Analysis of health and socio-economic characteristics of district level

Transcription

Biennial Collaborative Agreement between the Minister of Health of Poland
and the Regional Office for Europe of the World Health Organization 2010-2011
Analysis of health and socio-economic
characteristics of district level populations
in Poland
Agnieszka Chłoń-Domińczak, Michal Marek,
Daniel Rabczenko, Jakub Stokwiszewski, Bogdan Wojtyniak
Warsaw, February 2011
1
Content
1. POLISH DISTRICTS ..................................................................................................................... 18
1.1. Development of territorial units in Poland ..................................................................................................... 18
1.2. Demographic, economic and technical characteristics of Polish districts ...................................................... 23
1.3. Macro-level factors affecting district development ........................................................................................ 27
1.3.1. Social capital .......................................................................................................................................... 28
1.3.2. Polish metropolises................................................................................................................................. 29
1.3.3. Foreign investments ............................................................................................................................... 30
1.3.4. Public governance .................................................................................................................................. 32
1.3.5. European Union ...................................................................................................................................... 33
Summary ............................................................................................................................................................... 35
Annex 1.1. Lists .................................................................................................................................................... 37
Annex 1.2. Tables ................................................................................................................................................. 39
Annex 1.3. Figures ................................................................................................................................................ 44
2. SOCIAL AND ECONOMIC CHARACTERISTICS OF DISTRICTS IN POLAND .............. 48
2.1. Selection of variables ..................................................................................................................................... 48
2.2. Time and space characteristics of selected variables...................................................................................... 55
2.2.1. Demographic indicators.......................................................................................................................... 55
2.2.2. Economic and labour market indicators ................................................................................................. 60
2.2.3. Social cohesion indicators ...................................................................................................................... 66
2.2.4. Health care access indicators .................................................................................................................. 72
2.2.5. Educational indicators ............................................................................................................................ 75
2.3. Relations between selected variables ............................................................................................................. 83
References ............................................................................................................................................................. 92
ANNEX 2. VALUES OF SELECTED INDICATORS BY DISTRICT ............................................... 93
(TERYT: NATIONAL OFFICIAL REGISTER OF TERRITORIAL DIVISION OF THE COUNTRY) 117
3. DIFFERENCES IN HEALTH STATUS OF THE POPULATION ACROSS DISTRICTS IN
POLAND ............................................................................................................................................ 118
Introduction ......................................................................................................................................................... 118
3.1. Overall mortality .......................................................................................................................................... 120
3.1.1. Total population ................................................................................................................................... 120
3.1.2. Population below 65 years of age ......................................................................................................... 124
3.1.3. Population aged 65 years and over ....................................................................................................... 128
3.2. Mortality from cancer .................................................................................................................................. 132
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3.3. Mortality from circulatory system diseases.................................................................................................. 144
3.4. Mortality from diseases of the respiratory system ....................................................................................... 156
3.5. Mortality from diseases of the digestive system .......................................................................................... 168
3.6. Mortality from ill-defined causes (symptoms, signs and abnormal clinical and laboratory findings) ......... 180
3.7. Mortality from external causes ..................................................................................................................... 183
3.8. Infant mortality ............................................................................................................................................ 195
3.9. Life expectancy ............................................................................................................................................ 198
Summary ............................................................................................................................................................. 202
ANNEX 3 ............................................................................................................................................ 204
4. ASSOCIATION OF HEALTH STATUS OF DISTRICTS POPULATION WITH SOCIOECONOMIC CHARACTERISTIC OF DISTRICTS.................................................................... 284
4.1. Overall mortality .......................................................................................................................................... 285
4.2. Mortality from cancer .................................................................................................................................. 287
4.3. Mortality from circulatory system diseases.................................................................................................. 288
4.4. Mortality from respiratory system diseases.................................................................................................. 289
4.5. Mortality from digestive system diseases .................................................................................................... 290
4.6. Mortality from ill-defined causes (symptoms, signs and abnormal clinical and laboratory findings) ......... 291
4.7. Mortality from the external causes of death ................................................................................................. 292
4.8. Infant mortality ............................................................................................................................................ 293
4.9. Life expectancy at birth ................................................................................................................................ 294
Summary ............................................................................................................................................................. 295
Annex 4 ............................................................................................................................................................... 299
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Tables
Table 1. Characteristics of administrative regions (2010.06.30) ............................................. 18
Table 2. Extreme differences between districts (2002) ........................................................... 21
Table 3. Extreme differences between municipal districts (2002) ........................................... 21
Table 4. Budgetary revenues of municipal districts per capita in 2005 ................................... 21
Table 5. Number of districts and local communities in Poland (1999 –2010)......................... 22
Table 6. Quality of life in district municipalities (1999).......................................................... 23
Table 7. Development of rural district technical infrastructure measured with an index ........ 26
Table 8. Increase of budgetary revenues of municipal districts in year 2005 and 2003 .......... 27
Table 9. Share of people trusting other people (18 years of age and above) ........................... 28
Table 10. Total amount of foreign investments in Visegrad Group countries (billions of USD)
.................................................................................................................................................. 30
Table 11. New jobs created by foreign investments (2008) ..................................................... 30
Table 12. Number of employees working for firms with foreign capital (until 2008) ............ 31
Table 13. Countries with the lowest and the highest level of corruption ................................. 33
Table 14. Characteristics of the most underdeveloped regions (2008) .................................... 39
Table 15. Differences among districts (2002) .......................................................................... 39
Table 16. Rural district populations. Average values of three groups of districts ................... 41
Table 17. Economic development of rural districts. Average values of five groups of districts
.................................................................................................................................................. 41
Table 18. Rural districts with the best and the worst developed technical infrastructure ....... 42
Table 19 Characteristics of Polish metropolises ...................................................................... 43
Table 20. Indicators for district-level analysis of socio-economic determinants of health in
Poland ....................................................................................................................................... 51
Table 21. Descriptive statistics of feminization rate .............................................................. 57
Table 22. Descriptive statistics of old-age dempgraphic dependency ratio ............................. 58
Table 23. Descriptive statistics of population density.............................................................. 59
Table 24. Descriptive statistics of own revenue of local budgets per capita ........................... 62
Table 25. Descriptive statistics of unemployment rate ............................................................ 63
Table 26. Descriptive statistics of share of employment in agriculture ................................... 64
Table 27. Descriptive statistics of share of employment in hazardous conditions .................. 65
Table 28. Descriptive statistics of pre-school participation rate of children aged 3-5 ............. 68
Table 29. Descriptive statistics of library members per 1000 inhabitants ............................... 69
Table 30. Descriptive statistics of the share of households equipped with a bathroom ........... 70
Table 31. Descriptive statistics of local government elections turnout.................................... 71
Table 32. Descriptive statistics of the number of inhabitants per 1 health care institution...... 73
Table 33. Descriptive statistics of the number of inhabitants per 1 physician ......................... 74
Table 34. Descriptive statistics of the share of population with higher education................... 78
Table 35. Descriptive statistics of the share of population with vocational or lower education
.................................................................................................................................................. 78
Table 36. Descriptive statistics of average lower secondaryschool exam results (mathematics
and science) .............................................................................................................................. 79
Table 37. Descriptive statistics of upper secondary school matura results - mathematics (basic
level) ......................................................................................................................................... 79
Table 38. Descriptive statistics of average lower secondaryschool exam results (humanities)80
Table 39. Descriptive statistics of upper secondary school matura exam results - Polish
language (basic level) ............................................................................................................... 81
Table 40. Correlation matrix of the indicators ......................................................................... 89
Table 41. Age-standardized mortality ratio for overall mortality, total population, 2006–2008,
descriptive statistics................................................................................................................ 120
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Table 42. Age-standardized mortality ratio for overall mortality, population aged 0–64 years,
2006–2008, descriptive statistics............................................................................................ 124
Table 43. Age-standardized mortality ratio for overall mortality, population aged 65 years and
over, 2006–2008, descriptive statistics .................................................................................. 128
Table 44. Age-standardized mortality ratio for cancer, total population, 2006–2008,
Table 45. Age-standardized mortality ratio for cancer, population aged 0–64 years, 2006–
2008, descriptive statistics...................................................................................................... 136
Table 46. Age-standardized mortality ratio for cancer, population aged 65 years and over,
Table 47. Age-standardized mortality ratio for cardiovascular diseases, total population,
Table 48. Age-standardized mortality ratio for cardiovascular disases, population aged 0–64
years, 2006–2008, .................................................................................................................. 148
Table 49. Age-standardized mortality ratio for cardiovascular diseases, population aged 65
years and over, 2006–2008, descriptive statistics .................................................................. 152
Table 50. Age-standardized mortality ratio for diseases of the respiratory system, total
population, 2006–2008, descriptive statistics ........................................................................ 156
Table 51. Age-standardized mortality ratio for respiratory system diseases, population aged 0–
64 years, 2006–2008, descriptive statistics ............................................................................ 160
Table 52. Age-standardized mortality ratio for respiratory system diseases, population aged 65
years and over, 2006–2008, descriptive statistics .................................................................. 164
Table 53. Age-standardized mortality ratio for diseases of the digestive system, total
population, .............................................................................................................................. 168
Table 54. Age-standardized mortality ratio for diseases of the digestive system, population
aged 0–64 years, 2006–2008, descriptive statistics................................................................ 172
Table 55. Age-standardized mortality ratio for diseases of the digestive system, population
aged 65 years and over, 2006–2008, descriptive statistics ..................................................... 176
Table 56. Standardized mortality ratio for ill-defined causes of death, total population, 2006–
Table 57. Standardized mortality ratio for external causes, total population, 2006–2008,
Table 58. Standardized mortality ratio for external causes, population aged 0–64 years, 2006–
Table 59. Standardized mortality ratio for external causes, population aged 65 years and over,
Table 60. Descriptive statistics of infant mortality rates (per 1000 live births) by age in
districts ................................................................................................................................... 195
Table 61. Summary statistics of life expectancy at birth in districts by gender, 2001–2003 and
2006–2008 .............................................................................................................................. 198
Table 62. Age-standardized mortality ratio (SMR) by main groups of causes of deaths, district
of residence in 2006–2008, total population .......................................................................... 204
of residence in 2006–2008, males .......................................................................................... 211
of residence in 2006–2008, females ....................................................................................... 218
Table 65. Age-standardized mortality ratio (SMR) by main groups of causes of deaths,
district of residence in 2006–2008, total population 0–64 years old ..................................... 225
district of residence in 2006–2008, males 0–64 years old ..................................................... 232
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of residence in 2006–2008, females 0–64 years old .............................................................. 239
district of residence in 2006–2008, total population of age 65 years and more ..................... 246
district of residence in 2006–2008, males of age 65 years and more ..................................... 253
district of residence in 2006–2008, females of age 65 years and more.................................. 260
Table 71. Infant mortality rates by age and district of residence in 2001–2003 and 2006–
2008, per 1000 live births ....................................................................................................... 267
Table 72. Life expectancy at birth (in years) of males and females in 2001–2003 and 2006–
2008 ........................................................................................................................................ 276
Table 73 Standardized regression coefficients from the final multiple regression models for
mortality due to all causes for each age and sex group .......................................................... 286
Table 74. Standardized regression coefficients from the final multiple regression models for
cancer mortality for each age and sex group .......................................................................... 287
mortality due to circulatory system diseases for each age and sex group .............................. 289
mortality due to respiratory system diseases for each age and sex group .............................. 289
mortality due to digestive system diseases for each age and sex group ................................. 291
mortality due to ill-defined causes for each age and sex group ............................................. 292
mortality due to external causes for each age and sex group ................................................. 293
Table 80. Standardized regression coefficients for infant mortality rate, total and in age groups
................................................................................................................................................ 294
Table 81. Standardized regression coefficients for life expectancy at birth of males and
females ................................................................................................................................... 295
Table 82. Association between district SMR for all causes and socio-economic variables, total
population, all ages ................................................................................................................. 299
Table 83. Association between district SMR for all causes and socio-economic variables,
males, all ages ........................................................................................................................ 300
females, all ages ..................................................................................................................... 301
total population, aged 0–64 years ........................................................................................... 302
males, aged 0–64 years ........................................................................................................... 303
females, aged 0–64 years ....................................................................................................... 304
Table 88. Association between district SMR for all causes and socio-economic variables, total
population, aged 65 years and over ........................................................................................ 305
males, aged 65 years and over ................................................................................................ 306
females, aged 65 years and over............................................................................................. 307
Table 91. Association between district SMR for malignant neoplasms and socio-economic
variables, total population, all ages ........................................................................................ 308
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variables, males, all ages ........................................................................................................ 309
variables, females, all ages ..................................................................................................... 310
variables, total population, aged 0–64 years .......................................................................... 311
variables, males, aged 0–64 years .......................................................................................... 312
variables, females, aged 0–64 years ....................................................................................... 313
variables, total population, aged 65 years and over ............................................................... 314
variables, males, aged 65 years and over ............................................................................... 315
variables, females aged 65 years and over ............................................................................. 316
Table 100. Association between district SMR for circulatory system diseases and socioeconomic variables, total population, all ages ........................................................................ 317
Table 101. Association between district SMR for circulatory system diseases and socioeconomic variables, males, all ages ....................................................................................... 318
Table 102. Association between district SMR for circulatory system diseases and socioeconomic variables, females, all ages .................................................................................... 319
Table 103. Association between district SMR for circulatory system diseases and socioeconomic variables, total population, aged 0–64 ................................................................... 320
Table 104. Association between district SMR for circulatory system diseases and socioeconomic variables, males, aged 0–64 ................................................................................... 321
Table 105. Association between district SMR for circulatory system diseases and socioeconomic variables, females, aged 0–64 ................................................................................ 322
Table 106. Association between district SMR for circulatory system diseases and socioeconomic variables, total population, aged 65 years and over ............................................... 323
Table 107. Association between district SMR for circulatory system diseases and socioeconomic variables, males, aged 65 years and over ............................................................... 324
Table 108. Association between district SMR for circulatory system diseases and socioeconomic variables, females, aged 65 years and over............................................................ 325
Table 109. Association between district SMR for respiratory system diseases and socioeconomic variables, total population, all ages ........................................................................ 326
Table 110. Association between district SMR for respiratory system diseases and socioeconomic variables, males, all ages ....................................................................................... 327
Table 111. Association between district SMR for respiratory system diseases and socioeconomic variables, females, all ages .................................................................................... 328
Table 112. Association between district SMR for respiratory system diseases and socioeconomic variables, total population, aged 0–64 ................................................................... 329
Table 113. Association between district SMR for respiratory system diseases and socioeconomic variables, males, aged 0–64 ................................................................................... 330
Table 114. Association between district SMR for respiratory system diseases and socioeconomic variables, females, aged 0–64 ................................................................................ 331
Table 115. Association between district SMR for respiratory system diseases and socioeconomic variables, total population, aged 65 years and over ............................................... 332
Table 116. Association between district SMR for respiratory system diseases and socioeconomic variables, males, aged 65 years and over ............................................................... 333
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Table 117. Association between district SMR for respiratory system diseases and socioeconomic variables, females, aged 65 years and over............................................................ 334
Table 118. Association between district SMR for digestive system diseases and socioeconomic variables, total population, all ages ........................................................................ 335
Table 119. Association between district SMR for digestive system diseases and socioeconomic variables, males, all ages ....................................................................................... 336
Table 120. Association between district SMR for digestive system diseases and socioeconomic variables, females, all ages .................................................................................... 337
Table 121. Association between district SMR for digestive system diseases and socioeconomic variables, total population, aged 0–64 ................................................................... 338
Table 122. Association between district SMR for digestive system diseases and socioeconomic variables, males, aged 0–64 ................................................................................... 339
Table 123. Association between district SMR for digestive system diseases and socioeconomic variables, females, aged 0–64 ................................................................................ 340
Table 124. Association between district SMR for digestive system diseases and socioeconomic variables, total population, aged 65 years and over ............................................... 341
Table 125. Association between district SMR for digestive system diseases and socioeconomic variables, males, aged 65 years and over ............................................................... 342
Table 126. Association between district SMR for digestive system diseases and socioeconomic variables, females, aged 65 years and over............................................................ 343
Table 127. Association between district SMR for ill-defined causes and socio-economic
variables, total population, aged 0–64 .................................................................................... 347
variables, males, aged 0–64.................................................................................................... 348
variables, females, aged 0–64 ................................................................................................ 349
variables, females, aged 65 years and over ............................................................................ 352
Table 136. Association between district SMR for external causes and socio-economic
variables, total population, aged 0–64 .................................................................................... 356
variables, males, aged 0–64.................................................................................................... 357
variables, females, aged 0–64 ................................................................................................ 358
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variables, females, aged 65 years and over ............................................................................ 361
Table 145. Association between district infant mortality rate and socio-economic variables 362
Table 146. Association between districts infant neonatal (0-27 days) mortality rate and socioeconomic variables ................................................................................................................. 363
Table 147. Association between districts infant postneonatal (28–365 days) mortality rate and
socio-economic variables ....................................................................................................... 364
Table 148. Association between districts life expectancy and socio-economic variables, males
................................................................................................................................................ 365
Table 149. Association between districts life expectancy and socio-economic variables,
females ................................................................................................................................... 366
Figures
Fig. 1. Social determinants of health ........................................................................................ 13
Fig. 2. Effectiveness of governance in Poland and other countries in 2007 ........................... 32
Fig. 3. Effectiveness of Polish public administration in years 1966-2007 .............................. 32
Fig. 4. Conceptual framework of the Commission on Social Determinants of Health ............ 49
Fig. 5. Multiple levels of determination of health ................................................................... 50
Fig. 6 Histogram of feminization rate in 2007 (number of women per 100 men in age group
24-35) ....................................................................................................................................... 57
Fig. 7. Geographical distribution of feminization rate in 2007 ............................................... 57
Fig. 8. Histogram of old-age demographic dependency ratio in 2007 (people aged 60/65 and
above per 100 people aged 18-59/65) ...................................................................................... 58
Fig. 9. Geographical distribution of old-age dependency ratio in 2007 .................................. 58
Fig. 10. Histogram of population density in 2007 (people per one square km) ...................... 59
Fig. 11. Geographical distribution of population density in 2007 .......................................... 59
Fig. 12. Histogram of own revenue of local budgets per capita in 2007 (in PLN) ................. 62
Fig. 13. Geographical distribution of own revenue of local budgets per capita in 2007 ......... 62
Fig. 14. Histogram of unemployment rate in 2007 (percentage) ............................................ 63
Fig. 15. Geographical distribution of unemployment rate in 2007 ......................................... 63
Fig. 16. Histogram of share of employment in agriculture in 2007 (percentage) .................... 64
Fig. 17. Geographical distribution of share of employment in agriculture in 2007 ................. 64
Fig. 18. Histogram of share of employment in hazardous conditions in 2007 (percentage) .. 65
Fig. 19. Geographical distribution of share of employment in hazardous conditions in 2007 65
Fig. 20. Histogram of pre-school participation rate of children aged 3-5 in 2007 (percentage)
.................................................................................................................................................. 68
Fig. 21. Geographical distribution of the share of pre-school participation rate of children
aged 3-5 in 2007 ....................................................................................................................... 68
Fig. 22. Histogram of library members per 1000 inhabitants in 2007 .................................... 69
Fig. 23. Geographical distribution of library members per 1000 inhabitants in 2007 ............. 69
Fig. 24. Histogram of the share of households equipped with a bathroom in 2007 (percentage)
.................................................................................................................................................. 70
Fig. 25. Geographic distribution of the share of households equipped with a bathroom in 2007
(percentage) .............................................................................................................................. 70
Fig. 26. Histogram of local government elections turnout in 2006 (per cent) ........................ 71
9
Fig. 27. Geographical distribution of the share of local government elections turnout in 2006
.................................................................................................................................................. 71
Fig. 28. Histogram of the number of inhabitants per 1 health care institution in 2007 .......... 73
Fig. 29. Geographical distribution of the number of inhabitants per 1 health care institution in
2007 .......................................................................................................................................... 73
Fig. 30. Histogram of the share of the number of inhabitants per 1 physician in 2007 .......... 74
Fig. 31. Geographical distribution of the number of inhabitants per 1 physician in 2007 ....... 74
Fig. 32. Histogram of the share of population with higher education in 2002 (percentage)... 78
Fig. 33. Histogram of the share of population with vocational or lower education in 2002
(percentage) .............................................................................................................................. 78
Fig. 34. Histogram of average lower secondaryschool exam results (mathematics and
science) in 2007 ........................................................................................................................ 79
Fig. 35. Histogram of upper secondary school matura results - mathematics (basic level) in
2007 .......................................................................................................................................... 79
Fig. 36. Histogram of the share of average lower secondaryschool exam results (humanities)
in 2007 ...................................................................................................................................... 80
Fig. 37. Geographical distribution of average lower secondaryschool exam results
(humanities) in 2007................................................................................................................. 80
Fig. 38. Histogram of the share of upper secondary school matura exam results - Polish
language (basic level) in 2007 .................................................................................................. 81
Fig. 39. Geographical distribution of upper secondary school matura exam results - Polish
language (basic level) in 2007 .................................................................................................. 81
Fig. 40. Polarisation of district characteristics in Poland ........................................................ 88
Fig. 41. Age-standardized mortality ratio (SMR) for overall mortality, ................................ 122
Fig. 42. Age-standardized mortality ratio (SMR) for overall mortality, females, 2006–2008
................................................................................................................................................ 122
Fig. 43. Correlation between crude death rate ratio and age-standardized mortality ratio for
overall mortality, total population, 2006–2008 ...................................................................... 123
Fig. 44. Correlation between age-standardized mortality ratios for overall mortality in 2001–
2003 (03) and 2006–2008 (08), total population.................................................................... 123
Fig 46. Age-standardized mortality ratio (SMR) for overall mortality, ................................. 126
overall mortality, population aged 0–64 years, 2006–2008 ................................................... 127
2003 (03) and 2006–2008 (08), population aged 0–64 years ................................................. 127
overall mortality, population aged 65 years and over, 2006–2008 ........................................ 131
2003 (03) and 2006–2008 (08), population aged 65 years and over ...................................... 131
Fig. 53. Age-standardized mortality ratio (SMR) for cancer, males, ..................................... 134
Fig. 54. Age-standardized mortality ratio (SMR) for cancer, females, .................................. 134
cancer, total population, 2006–2008 ...................................................................................... 135
Fig. 56. Correlation between age-standardized mortality ratios for cancer in 2001–2003 (03)
and 2006–2008 (08), total population .................................................................................... 135
Fig. 57. Age-standardized mortality ratio (SMR) for cancer, ................................................ 138
10
cancer, population aged 0–64 years, 2006–2008 ................................................................... 139
Fig. 60. Correlation between age-standardized mortality ratios for cancer in 2001–2003 (03)
and 2006–2008 (08), population aged 0–64 years ................................................................. 139
Fig. 63. Correlation between crude death rate ratio and standardized mortality ratio for cancer,
population aged 65 years and over, 2006–2008 ..................................................................... 143
Fig. 64. Correlation between standardized mortality ratios for cancer in 2001–2003 (03) and
2006–2008 (08), population aged 65 years and over ............................................................. 143
Fig. 65. Age-standardized mortality ratio (SMR) for cardiovascular diseases, ..................... 146
Fig. 67. Correlation between crude death rate ratio and standardized mortality ratio for
cardiovascular diseases, ......................................................................................................... 147
Fig. 68. Correlation between age-standardized mortality ratios for cardiovascular diseases in
2001–2003 (03) and 2006–2008 (08), .................................................................................... 147
cardiovascular diseases, population aged 0–64 years, ........................................................... 151
2001–2003 (03) and 2006–2008 (08), population aged 0–64 years ....................................... 151
Fig. 73. Age-standardized mortality ratio (SMR for cardiovascular diseases, ...................... 154
Fig. 74. Age-standardized mortality ratio (SMR for cardiovascular diseases, ...................... 154
cardiovascular diseases, population aged 65 years and over, 2006–2008 ............................. 155
2001–2003 (03) and 2006–2008 (08), population aged 65 years and over ............................ 155
Fig. 77. Age-standardized mortality ratio (SMR) for respiratory system diseases, males, 2006–
2008 ........................................................................................................................................ 158
Fig. 78. Age-standardized mortality ratio (SMR) for respiratory system diseases, females,
2006–2008 .............................................................................................................................. 158
respiratory system diseases, total population, 2006–2008 ..................................................... 159
Fig. 80. Correlation between standardized mortality ratios for respiratory system diseases in
2001–2003 (03) and 2006–2008 (08), total population.......................................................... 159
Fig. 81. Age-standardized mortality ratio (SMR) for respiratory system diseases, males aged
0–64 years, 2006–2008 .......................................................................................................... 162
Fig. 82. Age-standardized mortality ratio (SMR) for respiratory system diseases, females aged
0–64 years, 2006–2008 .......................................................................................................... 162
respiratory system diseases, population aged 0–64 years, 2006–2008 .................................. 163
Fig. 84. Correlation between standardized mortality ratios for respiratory system diseases in
2001–2003 (03) and 2006–2008 (08), population aged 0–64 years ....................................... 163
Fig. 85. Age-standardized mortality ratio (SMR) for respiratory system diseases in 2006–
2008, males aged 65 years and over ....................................................................................... 166
Fig. 86. Age-standardized mortality ratio (SMR for respiratory system diseases in 2006–2008,
females aged 65 years and over.............................................................................................. 166
respiratory system diseases, population aged 65 years and over, 2006–2008 ....................... 167
11
Fig. 88. Correlation between age-standardized mortality ratios for respiratory system diseases
in 2001–2003 (03) and 2006–2008 (08), population aged 65years and over ......................... 167
Fig. 89. Age-standardized mortality ratio (SMR) for diseases of the digestive system, males,
2006–2008 .............................................................................................................................. 170
Fig. 90. Age-standardized mortality ratio (SMR) for diseases of the digestive system, females,
2006–2008 .............................................................................................................................. 170
diseases of the digestive system, total population, ................................................................. 171
Fig. 92. Correlation between age-standardized mortality ratios for diseases of the digestive
system in 2001–2003 (03) and 2006–2008 (08), .................................................................... 171
Fig. 93. Age-standardized mortality ratio (SMR) for diseases of the digestive system, males
aged 0–64 years, 2006–2008 .................................................................................................. 174
Fig. 94. Age-standardized mortality ratio (SMR) for diseases of the digestive system, females
aged 0–64 years, 2006–2008 .................................................................................................. 174
diseases of the digestive system, population aged ................................................................. 175
system in 2001–2003 (03) and 2006–2008 (08), population aged 0–64 years ...................... 175
Fig. 97. Age-standardized mortality ratio (SMR) for diseases of the digestive system, males
aged 65 years and over, 2006–2008 ....................................................................................... 178
Fig. 98. Age-standardized mortality ratio (SMR) for diseases of the digestive system, females
aged 65 years and over, 2006–2008 ....................................................................................... 178
diseases of the digestive system, population aged ................................................................. 179
system in 2001–2003 (03) and 2006–2008 (08), population aged 65 years and over............ 179
Fig. 101. Age-standardized mortality ratio (SMR) for ill-defined causes,............................. 182
Fig. 102. Age-standardized mortality ratio (SMR) for ill-defined causes, females, 2006–2008
................................................................................................................................................ 182
Fig. 103. Age-standardized mortality ratio (SMR) for external causes, males, 2006–2008 .. 185
Fig. 104. Age-standardized mortality ratio (SMR) for external causes, females, 2006–2008185
external causes, total population, 2006–2008 ........................................................................ 186
Fig. 106. Correlation between standardized mortality ratios for external causes in 2001–2003
(03) and 2006–2008 (08), total population............................................................................. 186
Fig. 107. Age-standardized mortality ratio (SMR) for external causes, ................................ 189
Fig. 108. Age-standardized mortality ratio (SMR) for external causes, ................................ 189
external causes, population aged 0–64 years, 2006–2008 ..................................................... 190
(03) and 2006–2008 (08), population aged 0–64 years .......................................................... 190
Fig. 111. Age-standardized mortality ratio (SMR) for external causes, males aged 65 years
and over, 2006–2008 .............................................................................................................. 193
Fig. 112. Age-standardized mortality ratio (SMR) for external causes, females aged 65 years
and over, 2006–2008 .............................................................................................................. 193
external causes, population aged 65 years and over,.............................................................. 194
(03) and 2006–2008 (08), population aged 65 years and over ............................................... 194
Fig. 115. Infant mortality rate, 2001–2003 (per 1000 live births) ......................................... 197
12
Fig. 116. Infant mortality rate, 2006–2008 (per 1000 live births) ......................................... 197
Fig. 117. Males life expectancy at birth, 2001–2003 ............................................................. 200
Fig. 118. Males life expectancy at birth, 2006–2008 ............................................................. 200
Fig. 119. Females life expectancy at birth, 2001–2003.......................................................... 201
Fig. 120. Females life expectancy at birth, 2006–2008.......................................................... 201
Introduction
In many countries, inequalities in health result from social determinants which affect the
conditions in which people are born and raised, find employment and access medical
treatment. They include “upstream” determinants, such as the type of economic policy,
poverty and unemployment levels, health hazards at workplace, social capital, and
organization and functioning of health care and welfare systems, as well as “downstream”
determinants, which include lifestyle and behaviour (e.g. tobacco use, alcohol abuse, physical
exercise, and diet), as well as the functioning of health care system [Dahlgren and Whitehead
(2007)]. All these factors affect various social groups to varying degrees, which largely
contributes to the existence of social inequalities in health. The figurebelow illustrates
complex interrelations between social determinants and health status.
Fig. 1. Social determinants of health
Source: Dahlgren and Whitehead (2007)
Impact of social determinants of health and their distribution varies across and within
countries. Therefore, a national policy - aiming at the reduction of social inequalities in health
13
- should be based not only on international experience, but also on studies focused on the
impact of social determinants on health in a particular country.
The main focus of the report is to study geographic differences in health status of all Polish
district populations as well as social factors behind these differences. The report could be used
as:
1) a basis for setting long-term priorities and goals of health policies developed at the
national, regional and local level,
2) a basis for development and evaluation of multi-sector, comprehensive strategies
focused on general improvement of health status of the entire population, and at the
reduction of social inequalities in health in particular districts,
3) a tool for periodic evaluation of the efficacy of long-term activities focused on health
status improvement and reduction of social inequalities in health in particular districts
or groups of districts.
Therefore, it is expected that such an analysis will be carried out periodically every few years.
The concept of the analysis was developed by Michal Marek in co-operation with Agnieszka
Chlon Dominczak and Bodan Wojtyniak. The importance of analysis of that kind was
emphasized in some governmental documents1. However, the analysis of such scope has been
performed for the first time since the territorial reform of 1999, which re-established districts
in Poland.
The report is addressed to all organisations which should participate in the development and
implementation of the multi-sector, comprehensive strategies mentioned above. Therefore, the
list of its addressees is long and includes stakeholders acting at the national, regional, district
and local levels, such as:
- Ministry of Health (responsible for national health policy),
- National Health Fund – the key public payer for health care services,
- Ministry of Regional Development – the key public organ with central level
responsibility for the development and implementation of regional strategies,
1
The documents such as: Obszary szczególnego zagrożenia życia [Areas of high risks to human life](M. Marek),
in: Narodowy Plan Zdrowotny na lata 2004-2013 [National Health Plan for 2004-2013], Ministry of Health,
Warsaw, pp. 223 et seq. (the plan was never implemented due to changes in legal regulations), Strategia
poprawy stanu zdrowia społeczeństwa polskiego 2007-2013 (projekt) [Strategy for improvement of health status
of the Polish population, Ministry of Health (draft), Warszawa, 26 November 2004 r. See also: Michal Marek,
Jan Rutkowski, 1994, Projekt oceny jakości życia w gminach [Quality of life in local communities], in J.B.
Karski (ed.) Problemy współpracy na rzecz zdrowia [Problems of co-operation for heath improvement], Annex
1, pp. 287-313 (based on empirical analysis conducted at the end of 1980’s.)
14
other ministries (e.g. Ministry of Education, Ministry of Labour and Social Policy,
Ministry of Sports, Ministry of Infrastructure),- regional (NUT-2), district (NUT-3) and
local authorities responsible for the well-being of their communities,
- associations of regional, district or local authorities,
- NGOs,
- international organisations such as WHO, developing programmes aimed at
improvement of health status and reduction of social inequalities in health.
The report is divided into four chapters. In Chapter One there is a brief presentation of the
history of administrative territorial reforms, followed by demographic and socio-economic
characteristics of districts based on research analyses conducted during the last ten years.
Selected (upstream) factors exerting positive as well as negative influence upon district
development, such as social capital, functioning of metropolises, distribution of foreign
investments, and Poland’s accession to the European Union, are also outlined.
In Chapter Two, socio-economic characteristics of districts are presented based on five
groups of indicators pertaining to the following areas: (a) demography; (b) economic and
labour market situation; (c) social cohesion; (d) access to health care, and (e) education. Links
between selected groups of indicators are presented and followed by analysis of their
geographic distribution. Resulting diversity is not only linked to a region, but also to a type of
district. Therefore, at the end of the chapter, features of a typical municipal and rural district
are presented. The analysis covers years 2002 and 2007.
In Chapter Three, health status of each district population is analysed based on standardized
mortality ratio (SMR) and crude death rates (CDR) for two periods: 2001 –2003 and 2006 –
2008. The analysis takes into account:
- total population and two age groups: below 65 (to include overall premature mortality)
and 65 +,
- two genders,
- causes of death: (a) all causes, (b) cardiovascular diseases, (b) cancer, (c) external causes,
(d) respiratory diseases, (e) digestive diseases, and additionally (f) ill-defined causes of
death (for the entire population, each age group, and the two genders).
-
life expectancy at birth,
- dynamics of changes in health status of district populations.
Moreover, a separate analysis of district infant mortality rates for total (IMR), neonatal (0 –27
days) and post-neonatal (28 days- under a year) age categories is included.
15
Chapter Four begins with the presentation and discussion of results of standardized
regression coefficients from final multiple regression models for mortality due to all causes
and according to each cause of death included in Chapter Three (for each age group and
gender.) Results of standardized regression coefficients for infant mortality rate are also
presented. Moreover, the results of similar analysis are presented for life expectancy at birth
for males and females. Regression analysis reveals that social determinants are strongly
associated with overall mortality outcomes of district populations. Such association suggests
that the most efficient improvement in health could only be achieved through the development
and implementation of multi-sector, comprehensive, long-term programmes based on health
in all policies principle.
The report is a follow-up on earlier work entitled: Social inequalities in health. The purpose
of the first study was to summarize the knowledge in the field of social inequalities in health
prevailing in Poland, and to present a preliminary set of recommendations for long-term,
multi-sector national health policy.
The first report is focused on the health status of the entire population as well as on
inequalities in health across the life course. In different sections of the report inequalities in
health are discussed in the context of such social determinants as: age, gender, educational
attainments, social and economic status, urban/rural areas, as well as non-health differences at
regional level (labour market, household income).
The authors of all sections of the report attempted to address at least two questions, namely:
1. What gap can be observed between Poland and the more developed European
countries?
2. What challenges are facing Poland in terms of social inequalities in health?
Furthermore, in several parts of the report a preliminary review is presented, detailing
pro-health measures initiated to date. The first report is divided into four sections.
In Section 1, selected upstream determinants of health in Poland are discussed, such as
relative poverty, early years development, education, and expenditures on health care,
including regional differences.
In Section 2, seven risk factors are presented, which exert a strong influence on the health
status of the entire population, as well as on social inequalities in health in Poland, namely:
tobacco use, high blood pressure, high level of blood cholesterol, obesity, alcohol
consumption, low consumption of fruit and vegetables, lack of physical exercise.
16
In Sections 3 and 4, social inequalities in the health of children and adolescents, as well as
health status of three age groups (25–44, 45–65, and 65 plus), are analysed.
The outcome of the analysis is used as the foundation for formulating a set of propositions
regarding next steps and actions aimed at the reduction of social inequalities in health.
References
Dahlgren G, Whitehead M. European strategies for tackling social inequities in health:
leveling up part 2. Copenhagen, WHO Regional Office for Europe, 2007, p. 23.
Obszary szczególnego zagrożenia życia [Areas of high risks to life](M. Marek), in: Narodowy
Plan Zdrowotny na lata 2004-2013 [National Health Plan for 2004-2013], Ministry of Health,
Warsaw, pp. 223 et seq.
Michal Marek, Jan Rutkowski, 1994, Projekt oceny jakości życia w gminach [Quality of life
in local communities], in J.B. Karski (ed.) Problemy współpracy na rzecz zdrowia [Problems
of co-operation for heath improvement], Annex 1, pp. 287-313.
Strategia poprawy stanu zdrowia społeczeństwa polskiego 2007-2013 (projekt) [Strategy for
improvement of health status of the Polish population, Ministry of Health (draft), Warszawa,
26 November 2004 r.
17
1. Polish Districts
Michał Marek
1.1. Development of territorial units in Poland
Polish regions (voivodships) and districts (poviats) were created in the fourteenth century.
Their territorial and institutional development continued till the fall of the Polish state at the
end of the eighteenth century. Following the collapse of the state, Poland, was divided among
Austria, Prussia and Russia. As a result, Polish administrative territorial units were replaced
by the units existing in the three countries. In 1918, when Poland regained independence,
regions and districts were re-established. Their role was defined in the Polish constitution in
March of 1921, but their size and role were finally shaped in 1933. All of the 16 regions were
divided into 264 districts (including 23 municipalities) and all non-municipal districts were
divided into 3,806 local communities (called gmina). The authorities were mostly responsible
for local economy, health protection and cultural issues.
Regions and districts were also present after the Second World War, though their selfgovernment role was limited due to restrictions imposed upon Polish political and social life
under socialist regime. In 1975, all districts were dissolved and the territory of Poland was
divided into 49 administrative regions, as well as into local communities.
Furthermore, in the post-war period serious changes concerning local communities were also
introduced. In 1952, three thousand local communities were replaced by eight thousand much
smaller units (called gromada). The reform increased administrative costs and had adverse
impact on the development of local communities. Therefore, in 1973, Poland was again
divided into 2,365 local communities.
In January 1999, twenty three years later, Poland was once more divided into 16 regions, 373
districts (including 65 municipal districts and 308 rural ones), and 2,489 local communities.
The territory of an average newly created administrative region was equal to 19,543 sq. kms,
and it was inhabited by 2.417 mln people2 .
Table 1. Characteristics of administrative regions (2010.06.30)
No.
Name of the region
and regional capital
Population (000)
Total
urban
Territory
(000 sq.
kms)
No. of
inhabitants
per sq.km
GDP/ per capita
(PPP)
2005
2007
EU27=10
EU27=
0%
100%
2
Grzegorz Gorzelak, B. Jałowiecki, M. Stec, „Reforma terytorialnej organizacji kraju: dwa lata doświadczeń,
Wydawnictwo Naukowe Scholar, Warszawa, 2001 p. 58. [Country territory administrative reform: two years of
experience.]
18
No.
Name of the region
and regional capital
Population (000)
Total
urban
Territory
(000 sq.
kms)
No. of
inhabitants
per sq.km
GDP/ per capita
(PPP)
2005
2007
EU27=10
EU27=
0%
100%
51.4
54.4
1
Total
38,187
23,284
312,679
122
2
Dolnośląskie
(Wrocław*)
Kujawsko-Pomorskie
(Bydgoszcz)
Lubelskie
(Lublin)
Lubuskie
(Zielona Góra)
Łódzkie
(Łódź)
Małopolskie
(Kraków)
Mazowieckiee
(Warszawa)
Opolskie
(Opole)
Podkarpackie
(Rzeszów)
Podlaskie
(Białystok)
Pomorskie
(Gdańsk)
Śląskie
(Katowice)
Świętokrzyskie
2,877
2,019
19,947
144
53.1
59.2
2,069
1,255
17,972
115
44.8
47.3
2,155
1,005
25,122
86
35.1
36.9
1,011
642
13,988
72
46.3
48.2
2,538
1,627
18,219
140
47.2
50.0
3,304
1,628
15,183
217
43.8
46.7
5,234
3,380
35,558
147
81.4
87.1
1,030
538
9,412
110
42.6
45.2
2,103
870
17,846
118
35.5
36.7
1,189
718
20,187
59
38.0
40.4
2,235
1,477
18,310
122
50.5
53.6
4,638
3,619
12,333
376
55.4
57.8
1,268
572
11,711
108
38.4
41.9
3
4
5
6
7
8
9
10
11
12
13
14
15
Warmińsko1,427
854
24,173
59
39.3
40.5
Mazurskie
(Olsztyn)
16
Wielkopolskie
3,414
1,912
29,826
114
54.9
56.9
(Poznań)
17
Zachodniopomorskie
1,693
1,166
22,892
74
47.7
48.9
(Szczecin)
*a regional capital of a particular region.
Source: Powierzchnia i ludność w przekroju terytorialnym [Area and Population in the territorial profile in
2010], Central Statistical Office, Warszawa, 2010, p. 17 www.stat.gov.pl; Eurostat- Statistical Office of the
European Communities; http//epp.eurostat.ec.europa.eu quoted after: Produkt Krajowy Brutto. Rachunki
Regionalne w 2008 r., GUS [GDP. Regional Accounts in 2008, Central Statistical Office ], Katowice 2010, p.
42.
In 2008, 53.6% of the total population of the employed in the national economy worked in the
five regions of Mazowieckie, Śląskie, Wielkopolskie, Dolnośląskie and Małopolskie (in 2005
it was 53.2%), and these five regions generated 59.5% of the national value of GDP. Two
regions – Mazowieckie and Śląskie – generated jointly 34.7% of the national value of GDP
(similarly as in 2005), with 28.3% of the total population of the employed in the national
economy working in their territory (in 2005 –28.4%).
19
In 2008, the group of regions with the lowest shares in the generation of GDP (3% and less
each) included the following: Lubuskie, Podlaskie, Podkarpackie, Opolskie, Świętokrzyskie
and Warmińsko-Mazurskie. The workforce of these regions represented 14.2% of the total
number of the employed in the national economy (in 2005 - 14.4%), but the total share of
these regions in GDP generation amounted to 12.4% (similarly as in 2005)3. Four of the
regions, namely: Podlaskie, Podkarpackie, Świetokrzyskie and Warmińsko- Mazurskie, are
located in the eastern part of the country (see table 14 in Annex 1.2).
Territory of an average newly created district was equal to 996 sq. kms, and it was inhabited
by 83.2 thousand people. In 1999, the districts were supposed to be created according to the
following criteria:
•
social acceptance,
•
historic, cultural and geographic factors,
•
economic potential,
•
institutional potential - future district towns should host the following institutions, at
the minimum: the court, prosecutor’s office and police local headquarters, fire
department, tax office, job agency, sanitary inspectorate, pension office (district unit
of ZUS), district hospital, high schools. Besides, it was decided that:
•
there will be no changes regarding the territories of local communities,
•
each district should be divided into at least five local communities,
•
district town should be inhabited by at least 10 000 citizens,
•
a rural district should be inhabited by at least 50 thousand people,
•
a municipal district should be inhabited by at least 100 thousand people.
However, these requirements were met by 242 (of 373) districts4. Therefore, there are big
discrepancies from one district to another (see Table 2).
3
GDP op. cit. GUS, Katowice, 2010 p. 40.
History of Poland’s territorial units was described, among others, by Jacek Petryszyn, Powiaty w Polsce,
Geografia w szkole, [Districts in Poland, Geography at school (teacher periodical)] No. 5, Nov.-Dec. 2004, pp.
260-268, Anna Tucholska, “Powiat między zbiorowością a wspólnotą”, Centrum Europejskich Studiów
regionalnych i lokalnych UW, [District: between a group of people and a community] Wydawnictwo Naukowe
Scholar, Warszawa, 2007, pp. 13-47.
4
20
Table 2. Extreme differences between districts (2002)
No.
Characteristics
Extreme
Differences
deferences
1.
Size (sq. kms)
2,985 vs. 13
229 [times]
2.
Population (in 000)
1,688 vs. 13
62
3.
Density of population (No. of 4,328 vs. 20
216
citizens per sq. km.)
4.
Revenues of local budgets per 3,243 vs. 186
17
capita
5.
Expenditures of local budgets per 3,753 vs. 179
20
capita
Source: Powiaty w Polsce, [Districts in Poland], Główny Urząd Statystyczny [Central Statistical
Office] Warszawa, 2003, pp.643 –645, 650, 651.
There are big differences not only between municipal and rural districts, but also within each
of these two groups – for instance, legal status of a municipal district has been assigned to
well-developed cities as well as to small towns (see Table 15 16 and 17 in Annex 1.2).
Table 3. Extreme differences between municipal districts (2002)
No.
Characteristics
Differences
Differences
1.
Size (sq. kms)
517 vs. 13
39,7 [times]
2.
Density (No. of citizens per sq. km.)
4,328 vs. 213
20,3
5.
Expenditures (PLN per capita)
6,338 vs.99.3
63,8
Source: Powiaty w Polsce, GUS, [Districts in Poland, Central Statistical Office] Warszawa, 2003.
Budgetary revenues of districts also vary to a significant extent (see Table 4).
Table 4. Budgetary revenues of municipal districts per capita in 2005
Total
LSMR > GSSB + EFRSB*
LSMR < GSSB +
budgetary
EFRSB
revenues
per
capita (PLN)
less than 2000
Sosnowiec, Świętochłowice, Żary
2001 –2500
Białystok,
Bydgoszcz,
Bytom,
Chorzów, Biała Podlaska, Chełm,
Radom,
Częstochowa, Jastrzębie Zdrój, Kielce, Lublin, Łódź, Łomża,
Mysłowice, Piekary Śląskie, Piotrków Trybunalski, Tarnobrzeg
Ruda Śląska, Siemianowice, Skierniewice, Szczecin,
Toruń, Tychy
2501 –3000
Bielsko-Biala, Dąbrowa Górnicza, Elbląg, Gdańsk, Grudziądz, Nowy Sącz,
Gdynia, Gliwice, Gorzów Wielkopolski, Jelenia Góra, Ostrołęka,
Siedlce,
Kalisz, Konin, Koszalin, Kraków, Legnica, Leszno, Suwałki,
Przemyśl,
Olsztyn, Opole, Poznań, Rybnik, Rzeszów, Słupsk, Tarnów, Zamość
Włocławek, Zabrze, Zielona Góra
3001 –3500
Katowice, Wrocław, Świnoujście
Krosno
more than 3500 Płock, Sopot, Warszawa
* LSMR - Local sources of municipal revenues, GSSB - general subvention from the state budget,
EFRSB - earmarked financial resources from the state budget allocated to specific tasks.
Source: A. Miszczak Finansowe aspekty funkcjonowania miast na prawach powiatu po wejściu Polski
do Unii Europejskiej, w: Polska regionalna i lokalna w świetle badań EUROREGu [Financial aspects of
21
the functioning of municipal districts after Poland’s accession to the European Union], G. Gorzelak
(ed.), Wydawnictwo Naukowe Scholar, Warszawa, 2007 p. 279.
In 2002, seven rural districts were created, in most cases as a result of dividing one district in
two, and the number of local communities was slightly reduced.
Table 5. Number of districts and local communities in Poland (1999 –2010)
Year
1999
2000
308
308
Rural
Districts
65
65
Municipal
Districts
2,489 2,489
Local
Communities
5
Source: www.stat.gov.pl .
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
308
314
314
314
314
314
314
314
314
314
65
66
65
65
65
65
65
65
65
65
2,489
2,478
2,478
2,478
2,478
2,478
2,478
2,478
2,478
2,478
Additionally, some rather minor territorial changes were made each year at the district level6.
The 1999 territorial reform is favourably perceived, in general, because it brought about many
important changes, such as de-centralization of responsibilities and strengthening of
democratic processes and local institutions. The reform also set the foundation for faster
social and economic development of many local communities. However, some negative
outcomes were also revealed. Too many small, economically weak districts were created,
which were inherently incapable of performing their statutory role in a proper manner. For
instance, there were 46 rural districts without any larger town. Therefore, their district
authorities were unable to provide adequate educational and health care services to the
inhabitants. Moreover, too many towns obtained the status of municipal districts. In such
cases, surrounding areas were separated from these towns by administrative barriers. As a
result, the supply of public services offered by municipal district administrators to the
residents of surrounding areas was not properly adjusted to their needs. Besides, the cooperation between distsrict and local authorities may have been rather limited7.
5
In: Zarys Strategii Sprawne Państwo 2011-2020 (projekt) [Outline of national strategy for the development of
effective governance 2011-2020, Ministry of Interior Affairs and Administration (draft), Warszawa, 2010, p. 7.
6
See annual reports of the Central Statistical Office: Powierzchnia i ludność w jednostkach podziału
terytorialnego kraju [Territory and population of administrative units in Poland], Warszawa.
7
Grzegorz Gorzelak, B. Jałowiecki, M. Stec, Reforma terytorialnej organizacji kraju: dwa lata doświadczeń
[Country territory administrative reform: two years of experience], Wydawnictwo Naukowe ”Scholar”,
Warszawa, 2001r, p. 13 and 14.
22
1.2. Demographic, economic and technical characteristics of Polish districts
In 2000, a multi-dementional analysis was conducted to evaluate the quality of life in 65
municipal districts (cf. List 1 in Annex 1.1) 8. In the study, all municipalities were divided
into three groups. The first group included all large cities and towns with population over
150,000 each, the second group included towns inhabited by 75 –150,000 citizens, and the
third one included municipalities with population below 75,000. Moreover, all municipalities
were divided into four additional groups (cities and towns with the best quality of life were
included in group A, and those with the worst one in group D). Additionally, municipalities in
groups A-D were listed in the order reflecting their position in the ranking (see Table 6)9.
Table 6. Quality of life in district municipalities (1999)
Municipalities
< 150,000
75 –150,000
> 75,000
Ranking
A
B
C
D
Lublin*,
Kraków, Nowy Sącz, Tarnów
Rzeszów, Olsztyn
Sopot
Zielona Góra, Jaworzno, Opole,
Koszalin, Chorzów
Słupsk, Dąbrowa Górnicza, Kalisz,
Gorzów Wielkopolski, Piotrków
Trybunalski, Mysłowice, Jelenia
Góra, Siedlce, Płock, Rybnik,
Konin, Jastrzębie Zdrój
Bytom,
Częstochowa, Elbląg,
Tychy,
Legnica,
Gliwice, Łódź, Zabrze, Włocławek, Siemianowice Śląskie,
Bydgoszcz, Sosnowiec, Grudziądz.
Radom, Ruda Śląska
Krosno, Przemyśl
Poznań,
Gdańsk,
Gdynia, Bielsko-Biała
Katowice,
Kielce,
Warszawa, Wrocław,
Szczecin,
Białystok,
Toruń
Biała Podlaska, Zamość,
Leszno, Chełm, Suwałki,
Tarnobrzeg, Świnoujście
Piekary Śląskie, Łomża,
Świetochłowice,
Ostrołęka, Skierniewice,
Żory
Source: K. Gawlikowska-Hueckel and others, op.cit., 2000, pp. 60 –62.
* names of regional capitals are in bold.
The quality of life in 16 regional capitals was varied. The best quality of life was observed in
four regional capitals, namely: Lublin, Kraków, Rzeszów and Olsztyn (three of them are
located in the poorest part of Poland), and the worst quality of life was reported in Łódź and
in Bydgoszcz ( group D).
8
Krystyna Gawlikowska-Hueckel,Anna Hildebrandt, Stanislaw Uminski, Jakosc zycia w miastach-powiatach
grodzkich [Quality of life in district municipalities], Instytut Badań nad Gospodarką Rynkową [The Gdansk
Institute for Market Economics], Gdańsk, 2000, p. 10 and 11. The authors stated that their studies took into
account similar analysis conducted by German research institutes.
9
There are also interesting rankings of the largest municipal districts conducted by Przekrój weekly magazine
(June 18, 2009 and Nov. 9, 2010 (No. 45/2010); http://www.przekroj.pl/pub/files/tabele/rankingmiast_2010.pdf .
The magazine takes into account 25 variables, but the methodology applied is not clear enough, therefore the
results of this ranking are not included in the report.
23
Moving on to the quality of life in large municipalities, included in group D, there was poor
environment, poor health status of their inhabitants, as well as high unemployment. In the
case of mid-size and small towns (from group D), there was poor access to educational
services, high unemployment and low living standards.
In a study concerning rural districts10, description of their populations was based on 9
indicators (list 2 in Annex 1.1). All the districts were divided into three groups, and each
group was characterized in the following way.
Group One includes 124 districts located in the northern, north-western, mid-western and
south-eastern parts of the country. Most of these districts (80%) belong to six regions:
Pomorskie, Warminsko-Mazurskie, Kujawsko-Pomorskie, Wielkopolskie, Małopolskie and
Podkarpackie. In all these districts density of population is similar to the average density in
Poland (excluding municipal districts). There is also relatively high fertility (per 1,000
inhabitants), high number of new marriages per 1,000 inhabitants, and rather low infant
mortality. Finally, balance of permanent migrations is at low negative level.
Group Two includes 74 districts, which are located mostly in the central and eastern part of
the country. Sixty percent of districts fromthis group belong to five regions – Łódzkie,
Mazowieckie, Świetokrzyskie, Podlaskie and Lubelskie. In this group there is low fertility,
low density of population, the number of new marriages is similar to average number for
Poland (excluding municipal districts). There is also the lowest level of divorces and very
high number of deaths per 1,000 inhabitants. Balance of permanent migrations is usually
negative and very high.
Group Three includes 109 districts located along western border of Poland as well as the
western part of the southern boarder; in the neighbourhood of municipal districts. Most of the
districts (84%) belong to five regions – Zachodniopomorskie, Lubuskie, Dolnosląskie,
Opolskie and Śląskie. In this case there is high density of population, low fertility, rather high
infant mortality, low number of new marriages and the highest number of divorces.
Furthermore, balance of negative migration is relatively high (see map 1 in Annex 1.3).
Description of rural district economies is based on 14 indicators (see List 3 in Annex 1.1).
All the districts were divided into five groups. The groups are characterized in the following
way.
10
Kamila Migdał –Najman, Krzysztof Najman „Zastosowanie sieci neuronowej typu SOM [self organizing map]
w badaniu przestrzennego zróżnicowania powiatów. Wiadomości Statystyczne nr 5/2004 pp. 74 et seq.
[Application of SOM (self-organizing map) neurone network in the research into spatial diversification of
districts.]
24
Group One includes 48 districts located in direct vicinity of large cities – Warsaw, Katowice,
Poznań, Wrocław, Szczecin, Opole, Gdańsk and Bielsko-Biała. In all the districts there is the
lowest share of people working in agriculture (30.7%), with high share of people working in
construction sector (30.4%) and market services (18.2%). These districts are characterized by
the highest average gross salary (almost 2,000 PLN). There is very low unemployment rate,
high labour productivity, very high revenues of local community budgets, the highest level of
budgetary expenditures for investment purposes per capita, as well as the highest gross value
of fixed assets in business per capita.
Group Two includes 75 districts located mostly in the western part of the country. In this
group there is relatively low number of registered companies11, relatively high unemployment
rate, and high employment in non-market services. Gross average salary is lower than in
Group One. In the districts gross value of fixed assets in business per capita is also relatively
low.
Group Three includes 31 districts located mostly in the central and the south-eastern part of
the country. The districts are located in neighbourhood of former regional capitals (which
played that role until 1999). In the districts there is relatively high level of employment, level
of unemployment is similar to country average, share of people working in agriculture is
rather high, and the share of those employed in industrial and construction sectors of the
economy is relatively low.
Group Four includes 48 districts, located mostly in the northern and the southern part of the
country. In the districts, there is the highest number of people working in non-market services,
and relatively high level of employment in market services and in industrial and construction
sectors. Additionally, there is relatively low level of employment in agriculture. Many of the
districts are located close to the seaside, where many people come for summer holidays.
However, at the same time, , unemployment rate in the group is very high, and the share of
working population per 1,000 inhabitants is very low. According to the authors of the
analysis, this is mostly due to massive bankruptcy of state owned farms (PGR), in the
northern part of the country, as well as reduction of personnel in the mining industry in the
southern part of the country. These two factors resulted in high level of structural
unemployment12.
11
In the national register called: REGON.
See also: Tomasz Tokarski, Aleksandra Rogut [2000], Zróżnicowanie struktury pracujących a odpływy z
bezrobocia” “Differentiation in the structure of employment and outflow from unemployment Wiadomości
Statystyczne [Statistical News], No. 3/2000, Barbara A. Despiney-Żochowska, From Marshallian District to
12
25
Group Five includes 105 districts located in the central and eastern part of the country. In this
group agricultural production dominates. Employment rate is high, but only a small portion of
the population works for industry, construction sector, and market and non-market services.
The number of businesses registered in these districts is the lowest one, but simultaneously
the level of registered unemployment is also among the lowest13. In the districts there is
domination of agricultural production. In this group, budgetary revenues of local communities
per capita, their investment expenditures, equity holdings in the local companies, as well as
the value of their fixed assets are at the lowest level. It means that the level of overall
development of the districts is low14 (see map 2 in Annex 1.3).
There are also significant differences in the development of infrastructure in rural
districts15. The level of development was measured with a composite index including six
indicators (see List 4 in Annex 1.1). The authors of the study present a list of 16 most
developed and 16 least developed districts (see Table 18 in Annex 1.2). Additionally, all
districts are divided into 5 groups (see Table 7).
Table 7. Development of rural district technical infrastructure measured with an index
Level
development
of Value
of
indicator
Very high
> 0.3
High
(0.1; - 0.3>
Average
(0,0; - 0.,1)
>
Low
the Number of districts in particular region
(-0.1; 0.0) >
Number
of
districts
Śląskie(7), Małopolskie (5), Mazowieckie (2),
Kujawsko-Pomorskie (1), Podkarpackie (1)
Małopolskie (11), Podkarpackie (8), Dolnośląskie
(5), Wielkopolskie (5), Łódzkie (4), Śląskie (4),
Mazowieckie (3), Świętokrzyskie (3), Lubelskie
(1), Pomorskie (1)
Wielkopolskie (17), Mazowieckie (12), Łódzkie
(10), Dolnośląskie (9), Kujawsko-Pomorskie (8),
Opolskie (7), Podarpackie (7), Śląskie (6),
Pomorskie (5), Świętokrzyskie (5), Lubelskie (4),
Małopolskie (2), Zachodniopomorskie (1)
Lubelskie (14), Dolnośląskie (12), Mazowieckie
(11), Kujawsko-Pomorskie (8), Podlaskie (7),
Warmińsko-Mazurskie (6), Zachodniopomorskie
(6), Łódzkie (5), Pomorskie (5), Świętokrzyskie (5),
Wielkopolskie (5), Opolskie (4), Lubuskie (3),
Podkarpackie (2), Małopolskie (1)
16
45
93
94
Local Product Systems: The Polish Case in: Z. B. Liberda, A. Grochowska (eds.) Civilizational Competences
and Regional Development in Poland, Warszawa, 2009, pp. 186 et seq.
13
In case of agriculture there is very often high level of hidden unemployment.
14
See tables No. 3 and No. 4 in Annex 2.
15
Jarosław Lira, Feliks Wysocki Zastosowanie pozycyjnego miernika rozwoju do pomiaru poziomu
zagospodarowania infrastrukturalnego powiatów [Application of positional index of development to measure the
level of infrastructural development of districts], Wiadomości Satystyczne, No. 9, 2004 pp. 39-47.
26
Level
development
Very low
of Value
of
the Number of districts in particular region
indicator
Number
of
districts
Warmińsko-Mazurskie (11), Zachodniopomorskie 59
(10), Mazowieckie (9), Lubuskie (8), Podlaskie (7),
Pomorskie (4), Wielkopolskie (4), KujawskoPomorskie (2), Podkarpackie (2), Lubelskie (1),
Łódzkie (1)
≤ -0.1
Source: J. Lira, F. Wysocki op. cit. p. 44.
1.3. Macro-level factors affecting district development
In years 1989-2009 development of Poland was very dynamic. Polish GDP per capita went up
from 35% to 55% of EU15 average. Average earnings in Poland increased five times thanks
to the appreciation of Polish currency. There were also significant social changes, e.g. the
number of higher education students increased four times (from 10% to 40%). Life
expectancy increased by about 5 years. Number of trips abroad increased five times (from 10
mln in 1988 to 50 mln in 2008)16. For the last two decades there was also dynamic regional
development strongly supported by central authorities which, among other things:
•
introduced a set of important legal regulations,
•
established the Ministry of Regional Development,
•
offered financial support to regional, district and local authorities,
•
introduced the National Strategy of Regional Development for years 2011-2020.
However, fast national and regional development aggravated the differences among districts
(see Table 8).
Table 8. Increase of budgetary revenues of municipal districts in year 2005 and 2003
Increase in budgetary
revenues in 2005
(2003=100%)*
100.1-110.0
110.1-120.0
120.1-130.0
Municipal districts
Bytom, Chełm, Grudziądz, Jelenia Góra, Katowice**, Legnica, Skierniewice,
Zamość
Biała Podlaska, Elbląg, Gliwice, Gorzów Wielkopolski, Jaworzno, Kalisz, Kielce,
Leszno, Lublin, Łomża, Łódź, Ostrołęka, Piotrków Trybunalski, Poznań, Przemyśl,
Radom, Rzeszów, Sosnowiec, Świętochłowice, Świnoujście, Tarnobrzeg, Tarnów,
Tychy, Żory
Białystok, Bielsko Biała, Bydgoszcz, Chorzów, Dąbrowa Górnicza, Gdańsk, Konin,
Kraków, Krosno, Mysłowice, Nowy Sącz, Olsztyn, Opole, Piekary Śląskie, Ruda
Śląska, Rybnik, Siedlce, Siemianowice, Słupsk, Sopot, Szczecin, Toruń, Włocławek,
Zabrze, Zielona Góra
130.1-140.0
Częstochowa, Jastrzębie Zdrój, Koszalin, Płock, Suwałki, Wrocław
16
Strategia Polska 2030 [Poland 2030 Strategy], Warszawa, 2010, p. 373. Fast development of Poland is also
mentioned in recent UNDP report: Human Development Report 2010, The Real Wealth of Nations: Pathways
to Human Development, p. 143, et seq.
27
Increase in budgetary
revenues in 2005
(2003=100%)*
140.1-150.0
Municipal districts
Gdynia, Warszawa
Source: A. Miszczak op.cit. p. 279.
*calculation of the dynamics based on PLN PPP, ** regional capitals in Poland.
Social, economic and technical development of regions, districts and local communities
depends on many factors such as social capital, functioning of public administration,
development of metropolies, foreign capital invested in Poland, as well as the influence of
European Union upon the country. These factors could also contribute to an increase or
decrease of social inequalities in health.
1.3.1. Social capital
Social capital has been recognized as a factor important not only for fast or low development,
but also as an important social determinat of health17. It was the subject of careful studies
conducted in Poland in years 2003, 2005 and 200718. In 2007, only 11.5% of young Polish
adults trusted other people – much less than in Nordic countries such as Denmark (64.3),
Norway (62.5%) or Finland (58.4), but also less than in the Czech Republik (19.1) and
Hungary (14.9) (see Table 9).
Denmark
Norway
Finland
Sweden
Netherlands
Ireland
Switzerland
Austria
UK
Belgium
Germany
Spain
France
Slovenia
Czech Rep.
Italy
Greece
Hungary
Portugal
Poland
Table 9. Share of people trusting other people (18 years of age and above)
64.3
62.3
58.4
52.1
47
45.8
42.3
31.7
30
27.5
27.4
25.1
20
19.7
19.1
18.6
15.3
14.9
13.6
10.9
Source: European Social Survey, Diagnoza społeczna [Social Diagnosis], 2007, see also: Poland 2030 Strategy,
p.249.
Moreover, participation rate of Polish citizens in different types of NGOs is the lowest in
comparison to the other countries – 50% lower than in the case of Denmark, Holland or
Ireland19. Therefore, the development of social capital was recognized as a top priority in
17
G.Dahlgren, M. Whitehead, European strategies for tackling social inequalities in heath. Leveling up, Part 2.
WHO, Copenhagen, 2007, p. 80, et seq.
18
Czapinski T, T. Panek, 2009, Diagnoza społeczna- Warunki i jakość życia Polaków [ Social Diagnosis,
Objective and Subjective Quality of Life in Poland]. The authors studied, among other, the following aspects of
social capital: level of social trust, level of citizen’s activities, structure of NGOs, level of cultural potential,
social attitudes and key social values.
19
Studies conducted by The Centre for Democracy and Civil Society.
28
‘Poland 2030’ strategy. However, it is expected that social capital increase will be slow, even
if all required measures are implemented.
1.3.2. Polish metropolises
Polish metropolises play a key role in the development of Polish regions, districts and local
communities, as well as the entire country. The following seven cities perform the function of
metropolitan centres: Gdańsk, Kraków, Katowice, Łódź, Poznań, Warszawa, Wrocław20.
There are significant differences among Polish metropolitan areas. In 2003, the population of
Katowice and Warszawa metropolitan areas was about 2.5 mln inhabitants each (respectively:
2.7 mln and 2.6 mln citizens). The population of each of the remaining metropolises is close
to one million. Warszawa, the most developed metropolitan centre, has the best prospects for
the fastest future development. There are also good prospects for development of Poznań,
Wrocław and Kraków. On the other hand, the development of Katowice metropolitan area21
has been relatively slow due to serious barriers resulting from historic development of mining
and steel industry in Górnośląskie region22. Key information on the Polish metropolies is
presented in Table 19, Annex 1.2.
Development of metropolitan cities has usually been faster than the development of regions in
which these cities are located (especially in the case of Kraków, Warszawa and Poznań).
Besides, metropolitan influence is positive mostly in the case of istricts (and local
communities) located in the distance of 20-50 kms from the city. However, more remote
districts frequently experience long lasting crisis due to daily or weekly commuting of their
most active citizens to the metropolitan area in search for much better career, educational and
cultural opportunities. Moreover, within several years, many commuters relocate with their
families to metropolitan city for a permanent residence.
All kinds of daily, weekly or occasional inter- or intra- regional migrations have significant
impact upon: household revenues, quality of life, access to health care services as well as
treatment costs. Therefore, the impact of these migrations on the life of district populations
20
Bohdan Jałowiecki, Metropolie jako bieguny rozwoju, w: Polska regionalna i lokalna w świetle badań
EUROREGu [Metropolises as growth poles, in: Regional and local Poland in the light of EUROREG research],
G.Gorzelak (ed.), Wydawnictwo naukowe Scholar, Warszawa, 2007, pp. 155 et seq. . Other experts also include
Szczecin and Bydgoszcz-Toruń as metropolises, and Lublin and Białystok as two emerging metropolitan areas,
see: M. Smętkowski Dynamika rozwoju regionalnego Dynamics of regional development in: G. Gorzelak,
A.Tucholska (eds.), Rozwój, region, przestrzeń [Development, region, space, Centrum Europejskich Studiów
Regionalnych i Lokalnych UW, Warszawa, marzec 2007, p.229.
21
More precisely, Górnośląskie conurbation comprising 12 towns.
22
Taking into account experiences of similar regions of Ruhra and Pas de Calais, it could be predicted that
revitalisation of Górnośląskie towns and post industrial areas that make up the conurbation could last even 3040 years (B. Jełowiecki, op.cit. p. 149).
29
should be included in further studies of social inequalities in health. Further analysis of
migrations should be focused, among others, on the structure of helth care serviced delivered
to patients from other regions. Moreover, train connections between district towns and
regional capitals should be included, side by side with real average travelling time (which, in
many cases, takes much longer than what is shown in the timetables), and the cost of travel.
1.3.3. Foreign investments
Geographic differences in development are caused, inter alia, by uneven allocation of foreign
investments in Poland. This kind of capital is very important because it improves innovation
of Polish industry and creates many new jobs.
In the country, total foreign investments were the highest in comparison to other former
socialist countries (including the Visegrad Group)23.
Table 10. Total amount of foreign investments in Visegrad Group countries (billions of USD)
Country
2004
2005
2006
2007
2008
13.1
10.4
19.2
22.6
16.5
Poland
5.0
11.7
6.0
10.4
10.7
The Czech
Republic
4.5
7.7
6.8
6.1
6.5
Hungary
3.0
2.1
4.2
3.3
3.4
The Slovak
Republic
Source: Sabina Krawczyk, “Implikacje napływu bezpośrednich inwestycji zagranicznych na gospodarki krajów
Grupy Wyszechradzkiej, Dom Wydawniczy „Agnus”, Gliwice, 2010 p.84.[Implications of foreing direct
investments for the economies of Visegrad Group countries]
Moreover, the number of new jobs created thanks to foreign investments in Poland was
among the highest in the EU.
Table 11. New jobs created by foreign investments (2008)
Position
in
the
ranking
1
2
5
10
Country
2007
Percentage*
(2007)
2008
Percentage *
(2008)
United Kingdom
24,186
14
20,196
14
Poland
18,399
10
15,512
10
Hungary
11,104
6
11,659
8
The
Czech
15,102
9
5,626
4
Republic
12
The
Slovak
8,479
5
3,660
2
Republic
* percentage of the total number of new jobs created by foreign investments in Europe.
Trend
2007 –2008
- 16
- 16
+5
- 63
- 57
23
However, Poland is at the bottom of the ranking list in terms of the amount of foreign investments per capita not only among Visegrad Group countries, but also in comparison to Romania, Bulgaria, Estonia, Lithuania,
Slovenia and Latvia.
30
Source: Ernst and Young, Reinvesting European Growth. Ernst and Young’s 2009 European Atractiveness
Survey. Ernst and Young, 2009, p. 16; quoted after: Sabina Krawczyk op. cit. p. 109.
Foreign investors usually prefer those regions in which Polish metropolies are located.
Therefore, the development of these regions has been faster, in comparison to other regions.
Moreover, foreign investors prefer some metropolises over others (see Table 12).
Table 12. Number of employees working for firms with foreign capital (until 2008)
Region
Number
of Foreign
capital
Per cent
Number of
employees
Per cent
Total
21,092
145996.9
100%
1,531,668
100%
Name of the
metropolis
located in the
region
-
Dolnośląskie
2,112
13410.3
9.19
149,644
9.77
Wrocław
Kujawsko-
537
2246.3
1.54
38,376
2.51
-
Lubelskie
329
722.1
0.49
21,647
1.41
-
Lubuskie
776
1939.5
1.33
37,455
2.45
-
Łódzkie
867
3860.3
2.64
68,781
4.49
Łódź
Małopolskie
1,251
10636.2
7.29
86,283
5.63
Kraków
Mazowieckie
7,622
73084.1
50.06
535,589
34.97
Warszawa
Opolskie
462
1455.8
1.00
25,292
1.65
-
Podkarpackie
317
2009.8
1.38
44,569
2.91
-
Podlaskie
127
275.4
0.19
10,130
0.66
-
Pomorskie
1,216
3948.8
2.70
67,890
4.43
Gdańsk
Śląskie
1,882
11739.0
8.04
157,527
10.28
Katowice
Świętokrzyskie
164
2709.0
1.86
17,571
1.15
-
Warmińsko-
291
1401.2
0.96
15,224
0.99
-
1,923
12880.1
8.82
205,417
13.41
Poznań
3679.2
2.52
50,273
3.28
-
firms
Pomorskie
Mazurskie
Wielkopolskie
Zachodniopomorskie 1,216
Source: Statistical data of the Central Statistical Office, Warszawa, 2009, p. 36 www.stat.gov.pl.
31
1.3.4. Public governance
Overall effectives of Polish public administration, in comparison to other countries, is low
Fig. 2. Effectiveness of governance in Poland and other countries in 2007
Source: The Effectiveness of governance in Poland and other countries in 2007, quoted after:
Strategia „Sprawne Państwo 2011-2020” [Draft of Effective Governance Strategy 20112020, p. 8.]
Moreover, in years 1966-2007, decrease in effectiveness was observed.
Fig. 3. Effectiveness of Polish public administration in years 1966-2007
Source: The Effectiveness of governance in Poland and other countries in 2007, quoted after:
Strategia „Sprawne Państwo 2011-2020” [Draft of Effective Governance Strategy 2011-2020], p. 8.
32
It has also been emphasized that governance of national development is not effective due to:
•
insufficient integration of socio-economic and territorial planning,
•
domination of sectoral approach over inter-sectoral one,
•
insufficient integration of budgetary planning with strategic development goals,
•
no continuity of strategic thinking (due to political shifts of power),
•
lack of qualified personnel with capacity to properly develop and implement different
strategies24.
International analysis also revealed that functioning of public administration in former
socialist countries is often not transparent. Unfortunately, Polish administration is not an
exception in this case (see Table 13).
Table 13. Countries with the lowest and the highest level of corruption
Countries with the lowest level of corruption (according to Control of Corruption Index)
2000
1. Holland
2. Ireland
3. The United Kingdom
4. Germany
5. Finland
2003
1. Finland
2. Denmark
3. Luxembourg
4. The Netherlands
5. Sweden
2006
1. Finland
2. Denmark
3. Sweden
4. Holland
5. Luxembourg
Countries with the highest level of corruption (according to the Control of Corruption Index)
2000
1. Latvia
2. The Slovak Republik
3. The Czech Republik
4. Lithuania
5. Poland
2003
1. Latvia
2. Lithuania
5. Poland
2006
1. Lithuania
2. Poland
3. Italy
Source: Word Bank, quoted after: Effective Governance Strategy op. cit. p. 33.
The weaknesses of Polish public administration could also negatively influence the
development and implementation of complex, long range inter-sectoral programmes aiming at
reduction of inequalities in health.
1.3.5. European Union
The influence of European Union upon general development of Poland has been very
significant. In 2004, Poland absorbed only 1.8 bln PLN, but in 2004-2008 the total amount of
EU financial resources spent in Poland was equal to 28.4 bln PLN; including 4.6 bln PLN for
24
See: Strategia Sprawne Państwo, [Effective Governance Strategy], pp. 14-15, see also: A. Zybała (ed.)
Wyzwania w systemie ochrony zdrowia- zasoby ludzkie i zasoby organizacyjne w centralnych instytucjach.
Challenges in heath care – central human and organizational resources., KSAP, Warsaw, 2009 (The report
commissioned by the World Health Organization (Regional Office for Europe).
33
human capital development. Thanks to this, 1.1 mln people (employed and unemployed)
participated in the programmes financed by European Union aimed at the development of new
professional skills, supply of new equipment to schools, and protecting people against social
exclusion.
It is estimated that, in 2004-2007, 15 – 20% of all new jobs in the country were created thanks
to EUfinancial resources. EU resources also played an important role in reducing negative
impact of global crisis on Polish economy25. Moreover, it is expected that unemployment rate
in Poland will drop in 2013 by half, from 11.6% to 6% percent, thanks to all the activities
based on EU financial support26 A significant increase in Polish labour market effectiveness is
also expected in 2010-2013. Furthermore, a strategy for reducing differences between the
most underdeveloped and the most developed Polish regions was created and will be
implemented in 2013-202027.
EU accession created new opportunities for massive migrations of Polish citizens abroad –
first, to the United Kingdom, Ireland and Sweden, and two years later to other countries such
as Holland, Spain, France and Italy. In the period of 2004-2007, about 2.3 million people
migrated abroad. Most intensive migrations occurred in 2005 and 2006 (450 000 and 500 000
people left Poland at that time, respectively). However, in 2008 and 2009 the number of
migrants dropped significantly due to the global crisis. Moreover, some migrants came back
to Poland (mostly from the UK and Ireland), but many others went to countries less affected
by the crisis.
It is estimated that 80% of all migrants were looking for better job opportunities. Among
those who left Poland, young people dominated – many of them with university or technical
education and with good command of foreign languages. Almost half of them (47%) had been
employed in Poland before departure, 22% were unemployed, and only 5% were inactive on
labour market28. Most of the migrants lived in poorly urbanized parts of the country, in such
regions as Podkarpackie, Opolskie, Świętokrzyskie and Zachodniopomorskie.
Those migrations bring about some benefits, but they also incur high costs. Many migrants
have improved their economic status and were able to offer higher financial support to their
families living in Poland. In the period of 2004-2007 their cash transfers to Poland increased
25
See governmental programme: Plan stabilności i rozwoju, [Plan for Stability and Development], Nov. 30,
2008. The Chancellery of the Prime Minister.
26
This very optimistic prediction might not come true due to negative influence of global crisis.
27
5 lat Polski w Unii Europejskiej, [Five years of Poland in the European Union], UKIE, Warszawa, 2009 pp.
259, et seq.
28
Paweł Kaczmarczyk, Poakcesyjne migracje Polaków –próba bilansu [Post Accession Migrations of Poles –
Attempt at Summation] in Studia Migracyjne i Polonijne (in print).
34
from 10 to 20 bln PLN. Families spent this money for a living, to improve their living
conditions, and to educate their children.
On the other hand, migrations generated many social and economic problems. Parents left
their families, therefore one parent (or only grantparents or relatives) were looking after
children. Besides, migration of many young people, often with the intention to stay abroad for
good, could reinforce negative demographic trends in Poland.
Due to migrations, serious and long lasting shortages on Polish regional and local labour
markets emerged. In this case, health care sector was not an exception. Many medical doctors
left Poland. Their migration has been more dangerous in Poland than in other countries. In
2008 there were only 2.2 medical doctors per 1,000 inhabitants in Poland, relative to 3.6 in
the Czech Republic, 3.1 in Hungary, and 3.0 in the Slovak Republic29.
Such shortages resulted in significant increase of earnings in Poland, despite the crisis, and
stimulated an increase in labour costs and inflation; it also exerted negative influence on
foreign investments in Poland. Moreover, they aggravated geographic differences, because in
some regions, especially in many local communities and small towns, serious shortages on
labour market brought about a decrease in investments30.
According to some projections, benefits and costs of massive migration, presented above,
might actually become even more pronounced in the near future, as Germany and Austria will
grant Poles non-restricted access to their labour markets in 2011.
Summary
Polish regions and districts were created six hundred years ago, but their development was not
stable due to the fall of Poland in the eighteenth century, and due to many changes introduced
in the twentieth century, followed by the re-gaining of country independence. In consequence
of the last reform of1999, Poland was divided into 16 regions, 373 municipal and rural
districts, and about 2,500 municipalities. This introductory chapter:
-
presents historic outline of districts,
-
describes their territorial, population, economic and technical development,
-
outlines discrepancies among the districts.
29
Value for Money in Health Spending, OECD Health Policy Studies, Paris, 2010 p. 49.
5 years of Poland in EU op. cit. pp. , 254, 261-269, http://www.cie.gov.pl/HLP/files.nsf/0/
B6319D9A6E54228AC1257619004A5F5B/$file/piec_lat_polski_w_unii_europejskiej.pdf
See also: Projekt opracowania: Polityka migracyjna Polski- stan obecny i postulowane działania [Draft.
Migration policy of Poland – characteristics of curent situation and proposed actions], Ministerstwo Spraw
Wewnętrznych i Administracji [Ministry of Interior Affairs and Administration], Warszawa, 2010 r.
30
35
Additionally, the chapter includes an analisis of the following factors stimulating and
hampering ongoing development of the districts:
- low level of social capital,
- permanent and temporrary migrations influencing the life of district and local
communities,
-
state policy strongly supporting fast development of local, distric and regional selfgovernance,
- low effectiveness of governance in Poland,
- positive and negative influence of metropolises upon regional, district and local
development,
- influence of uneven allocation of foreign capital in the country on regional and local
labour markets,
- positive and negative influence of the European Union on the social and economic
development of the country.
The above factors could also exert positive or negative influence on the implementation of
complex, inter-sectoral strategy for reducing social inequalities in health that should be
developed in the near future. Therefore, the analysis of issues discussed in this chapter
should be continued.
36
Annex 1.1. Lists
List 1. Groups of variables used in the index and sub-indexes to evaluate quality of life
in municipal districts
1. demography (0.5 – weight of the indicator based on a survey),
2. natural environment (1.0),
3. living conditions (housing) (1.0),
4. access to commercial services (0.25),
5. municipal transport (1.0),
6. access to educational services (pre-schools, schools, universities) (1.0),
7. access to cultural services (museums, public libraries, cinemas, theatres,
philharmonies) (0.75),
8. health status and access to health services (pre-mature deaths, deaths of infants, health
centres, hospitals, clinics, pharmacies, number of medical doctors and dentists) (2.0),
9. standard of living (1.0),
10. level of public safety (2.0),
11. access to sports and leisure services (swimming pools, sports fields, tennis courts,
recreational areas, etc.) (1.0).
Source: Krystyna Gawlikowska-Hueckel and others op. cit. p.15.
List 2. Variables used to characterise rural district populations
1. number of inhabitants in non-productive age per 100 people in productive age,
2. number of inhabitants per 1 sq. km,
3. fertility rate per 1,000 inhabitants,
4. number of marriages per 1,000 inhabitants,
5. number of divorces per 1,000 inhabitants,
6. number of new born live infants per 1,000 inhabitants,
7. number of deaths per 1,000 inhabitants,
8. balance of internal migrations (within Poland),
9. number of deaths of new born infants per 1,000 infants born alive.
Source: Kamila Migdał- Najman, Krzysztof Najman, op.cit. 74, 75.
37
List 3. Variables used to characterize economic development of rural districts
1. total number of employees per 1,000 inhabitants,
2. share of people employed in: agriculture, hunting, forestry, fishery (% of the total),
3. share of people employed in industry and construction (% of the total),
4. share of people employed in market services sector (% of the total),
5. share of people employed in non-market services sector (% of the total),
6. total number of registered unemployed inhabitants,
7. total number of unemployed women,
8. gross average monthly earnings (in PLN),
9. unemployment rate (%),
10. budgetary revenues of local communities per capita (in PLN),
11. budgetary expenditures of local communities per capita (in PLN),
12. investments in industrial firms located in a particular district per capita (in PLN),
13. gross value of durable assets of industrial firms per capita (in PLN),
14. number of companies registered in the REGON: companies (with legal personality)
and units of companies (without legal personality).
Source: Powiaty w Polsce [ Districts in Poland, The Central Statistical Office], Warszawa,
2001, in: Kamila Migdał-Najman, Krzysztof. Najman op.cit. p.75.
List 4. Variables used to measure the development of rural district technical
infrastructure
1. length of water pipelines in kms per 100 sq. kms,
2. length of communal disposal pipelines in kms per 100 sq. kms,
3.
length of gas pipelines in kms per 100 sq. kms,
4. inhabitants with access to wastewater treatment plants (% of the total number of
inhabitants),
5.
number of telephones per 1,000inhabitants,
6. length of public local and district roads.
Source: Jarosław Lira, Feliks Wysocki, op.cit. p. 42.
38
Annex 1.2. Tables
Table 14. Characteristics of the most underdeveloped regions (2008)
No.
Indicators
Lubelskie
Podlaskie
Podkarpackie
Świętokrzyskie
1.
Rate
of
unemployment
Number
of
employed
people
per
1000
inhabitants
Number
of
people
working
in
agriculture (%
of
all
employed)
Investments
per capita (in
PLN)
Number
of
registered
firms
in
REGON per
10,000
inhabitants
Average gross
monthly
earnings
(PLN)
11.2
9.7
13.0
355.9
351.5
36.2
2.
3.
4.
5.
6.
Poland
13.7
Warmińskomazurskie
16.8
329
368.4
298.8
359.5
33.2
23.0
30.6
15.9
15.6
3,526
4,046
3,759
4,384
4,140
5,700
715
757
687
852
812
985
2,604
2,610
2,490
2,549
2,474
2942
9.5
Source: Wiolleta Czermel Grzybowska (ed.) Finansowanie rozwoju regionalnego z funduszy
strukturalnych 2007-2013. Polska Wschodnia, szanse i możliwości rozwoju [Funding regional
development from EU structural funds 2007-2013. Eastern Poland, chances and opportunities
of development], Wydawnictwo Politechniki Białostockiej, Białystok, 2010, p. 27.
Table 15. Differences among districts (2002)
Territory
Population
Budgetary revenues
No
Budgetary expenditures
per capita
.
Maximum
District
sq.kms
District
District
PLN
District
PLN
Number
per
per
of
capita
capita
citizens
(000)
1.
Białostocki
2985
Warszawa
1 688
Warszawa
3243
Warszawa
3753
2.
Olsztyński
2840
Łódź
785
Sopot
3120
Sopot
3273
3.
Bialski
2754
Kraków
757
Nowy Sącz
2907
Płock
2946
4.
Słupski
2304
Wrocław
639
Płock
2855
Nowy Sącz
2732
39
Territory
Population
Budgetary revenues
No
Budgetary expenditures
per capita
.
Maximum
District
sq.kms
District
District
PLN
District
PLN
Number
per
per
of
capita
capita
citizens
(000)
5.
Kielecki
2248
Poznań
577
Krosno
2688
Katowice
2687
6.
Bytowski
2193
Gdańsk
461
Katowice
2688
Krosno
2675
7.
Ostrołęcki
2099
Szczecin
415
Świnoujście
2685
Wrocław
2594
8.
Sokolski
2054
Bydgoszcz
372
Gliwice
2523
Poznań
2560
9.
Szczycieński
1933
Lublin
358
Opole
2448
Leszno
2530
10.
Poznański
1900
Katowice
325
Ostrołęka
2397
Slupsk
2497
PLN
District
PLN
Minimum
District
Km2
District
Number
District
of
per
citizens
capita
(000)
1.
Świętochłowice
13
Sejneński
27
Siedlecki
186
Siedlecki
179
2.
Sopot
17
Bieszczadzki
22
Rybnicki
201
Rybnicki
205
3.
Siemianowice
25
Węgorzewski
24
Kaliski
209
Skierniewicki
227
Śląskie
4.
Ostrołęka
29
Leski
27
Przemyski
209
Kaliski
230
5.
Zamość
30
Gołdapski
27
Skierniewicki
228
Częstochowsk
244
i
6.
Leszno
32
Brzeziński
31
Koniński
235
Koniński
245
7.
Siedlce
32
Łosicki
33
Częstochowski
241
Przemyski
245
8.
Łomża
33
Bialobrzeski
34
Gliwicki
246
Gliwicki
246
9.
Skierniewice
33
Nidzicki
34
Tarnowski
248
Poznański
250
10.
Chorzów
34
Olecki
34
Bieruńsko-
249
Ppolski
251
lędziński
Source: Powiaty w Polsce [Districts in Poland], GUS [Central Statistical Office], , Warszawa, 2003 r. p.
643-651
40
Table 16. Rural district populations. Average values of three groups of districts
No.
Variable
Group 1
Group 2
Group 3
Average for
Poland
69.4
74.8
62.6
68.3
2
Number of inhabitants in non-productive
age per 100 people in productive age
Number of inhabitants per 1 sq. km
89.7
63.9
139.1
101.0
3
Fertility rate per 1,000 inhabitants
2.9
-0.7
0.1
1.1
4
Number of marriages per 1,000 inhabitants
5.8
5.6
5.3
5.6
5
Number of divorces per 1,000 inhabitants
0.7
0.5
1.0
0.8
6
Number of new born live infants per 1,000
11.7
10.5
9.5
10.6
1
inhabitants
7
Number of deaths per 1,000 inhabitants
8.7
11.3
9.4
9.6
8
Balance of internal migrations (within
-6.5
-115.9
89.0
1.0
7.7
7.8
8.3
8.0
Poland)
Number of deaths of new born infants per
1,000 infants born alive
9
Source: Kamila Migdał Majman, Krzysztof Najman op. cit. p. 78
Table 17. Economic development of rural districts. Average values of five groups of districts
and for the entire country
No.
Variable
Group 1
Group 2
Group 3
Group 4
Group
Poland
5
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Total number of employed
per 1,000 inhabitants
Employed in: agriculture,
hunting, forestry, fishery
(% of the total)
Employed in industry and
construction (% of the
total)
Employed
in
market
services sector (% of the
total)
Employed in non-market
services sector (% of the
total)
Total number of registered
unemployed inhabitants
Total
number
of
unemployed women
Gross average monthly
earnings (in PLN)
Rate of unemployment (%)
Budgetary revenues of
local communities per
capita (in PLN)
357.4
318.8
397.9
302.6
444.6
373.3
30.7
42.5
61.7
34.6
70.1
50.8
34.9
25.9
17.5
30.0
12.1
22.4
18.2
13.7
8.9
16.4
6.8
12.0
16.3
17.9
11.9
19.1
11.0
14.9
5,714.8
5292.6
10993.4
10466.8
5039.2
6656.6
3,311.8
2940.9
5861.5
5830.1
2648.0
3645.4
1,959.6
1573.3
1635.4
1626.7
1596.2
1656.2
13.3
22.2
17.9
25.7
15.3
18.6
1334.7
1180.5
1096.8
1172.2
1081.1
1160.9
41
No.
Variable
Group 1
Group 2
Group 3
Group 4
Group
Poland
5
11.
Budgetary investment
expenditures of local
communities per capita (%)
23.1
19.7
22.2
12.
Investments in industrial 2285.5
883.5
827.0
firms located in a particular
district per capita (in PLN)
13.
Gross value of fixed assets 25,752.4
8299.2
9559.8
of industrial firms per
capita (in PLN)
14.
Number of units registered 862.7
461.2
717.2
in the REGON: firms with
legal personality and units
of firms (without legal
personality)
Source: Kamila Migdał Najman, Krzysztof Najman op.cit. p. 82.
19.9
18.9
20.2
1085.3
644.2
1046.7
10549.8
6498.4
10891.3
789.9
373.6
571.3
Table 18. Rural districts with the best and the worst developed technical infrastructure
The best developed districts
The worst developed districts
Pruszkowski
Mazowieckie
0.784
Nowotomyski
Wielkopolskie
-0.143
Bielski
Śląskie
0.601
Szczecinecki
Zachodniopomorskie -0.143
Oświęcimski
Małopolskie
0.538
Hajnowski
Podlaskie
-0.146
Łanńcucki
Podkarpackie
0.483
Międzyrzecki
Lubuskie
-0.150
Cieszyński
Śląskie
0.461
Sulenciński
Lubuskie
0.150
Wodzisławski
Śląskie
0.459
Krośnieński
Lubuskie
-0.151
Wielicki
Małopolskie
0.444
Ostrołęcki
Mazowieckie
-0.152
Tyski
Śląskie
0.441
Przysuski
Mazowieckie
-0.153
Chrzanowski
Małopolskie
0.406
Słubicki
Lubuskie
-0.157
Krakowski
Małopolskie
0.402
Szczycieński
Warmińsko-
-0.159
Mazurskie
Wadowicki
Małopolskie
0.396
Czarnkowsko-
Wielkopolskie
-0.159
Lubuskie
0.160
trzcinecki
Piaseczyński
Mazowieckie
0.396
Strzeleckodrezdenecki
Aleksandrowski
Kujawsko-
0370
Drawski
Pomorskie
Mikołowski
Śląskie
0.364
Walecki
Będziński
Śląskie
0.361
Bieszczadzki
Podkarpackie
-0.195
Pszczyński
Śląskie
0.302
Sokołowski
Mazowieckie
- 0.302
Source: J. Lira, F. Wysocki op. cit. p. 44.
42
Table 19 Characteristics of Polish metropolises
Metropoli
tan capital
Popul
ation
Ad
de
d
val
ue
(A
V)
AV
Fixe
d
asse
ts
Revenue
s of local
commun
ities
(LCs)
No.
of
work
ing
peopl
e per
1,00
0
Investm
ents of
firms
Investm
ents of
LCs
No. of
registe
red
firms
Aver
age
salary
Balanc
e of
migrati
ons
Rema
rks
(000)
(00 (%)
mln mln
mln
mln
%
0)
PL
PLN
PLN
PLN
PL
N
N
Warszaw
1688. 72. 100.
104. 3058
473
13567
1020
509
100
++
a
2
5
0
7
Katowice
1926, 52. 72.7
40.7 2235
287
2246
305
220
68
*
6
7
Łódż
785.1 51. 70.9
24.5 1952
267
2341
266
248
60
4
Kraków
757.5 53. 74.3
43.9 2355
350
3359
463
353
68
+
9
Gdańsk** 756.6 57. 79.0
45.1 2198
296
2891
478
349
78
3
Wrocław
634.0 53. 74.3
34.3 2385
315
5172
483
378
70
++
9
Poznań
577.1 63. 87.0
51.6 2243
385
7427
272
362
74
0
1
*More precisely, Górnośląskie conurbation including 12 towns31.** More precisely, agglomeration of Gdańsk, Gdynia and
Sopot.
Source: B. Jałowiecki Metropolie jako bieguny rozwoju [Metropolises as growth poles], in: G. Gorzelak, A.
Tucholska op.cit. pp. 149, 150.
31
A conurbation is a region comprising a number of cities, large towns, and other urban areas that, through
population growth and physical expansion, have merged to form one continuous urban and industrially
developed area. In most cases, a conurbation is a polycentric urban agglomeration, in which transportation has
developed to link areas to create a single urban labour market or travel to work area. The term "conurbation" was
coined as a neologism in 1915 by Patrick Geddes in his book Cities In Evolution. See: the American Wikipedia.
43
Annex 1.3. Figures
44
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47
2. Social and economic characteristics of districts in Poland
Agnieszka Chłoń-Domińczak
This chapter presents the assessment of social and economic characteristics of districts in
Poland, with particular focus on those characteristics that may have impact on health status of
Polish population. It draws from earlier work presented in the report entitled Social
Inequalities in Health in Poland, which included the first description of selected social and
economic indicators of districts with the lowest and the highest mortality levels.
The analysis aims to assess changes in social and economic developments in all Polish
districts over time, as well as to monitor the district (powiat) variation of selected
characteristics. With the choice of this level it is possible, on the one hand, to grasp relatively
small administrative units in order to reflect geographical variation of social and economic
factors and, on the other hand, to select a relatively large set of available indicators.
One of the most important assumptions is that this analysis can be repeated in the future.
Thus, selection of variables is based on available statistical data, mainly from the Regional
Data Bank (Bank Danych Regionalnych) available from the Central Statistical Office. In order
to provide sufficient background information for further analysis of inter-relations between
economic and social characteristics and mortality, the analysis is conducted primarily for two
years: 2002 and 2007. In some cases, to ensure comparability of data, different years are used.
The chapter is organized as follows: first, it presents the choice of a selected groups of
variables as well as indicated limitations of data applied, followed by a brief analysis of
variables selected. The analysis is followed by the assessment of relations between selected
variables based on the correlations between them. The chapter ends with a summary and
conclusions.
2.1. Selection of variables
Social determinants of health are the conditions in which people are born, grow up, live, work
and age. These conditions influence a person’s opportunity to be healthy, his/her risk of
getting ill, and life expectancy. Social inequities in health – the unfair and avoidable
differences in health status across groups in society – are those that result from uneven
distribution of social determinants32.
Commission on Social Determinants of Health (CSDH) presented a conceptual framework for
understanding social determinants of health and health inequalities, which includes socio32
http://www.euro.who.int/en/what-we-do/health-topics/health-determinants/socioeconomic-determinants
48
economic and political context, social cohesion, as well as individual characteristics (Błąd!
Nie można odnaleźć źródła odwołania.4). This portrays the significance of socio-economic,
political and cultural contexts, an individual’s social position, health systems and health
behaviour in shaping the distribution of health and well-being. SDH are related to specific
features of societal conditions and the pathways by which they affect health. Examples include the
prevailing political structure, income, education, occupation, family structure, service availability,
sanitation, exposure to hazards, social support, racial discrimination, and access to resources
linked to health (Marmot and Wilkinson 1999). Correspondingly, inadequate income, housing,
and work environments are some of the SDH leading to health inequalities within and between
countries (Wilkinson and Marmot 2003).33
Fig. 4. Conceptual framework of the Commission on Social Determinants of Health
Source: Commission on Social Determinants of Health
Social determinants of health vary from one country to another, but also within countries and
within regions. The main goal of the report is to analyse socio-economic determinants of
health in Polish districts. Florey et al. (2007) present a conceptual framework of multiple
“levels” of determination of population health, including global, national and community
levels, in which global-level factors influence national-level factors, which in turn shape
33
Lee at al. (2007), p. 16
49
community-level factors. In such an approach, by monitoring community-level factors, we
can also control for higher-level factors that impact community outcomes (
5).
Fig. 5. Multiple levels of determination of health
Florey et al. (2007)
The framework of social determinants of health is based on interactions between individuals
and their health and their social and economic characteristics, as well as the environment.
However, the information on individual characteristics is usually scarce and based on sample
surveys. Data from such surveys (such as EU Survey on Income and Living Conditions or
Labour Force Survey) does not allow for detailed geographical decomposition of obtained
results. In an attempt to understand these determinants on local level, one must identify
indicators that are available on district level, mainly from administrative data or statistical
information based on surveys covering the entire population (such as census data). In this
chapter we aim to identify indicators that would serve as best proxy of social determinants of
health and health inequalities, and which would be easily available and regularly updated,
preferably based on statistical information. The main source of data for proposed indicators is
the Regional Data Bank (Bank Danych Regionalnych) of Central Statistical Office. Additional
complementary information is drawn from administrative sources: results of lower secondary
school and matura exams from the Central Examination Board (Centralna Komisja
50
Egzaminacyjna), and local government election turnout from the National Electoral
Commission (Państwowa Komisja Wyborcza), information on the number of physicians from
Health Care Information Systems Centre (Centrum Systemów Informacyjnych Ochrony
Zdrowia) 34.
Proposed indicators are grouped into five areas: (i) demography; (ii) economic and labour
market situation; (iii) social cohesion; (iv) access to health care, and (v) education. Selection
approach corresponds to the idea of socio-economic and political context of social
determinants of health proposed by the CSDH.
Table 20. Indicators for district-level analysis of socio-economic determinants of health in Poland
Area
Demography
Economic and labour
market situation
Indicator
Definition
feminization rate
Share of women aged 25-34 per 100 men in the same
age
old-age demographic dependency
ratio
Number of people aged 60/65 and above per 100
people aged 18-59/64
population density
Number of inhabitants per square kilometre
revenue of local budgets per capita
Own revenue at gmina (municipality) level (aggragated
for districts) from taxation per one inhabitant
unemployment rate
Registered unemployment rate (number of persons
registered as unemployed in relation to the total number
of employed and unemployed)
share of employment in agriculture
Proportion of people working in agriculture to the total
number of people employed
share of employment in hazardous
conditions
Proportion of people working in hazardous conditions
(in all defined groups of risk) to the total number of
people employed
pre-school participation rate of
children aged 3-5
Shareof children aged 3-5 attending pre-schools
library members per 1000 inhabitants
Number of registered library members per 1 000
inhabitants in a district
share of households with a bathroom
Number of houses/apartments equipped with bathroom
divided by the total number of houses / apartments
local government election turnout
Number of valid votes as percentage of total number of
voters in elections to gmina councils
number of inhabitants per 1 health
care institution
Number of inhabitants divided by the number of health
care institutions (Zakłady Opieki Zdrowotnej)
number of inhabitants per 1 physician
Number of inhabitants divided by the number of
physicians who work in a district as their primary
employment
share of population with higher
education
Number of people aged 15 and more with higher
education as percentage of total population aged 15
and more
share of population with vocational or
lower education
Number of people aged 15 and more with vocational
education as percentage of total population aged 15
and more
Average lower secondary school
exams results (mathematics and
science)
Average results of lowe secondary school tests
aggregated by district from mathematics and science
exams
average lower secondary school
exams results (humanities)
Average results of lower secondary school tests
aggregated by district from humanities exams
Matura examl results - Polish
language (basic level)
Average results of mandatory Imatura tests at Polish
language and literature on basic level. aggregated by
district
Social cohesion
Access to health care
Education
34
The author would like to thank Dorota Węziak-Białowolska and Henryk Szaleniec from Educational Reseach
Institute for their help in providing the information on the results of middle school and high school exams;
Marek Dmowski from CSIOZ for providing data on the number of physicians, and the National Electoral
Commission for providing information on election turnout results.
51
Area
Indicator
Definition
Matura exam results - mathematics
(basic level)
Average results of mandatory matura tests at
mathematics on basic level aggregated, by district
Source:Author’s analysis
In the area of demography three indicators are proposed for further analysis. Differences in
feminization rate reflect the outcome of long-term migration processes. In view of the fact
that young women are, on average, the most mobile group of population, districts affected by
long-lasting outflow due to migration have lower feminization rate. Thus, this indicator can be
applied to capture the outcomes of migration that could be caused by generally unfavourable
local social and economic conditions, which may be important from SDH perspective.
The second indicator in this area is the demographic dependency rate, showing the ratio of
people in the so-called post-productive age to those in the so-called productive age. As a
result, we can capture the share of population above retirement age. Assuming that the health
situation deteriorates with age, this indicator helps identify potential “risk” districts that have
more aged population.
The third indicator shows population density. According to the literature (Marmot and
Wilkinson (2006); WHO (2008), Blas and Kurup (2010), Wallace (2008), Galea (2007))
population density can be one of social determinants of health. In particular, higher population
density may predict many health effects, including infectious diseases. This is due to the fact
that high population density areas are more exposed to spread of infectious diseases. On the
other hand, very low population density may lead to reduced access to some health care
services.
As far as economic and labour market situation in the districts is concerned, we suggest
four indicators. The first one is related to the own income from taxation revenues per capita.
This shows the level of taxation income generated from various taxation revenue, both from
enterprises and individuals at the local level. As a result, this indicator is a good proxy for
general macroeconomic situation of districts. The remaining three indicators in this area are
related to labour market situation. The first indicator is unemployment rate. According to
available research (Bartley et al, 2006) unemployment is associated with higher prevalence of
ill health and mortality, as well as damage to psychological health. Second and third
indicators are related to the structure of employment. Employment in hazardous conditions or
in selected branches can also be a factor affecting health outcomes. In the case of Poland,
particularly employment in agriculture can be investigated. High share of employment in
agriculture can be related first to difficult working conditions, but secondly it may indicate
hidden unemployment.
52
Social cohesion indicators group various characteristics. The first indicator is the share of
children aged 3-5 in pre-school education. This indicator relates directly to the
recommendation presented in WHO (2008) on the focus on early child development –
education, including pre-school, shapes children’s lifetime trajectories and health. It helps to
equalize chances of children in future life, especially for children from deprived
environments. As 6-year-old children are covered by mandatory pre-school education, we
focus on these children who are in pre-school age, but their participation is based both on
institutional availability and parental decision.
The second indicator is the share of public library members per 1000 inhabitants. This
indicator can be used as a proxy to measure the actual educational level and approach of
people living in communities to the notion of life-long learning in informal way. However,
this indicator is only a proxy, as some people prefer to have their own (sometimes sizeable)
libraries, which may distort the observation. The use of this indicator is proposed due to
availability of regularly updated information in the Regional Data Bank.
The third indicator is intended to measure quality of living conditions through monitoring the
share of houses and apartments equipped with a bathroom. Access to bathrooms is a precondition for necessary hygiene level that has a direct influence on mitigation of various
health risks.
The fourth indicator is aimed to measure social activity of communities, which can be
approached through monitoring the level of election turnout. Local elections were chosen with
the purpose to focus on general public interest with the activity of local governments for
communities. Election turnout, according to Putnam’s theory, is one of measures of social
capital.
We also suggest looking at the indicators that could serve as a proxy of access to healthcare.
Two such indicators are applied. The first one relates to the number of inhabitants per one
health care institution (the so-called ‘Zakład Opieki Zdrowotnej’/ZOZ), and the other one to
the number of inhabitants per one physician. With these indicators we can monitor the
availability of resources compared to population level. It should be noted, however, that these
indicators can serve as a very rough approximation. In particular, there is a difference in the
number of physicians in large cities vs. the surrounding districts. There is a tendency to
cluster physicians in the cities, but they provide services to surrounding districts as well.
The last area with proposed indicators is education. Level of educational attainments is one
of the most frequently quoted determinants of health. In our database we put forward
indicators related to the educational structure of district population – including the share of
53
population with higher education, as well as the share of population with vocational or lower
education. These indicators are based on national census findings, thus they can be assessed
only for 2002. Another group of proposed indicators includes average results of country-wide
exams: at the level of lower secondary school and upper-secondary school (so-calle matura).
They include results from tests at humanities as well as at mathematics. It should be noted,
however, that at present exam results are not comparable in time, thus only the analysis of
space distribution across districts can be made. Matura exam results are presented only for
2007, as 2002 data was not based on standardized approach. Additionally, as matura
mathematics examinations have been mandatory only since 2010, results include only those
students who took this exam at their preference in 2007. This means that results are not
comparable to Polish language exams, which are mandatory.
54
2.2. Time and space characteristics of selected variables
In this section statistical description of proposed indicators is presented. For each indicator,
descriptive statistics include: non-weighted average, standard deviation, coefficient of
variation, median, first and third quartile minimum and maximum, as well as skewness and
kurtosis coefficients. These statistics are presented for both analysed years, so the direction of
changes in time can be assessed, both from the perspective of changes in values and also
distribution, which is important to evaluate potential improvements in cohesion between
districts.
There are also histograms for each indicator, presented for the last analysed year (in most
cases, 2007, with the exception of election turnout which is based on the indicator for 2006
elections, as well as the share of people with selected educational attainment, based on 2002
census results). Attached maps illustrate regional distribution of monitored characteristics.
2.2.1. Demographic indicators
Feminization rate
Between 2002 and 2007, average feminization rate in Polish districts slightly declined (Błąd!
Nie można odnaleźć źródła odwołania.). The decline was accompanied by deepening
variation of this indicator. In particular, the range of values between minimum and maximum
increased, due to a drop in minimum value and increase in maximum value.
Feminization rate is the highest in the case of large cities (it is the highest for Warszawa) and
surrounding districts (i.e. powiat piaseczyński). Geographical distribution shows that
feminization rate is much lower in the eastern part of Poland.
If we look at the distribution of values, depicted in Błąd! Nie można odnaleźć źródła
odwołania.6, we can see that the distribution of feminization rate across districts is a bit
skewed on the left. The change in skewness coefficient also shows that it deepened between
2002 and 2007. In 249 out of 379 districts feminization rate decreased. These developments
could be caused by migration processes that were observed, especially after the EU accession.
Significant portion of migrants are young women, which could explain the reduction of
feminization rate in the observed majority of districts.
Old-age demographic dependency ratio
Between 2002 and 2007, old-age demographic dependency ratio increased on average in
Polish districts, which is an expected consequence of population ageing. However,
geographical variance of this indicator decreased, which is shown by reduced value of the
55
coefficient of variation. Despite that, the variation of demographic dependency ratio is
relatively high. The change of the value of coefficient was to a large extent caused by
increased values in relatively “young” districts, rather than further decreases of the value in
the relatively “old” districts. This development can be attributed to the observed birth decline
in the analysed period, as well as to migration processes.
Distribution of this indicators is slightly skewed to the right, but between 2002 to 2007
skewness was reduced, which means that old-age dependency ratio increased in more
districts. In 30 districts there are more than 3 people in post-productive age per 10 people in
productive age.
Geographical distribution shows that eastern and southern-central parts of Poland are
relatively older, especially compared to the north-western districts.
Population density
Average population density decreased between 2002 and 2007, while median remained
almost unchanged. This is caused by some reduction in population density in most congested
districts.
Distribution of population density is heavily skewed to the right. Average population density
is highly affected by the districts with very high population density (cities), as reflected in a
significant difference between average and median value. Three quarters of districts have
population density below 185.5.
Variance of observed values is very high, leading to very high values of the coefficient of
variation.
As far as geographical distribution is concerned, we can observe that northern part of the
country as well as the eastern border are least populated, while the areas around Warszawa
and Katowice are the most dense.
56
Table 21. Descriptive statistics of feminization rate
Fig. 6 Histogram of feminization rate in 2007
(number of women per 100 men in age group 2435)
feminization rate
2002
2007
average
standard deviation
coefficient of variation
median
first quartile
third quartile
minimum
maximum
skewness coefficient
kurtosis coefficient
95.73
3.69
3.86
95.88
93.67
98.21
84.98
105.03
-0.168
0.104
94.32
5.60
5.93
94.55
91.74
97.40
75.26
111.59
-0.333
1.005
Fig. 7. Geographical distribution of feminization rate in 2007
Source: Author’s calculations based on Bank Danych Regionalnych (Regional Data Bank)
57
Table 22. Descriptive statistics of old-age
dempgraphic dependency ratio
Fig. 8. Histogram of old-age demographic
dependency ratio in 2007 (people aged 60/65 and
above per 100 people aged 18-59/65)
Old-age demographic
dependency ratio
2002
2007
average
standard deviation
median
first quartile
third quartile
minimum
maximum
24.12
4.43
18.35
23.50
21.00
26.65
9.80
41.30
0.666
0.951
24.25
3.92
16.18
23.90
21.50
26.55
13.00
41.40
0.603
1.157
Fig. 9. Geographical distribution of old-age dependency ratio in 2007
Source: Author’s calculations based on Bank Danych Regionalnych(Regional Data Bank)
58
Table 23. Descriptive statistics of population
density
Fig. 10. Histogram of population density in 2007
(people per one square km)
Population density
2002
2007
Average
standard deviation
median
first quartile
third quartile
minimum
maximum
392.47 385.08
716.85 694.20
182.65 180.27
88.00
89.00
61.50
61.00
184.00 185.50
20.00
19.00
4256.00 4097.00
2.456
2.421
5.624
5.423
Fig. 11. Geographical distribution of population density in 2007
59
2.2.2. Economic and labour market indicators
In this section we present basic description of economic and labour market characteristics of
districts. In the case of labour market indicators, the first year of analysis is changed to 2004
as regards unemployment rate, and to 2003 as regards employment structure indicators. Such
change was made in order to ensure comparability of results between the first year under
analysis and 2007 in the light of methodological changes to labour market statistics
introduced by Central Statistical Office. In that way, we can analyse not only district-level
distribution of the characteristics, but also changes in time.
Own revenue of local budgets per capita
Between 2002 and 2007, average nominal own revenue of local budget per capita in districts
increased, which was, of course, to be expected. The increase was accompanied by the
increase of variation, which shows that increases in the districts with lower revenues were
lower in comparison to the districts with higher revenues, which resulted in increased
skewness, already very high in 2002.
Distribution of own revenues, depicted in the histogram below, shows that own revenue of
districts has features similar to the distribution of income in the population – while the median
income is below average, there is a group of districts with higher income on the right side of
the distribution.
Distribution of own revenues of local budgets is also peaked, with high concentration of
districts around median value; as a result, kurtosis coefficient is high. Between 2002 and
2007, peakedness of own revenue distribution increased.
Unemployment rate
District-level analysis of unemployment can be based on registered unemployment data,
pertaining to individuals registered in labour offices. It should be noted that this indicator
differs from the economic (ILO) definition of unemployment, i.e. the one stipulating that
unemployed persons are not working, are actively seeking work and are able to start working.
All of these conditions are not necessarily fulfilled by those registered as unemployed (for
example, some people registered may not be ready to start work immediately). Nevertheless,
the link between these two concepts of unemployment is sufficient to validate the analysis
based on registered data.
Labour market situation, measured by the level of registered unemployment rate, improved
between 2004 and 2007 – the non-weighted average value of indicator dropped by more than
a third. However, again we can see an increase in the coefficient of variation, showing that
60
this change was unevenly redistributed, and improvements were faster in the districts with
already low unemployment and slower in those with relatively high unemployment. As a
result, distribution of unemployment rate became more skewed to the left.
There is significant variation of unemployment between districts. In one quarter of districts,
unemployment rate in 2007 was higher than 18 percent, and in one quarter of districts it was
below 9 percent. Distribution of unemployment rate is also relatively flat, as reflected in the
negative value of kurtosis coefficient. Yet, between 2004 and 2007 its value increased.
Share of employment in agriculture
Between 2003 and 2007, share of employment in agriculture decreased, on average, which
follows the gradual trend of a decreasing share of people employed in agriculture, observed
for the past decades. In the future, this trend should continue, as the employment structure in
Poland should converge to those observed in Europe. The decrease was relatively small,
which also confirms observed trends.
In half of the districts, share of employment in agriculture was lower than 26.77 percent –
among those, there is a share of urban districts (including cities with district rights), which can
be noted at the histogram, forming a peak at values below 4.3 percent of total employment. In
one quarter of all districts the share of employment in agriculture is high and exceeds 44.4
percent. Distribution of employment in agriculture is skewed to the left and relatively flat
(with negative kurtosis coefficient).
Relatively high share of employment in agriculture is thus observed in a significant part of all
districts in Poland, which can indicate that this represents a universal risk of poorer health
outcomes.
Share of employment in hazardous conditions
Average share of employment in hazardous conditions did not change between 2003 and
2007. However, standard deviation of such employment decreased, showing that there was a
reduction in variation of employment in hazardous conditions between districts, which is a
positive development. This is also confirmed by other statistics. In particular, the value of
third quartile was decreased. This translates into reduction in the number of districts with
higher shares of employment in hazardous conditions. Distribution of this labour market
characteristic is highly skewed to the right, with very high skewneness coefficient values. It is
also relatively steep, which is shown by high values of kurtosis coefficients.
61
Statistics show that high shares of employment in hazardous conditions are observed in
relatively few districts, i.e. potential risk from population health status perspective is not
universal.
Table 24. Descriptive statistics of own revenue of
local budgets per capita
Fig. 12. Histogram of own revenue of local budgets
per capita in 2007 (in PLN)
Own revenue of local
budgets per capita
2004
2007
average
standard deviation
median
first quartile
third quartile
minimum
maximum
725.98
295.65
40.72
644.36
542.68
842.92
304.20
2600.64
2.124
7.566
1154.79
556.19
48.16
979.29
797.93
1349.40
456.88
4885.13
2.312
8.703
Fig. 13. Geographical distribution of own revenue of local budgets per capita in 2007
62
Table 25. Descriptive statistics of
unemployment rate
Fig. 14. Histogram of unemployment rate in 2007
(percentage)
Unemployment rate
2004
2007
Average
standard deviation
Median
first quartile
third quartile
Minimum
Maximum
22.43
7.71
34.39
21.50
16.95
27.70
6.20
42.70
0.397
-0.388
14.05
6.19
44.05
13.10
9.30
18.00
2.40
33.60
0.549
-0.047
Fig. 15. Geographical distribution of unemployment rate in 2007
63
Table 26. Descriptive statistics of share of
employment in agriculture
Fig. 16. Histogram of share of employment in agriculture
in 2007 (percentage)
Share of
employment in
agriculture
2003
2007
average
standard deviation
median
first quartile
third quartile
minimum
maximum
30.98
21.88
70.61
28.85
13.40
48.12
0.21
79.79
0.302
-0.916
29.32
21.35
72.82
26.77
12.23
44.40
0.17
78.95
0.405
-0.823
Fig. 17. Geographical distribution of share of employment in agriculture in 2007
64
Table 27. Descriptive statistics of share of
employment in hazardous conditions
Fig. 18. Histogram of share of employment in
hazardous conditions in 2007 (percentage)
Share of employment
in hazardous
conditions
2003
2007
average
standard deviation
median
first quartile
third quartile
minimum
maximum
5.98
6.31
105.59
4.41
2.17
7.32
0.20
49.83
3.149
14.477
5.28
4.39
83.21
4.41
2.38
6.90
0.08
33.55
2.344
9.323
Fig. 19. Geographical distribution of share of employment in hazardous conditions in 2007
65
2.2.3. Social cohesion indicators
Pre-school participation rate of children aged 3-5
Between 2002 and 2007 pre-school participation rate of children aged 3-5 in Polish districts
increased, on average, which resulted from an increase in the number of available places (by
65.4 thousand) and a decrease in the number of children at this age by 126.1 thousand. Thus,
approximately a third of the observed decrease can be attributed to increased availability of
pre-schools. In a quarter of districts, less than 25 percent of children in the analysed age group
attend pre-schools. In the top quartile participation rate is above 51 percent.
Distribution of pre-school participation rate is skewed to the right, i.e. there are more districts
with relatively lower pre-school participation rates. It is also relatively flat: kurtosis
coefficient is negative.
Most of the districts in the top quartile are urban districts or districts adjacent to cities. On the
other hand, most of bottom quartile districts are rural. This is a factor that can further
aggravate existing gap between urban and rural areas of Poland, which requires policymakers’
attention.
Library members per 1000 inhabitants
The number of library members per 100 inhabitants decreased between 2002 and 2007, which
is probably caused by the liquidation of some of public libraries, particularly in rural areas. In
one fourth of districts, there are less than 139 library members per 1000 inhabitants, and in
another one fourth – more than 192.
Distribution of this indicator is skewed to the right, which deepened between 2002 and 2007.
Kurtosis coefficient also shows that the distribution is peaked (there was a significant increase
of the value of kurtosis coefficient between 2002 and 2007).
Looking at geographical distribution one can again observe relatively larger share of library
members in the cities, as well as in southern and western parts of Poland, with some
exceptions.
Lower participation in libraries can be observed in the case of eastern parts of Poland,
particularly in the rural parts of Mazowieckie voivodship, which is probably related to the
lack of access to public libraries in these districts.
Share of households equipped with a bathroom
Surprisingly, between 2002 and 2007 the share of households equipped with a bathroom
decreased. In 2007, in half of the districts there were less than 85.7 percent of households with
66
a bathroom, while in a quarter of districts less than 78.1 percent of households had a
bathroom.
Distribution of this indicator is skewed to the left, whereas kurtosis coefficient shows that it is
slightly flat. This is a change compared to 2002, when distribution of the share of households
equipped with a bathroom was more peaked. This change seems to result from an increase in
the number of districts with relatively smaller share of households with bathrooms.
Geographical distribution of this indicator is very interesting. Districts of the lowest quartile
are almost exclusively located in the central and eastern parts of Poland, which by and large
corresponds to the borders of Russian annexation, which lasted for about 150 years. On the
other hand, districts with the highest share of households with bathrooms are large city
districts (Warszawa, Katowice, Szczecin, Gdańsk, Wrocław and Poznań), and districts in their
close neighbourhood.
Local government election turnout
Local government election turnout was analysed for two election years: 2002 and 2006.
Statistics show that this indicator did not change significantly between elections. The third
quartile and median levels slightly decreased, while the first quartile slightly increased. This
confirms that the differences between districts decreased, which is also reflected in the
smaller value of coefficient of variation.
Distribution of this indicator shows that it is relatively close to normal distribution, slightly
skewed to the left and a little more peaked (coefficient of kurtosis is positive). These
characteristics did not change significantly between election years either, in 2002 the
distribution of election turnout was more skewed than in 2006.
Geographical distribution shows that the districts with the highest turnout are located mainly
in central and eastern parts of Poland. Moreover, election turnout seems lower in most of the
urban districts. Lowest turnout is observed in districts located in Śląskie and KujawskoPomorskie voivodships.
Interestingly enough, local election turnout statistics vary from the same indicator as regards
Parliamentary or Presidential elections. In the case of the latter, bigger cities usually exhibit
higher turnout. Such development can support the hypothesis that local election turnout may
be a good indicator to monitor the level of social engagement of local community.
67
Table 28. Descriptive statistics of preschool participation rate of children aged
3-5
Fig. 20. Histogram of pre-school participation rate of
children aged 3-5 in 2007 (percentage)
Pre-school
participation rate of
children aged 3-5
2002
2007
average
standard deviation
median
first quartile
third quartile
minimum
maximum
30.50
14.14
46.36
27.32
19.22
40.11
2.49
73.43
0.694
-0.151
39.11
17.35
44.36
35.57
25.82
51.18
0.00
89.52
0.505
-0.467
Fig. 21. Geographical distribution of the share of pre-school participation rate of children aged 3-5 in 2007
68
Table 29. Descriptive statistics of library
members per 1000 inhabitants
Fig. 22. Histogram of library members per 1000
inhabitants in 2007
Library members per
1000 inhabitants
2002
average
standard deviation
median
first quartile
third quartile
minimum
maximum
191.08
49.73
26.02
181.00
154.50
216.50
102.00
372.00
1.020
1.296
2007
169.49
43.84
25.87
162.00
139.00
192.00
82.00
380.00
1.293
3.096
Fig. 23. Geographical distribution of library members per 1000 inhabitants in 2007
69
Table 30. Descriptive statistics of the share of
households equipped with a bathroom
Fig. 24. Histogram of the share of households
equipped with a bathroom in 2007 (percentage)
Share of households
equippedwith
bathroom
2002
2007
average
standard deviation
median
first quartile
third quartile
minimum
maximum
87.29
8.96
10.26
89.46
81.79
93.95
58.55
100.00
-0.746
0.069
83.74
8.77
10.47
85.72
78.12
89.94
55.50
99.02
-0.681
-0.105
Fig. 25. Geographic distribution of the share of households equipped with a bathroom in 2007
(percentage)
70
Table 31. Descriptive statistics of local
government elections turnout
Fig. 26. Histogram of local government elections
turnout in 2006 (per cent)
Local government
elections turnout
2002
2006
average
standard deviation
median
first quartile
third quartile
minimum
maximum
45.70
7.44
16.29
47.15
41.77
50.85
22.86
63.30
-0.710
0.182
45.46
5.27
11.59
45.73
42.34
49.06
26.67
60.28
-0.310
0.216
Fig. 27. Geographical distribution of the share of local government elections turnout in 2006
Source: Author’s calculations based on Państwowa Komisja Wyborcza (National Electoral
Commission)
71
2.2.4. Health care access indicators
In this section we present the characteristics of proposed indicators related to access to health
care on a district level. However, it should be taken with some caution. First of all, the
network of healthcare institutions is to some extent developed not in line with the boundaries
of districts, so people living in some districts may use health care services provided in another
district. Secondly, physicians tend to work in many places, which is not fully recognised in
available statistics. Comparison of data provided by Central Statistical Office and CSIOZ also
shows some differences, presumably related to the differences in measurement. Due to lack of
comparability of data in the Regional Data Bank, this source of data was not used in the
analysis, but it can be used in the future, once comparable information is available.
Number of inhabitants per 1 health care institution
Access to health care institutions, measured as the number of inhabitants per one institution
(zakład opieki zdrowotnej/ZOZ) shows some improvement between 2002 and 2007, both
from the perspective of average value as well as variation. In a quarter of districts, there are
less than 2274 inhabitants per one institution, and in another quarter there are more than 3588.
There is significant range in the value of this indicator – from the minimum of 308 inhabitants
to the maximum of more than 18 thousand inhabitants.
Distribution of the indicator is highly skewed to the right. It is also peaked – half of the
districts have the value of the indicator ranging between 2.2 thousand and 3.6 thousand.
Geographical distribution of this indicator is rather scattered. The one feature that can be
observed is that the number of inhabitants per one health care institution is smaller in urban
districts (cities), which shows that the cities tend to attract health care institutions, which in
turn leads to potentially better access to health care services in urban areas. This is usually
accompanied by high number of inhabitants per one institution in districts surrounding the
cities, which shows that there is a tendency to “push” the development of institutions to the
nearest cities.
Number of inhabitants per 1 physician
The second indicator proposed to measure access to health care has similar distribution as the
previous one. However, between 2002 and 2007 the number of inhabitants per one physician
increased. This is mainly due to the reduced number of employed physicians, which decreased
by 11.8 thousand during these five years: from 90 thousand in 2002 to 78.2 thousand in 2007.
Decrease in the number of employed physicians results from that fact that more and more
doctors decide to become self-employed. Consequently, the findings cannot be fully
72
comparable between the two observations. The coefficient of variation of this indicator
decreased between 2002 and 2007, which is a positive development, just as the reduction of
the maximum value from 34.7 to 18.2 thousand inhabitants per one physician. In a quarter of
districts the number of inhabitants per one physician in 2007 was below 533, while in another
quarter it was more than 1 111. Analogically to the previous indicator, distribution of the
number of inhabitants per one physician is skewed to the right and highly peaked, it is also
scattered geographically.
Table 32. Descriptive statistics of the number of
inhabitants per 1 health care institution
Fig. 28. Histogram of the number of inhabitants per
1 health care institution in 2007
Number of inhabitants
per 1 health care
institution
2002
2007
average
4165.28 3218.55
standard deviation
3004.03 1827.43
72.12
56.78
median
3465.08 2839.26
first quartile
2680.43 2274.39
third quartile
4649.30 3588.10
minimum
320.94
307.08
maximum
34692.33 18210.40
4.664
3.737
34.555
22.416
Fig. 29. Geographical distribution of the number of inhabitants per 1 health care institution in 2007
73
Table 33. Descriptive statistics of the number of
inhabitants per 1 physician
Fig. 30. Histogram of the share of the number of
inhabitants per 1 physician in 2007
number of inhabitants
per 1 physician
2002
2007
average
standard deviation
median
first quartile
third quartile
minimum
maximum
920.75 943.66
1635.33 753.95
177.61
79.90
667.27 777.19
476.50 533.73
951.94 1111.23
17.60
24.37
25546.00 7312.57
11.736
3.421
160.542 19.526
Fig. 31. Geographical distribution of the number of inhabitants per 1 physician in 2007
Source: Author’s calculations based on Centrum Systemów Informacyjnych Ochrony Zdrowia
(Health Care Information Systems Centre)
74
2.2.5. Educational indicators
Education is frequently identified as one of the most important determinants of health. There
is strong correlation between the level of education and life expectancy, which can be
attributed, among others, to the impact of educational attainments on healthier lifestyles and
habits of population.
There are, however, limitations in the availability of data for the analysis of this group of
determinants at district level in Poland. The best potential indicator, i.e. share of population
with different levels of educational attainments at the district level, is available only from
national census data (i.e. since 2002). Given significant changes in educational attainments of
young population, as well as observed post-accession migration, census data may not be a
very good indicator of current characteristics of a district.
As mentioned in the introduction, annually available indicators linked to education are
associated with the results of lower secondary school and upper secondary school matura
exams. In the set of indicators we suggest taking into account both levels of exams, with
results from humanities as well as mathematics and science. These indicators are available at
district level. More importantly, these exams are conducted in such a way that guarantees
comparability of results between districts in each year. However, they are not comparable in
time, so we cannot assess the progress of results based on observed values.35.
Lower secondary school exams are taken by all students in compulsory education system.
Thus, they cover more or less the entire population of youth around age 15. A note of caution
is related to upper secondary school (matura) exams results, which are available at
comparable national level for 2007 observation (as unified exams were introduced in 2005).
Matura exams are taken only by a part of population around age 18, since not all the youth
choose to continue with upper secondary education that ends with this exam. Thus, the results
have selection bias. This is even more so in the case of mathematics, because prior to 2010
this examination was not compulsory. Thus, for further analysis of social determinants of
health in the next chapter, only the results of exams in humanities are used. In the future, both
types of exams may be used for analytical purposes, following the introduction of compulsory
high school mathematics exams.
As a result, only some of the identified indicators are used further for the analysis of health
indicators (namely, the results of lower secondary and upper secondary school exams in
35
Currently, Educational Research Institute in Warsaw is running a project aimed at introducing comparability in
the reporting of exams results.
75
humanities or Polish language). However, in this section we provide short description of all
proposed indicators, as they provide important contextual information.
Share of population with higher education
As indicated before, the share of population with given educational attainment is provided
only for year 2002 and is based on census results. Nevertheless, considering significant link
between education and health outcomes underlined in literature (Marmot and Wilkinson,
2006, Bartley et al., 2006), this indicator is included in the analysis framework. Education is
also linked to other social determinants of health, such as employment conditions,
unemployment, or lifestyle. Looking at geographical distribution of the share of population
with higher educational attainments in 2002, we can observe significant variation between
districts, similarly to other indicators. The distribution of the percentage of population with
higher education is skewed to the right. In half of the districts the share of people with such
educational attainment is above 6.3 percent, while in a quarter it is above 8.07 percent. Values
across districts range between 3 and 25 percent of total population. Again, we can see that
urban districts are at the top of the list (top 40 districts are all urban ones). Distribution of
higher educational attainments of the population is also peaked with high concentration of
districts, in which the share of such population ranges from 5 to 7 percent.
Share of population with vocational or lower education
This indicator can be treated as a “mirror” indicator relative to the previous one, showing the
distribution of lowest educational attainments. The analysis of descriptive statistics shows
that, first of all, the share of population with vocational or lower education is on average
much higher – ranging from the lowest 28 percent to the highest 78 percent of total
population. Variation of this indicator is lower compared to the previous one, as coefficient of
variation is below 15 percent. In half of the districts the share of the population with low
educational attainments is below 65.74 percent, and in a quarter – below 60.03 percent.
Distribution of this indicator, as shown in the histogram, indicates that it is skewed to the left,
matching the distribution of population with higher educational attainment. Distribution is
also relatively peaked. Concentration of population with vocational or lower education is
higher in rural districts, in particular in the eastern parts of Poland.
Average lower secondary school exam results (mathematics and science)
Average lower secondary school exam results in mathematics and science show relatively
little variation, with the coefficient of variation below 10 percent. 2002 results exhibit slightly
higher variation than those from 2007 High (one should remember that the results are not
76
comparable between the two years). In 2007, distribution of results was a little skewed to the
right (in 2002 it was skewed to the left), and more peaked than normal distribution.
Upper secondary school matura results - mathematics (basic level)
Upper secondary school matura mathematics exams scores show slightlyhigher variation,
compared to lower secondaryschool results, but the range is quite high – from the lowest 8.75
to the highest above 31. Half of the districts had average exam score below 20.85.
Distribution is skewed to the left and peaked. In contrast to lower secondaryschool exams,
there is no observed link between the type of district (municipal) and the outcome (highest
results).
Average lower secondaryschool exam results (humanities)
Average lower secondaryschool exam scores in humanities also show relatively little
variation, with the coefficient of variation around 5 percent. Variation of results in 2002 and
in 2007 is similar (one should remember that the results are not comparable between the two
years). In 2007, distribution of results was slightly` skewed to the right, with median (30.85)
slightly below average (30.87) and relatively flat, compared to normal distribution. Better
results are observed in central and south-eastern Poland. Similarly to mathematics and science
exams, municipal districts have, on average, better lower secondaryschool exam results in
humanities.
Upper secondary school matura exam results - Polish language (basic level)
Upper secondary school maturaPolish language exam results show slightly higher variation
compared to lower secondaryschool results (at 7.6 percent), though lower than in the case of
mathematics. Half of the districts had average exam score below 34.07. Distribution of results
is skewed to the left and a bit more peaked than normal distribution. Territorial distribution
shows that better results are obtained in bigger cities and around them, which indicates some
potential diffusion of knowledge between the cities and surrounding districts.
77
population with higher education
Fig. 32. Histogram of the share of population with
higher education in 2002 (percentage)
share of population
with higher education
2002
average
standard deviation
median
first quartile
third quartile
minimum
maximum
7.60
3.76
49.46
6.30
5.49
8.07
3.31
25.40
2.137
4.693
population with vocational or lower education
Fig. 33. Histogram of the share of population with
vocational or lower education in 2002 (percentage)
share of population
with vocational or
lower education
2002
average
standard deviation
median
first quartile
third quartile
minimum
maximum
63.20
9.27
14.66
65.74
60.34
69.01
28.54
78.91
-1.360
1.487
78
Table 36. Descriptive statistics of average
lower secondaryschool exam results
(mathematics and science)
Fig. 34. Histogram of average lower secondaryschool
exam results (mathematics and science) in 2007
average middle
school exam results
(mathematics and
science)
2002
2007
average
standard deviation
median
first quartile
third quartile
minimum
maximum
27.80
2.45
8.83
27.53
25.83
29.65
22.43
35.26
0.328
-0.490
24.74
1.43
5.78
24.65
23.65
25.48
21.70
29.72
0.696
0.451
Source: Author’s calculations based on Centralna Komisja Egzaminacyjna (Central
Examination Board)
Table 37. Descriptive statistics of upper
secondary school matura results mathematics (basic level)
Fig. 35. Histogram of upper secondary school matura
results - mathematics (basic level) in 2007
high school results mathematics (basic
level)
2007
average
standard deviation
median
first quartile
third quartile
minimum
maximum
20.72
2.84
13.73
20.85
19.05
22.70
8.75
31.09
-0.400
1.820
Examination Board)
79
Table 38. Descriptive statistics of average lower
secondaryschool exam results (humanities)
Fig. 36. Histogram of the share of average lower
secondaryschool exam results (humanities) in 2007
average middle
school exam results
(humanities)
2002
2007
average
standard deviation
median
first quartile
third quartile
minimum
maximum
29.61
1.58
5.33
29.52
28.44
30.66
25.00
34.19
0.193
0.118
30.87
1.59
5.15
30.85
29.70
31.94
26.43
35.41
0.264
-0.233
Fig. 37. Geographical distribution of average lower secondaryschool exam results (humanities) in 2007
Source: Author’s calculations based on Centralna Komisja Egzaminacyjna(Central
Examination Board)
80
Table 39. Descriptive statistics of upper
secondary school matura exam results - Polish
language (basic level)
Fig. 38. Histogram of the share of upper secondary
school matura exam results - Polish language (basic
level) in 2007
high school exam
results - Polish
language (basic
level)
2007
average
standard deviation
median
first quartile
third quartile
minimum
maximum
33.97
2.58
7.59
34.07
32.25
35.66
25.38
41.37
-0.176
0.192
Fig. 39. Geographical distribution of upper secondary school matura exam results - Polish language (basic
level) in 2007
Examination Board)
81
82
2.3. Relations between selected variables
In this section, correlations between selected indictors are analysed. This is done for several
purposes. First, it is important to identify whether there are some relations between variables,
in particular between those from different groups. Second, given that the indicators are also
used for further selection of variables explaining mortality and life expectancy, it is important
to identify those indicators which can reflect wider set of potential factors influencing social
determinants of health. The matrix presenting correlations between indicators is shown in
Błąd! Nie można odnaleźć źródła odwołania.. A short analysis of obtained results is
presented below. The analysis is carried out for each group of indicators. In order to avoid
repetitions, correlations that were not discussed before are discussed for each group.
At the end of this section we conclude with a proposal of classification of “typical” groups of
districts in Poland, selecting the factors that tend to co-exist together.
Demographic indicators
Feminization rate shows strong correlations (above 0.5 or below -0.5) with economic and
labour market as well as social indicators. There is a strong negative correlation between
feminization rate and the share of employment in agriculture. This confirms the interpretation
that feminization rate reflects the outcome of migration processes, usually from poorer,
agricultural districts to the cities. This indicator is also strongly positively correlated with
income from local taxation per inhabitant. This shows that young females tend to move to the
districts with better economic situation, which indicates potential direction of migrations. As
far as social indicators are concerned, there is a strong positive correlation between
feminization and the share of children aged 3-5 in pre-school education as well as library
members. The former can be potentially attributed to the fact that higher share of females
aged 24-35 leads to higher number of small children, which in turn increases the demand for
development of pre-school education. The latter can indicate that women, who participate in
higher education more frequently than men, also tend to use libraries more frequently, but this
would require additional research.
On the other hand, feminization rate is weakly correlated with indicators in the area of access
to healthcare. Also in the case of educational variables, correlations are not very strong, with
the exception of census data on the shares of population with higher, or vocational or lower
education. In the case of the former, there is a relatively strong positive correlation, mirrored
by relatively strong negative correlation in the case of the latter indicator. There is also
moderate positive correlation between feminization rate and lower secondary school exam
83
results. Such correlation again confirms that there is a link between educational outcomes of
children and the level of education of their parents, which is also corroborated by, for
example, the results of PISA survey in Poland36.
Old-age demographic dependency ratio is strongly and positively correlated with the share of
employment in agriculture. This shows that the outcomes of demographic processes, in scope
of ageing as well as migration, affect demographic structure of rural areas in Poland. This
indicator is also strongly and negatively correlated with the share of households equipped
with a bathroom, which may indicate that older people may inhabit lower quality dwellings.
Population density, higher in urban areas, is strongly and positively correlated with local
budget revenues, which once again confirms that urban areas are relatively more affluent. By
the same token, there is strong negative correlation between population density and the share
of employment in agriculture. This indicator is also strongly correlated with social indicators:
the correlation is positive in the case of pre-school participation, library membership and
quality of housing (houses equipped with a bathroom), and negative as regards local
government election turnout. Those positive correlations once again confirm the intuition
related to general features of urban districts. Negative correlation indicates that local elections
are of less priority to urban citizens.
Similarly to other demographic indicators, population density is moderately (around -0.3)
negatively correlated with indicators measuring access to health care, which shows that the
number of inhabitants per one physician is slightly lower in highly populated areas.
Taking into account correlation between population density and educational indicators, we
can again observe a strong correlation between this variable and population structure by
education – urban areas are inhabited byrelatively better educated population, which also
leads to relatively better exam results. However, as regards the latter, the correlation is not
that strong. There is weak (and, surprisingly, negative) correlation between population density
and high school mathematics exam results, yet, this can be influenced by the fact that this
measure relates to exams taken on a voluntary basis.
Economic and labour market indicators
Economic and labour market indicators show some strong correlations between one another.
Own revenue of local budgets per capita shows strong negative correlation with employment
in agriculture, which indicates that rural districts tend to be poorer than urban ones. It is also
36
The PISA (Programme of International Student Assessment) is an international survey measuring numeracy,
literacy and problem solving abilities of 15-year olds, developed and co-ordinated by the OECD
(http://www.pisa.oecd.org/)
84
moderately and negatively correlated with unemployment rate, which shows that better labour
market performance is observed in the districts with higher revenues. Employment in
agriculture, particularly for 2007, also shows relatively strong negative correlation with
employment in hazardous conditions, which indicates that such working conditions are
observedin non-rural parts of Poland.
There are also strong correlations between economic and labour market indicators, and social
indicators. Strong positive correlation between revenues of local budgets and pre-school
participation rate of children aged 3-5 shows that investment in pre-school education may
depend on availability of sources atthe local level, as the development of kindergartens is a
responsibility of local governments. Housing conditions also tend to be better in those
districts that have higher incomes. Own revenues of local budgets show moderate positive
correlation with library membership, which again confirms that relatively richer districts have
more developed social services. Interestingly, local budget revenues show moderate negative
correlation with local government election turnout, which indicates that this aspect of social
activity is observed more frequently in poorer areas of Poland.
Less efficient labour markets, i.e. those with higher unemployment rates or higher share of
employment in agriculture, are relatively strongly correlated with a lower number of children
attending pre-schools. Analogically, strong negative correlations are observed between the
share of employment in agriculture, library membership and housing quality. Only election
turnout shows positive correlation with employment in agriculture. Unemployment rate is
weakly correlated with social variables other than pre-school participation rate. There is
certain moderate positive correlation between the share of employment in hazardous
conditions and housing quality, and negative correlation with election turnout. This again
indicates that employment in hazardous conditions usually tends to occur in districts with
urban characteristics.
Similarly to the previous group of indicators, we cannot see strong correlations with access to
health care. These is a moderate positive correlation between the number of inhabitants per 1
physician and unemployment rate, as well as employment in agriculture. The latter, in
particular, confirms the belief that in rural areas there is worse access to health care.
Economic and labour market indicators are also strongly correlated with educational
indicators. Strong positive correlation with local budget revenue and the share of people with
higher education shows that richer districts are inhabited by better educated citizens. Labour
market conditions measured by lower unemployment and lower share of employment in
agriculture also tend to co-exist with higher share of people with higher education. Similar
85
direction of correlations (though moderately strong) is observed in the case of results of both
lower secondary and upper secondary school exams, with the exception of upper secondary
school matura mathematics exams.37 Specifically, correlation coefficient between lower
secondary school exam results and unemployment rate in 2007 was at around -0.5, which is
the strongest observed relation between analysed groups.
Social indicators
Social indicators are mutually inter-dependent. There is a relatively strong, positive
correlation between pre-school participation rate and the quality of housing, and negative
correlation between pre-school participation rate and election turnout, side by side witha
moderate positive correlation between pre-school participation rate and library membership.
Similarly to previous groups, there are weak (usually negative) correlations between social
indicators and access to health care services indicators.
Again, educational indicators are also strongly correlated with social indicators. The higher
the share of population with higher education, the higher the participation in pre-school
education and the quality of housing. There is also relatively strong correlation between
structure of educational attainments and library membership. On the other hand, local election
turnout is relatively strongly and negatively correlated with the share of population with
higher education. Strong to moderately strong correlation occurs between the first three social
indicators and the results of middle school exams, particularly in humanities. Similarly to
previous groups, there is only slight correlation with the results of upper secondary school
matura mathematics exams.
Health care access indicators
There is a relatively strong correlation between proposed health-care access variables,
particularly those from 2007 observations, which shows that a higher number of health care
institutions generally leads to a larger number of available physicians.
As mentioned before, health care access indicators demonstrate rather weak correlations with
other variables. There is a moderate positive correlation with the share of employment in
agriculture and the share of employment in hazardous condition (of about 0.2 and 0.3,
respectively), and the share of population with vocational or lower education (0.4), as well as
negative correlations of similar strength with population density (-0.3), pre-school
37
A note of caution: these are results of exams based on voluntary choice. As choices were non-random, this
affects the results of correlation analysis.
86
participation rate (-0.3), or with the share of population with higher education (-0.35) and
lower secondary school exam results (about -0.3). This shows some, but not very strong,
evidence, that inhabitants of rural districts have less access to health care services, as the
number of inhabitants per one health care institution or physician tends to be higher.
Educational indicators
Educational indicators, similarly to social indicators, exhibit strong correlations within the
group. In particular, the share of people with higher education is very strongly, negatively
correlated with the share of people with vocational or lower education, which of course
should be expected. Lower secondary school exams, particularly in 2007, demonstrate
relatively strong positive correlation (about 0.6) with the share of people with higher
education (observed in 2002). This may indicate that there is a tendency to replicate the
existing education structure in the new generation. Lower secondary school exam results for
humanities and mathematics and science also show similar positive correlation. Results of
upper secondary school maturaexams are not that strongly correlated with other variables,
particularly in the case of mathematics examination. There is certain weak positive correlation
(or about 0.3) observed in the case of 2007 lower secondary school exam results (in the case
of humanities and mathematics and science alike), and upper secondaryschool resuls in scope
of matura exam in Polish language.
To summarise, we can see inter-relations between selected groups of indicators, which shows
that districts in Poland develop in different ways. As shown in the analysis of geographical
distribution, such diversity is not only linked to the region, but also to the type of district.
Urban districts in particular are quite different in their characteristics, compared to the rural
ones. We may even say that there is certain polarisation of districts based on their socioeconomic situation.
Based on the correlation between selected indicators, we can present characteristics of two
groups of “typical” districts. The first one represents features of municipal districts, and the
other one has features frequently observed in the case of rural districts (see ). Needless to add,
such a breakdown does not cover all existing diversities (Fig. 40) nevertheless, it can illustrate
what kind of phenomena tend to co-exist in Polish districts.
87
Fig. 40. Polarisation of district characteristics in Poland
Typical municipal district
Typical rural district
High feminisation rate and high
population density
Low feminisation rate and low
population density
High revenues of local budgets
Low revenues of local budgets
Low employment in agriculture
High employment in agriculture
High participation of children in
pre-school education
Low participation of children in
pre-school educations
Higher library membership
Lower library membership
Better housing conditions
Worse housing conditions
Lower election turnout
Higher election turnout
High share of people with
higher education
Low share of people with
higher education
Low share of people with
vocational and lower education
High share of people with
vocational or lower education
88
share of population w ith vocational or low er education
average low er secondary school exam results (m athem atics
and science)
average low er secondary school exam results (hum anities)
ECO_4_2007
num ber of inhabitants per 1 physician
share of population w ith higher education
ECO_4_2003
num ber of inhabitants per 1 health care institution
ECO_3_2007
local governm ent election turnout
ECO_3_2003
share of households equipped w ith bathroom
ECO_2_2007
library m em bers per 1000 inhabitants
ECO_2_2004
pre-school participation rate of children aged 3-5
ECO_1_2007
share of em ploym ent in hazardous conditions
ECO_1_2002
share of em ploym ent in agriculture
DEM_3_2007
unem ploym ent rate
DEM_3_2002
population density
ow n revenue of local budgets per capita
DEM_2_2007
EDU_3_2002
EDU_3_2007
EDU_4_2002
EDU_4_2007
old-age dem ographic dependency ratio
DEM_2_2002
EDU_2_2002
fem inization rate
DEM_1_2007
DEM_1_2002
DEM_1_2007
DEM_2_2002
DEM_2_2007
DEM_3_2002
DEM_3_2007
ECO_1_2002
ECO_1_2007
ECO_2_2004
ECO_2_2007
ECO_3_2003
ECO_3_2007
ECO_4_2003
ECO_4_2007
SOC_1_2002
SOC_1_2007
SOC_2_2002
SOC_2_2007
SOC_3_2002
SOC_3_2007
SOC_4_2002
SOC_4_2006
HEALTH_1_2002
HEALTH_1_2007
HEALTH_2_2002
HEALTH_2_2007
EDU_1_2002
DEM_1_2002
Table 40. Correlation matrix of the indicators
1,00
0,81
- 0,44
- 0,32
0,38
0,38
0,50
0,48
- 0,21
- 0,30
- 0,61
- 0,61
0,19
0,28
0,52
0,57
0,24
0,23
0,60
0,59
- 0,43
- 0,43
- 0,11
- 0,05
- 0,10
- 0,13
0,48
- 0,49
0,81
1,00
- 0,50
- 0,35
0,47
0,48
0,57
0,57
- 0,26
- 0,37
- 0,68
- 0,68
0,22
0,34
0,59
0,64
0,27
0,26
0,69
0,68
- 0,53
- 0,50
- 0,12
- 0,01
- 0,13
- 0,16
0,55
- 0,55
- 0,44
- 0,50
1,00
0,95
- 0,16
- 0,16
- 0,34
- 0,32
- 0,25
- 0,04
0,62
0,64
- 0,33
- 0,45
- 0,25
- 0,31
- 0,36
- 0,26
- 0,72
- 0,76
0,37
0,39
- 0,03
- 0,03
0,14
0,04
- 0,18
0,22
- 0,32
- 0,35
0,95
1,00
0,06
0,06
- 0,17
- 0,13
- 0,35
- 0,16
0,41
0,43
- 0,28
- 0,34
- 0,02
- 0,08
- 0,21
- 0,12
- 0,55
- 0,59
0,17
0,22
- 0,09
- 0,09
0,05
- 0,10
0,03
- 0,01
0,38
0,47
- 0,16
0,06
1,00
1,00
0,53
0,59
- 0,31
- 0,33
- 0,59
- 0,57
0,09
0,17
0,65
0,65
0,40
0,36
0,39
0,41
- 0,58
- 0,48
- 0,21
- 0,19
- 0,15
- 0,32
0,76
- 0,74
0,38
0,48
- 0,16
0,06
1,00
1,00
0,54
0,60
- 0,31
- 0,34
- 0,60
- 0,58
0,09
0,17
0,65
0,65
0,40
0,36
0,39
0,41
- 0,58
- 0,48
- 0,21
- 0,19
- 0,15
- 0,33
0,76
- 0,75
0,50
0,57
- 0,34
- 0,17
0,53
0,54
1,00
0,93
- 0,29
- 0,39
- 0,69
- 0,68
0,25
0,40
0,62
0,65
0,35
0,32
0,58
0,58
- 0,46
- 0,33
- 0,06
- 0,03
- 0,16
- 0,21
0,65
- 0,65
0,48
0,57
- 0,32
- 0,13
0,59
0,60
0,93
1,00
- 0,32
- 0,43
- 0,71
- 0,70
0,21
0,37
0,65
0,68
0,36
0,34
0,60
0,60
- 0,49
- 0,34
- 0,09
- 0,06
- 0,15
- 0,23
0,74
- 0,73
- 0,21
- 0,26
- 0,25
- 0,35
- 0,31
- 0,31
- 0,29
- 0,32
1,00
0,91
0,06
0,05
0,06
- 0,10
- 0,45
- 0,44
- 0,10
- 0,19
- 0,10
- 0,05
0,17
0,06
0,21
0,14
0,04
0,22
- 0,41
0,37
- 0,30
- 0,37
- 0,04
- 0,16
- 0,33
- 0,34
- 0,39
- 0,43
0,91
1,00
0,22
0,23
- 0,04
- 0,20
- 0,50
- 0,51
- 0,13
- 0,18
- 0,26
- 0,22
0,26
0,17
0,18
0,11
0,06
0,21
- 0,41
0,38
- 0,61
- 0,68
0,62
0,41
- 0,59
- 0,60
- 0,69
- 0,71
0,06
0,22
1,00
1,00
- 0,32
- 0,52
- 0,70
- 0,76
- 0,53
- 0,44
- 0,81
- 0,83
0,64
0,59
0,13
0,11
0,25
0,33
- 0,65
0,72
- 0,61
- 0,68
0,64
0,43
- 0,57
- 0,58
- 0,68
- 0,70
0,05
0,23
1,00
1,00
- 0,34
- 0,52
- 0,69
- 0,75
- 0,52
- 0,42
- 0,81
- 0,84
0,63
0,59
0,13
0,10
0,25
0,31
- 0,63
0,71
0,19
0,22
- 0,33
- 0,28
0,09
0,09
0,25
0,21
0,06
- 0,04
- 0,32
- 0,34
1,00
0,55
0,15
0,18
0,21
0,18
0,29
0,32
- 0,25
- 0,32
0,02
0,04
- 0,05
0,04
0,01
- 0,03
0,28
0,34
- 0,45
- 0,34
0,17
0,17
0,40
0,37
- 0,10
- 0,20
- 0,52
- 0,52
0,55
1,00
0,31
0,34
0,33
0,28
0,45
0,49
- 0,30
- 0,33
- 0,07
- 0,01
- 0,12
- 0,11
0,12
- 0,16
- 0,14
0,24
0,25
0,29
- 0,12
0,29
0,34
0,32
0,40
0,10
0,09
0,08
0,43
0,23
0,25
0,23
0,11
0,39
0,50
0,45
0,11
0,39
0,50
0,45
- 0,10
0,31
0,42
0,28
- 0,07
0,38
0,47
0,35
- 0,36
- 0,58
- 0,53
- 0,58
- 0,22
- 0,50
- 0,52
- 0,50
0,23
- 0,23
- 0,37
- 0,27
0,24
- 0,22
- 0,35
- 0,26
- 0,30
- 0,09
- 0,05
- 0,10
- 0,29
- 0,03
0,10
0,03
EDU_5_2007
upper secondary school matura exam results - Polish language
(basic level)
0,17
0,19
- 0,17
- 0,05
0,25
0,25
0,12
0,13
- 0,26
- 0,24
- 0,24
- 0,24
0,02
0,13
EDU_6_2007
upper secondary school matura exam results - m athem atics
(basic level)
- 0,08
- 0,10
0,05
0,05
- 0,01
- 0,01
- 0,23
- 0,23
0,01
0,09
0,13
0,14
- 0,08
- 0,12
Source: Author’s calculations based on Bank Danych Regionalnych (Regional Data Bank), Centralna Komisja Egzaminacyjna (Central Examination Board),
Państwowa Komisja Wyborcza (National Electoral Commission) and Centrum Systemów Informacyjnych Ochrony Zdrowia (Health Care Information
Systems Centre)
89
and science)
(basic level)
(basic level)
HEALTH_2_2007
HEALTH_2_2002
HEALTH_1_2007
HEALTH_1_2002
SOC_4_2006
SOC_4_2002
SOC_3_2007
unem ploym ent rate
SOC_3_2002
SOC_2_2007
EDU_6_2007
population density
SOC_2_2002
EDU_5_2007
SOC_1_2007
EDU_3_2002
EDU_3_2007
EDU_4_2002
EDU_4_2007
fem inization rate
SOC_1_2002
DEM_1_2002
DEM_1_2007
DEM_2_2002
DEM_2_2007
DEM_3_2002
DEM_3_2007
ECO_1_2002
ECO_1_2007
ECO_2_2004
ECO_2_2007
ECO_3_2003
ECO_3_2007
ECO_4_2003
ECO_4_2007
SOC_1_2002
SOC_1_2007
SOC_2_2002
SOC_2_2007
SOC_3_2002
SOC_3_2007
SOC_4_2002
SOC_4_2006
HEALTH_1_2002
HEALTH_1_2007
HEALTH_2_2002
HEALTH_2_2007
EDU_1_2002
EDU_2_2002
0,52
0,59
- 0,25
- 0,02
0,65
0,65
0,62
0,65
- 0,45
- 0,50
- 0,70
- 0,69
0,15
0,31
1,00
0,96
0,45
0,45
0,61
0,60
- 0,59
- 0,48
- 0,17
- 0,16
- 0,22
- 0,35
0,73
- 0,76
0,01
0,43
0,54
0,47
0,57
0,64
- 0,31
- 0,08
0,65
0,65
0,65
0,68
- 0,44
- 0,51
- 0,76
- 0,75
0,18
0,34
0,96
1,00
0,48
0,46
0,67
0,66
- 0,61
- 0,51
- 0,16
- 0,14
- 0,23
- 0,34
0,74
- 0,78
- 0,01
0,45
0,53
0,49
0,24
0,27
- 0,36
- 0,21
0,40
0,40
0,35
0,36
- 0,10
- 0,13
- 0,53
- 0,52
0,21
0,33
0,45
0,48
1,00
0,90
0,46
0,48
- 0,38
- 0,36
- 0,11
- 0,13
- 0,15
- 0,26
0,43
- 0,49
- 0,05
0,19
0,30
0,28
0,23
0,26
- 0,26
- 0,12
0,36
0,36
0,32
0,34
- 0,19
- 0,18
- 0,44
- 0,42
0,18
0,28
0,45
0,46
0,90
1,00
0,40
0,41
- 0,30
- 0,26
- 0,09
- 0,12
- 0,13
- 0,24
0,40
- 0,45
0,00
0,23
0,31
0,32
0,60
0,69
- 0,72
- 0,55
0,39
0,39
0,58
0,60
- 0,10
- 0,26
- 0,81
- 0,81
0,29
0,45
0,61
0,67
0,46
0,40
1,00
0,98
- 0,55
- 0,53
0,03
- 0,02
- 0,17
- 0,20
0,54
- 0,58
- 0,27
0,21
0,27
0,21
0,59
0,68
- 0,76
- 0,59
0,41
0,41
0,58
0,60
- 0,05
- 0,22
- 0,83
- 0,84
0,32
0,49
0,60
0,66
0,48
0,41
0,98
1,00
- 0,56
- 0,55
0,02
- 0,01
- 0,17
- 0,19
0,54
- 0,57
- 0,30
0,18
0,24
0,17
- 0,43
- 0,53
0,37
0,17
- 0,58
- 0,58
- 0,46
- 0,49
0,17
0,26
0,64
0,63
- 0,25
- 0,30
- 0,59
- 0,61
- 0,38
- 0,30
- 0,55
- 0,56
1,00
0,89
- 0,02
- 0,15
0,09
0,12
- 0,52
0,56
0,11
- 0,20
- 0,32
- 0,25
- 0,43
- 0,50
0,39
0,22
- 0,48
- 0,48
- 0,33
- 0,34
0,06
0,17
0,59
0,59
- 0,32
- 0,33
- 0,48
- 0,51
- 0,36
- 0,26
- 0,53
- 0,55
0,89
1,00
- 0,00
- 0,10
0,10
0,12
- 0,35
0,40
0,22
- 0,08
- 0,20
- 0,14
- 0,11
- 0,12
- 0,03
- 0,09
- 0,21
- 0,21
- 0,06
- 0,09
0,21
0,18
0,13
0,13
0,02
- 0,07
- 0,17
- 0,16
- 0,11
- 0,09
0,03
0,02
- 0,02
- 0,00
1,00
0,71
0,19
0,49
- 0,16
0,18
- 0,11
- 0,17
- 0,22
- 0,26
- 0,05
- 0,01
- 0,03
- 0,09
- 0,19
- 0,19
- 0,03
- 0,06
0,14
0,11
0,11
0,10
0,04
- 0,01
- 0,16
- 0,14
- 0,13
- 0,12
- 0,02
- 0,01
- 0,15
- 0,10
0,71
1,00
0,21
0,58
- 0,14
0,18
- 0,10
- 0,14
- 0,15
- 0,21
- 0,10
- 0,13
0,14
0,05
- 0,15
- 0,15
- 0,16
- 0,15
0,04
0,06
0,25
0,25
- 0,05
- 0,12
- 0,22
- 0,23
- 0,15
- 0,13
- 0,17
- 0,17
0,09
0,10
0,19
0,21
1,00
0,57
- 0,19
0,23
- 0,00
- 0,14
- 0,13
- 0,17
- 0,13
- 0,16
0,04
- 0,10
- 0,32
- 0,33
- 0,21
- 0,23
0,22
0,21
0,33
0,31
0,04
- 0,11
- 0,35
- 0,34
- 0,26
- 0,24
- 0,20
- 0,19
0,12
0,12
0,49
0,58
0,57
1,00
- 0,35
0,40
- 0,14
- 0,29
- 0,32
- 0,33
0,20
0,23
0,24
0,26
0,24
0,23
- 0,23
- 0,21
- 0,17
- 0,16
- 0,24
- 0,29
- 0,11
- 0,12
- 0,01
0,00
- 0,04
- 0,04
0,04
- 0,01
- 0,06
- 0,08
- 0,07
- 0,14
Systems Centre)
90
and science)
EDU_6_2007
EDU_5_2007
EDU_4_2007
unem ploym ent rate
EDU_4_2002
EDU_3_2007
population density
EDU_3_2002
EDU_2_2002
EDU_3_2002
EDU_3_2007
EDU_4_2002
EDU_4_2007
fem inization rate
EDU_1_2002
DEM_1_2002
DEM_1_2007
DEM_2_2002
DEM_2_2007
DEM_3_2002
DEM_3_2007
ECO_1_2002
ECO_1_2007
ECO_2_2004
ECO_2_2007
ECO_3_2003
ECO_3_2007
ECO_4_2003
ECO_4_2007
SOC_1_2002
SOC_1_2007
SOC_2_2002
SOC_2_2007
SOC_3_2002
SOC_3_2007
SOC_4_2002
SOC_4_2006
HEALTH_1_2002
HEALTH_1_2007
HEALTH_2_2002
HEALTH_2_2007
EDU_1_2002
EDU_2_2002
0,48
0,55
- 0,18
0,03
0,76
0,76
0,65
0,74
- 0,41
- 0,41
- 0,65
- 0,63
0,01
0,12
0,73
0,74
0,43
0,40
0,54
0,54
- 0,52
- 0,35
- 0,16
- 0,14
- 0,19
- 0,35
1,00
- 0,96
0,22
0,65
0,62
0,62
- 0,49
- 0,55
0,22
- 0,01
- 0,74
- 0,75
- 0,65
- 0,73
0,37
0,38
0,72
0,71
- 0,03
- 0,16
- 0,76
- 0,78
- 0,49
- 0,45
- 0,58
- 0,57
0,56
0,40
0,18
0,18
0,23
0,40
- 0,96
1,00
- 0,20
- 0,59
- 0,63
- 0,61
- 0,14
- 0,12
0,40
0,43
0,11
0,11
- 0,10
- 0,07
- 0,36
- 0,22
0,23
0,24
- 0,30
- 0,29
0,01
- 0,01
- 0,05
0,00
- 0,27
- 0,30
0,11
0,22
- 0,11
- 0,10
- 0,00
- 0,14
0,22
- 0,20
1,00
0,48
0,52
0,42
0,24
0,29
0,10
0,23
0,39
0,39
0,31
0,38
- 0,58
- 0,50
- 0,23
- 0,22
- 0,09
- 0,03
0,43
0,45
0,19
0,23
0,21
0,18
- 0,20
- 0,08
- 0,17
- 0,14
- 0,14
- 0,29
0,65
- 0,59
0,48
1,00
0,63
0,80
0,25
0,34
0,09
0,25
0,50
0,50
0,42
0,47
- 0,53
- 0,52
- 0,37
- 0,35
- 0,05
0,10
0,54
0,53
0,30
0,31
0,27
0,24
- 0,32
- 0,20
- 0,22
- 0,15
- 0,13
- 0,32
0,62
- 0,63
0,52
0,63
1,00
0,69
0,29
0,32
0,08
0,23
0,45
0,45
0,28
0,35
- 0,58
- 0,50
- 0,27
- 0,26
- 0,10
0,03
0,47
0,49
0,28
0,32
0,21
0,17
- 0,25
- 0,14
- 0,26
- 0,21
- 0,17
- 0,33
0,62
- 0,61
0,42
0,80
0,69
1,00
0,17
0,19
- 0,17
- 0,05
0,25
0,25
0,12
0,13
- 0,26
- 0,24
- 0,24
- 0,24
0,02
0,13
0,20
0,23
0,24
0,26
0,24
0,23
- 0,23
- 0,21
- 0,17
- 0,16
- 0,24
- 0,29
0,26
- 0,27
0,07
0,32
0,19
0,34
- 0,08
- 0,10
0,05
0,05
- 0,01
- 0,01
- 0,23
- 0,23
0,01
0,09
0,13
0,14
- 0,08
- 0,12
- 0,11
- 0,12
- 0,01
0,00
- 0,04
- 0,04
0,04
- 0,01
- 0,06
- 0,08
- 0,07
- 0,14
- 0,04
0,07
0,05
0,10
- 0,04
0,01
EDU_5_2007
(basic level)
0,26
- 0,27
0,07
0,32
0,19
0,34
1,00
0,23
EDU_6_2007
(basic level)
- 0,04
0,07
0,05
0,10
- 0,04
0,01
0,23
1,00
Systems Centre)
91
References
WHO (2008) Commission on Social Determinants of Health. Closing the gap in a generation:
health equity through action on the social determinants of health. Final report of the
Commission on Social Determinants of Health. Geneva, World Health Organization, 2008
(http://www.who.int/social_determinants/resources/gkn_lee_al.pdf).
Florey, Lia S., Sandro Galea and Mark L. Wilson, Macrosocial Determinants of Population
Health in the Context of Globalisation, in: Sandro Galea (2007) Macrosocial determinants of
population health, Springer
Marmot, F.G, Richard G.Wilkinson (eds.), 2006, Social Determinants of Health, Oxford
University Press
McMurray, Anne (2006), Community Health and Wellness: A Socio-ecological Approach,
Elsevier
Blas, Erik and A.S. Kurup (eds), 2010, Equity, Social Determinants and Public Health
Programmes, World Health Organisation
Wallace, Barbara C. (ed.), 2008, Toward equity in health: a new global approach to health
disparities, Springer Publishing Company
Bartley M, J.Ferrie and S.M.Montgomery, 2006, Health and labour market disadvantage:
unemployment, non-employment and job insecurity, in: Marmot, F.G, Richard G.Wilkinson
(2006), Social Determinants of Health, Oxford University Press
92
Annex 2. Values of selected indicators by district
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
(TERYT: National Official Register of Territorial Division of the Country)
117
3. Differences in health status of the population across districts in Poland
Bogdan Wojtyniak, Daniel Rabczenko, Jakub Stokwiszewski – National Institute of Public
Health - National Institute of Hygiene
Introduction
Health status of the population across district units is rarely subject to analysis on a national
scale. The main reason is a large number of units (379), and lack of good quality data
collected within the framework of public statistics. Database with deaths of Polish population
represents one of the few reliable sources of information pertaining to the health status of
district residents. This chapter presents the assessment of discrepancies across districts in
health status of their population availing mortality based indicators.
Information on deaths and mortality based indicators is recognized as the best basis for
characteristics of health status of a population because of the legal obligation to record each
case of death, together with the cause of death. This requirement is also binding in Poland.
Information about deaths of Polish inhabitants is gathered by the Central Statistical Office
(CSO) by means of a Statistical Form to a Death Certificate (Pu - M67). The certificate is
filled in by a physician or another person authorised to issue death certificates (i.e. hospital
attendant /paramedic, midwife nurse). At the moment, almost all (99.6%) death certificates in
Poland are filled in by physicians.
A physician who fills in a death certificate specifies, among other things, the underlying,
direct and secondary causes of death. However, he/she does not assign the ICD code of the
cause of death. Coding is centrally conducted at the regional level by specially trained
physicians (about four in each of the 16 regions), who verify and code an underlying cause of
death in accordance with the 4-digit International Classification of Diseases (the ICD-10),
introduced to Poland in 1997.
The mortality database of the CSO makes it possible to calculate for each district different
mortality based indicators, such as life expectancy, mortality rates by cause of death, infant
mortality rates. In view of a relatively small number of deaths occurring in a district unit
within a year, especially in smaller districts, it is necessary to examine health status of district
population jointly for several consecutive years, and to analyse causes of death only for the
most frequent, main groups of causes.
Hereafter, we present the analysis of mortality across districts over a three-year period of
2006–2008 generated by all causes: cancer (ICD-10 C00-C97), cardiovascular diseases (ICD10 I00-I99), diseases of the respiratory system (ICD-10 J00-J99), diseases of the digestive
system (ICD-10 K00-K93), symptoms, signs and ill-defined conditions (ICD-10 R00-R99),
118
and external causes of death (ICD-10 V01-Y98), as well as mortality generated by selected
causes. Since the age structure of the population across district units is noticeably varied, it
was necessary to calculate mortality rates standardized for age in order to eliminate the impact
of those variations on the level of mortality across districts. Due to a small number of deaths
in most of the districts, the so-called indirect standardization had to be carried out, where
standardized mortality ratios (SMRs) for selected causes were calculated for each district unit,
on the basis of death rates in 5-year age groups computed for entire Poland (standard rates).
SMRs were calculated and mortality was analysed for the total population, those aged below
65 years (to be called premature mortality) and the elderly population of age 65 years and
over. Once the value of SMR is multiplied by 100, one can obtain a percentage of excess
mortality (when the ratio is higher than 100) in a given district unit versus average national
mortality level, or a percentage of ‘deficit’ of mortality (if the ratio is less than 100), i.e. the
percentage by which such mortality rate is lower than the national mortality level. We also
computed crude death rate ratios to compare them with corresponding SMRs, since both are
important to characterize health status of district population. Furthermore, we calculated
district infant mortality rates for total (IMR), neonatal (0-27 days), and post-neonatal (28 days
- below 1 year of age) age categories – IMR(0-27) and IMR(28+), respectively. Average life
expectancy was also estimated for males and females in each district unit. Additionally, we
calculated the same indicators for a three-year period of 2001–2003, therefore we could assess
the change in the health of district residents. When calculating SMRs for the earlier period,
we applied as a standard Polish age-specific mortality rates in those years. Thus, a difference
in the SMRs in the two periods for a given district reveals relative (in relation to mortality
level in the whole country) improvement or deterioration of health status in this district.
However, we have not presented all indicators for 2001–2003 due to space constrains; yet, all
these indicators are available from the authors upon request.
119
3.1. Overall mortality
3.1.1. Total population
In the three-year period of 2006–2008, there were 1,126.3 thousands deaths in Poland, i.e.
984.8 per 100 000 population per year. Total number of deaths varied between about 600 in
Bieszczadzki and Leski districts, inhabited by about 20 thousand people, and about 53
thousand in the city of Warsaw, with about 1,700 thousand permanent residents. Tables 1-3 in
Annex 3 present standardized mortality ratios (SMRs) for the total population by gender in
each of 379 districts in 2006–2008, and summary statistics of the SMRs are shown in Table
41. There is a noticeable difference in the overall mortality level across districts. For an
inhabitant of the district where the health status is the worst (Ruda Śląska), the risk of death
was 27% higher than in the case of an average inhabitant of Poland; and for an inhabitant
living in the district where mortality is the lowest (Rzeszów), the risk of death was about 25%
lower than the country average.
Table 41. Age-standardized mortality ratio for overall mortality, total population, 2006–2008, descriptive
statistics
Population
Mean
SD
Min
Max
Median
Q1
Q3
Kurtosis
Skewness
All
1.028
0.085
0.765
1.272
1.034
0.973
1.085
-0.005
-0.186
Females
1.019
0.083
0.800
1.306
1.022
0.960
1.077
0.124
0.168
Males
1.034
0.104
0.731
1.293
1.037
0.962
1.102
-0.315
-0.091
Four of the ten districts where mortality was the highest were located in Łódzkie region (rural
districts: Poddębicki, Brzeziński, Kutnowski, Lęczycki) and three were located in Śląskie
region (towns: Ruda Śląska, Siemianowice, Chorzów38). On the other hand, across the ten
districts with the lowest mortality, eight were towns from various regions (Rzeszów, Sopot,
Olsztyn, Białystok, Warszawa, Tarnobrzeg, Opole, Kielce). However, four of the ten districts
belong to Podkarpackie region, including the towns of Rzeszów and Tarnobrzeg, as well as
Leski and Mielecki districts. In each region there are districts where mortality was below the
national average, and districts where it was above the average. However, in Podkarpackie as
well as Małopolskie regions all districts but one demonstrated mortality below the national
average, while in Lódzkie region all districts but one demonstrated mortality above the
average.
Ranking of districts by female and male mortality differs to a limited extent (Spearman
correlation coefficients 0.66). However, in the territorial units with the highest overall
38
i.e. a municipal district.
120
SMR,as well as those with the lowest overall SMR, SMR of males and females were also
respectively high and low. As presented in two maps (Fig.41 and Fig. 42), districts with low
mortality are located mostly in the east and south-east of Poland, especially in the case of
females. Most elevated mortality is observed in the west, north-west, and in particular areas of
central Poland. Rankings of districts based on crude (real) death rate (CDR) and mortality
adjusted for differences in the age structure are not exactly the same (Spearman correlation
coefficients 0.52 - see Fig. 43). It means that there are districts where mortality is high due to
older age of the population, however, when adjusted for that difference, population health
status is better than the average (it could be mentioned that the highest overall crude mortality
is in Hajnowski district - 46.4% above the national average - while age standardised mortality
for this district is only 1.6% above the average). On the other hand, there are also districts
where mortality is low, but when age structure is taken into account the risk of death is
elevated, indicating rather poor health status of the population. Sztumski district, where crude
mortality level is 10% lower than the national average, while the age-adjusted mortality is
20% above the country level, can serve as a good example. It suggests that both indicators
must be taken into account when health care needs of the population are assessed, since a
different approach is necessary as regards the former and the latter group of the districts.
According to Fig. 44, correlation between district mortality level in the period 2006–2008 and
the level observed five years earlier, in 2001–2003, is rather high (rho=0.85). It means that the
districts where mortality was below average retained their good position, and those where
mortality was higher usually have not improved their situation to an extent greater than other
districts.
121
Fig. 41. Age-standardized mortality ratio (SMR) for overall mortality,
males, 2006–2008
females, 2006–2008
122
Fig. 43. Correlation between crude death rate ratio and age-standardized
mortality ratio for overall mortality, total population, 2006–2008
Fig. 44. Correlation between age-standardized mortality ratios for overall
mortality in 2001–2003 (03) and 2006–2008 (08), total population
123
3.1.2. Population below 65 years of age
Deaths in the population aged 0–64 years are usually treated and named as premature. In
Poland, in years 2006–2008, 30.1% of all deaths occurredin this age group, and crude death
rate was 348/100 000 per year. Tables 4-6 in Annex 3 present standardized mortality ratios for
the population of that age by gender in each district in 2006–2008; Table 42 shows SMR
summary statistics. A difference in premature mortality level across districts is quite
substantial. An inhabitant below 65 years of age living in a district where health status is the
worst (the town of Chorzów) experienced risk of death 50% higher than the average. An
inhabitant living in a district where mortality is the lowest (the town of Rzeszów) had a risk of
death 32% lower than the country average.
Table 42. Age-standardized mortality ratio for overall mortality, population aged 0–64 years, 2006–2008,
descriptive statistics
Population
Mean
SD
Min
Max
Median
Q1
Q3
Kurtosis
Skewness
All
1.021
0.144
0.682
1.503
1.027
0.920
1.115
0.065
0.268
Females
0.983
0.162
0.580
1.540
0.983
0.863
1.089
0.366
0.373
Males
1.025
0.157
0.666
1.503
1.028
0.901
1.133
-0.051
0.325
Five of the ten districts where mortality was the highest belonged to Łódzkie region
(Kutnowski, Poddębicki, Brzeziński districts, the city of Łódź, and Tomaszowski district),
and four are from Śląskie region (towns: Chorzów, Świętochłowice, Ruda Śląska and
Siemianowice). The only district located in neither of these two regions was Chełmski district
fromLubelskie. On the other hand, seven of the ten districts where premature mortality was
the lowest were from Podkarpackie region, two were from Opolskie, and one from
Małopolskie. In all regions but one there were districts where premature mortality was below
the national average and districts where it was above the average, and only in Podkarpackie
region all districts had mortality lower than the mean national level. In Łódzkie region all the
districts but two had premature mortality above Poland’s average.
Rankings of districts according to mortality of males and females are not very similar
(Spearman correlation coefficients 0.46). It is interesting to note that there were districts
where male premature mortality was high, while female mortality was at the average level
(Chełmski, Tomaszowski), or even much lower (Zwoleński, Makowski). On the other hand,
in some districts only female premature mortality was elevated, while male mortality was
below the average level (e.g. Kościerski, Grodziski, Górowski, Międzyrzecki, Starogardzki).
As shown on the maps (Fig. 45 and Fig. 46), districts with low premature mortality were
124
mostly concentrated in southern and central-western Poland in the case of males, and in
outhern and eastern Poland in the case of females.
Rankings of districts according to crude premature mortality level (CDRR) and premature
mortality adjusted for differences in age structure (SMR) are similar (Spearman correlation
coefficient 0.90) (Fig. 47). It means that in most cases high or low premature mortality level
in the districts was not a result of favourable or unfavourable population age structure, but
was a consequence of high or low risk of death. Therefore, in most of the districts crude death
rate may well represent the problem of premature mortality.
Correlation between district mortality levels in 2006–2008 and five years earlier, between
2001 and 2003, is high (rho=0.86) (Fig. 48). It means that those districts where mortality was
below the average retained their good position, and those where mortality was higher usually
were not able to improve their situation to an extent greater than other districts. The most
visible deterioration occurred in Poddębicki district in Łódzkie region, where mortality in
2001–2003 was 13% higher than the national average, and in 2006–2008 it was 43% higher.
The most significant improvement occured in Strzyżowski district (in Podkarpackie region),
where mortality in 2001–2003 was at the average national level, while in 2006–2008 it was
26% lower than the average.
125
males aged 0–64 years, 2006–2008
Fig 46. Age-standardized mortality ratio (SMR) for overall mortality,
females aged 0–64 years, 2006–2008
126
mortality ratio for overall mortality, population aged 0–64 years, 2006–2008
mortality in 2001–2003 (03) and 2006–2008 (08), population aged 0–64 years
127
3.1.3. Population aged 65 years and over
People aged 65 years and over are considered as an elderly population, and about 70% of all
deaths in Poland in 2006–2008 occurred in this age group, and the crude death rate was
5092/100 000 per year. Tables 7-9 in Annex 3 present standardized mortality ratios for the
population of this age by sex in each district in 2006–2008, and Table 43 shows SMR
summary statistics. A difference in mortality level of the elderly population between districts
is smaller than in the case of premature mortality. For an inhabitant living in the district where
health status is the worst (Sztumski district), the risk of death was 24% higher than for an
average elderly (65+), and an inhabitant living in the district where mortality is the lowest (the
town of Sopot) was exposed to the risk of death 22% lower than country average.
Table 43. Age-standardized mortality ratio for overall mortality, population aged 65 years and over,
2006–2008, descriptive statistics
Population
Mean
SD
Min
Max
Median
Q1
Q3
Kurtosis
Skewness
All
1.034
0.076
0.776
1.241
1.033
0.989
1.083
0.351
-0.287
Females
1.026
0.079
0.804
1.241
1.025
0.974
1.075
0.078
0.062
Males
1.044
0.093
0.738
1.351
1.048
0.986
1.104
0.467
-0.086
Across ten districts where mortality was the highest, two were from Dolnośląskie region
(Lwówecki and Złotoryjski), two from Wielkopolskie region (Międzychodzki and Śremski,)
and each of the other six districts was located in a different region. On the other hand, across
ten districts with the lowest mortality, as many as nine districts were towns, with two from
Podkarpackie region, another two from Mazowieckie region, and the remaining five towns all
located in different regions. In all regions but one there are districts where mortality of the
elderly was below the national average and districts where it was above the average; only in
Łódzkie region mortality was higher than Poland’s average in all the districts.
Rankings of districts according to mortality of males and females are not the same (Spearman
correlation coefficient 0.63). There are districts where mortality of elderly men was high,
while female mortality was at the average level (Nowodworski, Kętrzyński, Kwidzyński). On
the other hand, in some districts only female mortality was elevated, while male mortality was
about the average level (e.g. Kościerski, Wolsztyński). Districts with low elderly mortality are
located mostly in southern and the east-northern Poland (the maps - Fig. 49 and Fig. 50).
However, even in these regions there are some districts where inhabitants from that age group
were exposed to an increased risk of death when compared to country average level.
128
Rankings of districts according to crude mortality level of the elderly population (CDRR) and
mortality adjusted for differences in age structure (SMR) do not differ much (Spearman
correlation coefficient 0.76) (Fig. 51). It means that in many districts crude death rate will
reasonable well reflect the problem of total mortality of the elderly population.
Correlation between district mortality level in 2006–2008 and five years earlier, between 2001
and 2003, is rather high (rho=0.77) (Fig. 52). It means that changes in mortality of the elderly
population in the districts during this five-year period were mostly similar. The districts where
mortality was below the average retained their good position, and those where mortality was
higher usually were not able to improve their situation to an extent greater than other districts.
Correlation between district mortality level for all causes of younger (below 65 years) and
older (65 years and over) population is only moderate (Spearman correlation coefficient 0.55).
It suggests that in order to properly assess and address health needs of a district population it
is necessary to look independently at the younger and the older population groups.
129
males aged 65 years and over, 2006–2008
females aged 65 years and over, 2006–2008
130
mortality in 2001–2003 (03) and 2006–2008 (08), population aged 65 years
and over
mortality ratio for overall mortality, population aged 65 years and over,
2006–2008
131
3.2. Mortality from cancer
Total number of deaths caused by cancer accounted for 24.6% of all deaths in Poland in the
period of 2006–2008, and crude death rate was 243/100 000 per year. Tables 1-3 in Annex 3
present standardized mortality ratios for cancer (ICD-10 C00-C97), for the total population,
by sex, in each of 379 districts in 2006–2008, and SMR summary statistics are shown in
Table 44. There is a noticeable difference in overall cancer mortality level across districts. For
an inhabitant of the district where health status is the worst (Sztumski), the risk of death was
35% higher than in the case of an average inhabitant of Poland; and an inhabitant living in the
district where mortality is the lowest (Krasnostawski), was exposed to the risk of death 28%
Table 44. Age-standardized mortality ratio for cancer, total population, 2006–2008, descriptive statistics
Population
Mean
SD
Min
Max
Median
Q1
Q3
Kurtosis
Skewness
All
1.008
0.119
0.717
1.348
1.003
0.920
1.098
-0.410
0.040
Females
0.980
0.144
0.591
1.419
0.980
0.875
1.095
-0.371
-0.034
Males
1.028
0.131
0.700
1.626
1.023
0.934
1.110
0.606
0.449
Six of the ten districts where mortality was the highest were from Pomorskie region
(Sztumski, Nowodworski, Słupski, Kościerski, Lęborski, Pucki), and two from Śląskie region
(towns: Siemianowice, Mysłowice). On the other hand, six of the ten districts where mortality
was the lowest were from Lubelskie region (Krasnostawski, Bialski, Lubelski, Janowski,
Opolski, and the town of Chełm), and two districts were from Mazowieckie region (Przysuski
and Garwoliński). It should be pointed out that in two regions - Lubelskie and Podkarpackie in all the districts the risk of cancer death was below national average, and in Świętokrzyskie
region all districts but one also demonstrated mortality level below national average. On the
other hand, in Kujawsko-Pomorskie region mortality caused by cancer was above the average
in all the districts.
Correlation of district mortality of males and females is not very strong (Spearman correlation
coefficient 0.59). However, in the districts with the highest overall mortality and in those with
the lowest mortality SMRs of males and females were also high and low, respectively.
Nevertheless, there are some districts where SMRs of males and females differ quite
significantly. A good example is Sztumski district, where total cancer mortality is the highest.
In this case, male risk of death is 63% higher than the national average, however, female risk
of death is exactly at the average level. On the other hand, in the town of Koszalin female
132
mortality caused by cancer was 30% above the average, while male mortality was not
elevated above the average. Districts with low mortality have a tendency to concentrate in
eastern and the south-eastern Poland. In the west, north-west and in certain areas of central
Poland mortality is usually elevated (Fig.53 and Fig. 54).
Rankings of those districts according to crude mortality level (CDRR) and mortality adjusted
for differences in age structure are not exactly the same (Spearman correlation coefficient
0.62) (Fig. 55). There are districts where mortality is high due to older age structure of the
population, however, when adjusted for age, population health status is better than the
average. For example, high crude mortality caused by cancer is observed in Hajnowski,
Pińczowski, Sokołowski, Łosicki districts - about 40% above the national average - while age
standardised mortality is below or only slightly above the average. On the other hand, there
are districts where crude mortality is low, but when age structure is taken into account, the
risk of death is elevated, indicating rather poor health of the population. In this case, good
examples are Sztumski, Pszczyński, Grodziski and Wałecki districts. It demonstrates that both
indicators must be taken into consideration when health care needs of the population
regarding cancer prevention and treatment are assessed.
As illustrated in Fig. 56, correlation between district mortality levels in 2006–2008 and the
levels observed five years earlier, in 2001–2003, is more than moderate (rho=0.75). It means
that change in mortality in the districts during this five-year period was rather similar, and
those districts where mortality was below the average retained their good position, whereas
those where mortality was higher usually have not improved their status in relation to country
average to an extent greater than other districts. Yet, there are exceptions to this rule. For
example, in Radziejowski, Chełmiński, Słubick, Człuchowski and Świecki districts, recent
level of mortality is noticeably higher than the national level, while five years earlier it was
below the average. In Sztumski district SMR in the period of 2001–2003 revealed only 9%
excess of cancer deaths in comparison to average country level, while in the period of 2006–
2008 the excess was 35%.
133
Fig. 53. Age-standardized mortality ratio (SMR) for cancer, males,
2006–2008
Fig. 54. Age-standardized mortality ratio (SMR) for cancer, females,
2006–2008
134
mortality ratio for cancer, total population, 2006–2008
Fig. 56. Correlation between age-standardized mortality ratios for cancer in
2001–2003 (03) and 2006–2008 (08), total population
135
In 2006–2008 more than one-third (36.6%) of all cancer deaths occurred in the population
aged 0–64 years; they accounted for 29.4% of all premature deaths, and crude death rate was
103/100 000 per year. Thus, cancer is the most common cause of deaths in the younger
population of Poland. Tables 4-6 in Annex 3 present standardized mortality ratios for the
population from that age group, by sex, in each of 379 districts in 2006–2008, and SMR
summary statistics are shown in Table 45. A difference in the level of premature mortality
from cancer across districts is quite substantial. An inhabitant of that age group, living in a
district where health status is the worst (Sierpecki), had the risk of death 47% higher than an
average inhabitant of Poland. And for an inhabitant living in the districts where mortality is
the lowest (Włoszczowski district and the town of Rzeszów), the risk of death was 30% lower
than the country average.
Table 45. Age-standardized mortality ratio for cancer, population aged 0–64 years, 2006–2008, descriptive
statistics
Population
Mean
SD
Min
Max
Median
Q1
Q3
Kurtosis
Skewness
All
1.021
0.142
0.691
1.466
1.021
0.924
1.112
-0.022
0.186
Females
0.993
0.171
0.597
1.607
0.983
0.871
1.107
-0.102
0.272
Males
1.033
0.155
0.658
1.567
1.028
0.923
1.126
0.219
0.251
Ten districts where mortality was the highest were quite dispersed, they were located in seven
regions. On the other hand, five of the ten districts where premature cancer mortality was the
lowest were located in Lubelskie region (the town of Chełm as well as Krasnostawski,
Puławski, Lubelski and Janowski districts), and three in Podkarpackie region (the town of
Rzeszów and Kolbuszowski and Tarnobrzeski districts). In all the regions but two there are
districts where premature cancer mortality was below country average, and those where it was
above the average. The only exceptional regions in that regard were: Kujawsko-Pomorskie,
where in all the districts mortality was elevated above the national average level, and
Podkarpackie, where in all the districts mortality was lower than the mean national level.
Ranking of districts according to premature cancer mortality of males and females is not
highly correlated (Spearman correlation coefficient 0.50). Noticeably, there were districts
where male premature mortality was high while female mortality was at the average level
(e.g. Brodnicki, Płocki), or even substantially lower (e.g. Zwoleński, Pajęczański,
Białobrzeski). On the other hand, in some districts only female premature mortality was
elevated, while male mortality was below the average level (e.g. Gołdapski and the towns of
136
Jelenia Góra, Włocławek, Koszalin). Districts with low premature cancer mortality mostly
concentrated in the east of Poland, especially in south-eastern part of the country, while the
districts where mortality was elevated were located mostly in the north and in central-northern
part of Poland (Fig.57 and Fig. 58).
mortality adjusted for differences in age structure (SMR) are quite similar (Spearman
correlation coefficient 0.84) (Fig. 59). It means that in most cases high or low premature
mortality level in a district was not a result of favourable or unfavourable population age
structure,but instead it was a consequence of a high or low risk of death.
Correlation between district mortality levels in 2006–2008 and five years earlier, between
2001 and 2003, is rather moderate (rho=0.65) (Fig. 60). It means that changes in mortality in
districts during this five-year period were not very similar. However, in most cases the
districts where mortality was below the average retained their good position, and those where
mortality was higher usually were not able to improve their status to an extent greater than
other districts. The most significant deterioration was observed in the districts: StrzeleckoDrezdeński, Wąbrzeski, Kazimierski, Radziejowski and Słubicki, where in 2001–2003
mortality was at the national average, but in 2006–2008 it was 30-40% higher than the
average. The largest positive change occurred in Kamieński and Bełchatowski districts, where
in 2001–2003 mortality was 12% and 19% above the national average level, respectively,
while in 2006–2008 it was, respectively, 24% and 12% lower than the average.
137
Fig. 57. Age-standardized mortality ratio (SMR) for cancer,
females aged 0–64 years, 2006–2008,
138
mortality ratio for cancer, population aged 0–64 years, 2006–2008
Fig. 60. Correlation between age-standardized mortality ratios for cancer in
2001–2003 (03) and 2006–2008 (08), population aged 0–64 years
139
In 2006–2008, about 63% of all deaths due to cancer in Poland occurred in the age group of
65 years and above; they represented 22.5% of all deaths of the elderly population. Tables 7-9
in Annex 3 present standardized mortality ratios for the population from that age group, by
sex, in each district in 2006–2008;SMR summary statistics are shown in Table 46. Variation
in mortality of elderly population across districts is quite similar to premature mortality. An
inhabitant living in the district where health status was the worst (Działdowski district) had
the risk of death 40% higher than an average elderly inhabitant of Poland. For an inhabitant
living in the district where mortality was the lowest (Przysuski), the risk of death was 37%
Table 46. Age-standardized mortality ratio for cancer, population aged 65 years and over, 2006–2008,
Population
Mean
SD
Min
Max
Median
Q1
Q3
Kurtosis
Skewness
All
1.004
0.131
0.631
1.405
1.003
0.907
1.095
-0.272
0.097
Females
0.975
0.161
0.588
1.472
0.969
0.858
1.093
-0.278
0.104
Males
1.028
0.151
0.648
1.674
1.019
0.925
1.116
0.790
0.532
Five of the ten districts where mortality was the highest (Pucki, Nowodworski, Lęborski,
Kościerski, Sztumski) were from Pomorskie region, and two were from Śląskie region
(towns: Siemianowice and Mysłowice). On the other hand, six of the ten districts where
mortality was the lowest were located in Lubelskie region (Krasnostawski, Bialski, Lubelski,
Janowski, Opolski, Łukowski), and another two in Mazowieckie region (districts: Przysuski
and Garwoliński). In all the regions but two there were districts where cancer mortality of the
elderly was above the national average, and those where it was below the average;only in
Lubelskie and Świętokrzyskie region there were no district with mortality above Polish
average. On the other hand, in Pomorskie region in all the districts but one mortality of the
elderly due to cancer was higher than the national average.
District mortality levels of males and females are not highly correlated (Spearman correlation
coefficient 0.52). There are districts where male cancer mortality was considerably higher
(more than 30%) than the country average, while female mortality was at the average level or
below (e.g. Sztumski, Mogileński, Sejneński, Wąbrzeski, Słubicki, Toruński). Interestingly, in
the first of the above-mentioned districts the excess of male mortality over the national
average was 67%, whereas female mortality was 9% lower than the average. On the other
hand, in some districts only female mortality was elevated, while male mortality was about
the average level (e.g. the towns of: Suwałki, Opole, Gorzów, Słupsk, Grudziądz). As shown
140
on the maps (Fig. 61 and Fig. 62), districts with low cancer mortality of elderly population
typically concentrated in the south-east and the east Poland, with some areas in the west of the
country, while the districts where mortality is elevated are located mostly in the northern and
the central-northern part of Poland.
Rankings of the districts according to crude mortality level of the elderly population (CDRR)
and mortality adjusted for differences in age structure (SMR) are very similar (Spearman
correlation coefficient 0.99) (Fig. 63). It means that district crude death rate accurately
reflects the problem of cancer mortality of the elderly population.
Correlation between districts cancer mortality level of the elderly population in 2006–2008
and five years earlier, between 2001 and 2003, is rather high (rho=0.69) (Fig. 64). It means
that changes in mortality in the districts during this five-year period were not very diverse. In
most cases, the districts where mortality was below the average retained their good position,
and those where mortality was higher usually were not able to improve their status to the
extent greater than other districts. Significant deterioration was observed in Chełmiński,
Kościerski, Sztumski and Mogileński districts, where in 2001–2003 mortality was about the
national average level, but in 2006–2008 it was 25-30% higher than the average. The most
significant improvement was reported in the town of Świnoujście and in Kwidzyński district,
where in 2001–2003 mortality was, respectively, 29% and 46% above the national average
level, while in 2006–2008 it was equal to the average and only 16% above the average,
respectively.
Correlation between district mortality levels from cancer observed among the younger (below
65) and the older (65 and above) population is only moderate (Spearman correlation
coefficient 0.52).
141
142
Fig. 63. Correlation between crude death rate ratio and standardized
mortality ratio for cancer, population aged 65 years and over, 2006–2008
Fig. 64. Correlation between standardized mortality ratios for cancer in
2001–2003 (03) and 2006–2008 (08), population aged 65 years and over
143
3.3. Mortality from circulatory system diseases
Deaths caused by cardiovascular diseases (CVD) (ICD-10 I00-I99) accounted for 45.5% of all
deaths in Poland in 2006–2008, and the annual crude death rate was 448/100 000. Tables 1-3
in Annex 3 present standardized mortality ratios for CVD for the total population, by sex, in
each district in 2006–2008, and SMR summary statistics are shown in Table 47. There is a
noticeable variation in the overall CVD mortality level across districts, which is larger than in
the case of cancer mortality. An inhabitant of the district where cardiovascular health status is
the worst (Brzeziński) had a risk of death 44% higher than the national average. For an
inhabitant living in the district where mortality is the lowest (the town of Olsztyn), the risk of
death was 39% lower than the country level.
Table 47. Age-standardized mortality ratio for cardiovascular diseases, total population, 2006–2008,
Population
Mean
SD
Min
Max
Median
Q1
Q3
Kurtosis
Skewness
All
1.046
0.140
0.608
1.438
1.053
0.950
1.143
-0.038
-0.182
Females
1.042
0.144
0.639
1.461
1.046
0.949
1.139
-0.046
-0.192
Males
1.046
0.149
0.582
1.417
1.036
0.943
1.147
-0.191
0.034
Three of the ten districts with the highest mortality were located in Łódzkie region
(Brzeziński, Wieruszowski, Łęczycki), and two in Śląskie region (Żywiecki and the town of
Świętochłowice). On the other hand, among the ten districts where mortality was the lowest,
there were nine towns or cities, four of which were located in Pomorskie region (Sopot,
Gdynia, Gdańsk, Słupsk), and two in Podlaskie region (Białystok and Łomża). The other
towns or cities were dispersed all over the country (Olsztyn, Zielona Góra, Warszawa and
Leski district). In one region (Podlaskie), mortality from CVD was below the national average
in all districts.
District CVD mortality levels of males and females are quite strongly correlated (Spearman
correlation coefficient 0.83). Districts with low mortality were concentrated in the northern
and north-eastern Poland, while the districts where mortality was elevated demonstrated lower
concentration (Fig. 65 and Fig. 66).
Rankings of districts according to crude mortality level (CDRR) and mortality adjusted for
differences in age structure are not exactly the same (Spearman correlation coefficient 0.68)
(Fig. 67). It means that there are districts where mortality is high due to older age structure of
the population, but after adjustment for differences in age, mortality is actually lower than the
average. For example, high crude mortality caused by CVD was observed in Hajnowski and
144
Bielski districts and in the city of Łódź - about 30–40% above the national average - while
age-standardised mortality rate was below the average. On the other hand, there were districts
where crude mortality rate was low, but when age structure was taken into account, the risk of
death was elevated, indicating rather poor health of the population. Sztumski, Pszczyński or
Braniewski districts are good examples of that phenomenon. It demonstrates that both
indicators must be taken into account when assessing health care needs of the population
regarding CVD prevention and treatment.
Correlation between district mortality level in 2006–2008 and the level observed five years
earlier, in 2001–2003, is quite strong (rho=0.78) (Fig. 68). It means that change in mortality in
the districts during this five-year period was quite similar, and the districts where mortality
was below average retained their good position, whereas those where mortality was higher
usually have not improved their situation in relation to country average to an extent greater
than other districts. However, some exceptions must be noted; for example in Sulęciński,
Braniewski, Skarżyski or Krośnieński districts recent level of mortality is noticeably higher
than the average national level, while five years earlier it was below the average.
145
Fig. 65. Age-standardized mortality ratio (SMR) for cardiovascular diseases,
males, 2006–2008
females, 2006–2008
146
mortality ratio for cardiovascular diseases,
total population, 2006–2008
Fig. 68. Correlation between age-standardized mortality ratios for
cardiovascular diseases in 2001–2003 (03) and 2006–2008 (08),
total population
147
In 2006–2008, less than one-fifth (17.7%) of all CVD deaths occurred in the population aged
0–64 years. They accounted for 26.3% of all premature deaths, and the annual crude death
rate was 91.6/100 000. Thus, CVD deaths in this age group were less common than deaths
caused by cancer. Tables 4–6 in Annex 3 present standardized mortality ratios for the
population of that age, by sex, in each district in 2006–2008; and SMR summary statistics are
shown in Table 48. A difference in the level of premature mortality caused by CVD across
districts is large. An inhabitant below 65 years of age living in the district where
cardiovascular health status is the worst (the town of Świętochłowice), was exposed to the
risk of death 77% higher than the average inhabitant of Poland; and for an inhabitant living in
the district where mortality is the lowest (the town of Olsztyn), the risk of death was reduced
by 47% when compared to the country average.
Table 48. Age-standardized mortality ratio for cardiovascular disases, population aged 0–64 years, 2006–
2008,
Population
Mean
SD
Min
Max
Median
Q1
Q3
Kurtosis Skewness
All
1.033
0.203
0.531
1.772
1.012
0.886
1.145
0.340
0.553
Females
1.002
0.261
0.385
2.028
0.972
0.823
1.158
0.383
0.534
Males
1.029
0.207
0.539
1.703
1.002
0.877
1.145
0.225
0.595
Three of the ten districts where CVD premature mortality was the highest belong to Śląskie
region (towns: Świętochłowice, Chorzów, Siemianowice), three to Łódzkie region (districts:
Wieruszewski, Brzeziński, Łowicki), and two are located in Lubuskie region (districts:
Żagański and Żarski). On the other hand, across the ten districts where premature CVD
mortality was the lowest, three districts belong to Podlaskie region (towns: Białystok and
Łomża, and Zambrowski district), two to Lubelskie region (town of Zamość, Biłgorajski
district), and two to Podkarpackie region (Leski, Leżajski). In all the regions there are districts
where premature CVD mortality was below the country average, and districts where it was
above the average. However, in Podkarpackie region mortality in all the districts but one was
lower than Poland’s average.
Correlation between district male and female premature CVD mortality level is of medium
strength (Spearman correlation coefficient 0.53). There are districts where male premature
mortality was high while female mortality was below the average level (e.g. Suwalski,
Tomaszowski, Wieluński, Szydłowiecki, town of Piekary Śląskie). In Łowicki district male
mortality was 70% higher than the national level, while female mortality was elevated by only
9%. On the other hand, in some districts female premature mortality was increased, while
148
male mortality was below the average level (e.g. Gołdapski, Wołowski, GolubskoDobrzyński, Świecki). Districts with low female premature CVD mortality concentrated
primarily in eastern Poland, especially in the south-eastern part of the country, while the
districts where male mortality was low were more dispersed. However, they can also be found
in the south-east of Poland (Fig. 69 and Fig. 70).
mortality adjusted for differences in age structure (SMR) are very similar (Spearman
correlation coefficient 0.91) (Fig. 71). It means that in most cases high or low premature CVD
mortality in districts was not a result of favourable or unfavourable population age structure,
but rather high or low risk of death.
Correlation between district mortality level in 2006–2008 and five years earlier, in 2001–
2003, is rather moderate (rho=0.65) (Fig. 72). It indicates that changes in mortality in the
districts during this five-year period were not very similar. However, in most cases the
districts where mortality was below the average retained their good position, and those where
mortality was higher usually were not able to improve their situation to an extent greater than
other districts. The most significant deterioration was observed in Poddębicki and Skarżyski
districts, where in 2001–2003 mortality was below the national average, but in 2006–2008 it
was, respectively, 49% and 41% above the average. The most significant improvement took
place in Włodawski district, where in 2001–2003 mortality was 44% above the national
average level, while in 2006–2008 it was 14% lower than the average.
149
150
mortality ratio for cardiovascular diseases, population aged 0–64 years,
2006–2008
cardiovascular diseases in 2001–2003 (03) and 2006–2008 (08), population
aged 0–64 years
151
About 82% of all CVD deaths in Poland in 2006–2008 occurred in the age group of 65 years
and above; they represented 54.0% of all deaths of the elderly population; annual crude death
rate was 2751/100 000. Tables 7–9 in Annex 3 present standardized mortality ratios for the
population of that age, by sex, in each district in 2006–2008; SMR summary statistics are
shown in Table 49. Variation in mortality of elderly population between districts is smaller
than in the case of CVD premature mortality, however, it is of similar magnitude as cancer
mortality of elderly population. An inhabitant living in the district where cardiovascular
health status was the worst (Brzeziński) had a risk of death 41% higher than an average
elderly inhabitant of Poland; and in the case of an inhabitant living in the district where
mortality was the lowest (the town of Sopot), the risk of death was 39% lower than country
average.
Table 49. Age-standardized mortality ratio for cardiovascular diseases, population aged 65 years and
over, 2006–2008, descriptive statistics
Population
Mean
SD
Min
Max
Median
Q1
Q3
Kurtosis Skewness
1.049
0.139
0.611
1.410
1.057
0.958
1.147
0.042
-0.282
All
1.045
0.145
0.623
1.435
1.050
0.946
1.145
-0.040
-0.242
Females
1.056
0.150
0.591
1.462
1.057
0.953
1.154
0.030
-0.051
Males
Four of the ten districts where mortality was the highest belong to Łódzkie region (districts:
Brzeziński, Łęczycki, Wieruszowski, Opoczyński), and the remaining ones are dispersed all
over the country. On the other hand, among the ten districts where mortality was the lowest,
there were three towns located in Pomorskie region (Sopot, Gdańsk, Gdynia), two towns in
Podlaskie region (Białystok, Suwałki), the two districts from Podkarpackie region (Leski,
Mielecki). In all the regions but one there were districts where CVD mortality of the elderly
was above the national average, and districts where it was below the average; only in
Podlaskie region there was no district with mortality above Polish average. On the other hand,
in Łódzkie region all the districts but one exhibited higher CVD mortality of the elderly than
the national average.
Correlation of district male and female mortality rates is rather high (Spearman correlation
coefficient 0.80). There is only one district where male CVD mortality was substantially
higher (more than 20%) than the country average, while female mortality was below the
average level (Sępoliński), and only in one district (Tarnobrzeski) the situation was reversed,
i.e. female mortality was noticeably elevated (by 24%) while male mortality was at the
152
average level. Districts with low CVD mortality of elderly population were usually located in
central and eastern part of northern Poland. Districts where mortality is elevated are more
dispersed, but in the case of males and females some of those districts are concentrated in the
central part of the east of the country (Fig. 73 and Fig. 74).
mortality adjusted for differences in age structure (SMR) are very similar (Spearman
correlation coefficient 0.88) (Fig. 75). It means that district CVD crude death rates quite
accurately reflect the differences between districts in terms of life threat posed by CVD in the
population of this age.
Correlation between district CVD mortality level of the elderly population in2006–2008 and
five years earlier, in 2001–2003, is rather strong (rho=0.78) (Fig. 76). It means that changes in
mortality in the districts during this five-year period did not differ very much. In most cases,
the districts where mortality was below the average retained their good position, and those
where mortality was higher usually were not able to improve their status to an extent greater
than other districts. Significant deterioration was noted in Sulęciński and Olecki districts,
where in 2001–2003 mortality was below the national average, but in 2006–2008 it was about
20% higher than the country level. The largest relative improvement was observed in the
Ostrzeszowskiski district, where 2001–2003 mortality was 57% above the national average
level, while in 2006–2008 it was 29% below the average.
Correlation between district CVD mortality of younger (below 65 years) and older (65 years
and over) population is rather moderate (Spearman correlation coefficients 0.60).
153
Fig. 73. Age-standardized mortality ratio (SMR for cardiovascular diseases,
Fig. 74. Age-standardized mortality ratio (SMR for cardiovascular diseases,
154
mortality ratio for cardiovascular diseases, population aged 65 years and
over, 2006–2008
cardiovascular diseases in 2001–2003 (03) and 2006–2008 (08), population
aged 65 years and over
155
3.4. Mortality from diseases of the respiratory system
Deaths caused by diseases of the respiratory system (ICD-10 J00-J99) accounted for 5.1% of
all deaths in Poland in 2006–2008, and the annual crude death rate was 50.0/100 000. Tables
1–3 in Annex 3 present standardized mortality ratios due to respiratory diseases for the total
population, by sex, in each district in 2006–2008; SMRs summary statistics are shown in
Table 50. There is noticeable variation in the level of mortality from respiratory diseases
across districts, larger than in the case of cancer and CVD mortality. An inhabitant of the
district with the worst respiratory health status (Nidzicki) was exposed to the risk of death 2.6
times higher than the national average; and for an inhabitant living in the district where
mortality is the lowest (Niżański), the risk of death was 65% lower than the country level.
Table 50. Age-standardized mortality ratio for diseases of the respiratory system, total population, 2006–
2008, descriptive statistics
Skewness
Population
Mean
SD
Min
Max Median
Q1
Q3
Kurtosis
All
1.018
0.305
0.350 2.616
1.000
0.816
1.181
1.749
0.814
Females
0.970
0.361
0.281 2.732
0.923
0.719
1.184
1.373
0.781
Males
1.048
0.311
0.391 2.545
1.021
0.831
1.238
1.243
0.732
Eight of the ten districts where mortality was the highest were from Warmińsko-Mazurskie
region (Nidzicki, Olsztyński, Węgorzewski, Bartoszycki, Szczycieński, Elbląski, Kętrzyński,
Giżycki). On the other hand, across the ten districts where mortality was the lowest, five were
from the Podkarpackie region (Niżański, Jarosławski, Dębicki, Ropczycko-Sędziszowski and
the town of Tarnobrzeg), and the remaining ones were dispersed all over the country. In one
region (Warmińsko-Mazurskie) mortality caused by respiratory diseases was higher than the
national average in all the districts. On the other hand, in Opolskie region mortality in all the
districts but one was below the average.
Rankings of districts according to respiratory mortality of males and females is similar to a
certain extent (Spearman correlation coefficient 0.65). Districts with high mortality were
usually located in the north-east of Poland, while the districts where mortality was low were
dispersed throughout the rest of the country, with larger number observed in the south-east of
Poland(Fig. 77 and Fig. 78).
Rankings of districts according to crude mortality level (CDRR) and mortality adjusted for
differences in age structure (SMR) are quite similar (Spearman correlation coefficient 0.88). It
means that district’s crude death rate quite accurately reflects the problems of mortality
156
caused by respiratory diseases of the population. It may be pointed out that in Nidzicki district
both indicators were more than twice as high as Poland’s average.
Correlation between district mortality levels from the period of 2006–2008 and five years
earlier, between 2001 and2003, is moderate (rho=0.67) (Fig. 80), which means that changes in
mortality in the districts during this five-year period varied to some extent. However, in most
cases the districts where mortality was below average retained such relatively low level, and
those where mortality was higher usually were not able to improve their situation to an extent
greater than other districts. Significant deterioration occurred, for example, in Łęczyński
district and in the town of Elbląg, where mortality in 2001–2003 was below the average, and
five years later it was about 50% higher than the country level. On the other hand, in
Świebodziński and Sulęciński districts in 2001–2003 mortality caused by respiratory diseases
was more than 60% above the average, whereas in 2006–2008 it was elevated above the
average by less than 10%. Mortality in Nidzicki district is consistently the highest in analysed
period – about 2.6 times above the average country level in 2006-2008 as well as five years
earlier.
157
Fig. 77. Age-standardized mortality ratio (SMR) for respiratory system
diseases, males, 2006–2008
diseases, females, 2006–2008
158
mortality ratio for respiratory system diseases, total population, 2006–2008
Fig. 80. Correlation between standardized mortality ratios for respiratory
system diseases in 2001–2003 (03) and 2006–2008 (08), total population
159
In 2006–2008, 18.8% of all deaths caused by respiratory diseases occurred in the age group
below 65 years, they accounted for 3.1% of all premature deaths, and the annual crude death
rate was 10.9/100 000. Tables 4–6 in Annex 3 present standardized mortality ratios for the
population of that age, by sex, in each district in 2006–2008; SMR summary statistics are
presented in Table 51. A difference in the level of premature mortality from respiratory
diseases across districts is very large. A person under 65 years living in the district where the
respiratory health status is the worst (Węgorzewski) had a risk of death 2.7 times higher than
an average Polish inhabitant. For a person living in the district with the lowest mortality
(Przeworski), the risk of death was 80% lower than the country average.
Table 51. Age-standardized mortality ratio for respiratory system diseases, population aged 0–64 years,
Population
Mean
SD
Min
Max
Median
Q1
Q3
Kurtosis
Skewness
All
Females
Males
1.003
0.979
1.002
0.379
0.579
0.394
0.201
0.000
0.186
2.703
3.655
2.959
0.974
0.900
0.975
0.739
0.566
0.699
1.221
1.258
1.238
1.122
1.596
1.505
0.787
0.969
0.800
Six of the ten districts where premature mortality generated by respiratory diseases was the
highest belong to Warmińsko-Mazurskie region (Węgorzewski, Nidzicki, Giżycki, Elbląski,
Bartoszycki, Kętrzyński), and three are the towns in Śląskie region (Siemianowice, Chorzów,
Świętochłowice). On the other hand, of the ten districts where premature respiratory diseases
mortality was the lowest, as many as five districts belong to Podkarpackie region (Przeworski,
town of Tarnobrzeg, Niżański, Stalowolski, Strzyżowski), while the other five are dispersed
all over the country. In all the regions there are districts where premature mortality due to
respiratory diseases was below country average, and the districts where it was above the
average. Yet, in Podkarpackie region mortality in all the districts but one was lower than
Poland’s average, while in Warmińsko-Mazurskie region mortality level was above country
average in all the districts but one.
Correlation between male and female district mortality rates is rather low (Spearman
correlation coefficient 0.37). It could partly be explained by a small number of female deaths
and the presence of districts where there were no female deaths at all. Therefore, those
differences between males and females should be interpreted with caution. It may be
emphasised that in most of the districts where mortality was high, rates were elevated for
160
males as well as females, and in those where mortality was low, it was reduced for both sex
groups (Fig. 81 and Fig. 82). Districts with low premature mortality caused by respiratory
diseases usually concentrated in western and south-eastern parts of Poland, for men and
women alike. However, the former location applies more often to men than women, while the
reverse is true for the latter location. The districts where mortality was elevated concentrate
primarily in the north of Poland, especially in its central and eastern part.
mortality adjusted for differences in age structure (SMR) are almost the same (Spearman
correlation coefficient 0.98) (Fig. 83). It means that in most cases high or low level of
premature mortality caused by respiratory diseases in the district did not result from a
favourable or unfavourable population age structure, but was a result of high or low risk of
death.
Correlation between district mortality levels in 2006–2008 and five years earlier, in 2001–
2003, is rather weak (rho=0.37) (Fig. 84). It means that changes in mortality in the districts
during this five-year period were quite different. There are many districts where the difference
between district level of mortality and the national average has increased, and many where the
difference has declined. However, there is also a number of districts where mortality was
above the average in both periods. The most striking example is Węgorzewski district, where
mortality in 2001–2003 was 3.6 times higher than the Polish average level, and in 2006–2008
it was still 2.7 times higher.
161
diseases, males aged 0–64 years, 2006–2008
diseases, females aged 0–64 years, 2006–2008
162
mortality ratio for respiratory system diseases, population aged 0–64 years,
2006–2008
Fig. 84. Correlation between standardized mortality ratios for respiratory
system diseases in 2001–2003 (03) and 2006–2008 (08), population aged 0–64
years
163
About 81% of all deaths from respiratory diseases in Poland in 2006–2008 occurred in the age
group of 65 years and over; they represented 5.9% of all deaths of the elderly population, and
the annual crude death rate was 302/100 000. It means that respiratory diseases are the third
main cause of deaths (after CVD and cancer) in this age group. Tables 7-9 in Annex 3 present
standardized mortality ratios for the population of that age, by sex, in each district in 2006–
2008, and SMR summary statistics are shown in Table 52. Variation in mortality of the
elderly population across districts is slightly less pronounced than in the case of premature
mortality. An inhabitant living in the district where respiratory health status is the worst
(Nidzicki) was exposed to the risk of death 2.7 times higher than the average for elderly
inhabitants of Poland. For an inhabitant living in the district where mortality is the lowest
(Niżański), the risk of death was 65% lower than the country average.
Table 52. Age-standardized mortality ratio for respiratory system diseases, population aged 65 years and
over, 2006–2008, descriptive statistics
Population
Mean
All
Females
Males
1.021
0.967
1.062
SD
Min
Max
0.321 0.346 2.707
0.374 0.230 2.700
0.335 0.321 2.736
Median
Q1
Q3
Kurtosis
Skewness
0.980
0.928
1.027
0.793
0.712
0.822
1.202
1.191
1.247
1.702
0.980
1.549
0.805
0.680
0.795
Five of the ten districts where mortality was the highest belong to the Warmińsko-Mazurskie
region (Nidzicki, Olsztyński, Szczycieński, Bartoszycki, Elbląski), and two are from
Mazowieckie region (town of Ostrołęka and Ciechanowski district). On the other hand, across
the ten districts where mortality was the lowest five are located in Podkarpackie region
(Niżański, Jarosławski, Dębicki and the towns: Tarnobrzeg and Przemyśl), and two towns are
located in Śląskie region (Bielsko-Biała, Świętochłowice). In all the regions but one there are
districts where mortality of the elderly due to respiratory diseases was above the national
average, and districts where it was below the average, and only in Warmińsko-Mazurskie
region there was no district where mortality was below Polish average. On the other hand, in
Opolskie region respiratory mortality of the elderly was lower than the national average in all
the districts but one.
Correlation between district mortality level of males and females is rather moderate
(Spearman correlation coefficient 0.62). Analogically to the case of premature mortality,
differences in male and female rates have to be treated with some caution due to rather small
number of female deaths in many districts. Districts with low mortality caused by respiratory
164
diseases were mostly concentrated in western and the south-eastern parts of Poland, for men
and women alike. However, the former location is more visible in the case of male rather than
female mortality, while the reverse is true for the latter area. Districts with elevated mortality
are concentrated primarily in the north of Poland, especially in its central andeastern part (Fig.
85 and Fig. 86).
correlation coefficient 0.98) (Fig. 87). It means that district’s crude death rate reflects quite
accurately the problem of mortality of the elderly due to respiratory diseases.
Correlation between district respiratory mortality levels of the elderly population in 2006–
2008 and five years earlier, in 2001–2003, is rather moderate (rho=0.66) (Fig. 88). It means
that changes in mortality in the districts during this five-year period were not very similar.
There are many districts where the difference between district mortality and the national
average has increased, and many where the difference has declined. However, there is also a
number of districts where mortality was above the average in both periods. The most
noticeable example is Nidzicki district, where in 2001–2003 mortality was three times higher
than the average Polish level, and in 2006–2008 it was still 2.7 times higher. There are also
other districts in Warmińsko-Mazurskie region where mortality was elevated in both periods,
such as: Olsztyński, Bartoszycki, Piski, Węgorzewski. It means that excess mortality
generated by respiratory diseases in that part of Poland is quite persistent.
Correlation between respiratory mortality district levels of younger (below 65 years of age)
and older (aged 65 years and over) population is rather moderate (Spearman correlation
coefficient 0.62).
165
diseases in 2006–2008, males aged 65 years and over
Fig. 86. Age-standardized mortality ratio (SMR for respiratory system
diseases in 2006–2008, females aged 65 years and over
166
mortality ratio for respiratory system diseases, population aged 65 years and
over, 2006–2008
respiratory system diseases in 2001–2003 (03) and 2006–2008 (08), population
aged 65years and over
167
3.5. Mortality from diseases of the digestive system
Deaths caused by diseases of the digestive system (ICD-10 K00-K92) accounted for 4.5% of
all deaths in Poland in 2006–2008, and the annual crude death rate was 43.8/100 000. Tables
1-3 in Annex 3 present standardized mortality ratios for digestive system diseases for the total
population, by sex, in each district in 2006–2008; and SMR summary statistics are shown in
Table 53. There is noticeable variation in the level of mortality from digestive system diseases
across districts. An inhabitant of the district where health status is the worst (the town of
Chorzów) had a risk of death 1.95 times higher than the national average, and for an
inhabitant living in the district with the lowest mortality (Nowomiejski) the risk of death was
52% lower than the country level.
Table 53. Age-standardized mortality ratio for diseases of the digestive system, total population,
Population
Mean
SD
Min
Max Median
Q1
Q3
Kurtosis
Skewness
All
0.959 0.228 0.481 1.949 0.933
0.813
1.072
2.134
1.053
Females
0.945 0.263 0.314 2.194 0.921
0.779
1.076
2.255
0.950
Males
0.966 0.255 0.329 2.074 0.947
0.787
1.101
1.366
0.866
Four of the ten districts where mortality was the lowest were from the Podkarpackie region
(Kolbuszowski, Niżański, the town of Rzeszów, Dębicki), and two from the KujawskoPomorskie region (Radziejowski, Wąbrzeski). On the other hand, of the ten districts with the
highest mortality, five were from Śląskie region (towns: Chorzów, Ruda Śląska,
Siemianowice Śląskie, Świętochłowice and Mysłowice), two from Łódzkie region (the city of
Łódź and Zgierski district), and two from Warmińsko-Mazurskie region (Bartoszycki,
Lidzbarski).
Mortality generated by digestive system diseases was below the national average in all the
districts of Opolskie region and in all districts but one in Podkarpackie region. There was no
region with mortality elevated above national average in all the districts.
District mortality levels (SMRs) of males and females are only moderately correlated
(Spearman correlation coefficient 0.53).
Districts with high mortality are quite dispersed, with relatively small groupings in central and
south-western Poland, while the districts where mortality is low are concentrated in the southeastern and central-northern Poland (Fig. 89 and Fig. 90).
Rankings of the districts according to crude mortality level (CDRR) and mortality adjusted for
differences in age structure (SMR) are quite similar (Spearman correlation coefficient 0.91)
168
(Fig. 91). It means that a district’s crude death rate quite accurately reflects the problems of
population mortality due to respiratory diseases. It could be noted that there are few districts
where CDRR is below one (indicating mortality lower than the national level) while SMR is
above one (indicating mortality higher than the national level), or where the situation is
opposite. However, in those districts where CDRR and SMR show different relation to the
national level, absolute difference between indicators and average level is small.
Correlation between district mortality levels in 2006–2008 and five years earlier, in 2001–
2003, is only moderate (rho=0.60) (Fig. 92). It means that changes in mortality in the districts
during this five-year period were somewhat different. However, in most cases the districts
where mortality was below average retained their relatively good position, and those where
mortality was higher usually were not able to improve their situation to an extent greater than
other districts. Mortality in the Silesian towns Chorzów, Ruda Śląska, Siemianowice Śląskie,
Świętochłowice and Mysłowice and in Łódź city was very high in both the periods. It may be
pointed out that deaths caused by digestive system diseases are rather rare, so differences of
observed magnitude are not surprising.
169
Fig. 89. Age-standardized mortality ratio (SMR) for diseases of the digestive
system, males, 2006–2008
system, females, 2006–2008
170
mortality ratio for diseases of the digestive system, total population,
2006–2008
Fig. 92. Correlation between age-standardized mortality ratios for diseases of
the digestive system in 2001–2003 (03) and 2006–2008 (08),
total population
171
In 2006–2008, almost half (48.0%) of all deaths caused by diseases of the digestive system
occurred in the age group below 65 years of age; they accounted for 7.0% of all premature
deaths; and the annual crude death rate was 24.3/100 000. In this age group digestive system
diseases are responsible for more deaths than respiratory diseases. Tables 4-6 in Annex 3
present standardized mortality ratios for digestive system diseases for the population of that
age, by sex, in each district in 2006–2008; and SMR summary statistics are shown in Table
54. Variation in the level of mortality caused by digestive system diseases across districts is
higher than in total population. An inhabitants of the district where the situation is the worst
(the town of Chorzów) had a risk of death 2.3 times higher than the national average; whereas
for an inhabitant living in the district where mortality is the lowest (Nowomiejski), the risk of
death was 76% lower than the country level.
Table 54. Age-standardized mortality ratio for diseases of the digestive system, population aged 0–64
years, 2006–2008, descriptive statistics
Population
Mean
SD
Min
Max
Median
Q1
Q3
Kurtosis
Skewness
All
0.935 0.325 0.240 2.326
0.886
0.730 1.069
2.268
1.231
Females
0.879 0.458 0.000 2.893
0.810
0.561 1.094
1.953
1.134
Males
0.947 0.322 0.199 2.294
0.890
0.728 1.111
1.599
1.023
Five of the ten districts where digestive mortality was the highest are towns from Śląskie
region (Chorzów, Ruda Śląska, Siemianowice Śląskie, Świętochłowice and Mysłowice), and
three districts are located in Dolnośląskie region (Wałbrzyski, Złotoryjski, Dzierżoniowski).
On the other hand, of the ten districts where mortality from digestive system diseases was the
lowest, three districts belong to Podparpackie region (Bieszczadzki, Kolbuszowski,
Ropczycko-Sędziszowski), and the others are dispersed throughout the country. Mortality
caused by digestive system diseases was below national average in all the districts of
Opolskie region and in all the districts of Podkarpackie region, except one. There was no
region with mortality elevated above the national average in all the districts.
Districts mortality due to digestive system diseases of males and females are correlated only
moderately (Spearman correlation coefficient 0.54). For example, in Starogardzki and Słupski
districts, SMRs for males are much below one (0.694 and 0.728, respectively), while for
females they are high above one (1.49 and 1.51, respectively).
Female mortality demonstrates higher regional variation than male mortality (Table 54).
Districts with low male mortality are located mostly in south-eastern and the central-northern
Poland, and those with low female mortality are concentrated in south-eastern Poland (Fig. 93
172
and Fig. 94). Regions with elevated mortality are more or less the same for males and females
alike –Śląskie, Donośląskie and Łódzkie regions, and north-eastern Poland.
correlation coefficient 0.98) (Fig. 95). It means that, in most cases, high or low mortality level
due to digestive system diseases in districts was not a result of favourable or unfavourable
population age structure, but rather a consequence of high or low risk of death.
Correlation between district mortality levels in 2006–2008 and five years earlier, in2001–
2003, is only moderate (rho=0.59) (Fig. 96). It means that changes in mortality in the districts
during this five-year period were not very similar. The most significant deterioration occurred
in Dzierżoniowski district, where in 2001–2003 mortality was 24% below the national
average, but in 2006–2008 it was 81% above it. However, in the towns of Chorzów,
Świętochłowice, Ruda Śląska, Siemianowice Śląskie and Mysłowice (Śląskie region), and in
the city of Łódź, where mortality was the highest in 2006–2008, it was also very high in
2001–2003.
173
system, males aged 0–64 years, 2006–2008
system, females aged 0–64 years, 2006–2008
174
mortality ratio for diseases of the digestive system, population aged
0–64 years, 2006–2008
Fig. 96. Correlation between age-standardized mortality ratios for diseases of
the digestive system in 2001–2003 (03) and 2006–2008 (08), population aged
0–64 years
175
About half (52%) of all deaths from diseases of the digestive system in 2006–2008 occurred
in the age group of 65 years and above; they represented 3.3% of all deaths of the elderly
population; and the annual crude death rate was 170/100 000. It means that in the elderly
population deaths caused by this group of diseases are less frequent than deaths from
respiratory diseases. Tables 7-9 in Annex 3 present standardized mortality ratios for digestive
system diseases for the population of that age, by sex, in each district in 2006–2008; and SMR
summary statistics are shown in Table 55. Variation in elderly population mortality across
districts is smaller than in the case of mortality of younger population. A person living in the
district with the worst situation (Międzychodzki) was exposed to the risk of death 77% higher
than an average elderly inhabitant of Poland; and for a person living in the district with the
lowest mortality (Dębicki), the risk of death 51% lower than the country average.
Table 55. Age-standardized mortality ratio for diseases of the digestive system, population aged 65 years
and over, 2006–2008, descriptive statistics
Population
Mean
SD
Min
Max
Median
Q1
Q3
Kurtosis
Skewness
All
0.987 0.221 0.488 1.766
0.972
0.830 1.117
0.272
0.486
Females
0.972 0.256 0.237 1.895
0.951
0.798 1.116
0.905
0.516
Males
1.005 0.284 0.369 2.063
0.985
0.792 1.174
0.325
0.558
Five of the ten districts where mortality was the highest were from Śląskie region
(Wodzisławski district and the towns: Siemianowice Śląskie, Chorzów, Ruda Śląska,
Jastrzębie-Zdrój), and the others were dispersed all over the country. On the other hand, of the
ten districts with the lowest mortality five were located in Podkarpackie region (Dębicki,
Lubaczowski, Nizański, Kolbuszowski, and the town of Tarnobrzeg).
In all the districts of Opolskie region mortality of elderly population from digestive system
diseases is below the national average. There is no region where mortality in all districts is
higher than the country level.
Correlation between male and female mortality due to digestive system diseases is weak
(Spearman correlation coefficient 0.36).
Regional variation of mortality is high for both males and females, with some tendency of low
mortality districts to concentrate in the south-east of Poland; and high mortality districts in the
north-western and the central Poland and in Śląskie region (Fig. 97 and Fig. 98). Rankings of
districts according to crude mortality level of the elderly population (CDRR) and mortality
adjusted for differences in age structure (SMR) are almost the same (Spearman correlation
176
coefficient 0.98) (Fig. 99). It means that a district’s crude death rate accurately reflects
problems of district’s elderly population mortality caused by digestive diseases.
Correlation between district mortality of the elderly population due to digestive system
diseases in 2006–2008 and five years earlier, between 2001 and 2003, is rather weak
(rho=0.43) (Fig. 100). It means that the changes in mortality in the districts during this fiveyear period vary. However, in three towns from Śląskie region (Siemianowice, Chorzów,
Ruda Śląska), where in 2006–2008 mortality was one of the highest in Poland, it was also the
highest five years earlier.
Correlation between district mortality levels due to digestive system diseases of the younger
(under 65 years) and older (65 years and above) population is rather low (Spearman
correlation coefficient 0.39).
177
system, males aged 65 years and over, 2006–2008
system, females aged 65 years and over, 2006–2008
178
mortality ratio for diseases of the digestive system, population aged
65 years and over, 2006–2008
Fig. 100. Correlation between age-standardized mortality ratios for diseases
of the digestive system in 2001–2003 (03) and 2006–2008 (08), population aged
65 years and over
179
3.6. Mortality from ill-defined causes (symptoms, signs and abnormal clinical and
laboratory findings)
Deaths attributed to ill-defined causes such as symptoms, signs, abnormal findings, etc. (ICD10 R00-R99), without designation of any specific disease as the cause, accounted for 6.5% of
all deaths in Poland in 2006–2008; and the annual crude death rate was 64/100 000. It means
that they were more common as the cause of death than respiratory system or digestive system
diseases. These ill-defined causes of deaths are an indicator of the quality of the system of
classifying and coding causes of deaths, and do not provide useful information about health
status of the population. Therefore, differences between districts are described briefly to point
out the districts where the problem of poor quality of data is the most urgent. However, tables
and figures present the data in the same way as in the case of specific causes of deaths. It
should be underlined that in the districts where death rates for ill-defined conditions are high,
other, specific causes of deaths are underestimated, which affects proper assessment of the
volume of more exact health problems in those districts, and may distort district comparisons.
There is a striking variation in the level of mortality due to ill-defined conditions across
districts, much larger than in the case of mortality from specific causes (Table 56, Tables 1-3
in Annex 3). While in the district where the situation is the worst (Hrubieszowski), observed
number of deaths was 3.2 times higher than expected, in the district where the situation is the
best and mortality from these causes is the lowest (Żywiecki), observed number of deaths was
91% lower than expected on the basis of national prevalence.
Table 56. Standardized mortality ratio for ill-defined causes of death, total population, 2006–2008,
Population Mean
SD
Min
Max
Median
Q1
Q3
Kurtosis
Skewness
All
0.989 0.447 0.089 3.231
0.899
0.682 1.232
1.739
1.043
Females
1.024 0.508
0.05
3.397
0.942
0.637 1.262
1.118
0.932
Males
0.961 0.491
0.1
3.064
0.829
0.622 1.199
1.362
1.131
Four of the ten districts where mortality was the highest were from Lubelskie region
(Hrubieszowski, Zamojski, Biłgorajski, Chełmski), and the others were dispersed all over the
country. On the other hand, of the ten districts where mortality was the lowest, as many as
seven werelocated in Śląskie region (Żywiecki, Bielski, Pszczyński, Cieszyński, BieruńskoLędziński, towns: Bielsko-Biała and Żory), two districts are in Łódzkie region (Łowicki,
Skierniewicki), and one in Dolnośląskie region (Oleśnicki). Interestingly, in the districts
where mortality was the lowest, crude death rate was about 5 per 100 000 population; while in
the districts where mortality was the highest, crude death rate was about 200 per 100 000.
180
Correlation between district mortality levels of males and females is only moderate
(Spearman correlation coefficient 0.54). It suggests that the quality of coding of the causes of
deaths in a district may differ depending on the sex of deceased. As shown on the maps (Fig.
101 and Fig. 102), the districts where female mortality was elevated were rather dispersed all
over the country, while the districts where male mortality was high were usually concentrated
in central-eastern part of Poland (Lubelskie region).
It must be highlighted that those ill-defined causes of deaths are relatively more prevalent in
the case of premature mortality than in the case of deaths of the elderly - they are assigned to
8.9% of deaths below 65 years and to 5.4% of deaths in the age group 65 years and over.
181
Fig. 101. Age-standardized mortality ratio (SMR) for ill-defined causes,
males, 2006–2008
Fig. 102. Age-standardized mortality ratio (SMR) for ill-defined causes,
females, 2006–2008
182
3.7. Mortality from external causes
Deaths from external causes of mortality (ICD-10 V01-Y98) accounted for 6.7% of all deaths
in Poland in 2006–2008, and the annual crude death rate was 65.8/100 000. Tables 1-3 in the
Annex 3 present standardized mortality ratios for external causes for the total population, by
sex, in each district in 2006–2008; and SMR summary statistics are presented in Table 57.
Variation in mortality level across districts is noticeable. An inhabitant living in the district
where health status was the worst (Poddębicki) had a risk of death 78% higher than the
national average; and for a person living in the district where mortality was the lowest (the
town of Lublin), the risk of death was 51% lower than the country level.
Table 57. Standardized mortality ratio for external causes, total population, 2006–2008, descriptive
statistics
Population Mean
SD
Min
Max
Median
Q1
Q3
Kurtosis
Skewness
All
1.070 0.229 0.492 1.781
1.049
0.920 1.223
-0.114
0.341
Females
1.006 0.281 0.382 2.192
0.976
0.815 1.172
0.986
0.715
Males
1.080 0.252 0.440 1.764
1.055
0.900 1.248
-0.270
0.323
Five of the ten districts with the highest mortality (Poddębicki, Brzeziński, Piotrkówski,
Łęczycki and Rawski) belong to Łódzkie region, and three (Lipski, Pułtuski and Płoński) to
Mazowieckie region. On the other hand, of the ten districts where mortality was the lowest,
three (the town of Lublin, Janowski, and Świdnicki) are from Lubelskie region, two from
Podkarpackie region (towns: Rzeszów and Tarnobrzeg), and two (the town of Bydgoszcz and
Wąbrzeski district) from Kujawsko-Pomorskie region. Mortality in Łódzkie region in all the
districts except the city of Łódź and the town of Skierniewice was above the national average.
District mortality for males and females is weakly correlated (Spearman correlation
coefficient 0.32). Mortality in districts located in the south-east of Poland was (with few
exceptions such as, for example, female mortality in Bieszczadzki district) lower than the
national average, both for males and females (Fig. 103 and Fig. 104). Male and female
mortality varies substantially in the districts located in the following regions: Wielkopolskie
(low male and high female mortality), Warmińsko-Mazurskie and Podlaskie (low female and
high male mortality).
There is very high correlation between crude mortality (CDRR) level and mortality adjusted
for age structure (SMR) - Spearman correlation coefficient is 0.98 (Fig. 105).
Correlation between district mortality level in the period of 2006–2008 and the level observed
five years earlier, between 2001 and 2003, is more than moderate (rho=0.74) (Fig. 106). It
183
means that the change in mortality in the districts during this five-year period was often quite
similar: the districts where mortality was below average retained their good position, and
those where mortality was higher usually have not changed their position in relation to the
country average to an extent greater than other districts.
184
Fig. 103. Age-standardized mortality ratio (SMR) for external causes, males,
2006–2008
Fig. 104. Age-standardized mortality ratio (SMR) for external causes,
females, 2006–2008
185
mortality ratio for external causes, total population, 2006–2008
Fig. 106. Correlation between standardized mortality ratios for external
causes in 2001–2003 (03) and 2006–2008 (08), total population
186
In 2006–2008, almost three-quarters (73.7%) of all deaths due to external causes occurred in
the age group below 65, they accounted for as much as 16.1% of all premature deaths; and the
annual crude death rate was 56.0/100 000. Tables 4-6 in Annex 3 present standardized
mortality ratios for external causes, by sex, in each district in 2006–2008; and SMR summary
statistics are shown in Table 58. A difference in the level of premature mortality due to
external causes across districts is large and varies from more than 83% above the average (in
Piotrkowski district), to 59% below the national average (the town of Lublin).
Table 58. Standardized mortality ratio for external causes, population aged 0–64 years, 2006–2008,
Population
Mean
SD
Min
Max
Median
Q1
Q3
Kurtosis
Skewness
All
1.089 0.271 0.412 1.832
1.063
0.884 1.271
-0.284
0.381
Females
1.019 0.335 0.174 2.412
0.972
0.792 1.211
0.886
0.665
Males
1.087 0.280 0.412 1.887
1.063
0.881 1.280
-0.241
0.423
Six of the ten districts where mortality was the highest (Piotrkowski, Łęczycki, Brzeziński,
Rawski, Skierniewicki and Poddębicki) are from Łódzkie region. On the other hand, of the ten
districts with the lowest mortality, three (the town of Lublin, Janowski and Świdnicki) belong
to Lubelskie region, and another two (the town of Rzeszów and Mielecki district) are located
in Podkarpackie region. In Łódzkie region in all the districts but one (the town of
Skierniewice), and in Warmińsko-Mazurskie region in all the districts but two, (the towns of
Olsztyn and Elbląg), mortality was higher than Poland’s average.
Correlation between male and female mortality from external causes is rather weak
(Spearman correlation coefficient 0.39). It means that high level of male mortality is not
necessarily associated with high level of female mortality (eg. in Bieszczadzki district male
mortality is 7%, while female mortality is 140% above the national average, while in
Brzeziński district female mortality is equal to the national average, and male mortality is
88% higher than the average). There are regions where the relation of both male and female
mortality to the national average is similar (the south-east and the north-west of Poland), as
well as regions where mortality of males and females differs – as in the north-east of Poland
(Fig. 107 and Fig. 108). Districts with increased male mortality tend to be concentrated in the
central and the north-eastern parts of Poland, while in the case of females such districts are
rather dispersed all over the country.
187
Rankings of districts according to crude mortality rates (CDRR) and mortality adjusted for
differences in age structure (SMR) are very similar (Spearman correlation coefficients 0.99)
(Fig. 109). It means that, in most cases, high or low level of mortality due to external causes
in the districts was not a result of favourable or unfavourable population age structure, but
was a consequence of high or low risk of death.
Correlation between district mortality levels from the period of 2006–2008 and five years
earlier, between 2001 and 2003, is quite strong (rho=0.79) (Fig. 110). It means that changes in
mortality in the districts during this five-year period were similar. With that, one should note
the relative deterioration of the situation in Bieszczadzki (SMR changed from 33% below the
national average to 29% above the average) and Starogardzki districts (from 33% below the
national average to 30% above the average).
188
189
mortality ratio for external causes, population aged 0–64 years, 2006–2008
causes in 2001–2003 (03) and 2006–2008 (08), population aged 0–64 years
190
About one-fourth (26.3%) of all deaths due to external causes in 2006–2008 occurred in the
age group of 65 years and above, they represented only 2.5% of all deaths of the elderly
population, and the annual crude death rate was 129/100 000. Tables 7-9 in Annex 3 present
standardized mortality ratios for the population of that age, by sex, in each district in 2006–
2008, and SMR summary statistics are shown in Table 59. Variation in mortality due to
external causes across districts is large. A person living in the district where mortality was the
highest (Wągrowiecki), had a risk of death 148% higher than the average for elderly
inhabitants of Poland; and in the district where mortality was the lowest (GolubskoDobrzyński), the risk of death was 78% below the country average.
Table 59. Standardized mortality ratio for external causes, population aged 65 years and over, 2006–2008,
Population
Mean
SD
Min
Max
Median
Q1
Q3
Kurtosis
Skewness
All
1.027 0.308 0.232 2.484
1.001
0.826 1.182
2.013
0.861
Females
0.994 0.416 0.088 2.846
0.945
0.735 1.197
1.932
1.019
Males
1.056 0.324 0.088 2.249
1.045
0.841 1.238
0.877
0.485
Eight of the ten districts where mortality was the highest were located in Wielkopolskie
region (Wągrowiecki, Obornicki, Złotowski, Międzychodzki, Pleszewski, Nowotomyski,
Gostyński, Rawicki). On the other hand, of the ten districts with the lowest mortality, six
belong to Kujawsko-Pomorskie region (Golubsko-Dobrzyński, Bydgoski, Tucholski,
Chełmiński, Wąbrzeski, the town of Bydgoszcz), and two to Warmińsko-Mazurskie region
(Elbląski district and the town of Elbląg).
In all the districts of Kujawsko-Pomorskie region and in all but one in Warmińsko-Mazurskie,
mortality of the elderly due to external causes was below the national average. On the other
hand, in Wielkopolskie region in all the districts but one mortality was higher than Poland’s
average.
Correlation between male and female mortality due to external causes is rather weak
(Spearman correlation coefficient 0.34). It means that high level of male mortality is not
necessarily associated with high level of female mortality (eg. in Leszczyński district male
mortality is 15%, and female mortality 112%, above the national average, while in Łobeski
district female mortality is 8% below the national average, and male mortality is 94% higher
than the average).
191
Districts with high mortality of elderly population due to external causes are rather scattered
all over the country in the case of male mortality, and tend to concentrate in central-western
part of Poland in the case of female mortality (Fig. 111 and Fig. 112). It is noticeable that the
districts with low female mortality are located mostly in northern and central-northern parts of
the country.
Rankings of districts according to crude mortality due to external causes among the elderly
population (CDRR) and mortality adjusted for differences in age structure (SMR) are almost
identical (Spearman correlation coefficient 0.99) (Fig. 113). It means that a district’s crude
death rate reflects quite accurately mortality problems of the population of this age group.
Correlation between mortality of the elderly population from external causes in 2006–2008
and five years earlier, between 2001 and 2003, is relatively weak (rho=0.42) (Fig. 114)
indicating differences in temporal changes of districts mortality. However, as can be seen on
the figure, there are districts with very high mortality in both periods - for example,
Wągrowiecki (excess of mortality by 78% and 148% in the first and second period
respectively) and Międzychodzki (excess by 112% and 92%).
Correlation between district mortality levels for external causes of the younger (under 65
years) and older (65 years and above) population is low (Spearman correlation coefficient
0.24) indicating many “disagreements” in district’s risk of death for younger and elderly
population. For example in districts Gostyński and Kościański mortality of the elderly
population was about 75% above the country average while mortality of those below 65 years
was 15% below the average. On the other hand in Bartoszycki and Hajnowski districts
mortality of elderly was 15% lower than national level while mortality of younger population
was some 70% higher than average for Poland.
192
Fig. 111. Age-standardized mortality ratio (SMR) for external causes, males
aged 65 years and over, 2006–2008
Fig. 112. Age-standardized mortality ratio (SMR) for external causes, females
aged 65 years and over, 2006–2008
193
mortality ratio for external causes, population aged 65 years and over,
2006–2008
causes in 2001–2003 (03) and 2006–2008 (08), population aged 65 years and
over
194
3.8. Infant mortality
Infant mortality rate (IMR), and especially neonatal mortality, is considered as one of the key
indicators of health care development and performance, even in developed countries. In
Poland, there were 6 897 deaths of children below one year of age (infants), i.e. 5.86 per 1000
live birth per year in the three-year period of 2006–2008. The absolute number of infant
deaths in districts varied between 2 in the town of Sopot, where mothers gave birth to 848 live
born children, and about 252 in Warsaw city, where 52 250 children were born alive. Most of
infant deaths (71.3%) occurred during the first four weeks of life (below 28 days), and only
28.7% occurred later but before the child’s 1st birthday.
Table 10 of Annex 3 presents total infant mortality rates (IMR), rates in neonatal period
IMR(0-27) and in the post neonatal period IMR(28+) in each of 379 districts from 2006–2008
and five years earlier from 2001–2003. The summary statistics of the IMRs are shown in
Table 60. The interpretation of differences in IMR across districts and observed changes
during the 5-year period should be cautious due to small number of deaths in many districts
and possible large random variation.
Table 60. Descriptive statistics of infant mortality rates (per 1000 live births) by age in districts
Period
2001–
2003
2006–
2008
Indicator
Mean
SD
Min
Max
Median
Q1
Q3
Kurtosis
Skewness
IMR(0-27)
5.173
1.914
0.493
16.092
5.058
3.778
6.315
2.358
0.697
IMR(28+)
2.184
1.097
0
7.789
2.095
1.345
2.787
1.72
0.823
IMR
7.358
2.289
2.392
18.673
7.315
5.864
8.609
1.326
0.584
IMR(0-27)
4.173
1.48
0
8.929
4.071
3.155
5.183
0.078
0.282
IMR(28+)
1.721
0.99
0
4.862
1.603
1.04
2.258
0.404
0.688
IMR
5.894
1.788
1.951
12.542
5.753
4.659
7.08
0.184
0.418
There is a noticeable difference between districts in the overall infant mortality level. In nine
districts more than ten infant deaths per 1000 live births were registered in years 2006–2008
and in the Zwoleński district, where mortality was the highest, IMR was 12.5. Three of the ten
districts where IMR was the highest are located in the Dolnośląskie region (Strzeliński,
Oławski, Trzebnicki), two in the Mazowieckie region (Zwoleński, Białobrzeski) and the other
five are placed all over the country. On the other hand, in 17 districts IMR was even below 3
per 1000 live births and in the best the Prudnicki district (belonging to the Opolski region) it
was only 1.95. Three of the ten districts where IMR was the lowest are from the Podlaski
195
region (Wysokomazowiecki, Augustowski, Sokólski), two are located in the Mazowiecki
region (Sokołowski, Nowodworski) and the other five are dispersed over the country. In all
regions there are districts where infant mortality was above the national average and districts
where it was below the average (Fig.116). Such a dispersion was also observed five years
earlier in years 2001–2003 (Fig. 115), however, districts where IMR was high or low very
often differ - the correlation between IMR in 2001–2003 and in 2006–2008 is low
(rho=0.163). Nevertheless, it is necessary to emphasise that there are more than a few districts
where IMR was very high in the recent years and five years earlier as well, for instance: the
districts Leski (10.4 and 12.2), Strzeliński (10.3 and 8.1), Oławski (10.1 and 8.5), Trzebnicki
(10.1 and 13.0), the towns: Bytom (9.9 and 11.3), Rybnik (9.8 and 10.4), Katowice (9.3 and
12.0) and several others. In total, there are 52 districts where IMR in the both periods: 2001–
2003 and 2006–2008 was in the highest quartile.
196
Fig. 115. Infant mortality rate, 2001–2003 (per 1000 live births)
Fig. 116. Infant mortality rate, 2006–2008 (per 1000 live births)
197
3.9. Life expectancy
In Poland, in the period of 2006–2008 the average life expectancy of a new-born boy was
71.0 years, and of a new-born girl 79.8 years. It was about a year longer than five years
earlier, in 2001–2003, when these values were 70.2 and 78.6 years for boys and girls,
respectively. Table 11 in Annex 3 presents life expectancy values in each of 379 districts, in
2006–2008 and five years earlier, in 2001–2003, with summary statistics shown in Table 61.
There is a noticeable variation in life expectancy of people living in different districts. Men in
the district where health status is the worst (Kutnowski and Chełmski) could expect to live on
average 66.5 years, i.e. 4.5 years less than an average male inhabitant in Poland. A man living
in the district where mortality is the lowest (Rzeszów) could expect to live 75.3 years, i.e. 4.3
years longer than an average man in Poland, and 8.8 years longer than a man in the districts
where life expectancy is the shortest. Five years earlier the shortest life could also be expected
by a man in Chełmski district, and its length was the same as in 2006–2008, while life
expectancy of a man living in the town of Sopot was 7.7 years longer (74.2 years). Thus, in
recent years the disparity in male life expectancy slightly increased. In 63 districts there was
no improvement in male life expectancy in the 5-year period between 2001–2003 and 2006–
2008, or life expectancy prospects deteriorated (in Poddębicki district even by two years).
Table 61. Summary statistics of life expectancy at birth in districts by gender, 2001–2003 and 2006–2008
Sex
Period
Mean
SD
Min
Max
Median
Q1
Q3
Kurtosis
Skewness
Males
2001–2003
69.9
1.3
66.5
74.2
69.9
69.1
70.8
0.0
0.2
2006–2008
70.6
1.5
66.5
75.3
70.5
69.6
71.7
-0.2
0.2
2001–2003
78.6
1.0
75.3
81.0
78.6
77.9
79.3
0.3
-0.2
2006–2008
79.7
1.0
76.3
82.5
79.8
78.9
80.5
-0.3
-0.1
Females
Five of the ten districts where in 2006–2008 male life expectancy was the shortest were
located in Łódzkie region (Kutnowski, Brzeziński, Poddębicki, Tomaszowski, and the city of
Łódź), and the remaining five were dispersed throughout the country. On the other hand, of
the ten districts where men could expect the longest life, eight are towns from different
regions (Rzeszów, Opole, Sopot, Olsztyn, Warszawa, Gdynia, Kraków, Koszalin), and the
remaining two districts belong to Podkarpackie and Opolskie regions (in addition to the towns
of Rzeszów and Opole, Mielecki and Opolski districts as well). In all regions there were
districts where male life expectancy was below the national average and districts where it was
above the average, however, in Podkarpackie region in all the districts but one a man could
198
expect to live longer than an average male inhabitant of Poland. In contrast, in Łódzkie region
in all the districts but one a man could expect to live shorter than the country average. In both
periods of 2001–2003 and 2006–2008, the districts with the highest male life expectancy were
concentrated in the south of Poland, as well as in central-northern and central-western parts of
the country (Fig. 117 and Fig. 118).
In the case of women, variation in life expectancy associated with the district of residence is
much smaller than in the case of men. A woman in the districts where health status is the
worst (towns of Siemianowice Śląskie and Ruda Śląska) could expect to live to the age of
76.3 and 76.5, respectively i.e. about three and a half years less than an average female in
Poland. A woman living in a district where mortality is the lowest (Leski) could expect to live
to the age of 82.5, i.e. 2.7 years longer than an average woman in Poland, and 6.2 years longer
than a woman in the district with the shortest life expectancy. Five years earlier, in 2001–
2003, a woman who could expect the shortest life was living in Międzychodzki district (75.3
years), while life expectancy of those living in the town of Zamość was 5.7 years longer (81.0
years). Thus, in recent years the disparity between female and man life expectancy slightly
increased. Only in 17 districts there was no improvement in female life expectancy over the 5year period between 2001–2003 and 2006–2008, or life expectancy deteriorated (in
Kościerski district by 1.7 years). On the other hand, in 31 districts female life expectancy
increased by 2.0-2.4 years.
Of the ten districts where in 2006–2008 female life expectancy was the shortest, five were the
towns from Śląskie region (Siemianowice Śląskie, Ruda Śląska, Chorzów, Świętochłowice,
Mysłowice), and two were districts from Łódzkie region (Kutnoski and the city of Łódź). On
the other hand, five of the ten districts where female life expectancy was the longest belonged
to Podkarpackie region (Leski, Niżański, Ropczycko-Sędziszowski, towns of Rzeszów and
Tarnobrzeg), and two districts were located in Lubelskie region (towns: Chełm and Zamość).
Interestingly enough, in Podkarpackie and Podlaskie regions there are no districts where a
woman could expect to live shorter than an average female inhabitant in Poland, whereas in
Łódzkie region in all the districts but one women lived shorter than the country average. In
both periods of 2001–2003 and 2006–2008, districts with the highest female life expectancy
were located mostly in the east of Poland, while those with low life expectancy were
prevailing in the western parts of the country (Fig. 119 and Fig. 120).
199
Fig. 117. Males life expectancy at birth, 2001–2003
Fig. 118. Males life expectancy at birth, 2006–2008
200
Fig. 119. Females life expectancy at birth, 2001–2003
Fig. 120. Females life expectancy at birth, 2006–2008
201
Summary
There are substantial differences in mortality and, consequently, in life expectancy in districts
in Poland. Most of the analysed mortality ratios in 2006–2008 have shown moderate
correlation with the values reported five years earlier, in 2001–2003. It means that the change
in mortality in the districts during this five-year period was rather similar, and the districts
where mortality was below average retained their good position, and those where mortality
was higher usually have not improved their situation in relation to the country average more
than other districts. However, there are examples of districts where mortality indicators
improved, and districts where they deteriorated.
Correlation between district mortality indicators (SMRs) for younger (below 65 years of age)
and older (65 years and above) population for all analysed causes was not very strong. It
indicates that in order to properly assess and address health needs of a district population it
could be necessary to look independently at the younger and older population groups.
In several cases correlation between district crude death rates (CDR) and age-standardised
mortality ratios has shown only average strength. It means that both indicators must be taken
into account when health care needs of the population are assessed, since a different approach
is necessary in the districts where CDR is high and SMR is not elevated, than in those where
situation is reversed, or where both indicators are high.
To summarize differences in mortality across districts in Poland, and especially to
characterize mortality in the ten districts where total SMR was the highest, it is worth noting
that such a high risk of death does not result from extremely high mortality caused by one
particular group of diseases, although high mortality generated by cardiovascular diseases
does play the most important role. The number of CVD deaths observed in those districts was
higher than expected by 20 to 40 %. The situation in each of the ten districts where total
mortality was the most elevated can be summarized as follows:
1. Ruda Śląska (town in Śląskie voivodship) – the second highest mortality across
districts generated by digestive system diseases, very high mortality due to cancer and
CVD, high mortality due to respiratory system diseases;
2. Poddębicki district (Łódzkie voivodship) – the highest mortality due to external
causes, high mortality from CVD, respiratory diseases and ill-defined causes;
3. Siemianowice Śląskie (town in Śląskie voivodship) – very high mortality caused by
cancer and digestive system diseases; high mortality due to CVD and respiratory
diseases;
202
4. Chorzów (town in Śląskie voivodship) – the highest mortality from digestive system
diseases, very high mortality from respiratory diseases, high mortality due to external
causes, CVD and cancer;
5. Brzeziński district (Łódzkie voivodship) – the highest mortality across districts from
CVD and the second highest due to external causes, very high mortality caused by
digestive system diseases;
6. Lwówecki district (Dolnośląskie voivodship) – very high mortality from CVD, high
mortality due to digestive system diseases and ill-defined causes;
7. Kutnowski district (Łódzkie voivodship) – high mortality from ill-defined causes,
CVD and external causes;
8. Sztumski district (Pomorskie voivodship) – the highest mortality across districts
generated by cancer, very high mortality caused by CVD;
9. Łęczycki district (Łódzkie voivodship) – very high mortality from CVD and external
causes, elevated mortality due to respiratory diseases;
10. Sulęciński district (Lubuskie voivodship) – mortality from all analysed causes is
increased, but in no case it is elevated to a very high level.
It should be pointed out that among the districts with the highest mortality according to each
analysed specific group of diseases, only few are included in the group of the ten districts
with the highest overall mortality. Of the 20 districts with the highest cancer mortality, only
two belong to that group, of the 20 districts with the highest CVD mortality – four, of those
with the highest mortality due to respiratory diseases – none, mortality due to digestive
system diseases – three, and with the highest external causes – also three.
It may be noted that – with regard to improvement plans for the districts where population
health status is really life-threatening - it would seem reasonable to select districts not only on
the basis of overall mortality rate (or life expectancy), but also to take into account the risk of
death from particular causes (groups of diseases): with such an approach, action steps can be
more focused and more effective.
203
Colours reflect ranking of the districts sorted from the lowest value of SMR (1) to the highest value (379)
1
Annex 3
Table 62. Age-standardized mortality ratio (SMR) by main groups of causes of deaths, district of
residence in 2006–2008, total population
TERYT
District
bolesławiecki
dzierżoniowski
głogowski
górowski
jaworski
jeleniogórski
kamiennogórski
kłodzki
legnicki
lubański
lubiński
lwówecki
milicki
oleśnicki
oławski
polkowicki
strzeliński
średzki
świdnicki
trzebnicki
wałbrzyski
wołowski
wrocławski
ząbkowicki
zgorzelecki
złotoryjski
m. Jelenia Góra
m. Legnica
m. Wrocław
0201
0202
0203
0204
0205
0206
0207
0208
0209
0210
0211
0212
0213
0214
0215
0216
0217
0218
0219
0220
0221
0222
0223
0224
0225
0226
0261
0262
0264
Total
1.009
1.036
1.013
1.015
1.080
1.107
1.111
1.127
1.048
1.095
0.971
1.218
1.029
1.022
0.982
1.060
1.055
1.171
1.082
1.085
1.174
1.094
1.015
1.076
1.130
1.163
0.986
1.056
0.901
Cancer
1.122
1.021
1.054
1.079
1.070
1.104
1.003
1.107
1.060
0.992
1.080
1.049
0.993
1.036
1.035
1.109
1.116
1.110
1.155
1.166
1.148
1.127
1.001
1.048
1.133
1.195
1.057
1.153
0.964
CVD
1.010
1.077
1.054
1.139
1.142
1.094
1.085
1.234
1.113
1.309
0.970
1.273
1.208
1.163
1.026
1.095
1.232
1.291
1.131
1.198
1.196
1.245
1.095
1.148
1.211
1.220
0.921
0.999
0.934
aleksandrowski
brodnicki
bydgoski
chełmiński
golubsko-dobrzyński
grudziądzki
inowrocławski
lipnowski
mogileński
nakielski
radziejowski
rypiński
sępoleński
świecki
toruński
tucholski
wąbrzeski
włocławski
żniński
m. Bydgoszcz
m. Grudziądz
m. Toruń
0401
0402
0403
0404
0405
0406
0407
0408
0409
0410
0411
0412
0413
0414
0415
0416
0417
0418
0419
0461
0462
0463
1.085
1.070
1.013
1.155
0.976
1.114
1.085
1.161
1.105
1.153
1.096
1.086
0.981
1.098
1.083
1.026
1.085
1.130
1.030
0.935
1.092
0.908
1.027
1.113
1.103
1.240
1.026
1.145
1.140
1.038
1.172
1.164
1.175
1.157
1.054
1.169
1.230
1.081
1.234
1.045
1.131
1.113
1.166
1.075
1.228
1.079
1.005
1.054
0.936
1.114
1.177
1.197
1.192
1.384
1.122
1.163
1.072
1.203
0.949
1.113
1.009
1.247
1.017
0.884
1.078
0.773
Page 204
Respiratory Digestive Ill-defined
0.866
0.880
0.615
0.961
1.291
1.100
0.882
1.233
0.713
0.818
0.794
0.809
1.035
1.259
0.700
1.168
1.438
0.803
1.131
1.179
2.041
1.168
1.113
0.862
1.034
0.987
1.141
0.829
0.815
0.800
0.832
1.134
0.758
1.158
1.371
1.533
0.529
0.834
0.869
0.909
1.010
0.357
0.547
0.929
1.154
0.826
0.904
1.033
0.493
0.789
0.744
0.970
1.256
1.052
0.761
1.196
1.044
0.802
0.866
0.818
1.168
1.476
1.488
0.832
1.039
0.816
0.863
1.104
0.813
0.945
1.178
1.043
1.017
1.146
0.846
1.230
1.413
0.787
0.995
1.223
1.012
0.787
1.416
1.460
0.638
1.057
1.003
0.745
1.248
1.480
1.204
1.399
1.056
1.333
1.499
0.912
0.913
1.652
1.021
1.117
1.002
1.458
1.336
1.225
1.206
1.019
1.045
1.065
1.115
0.891
0.902
0.944
0.978
0.678
0.716
1.248
0.835
0.801
0.837
0.599
0.689
0.790
0.902
0.877
0.719
0.578
0.846
0.762
0.892
0.945
1.011
0.873
1.198
0.806
1.580
1.029
1.224
0.427
1.201
1.029
0.608
0.847
0.626
0.548
0.763
1.113
0.693
1.144
0.606
1.128
1.033
0.960
1.141
External
1.117
0.964
0.925
0.973
1.306
1.094
0.860
1.115
0.924
0.923
1.038
1.190
0.934
1.073
1.072
1.163
0.986
1.165
1.137
1.101
1.071
0.817
1.063
0.981
1.213
1.273
0.988
0.943
0.702
1.018
0.863
0.789
0.945
0.858
1.220
0.971
1.307
0.956
0.943
0.964
1.072
0.887
1.035
1.215
0.870
0.644
1.362
0.939
0.603
0.946
0.721
1
TERYT
District
m. Włocławek
0464
Total
1.051
Cancer
1.102
CVD
1.030
bialski
biłgorajski
chełmski
hrubieszowski
janowski
krasnostawski
kraśnicki
lubartowski
lubelski
łęczyński
łukowski
opolski
parczewski
puławski
radzyński
rycki
świdnicki
tomaszowski
włodawski
zamojski
m. Biała Podlaska
m. Chełm
m. Lublin
m. Zamość
0601
0602
0603
0604
0605
0606
0607
0608
0609
0610
0611
0612
0613
0614
0615
0616
0617
0618
0619
0620
0661
0662
0663
0664
1.117
0.982
1.188
1.053
1.043
1.078
1.000
1.076
1.060
1.085
1.040
1.074
1.040
0.963
1.088
1.027
0.978
0.999
1.160
1.037
0.944
0.932
0.926
0.854
0.743
0.785
0.837
0.834
0.748
0.717
0.842
0.919
0.746
0.804
0.816
0.754
0.909
0.864
0.849
0.865
0.833
0.797
0.862
0.813
0.820
0.774
0.887
0.802
1.280
0.956
1.223
0.868
1.184
1.163
1.158
1.171
1.225
1.134
1.164
1.263
1.117
0.921
1.126
1.158
1.030
1.059
1.284
0.932
0.959
0.982
0.911
0.791
0.657
0.919
0.948
0.850
1.004
1.338
0.826
0.789
0.818
1.560
0.707
0.783
0.862
0.665
0.935
0.689
0.445
0.537
0.636
0.853
0.732
0.682
0.822
0.725
0.849
0.589
1.031
0.851
0.823
0.929
0.781
0.903
0.990
0.846
0.672
0.871
0.893
0.922
1.026
0.855
0.793
0.887
1.047
0.710
0.717
1.043
0.971
0.781
1.346
2.308
2.098
3.231
1.700
1.664
0.882
1.256
1.282
1.675
1.527
1.192
1.415
1.958
1.538
1.206
1.972
1.438
1.533
2.506
1.246
1.250
1.500
1.260
1.483
0.747
1.441
1.148
0.588
0.960
0.861
1.084
0.889
0.902
0.931
0.978
0.861
0.792
1.300
0.907
0.648
1.197
1.249
1.251
0.967
0.880
0.492
0.811
gorzowski
krośnieński
międzyrzecki
nowosolski
słubicki
strzelecko-drezdenecki
sulęciński
świebodziński
zielonogórski
żagański
żarski
wschowski
m. Gorzów Wielkopolski
m. Zielona Góra
0801
0802
0803
0804
0805
0806
0807
0808
0809
0810
0811
0812
0861
0862
1.034
1.120
1.099
1.018
1.168
1.083
1.188
1.079
1.050
1.115
1.131
1.068
0.939
0.895
1.015
0.974
1.022
0.969
1.202
1.161
1.069
0.921
1.020
1.048
1.034
1.025
0.986
0.964
0.992
1.174
1.074
0.981
1.136
0.977
1.196
1.161
0.944
1.287
1.320
0.989
0.790
0.711
0.842
1.365
1.222
0.775
1.210
1.078
1.085
1.013
0.803
0.707
0.694
0.595
0.822
0.697
0.846
0.916
1.053
1.171
1.257
0.916
1.114
1.043
0.970
1.027
0.990
0.873
0.805
0.900
1.572
0.873
1.678
0.908
1.122
1.812
1.119
1.135
1.955
0.458
0.814
1.404
1.974
1.866
1.175
1.107
0.955
1.104
1.161
1.174
1.283
0.888
1.084
1.119
1.111
1.009
0.664
0.807
bełchatowski
kutnowski
łaski
łęczycki
łowicki
łódzki wschodni
opoczyński
pabianicki
pajęczański
piotrkowski
poddębicki
radomszczański
rawski
sieradzki
skierniewicki
tomaszowski
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1.039
1.217
1.066
1.192
1.076
1.143
1.091
1.101
1.018
1.154
1.241
1.096
1.018
1.037
1.024
1.133
0.999
1.142
0.916
1.009
0.981
0.969
0.858
0.948
0.989
0.853
1.106
0.924
0.913
1.019
0.966
0.973
1.082
1.236
1.115
1.373
1.290
1.183
1.291
1.071
1.018
1.274
1.245
1.171
0.994
1.070
1.103
1.140
1.123
0.920
1.035
1.309
1.062
1.199
0.916
1.073
1.101
1.108
1.220
1.058
1.397
1.236
0.964
1.135
1.162
1.163
1.144
1.129
1.059
1.367
1.038
1.373
0.862
1.223
0.989
0.826
1.114
1.030
0.976
1.247
0.899
1.729
1.023
0.438
0.251
1.095
0.618
1.334
1.089
0.918
1.425
1.540
0.790
0.612
0.327
1.322
1.160
1.286
1.348
1.630
1.005
1.234
1.412
1.377
1.037
1.655
1.781
1.206
1.600
1.282
1.536
1.241
Page 205
0.924
1.382
0.618
External
1.213
1
District
TERYT
wieluński
wieruszowski
zduńskowolski
zgierski
brzeziński
m. Łódź
m. Piotrków Trybunalski
m. Skierniewice
1017
1018
1019
1020
1021
1061
1062
1063
Total
1.059
1.137
1.101
1.126
1.225
1.134
1.080
0.990
Cancer
1.003
0.984
1.058
0.987
0.915
1.005
0.926
1.038
CVD
1.167
1.393
1.100
1.113
1.438
0.972
1.131
1.065
bocheński
brzeski
chrzanowski
dąbrowski
gorlicki
krakowski
limanowski
miechowski
myślenicki
nowosądecki
nowotarski
olkuski
oświęcimski
proszowicki
suski
tarnowski
tatrzański
wadowicki
wielicki
m. Kraków
m. Nowy Sącz
m. Tarnów
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1261
1262
1263
0.960
0.955
0.988
0.915
0.926
0.968
0.947
1.020
0.980
0.940
0.898
0.945
0.936
0.996
1.000
0.919
0.985
0.961
0.960
0.860
0.863
0.919
0.880
0.761
0.977
0.914
0.811
0.963
0.921
0.941
0.935
0.893
0.861
0.907
1.018
0.914
1.010
0.892
0.992
0.971
0.932
0.955
0.954
0.969
1.047
1.166
1.022
0.977
1.066
1.061
0.989
1.086
1.113
0.980
0.983
0.950
0.975
1.030
1.093
0.989
1.041
1.070
1.088
0.868
0.835
0.969
1.057
0.777
1.105
0.803
0.893
0.913
1.284
1.014
0.748
1.034
0.778
1.086
1.140
1.714
0.930
0.988
0.763
0.878
0.636
0.720
0.736
0.852
0.771
0.738
0.994
0.616
0.889
0.780
0.549
0.836
1.088
0.842
0.647
0.768
0.876
0.609
0.677
0.678
0.966
0.780
0.849
0.850
0.833
0.846
0.957
0.905
0.961
1.128
0.716
0.726
0.811
0.528
0.502
1.045
0.869
1.027
0.560
0.480
0.815
1.041
0.664
0.595
0.628
0.733
0.993
0.803
0.855
0.775
0.950
0.673
0.692
0.900
1.026
1.491
1.055
0.906
0.836
0.920
0.723
1.375
1.008
0.779
1.076
0.929
0.934
0.724
0.613
0.760
białobrzeski
ciechanowski
garwoliński
gostyniński
grodziski
grójecki
kozienicki
legionowski
lipski
łosicki
makowski
miński
mławski
nowodworski
ostrołęcki
ostrowski
otwocki
piaseczyński
płocki
płoński
pruszkowski
przasnyski
przysuski
pułtuski
radomski
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1.093
1.071
1.003
1.071
1.018
1.100
0.966
0.931
1.021
0.999
1.051
1.044
1.092
1.123
1.055
0.991
0.937
0.962
1.121
1.127
0.890
1.175
1.002
1.083
1.044
0.882
1.115
0.776
1.050
1.125
0.971
0.917
0.999
0.814
0.889
0.987
0.973
1.186
1.222
0.966
0.880
0.913
0.962
1.082
1.027
1.012
1.059
0.736
1.116
0.951
1.133
1.005
1.121
0.966
1.013
1.208
0.972
0.890
1.078
1.047
1.090
1.070
1.057
1.061
1.045
1.054
0.923
0.945
1.063
1.166
0.849
1.251
1.157
1.064
1.080
0.891
1.682
0.918
1.278
1.174
1.150
1.019
1.295
1.102
1.087
1.063
1.417
1.312
1.231
1.633
0.966
0.739
1.164
1.425
1.005
0.953
1.223
1.121
1.207
1.090
1.041
1.014
0.998
0.746
0.908
1.027
0.756
0.938
0.958
1.037
0.641
0.957
0.999
1.108
0.645
0.956
1.035
1.040
0.861
1.044
0.976
0.847
0.816
0.672
0.763
1.475
0.441
0.897
2.052
0.828
0.720
0.920
0.715
0.990
0.703
0.599
0.579
0.894
0.785
0.936
0.902
1.251
0.761
1.579
0.938
0.633
0.998
0.666
0.998
1.238
1.463
1.462
1.343
1.101
1.044
1.414
1.337
1.080
1.665
1.340
1.554
1.481
1.298
1.555
1.367
1.297
1.047
1.087
1.232
1.606
0.946
1.491
1.255
1.660
1.294
Page 206
1.102
0.868
0.555
0.638
1.006
0.601
1.063
1.161
1.279
1.276
1.588
1.233
0.847
1.352
0.754
1.367
1.596
2.481
1.002
1.324
0.887
0.884
0.901
0.585
External
1.080
1.168
1.048
1.256
1.698
0.986
1.223
0.945
1
District
TERYT
siedlecki
sierpecki
sochaczewski
sokołowski
szydłowiecki
warszawski zachodni
węgrowski
wołomiński
wyszkowski
zwoleński
żuromiński
żyrardowski
m. Ostrołęka
m. Płock
m. Radom
m. Siedlce
m. st. Warszawa
1426
1427
1428
1429
1430
1432
1433
1434
1435
1436
1437
1438
1461
1462
1463
1464
1465
Total
1.040
1.079
1.102
0.979
1.053
0.887
1.027
1.055
1.035
1.134
1.015
1.144
0.948
1.029
0.982
0.869
0.832
Cancer
0.881
1.174
1.064
0.870
0.861
0.957
0.880
1.003
0.894
0.943
1.174
1.139
0.976
1.156
0.953
0.895
0.956
CVD
1.057
1.053
1.186
1.044
1.135
0.838
1.034
1.028
1.036
1.125
1.041
1.114
0.795
0.874
0.918
0.903
0.715
brzeski
głubczycki
kędzierzyńsko-kozielski
kluczborski
krapkowicki
namysłowski
nyski
oleski
opolski
prudnicki
strzelecki
m. Opole
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1661
1.006
1.109
0.926
1.029
0.961
1.019
1.008
0.948
0.877
1.059
0.948
0.844
0.968
1.128
0.925
0.995
0.834
1.102
1.023
0.960
0.885
0.995
0.820
0.969
0.985
1.281
0.980
1.161
1.090
0.955
1.145
1.115
0.948
1.221
1.178
0.839
0.764
0.820
0.755
0.762
0.990
1.101
0.889
0.471
0.960
0.908
0.816
0.944
0.856
0.868
0.815
0.838
0.775
0.690
0.704
0.682
0.660
0.733
0.780
0.775
1.792
0.535
1.060
0.762
0.690
1.574
0.537
0.748
0.592
0.801
0.476
0.583
1.005
1.104
0.766
1.055
0.827
1.133
1.008
0.827
0.795
0.960
0.720
0.677
bieszczadzki
brzozowski
dębicki
jarosławski
jasielski
kolbuszowski
krośnieński
leżajski
lubaczowski
łańcucki
mielecki
niżański
przemyski
przeworski
ropczycko-sędziszowski
rzeszowski
sanocki
stalowowolski
strzyżowski
tarnobrzeski
leski
m. Krosno
m. Przemyśl
m. Rzeszów
m. Tarnobrzeg
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1861
1862
1863
1864
1.024
0.942
0.906
0.991
0.949
0.885
0.910
0.923
0.976
0.916
0.847
0.931
0.975
0.947
0.921
0.915
0.884
0.956
0.938
0.930
0.847
0.859
0.975
0.765
0.835
0.938
0.939
0.968
0.891
0.909
0.780
0.862
0.855
0.898
0.818
0.899
0.854
0.850
0.838
0.839
0.821
0.840
0.920
0.892
0.811
0.994
0.940
0.978
0.836
0.864
1.042
0.925
1.050
1.189
0.987
1.039
0.861
0.987
1.109
1.059
0.763
1.072
1.031
1.089
1.133
1.075
0.866
1.080
0.923
1.113
0.710
0.771
1.024
0.810
0.938
1.294
1.126
0.493
0.451
0.735
0.832
0.897
0.598
0.782
0.575
1.050
0.350
0.518
0.758
0.508
0.716
0.831
0.620
0.823
0.567
0.893
0.656
0.540
0.531
0.396
0.824
0.698
0.606
0.715
0.985
0.514
0.661
0.703
0.616
0.816
0.653
0.557
0.914
0.633
0.671
0.671
0.804
0.784
0.664
0.746
0.903
0.902
1.028
0.577
0.683
0.956
1.341
0.425
1.030
1.387
0.406
1.520
1.176
0.774
0.630
1.019
1.275
1.723
0.815
0.419
0.663
1.155
0.823
1.723
0.399
1.083
1.219
1.142
0.523
0.424
1.126
0.793
0.763
0.896
0.781
0.968
0.860
0.942
0.991
0.989
0.733
0.845
0.973
0.903
0.958
0.831
0.859
0.758
0.797
0.847
0.934
0.754
0.759
0.642
0.620
augustowski
2001
0.962
0.983
0.875
0.859
1.073
1.274
1.181
Page 207
1.352
0.965
0.894
1.086
0.881
0.839
1.271
0.888
0.680
1.408
0.700
0.428
1.163
0.854
0.864
1.141
0.994
0.800
1.157
1.172
0.960
1.358
1.135
1.239
1.660
0.921
0.682
1.623
0.836
1.243
1.083
0.585
0.558
1.588
1.117
1.214
1.683
0.817
0.872
1.313
1.149
1.280
0.952
1.082
1.565
0.742
1.053
0.511
1.118
1.077
0.892
External
1.425
1.302
1.244
1.251
1.470
0.960
1.471
1.179
1.500
1.355
0.997
1.167
1.024
0.951
0.993
0.955
0.739
1
TERYT
District
białostocki
bielski
grajewski
hajnowski
kolneński
łomżyński
moniecki
sejneński
siemiatycki
sokólski
suwalski
wysokomazowiecki
zambrowski
m. Białystok
m. Łomża
m. Suwałki
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2061
2062
2063
Total
0.994
0.936
0.978
1.016
1.019
0.962
0.908
1.041
0.999
1.010
1.011
0.965
0.959
0.823
0.910
0.891
Cancer
0.906
0.859
0.999
0.914
0.871
0.902
0.909
1.127
0.936
0.912
1.019
0.888
0.974
0.918
1.098
1.012
CVD
0.924
0.928
0.942
0.925
0.893
0.827
0.797
0.897
0.942
0.983
0.967
0.965
0.920
0.702
0.748
0.759
bytowski
chojnicki
człuchowski
gdański
kartuski
kościerski
kwidzyński
lęborski
malborski
nowodworski
pucki
słupski
starogardzki
tczewski
wejherowski
sztumski
m. Gdańsk
m. Gdynia
m. Słupsk
m. Sopot
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2261
2262
2263
2264
1.022
1.006
1.017
0.972
0.916
1.055
1.083
1.072
1.089
1.123
1.047
1.091
1.082
1.048
0.992
1.196
0.897
0.867
0.977
0.795
0.985
1.139
1.169
1.078
0.993
1.252
1.179
1.251
1.009
1.277
1.248
1.262
1.134
1.224
1.112
1.348
1.078
1.101
1.056
1.121
0.954
0.970
0.892
0.880
0.913
0.956
1.098
0.948
1.081
1.048
0.948
0.836
0.949
0.972
0.820
1.314
0.750
0.738
0.752
0.645
1.408
1.117
0.882
1.238
0.822
1.466
1.122
1.113
1.107
1.250
0.935
1.040
1.729
1.152
1.459
0.620
1.025
0.814
0.723
0.770
0.949
0.887
0.846
0.994
0.799
0.632
1.162
1.010
1.176
1.095
0.974
1.072
0.935
1.031
0.973
1.097
1.091
0.942
1.387
0.847
1.039
0.973
1.786
0.690
0.816
0.948
0.630
0.795
1.705
1.231
1.027
1.945
0.811
0.641
1.105
0.670
0.922
0.825
2.046
0.829
1.079
0.874
0.871
1.166
0.922
0.879
1.098
1.381
1.028
0.967
0.856
1.105
1.306
0.998
0.962
1.212
0.885
0.776
0.884
0.640
będziński
bielski
cieszyński
częstochowski
gliwicki
kłobucki
lubliniecki
mikołowski
myszkowski
pszczyński
raciborski
rybnicki
tarnogórski
bieruńsko-lędziński
wodzisławski
zawierciański
żywiecki
m. Bielsko-Biała
m. Bytom
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2461
2462
1.147
0.937
0.985
1.093
1.064
1.013
0.971
1.000
1.093
1.001
1.000
1.067
0.973
1.055
1.028
1.141
1.069
0.876
1.088
1.087
0.868
0.960
0.993
1.060
0.916
0.867
0.942
0.962
0.970
1.000
1.016
0.972
1.062
1.087
1.059
0.919
0.893
1.059
1.211
1.168
1.100
1.204
1.071
1.149
1.141
1.141
1.211
1.239
0.968
1.122
1.046
1.169
0.992
1.167
1.343
1.030
0.980
0.961
0.565
1.115
1.002
1.122
0.988
0.820
1.126
1.031
0.661
1.012
1.572
1.028
1.120
1.244
1.256
0.602
0.462
1.018
1.485
0.759
0.788
1.088
1.100
0.841
0.946
1.057
0.988
0.735
1.168
1.154
0.974
1.177
1.321
1.216
1.160
0.745
1.452
0.699
0.100
0.167
0.713
0.963
0.461
0.523
0.423
0.909
0.101
1.443
0.736
0.747
0.313
0.678
0.597
0.089
0.166
1.467
1.274
0.841
0.928
1.253
1.010
1.202
0.786
0.759
1.197
1.032
0.662
0.943
0.766
0.909
0.836
1.243
1.135
0.830
1.264
Page 208
1.295
0.954
1.420
1.029
0.817
0.776
0.920
0.820
0.663
1.349
1.371
0.968
1.684
0.665
2.082
1.235
0.823
1.519
1.435
1.015
1.002
0.865
0.922
1.180
1.369
0.801
0.780
1.089
1.107
0.930
0.851
0.868
1.070
1.237
0.769
0.850
0.986
0.823
0.668
0.817
1.037
1.031
0.665
0.784
0.839
0.923
0.968
0.507
External
1.049
1.567
1.105
1.378
0.878
1.392
0.920
1.352
1.539
1.323
1.376
1.239
1.329
0.759
0.998
0.994
1
TERYT
District
m. Chorzów
m. Częstochowa
m. Dąbrowa Górnicza
m. Gliwice
m. Jastrzębie-Zdrój
m. Jaworzno
m. Katowice
m. Mysłowice
m. Piekary Śląskie
m. Ruda Śląska
m. Rybnik
m. Siemianowice Śląskie
m. Sosnowiec
m. Świętochłowice
m. Tychy
m. Zabrze
m. Żory
2463
2464
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
Total
1.230
1.033
1.106
0.929
1.006
1.050
1.023
1.146
1.099
1.272
0.972
1.231
1.115
1.185
0.972
0.951
0.923
Cancer
1.175
0.985
1.074
1.052
1.082
1.107
1.058
1.250
1.046
1.189
0.985
1.283
1.129
1.034
1.021
1.003
1.041
CVD
1.205
1.065
1.149
0.837
0.913
1.122
0.994
1.176
1.234
1.271
0.981
1.254
1.119
1.309
1.013
0.820
0.926
buski
jędrzejowski
kazimierski
kielecki
konecki
opatowski
ostrowiecki
pińczowski
sandomierski
skarżyski
starachowicki
staszowski
włoszczowski
m. Kielce
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2661
0.987
1.010
1.118
1.022
1.054
1.073
1.029
0.997
0.929
1.052
1.012
1.013
1.034
0.846
0.884
0.865
1.082
0.932
0.943
0.941
0.975
0.879
0.925
0.921
0.913
0.921
0.736
0.854
0.910
1.088
1.074
1.006
1.164
1.146
1.178
1.039
0.950
1.184
1.031
1.100
1.176
0.762
1.075
1.249
1.286
1.294
0.946
0.890
0.503
0.786
0.607
0.822
1.106
0.636
0.927
1.036
0.800
0.744
0.742
0.775
0.956
0.720
0.812
0.866
1.003
1.042
0.961
0.828
0.759
0.853
1.974
0.801
1.416
1.235
0.691
1.259
0.567
1.445
0.856
0.511
0.619
0.920
0.713
1.300
1.036
1.091
1.101
1.144
1.111
1.261
0.950
1.062
0.871
1.113
1.165
1.141
1.186
0.659
bartoszycki
braniewski
działdowski
elbląski
ełcki
giżycki
iławski
kętrzyński
lidzbarski
mrągowski
nidzicki
nowomiejski
olecki
olsztyński
ostródzki
piski
szczycieński
gołdapski
węgorzewski
m. Elbląg
m. Olsztyn
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2861
2862
1.131
1.136
1.092
1.117
1.035
1.045
0.983
1.130
1.117
1.051
1.149
0.987
1.077
1.087
1.005
1.033
1.045
1.072
1.086
1.033
0.804
1.049
1.099
1.321
1.123
1.020
0.948
1.078
1.203
1.203
1.100
1.236
1.129
0.957
1.103
1.057
0.978
0.935
0.985
1.030
1.149
0.929
0.998
1.184
0.958
0.932
0.882
0.969
0.907
1.029
1.053
0.939
0.931
0.864
1.203
0.938
0.902
0.961
0.935
0.955
0.905
0.835
0.608
1.827
1.415
1.436
1.787
1.084
1.732
1.316
1.748
1.242
1.587
2.616
1.527
1.088
2.061
1.441
1.672
1.803
1.372
1.851
1.509
1.339
1.594
1.095
0.786
0.929
1.005
1.085
0.767
1.134
1.476
0.931
1.116
0.481
0.667
0.712
1.057
0.643
0.835
0.769
0.952
1.056
0.828
0.752
0.805
0.803
2.021
2.017
1.009
1.022
0.956
1.126
1.245
1.421
1.029
0.696
1.253
1.014
1.020
1.598
2.060
1.730
1.760
1.284
1.501
1.042
1.014
1.016
0.953
1.280
0.993
1.251
1.237
1.105
1.138
1.042
1.153
1.398
1.127
1.286
1.145
1.216
1.264
0.767
0.804
chodzieski
czarnkowsko-trzcianecki
gnieźnieński
3001
3002
3003
1.048
1.067
1.029
1.123
1.182
1.117
0.929
0.958
0.989
1.073
0.845
0.728
0.610
0.722
0.987
1.528
1.510
0.896
1.036
1.122
1.021
Page 209
1.418
1.949
0.594
0.991
1.269
0.856
1.115
1.357
0.673
0.838
1.190
0.917
1.056
1.147
0.906
0.881
1.005
0.674
1.107
1.383
0.623
1.188
1.715
0.423
0.934
1.370
0.608
1.214
1.914
0.929
1.198
1.048
0.534
1.407
1.835
0.388
1.161
1.450
0.726
0.784
1.795
0.588
1.161
1.115
0.424
1.000
1.345
1.064
0.845
1.389
0.228
External
1.291
1.062
1.040
0.819
0.907
0.941
1.081
0.999
0.797
1.096
0.869
1.092
1.090
1.337
0.784
0.945
0.871
1
District
TERYT
gostyński
grodziski
jarociński
kaliski
kępiński
kolski
koniński
kościański
krotoszyński
leszczyński
międzychodzki
nowotomyski
obornicki
ostrowski
ostrzeszowski
pilski
pleszewski
poznański
rawicki
słupecki
szamotulski
średzki
śremski
turecki
wągrowiecki
wolsztyński
wrzesiński
złotowski
m. Kalisz
m. Konin
m. Leszno
m. Poznań
3004
3005
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3061
3062
3063
3064
Total
1.045
1.102
1.005
1.029
0.979
1.065
0.985
0.994
1.079
1.009
1.151
1.058
1.123
0.973
1.076
1.043
1.081
0.983
1.055
1.040
1.059
1.047
1.091
1.093
1.061
1.061
1.036
1.043
0.970
0.861
0.955
0.910
Cancer
1.106
0.935
1.076
0.961
0.971
1.090
1.070
1.092
1.093
1.079
0.998
1.089
1.247
1.114
0.925
1.127
1.063
1.109
1.091
1.157
1.193
1.059
1.169
1.107
1.093
1.079
1.098
1.099
1.052
1.026
1.105
1.060
CVD
1.000
1.280
1.063
0.949
1.039
1.157
0.956
0.879
1.134
1.019
1.078
1.038
1.047
0.910
1.247
0.964
1.167
0.983
1.139
1.002
1.038
1.067
1.069
1.052
0.923
1.187
1.037
1.036
0.938
0.779
0.895
0.853
białogardzki
choszczeński
drawski
goleniowski
gryficki
gryfiński
kamieński
kołobrzeski
koszaliński
myśliborski
policki
pyrzycki
sławieński
stargardzki
szczecinecki
świdwiński
wałecki
łobeski
m. Koszalin
m. Szczecin
m. Świnoujście
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
3261
3262
3263
1.138
1.082
1.112
1.059
1.138
1.058
1.036
0.951
1.138
1.087
0.973
1.059
1.063
1.034
1.156
1.066
1.127
1.083
0.859
0.969
1.019
1.179
1.043
1.018
1.046
1.048
1.042
0.901
1.046
1.200
0.977
0.957
1.014
1.195
1.026
0.996
1.133
1.149
0.993
1.145
1.057
1.037
1.237
1.115
1.185
1.050
1.140
1.135
1.096
0.798
1.145
1.160
0.926
1.054
1.124
1.018
1.293
1.059
1.201
1.118
0.780
0.934
1.070
0.954
1.452
0.916
1.065
1.374
0.878
1.039
0.634
0.936
1.384
0.976
0.950
0.518
1.236
0.809
1.055
1.083
1.213
0.679
0.864
0.768
1.134
0.904
0.934
1.164
1.215
0.833
1.035
0.886
0.845
0.996
1.017
1.092
1.049
0.970
1.028
1.091
1.155
1.073
0.855
1.155
1.039
0.481
0.609
0.759
0.888
1.582
0.694
0.916
1.912
1.357
0.924
1.162
1.292
0.629
0.886
1.006
0.852
0.468
0.844
0.682
0.708
1.249
1.392
1.255
1.400
1.283
1.087
1.168
1.031
0.952
0.980
1.129
0.982
1.216
1.320
1.171
1.183
1.181
1.364
1.338
0.738
0.963
0.777
1.000
1.000
1.000
1.000
1.000
1.000
1.000
Polska
Page 210
0.904
0.930
1.176
0.815
1.254
0.614
0.630
0.673
0.502
0.960
0.890
1.876
0.983
0.722
0.555
0.659
1.066
0.535
0.896
0.882
0.828
0.581
0.983
1.640
0.917
1.121
0.494
0.721
0.808
0.891
0.829
1.101
1.997
0.659
0.976
1.021
0.665
0.968
1.023
0.872
0.858
0.653
0.713
0.831
0.360
1.068
0.867
1.070
0.732
0.936
0.790
0.759
0.932
0.776
0.901
0.860
0.482
1.394
0.923
0.435
0.660
0.779
1.236
0.849
0.933
0.412
1.082
0.992
0.830
1.125
1.050
1.195
1.092
0.924
0.699
0.679
0.987
0.471
1.165
1.004
0.442
0.722
0.640
0.938
0.768
1.050
1.035
0.829
0.977
0.644
0.705
0.958
0.748
0.698
0.911
0.846
External
1.075
1.149
1.287
1.205
1.302
1.281
1.138
1.049
1.131
0.992
1.400
1.193
1.270
1.042
1.244
1.099
1.198
0.868
1.222
1.182
1.079
1.086
0.977
1.252
1.643
1.002
1.126
1.370
0.927
0.923
0.922
0.786
1
residence in 2006–2008, males
District
bolesławiecki
dzierżoniowski
głogowski
górowski
jaworski
jeleniogórski
kamiennogórski
kłodzki
legnicki
lubański
lubiński
lwówecki
milicki
oleśnicki
oławski
polkowicki
strzeliński
średzki
świdnicki
trzebnicki
wałbrzyski
wołowski
wrocławski
ząbkowicki
zgorzelecki
złotoryjski
m. Jelenia Góra
m. Legnica
m. Wrocław
TERYT
0201
0202
0203
0204
0205
0206
0207
0208
0209
0210
0211
0212
0213
0214
0215
0216
0217
0218
0219
0220
0221
0222
0223
0224
0225
0226
0261
0262
0264
Total
1.018
1.074
0.989
0.979
1.098
1.132
1.142
1.144
1.065
1.126
0.949
1.281
1.012
1.081
0.996
1.068
1.082
1.231
1.101
1.112
1.172
1.056
1.039
1.061
1.187
1.212
0.978
1.076
0.890
Cancer
1.124
1.048
0.994
1.099
1.134
1.117
0.973
1.161
1.103
1.099
1.098
1.190
1.051
1.087
1.080
1.215
1.195
1.233
1.152
1.179
1.123
1.114
1.003
1.011
1.139
1.292
0.980
1.107
0.919
CVD
1.017
1.121
1.094
1.137
1.122
1.120
1.121
1.270
1.142
1.325
0.932
1.406
1.177
1.280
1.028
1.048
1.248
1.374
1.202
1.252
1.156
1.210
1.146
1.135
1.311
1.250
0.976
1.044
0.940
aleksandrowski
brodnicki
bydgoski
chełmiński
grudziądzki
inowrocławski
lipnowski
mogileński
nakielski
radziejowski
rypiński
sępoleński
świecki
toruński
tucholski
wąbrzeski
włocławski
żniński
m. Bydgoszcz
m. Grudziądz
m. Toruń
m. Włocławek
0401
0402
0403
0404
0405
0406
0407
0408
0409
0410
0411
0412
0413
0414
0415
0416
0417
0418
0419
0461
0462
0463
0464
1.120
1.060
0.979
1.144
1.015
1.116
1.092
1.197
1.093
1.133
1.117
1.046
0.993
1.032
1.075
1.019
1.096
1.185
1.015
0.924
1.069
0.919
1.052
1.056
1.228
1.058
1.209
1.160
1.191
1.152
1.071
1.316
1.156
1.163
1.137
1.061
1.128
1.333
1.047
1.342
1.168
1.129
1.067
1.120
1.090
1.013
1.210
1.049
0.987
1.012
0.924
1.097
1.194
1.185
1.134
1.393
1.178
1.069
1.179
1.103
0.856
1.131
0.961
1.242
1.001
0.891
1.063
0.788
1.028
0.938
1.549
1.395
1.355
1.563
1.016
1.419
1.742
0.802
1.017
1.692
1.141
1.133
0.969
1.461
1.296
1.165
1.399
1.069
0.919
0.985
1.062
0.996
0.786
0.761
0.816
1.011
0.685
0.487
1.165
0.805
0.677
0.735
0.577
0.636
0.745
0.955
0.757
0.800
0.751
0.859
0.638
0.827
0.956
1.030
1.437
1.143
0.847
0.929
1.452
1.080
1.344
0.455
1.294
0.999
0.585
0.794
0.518
0.185
0.580
1.047
0.629
1.223
0.501
1.113
1.205
0.956
1.110
0.554
1.182
0.857
0.820
1.020
0.934
1.352
1.061
1.416
1.045
0.967
1.078
1.224
0.964
1.070
1.282
0.874
0.716
1.536
1.006
0.620
0.983
0.750
1.274
bialski
0601
1.160
0.730
1.327
0.744
0.885
1.577
1.499
Page 211
0.778
0.760
0.737
1.031
1.598
0.976
1.014
1.172
0.495
0.782
0.930
0.561
1.127
1.292
0.611
1.244
1.225
0.787
1.225
1.247
2.146
1.291
1.123
0.706
1.021
1.164
1.158
0.892
1.030
0.712
0.853
1.165
0.554
1.300
1.252
1.056
0.595
1.150
0.749
1.070
1.082
0.447
0.567
0.870
1.237
0.938
0.929
0.930
0.551
0.791
0.923
1.052
1.322
1.043
0.867
1.368
0.815
0.898
0.863
0.942
1.396
1.590
1.505
0.861
1.065
0.745
0.899
1.028
1.008
1.065
1.315
0.782
1.022
1.149
0.964
1.276
1.714
0.560
0.955
1.187
0.808
0.832
1.546
1.369
0.579
1.055
1.155
External
1.136
1.040
0.949
0.857
1.341
1.223
0.906
1.172
0.834
0.966
1.001
1.279
0.889
1.078
1.054
1.197
0.998
1.241
1.139
1.128
1.102
0.837
1.082
0.998
1.269
1.358
1.014
0.974
0.667
1
District
biłgorajski
chełmski
hrubieszowski
janowski
krasnostawski
kraśnicki
lubartowski
lubelski
łęczyński
łukowski
opolski
parczewski
puławski
radzyński
rycki
świdnicki
tomaszowski
włodawski
zamojski
m. Biała Podlaska
m. Chełm
m. Lublin
m. Zamość
TERYT
0602
0603
0604
0605
0606
0607
0608
0609
0610
0611
0612
0613
0614
0615
0616
0617
0618
0619
0620
0661
0662
0663
0664
Total
0.996
1.293
1.116
1.043
1.130
0.995
1.115
1.102
1.161
1.075
1.113
1.088
1.000
1.090
1.078
1.019
1.017
1.212
1.078
0.964
1.024
0.927
0.869
Cancer
0.873
0.902
0.898
0.808
0.803
0.850
0.964
0.862
0.877
0.828
0.834
0.929
0.913
0.859
0.920
0.879
0.870
0.934
0.870
0.700
0.812
0.895
0.767
CVD
0.894
1.278
0.864
1.115
1.184
1.147
1.165
1.208
1.129
1.169
1.253
1.129
0.909
1.114
1.147
0.998
1.059
1.269
0.915
0.947
1.057
0.884
0.763
gorzowski
krośnieński
międzyrzecki
nowosolski
słubicki
sulęciński
świebodziński
zielonogórski
żagański
żarski
wschowski
m. Zielona Góra
0801
0802
0803
0804
0805
0806
0807
0808
0809
0810
0811
0812
0861
0862
1.006
1.161
1.112
1.000
1.214
1.110
1.219
1.093
1.089
1.137
1.118
1.058
0.904
0.868
0.978
1.040
1.068
0.940
1.282
1.275
1.123
0.945
1.044
1.092
0.974
1.016
0.912
0.905
0.941
1.210
1.102
0.964
1.173
0.957
1.269
1.197
1.000
1.361
1.372
1.010
0.792
0.724
0.803
1.423
1.241
0.755
1.347
1.259
1.087
0.950
0.859
0.709
0.749
0.770
0.765
0.705
0.845
1.038
1.025
1.086
1.142
0.787
0.950
1.197
0.946
1.103
0.999
0.705
0.756
0.864
1.572
0.644
1.581
0.668
1.090
1.627
0.948
1.069
1.826
0.437
0.733
1.295
1.825
1.555
1.230
1.236
0.981
1.146
1.196
1.212
1.373
0.982
1.163
1.161
1.134
0.965
0.646
0.816
bełchatowski
kutnowski
łaski
łęczycki
łowicki
łódzki wschodni
opoczyński
pabianicki
pajęczański
piotrkowski
poddębicki
radomszczański
rawski
sieradzki
skierniewicki
tomaszowski
wieluński
wieruszowski
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1.060
1.292
1.096
1.194
1.131
1.181
1.092
1.142
1.034
1.178
1.252
1.144
1.028
1.043
1.059
1.251
1.126
1.133
1.039
1.214
0.971
1.007
1.036
1.004
0.944
1.019
1.082
0.852
1.201
0.991
0.898
1.024
1.042
1.013
1.103
1.013
1.039
1.189
1.091
1.337
1.389
1.192
1.201
1.073
0.929
1.290
1.174
1.166
0.959
1.056
1.040
1.250
1.222
1.396
1.034
1.087
1.042
1.564
1.235
1.209
1.124
1.026
1.112
1.162
1.279
1.163
1.319
1.371
1.032
1.235
1.350
0.730
1.290
1.345
1.062
1.005
1.052
1.609
0.947
1.273
0.927
1.168
0.921
0.870
1.106
1.033
0.975
1.505
0.926
1.050
1.058
2.374
1.250
0.546
0.306
1.228
0.768
1.582
1.279
0.982
1.583
1.755
1.005
0.594
0.360
1.668
0.654
0.599
1.233
1.289
1.494
1.714
1.045
1.158
1.392
1.320
1.056
1.667
1.742
1.248
1.598
1.269
1.650
1.353
1.103
1.135
Page 212
0.950
0.550
2.284
1.217
1.130
2.350
1.017
0.915
3.064
1.376
1.179
1.582
1.574
0.831
1.789
1.020
0.761
0.896
0.957
0.845
1.548
0.928
1.016
1.697
1.641
0.785
2.279
0.874
0.703
1.932
0.982
0.887
1.292
0.830
0.978
1.810
0.730
0.934
2.228
0.815
1.021
1.711
0.822
0.984
1.600
0.520
0.806
2.499
0.631
0.973
1.263
0.757
1.351
1.516
1.033
0.751
2.378
0.806
0.740
1.608
0.768
1.141
1.656
0.809
0.986
1.847
0.755
1.011
1.479
External
0.794
1.547
1.284
0.592
0.939
0.892
1.164
0.842
0.872
0.896
1.094
0.937
0.795
1.269
0.916
0.653
1.230
1.303
1.356
1.069
0.924
0.440
0.833
1
District
zduńskowolski
zgierski
brzeziński
m. Łódź
m. Skierniewice
TERYT
1019
1020
1021
1061
1062
1063
Total
1.100
1.182
1.243
1.195
1.096
0.969
Cancer
1.054
1.040
0.951
0.980
0.959
0.991
CVD
1.013
1.130
1.417
1.021
1.134
1.011
bocheński
brzeski
chrzanowski
dąbrowski
gorlicki
krakowski
limanowski
miechowski
myślenicki
nowosądecki
nowotarski
olkuski
oświęcimski
proszowicki
suski
tarnowski
tatrzański
wadowicki
wielicki
m. Kraków
m. Nowy Sącz
m. Tarnów
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1261
1262
1263
0.911
0.936
0.964
0.842
0.892
0.965
0.920
0.990
0.971
0.934
0.893
0.935
0.902
1.054
1.025
0.906
0.998
0.945
0.973
0.821
0.844
0.908
0.934
0.786
0.972
0.874
0.768
0.992
0.991
0.875
0.958
0.982
0.847
0.895
1.027
1.046
1.060
0.946
1.023
1.028
0.979
0.891
0.929
0.952
0.991
1.150
1.016
0.928
1.028
1.048
0.933
1.027
1.116
0.951
0.971
0.896
0.930
0.956
1.165
1.005
1.073
1.029
1.099
0.833
0.877
1.004
0.991
0.945
0.975
0.908
1.029
0.893
1.104
1.109
0.808
0.995
0.897
1.020
1.148
1.876
0.905
0.939
0.730
0.877
0.532
0.632
0.723
0.741
0.707
0.722
0.917
0.597
0.841
0.756
0.533
0.779
0.880
0.813
0.677
0.739
0.889
0.716
0.813
0.632
0.905
0.701
0.882
0.818
0.775
0.777
0.810
0.662
0.964
0.746
0.712
0.829
0.617
0.554
0.451
0.923
0.827
1.212
0.498
0.715
0.634
0.772
0.528
0.642
0.756
0.811
0.905
0.709
0.837
0.844
0.893
0.660
0.699
0.915
1.057
1.616
1.062
0.902
0.852
0.941
0.701
1.470
0.974
0.806
1.072
0.934
0.928
0.703
0.548
0.794
białobrzeski
ciechanowski
garwoliński
gostyniński
grodziski
grójecki
kozienicki
legionowski
lipski
łosicki
makowski
miński
mławski
nowodworski
ostrołęcki
ostrowski
otwocki
piaseczyński
płocki
płoński
pruszkowski
przasnyski
przysuski
pułtuski
radomski
siedlecki
sierpecki
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1.101
1.092
1.047
1.074
1.037
1.094
0.993
0.922
1.052
1.005
1.130
1.089
1.170
1.140
1.094
1.033
0.957
0.959
1.147
1.180
0.877
1.239
1.037
1.098
1.066
1.037
1.110
0.949
1.118
0.823
1.119
1.135
0.930
0.979
0.982
0.792
0.840
1.059
1.028
1.262
1.252
1.066
0.962
0.899
0.947
1.173
1.105
0.964
1.190
0.763
1.143
0.982
0.870
1.242
1.125
1.018
1.143
0.943
1.019
1.214
0.958
0.920
1.127
1.065
1.175
1.104
1.102
1.030
1.007
1.062
0.915
0.912
1.036
1.207
0.821
1.331
1.186
1.016
1.064
1.018
1.040
1.013
1.674
0.939
1.210
1.014
1.171
1.164
1.171
1.216
0.963
1.137
1.409
1.438
1.266
1.868
1.148
0.750
1.135
1.540
0.952
0.967
1.244
1.242
1.346
1.226
1.362
1.273
1.125
1.032
1.000
0.663
0.902
1.131
0.787
0.832
1.011
1.122
0.588
1.062
1.138
1.258
0.742
0.978
1.042
1.098
0.748
1.004
0.995
0.978
0.886
0.675
0.769
0.962
0.923
1.129
0.372
1.092
2.024
0.993
0.620
1.023
0.766
0.919
0.745
0.625
0.604
0.981
0.839
0.982
0.914
1.521
0.813
1.696
1.045
0.721
0.674
0.686
0.903
1.352
0.952
0.934
1.398
1.539
1.398
1.069
1.097
1.493
1.250
0.941
1.735
1.437
1.675
1.566
1.389
1.523
1.393
1.337
1.088
1.090
1.242
1.702
0.947
1.572
1.311
1.663
1.324
1.469
1.310
Page 213
0.830
1.274
1.666
1.384
1.677
1.380
0.965
1.364
0.619
1.276
1.686
2.870
1.012
1.232
0.884
0.792
0.892
0.775
External
1.126
1.280
1.764
0.999
1.201
0.955
1
District
sochaczewski
sokołowski
szydłowiecki
warszawski zachodni
węgrowski
wołomiński
wyszkowski
zwoleński
żuromiński
żyrardowski
m. Ostrołęka
m. Płock
m. Radom
m. Siedlce
m. st. Warszawa
TERYT
1428
1429
1430
1432
1433
1434
1435
1436
1437
1438
1461
1462
1463
1464
1465
Total
1.121
0.977
1.124
0.883
1.023
1.090
1.083
1.131
0.999
1.182
0.959
1.062
1.014
0.894
0.803
Cancer
1.115
0.919
0.942
0.956
0.850
1.031
0.893
1.034
1.229
1.162
1.067
1.177
0.956
0.829
0.874
CVD
1.167
1.045
1.219
0.837
1.030
1.019
1.079
1.122
0.957
1.109
0.811
0.870
0.949
0.953
0.699
brzeski
głubczycki
kluczborski
krapkowicki
namysłowski
nyski
oleski
opolski
prudnicki
strzelecki
m. Opole
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1661
1.054
1.151
0.894
1.049
0.915
1.046
0.987
0.911
0.836
1.073
0.916
0.792
1.031
1.135
0.879
1.050
0.837
1.164
1.041
0.969
0.870
1.029
0.857
0.871
1.021
1.392
0.975
1.219
1.031
0.978
1.103
1.113
0.916
1.247
1.129
0.799
0.896
0.893
0.763
0.737
1.082
1.275
0.963
0.566
0.932
0.963
0.765
0.906
0.904
0.909
0.851
0.825
0.632
0.729
0.793
0.679
0.595
0.847
0.749
0.752
1.829
0.535
1.018
0.626
0.693
1.370
0.586
0.472
0.655
0.821
0.484
0.625
1.044
1.115
0.735
1.126
0.839
1.151
0.999
0.818
0.804
1.032
0.788
0.651
bieszczadzki
brzozowski
dębicki
jarosławski
jasielski
kolbuszowski
krośnieński
leżajski
lubaczowski
łańcucki
mielecki
niżański
przemyski
przeworski
rzeszowski
sanocki
stalowowolski
strzyżowski
tarnobrzeski
leski
m. Krosno
m. Przemyśl
m. Rzeszów
m. Tarnobrzeg
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1861
1862
1863
1864
1.034
0.908
0.906
0.961
0.939
0.850
0.882
0.878
1.009
0.883
0.808
0.954
0.971
0.916
0.955
0.892
0.856
0.901
0.917
0.918
0.848
0.830
0.981
0.731
0.831
1.011
0.971
0.970
0.881
0.978
0.840
0.877
0.874
0.929
0.827
0.910
0.921
0.888
0.874
0.880
0.870
0.815
0.919
0.930
0.948
0.973
0.993
0.982
0.810
0.848
1.155
0.885
1.061
1.132
1.006
0.994
0.895
0.904
1.142
0.991
0.760
1.115
0.979
1.002
1.226
1.044
0.919
1.015
0.937
1.018
0.732
0.797
0.980
0.777
0.964
1.251
1.185
0.618
0.522
0.870
0.831
0.931
0.635
0.887
0.654
0.956
0.391
0.624
0.841
0.613
0.810
0.755
0.670
0.861
0.694
0.862
0.557
0.569
0.481
0.395
0.630
0.749
0.647
0.758
0.993
0.519
0.582
0.745
0.595
0.717
0.660
0.657
0.890
0.591
0.547
0.638
0.718
0.847
0.546
0.669
0.987
0.815
1.021
0.504
0.885
0.701
0.759
0.431
1.031
0.831
0.232
0.861
1.003
0.740
0.670
0.660
0.940
1.788
0.844
0.363
0.555
0.644
0.540
1.145
0.374
0.723
0.553
1.308
0.558
0.421
0.988
0.863
0.754
0.926
0.830
0.950
0.876
0.845
1.048
1.024
0.655
0.909
0.964
0.938
1.011
0.833
0.901
0.735
0.860
0.887
1.039
0.799
0.846
0.620
0.632
augustowski
białostocki
bielski
2001
2002
2003
0.976
1.025
0.913
0.996
0.939
0.820
0.898
0.984
0.876
0.851
1.268
1.044
1.169
0.976
0.864
1.080
1.142
0.652
1.222
1.050
1.573
Page 214
1.293
0.969
0.904
1.358
0.558
0.407
1.382
0.800
0.796
0.913
0.902
0.902
1.102
1.125
1.059
1.412
1.142
1.407
1.759
0.964
0.711
1.556
0.875
0.910
1.148
0.542
0.536
1.401
1.358
1.584
1.581
0.882
0.727
1.250
1.254
1.597
0.984
1.135
1.666
0.875
1.065
0.653
0.952
1.052
0.999
External
1.240
1.270
1.497
0.953
1.376
1.233
1.566
1.468
0.904
1.169
0.991
0.904
1.003
0.899
0.684
1
District
grajewski
hajnowski
kolneński
łomżyński
moniecki
sejneński
siemiatycki
sokólski
suwalski
wysokomazowiecki
zambrowski
m. Białystok
m. Łomża
m. Suwałki
TERYT
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2061
2062
2063
Total
1.039
1.031
1.047
0.984
0.902
1.051
1.005
1.058
1.043
0.952
0.967
0.834
0.907
0.887
Cancer
1.081
0.957
0.905
0.952
0.860
1.239
0.980
0.953
1.117
0.828
1.026
0.882
1.095
0.947
CVD
0.969
0.926
0.949
0.840
0.814
0.843
0.933
1.030
1.056
0.933
0.887
0.741
0.789
0.796
bytowski
chojnicki
człuchowski
gdański
kartuski
kościerski
kwidzyński
lęborski
malborski
nowodworski
pucki
słupski
starogardzki
tczewski
wejherowski
sztumski
m. Gdańsk
m. Gdynia
m. Słupsk
m. Sopot
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2261
2262
2263
2264
0.992
0.953
1.023
0.966
0.900
0.901
1.130
1.103
1.098
1.206
0.987
1.106
1.026
1.039
0.949
1.252
0.882
0.816
1.002
0.770
0.965
1.100
1.248
1.118
1.054
1.100
1.293
1.321
1.070
1.411
1.165
1.335
1.085
1.239
1.072
1.626
1.026
1.004
1.023
1.110
0.919
0.938
0.879
0.891
0.910
0.861
1.209
0.961
1.087
1.138
0.964
0.839
0.924
0.996
0.798
1.303
0.786
0.753
0.773
0.646
1.388
1.210
0.920
1.133
0.844
1.389
1.076
1.109
1.018
1.310
0.822
1.072
1.576
1.063
1.252
0.587
0.934
0.734
0.621
0.575
0.948
0.692
0.833
0.955
0.778
0.550
1.302
1.080
1.333
1.124
0.985
0.952
0.848
1.056
0.975
1.205
1.036
0.840
1.258
0.947
0.891
0.755
1.696
0.481
0.527
0.593
0.362
0.526
1.670
1.244
0.671
1.742
0.469
0.403
0.816
0.459
0.678
0.541
2.165
0.495
1.075
0.886
0.879
1.127
0.937
0.799
1.143
1.434
0.967
0.968
0.837
1.039
1.284
0.988
0.957
1.310
0.852
0.719
0.912
0.577
będziński
bielski
cieszyński
częstochowski
gliwicki
kłobucki
lubliniecki
mikołowski
myszkowski
pszczyński
raciborski
rybnicki
tarnogórski
wodzisławski
zawierciański
żywiecki
m. Bielsko-Biała
m. Bytom
m. Chorzów
m. Częstochowa
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2461
2462
2463
2464
1.184
0.880
0.930
1.109
1.045
1.009
0.913
0.940
1.096
0.968
0.951
1.029
0.923
1.021
0.990
1.148
1.064
0.862
1.078
1.254
1.034
1.092
0.834
0.960
1.031
1.053
0.971
0.833
0.937
1.004
0.937
0.990
1.024
0.915
1.015
1.065
1.088
0.978
0.873
1.000
1.153
0.941
1.242
1.145
1.058
1.164
1.050
1.108
1.082
1.106
1.222
1.260
0.943
1.126
0.995
1.191
0.982
1.172
1.374
1.075
0.972
1.252
1.038
1.061
0.483
0.964
1.178
1.087
1.191
0.846
0.940
1.084
0.622
1.048
1.473
1.045
1.181
1.124
1.267
0.536
0.400
1.094
1.383
0.972
1.613
0.727
0.831
1.138
1.032
0.802
1.058
0.962
1.079
0.616
1.058
1.092
0.992
1.076
1.300
1.173
1.138
0.725
1.397
2.074
1.305
0.778
0.104
0.205
0.893
1.054
0.426
0.431
0.373
0.745
0.130
1.248
0.671
0.691
0.347
0.608
0.475
0.100
0.209
1.461
0.677
1.088
1.266
0.822
0.835
1.274
0.975
1.196
0.783
0.723
1.209
1.048
0.544
0.848
0.726
0.854
0.799
1.283
1.120
0.755
1.230
1.201
1.069
Page 215
1.062
0.922
0.482
1.210
1.406
0.668
1.818
0.771
1.993
1.353
0.675
1.212
1.313
1.089
0.836
0.920
0.998
0.676
1.260
0.840
0.573
1.109
1.261
0.701
0.806
0.687
0.687
1.327
0.757
0.779
1.070
0.912
0.561
0.722
1.032
0.932
0.697
0.750
0.746
0.877
1.095
0.324
External
1.149
1.388
0.852
1.437
1.031
1.490
1.408
1.379
1.444
1.294
1.332
0.744
0.949
1.032
1
District
m. Gliwice
m. Jaworzno
m. Katowice
m. Mysłowice
m. Ruda Śląska
m. Rybnik
m. Sosnowiec
m. Tychy
m. Zabrze
m. Żory
TERYT
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
Total
1.124
0.891
0.947
1.051
0.991
1.095
1.121
1.243
0.920
1.178
1.109
1.209
0.931
0.941
0.881
Cancer
1.067
0.972
0.985
1.160
0.993
1.161
1.047
1.140
0.918
1.269
1.093
1.018
1.038
0.928
1.046
CVD
1.164
0.808
0.877
1.137
0.980
1.129
1.326
1.264
0.968
1.207
1.115
1.330
0.963
0.822
0.932
buski
jędrzejowski
kazimierski
kielecki
konecki
opatowski
ostrowiecki
pińczowski
sandomierski
skarżyski
starachowicki
staszowski
włoszczowski
m. Kielce
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2661
0.985
1.000
1.181
1.064
1.101
1.101
1.037
1.054
0.961
1.082
1.037
1.010
0.989
0.831
0.934
0.913
1.155
0.998
0.975
1.021
1.034
0.932
0.933
0.937
0.921
0.910
0.731
0.830
0.934
1.070
1.118
1.015
1.166
1.161
1.187
1.149
1.021
1.235
1.029
1.102
1.101
0.745
1.103
1.136
1.554
1.316
1.251
1.036
0.579
0.865
0.601
0.838
1.228
0.738
0.883
0.901
0.829
0.810
0.755
0.830
1.057
0.644
0.880
0.997
1.073
1.203
1.059
0.849
0.710
0.844
1.551
0.649
1.497
1.328
0.815
1.067
0.677
1.193
0.763
0.593
0.810
0.720
0.679
1.412
1.092
1.109
1.038
1.180
1.209
1.302
0.912
1.059
0.931
1.181
1.059
1.244
1.198
0.643
bartoszycki
braniewski
działdowski
elbląski
ełcki
giżycki
iławski
kętrzyński
lidzbarski
mrągowski
nidzicki
nowomiejski
olecki
olsztyński
ostródzki
piski
szczycieński
gołdapski
węgorzewski
m. Elbląg
m. Olsztyn
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2861
2862
1.210
1.165
1.102
1.137
1.104
1.126
0.996
1.233
1.179
1.089
1.201
1.002
1.074
1.110
1.049
1.081
1.093
1.075
1.090
1.051
0.802
1.110
1.234
1.371
1.201
1.101
0.969
1.189
1.326
1.250
1.179
1.338
1.176
1.038
1.137
1.099
0.976
0.986
0.867
1.036
1.134
0.896
1.018
1.200
0.948
0.856
0.905
1.015
0.848
1.119
1.064
0.938
0.891
0.926
1.137
0.940
0.913
0.996
0.932
0.966
0.850
0.840
0.582
1.960
1.206
1.377
1.792
0.970
1.670
1.339
1.593
1.364
1.667
2.545
1.598
1.069
1.922
1.347
1.664
1.921
1.506
2.027
1.376
1.167
1.455
0.811
0.794
0.911
1.050
1.234
0.763
1.328
1.390
0.872
1.271
0.329
0.709
0.716
1.197
0.748
0.897
0.744
0.952
1.124
0.867
0.782
0.906
0.707
2.084
2.250
1.163
0.957
0.985
1.342
1.192
1.470
0.829
0.800
1.126
1.105
1.001
1.518
2.089
1.676
2.029
1.389
1.673
1.075
1.053
1.113
1.017
1.423
0.988
1.328
1.409
1.246
1.244
1.026
1.171
1.435
1.210
1.406
1.247
1.365
1.271
0.714
0.801
chodzieski
gnieźnieński
gostyński
grodziski
3001
3002
3003
3004
3005
1.017
1.035
0.987
0.980
1.046
1.095
1.177
1.067
1.060
0.902
0.933
0.942
1.000
0.999
1.284
1.225
0.822
0.800
0.993
0.822
0.637
0.681
0.988
0.881
1.246
1.213
1.410
0.558
0.877
0.442
0.957
1.053
0.961
0.846
1.029
Page 216
1.039
1.464
0.780
0.729
1.168
0.981
0.961
1.114
0.980
0.847
0.990
0.624
1.030
1.287
0.673
1.316
1.682
0.428
0.929
1.422
0.788
1.243
1.717
0.947
1.211
0.986
0.476
1.353
1.672
0.316
1.123
1.519
0.842
0.908
1.957
0.667
1.164
1.100
0.393
1.028
1.341
1.086
0.578
1.362
0.148
External
1.055
0.761
0.858
0.947
1.010
0.852
0.791
1.058
0.777
0.985
1.023
1.342
0.750
0.897
0.794
1
District
jarociński
kaliski
kępiński
kolski
koniński
kościański
krotoszyński
leszczyński
międzychodzki
nowotomyski
obornicki
ostrowski
ostrzeszowski
pilski
pleszewski
poznański
rawicki
słupecki
szamotulski
średzki
śremski
turecki
wągrowiecki
wolsztyński
wrzesiński
złotowski
m. Kalisz
m. Konin
m. Leszno
m. Poznań
TERYT
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3061
3062
3063
3064
Total
0.945
1.032
0.944
1.088
1.012
1.007
1.035
0.986
1.094
1.049
1.096
0.980
1.053
1.050
1.009
0.934
0.999
1.033
1.014
1.033
1.021
1.110
1.020
0.989
1.058
1.043
0.980
0.867
0.929
0.882
Cancer
1.010
0.998
0.974
1.117
1.170
1.110
1.047
1.180
0.914
1.077
1.230
1.152
0.957
1.158
1.018
1.106
1.103
1.145
1.235
1.021
1.241
1.102
1.021
1.045
1.114
1.103
1.040
0.961
1.061
0.993
CVD
0.999
0.887
0.972
1.249
0.958
0.952
1.066
0.986
1.097
1.078
1.057
0.925
1.199
0.989
1.052
0.951
1.088
0.970
0.998
1.132
0.994
1.078
0.868
1.118
1.131
1.033
0.945
0.850
0.890
0.862
białogardzki
choszczeński
drawski
goleniowski
gryficki
gryfiński
kamieński
kołobrzeski
koszaliński
myśliborski
policki
pyrzycki
sławieński
stargardzki
szczecinecki
świdwiński
wałecki
łobeski
m. Koszalin
m. Szczecin
m. Świnoujście
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
3261
3262
3263
1.225
1.069
1.135
1.083
1.154
1.027
1.026
0.948
1.155
1.113
0.965
1.110
1.092
1.018
1.162
1.128
1.137
1.177
0.833
0.961
0.988
1.230
1.062
1.027
1.010
1.075
1.093
0.875
1.080
1.237
1.024
0.901
1.158
1.222
1.050
1.076
1.171
1.213
1.078
1.022
0.998
1.026
1.330
1.074
1.249
1.070
1.123
1.107
1.130
0.750
1.201
1.157
0.946
1.019
1.166
1.000
1.281
1.148
1.210
1.195
0.811
0.965
1.035
1.083
1.624
0.911
1.068
1.393
0.862
0.934
0.608
0.967
1.393
1.097
0.908
0.601
1.116
0.823
1.083
1.056
1.566
0.595
0.802
0.587
1.397
0.808
0.966
1.172
1.001
0.814
0.988
0.974
0.814
0.984
0.844
1.218
0.941
0.862
1.078
1.100
1.367
1.259
0.955
1.158
0.963
0.380
0.612
0.471
0.925
1.654
0.542
0.810
2.108
1.358
1.054
1.171
1.584
0.673
0.873
0.987
0.865
0.373
0.828
0.739
0.687
1.396
1.536
1.293
1.456
1.345
1.128
1.122
1.000
0.806
0.935
1.152
0.993
1.278
1.340
1.184
1.269
1.245
1.357
1.389
0.600
0.931
0.787
1.000
1.000
1.000
1.000
1.000
1.000
1.000
Polska
Page 217
0.702
0.620
0.436
1.047
0.919
1.818
1.141
0.754
0.555
0.756
0.980
0.577
1.082
0.855
0.774
0.543
0.972
1.500
1.109
1.232
0.472
0.809
0.783
0.792
0.913
0.898
1.676
0.690
0.877
0.902
0.748
0.948
0.942
0.960
0.846
0.723
0.979
0.843
0.419
1.100
0.822
0.995
0.814
0.849
0.829
0.728
0.849
0.688
1.029
0.607
0.463
1.515
0.982
0.355
0.658
0.622
0.869
0.849
0.805
0.342
1.050
1.000
0.538
1.354
1.063
1.174
1.176
0.984
0.604
0.713
0.831
0.496
1.150
0.980
0.306
0.817
0.590
0.816
0.757
1.125
1.144
0.872
1.102
0.615
0.827
1.073
0.586
0.601
0.889
0.948
External
1.231
1.282
1.225
1.197
1.096
0.916
1.084
0.822
1.155
1.119
1.117
0.912
1.290
1.053
1.080
0.763
1.060
1.186
1.027
1.031
0.798
1.228
1.490
0.986
1.085
1.287
0.921
0.889
0.905
0.670
1
residence in 2006–2008, females
District
bolesławiecki
dzierżoniowski
głogowski
górowski
jaworski
jeleniogórski
kamiennogórski
kłodzki
legnicki
lubański
lubiński
lwówecki
milicki
oleśnicki
oławski
polkowicki
strzeliński
średzki
świdnicki
trzebnicki
wałbrzyski
wołowski
wrocławski
ząbkowicki
zgorzelecki
złotoryjski
m. Jelenia Góra
m. Legnica
m. Wrocław
TERYT
0201
0202
0203
0204
0205
0206
0207
0208
0209
0210
0211
0212
0213
0214
0215
0216
0217
0218
0219
0220
0221
0222
0223
0224
0225
0226
0261
0262
0264
Total
1.009
1.030
1.045
1.048
1.062
1.111
1.104
1.132
1.046
1.094
0.989
1.167
1.042
0.959
0.956
1.039
1.033
1.109
1.085
1.054
1.200
1.142
0.982
1.109
1.078
1.130
1.018
1.059
0.924
Cancer
1.139
1.041
1.133
1.056
1.001
1.137
1.081
1.078
1.038
0.912
1.030
0.912
0.915
0.990
0.963
0.968
1.046
0.978
1.195
1.162
1.210
1.158
1.002
1.122
1.151
1.123
1.180
1.241
1.031
CVD
1.013
1.068
1.011
1.135
1.161
1.095
1.077
1.223
1.100
1.325
1.002
1.177
1.230
1.066
1.017
1.137
1.227
1.227
1.089
1.151
1.249
1.283
1.046
1.171
1.135
1.212
0.891
0.977
0.938
aleksandrowski
brodnicki
bydgoski
chełmiński
grudziądzki
inowrocławski
lipnowski
mogileński
nakielski
radziejowski
rypiński
sępoleński
świecki
toruński
tucholski
wąbrzeski
włocławski
żniński
m. Bydgoszcz
m. Grudziądz
m. Toruń
m. Włocławek
0401
0402
0403
0404
0405
0406
0407
0408
0409
0410
0411
0412
0413
0414
0415
0416
0417
0418
0419
0461
0462
0463
0464
1.056
1.077
1.035
1.167
0.922
1.089
1.082
1.123
1.117
1.168
1.062
1.125
0.951
1.168
1.079
1.014
1.069
1.050
1.028
0.960
1.136
0.916
1.077
1.005
0.966
1.139
1.282
0.848
1.065
1.131
1.009
0.985
1.165
1.186
1.177
1.031
1.211
1.077
1.102
1.099
0.869
1.106
1.179
1.242
1.078
1.235
1.251
1.102
1.007
1.090
0.941
1.114
1.166
1.209
1.245
1.367
1.066
1.239
0.963
1.286
1.026
1.079
1.049
1.240
1.016
0.886
1.105
0.773
1.048
0.484
0.809
1.562
0.996
1.162
1.107
1.221
1.183
1.075
0.727
1.602
0.839
1.083
1.050
1.438
1.363
1.327
0.894
0.906
1.241
1.215
1.230
0.871
1.035
1.089
1.121
0.926
0.656
1.043
1.373
0.868
0.975
0.981
0.615
0.752
0.833
0.807
1.035
0.577
0.331
0.802
0.931
1.002
0.952
1.023
1.354
0.534
1.667
0.586
1.752
0.936
0.981
0.390
1.057
1.064
0.636
0.890
0.762
1.043
1.030
1.182
0.763
1.018
0.729
1.122
0.842
0.993
1.229
0.729
0.520
0.847
0.635
0.681
0.551
0.636
0.683
0.891
0.628
0.816
0.534
0.536
0.556
0.877
0.916
0.794
0.382
0.672
0.657
0.597
0.890
0.718
1.136
bialski
0601
1.046
0.744
1.224
0.482
0.782
1.006
1.312
Page 218
1.024
1.061
0.428
0.938
0.944
1.292
0.688
1.333
1.100
0.880
0.581
1.144
0.923
1.200
0.817
1.169
1.728
0.834
1.069
1.105
1.926
1.056
1.114
1.092
1.109
0.749
1.116
0.803
0.548
0.942
0.763
1.075
1.152
1.012
1.526
2.215
0.437
0.369
1.020
0.709
0.903
0.225
0.512
1.011
1.008
0.658
0.841
1.184
0.430
0.781
0.493
0.882
1.147
1.047
0.653
0.983
1.384
0.679
0.855
0.621
0.884
1.354
1.499
0.794
0.996
0.919
0.816
1.192
0.500
0.830
1.004
1.388
1.050
1.147
0.672
1.255
0.993
1.113
1.098
1.303
1.307
0.758
1.277
1.645
0.728
1.086
0.832
External
1.066
0.787
0.852
1.288
1.140
0.727
0.716
0.958
1.204
0.808
1.186
0.874
1.032
1.031
1.114
0.962
0.896
0.846
1.163
0.954
1.018
0.728
0.945
0.927
1.024
0.964
0.975
0.916
0.852
1
District
biłgorajski
chełmski
hrubieszowski
janowski
krasnostawski
kraśnicki
lubartowski
lubelski
łęczyński
łukowski
opolski
parczewski
puławski
radzyński
rycki
świdnicki
tomaszowski
włodawski
zamojski
m. Biała Podlaska
m. Chełm
m. Lublin
m. Zamość
TERYT
0602
0603
0604
0605
0606
0607
0608
0609
0610
0611
0612
0613
0614
0615
0616
0617
0618
0619
0620
0661
0662
0663
0664
Total
0.958
1.078
0.975
1.022
1.023
0.992
1.032
1.004
0.990
0.982
1.027
0.978
0.917
1.063
0.954
0.927
0.969
1.090
0.977
0.935
0.848
0.949
0.853
Cancer
0.666
0.765
0.747
0.652
0.610
0.815
0.867
0.591
0.713
0.785
0.650
0.875
0.790
0.815
0.779
0.764
0.698
0.763
0.727
0.977
0.739
0.898
0.858
CVD
1.006
1.181
0.869
1.233
1.145
1.159
1.174
1.235
1.138
1.148
1.268
1.100
0.929
1.122
1.158
1.057
1.054
1.291
0.939
0.982
0.929
0.951
0.826
gorzowski
krośnieński
międzyrzecki
nowosolski
słubicki
sulęciński
świebodziński
zielonogórski
żagański
żarski
wschowski
m. Zielona Góra
0801
0802
0803
0804
0805
0806
0807
0808
0809
0810
0811
0812
0861
0862
1.064
1.077
1.076
1.045
1.128
1.050
1.160
1.070
0.993
1.112
1.166
1.081
0.996
0.936
1.067
0.907
0.961
1.016
1.130
1.028
1.025
0.901
0.986
1.031
1.137
1.040
1.091
1.041
1.037
1.146
1.042
1.003
1.113
0.996
1.140
1.134
0.887
1.242
1.293
0.972
0.796
0.703
0.911
1.312
1.207
0.819
1.046
0.835
1.132
1.131
0.722
0.739
0.645
0.345
0.923
0.676
0.831
0.730
1.077
1.294
1.429
1.086
1.327
0.822
0.986
0.929
0.985
1.107
0.897
0.971
1.540
1.228
1.798
1.273
1.177
2.047
1.339
1.235
2.113
0.494
0.944
1.549
2.261
2.392
0.930
0.621
0.829
0.971
1.012
0.982
0.892
0.572
0.748
0.979
1.046
1.126
0.766
0.842
bełchatowski
kutnowski
łaski
łęczycki
łowicki
łódzki wschodni
opoczyński
pabianicki
pajęczański
piotrkowski
poddębicki
radomszczański
rawski
sieradzki
skierniewicki
tomaszowski
wieluński
wieruszowski
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
0.999
1.147
1.025
1.177
1.010
1.104
1.081
1.085
0.978
1.117
1.208
1.033
0.983
1.017
0.963
1.015
0.991
1.128
0.931
1.067
0.839
0.999
0.907
0.927
0.746
0.891
0.842
0.851
0.959
0.831
0.911
0.997
0.840
0.939
0.887
0.937
1.111
1.282
1.133
1.397
1.202
1.177
1.367
1.087
1.085
1.255
1.298
1.171
1.015
1.075
1.145
1.055
1.125
1.381
1.242
0.710
1.007
0.879
0.804
1.193
0.596
1.187
1.035
1.009
1.072
0.890
1.448
0.985
0.806
1.013
0.781
0.489
0.945
0.934
1.246
1.282
1.060
1.037
1.139
1.538
0.744
1.273
1.061
0.754
1.098
1.012
0.947
0.922
0.787
0.928
0.636
0.931
0.711
0.294
0.179
0.915
0.399
1.070
0.800
0.817
1.190
1.233
0.452
0.628
0.276
0.882
0.431
0.591
0.861
1.296
0.845
1.313
0.858
1.485
1.352
1.648
0.864
1.513
1.757
1.014
1.475
1.278
1.035
0.917
0.974
1.192
Page 219
0.860
0.631
2.303
0.582
0.891
1.761
0.589
0.757
3.397
0.370
0.314
1.803
0.991
1.041
1.514
0.477
0.798
0.852
0.558
0.966
0.886
0.640
0.941
0.724
1.446
0.927
0.718
0.426
0.615
0.949
0.492
0.839
1.051
0.894
0.772
0.911
0.556
0.910
1.616
1.081
1.007
1.257
0.465
0.666
0.662
0.318
0.779
1.245
0.390
0.757
1.632
0.442
0.616
1.531
0.562
0.641
2.593
0.638
0.705
0.721
0.570
0.950
0.738
0.866
0.995
1.127
0.710
0.483
0.998
External
0.557
1.019
0.675
0.510
0.987
0.727
0.791
0.994
0.976
0.964
0.587
0.578
0.788
1.274
0.800
0.636
1.011
0.999
0.845
0.681
0.828
0.712
0.810
1
District
zduńskowolski
zgierski
brzeziński
m. Łódź
m. Skierniewice
TERYT
1019
1020
1021
1061
1062
1063
Total
1.102
1.077
1.206
1.123
1.084
1.028
Cancer
1.060
0.935
0.872
1.085
0.902
1.108
CVD
1.182
1.108
1.461
0.964
1.145
1.124
bocheński
brzeski
chrzanowski
dąbrowski
gorlicki
krakowski
limanowski
miechowski
myślenicki
nowosądecki
nowotarski
olkuski
oświęcimski
proszowicki
suski
tarnowski
tatrzański
wadowicki
wielicki
m. Kraków
m. Nowy Sącz
m. Tarnów
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1261
1262
1263
1.005
0.967
1.010
0.982
0.957
0.959
0.960
1.031
0.977
0.925
0.888
0.936
0.967
0.917
0.955
0.917
0.979
0.978
0.942
0.913
0.890
0.944
0.802
0.724
0.973
0.946
0.864
0.910
0.811
1.002
0.900
0.751
0.864
0.894
0.987
0.719
0.933
0.801
0.963
0.899
0.872
1.039
0.982
0.998
1.086
1.172
1.022
1.009
1.093
1.063
1.026
1.121
1.100
0.991
0.983
0.985
1.011
1.080
1.017
0.963
1.020
1.105
1.076
0.905
0.798
0.946
1.153
0.518
1.297
0.593
0.682
0.931
1.535
0.832
0.670
1.061
0.548
1.133
1.090
1.439
0.950
1.035
0.817
0.886
0.795
0.852
0.753
1.030
0.843
0.744
1.096
0.623
0.944
0.800
0.553
0.887
1.351
0.857
0.590
0.797
0.851
0.453
0.467
0.723
1.057
0.884
0.795
0.913
0.929
0.961
1.131
1.222
0.951
1.639
0.710
0.574
1.054
0.487
0.555
1.184
0.916
0.736
0.650
0.186
1.042
1.380
0.853
0.522
0.449
0.655
1.137
0.954
0.873
0.503
1.122
0.650
0.640
0.815
0.818
1.034
0.940
0.841
0.750
0.812
0.781
0.998
1.036
0.632
1.114
0.884
0.938
0.841
0.860
0.703
białobrzeski
ciechanowski
garwoliński
gostyniński
grodziski
grójecki
kozienicki
legionowski
lipski
łosicki
makowski
miński
mławski
nowodworski
ostrołęcki
ostrowski
otwocki
piaseczyński
płocki
płoński
pruszkowski
przasnyski
przysuski
pułtuski
radomski
siedlecki
sierpecki
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1.053
1.046
0.935
1.068
1.007
1.091
0.922
0.943
0.974
0.963
0.936
0.979
0.997
1.091
0.978
0.920
0.919
0.962
1.069
1.056
0.908
1.089
0.953
1.046
1.001
1.003
1.047
0.760
1.114
0.696
0.962
1.126
1.007
0.821
1.013
0.832
0.921
0.866
0.889
1.083
1.169
0.805
0.743
0.936
0.974
0.939
0.915
1.067
0.882
0.701
1.058
0.894
0.850
1.099
1.120
0.994
1.089
0.984
1.015
1.192
0.975
0.859
1.029
1.013
1.000
1.032
1.014
1.084
1.061
1.031
0.933
0.972
1.075
1.124
0.875
1.174
1.129
1.096
1.084
1.067
1.065
0.631
1.699
0.848
1.392
1.426
1.076
0.778
1.479
0.918
1.204
0.896
1.390
1.099
1.145
1.169
0.628
0.724
1.194
1.199
1.068
0.922
1.185
0.920
0.945
0.849
1.206
0.824
0.880
0.985
0.971
0.856
0.926
0.866
0.700
1.103
0.866
0.888
0.692
0.799
0.803
0.878
0.478
0.899
1.034
0.963
0.994
1.084
0.965
0.647
0.708
0.652
0.734
0.926
0.820
1.898
0.534
0.624
2.080
0.634
0.838
0.772
0.647
1.041
0.626
0.545
0.537
0.768
0.698
0.825
0.859
0.919
0.693
1.369
0.781
0.526
1.389
0.625
1.104
1.045
0.777
0.712
1.490
1.172
1.059
1.188
0.924
1.080
1.513
1.596
1.285
0.873
1.011
1.151
0.953
1.631
1.069
1.054
0.947
1.108
1.091
1.218
0.999
1.090
0.969
1.532
1.090
1.090
1.242
Page 220
1.409
1.012
0.755
1.147
1.494
1.062
0.654
1.333
0.953
1.600
1.566
2.174
1.018
1.488
0.922
1.043
0.932
0.317
External
0.812
1.231
1.456
1.070
1.384
0.967
1
District
sochaczewski
sokołowski
szydłowiecki
warszawski zachodni
węgrowski
wołomiński
wyszkowski
zwoleński
żuromiński
żyrardowski
m. Ostrołęka
m. Płock
m. Radom
m. Siedlce
m. st. Warszawa
TERYT
1428
1429
1430
1432
1433
1434
1435
1436
1437
1438
1461
1462
1463
1464
1465
Total
1.069
0.953
0.976
0.883
1.005
1.014
0.964
1.114
1.020
1.120
0.941
0.999
0.963
0.856
0.873
Cancer
0.980
0.765
0.768
0.940
0.891
0.968
0.883
0.805
1.088
1.133
0.854
1.130
0.967
0.988
1.063
CVD
1.195
1.025
1.066
0.832
1.022
1.037
0.988
1.114
1.105
1.130
0.781
0.882
0.904
0.869
0.734
brzeski
głubczycki
kluczborski
krapkowicki
namysłowski
nyski
oleski
opolski
prudnicki
strzelecki
m. Opole
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1661
0.963
1.067
0.960
1.001
1.007
0.993
1.039
0.978
0.916
1.058
0.971
0.916
0.911
1.140
0.980
0.919
0.819
1.042
1.016
0.934
0.896
0.979
0.756
1.098
0.963
1.190
0.978
1.105
1.135
0.938
1.187
1.104
0.968
1.210
1.208
0.882
0.612
0.741
0.749
0.803
0.848
0.892
0.808
0.322
1.007
0.888
0.892
1.008
0.792
0.798
0.755
0.849
0.979
0.629
0.578
0.671
0.741
0.586
0.807
0.827
1.741
0.525
1.111
0.952
0.666
1.834
0.465
1.112
0.487
0.772
0.448
0.541
0.865
1.005
0.865
0.793
0.765
1.030
1.022
0.817
0.740
0.742
0.452
0.815
bieszczadzki
brzozowski
dębicki
jarosławski
jasielski
kolbuszowski
krośnieński
leżajski
lubaczowski
łańcucki
mielecki
niżański
przemyski
przeworski
rzeszowski
sanocki
stalowowolski
strzyżowski
tarnobrzeski
leski
m. Krosno
m. Przemyśl
m. Rzeszów
m. Tarnobrzeg
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1861
1862
1863
1864
0.981
0.962
0.893
1.026
0.954
0.900
0.938
0.963
0.914
0.950
0.875
0.891
0.961
0.970
0.866
0.928
0.907
1.011
0.949
0.934
0.800
0.902
0.992
0.809
0.843
0.804
0.873
0.950
0.906
0.817
0.669
0.845
0.818
0.834
0.804
0.850
0.756
0.787
0.778
0.771
0.745
0.860
0.903
0.834
0.626
0.960
0.879
0.995
0.863
0.877
0.901
0.948
1.029
1.242
0.966
1.059
0.830
1.053
1.062
1.116
0.751
1.024
1.065
1.154
1.037
1.092
0.807
1.133
0.902
1.190
0.657
0.753
1.077
0.845
0.912
1.299
0.983
0.281
0.344
0.524
0.791
0.849
0.526
0.568
0.450
1.142
0.283
0.339
0.616
0.340
0.566
0.931
0.516
0.755
0.374
0.843
0.818
0.519
0.594
0.393
1.112
0.608
0.535
0.655
0.963
0.488
0.760
0.629
0.625
0.941
0.626
0.401
0.919
0.676
0.830
0.700
0.921
0.683
0.809
0.842
0.721
1.045
1.077
0.695
0.384
1.360
2.100
0.408
1.026
2.133
0.634
2.369
1.395
0.797
0.574
1.555
1.685
1.574
0.760
0.484
0.788
1.893
1.247
2.456
0.426
1.624
2.148
0.982
0.488
0.438
1.559
0.506
0.762
0.795
0.590
0.932
0.779
1.214
0.693
0.866
0.960
0.561
0.908
0.744
0.715
0.776
0.693
0.824
0.530
0.671
0.452
0.669
0.586
0.761
0.616
augustowski
białostocki
bielski
2001
2002
2003
0.926
0.937
0.930
0.936
0.838
0.870
0.843
0.858
0.958
0.822
1.276
0.895
0.921
0.899
0.729
1.511
1.753
0.916
0.989
0.972
1.389
Page 221
1.198
0.768
0.359
1.375
0.869
0.441
0.878
0.908
0.933
1.458
1.122
0.658
1.172
1.196
0.803
1.276
1.127
0.999
1.471
0.842
0.625
1.673
0.759
1.627
0.963
0.630
0.577
1.909
0.821
0.759
1.868
0.731
1.129
1.430
1.024
0.844
0.927
1.043
1.479
0.573
1.068
0.330
1.355
1.137
0.794
External
1.235
1.078
1.291
0.985
1.599
1.010
1.186
0.867
1.214
1.202
1.196
1.172
1.023
1.223
0.955
1
District
grajewski
hajnowski
kolneński
łomżyński
moniecki
sejneński
siemiatycki
sokólski
suwalski
wysokomazowiecki
zambrowski
m. Białystok
m. Łomża
m. Suwałki
TERYT
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2061
2062
2063
Total
0.886
0.984
0.950
0.898
0.877
0.988
0.960
0.938
0.931
0.935
0.927
0.829
0.918
0.907
Cancer
0.862
0.838
0.791
0.783
0.926
0.905
0.831
0.842
0.828
0.913
0.873
0.977
1.096
1.104
CVD
0.903
0.917
0.821
0.794
0.761
0.927
0.932
0.933
0.856
0.965
0.939
0.679
0.709
0.733
bytowski
chojnicki
człuchowski
gdański
kartuski
kościerski
kwidzyński
lęborski
malborski
nowodworski
pucki
słupski
starogardzki
tczewski
wejherowski
sztumski
m. Gdańsk
m. Gdynia
m. Słupsk
m. Sopot
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2261
2262
2263
2264
1.041
1.060
1.004
0.963
0.903
1.225
1.028
1.041
1.090
1.031
1.101
1.059
1.143
1.052
1.026
1.127
0.916
0.919
0.964
0.825
0.995
1.176
1.066
1.003
0.860
1.419
1.046
1.177
0.953
1.126
1.330
1.160
1.191
1.194
1.137
1.001
1.139
1.210
1.113
1.140
0.977
0.992
0.901
0.856
0.888
1.029
0.998
0.938
1.083
0.972
0.912
0.826
0.966
0.944
0.827
1.326
0.719
0.719
0.743
0.648
1.413
0.952
0.831
1.391
0.740
1.504
1.225
1.164
1.289
1.206
1.090
0.995
1.963
1.294
1.756
0.692
1.153
0.914
0.895
1.033
0.921
1.166
0.852
1.037
0.796
0.742
0.939
0.903
0.951
1.039
0.927
1.231
1.055
0.986
0.944
0.913
1.176
1.085
1.600
0.752
1.257
1.282
1.894
1.027
1.240
1.490
1.045
1.190
1.758
1.190
1.598
2.219
1.321
0.996
1.569
1.025
1.248
1.205
1.937
1.170
1.022
0.803
0.809
1.274
0.782
1.106
0.900
1.195
1.238
0.918
0.878
1.270
1.341
1.026
0.940
0.803
1.017
0.972
0.859
0.823
będziński
bielski
cieszyński
częstochowski
gliwicki
kłobucki
lubliniecki
mikołowski
myszkowski
pszczyński
raciborski
rybnicki
tarnogórski
wodzisławski
zawierciański
żywiecki
m. Bielsko-Biała
m. Bytom
m. Chorzów
m. Częstochowa
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2461
2462
2463
2464
1.114
0.997
1.048
1.066
1.069
1.006
1.028
1.066
1.085
1.030
1.056
1.100
1.017
1.082
1.063
1.129
1.068
0.904
1.090
1.219
1.055
1.091
0.901
0.953
0.941
1.046
0.836
0.901
0.939
0.901
1.004
1.011
0.993
1.023
1.110
1.099
1.020
0.837
0.930
1.127
1.223
1.063
1.189
1.180
1.134
1.233
1.075
1.175
1.181
1.165
1.198
1.204
0.985
1.107
1.079
1.130
0.990
1.161
1.308
1.001
0.979
1.175
1.104
0.836
0.686
1.344
0.739
1.153
0.675
0.783
1.419
0.937
0.724
0.980
1.718
0.961
1.019
1.425
1.244
0.705
0.566
0.898
1.530
1.061
1.328
0.800
0.727
1.002
1.185
0.874
0.764
1.187
0.861
0.908
1.319
1.227
0.929
1.306
1.335
1.266
1.172
0.788
1.519
1.800
1.255
0.600
0.091
0.113
0.469
0.791
0.494
0.638
0.492
1.127
0.050
1.712
0.819
0.815
0.245
0.770
0.754
0.073
0.116
1.460
0.491
0.582
1.332
0.892
1.240
1.112
1.111
1.140
0.761
0.875
1.148
0.945
1.059
1.236
0.881
1.071
0.954
1.095
1.127
1.114
1.360
1.594
1.114
Page 222
0.661
0.653
0.892
1.510
1.308
1.331
1.346
0.484
2.110
0.926
0.985
1.850
1.475
0.873
1.190
0.681
0.779
1.806
1.433
0.724
1.027
0.997
0.884
1.205
0.852
1.080
1.531
0.934
0.751
0.902
0.781
0.680
0.809
0.971
1.083
1.219
0.611
0.853
1.005
1.014
0.806
0.787
External
0.884
1.265
0.847
1.040
0.462
0.757
1.787
1.043
0.917
0.913
1.236
0.887
1.232
0.938
1
District
m. Gliwice
m. Jaworzno
m. Katowice
m. Mysłowice
m. Ruda Śląska
m. Rybnik
m. Sosnowiec
m. Tychy
m. Zabrze
m. Żory
TERYT
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
Total
1.085
0.967
1.065
1.042
1.064
1.203
1.077
1.306
1.022
1.295
1.138
1.152
1.024
0.948
0.958
Cancer
1.084
1.140
1.181
1.026
1.142
1.361
1.053
1.256
1.058
1.296
1.191
1.054
0.989
1.083
0.991
CVD
1.134
0.858
0.929
1.102
1.008
1.214
1.154
1.272
0.980
1.295
1.133
1.283
1.061
0.807
0.888
buski
jędrzejowski
kazimierski
kielecki
konecki
opatowski
ostrowiecki
pińczowski
sandomierski
skarżyski
starachowicki
staszowski
włoszczowski
m. Kielce
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2661
0.975
0.997
1.033
0.954
0.997
1.030
1.026
0.922
0.881
1.027
0.981
0.990
1.062
0.867
0.804
0.774
0.967
0.830
0.907
0.831
0.907
0.801
0.899
0.912
0.899
0.907
0.725
0.880
0.883
1.089
1.027
0.984
1.159
1.124
1.174
0.940
0.881
1.148
1.032
1.080
1.227
0.780
1.000
1.360
0.856
1.235
0.514
0.665
0.398
0.662
0.599
0.812
0.899
0.434
0.950
1.232
0.746
0.633
0.705
0.668
0.805
0.799
0.726
0.678
0.897
0.843
0.827
0.771
0.801
0.887
2.413
0.975
1.285
1.060
0.520
1.452
0.425
1.693
0.958
0.408
0.351
1.160
0.733
1.174
0.812
0.943
1.181
0.903
0.727
1.024
1.086
0.975
0.663
0.919
1.482
0.696
1.020
0.757
bartoszycki
braniewski
działdowski
elbląski
ełcki
giżycki
iławski
kętrzyński
lidzbarski
mrągowski
nidzicki
nowomiejski
olecki
olsztyński
ostródzki
piski
szczycieński
gołdapski
węgorzewski
m. Elbląg
m. Olsztyn
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2861
2862
1.059
1.090
1.076
1.094
0.958
0.962
0.965
1.036
1.046
1.008
1.080
0.962
1.081
1.050
0.956
0.955
0.979
1.063
1.068
1.033
0.819
1.002
0.918
1.257
1.044
0.925
0.938
0.936
1.093
1.154
1.008
1.105
1.064
0.862
1.054
1.012
0.971
0.869
1.143
1.023
1.195
0.978
0.991
1.164
0.966
1.000
0.865
0.937
0.959
0.967
1.046
0.943
0.967
0.805
1.265
0.932
0.895
0.917
0.936
0.943
0.949
0.844
0.639
1.739
1.750
1.526
1.838
1.279
1.882
1.296
2.070
1.074
1.503
2.732
1.439
1.135
2.297
1.614
1.642
1.606
1.150
1.575
1.769
1.602
1.793
1.516
0.771
0.934
0.941
0.877
0.767
0.877
1.589
1.018
0.873
0.683
0.598
0.693
0.849
0.465
0.727
0.794
0.922
0.984
0.805
0.711
0.615
0.939
1.879
1.650
0.788
1.112
0.922
0.807
1.328
1.324
1.266
0.527
1.431
0.871
1.023
1.699
1.971
1.747
1.409
1.193
0.919
0.847
0.863
0.614
0.722
0.782
0.988
0.993
0.618
0.586
0.702
1.033
1.026
1.197
0.811
0.739
0.709
0.626
1.097
0.979
0.906
chodzieski
gnieźnieński
gostyński
grodziski
3001
3002
3003
3004
3005
1.088
1.099
1.078
1.109
1.150
1.169
1.189
1.182
1.148
0.960
0.927
0.969
0.977
0.990
1.259
0.883
0.886
0.626
0.726
0.771
0.569
0.767
0.981
0.986
1.229
1.986
1.628
1.375
1.609
0.857
1.291
1.301
1.214
1.821
1.489
Page 223
1.249
1.202
0.514
0.988
1.220
0.825
1.170
1.185
0.710
0.926
1.023
0.750
1.233
1.528
0.565
1.001
1.752
0.412
0.971
1.294
0.354
1.209
2.194
0.887
1.166
1.125
0.612
1.509
2.077
0.496
1.268
1.375
0.570
0.623
1.540
0.468
1.147
1.141
0.482
0.931
1.325
1.011
1.301
1.400
0.392
External
1.000
1.017
1.078
0.910
1.340
1.509
0.816
1.212
1.166
1.470
1.351
1.306
0.917
1.091
1.172
1
District
jarociński
kaliski
kępiński
kolski
koniński
kościański
krotoszyński
leszczyński
międzychodzki
nowotomyski
obornicki
ostrowski
ostrzeszowski
pilski
pleszewski
poznański
rawicki
słupecki
szamotulski
średzki
śremski
turecki
wągrowiecki
wolsztyński
wrzesiński
złotowski
m. Kalisz
m. Konin
m. Leszno
m. Poznań
TERYT
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3061
3062
3063
3064
Total
1.067
1.010
1.010
1.037
0.944
0.978
1.125
1.018
1.212
1.071
1.151
0.957
1.090
1.034
1.151
1.037
1.115
1.029
1.110
1.057
1.172
1.068
1.095
1.140
1.007
1.034
0.986
0.861
0.994
0.956
Cancer
1.147
0.894
0.960
1.054
0.933
1.070
1.148
0.920
1.108
1.114
1.263
1.054
0.876
1.084
1.104
1.099
1.069
1.149
1.141
1.102
1.057
1.108
1.172
1.116
1.076
1.092
1.097
1.109
1.164
1.158
CVD
1.115
0.993
1.094
1.076
0.947
0.813
1.191
1.036
1.058
1.006
1.032
0.891
1.277
0.940
1.262
1.007
1.179
1.019
1.071
0.999
1.136
1.027
0.963
1.246
0.951
1.031
0.949
0.714
0.905
0.855
białogardzki
choszczeński
drawski
goleniowski
gryficki
gryfiński
kamieński
kołobrzeski
koszaliński
myśliborski
policki
pyrzycki
sławieński
stargardzki
szczecinecki
świdwiński
wałecki
łobeski
m. Koszalin
m. Szczecin
m. Świnoujście
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
3261
3262
3263
1.052
1.083
1.083
1.027
1.125
1.090
1.046
0.962
1.109
1.054
0.977
0.997
1.032
1.052
1.154
0.996
1.123
0.978
0.899
0.983
1.061
1.144
1.008
1.014
1.099
1.033
0.975
0.935
1.012
1.158
0.921
1.027
0.840
1.176
0.996
0.902
1.101
1.086
0.903
1.304
1.136
1.050
1.168
1.142
1.126
1.030
1.161
1.156
1.060
0.849
1.087
1.162
0.897
1.085
1.091
1.034
1.308
0.982
1.200
1.053
0.753
0.909
1.110
0.829
1.178
0.947
1.083
1.399
0.915
1.211
0.682
0.916
1.382
0.792
1.037
0.404
1.432
0.801
1.046
1.157
0.748
0.807
0.963
1.084
0.763
1.025
0.874
1.133
1.512
0.845
1.095
0.764
0.870
1.002
1.301
0.889
1.197
1.126
0.955
1.064
0.839
0.792
0.734
1.162
1.165
0.618
0.584
1.196
0.809
1.440
0.930
1.084
1.621
1.311
0.714
1.120
0.818
0.555
0.901
1.035
0.820
0.616
0.851
0.615
0.748
0.978
0.880
1.024
1.151
1.000
0.882
1.267
1.110
1.480
1.076
1.012
0.923
0.914
1.204
1.101
0.887
0.911
1.375
1.098
1.239
1.094
0.740
1.000
1.000
1.000
1.000
1.000
1.000
1.000
Polska
Page 224
0.491
0.745
0.597
0.796
0.832
1.912
0.718
0.661
0.543
0.514
1.175
0.474
0.620
0.899
0.878
0.642
0.993
1.819
0.623
0.953
0.520
0.567
0.824
1.009
0.711
1.383
2.437
0.633
1.110
1.176
0.538
0.990
1.136
0.721
0.869
0.549
0.329
0.796
0.274
1.022
0.934
1.188
0.581
1.049
0.720
0.797
1.055
0.913
0.696
1.216
0.504
1.162
0.817
0.533
0.670
0.997
1.737
0.845
1.113
0.511
1.121
0.972
1.294
0.758
1.029
1.218
0.941
0.813
0.820
0.620
1.208
0.427
1.199
1.034
0.635
0.583
0.700
1.103
0.818
0.992
0.942
0.763
0.815
0.706
0.524
0.817
0.997
0.849
0.966
0.751
External
1.460
0.891
1.494
1.517
1.201
1.475
1.259
1.528
2.192
1.432
1.783
1.458
1.024
1.266
1.555
1.248
1.729
1.091
1.235
1.248
1.610
1.308
2.086
1.024
1.255
1.590
1.030
1.095
1.038
1.191
1
residence in 2006–2008, total population 0–64 years old
District
bolesławiecki
dzierżoniowski
głogowski
górowski
jaworski
jeleniogórski
kamiennogórski
kłodzki
legnicki
lubański
lubiński
lwówecki
milicki
oleśnicki
oławski
polkowicki
strzeliński
średzki
świdnicki
trzebnicki
wałbrzyski
wołowski
wrocławski
ząbkowicki
zgorzelecki
złotoryjski
m. Jelenia Góra
m. Legnica
m. Wrocław
TERYT
0201
0202
0203
0204
0205
0206
0207
0208
0209
0210
0211
0212
0213
0214
0215
0216
0217
0218
0219
0220
0221
0222
0223
0224
0225
0226
0261
0262
0264
Total
0.956
1.122
0.879
0.992
1.152
1.175
1.155
1.155
1.100
1.114
0.894
1.218
1.059
1.045
0.920
1.020
1.116
1.223
1.137
1.120
1.324
1.070
1.010
1.126
1.236
1.092
0.999
1.104
0.927
Cancer
0.975
1.110
0.945
1.291
1.154
1.077
1.037
1.065
1.132
1.060
1.016
1.139
1.103
1.081
0.929
0.990
1.142
1.080
1.124
1.083
1.201
1.147
0.957
1.065
1.169
1.089
1.018
1.170
0.922
CVD
0.916
1.153
0.897
1.001
1.214
1.314
1.099
1.417
1.157
1.488
0.843
1.217
1.353
1.268
0.899
1.198
1.250
1.449
1.333
1.339
1.353
1.130
1.095
1.311
1.322
1.129
1.026
0.986
0.936
aleksandrowski
brodnicki
bydgoski
chełmiński
grudziądzki
inowrocławski
lipnowski
mogileński
nakielski
radziejowski
rypiński
sępoleński
świecki
toruński
tucholski
wąbrzeski
włocławski
żniński
m. Bydgoszcz
m. Grudziądz
m. Toruń
m. Włocławek
0401
0402
0403
0404
0405
0406
0407
0408
0409
0410
0411
0412
0413
0414
0415
0416
0417
0418
0419
0461
0462
0463
0464
1.216
1.038
0.964
1.130
1.017
1.032
1.083
1.217
0.997
1.135
1.075
1.052
0.884
1.017
1.056
0.970
1.099
1.192
0.999
0.916
1.112
0.904
1.114
1.210
1.210
1.063
1.247
1.164
1.141
1.170
1.044
1.054
1.253
1.358
1.152
1.042
1.114
1.262
1.204
1.430
1.012
1.100
1.049
1.289
1.060
1.107
1.236
1.123
0.986
0.849
0.960
0.930
1.287
1.209
1.059
1.507
1.029
1.047
1.043
1.135
0.804
1.074
0.986
1.359
1.059
0.878
1.050
0.774
1.001
0.946
1.386
1.480
1.588
1.541
0.688
1.549
1.705
1.080
0.873
1.393
0.839
1.013
1.158
1.256
1.207
1.175
1.345
1.263
0.838
1.262
1.275
1.141
1.055
0.754
0.739
0.869
0.609
0.483
1.063
0.917
0.689
0.837
0.544
0.488
0.533
0.745
0.705
0.568
0.476
0.848
0.566
0.824
0.988
0.985
1.415
1.489
0.672
0.979
1.648
1.032
1.153
0.415
1.358
1.045
0.483
0.574
0.553
0.159
0.450
0.988
0.413
1.367
0.705
1.057
1.313
0.867
0.977
0.499
1.121
0.978
0.886
1.093
1.070
1.419
1.035
1.495
0.967
1.047
1.126
1.236
0.954
1.172
1.326
1.007
0.701
1.537
0.983
0.647
0.988
0.740
1.346
bialski
0601
1.158
0.776
1.245
0.806
0.774
1.677
1.410
Page 225
0.680
0.653
0.825
1.333
1.808
0.921
0.867
1.086
0.456
1.209
0.630
0.768
1.142
1.503
0.644
1.624
1.469
0.665
1.360
1.268
1.927
1.631
1.389
0.561
1.872
1.232
1.120
1.143
0.933
0.500
0.890
1.180
0.536
1.171
1.448
0.955
0.673
1.084
0.674
0.785
0.994
0.476
0.516
0.822
1.152
0.527
0.992
0.833
0.569
0.858
1.227
0.974
1.506
1.120
1.078
1.479
0.644
0.876
0.897
1.095
1.380
1.969
1.712
0.984
1.466
0.909
0.838
1.023
1.058
1.243
1.587
0.603
1.132
1.268
1.154
1.007
1.828
0.633
1.417
1.350
0.511
0.996
1.797
1.353
0.668
1.158
1.491
External
1.113
1.015
0.894
0.971
1.338
1.137
0.834
1.228
0.928
0.955
1.022
1.269
0.915
1.108
1.077
1.217
1.101
1.246
1.169
1.110
1.188
0.905
1.084
1.072
1.293
1.281
0.968
0.917
0.664
1
District
biłgorajski
chełmski
hrubieszowski
janowski
krasnostawski
kraśnicki
lubartowski
lubelski
łęczyński
łukowski
opolski
parczewski
puławski
radzyński
rycki
świdnicki
tomaszowski
włodawski
zamojski
m. Biała Podlaska
m. Chełm
m. Lublin
m. Zamość
TERYT
0602
0603
0604
0605
0606
0607
0608
0609
0610
0611
0612
0613
0614
0615
0616
0617
0618
0619
0620
0661
0662
0663
0664
Total
0.885
1.396
1.131
0.892
1.060
0.921
1.073
1.043
1.103
1.067
1.076
1.069
0.939
1.102
1.095
0.954
0.997
1.113
1.057
0.968
0.979
0.935
0.804
Cancer
0.803
0.995
0.956
0.754
0.730
0.826
0.961
0.752
0.861
0.929
0.760
1.109
0.747
1.016
0.938
0.781
0.769
0.843
0.795
0.796
0.727
0.862
0.806
CVD
0.637
1.287
0.734
1.035
1.093
1.009
0.978
1.109
0.967
0.927
1.331
0.950
0.749
0.813
1.184
0.727
0.962
0.859
0.746
0.904
1.081
0.798
0.557
gorzowski
krośnieński
międzyrzecki
nowosolski
słubicki
sulęciński
świebodziński
zielonogórski
żagański
żarski
wschowski
m. Zielona Góra
0801
0802
0803
0804
0805
0806
0807
0808
0809
0810
0811
0812
0861
0862
1.033
1.080
1.043
1.040
1.227
1.148
1.145
1.120
1.065
1.179
1.165
1.032
0.907
0.868
1.043
0.901
1.036
1.024
1.319
1.438
1.110
1.003
1.104
1.069
1.092
1.058
0.876
0.946
0.881
1.403
0.975
0.914
1.253
0.848
1.114
1.306
0.930
1.554
1.497
0.881
0.775
0.728
0.848
0.999
1.259
0.914
1.356
1.478
0.423
1.530
1.161
0.913
0.792
0.566
0.799
0.587
0.618
1.075
1.036
1.175
1.052
0.630
0.743
0.909
0.951
1.072
1.030
0.591
0.740
0.894
1.633
0.558
1.470
0.527
1.007
1.396
0.937
0.929
1.397
0.373
0.716
1.245
1.654
1.026
1.231
1.135
0.926
1.175
1.206
1.230
1.321
1.025
1.129
1.217
1.265
0.992
0.676
0.853
bełchatowski
kutnowski
łaski
łęczycki
łowicki
łódzki wschodni
opoczyński
pabianicki
pajęczański
piotrkowski
poddębicki
radomszczański
rawski
sieradzki
skierniewicki
tomaszowski
wieluński
wieruszowski
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
0.994
1.452
1.171
1.213
1.129
1.225
1.164
1.211
1.064
1.291
1.430
1.150
1.062
1.038
1.090
1.348
1.051
1.167
0.885
1.306
1.063
0.987
1.025
1.016
1.003
0.981
1.084
0.980
1.203
1.005
0.890
1.014
0.996
1.088
0.985
1.181
1.006
1.117
1.078
1.355
1.565
1.267
1.202
0.978
0.967
1.478
1.494
1.047
0.809
1.118
1.017
1.364
1.228
1.610
0.674
1.760
0.952
1.379
1.415
0.937
0.958
1.215
0.686
0.949
1.078
1.173
0.932
1.065
0.808
1.184
1.147
0.943
1.069
1.445
1.110
1.014
1.026
1.379
1.207
1.600
0.807
1.200
1.056
0.946
1.127
0.977
1.050
1.518
0.702
1.047
1.056
2.851
1.492
0.745
0.423
1.133
1.036
1.766
1.256
0.903
2.092
1.966
1.179
0.646
0.390
2.101
0.788
0.512
1.236
1.491
1.477
1.829
1.106
1.322
1.536
1.371
1.131
1.832
1.744
1.292
1.753
1.252
1.753
1.345
1.117
1.144
Page 226
0.737
0.446
2.050
1.107
0.971
2.863
0.935
0.965
2.311
0.718
0.758
1.291
1.337
0.892
1.924
0.444
0.784
1.117
0.535
0.897
1.958
1.022
0.777
2.044
1.370
0.889
2.773
0.544
0.713
2.189
0.573
0.963
1.539
0.408
0.786
2.047
0.634
0.744
2.451
0.750
0.931
1.880
0.494
0.752
2.188
0.559
0.830
2.658
0.424
1.011
1.142
0.730
1.103
1.588
0.910
0.680
2.285
0.872
0.624
1.689
0.740
0.984
1.913
0.859
0.987
2.336
0.717
0.792
1.446
External
0.689
1.556
1.230
0.533
0.916
0.817
1.035
0.841
0.824
0.971
0.931
0.864
0.741
1.295
0.893
0.595
1.342
1.429
1.355
0.887
0.787
0.412
0.828
1
District
zduńskowolski
zgierski
brzeziński
m. Łódź
m. Skierniewice
TERYT
1019
1020
1021
1061
1062
1063
Total
1.219
1.234
1.384
1.368
1.212
0.930
Cancer
1.122
1.049
1.069
1.061
1.068
1.016
CVD
1.076
1.159
1.566
1.134
1.248
0.848
bocheński
brzeski
chrzanowski
dąbrowski
gorlicki
krakowski
limanowski
miechowski
myślenicki
nowosądecki
nowotarski
olkuski
oświęcimski
proszowicki
suski
tarnowski
tatrzański
wadowicki
wielicki
m. Kraków
m. Nowy Sącz
m. Tarnów
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1261
1262
1263
0.815
0.790
0.913
0.820
0.775
0.882
0.851
1.031
0.858
0.858
0.801
0.900
0.866
1.027
0.932
0.804
0.929
0.912
0.884
0.802
0.733
0.856
0.999
0.805
0.872
0.891
0.778
0.936
0.915
1.006
0.958
0.919
0.830
0.925
1.063
0.978
1.026
0.869
0.894
1.022
0.932
0.878
0.796
0.859
0.832
1.000
0.962
1.086
0.841
0.957
0.858
1.056
0.862
0.910
0.824
0.810
0.916
0.976
1.121
0.985
1.055
0.969
0.937
0.739
0.785
1.031
0.667
0.753
0.914
0.630
0.956
0.879
1.044
0.414
0.534
0.861
0.761
0.600
1.154
0.752
0.489
0.587
0.641
0.847
0.643
0.698
0.677
0.786
0.616
0.675
0.980
0.466
0.738
0.728
0.533
0.573
0.769
0.740
0.582
0.722
0.859
0.610
0.700
0.474
1.253
0.845
0.745
0.824
0.650
0.829
0.557
0.384
1.009
0.361
0.584
0.828
0.446
0.646
0.448
0.742
0.774
1.430
0.460
0.890
0.474
0.455
0.548
0.638
0.861
0.921
0.653
0.720
0.830
0.769
0.921
0.708
0.684
0.839
1.057
1.706
1.026
0.813
0.834
0.848
0.664
1.492
1.009
0.836
0.970
0.868
0.854
0.720
0.551
0.745
białobrzeski
ciechanowski
garwoliński
gostyniński
grodziski
grójecki
kozienicki
legionowski
lipski
łosicki
makowski
miński
mławski
nowodworski
ostrołęcki
ostrowski
otwocki
piaseczyński
płocki
płoński
pruszkowski
przasnyski
przysuski
pułtuski
radomski
siedlecki
sierpecki
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1.154
1.105
1.077
1.055
1.029
1.201
0.995
0.884
1.058
1.047
1.134
1.143
1.252
1.189
1.094
1.042
0.997
0.890
1.156
1.267
0.889
1.303
1.103
1.144
1.018
1.106
1.197
1.073
1.151
0.898
1.136
1.136
1.116
1.070
0.924
0.789
0.944
1.023
1.006
1.338
1.239
0.994
0.906
0.934
0.887
1.146
1.144
0.965
1.130
0.992
1.202
0.988
0.980
1.466
1.171
1.120
1.110
0.861
1.011
1.474
0.844
0.882
1.162
1.016
1.144
1.272
1.269
1.110
1.168
1.050
0.958
0.800
0.974
1.276
0.813
1.594
1.267
1.040
0.881
1.053
1.055
1.061
1.237
1.246
1.111
0.982
1.147
0.811
0.996
1.472
0.690
1.421
1.450
1.099
1.151
1.318
1.080
0.942
1.062
1.386
1.006
1.000
1.336
1.236
1.224
1.029
1.306
1.143
0.902
1.016
1.020
0.669
0.877
0.994
0.775
0.723
0.708
1.150
0.592
0.940
0.930
1.016
0.483
1.034
0.916
1.043
0.751
0.994
1.079
1.021
0.678
0.577
0.699
0.727
0.858
0.697
0.436
1.168
1.982
1.136
0.581
1.227
0.900
0.828
0.895
0.747
0.639
1.066
0.955
0.868
0.874
1.609
0.795
1.923
1.196
0.820
0.714
0.558
0.473
1.295
0.984
1.036
1.420
1.430
1.403
1.011
1.035
1.539
1.189
0.958
1.681
1.394
1.700
1.569
1.378
1.584
1.478
1.413
1.115
1.049
1.189
1.683
0.906
1.520
1.451
1.807
1.308
1.538
1.388
Page 227
0.814
1.452
1.995
1.389
1.702
1.413
1.223
1.760
0.667
1.529
1.911
3.276
1.621
1.420
1.011
0.799
1.010
0.696
External
1.058
1.357
1.760
1.020
1.250
0.956
1
District
sochaczewski
sokołowski
szydłowiecki
warszawski zachodni
węgrowski
wołomiński
wyszkowski
zwoleński
żuromiński
żyrardowski
m. Ostrołęka
m. Płock
m. Radom
m. Siedlce
m. st. Warszawa
TERYT
1428
1429
1430
1432
1433
1434
1435
1436
1437
1438
1461
1462
1463
1464
1465
Total
1.127
0.949
1.153
0.848
1.155
1.066
1.087
1.167
0.991
1.241
0.869
1.061
1.016
0.898
0.843
Cancer
1.061
0.931
1.056
0.872
1.025
0.981
0.930
1.117
1.106
1.128
0.827
1.149
0.900
0.899
0.884
CVD
1.315
1.074
1.298
0.794
1.109
1.034
1.058
1.225
1.072
1.233
0.778
0.848
0.985
0.956
0.775
brzeski
głubczycki
kluczborski
krapkowicki
namysłowski
nyski
oleski
opolski
prudnicki
strzelecki
m. Opole
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1661
1.023
1.051
0.888
0.960
0.843
0.974
0.958
0.816
0.735
1.023
0.855
0.763
1.061
1.143
0.984
1.060
0.885
1.115
1.022
0.865
0.735
1.243
0.854
0.831
0.848
1.264
0.910
1.145
0.904
0.745
1.055
1.127
0.811
1.046
1.087
0.808
1.082
1.195
1.039
0.739
1.005
1.383
1.150
0.996
0.893
1.061
1.070
1.034
0.924
0.752
0.890
0.770
0.623
0.793
0.849
0.625
0.562
0.494
0.812
0.756
1.761
0.514
0.960
0.509
0.636
1.293
0.738
0.225
0.572
0.963
0.519
0.611
1.006
1.107
0.729
1.012
0.829
1.146
0.997
0.760
0.784
1.032
0.733
0.653
bieszczadzki
brzozowski
dębicki
jarosławski
jasielski
kolbuszowski
krośnieński
leżajski
lubaczowski
łańcucki
mielecki
niżański
przemyski
przeworski
rzeszowski
sanocki
stalowowolski
strzyżowski
tarnobrzeski
leski
m. Krosno
m. Przemyśl
m. Rzeszów
m. Tarnobrzeg
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1861
1862
1863
1864
0.933
0.751
0.801
0.897
0.850
0.719
0.804
0.772
0.914
0.810
0.755
0.856
0.894
0.798
0.816
0.772
0.744
0.773
0.742
0.835
0.788
0.740
0.917
0.682
0.780
0.890
0.822
0.846
0.868
0.958
0.734
0.810
0.785
0.810
0.832
0.841
0.857
0.837
0.816
0.858
0.852
0.763
0.792
0.796
0.739
0.906
0.866
0.877
0.703
0.797
1.118
0.804
0.893
0.963
0.925
0.851
0.921
0.669
0.980
0.771
0.876
0.968
0.721
0.808
0.986
0.804
0.845
0.895
0.935
0.969
0.628
0.713
0.799
0.713
0.839
0.516
0.810
0.509
0.726
0.610
0.814
0.821
0.514
1.109
0.506
0.553
0.366
0.464
0.201
0.513
0.610
0.618
0.386
0.392
0.633
0.436
0.482
0.877
0.602
0.326
0.301
0.532
0.731
0.566
0.794
0.424
0.493
0.762
0.739
0.708
0.635
0.586
0.772
0.519
0.461
0.566
0.515
0.873
0.527
0.657
0.770
0.806
1.114
0.518
0.798
0.466
0.260
0.380
1.052
0.419
0.140
0.320
0.768
0.596
0.601
0.311
0.681
1.510
0.699
0.395
0.438
0.307
0.346
0.159
0.340
0.498
0.218
1.250
0.591
0.462
1.294
0.811
0.750
0.935
0.834
0.908
0.896
0.816
1.109
0.910
0.627
0.876
1.012
0.913
0.991
0.825
0.912
0.728
0.805
0.934
0.980
0.785
0.723
0.558
0.667
augustowski
białostocki
bielski
2001
2002
2003
0.968
0.986
1.042
0.933
0.906
1.075
0.752
0.944
0.965
0.731
1.231
0.812
1.246
0.940
0.854
0.783
0.933
0.422
1.192
1.070
1.564
Page 228
1.039
0.868
0.976
1.227
0.642
0.500
0.869
0.860
0.600
0.852
0.889
0.895
1.619
1.035
1.392
1.354
1.091
1.425
1.704
0.954
0.681
1.560
0.560
0.471
1.450
0.434
0.639
1.284
1.221
1.922
1.299
0.677
0.878
1.263
1.081
1.809
0.942
1.010
1.542
0.891
0.999
0.607
0.960
1.104
1.101
External
1.245
1.249
1.608
0.884
1.505
1.139
1.553
1.544
0.996
1.230
0.882
0.862
1.062
1.009
0.645
1
District
grajewski
hajnowski
kolneński
łomżyński
moniecki
sejneński
siemiatycki
sokólski
suwalski
wysokomazowiecki
zambrowski
m. Białystok
m. Łomża
m. Suwałki
TERYT
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2061
2062
2063
Total
0.985
1.165
0.935
0.975
0.903
1.106
1.084
1.160
1.051
0.979
0.914
0.796
0.855
0.914
Cancer
0.995
0.962
0.911
0.885
0.800
0.924
1.028
0.994
0.998
0.903
0.940
0.785
0.986
0.963
CVD
0.920
1.069
0.719
0.772
0.791
1.120
1.111
1.269
1.378
0.978
0.688
0.660
0.689
0.856
bytowski
chojnicki
człuchowski
gdański
kartuski
kościerski
kwidzyński
lęborski
malborski
nowodworski
pucki
słupski
starogardzki
tczewski
wejherowski
sztumski
m. Gdańsk
m. Gdynia
m. Słupsk
m. Sopot
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2261
2262
2263
2264
0.897
0.916
1.014
0.920
0.811
0.926
1.073
1.056
1.032
1.086
0.935
1.169
1.067
1.018
0.931
1.124
0.914
0.831
1.069
0.863
0.870
0.980
1.146
1.068
0.952
1.169
1.205
1.149
1.049
1.191
1.121
1.286
1.193
1.196
1.046
1.407
1.021
0.961
0.997
1.036
0.887
1.005
0.906
0.904
0.877
0.933
1.410
0.992
1.003
1.128
0.888
0.891
1.016
1.018
0.881
1.130
0.920
0.825
0.772
0.893
1.012
0.836
0.577
0.923
0.622
1.294
0.606
0.858
1.031
1.483
0.824
1.233
1.358
1.043
1.125
0.275
1.121
1.015
0.494
1.100
0.693
0.795
0.791
0.773
0.617
0.472
0.967
0.785
1.159
0.923
0.885
0.943
0.908
1.034
0.852
1.202
1.078
0.915
1.493
0.985
0.605
0.730
1.534
0.254
0.237
0.344
0.191
0.374
1.000
1.111
0.703
1.530
0.281
0.316
0.557
0.279
0.323
0.299
2.027
0.345
1.069
0.864
0.855
1.131
0.905
0.862
1.079
1.471
1.057
0.985
0.754
1.133
1.302
0.975
0.933
1.361
0.879
0.774
0.919
0.612
będziński
bielski
cieszyński
częstochowski
gliwicki
kłobucki
lubliniecki
mikołowski
myszkowski
pszczyński
raciborski
rybnicki
tarnogórski
wodzisławski
zawierciański
żywiecki
m. Bielsko-Biała
m. Bytom
m. Chorzów
m. Częstochowa
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2461
2462
2463
2464
1.206
0.807
0.897
1.117
1.031
0.965
0.835
0.935
1.119
0.885
0.875
0.943
0.960
0.960
0.926
1.184
1.034
0.824
1.271
1.503
1.086
1.068
0.846
1.019
1.094
1.022
0.993
0.844
1.031
1.003
0.869
0.951
0.920
0.949
1.035
0.969
1.053
1.009
0.893
1.126
1.351
1.004
1.277
1.080
1.069
1.105
0.988
1.033
0.917
1.067
1.224
1.124
0.886
1.122
1.144
1.181
1.036
1.259
1.373
1.023
1.156
1.652
0.952
1.173
0.407
0.834
1.125
1.117
1.110
0.742
0.975
1.003
0.905
0.683
1.425
1.217
0.819
0.956
1.102
0.782
0.552
1.384
2.153
1.480
1.703
0.651
0.728
1.050
1.054
0.595
0.914
0.900
1.155
0.729
1.057
1.075
1.076
1.150
0.983
1.437
1.141
0.805
1.595
2.326
1.446
0.818
0.149
0.240
0.913
1.068
0.583
0.440
0.431
0.819
0.125
1.053
0.626
0.729
0.344
0.566
0.409
0.105
0.276
1.722
0.858
1.297
1.257
0.747
0.843
1.300
0.963
1.185
0.758
0.782
1.240
1.037
0.606
0.825
0.709
0.851
0.769
1.281
1.158
0.779
1.295
1.277
1.070
Page 229
0.707
1.044
0.296
0.797
1.707
0.386
1.051
0.469
2.135
0.750
0.818
0.862
1.503
1.090
0.585
0.366
0.981
0.381
0.628
0.672
0.414
0.581
1.401
0.684
0.720
0.644
0.207
0.867
0.900
0.581
1.054
0.814
0.661
0.830
1.071
0.851
0.746
0.669
0.714
1.057
0.999
0.222
External
1.120
1.696
0.874
1.518
1.085
1.474
1.564
1.450
1.517
1.296
1.324
0.701
0.889
1.063
1
District
m. Gliwice
m. Jaworzno
m. Katowice
m. Mysłowice
m. Ruda Śląska
m. Rybnik
m. Sosnowiec
m. Tychy
m. Zabrze
m. Żory
TERYT
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
Total
1.143
0.985
0.935
1.062
1.131
1.137
1.147
1.382
0.953
1.339
1.164
1.441
0.925
1.115
0.825
Cancer
1.069
1.079
1.022
1.069
1.063
1.195
1.133
1.249
1.025
1.271
1.126
1.118
0.947
1.090
0.950
CVD
1.270
0.874
0.737
1.265
1.165
1.144
1.340
1.451
0.988
1.528
1.218
1.772
1.033
1.019
0.830
buski
jędrzejowski
kazimierski
kielecki
konecki
opatowski
ostrowiecki
pińczowski
sandomierski
skarżyski
starachowicki
staszowski
włoszczowski
m. Kielce
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2661
0.983
0.995
1.157
1.036
1.054
1.111
0.976
1.031
0.879
1.124
1.066
0.975
0.983
0.809
0.918
0.990
1.296
0.996
0.986
1.088
0.960
1.045
0.911
1.012
0.936
0.907
0.691
0.860
1.138
1.216
0.907
1.002
1.165
1.187
1.218
1.172
0.912
1.414
1.062
1.116
1.116
0.662
0.821
1.033
0.816
0.927
1.199
1.119
0.457
1.084
0.483
0.794
1.102
0.595
1.005
0.678
0.900
0.792
0.786
0.674
0.860
0.641
0.765
0.907
0.918
1.343
1.075
0.797
0.669
0.714
0.565
0.519
1.658
1.400
0.890
1.022
0.779
0.709
0.716
0.584
0.917
0.345
0.712
1.574
1.109
1.104
1.207
1.151
1.188
1.323
0.895
1.091
0.911
1.114
1.195
1.250
1.335
0.572
bartoszycki
braniewski
działdowski
elbląski
ełcki
giżycki
iławski
kętrzyński
lidzbarski
mrągowski
nidzicki
nowomiejski
olecki
olsztyński
ostródzki
piski
szczycieński
gołdapski
węgorzewski
m. Elbląg
m. Olsztyn
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2861
2862
1.249
1.182
1.099
1.201
1.118
1.110
0.971
1.242
1.241
0.995
1.263
0.939
1.107
1.114
1.063
1.093
1.088
1.133
1.234
1.104
0.783
1.135
1.171
1.188
1.327
1.036
0.995
1.153
1.227
1.249
0.932
1.293
1.146
0.984
1.078
1.071
0.872
0.993
1.076
1.179
1.091
0.838
1.023
1.317
1.027
0.800
0.872
0.929
0.856
1.218
1.086
0.923
1.049
0.934
1.154
0.983
0.896
1.088
0.932
0.884
0.801
0.805
0.531
2.048
1.645
1.218
2.073
1.104
2.234
1.214
1.898
1.645
1.850
2.261
1.255
0.788
1.794
1.504
1.634
1.678
1.752
2.703
1.644
1.124
1.641
1.018
0.821
1.025
1.124
1.069
0.666
1.458
1.562
0.808
1.052
0.240
0.750
0.736
1.022
0.575
0.785
0.772
0.924
1.195
0.853
0.719
0.674
0.657
1.960
2.222
1.116
0.759
0.949
1.482
1.280
1.552
0.558
1.199
1.164
1.082
1.194
1.443
2.090
1.853
2.356
1.270
1.703
1.178
1.162
1.225
1.053
1.406
1.100
1.382
1.418
1.119
1.322
1.066
1.254
1.531
1.274
1.477
1.258
1.305
1.483
0.864
0.837
chodzieski
gnieźnieński
gostyński
grodziski
3001
3002
3003
3004
3005
0.999
1.004
0.984
0.929
0.959
1.212
1.197
1.103
1.061
0.929
0.877
0.937
1.042
0.893
0.980
0.975
0.666
0.976
0.908
0.714
0.641
0.616
0.958
0.784
1.261
0.971
1.044
0.429
0.920
0.542
0.943
0.978
0.941
0.858
1.180
Page 230
0.811
1.473
0.864
0.865
1.194
1.093
1.082
0.955
0.917
1.414
1.019
0.709
1.599
1.506
0.839
1.821
1.954
0.503
1.649
1.461
0.807
1.580
2.186
1.084
1.132
1.145
0.595
2.158
1.981
0.528
1.271
1.591
0.971
1.851
2.300
0.732
1.070
1.138
0.555
1.364
1.392
1.309
0.811
1.376
0.158
External
1.024
0.781
0.872
0.902
1.026
0.884
0.786
1.070
0.782
1.106
1.042
1.348
0.800
0.937
0.806
1
District
jarociński
kaliski
kępiński
kolski
koniński
kościański
krotoszyński
leszczyński
międzychodzki
nowotomyski
obornicki
ostrowski
ostrzeszowski
pilski
pleszewski
poznański
rawicki
słupecki
szamotulski
średzki
śremski
turecki
wągrowiecki
wolsztyński
wrzesiński
złotowski
m. Kalisz
m. Konin
m. Leszno
m. Poznań
TERYT
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3061
3062
3063
3064
Total
0.906
1.005
0.831
1.096
0.966
0.921
0.963
0.829
1.063
0.983
1.050
0.925
0.938
1.004
0.953
0.840
0.965
0.956
0.976
1.010
0.921
1.065
1.053
0.927
0.981
1.012
1.027
0.826
0.899
0.888
Cancer
1.021
0.987
0.903
1.153
1.002
1.133
1.098
1.019
1.119
1.074
1.220
1.111
0.931
1.079
1.069
1.006
1.150
1.092
1.107
1.223
1.085
1.165
1.190
1.142
1.046
1.156
1.076
0.939
1.091
1.010
CVD
0.922
0.864
0.826
1.197
0.970
0.801
0.915
0.827
0.953
1.000
1.056
0.883
1.058
0.984
0.845
0.869
1.081
0.930
1.072
1.068
1.016
0.844
0.951
1.116
1.182
0.954
0.976
0.846
0.880
0.906
białogardzki
choszczeński
drawski
goleniowski
gryficki
gryfiński
kamieński
kołobrzeski
koszaliński
myśliborski
policki
pyrzycki
sławieński
stargardzki
szczecinecki
świdwiński
wałecki
łobeski
m. Koszalin
m. Szczecin
m. Świnoujście
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
3261
3262
3263
1.242
1.022
1.097
1.141
1.137
1.034
0.987
0.920
1.099
1.097
0.937
1.074
1.159
1.025
1.088
1.129
1.106
1.161
0.813
0.976
0.936
1.364
0.989
1.007
1.134
1.097
1.053
0.764
0.974
1.154
1.016
0.847
1.001
1.279
1.012
1.041
1.184
1.174
1.132
1.065
0.961
1.090
1.289
1.050
1.193
1.064
1.118
1.098
1.216
0.603
1.186
1.102
0.906
1.050
1.313
0.975
1.279
1.123
1.263
1.290
0.744
1.012
0.791
1.118
1.340
0.782
0.979
1.334
0.858
1.387
0.662
0.634
1.302
0.760
0.786
0.498
1.225
0.676
1.148
1.216
1.113
0.531
1.048
0.365
1.125
0.785
0.906
1.102
1.022
0.850
0.857
0.784
0.803
0.898
0.886
0.936
1.021
0.863
0.847
0.855
1.052
0.988
0.655
1.220
1.054
0.412
0.684
0.394
1.027
1.432
0.646
0.646
1.885
1.190
1.202
1.325
1.549
0.601
0.944
0.707
0.912
0.332
0.865
0.725
0.591
1.218
1.540
1.292
1.469
1.408
1.175
1.185
1.089
0.993
1.002
1.162
0.961
1.299
1.420
1.200
1.272
1.312
1.215
1.311
0.700
0.948
0.730
1.000
1.000
1.000
1.000
1.000
1.000
1.000
Polska
Page 231
0.771
0.459
0.339
0.567
0.846
1.379
0.467
0.538
0.441
1.037
1.101
0.464
0.834
0.811
0.605
0.475
0.799
1.078
0.954
1.260
0.514
0.836
0.475
0.499
0.875
0.524
1.392
0.766
0.574
0.730
0.647
1.070
0.829
0.652
0.776
0.746
0.936
0.603
0.421
1.082
0.771
0.859
0.528
0.715
0.884
0.738
0.765
0.603
0.722
0.681
0.423
1.066
0.842
0.283
0.634
0.849
0.513
0.878
0.747
0.348
0.799
0.962
0.256
1.387
1.002
0.948
0.872
0.804
0.491
0.338
0.610
0.237
1.018
0.826
0.338
0.652
0.530
0.709
0.801
1.278
1.109
0.830
1.117
0.395
0.781
0.778
0.423
0.672
0.839
0.975
External
1.218
1.193
1.189
1.278
1.205
0.833
0.954
0.808
1.253
1.019
0.996
0.841
1.111
1.057
0.985
0.767
1.059
1.126
1.046
1.003
0.811
1.250
1.394
0.929
0.977
1.191
0.884
0.807
0.869
0.629
1
residence in 2006–2008, males 0–64 years old
Cancer
District
bolesławiecki
dzierżoniowski
głogowski
górowski
jaworski
jeleniogórski
kamiennogórski
kłodzki
legnicki
lubański
lubiński
lwówecki
milicki
oleśnicki
oławski
polkowicki
strzeliński
średzki
świdnicki
trzebnicki
wałbrzyski
wołowski
wrocławski
ząbkowicki
zgorzelecki
złotoryjski
m. Jelenia Góra
m. Legnica
m. Wrocław
TERYT
0201
0202
0203
0204
0205
0206
0207
0208
0209
0210
0211
0212
0213
0214
0215
0216
0217
0218
0219
0220
0221
0222
0223
0224
0225
0226
0261
0262
0264
Total
0.984
1.143
0.830
0.886
1.141
1.179
1.132
1.155
1.060
1.123
0.858
1.245
1.043
1.051
0.917
0.991
1.087
1.208
1.129
1.110
1.296
1.006
0.983
1.114
1.257
1.111
0.954
1.069
0.912
1.004
1.172
0.842
1.162
1.190
1.099
0.997
1.127
1.196
1.092
0.984
1.162
1.147
1.066
0.911
1.053
1.191
1.087
1.085
1.085
1.144
1.118
0.941
1.006
1.097
1.166
0.871
1.090
0.886
CVD
0.941
1.182
0.887
0.920
1.214
1.319
1.058
1.387
1.109
1.443
0.818
1.276
1.304
1.321
0.906
1.057
1.183
1.404
1.340
1.304
1.318
0.983
1.013
1.343
1.393
1.142
1.037
0.963
0.951
aleksandrowski
brodnicki
bydgoski
chełmiński
grudziądzki
inowrocławski
lipnowski
mogileński
nakielski
radziejowski
rypiński
sępoleński
świecki
toruński
tucholski
wąbrzeski
włocławski
żniński
m. Bydgoszcz
m. Grudziądz
m. Toruń
m. Włocławek
0401
0402
0403
0404
0405
0406
0407
0408
0409
0410
0411
0412
0413
0414
0415
0416
0417
0418
0419
0461
0462
0463
0464
1.243
1.021
0.900
1.121
0.993
0.993
1.073
1.225
1.009
1.106
1.032
1.068
0.834
0.936
1.036
0.935
1.029
1.211
1.016
0.880
1.088
0.897
1.085
1.241
1.383
0.980
1.231
1.196
1.042
1.172
1.052
1.124
1.249
1.321
1.202
0.963
1.009
1.267
1.082
1.394
1.096
1.191
1.028
1.297
1.085
0.969
1.250
1.100
0.902
0.834
0.816
0.872
1.263
1.147
1.101
1.487
1.032
1.074
1.014
1.002
0.775
1.137
1.054
1.236
1.028
0.858
1.020
0.788
0.985
1.054
1.475
1.228
1.584
1.500
0.517
1.540
1.989
1.067
1.065
1.272
0.683
0.732
1.063
1.272
1.184
1.016
1.264
1.088
0.865
1.221
1.290
1.302
0.898
0.703
0.782
0.958
0.603
0.398
1.024
0.799
0.653
0.674
0.507
0.500
0.697
0.730
0.652
0.607
0.443
0.775
0.513
0.778
1.051
1.022
1.549
1.526
0.594
0.934
1.514
1.049
1.184
0.429
1.423
0.975
0.523
0.637
0.592
0.112
0.463
0.941
0.390
1.191
0.646
1.138
1.271
0.810
0.946
0.471
1.211
0.932
0.881
1.131
1.110
1.485
1.082
1.510
0.997
1.015
1.101
1.331
0.960
1.127
1.346
0.926
0.700
1.625
1.045
0.638
0.969
0.745
1.327
bialski
0601
1.236
0.762
1.261
0.998
0.831
1.777
1.453
Page 232
0.668
0.578
0.909
1.269
1.910
0.911
0.938
0.908
0.434
0.919
0.639
0.769
0.988
1.440
0.626
1.634
1.145
0.694
1.555
1.169
2.012
1.540
1.362
0.558
1.349
1.364
0.981
1.077
1.012
0.557
0.860
1.143
0.465
1.577
1.324
0.777
0.385
1.372
0.678
0.885
1.066
0.501
0.513
0.800
1.139
0.628
0.998
0.817
0.435
0.845
1.149
1.095
1.496
1.044
1.147
1.616
0.611
0.921
0.881
1.138
1.506
1.795
1.671
1.134
1.375
0.869
0.922
0.999
1.117
1.489
1.567
0.601
1.099
1.302
1.139
0.619
1.913
0.622
1.352
1.244
0.551
1.107
1.788
1.267
0.675
1.140
1.472
External
1.129
1.033
0.892
0.893
1.326
1.196
0.803
1.234
0.874
0.945
0.989
1.342
0.906
1.096
1.084
1.179
1.083
1.271
1.162
1.111
1.215
0.885
1.089
1.016
1.320
1.282
0.970
0.940
0.636
1
Cancer
District
biłgorajski
chełmski
hrubieszowski
janowski
krasnostawski
kraśnicki
lubartowski
lubelski
łęczyński
łukowski
opolski
parczewski
puławski
radzyński
rycki
świdnicki
tomaszowski
włodawski
zamojski
m. Biała Podlaska
m. Chełm
m. Lublin
m. Zamość
TERYT
0602
0603
0604
0605
0606
0607
0608
0609
0610
0611
0612
0613
0614
0615
0616
0617
0618
0619
0620
0661
0662
0663
0664
Total
0.941
1.492
1.212
0.931
1.136
0.970
1.132
1.099
1.175
1.134
1.104
1.097
1.007
1.116
1.157
1.015
1.042
1.203
1.140
0.974
1.102
0.957
0.849
0.923
1.158
1.034
0.831
0.804
0.905
0.949
0.854
0.949
0.949
0.777
1.146
0.782
0.992
0.985
0.741
0.789
0.877
0.883
0.673
0.731
0.855
0.807
CVD
0.616
1.224
0.742
0.997
1.135
1.084
1.058
1.078
0.955
0.985
1.278
0.821
0.761
0.854
1.181
0.719
0.954
0.916
0.757
0.832
1.216
0.825
0.555
gorzowski
krośnieński
międzyrzecki
nowosolski
słubicki
sulęciński
świebodziński
zielonogórski
żagański
żarski
wschowski
m. Zielona Góra
0801
0802
0803
0804
0805
0806
0807
0808
0809
0810
0811
0812
0861
0862
1.000
1.107
1.033
0.984
1.200
1.140
1.136
1.122
1.061
1.172
1.177
1.017
0.858
0.838
1.017
0.908
1.006
0.980
1.218
1.551
1.137
1.044
1.077
1.095
1.044
1.097
0.838
0.897
0.844
1.369
1.021
0.816
1.191
0.827
1.106
1.257
0.894
1.577
1.583
0.922
0.744
0.736
0.753
0.756
1.206
0.437
1.623
1.599
0.424
1.228
1.279
0.651
0.691
0.387
0.809
0.482
0.617
1.097
1.022
1.114
1.076
0.620
0.729
0.923
0.951
1.103
1.070
0.568
0.620
0.824
1.473
0.485
1.355
0.541
0.978
1.299
0.931
0.939
1.441
0.376
0.687
1.226
1.587
1.004
1.248
1.251
0.958
1.133
1.219
1.201
1.311
1.067
1.137
1.213
1.220
0.891
0.625
0.844
bełchatowski
kutnowski
łaski
łęczycki
łowicki
łódzki wschodni
opoczyński
pabianicki
pajęczański
piotrkowski
poddębicki
radomszczański
rawski
sieradzki
skierniewicki
tomaszowski
wieluński
wieruszowski
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1.041
1.502
1.222
1.255
1.159
1.266
1.205
1.226
1.107
1.320
1.496
1.192
1.113
1.070
1.144
1.483
1.128
1.199
0.949
1.312
1.120
0.945
1.069
1.107
1.069
1.013
1.262
1.007
1.341
1.034
0.826
1.024
1.070
1.172
1.055
1.230
0.967
1.145
1.033
1.436
1.703
1.247
1.215
0.956
0.953
1.457
1.413
1.038
0.846
1.096
0.971
1.524
1.344
1.614
0.515
2.110
0.842
1.616
1.296
1.083
0.832
1.132
0.635
0.664
1.243
1.077
1.058
1.134
0.937
1.260
1.308
1.041
1.160
1.525
1.166
0.921
0.966
1.598
1.151
1.457
0.801
1.204
1.050
0.941
1.146
1.116
0.999
1.719
0.853
1.229
1.142
2.878
1.630
0.756
0.420
1.186
1.075
1.846
1.258
0.959
2.239
2.013
1.275
0.729
0.410
2.171
0.788
0.571
1.283
1.430
1.570
1.827
1.028
1.176
1.502
1.310
1.083
1.793
1.752
1.295
1.726
1.290
1.781
1.404
1.132
1.069
Page 233
0.696
0.415
2.146
1.148
1.063
2.921
1.060
0.944
2.349
0.845
0.934
1.367
1.483
0.884
2.053
0.510
0.877
1.113
0.669
0.868
1.985
0.985
0.855
2.090
1.105
0.943
2.919
0.637
0.810
2.353
0.698
1.070
1.469
0.418
0.800
2.355
0.699
0.819
2.615
0.588
0.928
2.012
0.580
0.739
2.289
0.657
0.917
2.977
0.565
1.173
1.183
0.615
1.409
1.549
1.026
0.718
2.331
0.886
0.654
1.795
1.009
1.140
2.123
0.738
1.039
2.457
0.809
1.001
1.600
External
0.732
1.622
1.363
0.551
0.898
0.838
1.106
0.836
0.848
0.936
0.976
0.949
0.774
1.238
0.925
0.621
1.359
1.428
1.405
0.969
0.831
0.412
0.859
1
Cancer
District
zduńskowolski
zgierski
brzeziński
m. Łódź
m. Skierniewice
TERYT
1019
1020
1021
1061
1062
1063
Total
1.252
1.298
1.469
1.381
1.220
0.938
1.158
0.998
1.118
1.015
1.148
0.890
CVD
1.049
1.193
1.512
1.118
1.226
0.899
bocheński
brzeski
chrzanowski
dąbrowski
gorlicki
krakowski
limanowski
miechowski
myślenicki
nowosądecki
nowotarski
olkuski
oświęcimski
proszowicki
suski
tarnowski
tatrzański
wadowicki
wielicki
m. Kraków
m. Nowy Sącz
m. Tarnów
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1261
1262
1263
0.773
0.778
0.911
0.806
0.764
0.898
0.841
1.034
0.849
0.874
0.797
0.900
0.870
1.096
0.938
0.815
0.920
0.888
0.891
0.791
0.738
0.870
1.019
0.829
0.873
0.887
0.746
0.976
0.975
0.912
0.925
0.972
0.802
0.946
1.073
1.045
1.071
0.920
0.879
1.064
0.923
0.855
0.763
0.907
0.796
0.935
0.898
1.123
0.845
0.976
0.815
1.088
0.855
0.905
0.828
0.756
0.944
0.998
1.131
1.005
1.000
0.946
0.955
0.751
0.860
1.047
0.628
0.714
0.888
0.654
1.053
0.919
1.004
0.553
0.611
0.884
0.824
0.486
1.215
0.893
0.520
0.525
0.738
0.916
0.727
0.696
0.792
0.631
0.601
0.704
1.091
0.483
0.810
0.743
0.541
0.545
0.770
0.845
0.615
0.627
1.002
0.753
0.822
0.518
1.227
0.759
0.827
0.817
0.704
0.882
0.514
0.417
1.060
0.392
0.578
0.856
0.373
0.645
0.408
0.734
0.789
1.494
0.443
1.016
0.434
0.465
0.427
0.620
0.901
0.930
0.753
0.761
0.781
0.778
0.895
0.695
0.671
0.843
1.107
1.751
1.046
0.845
0.835
0.878
0.668
1.533
0.990
0.854
1.029
0.881
0.849
0.721
0.523
0.782
białobrzeski
ciechanowski
garwoliński
gostyniński
grodziski
grójecki
kozienicki
legionowski
lipski
łosicki
makowski
miński
mławski
nowodworski
ostrołęcki
ostrowski
otwocki
piaseczyński
płocki
płoński
pruszkowski
przasnyski
przysuski
pułtuski
radomski
siedlecki
sierpecki
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1.180
1.108
1.143
1.020
1.031
1.219
1.004
0.872
1.127
1.076
1.217
1.219
1.298
1.226
1.167
1.120
1.058
0.927
1.194
1.296
0.891
1.323
1.162
1.164
1.060
1.174
1.155
1.235
1.109
0.922
1.028
1.114
1.108
1.108
0.914
0.855
0.772
1.091
1.076
1.368
1.247
1.115
1.005
0.953
0.966
1.268
1.176
0.960
1.211
1.013
1.139
1.044
1.015
1.364
1.067
1.119
1.161
0.881
1.022
1.481
0.931
0.903
1.205
1.128
1.250
1.357
1.290
1.140
1.239
1.110
1.002
0.817
0.989
1.292
0.808
1.588
1.326
1.001
0.911
1.096
1.011
1.089
1.035
1.313
0.914
0.880
1.048
0.767
1.057
1.659
0.921
1.323
1.433
1.070
1.245
1.303
1.298
1.027
0.984
1.511
0.855
1.078
1.284
1.287
1.328
1.122
1.519
1.466
0.955
1.179
1.113
0.726
0.885
1.121
0.848
0.705
0.849
1.223
0.621
1.032
1.056
1.222
0.549
1.018
0.906
1.086
0.751
1.028
1.064
1.112
0.822
0.668
0.695
0.783
0.891
0.758
0.412
1.235
1.998
1.161
0.583
1.161
0.896
0.792
0.947
0.799
0.698
1.147
1.031
0.921
0.918
1.740
0.820
1.990
1.258
0.836
0.625
0.538
0.526
1.315
1.048
0.917
1.407
1.471
1.421
1.003
1.045
1.590
1.096
0.893
1.664
1.417
1.788
1.637
1.420
1.575
1.495
1.460
1.149
1.067
1.178
1.692
0.939
1.524
1.445
1.806
1.333
1.582
1.420
Page 234
0.714
1.530
2.047
1.624
1.766
1.553
1.235
1.641
0.765
1.515
1.907
3.236
1.647
1.268
0.943
0.754
1.002
0.846
External
1.094
1.413
1.887
1.016
1.159
0.965
1
Cancer
District
sochaczewski
sokołowski
szydłowiecki
warszawski zachodni
węgrowski
wołomiński
wyszkowski
zwoleński
żuromiński
żyrardowski
m. Ostrołęka
m. Płock
m. Radom
m. Siedlce
m. st. Warszawa
TERYT
1428
1429
1430
1432
1433
1434
1435
1436
1437
1438
1461
1462
1463
1464
1465
Total
1.133
1.029
1.201
0.872
1.169
1.126
1.151
1.259
0.984
1.251
0.910
1.081
1.062
0.907
0.819
1.004
1.052
1.050
0.974
1.111
1.017
0.983
1.342
1.145
1.086
0.876
1.183
0.914
0.814
0.806
CVD
1.307
1.162
1.360
0.808
1.124
1.035
1.106
1.234
1.015
1.222
0.827
0.844
1.019
0.980
0.780
brzeski
głubczycki
kluczborski
krapkowicki
namysłowski
nyski
oleski
opolski
prudnicki
strzelecki
m. Opole
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1661
1.028
1.035
0.837
0.988
0.811
0.951
0.946
0.823
0.722
1.008
0.863
0.718
1.069
1.056
0.987
1.133
0.991
1.142
1.045
0.902
0.738
1.282
0.928
0.745
0.899
1.248
0.860
1.141
0.808
0.694
1.070
1.112
0.822
1.001
1.028
0.781
1.103
1.124
0.907
0.731
0.829
1.515
1.069
1.036
0.774
1.100
1.241
0.968
0.941
0.862
0.792
0.790
0.447
0.805
0.879
0.635
0.566
0.549
0.797
0.697
1.747
0.518
0.876
0.573
0.648
1.226
0.686
0.200
0.588
0.973
0.500
0.640
0.977
1.144
0.697
1.064
0.845
1.109
0.954
0.812
0.791
1.061
0.765
0.619
bieszczadzki
brzozowski
dębicki
jarosławski
jasielski
kolbuszowski
krośnieński
leżajski
lubaczowski
łańcucki
mielecki
niżański
przemyski
przeworski
rzeszowski
sanocki
stalowowolski
strzyżowski
tarnobrzeski
leski
m. Krosno
m. Przemyśl
m. Rzeszów
m. Tarnobrzeg
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1861
1862
1863
1864
0.886
0.735
0.790
0.901
0.834
0.715
0.806
0.776
0.973
0.841
0.733
0.898
0.873
0.836
0.847
0.795
0.762
0.787
0.756
0.894
0.841
0.731
0.952
0.666
0.788
0.893
0.796
0.848
0.822
0.951
0.699
0.779
0.795
0.874
0.917
0.853
0.942
0.819
0.842
0.902
0.919
0.776
0.808
0.834
0.799
0.998
0.868
0.885
0.658
0.746
1.069
0.781
0.877
0.938
0.920
0.861
0.965
0.683
1.014
0.812
0.860
0.958
0.699
0.871
1.084
0.842
0.895
0.924
0.978
1.068
0.673
0.724
0.801
0.741
0.893
0.688
0.755
0.572
0.740
0.510
0.811
0.825
0.613
1.203
0.417
0.710
0.403
0.384
0.272
0.309
0.657
0.637
0.308
0.440
0.664
0.383
0.397
0.604
0.589
0.186
0.199
0.551
0.747
0.690
0.881
0.471
0.529
0.839
0.639
0.738
0.690
0.653
0.730
0.538
0.473
0.559
0.511
0.970
0.535
0.623
0.917
0.800
1.147
0.467
0.965
0.552
0.280
0.420
1.066
0.418
0.136
0.353
0.770
0.667
0.598
0.324
0.694
1.404
0.747
0.422
0.478
0.291
0.339
0.159
0.376
0.465
0.217
1.318
0.632
0.440
1.072
0.845
0.739
0.960
0.827
0.944
0.877
0.756
1.164
0.962
0.577
0.890
0.965
0.942
1.007
0.845
0.929
0.722
0.788
0.951
1.099
0.812
0.837
0.547
0.701
augustowski
białostocki
bielski
2001
2002
2003
0.980
1.036
1.059
0.970
0.963
1.111
0.739
1.003
0.950
0.638
1.241
0.666
1.272
0.930
0.856
0.830
0.899
0.442
1.176
1.102
1.615
Page 235
0.953
0.890
1.096
1.374
0.688
0.507
1.157
0.897
0.624
0.757
0.889
0.898
1.618
0.978
1.340
1.603
1.164
1.511
1.942
1.040
0.767
1.529
0.728
0.506
1.325
0.520
0.643
1.099
1.380
1.988
1.467
0.713
0.856
1.058
1.119
1.935
1.028
1.086
1.629
1.021
1.197
0.616
0.969
1.099
1.119
External
1.235
1.285
1.573
0.889
1.363
1.174
1.580
1.663
0.984
1.222
0.864
0.877
1.061
0.939
0.636
1
Cancer
District
grajewski
hajnowski
kolneński
łomżyński
moniecki
sejneński
siemiatycki
sokólski
suwalski
wysokomazowiecki
zambrowski
m. Białystok
m. Łomża
m. Suwałki
TERYT
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2061
2062
2063
Total
1.067
1.230
0.986
0.997
0.901
1.178
1.119
1.219
1.142
1.033
0.967
0.816
0.829
0.922
1.072
1.082
0.958
0.915
0.718
0.979
1.178
1.071
1.115
0.911
1.110
0.737
0.938
0.963
CVD
0.991
1.103
0.789
0.772
0.821
1.135
1.076
1.271
1.483
1.046
0.721
0.698
0.719
0.862
bytowski
chojnicki
człuchowski
gdański
kartuski
kościerski
kwidzyński
lęborski
malborski
nowodworski
pucki
słupski
starogardzki
tczewski
wejherowski
sztumski
m. Gdańsk
m. Gdynia
m. Słupsk
m. Sopot
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2261
2262
2263
2264
0.864
0.840
0.981
0.907
0.796
0.801
1.062
1.066
1.029
1.107
0.887
1.108
0.987
0.996
0.904
1.154
0.884
0.779
1.099
0.842
0.833
0.905
1.212
1.172
0.971
1.015
1.233
1.171
1.096
1.199
1.046
1.372
1.141
1.226
1.022
1.567
0.982
0.872
1.048
1.033
0.815
0.937
0.854
0.831
0.901
0.851
1.406
0.990
1.002
1.192
0.900
0.844
0.931
1.010
0.880
1.103
0.920
0.826
0.747
0.859
1.146
1.031
0.688
0.887
0.619
1.351
0.629
1.006
1.170
1.425
0.695
0.893
1.213
0.897
1.089
0.245
1.133
0.939
0.471
1.140
0.675
0.622
0.709
0.797
0.557
0.382
0.958
0.884
1.218
0.854
0.964
0.728
0.694
1.126
0.785
1.268
1.040
0.867
1.342
1.099
0.572
0.605
1.404
0.268
0.216
0.313
0.190
0.308
0.969
1.190
0.649
1.459
0.274
0.275
0.511
0.257
0.316
0.276
2.160
0.331
1.083
0.845
0.858
1.087
0.949
0.829
1.089
1.484
0.979
1.006
0.717
1.018
1.245
0.989
0.952
1.412
0.859
0.733
0.966
0.578
będziński
bielski
cieszyński
częstochowski
gliwicki
kłobucki
lubliniecki
mikołowski
myszkowski
pszczyński
raciborski
rybnicki
tarnogórski
wodzisławski
zawierciański
żywiecki
m. Bielsko-Biała
m. Bytom
m. Chorzów
m. Częstochowa
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2461
2462
2463
2464
1.242
0.788
0.887
1.142
1.006
0.972
0.818
0.890
1.114
0.881
0.841
0.882
0.926
0.932
0.906
1.208
1.055
0.811
1.201
1.503
1.091
1.122
0.825
1.068
1.121
1.066
1.028
0.864
0.961
0.998
0.933
0.936
0.909
0.888
1.016
1.067
1.095
1.078
0.908
1.064
1.379
0.933
1.299
1.092
1.058
1.104
0.927
1.070
0.883
1.090
1.222
1.107
0.877
1.075
1.122
1.218
1.025
1.304
1.375
1.038
1.033
1.596
0.966
1.175
0.457
0.843
1.178
0.725
1.146
0.738
0.911
1.115
0.972
0.655
1.035
1.297
0.922
0.975
0.960
0.715
0.661
1.551
2.425
1.599
1.775
0.707
0.772
1.112
0.979
0.555
0.899
0.868
1.075
0.605
0.893
1.016
1.008
1.016
0.879
1.437
1.109
0.775
1.453
2.294
1.411
0.814
0.121
0.242
0.975
1.151
0.547
0.425
0.360
0.834
0.120
1.026
0.583
0.664
0.300
0.497
0.403
0.094
0.234
1.538
0.843
1.330
1.230
0.749
0.816
1.292
0.979
1.156
0.746
0.750
1.220
1.044
0.588
0.810
0.688
0.795
0.757
1.330
1.158
0.724
1.257
1.221
1.061
Page 236
0.951
1.101
0.287
0.692
1.799
0.339
1.116
0.542
2.132
0.866
0.798
0.912
1.491
1.191
0.603
0.244
0.954
0.370
0.731
0.741
0.421
0.568
1.460
0.672
0.785
0.679
0.235
1.051
0.890
0.647
1.071
0.929
0.574
0.823
1.069
0.931
0.732
0.733
0.681
1.102
1.131
0.232
External
1.133
1.642
0.861
1.480
1.115
1.596
1.513
1.443
1.544
1.302
1.314
0.720
0.942
1.080
1
Cancer
District
m. Gliwice
m. Jaworzno
m. Katowice
m. Mysłowice
m. Ruda Śląska
m. Rybnik
m. Sosnowiec
m. Tychy
m. Zabrze
m. Żory
TERYT
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
Total
1.137
0.924
0.891
1.049
1.088
1.063
1.156
1.321
0.894
1.279
1.136
1.430
0.887
1.054
0.801
1.023
1.021
0.903
1.151
1.025
1.194
1.126
1.217
0.953
1.227
1.070
1.131
0.918
1.080
0.969
CVD
1.199
0.837
0.763
1.229
1.140
1.041
1.449
1.346
0.959
1.540
1.194
1.684
1.018
0.926
0.838
buski
jędrzejowski
kazimierski
kielecki
konecki
opatowski
ostrowiecki
pińczowski
sandomierski
skarżyski
starachowicki
staszowski
włoszczowski
m. Kielce
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2661
1.019
1.007
1.174
1.062
1.120
1.117
1.024
1.072
0.903
1.172
1.084
1.000
0.994
0.818
1.021
1.002
1.399
1.058
1.000
1.081
1.046
1.070
0.960
1.003
0.882
0.875
0.693
0.851
1.159
1.215
0.834
1.011
1.242
1.210
1.282
1.226
0.959
1.451
1.111
1.164
1.063
0.667
0.908
0.944
1.078
0.916
1.272
1.295
0.516
1.327
0.480
0.933
1.383
0.650
1.316
0.734
0.952
0.823
0.778
0.734
0.958
0.673
0.878
0.980
0.885
1.390
1.086
0.858
0.724
0.762
0.606
0.542
1.759
1.398
0.963
1.020
0.813
0.755
0.731
0.686
1.013
0.340
0.790
1.622
1.142
1.104
1.140
1.144
1.226
1.225
0.889
1.083
0.918
1.183
1.099
1.327
1.303
0.570
bartoszycki
braniewski
działdowski
elbląski
ełcki
giżycki
iławski
kętrzyński
lidzbarski
mrągowski
nidzicki
nowomiejski
olecki
olsztyński
ostródzki
piski
szczycieński
gołdapski
węgorzewski
m. Elbląg
m. Olsztyn
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2861
2862
1.305
1.179
1.088
1.154
1.153
1.162
0.957
1.277
1.295
0.991
1.305
0.910
1.097
1.096
1.049
1.132
1.108
1.073
1.210
1.057
0.773
1.207
1.278
1.256
1.344
1.141
1.004
1.232
1.294
1.342
0.908
1.407
1.205
1.004
1.034
0.970
0.833
1.013
0.921
1.253
1.067
0.822
1.023
1.327
1.064
0.753
0.820
0.970
0.799
1.217
1.123
0.905
1.074
0.940
1.077
0.984
0.878
1.136
0.914
0.690
0.775
0.776
0.539
2.135
1.159
0.798
1.453
1.008
2.187
1.427
1.935
1.772
1.815
1.972
1.087
1.064
1.511
1.426
1.376
1.436
1.949
2.959
1.409
1.030
1.567
0.841
0.795
0.895
1.113
1.137
0.645
1.518
1.427
0.696
1.193
0.264
0.663
0.705
1.078
0.679
0.875
0.674
0.934
1.155
0.823
0.695
0.656
0.673
1.759
2.280
1.217
0.742
1.014
1.457
1.151
1.520
0.550
1.152
1.048
1.126
1.137
1.419
2.166
1.603
2.324
1.267
1.736
1.157
1.129
1.272
1.069
1.435
1.047
1.380
1.479
1.205
1.361
1.007
1.212
1.481
1.306
1.531
1.304
1.359
1.444
0.762
0.794
chodzieski
gnieźnieński
gostyński
grodziski
3001
3002
3003
3004
3005
0.987
0.972
0.938
0.878
0.838
1.243
1.175
1.006
1.027
0.767
0.892
0.888
1.056
0.973
0.891
1.229
0.616
1.132
0.819
0.527
0.547
0.715
0.887
0.686
1.054
1.017
1.032
0.405
0.892
0.477
0.921
0.986
0.912
0.722
1.001
Page 237
0.848
1.519
0.903
0.810
1.143
1.062
1.102
0.897
0.942
1.215
1.043
0.593
1.548
1.374
0.808
1.697
1.754
0.422
1.332
1.334
0.911
1.568
1.920
1.099
1.211
1.131
0.493
2.032
1.833
0.423
1.333
1.597
0.981
1.891
2.249
0.763
0.993
1.109
0.503
1.480
1.295
1.136
0.677
1.365
0.148
External
1.061
0.750
0.883
0.927
0.988
0.807
0.796
1.056
0.747
0.955
0.966
1.339
0.784
0.879
0.764
1
Cancer
District
jarociński
kaliski
kępiński
kolski
koniński
kościański
krotoszyński
leszczyński
międzychodzki
nowotomyski
obornicki
ostrowski
ostrzeszowski
pilski
pleszewski
poznański
rawicki
słupecki
szamotulski
średzki
śremski
turecki
wągrowiecki
wolsztyński
wrzesiński
złotowski
m. Kalisz
m. Konin
m. Leszno
m. Poznań
TERYT
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3061
3062
3063
3064
Total
0.869
1.070
0.822
1.079
0.968
0.926
0.967
0.817
0.975
0.955
1.060
0.932
0.953
1.000
0.947
0.819
0.934
0.984
0.914
0.990
0.875
1.050
0.985
0.913
1.021
0.958
1.033
0.833
0.846
0.869
0.956
1.090
0.855
1.153
1.104
1.194
1.147
1.173
1.001
1.069
1.294
1.108
1.001
1.109
1.037
1.025
1.125
1.168
1.125
1.152
1.130
1.050
1.123
1.191
1.125
1.039
1.084
0.910
1.002
0.975
CVD
0.856
0.882
0.851
1.222
0.948
0.831
0.904
0.767
0.887
0.984
1.085
0.939
0.998
0.948
0.861
0.840
1.132
0.920
0.973
1.146
0.994
0.877
0.874
1.092
1.233
0.924
0.980
0.893
0.846
0.932
białogardzki
choszczeński
drawski
goleniowski
gryficki
gryfiński
kamieński
kołobrzeski
koszaliński
myśliborski
policki
pyrzycki
sławieński
stargardzki
szczecinecki
świdwiński
wałecki
łobeski
m. Koszalin
m. Szczecin
m. Świnoujście
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
3261
3262
3263
1.253
1.006
1.086
1.095
1.155
0.981
0.972
0.914
1.037
1.086
0.921
1.093
1.114
0.988
1.093
1.155
1.094
1.213
0.782
0.953
0.918
1.489
1.004
0.994
1.005
1.132
1.065
0.728
1.045
1.128
1.029
0.788
1.090
1.205
1.017
1.118
1.256
1.189
1.243
0.968
0.934
1.074
1.213
0.995
1.174
1.017
1.050
1.004
1.162
0.587
1.110
1.044
0.896
0.957
1.300
0.938
1.219
1.135
1.230
1.268
0.784
0.998
0.845
0.804
1.116
0.804
0.892
1.455
0.796
1.024
0.671
0.530
1.406
0.723
0.699
0.418
0.990
0.801
1.145
1.214
1.474
0.467
1.007
0.305
1.232
0.819
0.916
1.092
1.002
0.917
0.896
0.915
0.816
0.829
0.744
1.024
0.913
0.778
0.761
0.832
1.219
1.182
0.802
1.193
0.862
0.399
0.600
0.345
0.927
1.469
0.588
0.685
1.867
1.138
1.200
1.279
1.572
0.637
0.858
0.684
0.959
0.373
0.863
0.817
0.579
1.278
1.613
1.391
1.403
1.398
1.200
1.117
1.048
0.885
0.933
1.169
0.967
1.296
1.348
1.205
1.294
1.296
1.211
1.284
0.605
0.920
0.792
1.000
1.000
1.000
1.000
1.000
1.000
1.000
Polska
Page 238
0.847
0.498
0.391
0.512
1.009
1.446
0.451
0.555
0.387
1.036
1.142
0.526
0.904
0.708
0.622
0.454
0.877
1.084
1.109
1.378
0.480
0.814
0.538
0.467
0.523
0.523
1.337
0.628
0.556
0.752
0.795
0.975
0.813
0.743
0.779
0.772
1.179
0.721
0.447
0.993
0.738
0.919
0.637
0.710
0.865
0.676
0.760
0.627
0.900
0.548
0.407
1.267
0.856
0.311
0.577
0.761
0.460
0.741
0.764
0.364
0.926
0.932
0.260
1.560
1.065
1.006
0.879
0.871
0.449
0.368
0.696
0.284
0.939
0.944
0.329
0.582
0.507
0.640
0.695
1.356
1.171
0.801
1.288
0.451
0.893
0.970
0.397
0.689
0.864
0.977
External
1.212
1.221
1.235
1.199
1.132
0.824
1.005
0.762
1.112
1.021
1.000
0.846
1.158
1.063
0.985
0.748
0.968
1.180
1.015
0.988
0.686
1.278
1.350
0.924
1.001
1.161
0.892
0.836
0.869
0.604
1
residence in 2006–2008, females 0–64 years old
Cancer
District
bolesławiecki
dzierżoniowski
głogowski
górowski
jaworski
jeleniogórski
kamiennogórski
kłodzki
legnicki
lubański
lubiński
lwówecki
milicki
oleśnicki
oławski
polkowicki
strzeliński
średzki
świdnicki
trzebnicki
wałbrzyski
wołowski
wrocławski
ząbkowicki
zgorzelecki
złotoryjski
m. Jelenia Góra
m. Legnica
m. Wrocław
TERYT
0201
0202
0203
0204
0205
0206
0207
0208
0209
0210
0211
0212
0213
0214
0215
0216
0217
0218
0219
0220
0221
0222
0223
0224
0225
0226
0261
0262
0264
Total
0.891
1.079
0.991
1.192
1.121
1.138
1.193
1.154
1.159
1.078
0.983
1.091
1.038
1.003
0.911
1.044
1.130
1.181
1.165
1.074
1.421
1.204
1.012
1.128
1.165
0.974
1.151
1.235
1.015
0.941
1.029
1.076
1.441
1.077
1.031
1.089
0.980
1.019
1.011
1.053
1.089
1.010
1.095
0.942
0.877
1.053
1.047
1.180
1.054
1.290
1.181
0.952
1.137
1.261
0.949
1.224
1.290
0.986
CVD
0.850
1.080
0.904
1.130
1.104
1.238
1.209
1.511
1.234
1.615
0.919
0.932
1.393
1.047
0.846
1.575
1.373
1.449
1.327
1.320
1.513
1.564
1.250
1.147
1.058
0.966
1.074
1.117
0.975
aleksandrowski
brodnicki
bydgoski
chełmiński
grudziądzki
inowrocławski
lipnowski
mogileński
nakielski
radziejowski
rypiński
sępoleński
świecki
toruński
tucholski
wąbrzeski
włocławski
żniński
m. Bydgoszcz
m. Grudziądz
m. Toruń
m. Włocławek
0401
0402
0403
0404
0405
0406
0407
0408
0409
0410
0411
0412
0413
0414
0415
0416
0417
0418
0419
0461
0462
0463
0464
1.158
1.055
1.094
1.138
1.033
1.052
1.113
1.147
0.958
1.170
1.114
0.974
0.956
1.194
1.053
1.007
1.242
1.050
0.913
1.061
1.223
0.993
1.253
1.173
0.960
1.164
1.261
1.103
1.256
1.166
1.021
0.961
1.242
1.385
1.069
1.132
1.249
1.225
1.359
1.468
0.865
0.944
1.099
1.299
1.053
1.304
1.206
1.156
1.198
0.877
1.374
1.002
1.372
1.340
0.922
1.483
0.903
0.900
1.023
1.513
0.825
0.758
0.709
1.629
1.087
1.031
1.220
0.826
1.146
Page 239
0.717
0.859
0.454
1.517
1.549
0.983
0.658
1.577
0.557
1.915
0.535
0.607
1.502
1.598
0.654
1.542
2.352
0.502
0.824
1.517
1.450
1.865
1.457
0.571
3.220
0.791
1.679
1.307
0.702
0.230
0.964
1.294
0.859
0.000
1.734
1.703
1.427
0.168
0.572
0.496
0.756
0.332
0.513
0.872
1.183
0.216
0.910
0.807
0.905
0.834
1.434
0.561
1.413
1.319
0.910
1.116
0.802
0.688
0.875
0.725
1.100
2.484
1.974
0.554
1.662
1.023
0.526
1.018
0.616
0.546
1.592
0.578
1.198
1.154
1.186
2.030
1.450
0.603
1.656
1.703
0.407
0.768
1.912
1.857
0.695
1.280
1.761
0.675
1.118
2.095
1.568
1.578
1.109
1.577
0.861
1.106
0.319
1.626
1.226
1.722
1.368
1.127
1.194
1.561
1.460
1.678
0.838
1.434
1.349
0.829
1.486
0.873
0.583
0.608
0.589
0.694
1.181
1.214
0.780
1.268
0.609
0.432
0.000
0.761
0.818
0.415
0.547
0.986
0.693
1.006
0.882
0.991
1.199
1.332
0.988
1.106
2.213
0.836
0.783
0.359
0.914
1.332
0.254
0.173
0.326
0.363
0.361
1.095
0.470
2.075
0.864
0.593
1.696
1.203
1.290
0.691
External
1.024
0.943
0.926
1.250
1.266
0.799
0.950
1.181
1.158
0.985
1.235
0.735
0.852
1.102
1.015
1.294
1.025
0.917
1.223
0.972
1.089
0.935
0.939
1.323
1.096
1.143
1.054
0.901
0.897
0.633
1.152
0.863
0.862
0.715
0.823
0.803
1.252
0.753
1.151
1.086
0.611
0.779
1.356
1.106
1.348
0.646
0.754
0.566
0.796
1.188
0.867
1.658
1
Cancer
District
bialski
biłgorajski
chełmski
hrubieszowski
janowski
krasnostawski
kraśnicki
lubartowski
lubelski
łęczyński
łukowski
opolski
parczewski
puławski
radzyński
rycki
świdnicki
tomaszowski
włodawski
zamojski
m. Biała Podlaska
m. Chełm
m. Lublin
m. Zamość
TERYT
0601
0602
0603
0604
0605
0606
0607
0608
0609
0610
0611
0612
0613
0614
0615
0616
0617
0618
0619
0620
0661
0662
0663
0664
Total
0.898
0.695
1.023
0.884
0.712
0.847
0.784
0.873
0.859
0.865
0.841
0.958
0.961
0.815
1.001
0.870
0.833
0.810
0.824
0.755
0.998
0.779
0.986
0.748
0.793
0.617
0.731
0.839
0.620
0.626
0.717
0.968
0.597
0.720
0.891
0.729
1.053
0.715
1.038
0.853
0.842
0.726
0.781
0.651
0.959
0.739
0.903
0.816
CVD
1.114
0.662
1.355
0.676
1.040
0.937
0.754
0.650
1.151
0.933
0.673
1.420
1.333
0.764
0.614
1.105
0.784
0.896
0.606
0.632
1.187
0.833
0.855
0.611
gorzowski
krośnieński
międzyrzecki
nowosolski
słubicki
sulęciński
świebodziński
zielonogórski
żagański
żarski
wschowski
m. Zielona Góra
0801
0802
0803
0804
0805
0806
0807
0808
0809
0810
0811
0812
0861
0862
1.061
0.959
1.018
1.170
1.257
1.101
1.100
1.101
1.012
1.163
1.117
1.047
1.065
0.987
1.058
0.873
1.059
1.077
1.448
1.239
1.055
0.941
1.114
1.023
1.152
0.993
0.937
1.024
0.919
1.411
0.740
1.213
1.394
0.837
1.043
1.441
0.961
1.410
1.178
0.717
0.921
0.767
1.056
1.616
1.333
2.160
0.585
1.046
0.393
2.302
0.749
1.588
1.046
1.025
0.815
0.878
0.579
0.957
1.024
1.338
0.942
0.612
0.727
0.858
0.890
0.950
0.901
0.640
1.089
1.133
2.200
0.849
1.870
0.458
1.071
1.670
0.825
0.855
1.016
0.334
0.823
1.274
2.120
1.252
1.013
0.385
0.669
1.391
1.050
1.234
1.153
0.779
0.975
1.170
1.485
1.481
1.012
1.034
bełchatowski
kutnowski
łaski
łęczycki
łowicki
łódzki wschodni
opoczyński
pabianicki
pajęczański
piotrkowski
poddębicki
radomszczański
rawski
sieradzki
skierniewicki
tomaszowski
wieluński
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
0.822
1.352
1.026
1.077
1.034
1.124
0.976
1.247
0.856
1.138
1.203
1.004
0.869
0.936
0.851
1.032
0.816
0.770
1.305
0.983
1.042
0.960
0.890
0.889
0.961
0.788
0.925
1.003
0.958
0.972
0.995
0.865
0.978
0.876
1.052
1.061
1.206
1.045
1.085
1.330
1.042
1.133
0.891
1.418
1.691
1.026
0.621
1.154
1.051
0.894
0.786
1.081
0.898
1.227
0.715
1.706
0.561
1.240
1.497
0.773
1.674
0.604
1.390
0.530
0.858
0.377
1.004
0.670
0.754
1.255
0.928
1.243
1.169
0.785
1.266
2.073
0.735
1.092
1.011
0.915
0.996
0.557
1.083
0.992
0.237
0.552
2.826
0.797
0.645
0.417
0.900
0.667
1.674
0.974
0.495
1.155
1.568
0.567
0.231
0.204
1.838
0.720
0.911
1.838
0.872
1.698
1.474
2.121
1.425
1.860
1.147
1.773
1.411
1.142
1.646
0.962
1.159
1.045
0.927
Page 240
0.256
0.551
0.967
0.813
0.509
1.373
0.900
0.594
2.022
0.569
0.985
1.942
0.298
0.142
0.646
0.930
0.886
1.213
0.267
0.505
1.077
0.151
0.933
1.627
1.081
0.512
1.638
2.029
0.689
1.825
0.265
0.383
1.149
0.209
0.599
1.699
0.371
0.707
0.401
0.503
0.579
1.933
1.150
0.874
0.989
0.228
0.750
1.461
0.329
0.624
1.426
0.000
0.446
0.760
1.008
0.157
1.609
0.520
0.496
1.619
0.882
0.593
1.471
0.170
0.700
1.430
1.236
0.980
2.408
0.539
0.326
1.029
External
0.932
0.359
0.837
0.332
0.302
0.939
0.646
0.512
0.791
0.622
1.010
0.580
0.255
0.599
1.386
0.547
0.477
1.008
1.247
0.789
0.579
0.713
0.511
0.796
1
Cancer
District
wieruszowski
zduńskowolski
zgierski
brzeziński
m. Łódź
m. Skierniewice
TERYT
1018
1019
1020
1021
1061
1062
1063
Total
1.027
1.161
1.122
1.168
1.456
1.273
0.945
1.098
1.080
1.130
1.008
1.150
0.991
1.190
CVD
1.484
1.189
1.113
1.740
1.326
1.440
0.739
bocheński
brzeski
chrzanowski
dąbrowski
gorlicki
krakowski
limanowski
miechowski
myślenicki
nowosądecki
nowotarski
olkuski
oświęcimski
proszowicki
suski
tarnowski
tatrzański
wadowicki
wielicki
m. Kraków
m. Nowy Sącz
m. Tarnów
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1261
1262
1263
0.877
0.779
0.912
0.801
0.780
0.802
0.811
0.957
0.813
0.756
0.788
0.870
0.854
0.784
0.842
0.727
0.973
0.945
0.839
0.884
0.760
0.876
0.945
0.757
0.867
0.883
0.817
0.863
0.806
1.125
0.982
0.820
0.863
0.877
1.050
0.858
0.937
0.783
0.922
0.950
0.932
0.928
0.853
0.816
0.867
1.133
1.156
0.843
0.792
0.825
0.898
0.844
0.767
0.823
0.775
0.939
0.832
0.812
0.955
0.834
1.261
0.996
0.825
0.784
0.630
1.083
0.725
0.814
0.974
0.520
0.670
0.727
1.067
0.000
0.266
0.735
0.567
0.885
0.997
0.309
0.364
0.721
0.413
0.637
0.393
0.757
0.441
1.214
0.616
0.540
0.673
0.367
0.503
0.648
0.449
0.614
0.687
0.350
0.461
0.968
0.465
0.147
0.259
0.303
1.353
1.064
0.486
0.909
0.552
0.756
0.695
0.174
0.772
0.139
0.569
0.621
0.716
0.559
0.561
0.653
0.651
1.050
0.531
0.168
0.585
0.342
1.103
0.676
0.622
1.037
0.315
0.651
1.017
0.624
1.051
0.655
0.693
0.757
0.568
1.196
0.735
0.515
0.793
0.648
0.631
1.068
0.939
0.622
0.694
0.740
0.853
0.845
0.758
0.653
białobrzeski
ciechanowski
garwoliński
gostyniński
grodziski
grójecki
kozienicki
legionowski
lipski
łosicki
makowski
miński
mławski
nowodworski
ostrołęcki
ostrowski
otwocki
piaseczyński
płocki
płoński
pruszkowski
przasnyski
przysuski
pułtuski
radomski
siedlecki
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
0.978
1.073
0.835
1.126
1.050
1.100
0.902
0.934
0.751
0.886
0.807
0.906
1.096
1.070
0.753
0.757
0.873
0.822
0.971
1.151
0.933
1.153
0.847
1.022
0.843
0.807
0.799
1.201
0.848
1.281
1.174
1.112
0.985
0.939
0.658
1.186
0.895
0.897
1.286
1.218
0.781
0.738
0.918
0.788
0.938
1.091
0.990
0.990
0.942
1.273
0.886
0.905
1.379
1.090
0.832
0.774
1.020
1.353
0.450
0.841
0.823
0.532
0.619
0.925
1.147
0.972
0.698
0.725
0.857
0.772
0.816
1.167
0.894
1.445
0.915
1.078
0.695
0.747
0.882
1.752
0.976
1.612
1.267
1.365
0.869
0.859
0.748
0.000
1.571
1.447
1.144
0.875
1.204
0.384
0.753
1.286
0.926
1.379
0.872
1.382
0.989
0.851
0.704
0.565
0.632
0.533
0.663
0.502
0.882
0.574
0.502
0.793
0.176
0.818
0.440
0.632
0.534
0.403
0.188
0.991
0.960
0.956
0.686
0.859
1.186
0.649
0.157
0.264
0.659
0.454
0.237
0.533
0.672
1.868
1.095
0.509
1.373
0.976
0.803
0.463
0.332
0.308
0.606
0.552
0.317
0.511
1.096
0.729
1.277
0.815
0.852
1.025
0.535
0.150
1.011
0.409
1.153
1.117
1.080
1.029
1.053
1.083
1.542
1.376
1.298
0.891
0.823
1.070
1.010
1.575
0.904
0.887
0.981
1.027
1.055
1.461
0.863
1.172
1.117
1.567
0.963
0.864
Page 241
0.630
0.448
0.170
1.084
1.275
1.848
0.851
1.601
0.921
1.198
2.071
0.216
1.710
2.115
4.081
1.679
1.911
1.437
0.944
1.084
0.124
External
1.407
0.892
1.156
1.002
1.245
1.873
1.004
1
Cancer
District
sierpecki
sochaczewski
sokołowski
szydłowiecki
warszawski zachodni
węgrowski
wołomiński
wyszkowski
zwoleński
żuromiński
żyrardowski
m. Ostrołęka
m. Płock
m. Radom
m. Siedlce
m. st. Warszawa
TERYT
1427
1428
1429
1430
1432
1433
1434
1435
1436
1437
1438
1461
1462
1463
1464
1465
Total
1.274
1.099
0.673
0.947
0.796
1.013
0.926
0.883
0.835
0.980
1.246
0.800
1.078
0.967
0.927
0.967
1.607
1.135
0.729
1.048
0.734
0.867
0.937
0.849
0.772
1.053
1.193
0.766
1.127
0.901
1.019
1.006
CVD
1.161
1.322
0.664
0.955
0.755
0.907
1.041
0.849
1.077
1.224
1.312
0.670
0.939
0.980
0.962
0.855
brzeski
głubczycki
kluczborski
krapkowicki
namysłowski
nyski
oleski
opolski
prudnicki
strzelecki
m. Opole
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1661
0.983
1.026
1.014
0.883
0.899
0.974
0.957
0.757
0.739
1.031
0.798
0.910
1.039
1.249
0.980
0.965
0.722
1.047
0.975
0.794
0.719
1.174
0.736
0.957
0.644
1.189
1.060
1.154
1.175
0.843
0.943
1.085
0.715
1.141
1.208
0.959
0.991
1.308
1.375
0.761
1.446
0.912
1.323
0.824
1.180
0.915
0.542
1.260
0.848
0.366
1.155
0.705
1.110
0.702
0.729
0.560
0.523
0.316
0.810
0.961
1.718
0.419
1.329
0.202
0.533
1.443
0.931
0.322
0.439
0.844
0.558
0.578
1.103
0.719
0.903
0.697
0.724
1.217
1.173
0.399
0.702
0.834
0.508
0.927
bieszczadzki
brzozowski
dębicki
jarosławski
jasielski
kolbuszowski
krośnieński
leżajski
lubaczowski
łańcucki
mielecki
niżański
przemyski
przeworski
rzeszowski
sanocki
stalowowolski
strzyżowski
tarnobrzeski
leski
m. Krosno
m. Przemyśl
m. Rzeszów
m. Tarnobrzeg
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1861
1862
1863
1864
1.005
0.754
0.810
0.880
0.870
0.678
0.774
0.723
0.689
0.719
0.784
0.666
0.878
0.661
0.699
0.673
0.686
0.726
0.662
0.644
0.580
0.806
0.913
0.760
0.799
0.864
0.849
0.837
0.930
0.965
0.772
0.848
0.756
0.703
0.710
0.813
0.709
0.849
0.768
0.787
0.741
0.741
0.760
0.733
0.642
0.739
0.878
0.887
0.771
0.874
1.176
0.813
0.908
1.032
0.913
0.718
0.731
0.565
0.762
0.616
0.882
0.879
0.710
0.544
0.592
0.610
0.662
0.775
0.713
0.577
0.385
0.748
0.893
0.696
0.745
0.000
0.916
0.327
0.682
0.861
0.757
0.780
0.211
0.772
0.731
0.107
0.230
0.651
0.000
1.041
0.449
0.561
0.587
0.241
0.518
0.548
0.719
1.598
0.675
0.685
0.586
0.436
0.661
0.213
0.516
0.239
0.366
0.492
0.971
0.606
0.447
0.317
0.817
0.436
0.396
0.550
0.519
0.594
0.458
0.722
0.253
0.888
1.150
0.690
0.426
0.000
0.123
0.173
0.968
0.392
0.135
0.138
0.671
0.137
0.589
0.226
0.481
1.729
0.394
0.224
0.192
0.370
0.371
0.130
0.137
0.575
0.255
1.226
0.514
0.613
2.412
0.512
0.760
0.781
0.800
0.549
0.930
1.072
0.576
0.599
0.856
0.634
1.079
0.657
0.793
0.626
0.790
0.752
0.774
0.758
0.174
0.769
0.312
0.701
0.571
augustowski
białostocki
2001
2002
0.915
0.805
0.878
0.813
0.775
0.675
0.963
1.137
1.130
0.911
0.514
0.984
1.187
0.747
Page 242
0.259
0.729
1.518
1.256
0.800
0.415
0.723
0.453
0.387
0.000
0.673
0.382
1.104
0.900
0.905
1.467
1.096
1.353
0.729
0.894
1.051
1.004
0.659
0.213
1.543
0.000
0.208
1.749
0.166
0.564
1.785
0.837
1.741
0.927
0.621
1.068
1.842
1.069
1.560
0.793
0.886
1.380
0.625
0.576
0.659
1.027
1.219
1.224
External
1.113
1.275
0.832
1.527
0.890
1.985
0.980
1.232
0.515
0.936
1.332
1.066
0.904
1.178
1.514
0.824
1
Cancer
District
bielski
grajewski
hajnowski
kolneński
łomżyński
moniecki
sejneński
siemiatycki
sokólski
suwalski
wysokomazowiecki
zambrowski
m. Białystok
m. Łomża
m. Suwałki
TERYT
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2061
2062
2063
Total
0.925
0.719
0.965
0.717
0.794
0.836
0.829
0.918
0.934
0.676
0.741
0.739
0.823
0.954
0.929
1.002
0.870
0.788
0.828
0.810
0.907
0.827
0.785
0.873
0.795
0.864
0.688
0.868
1.056
0.969
CVD
0.922
0.611
0.924
0.390
0.634
0.591
0.951
1.124
1.159
0.778
0.599
0.542
0.645
0.645
0.887
bytowski
chojnicki
człuchowski
gdański
kartuski
kościerski
kwidzyński
lęborski
malborski
nowodworski
pucki
słupski
starogardzki
tczewski
wejherowski
sztumski
m. Gdańsk
m. Gdynia
m. Słupsk
m. Sopot
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2261
2262
2263
2264
0.934
1.096
1.050
0.917
0.794
1.212
1.060
0.998
1.014
0.976
1.021
1.248
1.250
1.064
0.965
0.984
1.019
0.977
1.054
0.966
0.903
1.080
1.025
0.894
0.898
1.375
1.146
1.096
0.969
1.160
1.211
1.120
1.262
1.146
1.060
1.139
1.084
1.087
0.946
1.064
1.044
1.199
1.001
1.084
0.681
1.142
1.332
0.942
0.961
0.825
0.783
0.948
1.263
1.023
0.825
1.116
0.975
0.852
0.904
1.078
0.564
0.302
0.238
0.973
0.578
1.077
0.515
0.420
0.629
1.567
1.135
2.096
1.714
1.411
1.170
0.342
1.136
1.231
0.576
1.083
0.697
1.266
0.986
0.670
0.744
0.710
0.951
0.483
0.960
1.074
0.614
1.510
1.492
0.769
1.010
0.936
1.225
1.072
1.973
0.776
0.690
1.277
1.988
0.169
0.303
0.457
0.180
0.660
1.096
0.612
0.894
1.636
0.302
0.499
0.735
0.354
0.383
0.420
1.721
0.449
0.888
0.930
0.767
1.329
0.560
0.960
0.937
1.376
1.458
0.757
0.902
1.631
1.545
0.897
0.787
0.959
1.069
1.051
0.796
0.869
będziński
bielski
cieszyński
częstochowski
gliwicki
kłobucki
lubliniecki
mikołowski
myszkowski
pszczyński
raciborski
rybnicki
tarnogórski
wodzisławski
zawierciański
żywiecki
m. Bielsko-Biała
m. Bytom
m. Chorzów
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2461
2462
2463
1.133
0.842
0.934
0.982
1.082
0.894
0.851
1.039
1.120
0.871
0.953
1.071
1.036
0.999
0.974
1.086
0.935
0.896
1.451
1.540
0.994
0.867
0.952
1.031
0.957
0.929
0.808
1.122
1.003
0.770
0.965
0.926
1.031
1.052
0.829
0.976
0.894
0.886
1.217
1.334
1.228
1.003
1.121
1.002
1.164
0.826
0.983
0.975
1.207
1.121
0.897
1.222
1.192
0.987
1.063
1.045
1.270
1.060
1.554
1.889
1.181
0.261
0.819
0.900
2.141
0.946
0.726
1.132
0.694
0.693
0.748
2.428
0.992
0.504
0.901
1.445
0.926
0.321
0.965
1.538
1.545
0.484
0.623
0.789
1.245
0.669
0.920
0.980
1.366
1.062
1.508
1.204
1.252
1.486
1.265
1.393
1.174
0.934
1.987
2.454
0.871
0.276
0.238
0.478
0.653
0.678
0.478
0.747
0.738
0.142
1.155
0.784
1.007
0.521
0.876
0.413
0.148
0.484
2.558
0.958
1.463
0.729
1.025
1.162
0.874
1.185
0.772
0.957
1.337
0.970
0.716
0.855
0.818
1.109
0.845
0.944
1.045
1.160
1.503
1.612
Page 243
1.165
0.774
0.252
0.000
0.810
0.306
1.058
1.371
0.570
0.784
0.186
1.680
0.318
0.754
0.340
1.410
0.662
0.376
0.682
0.965
0.365
0.293
0.413
0.314
0.585
1.110
0.631
0.453
0.434
0.000
0.251
0.831
0.134
0.967
0.447
1.016
0.932
1.188
0.692
0.816
0.545
0.938
0.988
0.703
0.201
External
0.978
0.922
1.761
0.737
1.294
0.682
0.453
1.590
1.193
0.858
0.987
1.246
0.747
0.724
1.102
1
Cancer
District
m. Częstochowa
m. Gliwice
m. Jaworzno
m. Katowice
m. Mysłowice
m. Ruda Śląska
m. Rybnik
m. Sosnowiec
m. Tychy
m. Zabrze
m. Żory
TERYT
2464
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
Total
1.121
1.147
1.147
1.052
1.103
1.286
1.322
1.119
1.524
1.087
1.507
1.259
1.461
1.017
1.251
0.854
1.110
1.119
1.159
1.190
0.968
1.133
1.195
1.143
1.293
1.119
1.338
1.205
1.097
0.980
1.094
0.887
CVD
0.971
1.470
1.002
0.679
1.400
1.330
1.464
0.980
1.767
1.051
1.527
1.333
2.028
1.074
1.286
0.731
buski
jędrzejowski
kazimierski
kielecki
konecki
opatowski
ostrowiecki
pińczowski
sandomierski
skarżyski
starachowicki
staszowski
włoszczowski
m. Kielce
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2661
0.833
0.887
1.017
0.849
0.812
0.988
0.865
0.838
0.798
1.022
1.029
0.817
0.856
0.838
0.754
0.944
1.109
0.863
0.949
1.070
0.842
0.986
0.833
1.028
1.017
0.928
0.666
0.887
0.975
1.070
1.049
0.787
0.800
0.932
1.029
0.851
0.723
1.332
0.930
0.781
1.135
0.713
0.534
1.191
0.000
0.846
0.920
0.499
0.308
0.312
0.478
0.449
0.394
0.375
0.000
0.588
0.678
0.631
0.726
0.393
0.507
0.464
0.473
0.592
0.981
1.242
1.047
0.525
0.419
0.650
0.290
0.320
0.828
1.052
0.413
0.794
0.652
0.338
0.603
0.159
0.493
0.299
0.158
1.613
0.758
0.926
1.334
0.932
0.760
1.594
0.954
0.889
0.832
0.755
1.685
0.562
1.205
0.672
bartoszycki
braniewski
działdowski
elbląski
ełcki
giżycki
iławski
kętrzyński
lidzbarski
mrągowski
nidzicki
nowomiejski
olecki
olsztyński
ostródzki
piski
szczycieński
gołdapski
węgorzewski
m. Elbląg
m. Olsztyn
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2861
2862
1.077
1.124
1.109
1.229
1.014
0.956
0.983
1.135
1.068
0.982
1.087
0.969
1.084
1.104
1.068
0.934
0.986
1.226
1.186
1.242
0.870
1.021
0.998
1.091
1.263
0.879
0.978
1.029
1.128
1.112
0.954
1.101
1.044
0.943
1.119
1.202
0.917
0.953
1.282
1.035
1.124
0.879
0.983
1.172
0.885
0.852
1.018
0.773
1.011
1.198
0.923
0.948
0.885
0.841
1.343
0.890
0.913
0.849
0.931
1.466
0.785
0.919
0.575
1.761
2.896
2.309
3.655
1.340
2.320
0.610
1.777
1.266
1.895
2.928
1.644
0.000
2.475
1.663
2.258
2.266
1.085
1.711
2.278
1.449
1.814
1.474
0.878
1.319
1.145
0.855
0.706
1.268
1.895
1.110
0.581
0.155
0.964
0.786
0.829
0.234
0.475
1.007
0.788
1.341
1.012
0.803
0.672
0.553
2.576
1.907
0.609
0.800
0.621
1.497
1.824
1.541
0.526
1.306
1.597
0.808
1.327
1.401
1.422
2.697
2.640
1.529
1.436
1.132
1.297
0.734
0.944
1.164
1.335
1.321
0.943
0.608
0.941
1.271
1.332
1.676
1.011
0.963
0.853
0.848
1.341
1.453
1.231
chodzieski
gnieźnieński
gostyński
3001
3002
3003
3004
1.010
1.039
1.087
1.030
1.154
1.210
1.229
1.095
0.796
1.029
0.970
0.572
0.273
0.768
0.544
1.111
0.892
0.288
1.145
1.032
0.727
0.984
0.531
0.977
1.025
0.827
1.079
1.549
Page 244
1.257
1.612
1.289
0.700
1.351
0.687
1.016
1.353
1.270
1.038
1.125
0.840
1.951
0.955
1.231
1.814
1.924
1.059
2.140
2.499
0.867
2.473
1.800
0.326
1.598
2.893
0.984
0.899
1.159
1.041
2.515
2.415
1.009
1.146
1.631
0.993
1.730
2.413
0.576
1.270
1.229
0.798
1.013
1.634
2.053
1.136
1.375
0.197
External
1.233
0.849
0.989
0.828
0.760
1.325
1.296
0.723
1.119
0.945
1.960
1.512
1.389
0.910
1.252
1.030
1
Cancer
District
grodziski
jarociński
kaliski
kępiński
kolski
koniński
kościański
krotoszyński
leszczyński
międzychodzki
nowotomyski
obornicki
ostrowski
ostrzeszowski
pilski
pleszewski
poznański
rawicki
słupecki
szamotulski
średzki
śremski
turecki
wągrowiecki
wolsztyński
wrzesiński
złotowski
m. Kalisz
m. Konin
m. Leszno
m. Poznań
TERYT
3005
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3061
3062
3063
3064
Total
1.221
0.990
0.794
0.818
1.114
0.906
0.889
0.934
0.817
1.247
1.033
1.004
0.899
0.862
1.007
0.938
0.889
1.013
0.841
1.112
1.043
1.025
1.106
1.180
0.936
0.876
1.102
1.090
0.860
1.066
0.995
1.147
1.105
0.829
0.960
1.146
0.838
1.039
1.021
0.767
1.271
1.071
1.102
1.109
0.820
1.029
1.099
0.970
1.171
0.965
1.069
1.317
1.011
1.329
1.264
1.057
0.937
1.305
1.095
0.998
1.219
1.079
CVD
1.181
1.115
0.750
0.685
1.080
0.958
0.676
0.918
0.957
1.103
1.014
0.924
0.687
1.194
1.086
0.750
0.944
0.858
0.901
1.354
0.786
1.059
0.754
1.133
1.142
1.016
0.965
1.083
0.794
1.045
0.935
białogardzki
choszczeński
drawski
goleniowski
gryficki
gryfiński
kamieński
kołobrzeski
koszaliński
myśliborski
policki
pyrzycki
sławieński
stargardzki
szczecinecki
świdwiński
wałecki
łobeski
m. Koszalin
m. Szczecin
m. Świnoujście
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
3261
3262
3263
1.162
0.986
1.071
1.201
1.005
1.107
0.982
0.940
1.178
1.082
0.938
0.954
1.223
1.092
1.056
0.992
1.104
0.934
0.931
1.054
0.986
1.160
0.936
1.002
1.300
1.010
1.004
0.801
0.872
1.153
0.981
0.907
0.848
1.363
0.992
0.921
1.049
1.135
0.930
1.207
1.001
1.115
1.454
1.110
1.149
1.129
1.217
1.301
1.308
0.654
1.290
1.225
0.856
1.264
1.252
1.051
1.430
0.967
1.303
1.211
0.691
1.090
0.633
1.926
1.858
0.671
1.158
0.874
0.966
2.320
0.636
0.875
0.961
0.816
0.977
0.691
1.819
0.328
1.072
1.173
0.000
0.720
1.175
0.525
0.764
0.615
0.824
1.067
1.001
0.590
0.706
0.438
0.694
1.061
1.263
0.607
1.278
1.078
1.068
0.862
0.542
0.322
0.335
1.330
1.583
0.435
0.981
0.587
1.392
1.028
0.840
0.404
2.013
1.242
1.110
1.450
1.210
0.364
1.291
0.783
0.562
0.124
0.733
0.442
0.675
0.985
1.006
0.530
1.711
1.326
0.866
1.428
1.236
1.605
1.250
1.032
0.905
1.108
1.686
1.118
1.119
1.247
1.181
1.276
1.275
1.158
0.398
1.000
1.000
1.000
1.000
1.000
1.000
1.000
Polska
Page 245
1.181
1.784
0.776
0.558
0.346
0.099
0.691
0.325
0.925
0.489
0.460
0.654
1.013
0.956
0.156
0.592
1.055
0.440
0.519
0.559
1.003
0.520
0.892
0.645
0.843
0.263
0.596
1.790
0.503
1.525
1.109
0.610
0.593
0.236
1.315
0.872
0.407
0.760
0.607
0.244
0.231
0.262
1.305
0.863
0.590
0.214
0.705
0.917
0.891
0.779
0.491
0.219
1.037
0.471
0.462
0.756
0.123
0.769
1.075
0.734
1.224
0.681
0.258
0.446
1.036
0.236
0.957
0.826
0.683
0.805
0.565
0.639
0.246
0.348
0.000
1.217
0.490
0.372
0.807
0.562
0.956
1.117
1.203
1.052
0.958
0.773
0.222
0.546
0.340
0.585
0.688
0.854
1.135
External
2.037
1.241
0.915
0.812
1.648
1.463
0.856
0.635
0.993
1.932
0.983
0.937
0.800
0.742
1.034
0.937
0.888
1.495
0.694
1.180
1.049
1.480
1.069
1.516
0.898
0.825
1.235
0.996
0.759
0.978
0.867
1
residence in 2006–2008, total population of age 65 years and more
District
bolesławiecki
dzierżoniowski
głogowski
górowski
jaworski
jeleniogórski
kamiennogórski
kłodzki
legnicki
lubański
lubiński
lwówecki
milicki
oleśnicki
oławski
polkowicki
strzeliński
średzki
świdnicki
trzebnicki
wałbrzyski
wołowski
wrocławski
ząbkowicki
zgorzelecki
złotoryjski
m. Jelenia Góra
m. Legnica
m. Wrocław
TERYT
0201
0202
0203
0204
0205
0206
0207
0208
0209
0210
0211
0212
0213
0214
0215
0216
0217
0218
0219
0220
0221
0222
0223
0224
0225
0226
0261
0262
0264
Total
1.036
1.001
1.116
1.025
1.047
1.076
1.092
1.114
1.024
1.087
1.028
1.219
1.015
1.009
1.018
1.085
1.029
1.147
1.056
1.068
1.111
1.104
1.018
1.055
1.077
1.199
0.981
1.030
0.890
Cancer
1.217
0.974
1.161
0.952
1.019
1.120
0.985
1.131
1.016
0.950
1.137
0.995
0.920
1.006
1.114
1.202
1.102
1.128
1.173
1.221
1.120
1.116
1.030
1.039
1.111
1.264
1.079
1.142
0.987
CVD
1.034
1.062
1.115
1.170
1.126
1.045
1.082
1.194
1.103
1.269
1.018
1.285
1.175
1.136
1.062
1.063
1.228
1.257
1.084
1.165
1.163
1.270
1.096
1.114
1.184
1.243
0.899
1.002
0.934
aleksandrowski
brodnicki
bydgoski
chełmiński
grudziądzki
inowrocławski
lipnowski
mogileński
nakielski
radziejowski
rypiński
sępoleński
świecki
toruński
tucholski
wąbrzeski
włocławski
żniński
m. Bydgoszcz
m. Grudziądz
m. Toruń
m. Włocławek
0401
0402
0403
0404
0405
0406
0407
0408
0409
0410
0411
0412
0413
0414
0415
0416
0417
0418
0419
0461
0462
0463
0464
1.029
1.084
1.041
1.168
0.957
1.158
1.086
1.136
1.158
1.163
1.105
1.101
1.026
1.142
1.097
1.054
1.078
1.106
1.046
0.944
1.082
0.909
1.017
0.921
1.057
1.133
1.236
0.947
1.148
1.120
1.035
1.246
1.102
1.080
1.159
1.062
1.207
1.209
1.003
1.120
1.062
1.152
1.151
1.090
1.085
1.098
1.227
1.069
1.010
1.105
0.931
1.162
1.150
1.194
1.224
1.351
1.140
1.186
1.078
1.221
0.985
1.122
1.014
1.226
1.006
0.885
1.085
0.773
1.038
0.699
1.215
1.480
1.101
1.366
1.159
1.275
1.451
0.869
0.924
1.706
1.062
1.142
0.958
1.512
1.369
1.237
1.177
0.953
1.093
1.016
1.072
0.861
0.744
1.042
1.189
1.094
0.743
0.975
1.444
0.759
0.916
0.837
0.645
0.868
1.040
1.078
1.065
0.874
0.675
0.844
0.972
0.954
0.902
1.037
1.344
0.454
1.594
0.646
1.521
1.027
1.288
0.438
1.084
1.016
0.727
1.020
0.677
0.844
1.046
1.219
0.916
0.975
0.540
1.188
0.835
1.035
1.271
0.725
0.740
0.514
0.422
0.451
0.232
0.511
0.764
0.742
0.921
0.563
0.551
0.617
0.682
0.560
0.821
0.428
0.475
0.894
0.796
0.486
0.825
0.666
0.781
bialski
0601
1.104
0.729
1.286
0.631
0.900
1.163
1.642
Page 246
0.917
1.125
0.420
0.884
0.857
1.224
0.888
1.466
1.050
0.724
0.952
0.841
1.010
1.024
0.743
1.058
1.407
0.913
1.079
1.098
2.129
1.060
0.857
1.091
0.834
0.748
1.156
0.753
0.701
1.038
0.810
1.065
1.046
1.155
1.299
1.964
0.492
0.578
1.020
0.943
1.028
0.254
0.557
1.057
1.156
0.926
0.788
1.259
0.477
0.729
0.399
0.969
1.014
1.000
0.683
0.916
1.369
0.783
0.834
0.592
1.122
1.046
1.325
0.797
0.646
0.746
0.869
1.190
0.607
0.879
0.812
1.354
0.987
1.019
0.581
1.288
0.972
0.923
0.903
1.112
1.362
0.730
1.003
1.554
0.631
0.975
0.685
External
1.131
0.835
1.072
0.977
1.210
0.968
0.933
0.806
0.913
0.829
1.105
0.964
0.994
0.956
1.055
0.945
0.674
0.921
1.042
1.073
0.775
0.569
0.992
0.735
0.966
1.246
1.037
1.024
0.792
1
District
biłgorajski
chełmski
hrubieszowski
janowski
krasnostawski
kraśnicki
lubartowski
lubelski
łęczyński
łukowski
opolski
parczewski
puławski
radzyński
rycki
świdnicki
tomaszowski
włodawski
zamojski
m. Biała Podlaska
m. Chełm
m. Lublin
m. Zamość
TERYT
0602
0603
0604
0605
0606
0607
0608
0609
0610
0611
0612
0613
0614
0615
0616
0617
0618
0619
0620
0661
0662
0663
0664
Total
1.018
1.119
1.025
1.094
1.084
1.028
1.076
1.066
1.075
1.030
1.074
1.031
0.973
1.083
1.001
0.989
0.999
1.179
1.031
0.929
0.907
0.921
0.882
Cancer
0.776
0.766
0.776
0.746
0.711
0.849
0.899
0.743
0.765
0.759
0.750
0.818
0.928
0.769
0.828
0.863
0.809
0.871
0.821
0.839
0.808
0.902
0.800
CVD
1.012
1.213
0.891
1.209
1.173
1.184
1.204
1.246
1.175
1.207
1.252
1.143
0.956
1.180
1.153
1.097
1.075
1.367
0.958
0.976
0.956
0.937
0.857
gorzowski
krośnieński
międzyrzecki
nowosolski
słubicki
sulęciński
świebodziński
zielonogórski
żagański
żarski
wschowski
m. Zielona Góra
0801
0802
0803
0804
0805
0806
0807
0808
0809
0810
0811
0812
0861
0862
1.035
1.142
1.129
1.007
1.135
1.050
1.209
1.058
1.041
1.084
1.114
1.087
0.957
0.908
0.996
1.026
1.012
0.933
1.119
0.980
1.044
0.866
0.958
1.035
0.997
1.002
1.067
0.975
1.020
1.113
1.099
0.997
1.103
1.008
1.214
1.124
0.948
1.223
1.277
1.015
0.794
0.707
0.841
1.470
1.212
0.739
1.166
0.973
1.249
0.874
0.697
0.654
0.668
0.603
0.829
0.726
1.099
0.733
1.071
1.166
1.501
1.215
1.486
1.185
0.992
0.981
0.947
1.177
0.883
0.905
1.519
1.170
1.869
1.229
1.239
2.154
1.265
1.312
2.481
0.532
0.900
1.533
2.278
2.571
0.978
1.010
1.056
0.880
0.990
0.994
1.164
0.440
0.916
0.809
0.604
1.066
0.622
0.671
bełchatowski
kutnowski
łaski
łęczycki
łowicki
łódzki wschodni
opoczyński
pabianicki
pajęczański
piotrkowski
poddębicki
radomszczański
rawski
sieradzki
skierniewicki
tomaszowski
wieluński
wieruszowski
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1.064
1.116
1.022
1.184
1.056
1.106
1.063
1.055
1.002
1.104
1.173
1.074
1.000
1.036
1.005
1.043
1.062
1.124
1.084
1.046
0.834
1.019
0.959
0.940
0.784
0.930
0.944
0.791
1.059
0.881
0.926
1.021
0.955
0.909
1.012
0.873
1.102
1.261
1.123
1.376
1.240
1.164
1.308
1.090
1.027
1.239
1.202
1.194
1.032
1.061
1.115
1.094
1.156
1.351
1.255
0.729
1.053
1.296
0.993
1.262
0.907
1.042
1.178
1.139
1.246
1.035
1.500
1.272
0.988
1.124
1.093
0.572
1.277
0.909
1.173
1.216
1.085
1.355
0.899
1.176
0.903
1.241
0.938
0.728
1.103
1.074
0.930
1.011
1.000
0.971
0.754
0.959
0.713
0.258
0.146
1.066
0.347
1.042
0.995
0.926
1.049
1.275
0.521
0.590
0.297
0.781
0.418
0.658
0.869
0.753
1.015
1.183
0.765
0.981
1.092
1.392
0.823
1.240
1.863
0.997
1.194
1.361
1.121
0.970
0.991
1.231
Page 247
0.954
0.699
2.465
0.920
1.073
1.688
0.834
0.767
3.758
1.055
0.869
1.930
1.338
0.952
1.542
0.898
0.778
0.744
0.837
0.908
0.849
0.779
1.154
0.827
1.612
0.798
0.684
0.740
0.640
1.115
0.823
0.802
0.991
0.941
0.968
1.077
0.672
1.073
1.638
0.970
1.099
1.332
0.728
0.938
0.608
0.418
0.760
1.470
0.558
0.796
1.606
0.616
0.999
1.495
0.844
0.729
2.606
0.686
0.843
0.762
0.665
1.109
0.671
0.812
0.956
0.866
0.728
0.769
1.087
External
0.890
1.185
0.965
0.708
1.042
0.961
1.200
1.004
1.191
0.829
1.084
0.855
0.918
1.311
0.942
0.793
0.867
0.777
1.052
1.296
1.192
0.719
0.750
1
District
zduńskowolski
zgierski
brzeziński
m. Łódź
m. Skierniewice
TERYT
1019
1020
1021
1061
1062
1063
Total
1.047
1.078
1.155
1.045
1.012
1.025
Cancer
1.020
0.950
0.826
0.974
0.830
1.055
CVD
1.106
1.103
1.410
0.942
1.101
1.128
bocheński
brzeski
chrzanowski
dąbrowski
gorlicki
krakowski
limanowski
miechowski
myślenicki
nowosądecki
nowotarski
olkuski
oświęcimski
proszowicki
suski
tarnowski
tatrzański
wadowicki
wielicki
m. Kraków
m. Nowy Sącz
m. Tarnów
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1261
1262
1263
1.022
1.024
1.022
0.951
0.987
1.003
0.986
1.016
1.034
0.975
0.942
0.965
0.968
0.985
1.027
0.963
1.008
0.983
0.993
0.884
0.930
0.948
0.815
0.738
1.036
0.925
0.828
0.977
0.924
0.912
0.922
0.879
0.879
0.897
0.993
0.881
1.003
0.903
1.043
0.943
0.932
0.996
1.057
1.034
1.091
1.199
1.036
0.958
1.109
1.082
1.013
1.091
1.167
0.994
1.018
0.980
0.989
1.039
1.088
0.990
1.038
1.091
1.120
0.894
0.848
0.955
1.144
0.782
1.150
0.837
0.879
0.921
1.336
1.110
0.798
1.073
0.782
1.197
1.137
1.898
1.021
1.068
0.789
0.885
0.634
0.725
0.752
0.868
0.909
0.793
1.006
0.735
1.018
0.827
0.563
1.005
1.387
0.935
0.709
0.811
0.893
0.609
0.659
0.840
0.724
0.720
0.946
0.873
1.026
0.863
1.236
1.266
0.926
1.615
0.806
0.657
1.061
0.474
0.544
1.266
0.943
0.735
0.639
0.250
1.038
1.415
0.741
0.563
0.458
0.611
1.276
0.866
0.930
0.794
1.030
0.582
0.715
1.065
0.939
1.078
1.145
1.192
0.841
1.117
0.891
1.103
1.007
0.629
1.354
1.109
1.169
0.733
0.814
0.802
białobrzeski
ciechanowski
garwoliński
gostyniński
grodziski
grójecki
kozienicki
legionowski
lipski
łosicki
makowski
miński
mławski
nowodworski
ostrołęcki
ostrowski
otwocki
piaseczyński
płocki
płoński
pruszkowski
przasnyski
przysuski
pułtuski
radomski
siedlecki
sierpecki
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1.071
1.055
0.977
1.079
1.014
1.061
0.955
0.958
1.010
0.985
1.022
1.004
1.030
1.091
1.041
0.974
0.912
0.997
1.107
1.072
0.891
1.126
0.970
1.060
1.056
1.019
1.034
0.788
1.094
0.717
1.000
1.119
0.894
0.834
1.054
0.824
0.866
0.971
0.956
1.110
1.212
0.955
0.867
0.902
1.012
1.049
0.965
1.040
1.024
0.631
1.072
0.930
0.838
1.028
1.126
0.981
1.123
0.988
1.014
1.159
0.996
0.892
1.066
1.052
1.081
1.032
1.018
1.050
1.025
1.054
0.917
0.980
1.080
1.145
0.857
1.190
1.141
1.068
1.120
1.057
1.052
0.858
1.787
0.857
1.316
1.216
1.150
1.060
1.385
1.044
1.150
0.999
1.410
1.355
1.251
1.688
0.946
0.694
1.191
1.433
1.005
0.942
1.201
1.102
1.204
1.103
1.359
1.074
1.148
1.012
0.981
0.816
0.935
1.055
0.741
1.195
1.116
0.965
0.676
0.971
1.055
1.199
0.761
0.900
1.139
1.038
0.953
1.086
0.878
0.707
0.905
0.750
0.820
1.122
0.900
1.934
0.445
0.742
2.100
0.620
0.807
0.736
0.544
1.065
0.611
0.516
0.540
0.784
0.651
0.976
0.918
1.013
0.735
1.358
0.771
0.497
1.178
0.720
1.330
1.199
0.850
0.715
1.570
1.559
1.194
1.344
1.065
1.100
1.708
1.513
1.633
1.235
1.219
1.252
1.095
1.467
1.095
1.033
0.871
1.209
1.343
1.408
1.057
1.418
0.857
1.281
1.254
1.186
1.083
Page 248
1.122
0.887
0.764
1.249
1.481
1.103
0.759
0.975
0.818
1.335
1.344
1.993
0.834
1.222
0.783
0.911
0.766
0.476
External
1.022
0.984
1.528
0.910
1.143
0.904
1
District
sochaczewski
sokołowski
szydłowiecki
warszawski zachodni
węgrowski
wołomiński
wyszkowski
zwoleński
żuromiński
żyrardowski
m. Ostrołęka
m. Płock
m. Radom
m. Siedlce
m. st. Warszawa
TERYT
1428
1429
1430
1432
1433
1434
1435
1436
1437
1438
1461
1462
1463
1464
1465
Total
1.092
0.988
1.016
0.905
0.985
1.049
1.012
1.122
1.023
1.101
1.003
1.010
0.965
0.852
0.828
Cancer
1.066
0.844
0.764
1.011
0.813
1.017
0.874
0.864
1.205
1.145
1.103
1.161
0.986
0.892
0.991
CVD
1.159
1.040
1.106
0.848
1.023
1.027
1.032
1.109
1.035
1.089
0.800
0.882
0.902
0.888
0.705
brzeski
głubczycki
kluczborski
krapkowicki
namysłowski
nyski
oleski
opolski
prudnicki
strzelecki
m. Opole
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1661
0.997
1.134
0.944
1.060
1.020
1.040
1.031
1.002
0.938
1.073
0.988
0.884
0.913
1.121
0.892
0.958
0.805
1.094
1.024
1.008
0.963
0.877
0.804
1.056
1.015
1.285
0.997
1.165
1.136
1.002
1.166
1.112
0.977
1.254
1.197
0.846
0.688
0.737
0.685
0.767
0.986
1.033
0.827
0.358
0.975
0.877
0.759
0.921
0.791
0.970
0.744
0.902
0.930
0.591
0.566
0.732
0.750
0.925
0.752
0.794
1.817
0.549
1.142
0.958
0.738
1.787
0.381
1.113
0.608
0.693
0.443
0.560
1.001
1.096
0.870
1.179
0.821
1.096
1.041
1.008
0.829
0.781
0.683
0.747
bieszczadzki
brzozowski
dębicki
jarosławski
jasielski
kolbuszowski
krośnieński
leżajski
lubaczowski
łańcucki
mielecki
niżański
przemyski
przeworski
rzeszowski
sanocki
stalowowolski
strzyżowski
tarnobrzeski
leski
m. Krosno
m. Przemyśl
m. Rzeszów
m. Tarnobrzeg
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1861
1862
1863
1864
1.072
1.013
0.953
1.031
0.989
0.949
0.950
0.987
1.000
0.955
0.888
0.961
1.006
1.001
0.965
0.969
0.951
1.045
1.010
0.973
0.874
0.921
1.001
0.805
0.870
0.968
0.996
1.036
0.903
0.884
0.802
0.888
0.893
0.940
0.811
0.930
0.853
0.856
0.848
0.828
0.806
0.887
1.002
0.937
0.853
1.043
0.991
1.038
0.919
0.917
1.023
0.946
1.084
1.235
0.999
1.073
0.850
1.050
1.132
1.110
0.739
1.092
1.086
1.137
1.163
1.124
0.871
1.124
0.920
1.145
0.728
0.786
1.074
0.833
0.969
1.506
1.188
0.489
0.390
0.762
0.836
0.913
0.617
0.717
0.588
1.164
0.346
0.528
0.864
0.507
0.738
0.884
0.680
0.907
0.552
1.001
0.704
0.459
0.513
0.420
1.394
0.827
0.488
0.846
1.147
0.588
0.798
0.651
0.517
0.899
0.670
0.532
1.028
0.718
0.856
0.755
1.091
0.693
0.770
0.830
1.028
1.004
0.946
0.635
0.535
1.411
2.005
0.459
1.014
2.054
0.579
2.293
1.465
0.888
0.647
1.558
1.665
1.860
0.882
0.435
0.805
1.833
1.218
2.714
0.444
1.550
2.077
1.062
0.468
0.384
0.539
0.746
0.804
0.787
0.638
1.133
0.765
1.301
0.682
1.185
1.042
0.760
0.870
0.879
0.860
0.845
0.695
0.854
0.776
0.584
0.797
0.654
0.861
0.897
0.443
augustowski
białostocki
bielski
2001
2002
2003
0.960
0.997
0.908
1.006
0.906
0.782
0.896
0.921
0.923
0.883
1.307
1.060
0.944
0.964
0.797
1.559
1.704
0.935
1.156
1.001
1.572
Page 249
1.324
0.907
0.469
1.437
0.739
0.393
1.220
0.849
1.027
1.211
1.098
0.730
1.077
1.268
0.738
1.359
1.180
1.088
1.650
0.892
0.682
1.634
1.032
1.672
1.017
0.695
0.513
1.658
1.021
0.706
1.820
1.020
0.865
1.328
1.230
0.771
0.955
1.155
1.584
0.696
1.118
0.422
1.147
1.057
0.774
External
1.241
1.254
1.124
1.181
1.395
1.307
1.348
0.929
0.999
0.997
1.642
1.257
0.786
0.761
0.940
1
District
grajewski
hajnowski
kolneński
łomżyński
moniecki
sejneński
siemiatycki
sokólski
suwalski
wysokomazowiecki
zambrowski
m. Białystok
m. Łomża
m. Suwałki
TERYT
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2061
2062
2063
Total
0.976
0.975
1.049
0.959
0.909
1.020
0.975
0.964
0.998
0.961
0.977
0.835
0.944
0.877
Cancer
1.001
0.896
0.854
0.909
0.952
1.212
0.901
0.878
1.028
0.882
0.991
0.995
1.184
1.048
CVD
0.946
0.906
0.923
0.834
0.798
0.863
0.919
0.942
0.904
0.963
0.964
0.712
0.765
0.733
bytowski
chojnicki
człuchowski
gdański
kartuski
kościerski
kwidzyński
lęborski
malborski
nowodworski
pucki
słupski
starogardzki
tczewski
wejherowski
sztumski
m. Gdańsk
m. Gdynia
m. Słupsk
m. Sopot
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2261
2262
2263
2264
1.096
1.051
1.019
1.005
0.974
1.124
1.088
1.082
1.118
1.142
1.115
1.046
1.090
1.065
1.030
1.241
0.890
0.882
0.931
0.776
1.069
1.239
1.186
1.086
1.019
1.306
1.159
1.324
0.984
1.332
1.342
1.244
1.095
1.244
1.161
1.305
1.110
1.181
1.096
1.158
0.973
0.962
0.889
0.873
0.922
0.961
1.010
0.936
1.100
1.029
0.966
0.820
0.932
0.960
0.802
1.369
0.715
0.719
0.748
0.611
1.530
1.190
0.973
1.344
0.879
1.514
1.281
1.188
1.127
1.189
0.970
0.980
1.831
1.183
1.566
0.729
1.004
0.767
0.785
0.721
1.264
0.983
0.913
1.291
1.005
0.808
1.404
1.275
1.194
1.279
1.087
1.233
0.965
1.028
1.125
0.965
1.103
0.965
1.276
0.765
1.474
1.175
2.030
1.169
1.345
1.491
1.068
1.194
2.345
1.334
1.362
2.369
1.284
0.941
1.679
1.104
1.319
1.194
2.061
1.028
1.114
0.905
0.933
1.314
0.984
0.942
1.175
1.052
0.932
0.909
1.262
0.997
1.320
1.081
1.080
0.604
0.898
0.782
0.776
0.687
będziński
bielski
cieszyński
częstochowski
gliwicki
kłobucki
lubliniecki
mikołowski
myszkowski
pszczyński
raciborski
rybnicki
tarnogórski
wodzisławski
zawierciański
żywiecki
m. Bielsko-Biała
m. Bytom
m. Chorzów
m. Częstochowa
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2461
2462
2463
2464
1.121
0.998
1.026
1.084
1.081
1.032
1.032
1.033
1.082
1.067
1.057
1.127
0.980
1.109
1.078
1.123
1.084
0.899
1.003
1.114
1.010
1.099
0.880
0.925
0.942
1.083
0.876
0.879
0.886
0.939
1.038
1.026
1.071
0.984
1.079
1.158
1.062
0.871
0.893
1.020
1.079
0.973
1.196
1.188
1.107
1.222
1.092
1.171
1.190
1.159
1.208
1.270
0.986
1.122
1.023
1.166
0.981
1.148
1.337
1.031
0.939
1.110
1.090
0.911
0.604
1.183
0.978
1.123
0.963
0.839
1.167
1.037
0.588
1.090
1.610
0.981
1.209
1.318
1.290
0.561
0.441
0.927
1.251
0.878
1.285
0.866
0.846
1.119
1.148
1.043
0.976
1.222
0.845
0.741
1.273
1.233
0.875
1.209
1.657
1.028
1.176
0.688
1.315
1.624
1.106
0.612
0.060
0.111
0.591
0.869
0.382
0.584
0.417
0.970
0.077
1.752
0.827
0.761
0.281
0.768
0.722
0.078
0.086
1.273
0.412
0.535
1.317
1.123
1.173
1.138
1.157
1.245
0.869
0.685
1.086
1.013
0.823
1.308
0.928
1.124
1.034
1.151
1.072
0.971
1.178
1.327
1.040
Page 250
0.965
0.627
0.912
1.428
1.180
1.238
1.803
0.812
2.049
1.308
0.827
1.834
1.425
0.969
1.199
0.948
0.882
1.600
1.477
0.876
0.947
1.168
0.921
1.046
0.873
1.020
1.535
1.294
0.688
0.973
0.972
0.831
0.673
0.813
1.003
1.171
0.639
0.934
0.970
0.883
0.930
0.781
External
1.066
0.850
0.888
1.132
0.589
1.093
1.493
1.074
1.059
1.128
1.341
0.931
1.435
0.723
1
District
m. Gliwice
m. Jaworzno
m. Katowice
m. Mysłowice
m. Ruda Śląska
m. Rybnik
m. Sosnowiec
m. Tychy
m. Zabrze
m. Żory
TERYT
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
Total
1.086
0.904
1.068
1.044
0.978
1.151
1.076
1.211
0.982
1.174
1.089
1.051
1.003
0.875
1.032
Cancer
1.078
1.035
1.142
1.129
1.055
1.289
0.995
1.148
0.957
1.291
1.131
0.979
1.078
0.952
1.165
CVD
1.116
0.828
0.996
1.087
0.957
1.186
1.209
1.219
0.979
1.180
1.093
1.187
1.006
0.775
0.979
buski
jędrzejowski
kazimierski
kielecki
konecki
opatowski
ostrowiecki
pińczowski
sandomierski
skarżyski
starachowicki
staszowski
włoszczowski
m. Kielce
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2661
0.988
1.015
1.107
1.017
1.053
1.061
1.052
0.986
0.947
1.023
0.991
1.028
1.052
0.864
0.869
0.802
0.994
0.898
0.921
0.878
0.984
0.805
0.932
0.874
0.901
0.928
0.758
0.851
0.877
1.066
1.097
1.006
1.164
1.139
1.169
1.018
0.957
1.139
1.024
1.097
1.186
0.787
1.116
1.290
1.357
1.371
0.894
0.852
0.514
0.736
0.630
0.828
1.107
0.644
0.913
1.131
0.735
0.708
0.714
0.862
1.032
0.774
0.854
0.838
1.066
0.792
0.868
0.854
0.825
0.997
2.629
0.959
1.306
1.128
0.567
1.382
0.413
1.809
0.934
0.461
0.418
1.267
0.714
1.073
0.895
1.060
0.904
1.126
0.926
1.131
1.094
1.005
0.779
1.110
1.091
0.869
0.853
0.916
bartoszycki
braniewski
działdowski
elbląski
ełcki
giżycki
iławski
kętrzyński
lidzbarski
mrągowski
nidzicki
nowomiejski
olecki
olsztyński
ostródzki
piski
szczycieński
gołdapski
węgorzewski
m. Elbląg
m. Olsztyn
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2861
2862
1.073
1.112
1.088
1.077
0.991
1.014
0.989
1.077
1.061
1.083
1.092
1.009
1.063
1.072
0.976
1.002
1.023
1.043
1.021
0.996
0.814
0.997
1.052
1.405
1.003
1.009
0.919
1.029
1.189
1.177
1.216
1.201
1.119
0.942
1.120
1.048
1.041
0.899
0.932
0.947
1.188
0.988
0.992
1.150
0.941
0.962
0.885
0.979
0.920
0.985
1.046
0.944
0.903
0.849
1.214
0.927
0.904
0.930
0.936
0.972
0.927
0.843
0.626
1.771
1.352
1.493
1.718
1.078
1.607
1.344
1.711
1.148
1.508
2.707
1.590
1.161
2.139
1.425
1.682
1.836
1.277
1.655
1.473
1.394
1.546
1.181
0.750
0.833
0.873
1.100
0.879
0.810
1.396
1.077
1.182
0.704
0.585
0.684
1.094
0.713
0.888
0.766
0.977
0.904
0.803
0.779
0.932
0.924
2.069
1.826
0.922
1.261
0.962
0.855
1.210
1.312
1.362
0.285
1.340
0.957
0.863
1.741
2.035
1.636
1.232
1.296
0.855
0.557
0.527
0.354
0.591
0.891
0.618
0.849
0.708
1.055
0.538
0.971
0.838
0.899
0.644
0.652
0.758
0.928
0.645
0.454
0.703
chodzieski
gnieźnieński
gostyński
grodziski
3001
3002
3003
3004
3005
1.074
1.100
1.054
1.099
1.176
1.060
1.172
1.128
1.133
0.939
0.943
0.963
0.975
1.024
1.352
1.101
0.894
0.657
0.904
0.842
0.575
0.837
1.020
1.073
1.246
2.036
1.907
1.301
1.377
0.675
1.360
1.604
1.298
1.751
1.039
Page 251
1.203
1.223
0.497
0.832
1.186
0.787
1.043
1.469
0.888
0.743
0.991
0.644
0.995
1.275
0.473
0.998
1.436
0.347
0.755
1.282
0.451
1.103
1.600
0.777
1.217
0.939
0.479
1.193
1.676
0.263
1.130
1.295
0.507
0.485
1.248
0.466
1.192
1.086
0.270
0.910
1.298
0.879
0.864
1.419
0.357
External
1.091
0.923
1.072
1.063
1.218
1.398
0.828
1.184
1.164
1.050
1.237
1.305
0.720
0.968
1.293
1
District
jarociński
kaliski
kępiński
kolski
koniński
kościański
krotoszyński
leszczyński
międzychodzki
nowotomyski
obornicki
ostrowski
ostrzeszowski
pilski
pleszewski
poznański
rawicki
słupecki
szamotulski
średzki
śremski
turecki
wągrowiecki
wolsztyński
wrzesiński
złotowski
m. Kalisz
m. Konin
m. Leszno
m. Poznań
TERYT
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3061
3062
3063
3064
Total
1.053
1.039
1.050
1.052
0.994
1.030
1.134
1.101
1.199
1.097
1.166
0.995
1.142
1.065
1.143
1.069
1.101
1.077
1.101
1.067
1.194
1.105
1.066
1.133
1.065
1.060
0.945
0.881
0.985
0.919
Cancer
1.111
0.947
1.015
1.053
1.108
1.066
1.090
1.120
0.910
1.099
1.267
1.116
0.922
1.162
1.059
1.189
1.052
1.195
1.249
0.952
1.232
1.073
1.028
1.035
1.133
1.060
1.038
1.092
1.116
1.088
CVD
1.096
0.964
1.088
1.149
0.953
0.897
1.185
1.066
1.112
1.047
1.044
0.916
1.289
0.959
1.242
1.015
1.152
1.017
1.029
1.067
1.085
1.098
0.916
1.206
1.000
1.057
0.929
0.759
0.899
0.842
białogardzki
choszczeński
drawski
goleniowski
gryficki
gryfiński
kamieński
kołobrzeski
koszaliński
myśliborski
policki
pyrzycki
sławieński
stargardzki
szczecinecki
świdwiński
wałecki
łobeski
m. Koszalin
m. Szczecin
m. Świnoujście
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
3261
3262
3263
1.085
1.114
1.120
1.012
1.139
1.073
1.064
0.968
1.161
1.082
1.003
1.051
1.014
1.039
1.192
1.034
1.139
1.045
0.883
0.966
1.072
1.058
1.079
1.026
0.981
1.014
1.034
1.002
1.097
1.235
0.951
1.072
1.022
1.139
1.036
0.967
1.099
1.131
0.909
1.197
1.112
0.996
1.224
1.132
1.183
1.046
1.146
1.146
1.062
0.852
1.133
1.175
0.934
1.054
1.077
1.029
1.296
1.043
1.184
1.077
0.789
0.917
1.165
0.912
1.483
0.954
1.090
1.385
0.884
0.935
0.626
1.027
1.406
1.071
0.994
0.524
1.239
0.846
1.031
1.044
1.238
0.718
0.822
0.906
1.142
1.035
0.967
1.239
1.435
0.813
1.249
1.004
0.896
1.102
1.252
1.260
1.081
1.094
1.226
1.342
1.277
1.158
1.061
1.095
1.019
0.538
0.543
1.097
0.749
1.724
0.744
1.185
1.938
1.525
0.684
0.919
1.067
0.654
0.830
1.273
0.801
0.599
0.827
0.645
0.797
1.287
0.913
1.133
1.157
0.819
0.775
1.104
0.825
0.809
0.897
1.022
1.096
0.942
0.986
1.068
0.892
0.756
1.905
1.423
0.852
1.002
0.943
1.000
1.000
1.000
1.000
1.000
1.000
1.000
Polska
Page 252
0.594
0.890
0.633
1.040
0.927
2.176
1.114
0.907
0.642
0.573
1.034
0.583
0.910
0.947
0.983
0.608
1.169
2.082
0.908
0.982
0.479
0.690
1.165
1.215
0.816
1.766
2.534
0.631
1.409
1.258
0.670
0.844
1.210
0.927
0.940
0.581
0.656
1.060
0.313
1.064
0.981
1.272
0.785
1.162
0.717
0.766
1.144
0.950
0.948
1.045
0.530
1.471
0.999
0.542
0.667
0.707
1.813
0.842
1.135
0.468
1.172
1.029
1.414
1.063
1.096
1.375
1.151
1.054
0.874
0.777
1.412
0.684
1.207
1.201
0.533
0.742
0.764
1.151
0.760
0.837
0.983
0.829
0.807
0.884
0.683
1.160
1.027
0.703
0.972
0.762
External
1.504
1.236
1.659
1.289
0.939
1.725
1.680
1.622
1.920
1.779
2.313
1.658
1.656
1.251
1.870
1.268
1.741
1.346
1.185
1.365
1.616
1.257
2.484
1.264
1.634
2.014
1.041
1.323
1.097
1.179
1
residence in 2006–2008, males of age 65 years and more
District
bolesławiecki
dzierżoniowski
głogowski
górowski
jaworski
jeleniogórski
kamiennogórski
kłodzki
legnicki
lubański
lubiński
lwówecki
milicki
oleśnicki
oławski
polkowicki
strzeliński
średzki
świdnicki
trzebnicki
wałbrzyski
wołowski
wrocławski
ząbkowicki
zgorzelecki
złotoryjski
m. Jelenia Góra
m. Legnica
m. Wrocław
TERYT
0201
0202
0203
0204
0205
0206
0207
0208
0209
0210
0211
0212
0213
0214
0215
0216
0217
0218
0219
0220
0221
0222
0223
0224
0225
0226
0261
0262
0264
Total
1.045
1.027
1.183
1.053
1.065
1.092
1.150
1.135
1.069
1.128
1.043
1.309
0.987
1.108
1.067
1.148
1.079
1.249
1.079
1.114
1.090
1.091
1.085
1.022
1.131
1.305
0.994
1.081
0.877
Cancer
1.206
0.971
1.153
1.055
1.096
1.130
0.956
1.183
1.036
1.105
1.198
1.209
0.980
1.102
1.212
1.353
1.197
1.334
1.198
1.250
1.112
1.111
1.049
1.014
1.169
1.394
1.045
1.120
0.936
CVD
1.053
1.097
1.248
1.236
1.082
1.022
1.147
1.219
1.157
1.272
1.003
1.462
1.118
1.260
1.091
1.043
1.275
1.361
1.139
1.227
1.093
1.301
1.210
1.047
1.272
1.307
0.952
1.082
0.937
aleksandrowski
brodnicki
bydgoski
chełmiński
grudziądzki
inowrocławski
lipnowski
mogileński
nakielski
radziejowski
rypiński
sępoleński
świecki
toruński
tucholski
wąbrzeski
włocławski
żniński
m. Bydgoszcz
m. Grudziądz
m. Toruń
m. Włocławek
0401
0402
0403
0404
0405
0406
0407
0408
0409
0410
0411
0412
0413
0414
0415
0416
0417
0418
0419
0461
0462
0463
0464
1.034
1.088
1.049
1.163
1.032
1.224
1.108
1.176
1.156
1.156
1.173
1.031
1.115
1.114
1.107
1.083
1.145
1.169
1.014
0.952
1.056
0.937
1.023
0.940
1.132
1.117
1.193
1.138
1.299
1.139
1.083
1.443
1.088
1.072
1.099
1.126
1.214
1.381
1.025
1.309
1.211
1.085
1.089
1.009
1.093
1.046
1.194
1.027
1.029
1.097
0.968
1.209
1.162
1.201
1.148
1.346
1.234
1.067
1.251
1.153
0.894
1.129
0.922
1.244
0.989
0.903
1.081
0.787
1.051
0.904
1.571
1.457
1.275
1.582
1.202
1.378
1.665
0.717
1.000
1.809
1.272
1.263
0.935
1.527
1.333
1.211
1.436
1.063
0.933
0.911
0.988
0.882
0.612
0.855
0.883
1.109
0.817
0.660
1.417
0.816
0.720
0.852
0.683
0.845
0.826
1.380
0.956
1.137
1.260
0.986
0.861
0.897
0.801
1.043
1.228
0.590
1.247
0.919
1.338
1.129
1.648
0.499
1.088
1.038
0.699
1.021
0.409
0.304
0.795
1.233
1.027
1.274
0.296
1.071
1.114
1.186
1.373
0.708
1.053
0.488
0.473
0.427
0.088
0.589
0.955
0.943
1.286
0.696
0.983
0.744
0.984
0.760
0.922
0.609
0.792
1.146
0.808
0.546
1.046
0.770
0.999
bialski
0601
1.122
0.716
1.345
0.692
0.947
1.358
1.656
Page 253
0.816
1.090
0.425
0.963
1.109
1.072
1.055
1.869
0.657
0.737
1.448
0.207
1.171
1.031
0.587
1.106
1.380
0.958
1.123
1.379
2.366
1.212
0.724
0.940
0.910
0.794
1.467
0.831
1.061
0.968
0.850
1.214
0.753
1.213
1.126
1.489
0.667
0.740
0.868
1.138
1.113
0.348
0.588
1.007
1.420
1.076
0.774
1.185
0.586
0.701
0.568
1.038
1.007
1.040
0.775
0.929
1.162
0.890
0.829
0.595
1.365
1.286
1.265
0.781
0.569
0.559
0.891
1.085
0.814
0.935
0.893
1.062
0.996
0.870
0.659
1.526
1.299
0.436
0.844
1.101
1.173
0.736
1.102
1.546
0.556
0.942
0.761
External
1.172
1.068
1.360
0.673
1.418
1.363
1.409
0.887
0.625
1.073
1.070
0.970
0.796
0.978
0.894
1.316
0.592
1.086
1.025
1.227
0.640
0.614
1.045
0.910
1.013
1.804
1.192
1.143
0.780
1
District
biłgorajski
chełmski
hrubieszowski
janowski
krasnostawski
kraśnicki
lubartowski
lubelski
łęczyński
łukowski
opolski
parczewski
puławski
radzyński
rycki
świdnicki
tomaszowski
włodawski
zamojski
m. Biała Podlaska
m. Chełm
m. Lublin
m. Zamość
TERYT
0602
0603
0604
0605
0606
0607
0608
0609
0610
0611
0612
0613
0614
0615
0616
0617
0618
0619
0620
0661
0662
0663
0664
Total
1.029
1.179
1.062
1.103
1.127
1.009
1.104
1.103
1.149
1.040
1.118
1.084
0.996
1.075
1.029
1.021
1.003
1.218
1.049
0.954
0.961
0.907
0.888
Cancer
0.847
0.770
0.829
0.797
0.802
0.823
0.973
0.865
0.823
0.765
0.865
0.825
0.984
0.793
0.885
0.959
0.912
0.967
0.864
0.723
0.869
0.918
0.736
CVD
0.987
1.296
0.903
1.152
1.198
1.166
1.202
1.251
1.212
1.231
1.245
1.222
0.962
1.197
1.136
1.106
1.093
1.400
0.957
1.011
0.983
0.908
0.872
gorzowski
krośnieński
międzyrzecki
nowosolski
słubicki
sulęciński
świebodziński
zielonogórski
żagański
żarski
wschowski
m. Zielona Góra
0801
0802
0803
0804
0805
0806
0807
0808
0809
0810
0811
0812
0861
0862
1.012
1.211
1.182
1.014
1.227
1.085
1.287
1.069
1.115
1.108
1.068
1.092
0.943
0.890
0.947
1.146
1.115
0.912
1.332
1.069
1.112
0.874
1.016
1.091
0.923
0.956
0.966
0.910
0.989
1.126
1.144
1.032
1.163
1.020
1.345
1.169
1.057
1.260
1.269
1.051
0.816
0.719
0.821
1.679
1.254
0.862
1.238
1.138
1.316
0.853
0.693
0.729
0.770
0.905
0.749
0.772
1.302
0.914
1.031
1.036
1.281
1.104
1.362
1.703
0.936
1.104
0.862
0.963
1.010
0.927
1.755
0.956
2.010
0.881
1.315
2.214
0.980
1.295
2.585
0.546
0.816
1.413
2.255
2.394
1.123
1.150
1.107
1.209
1.056
1.271
1.711
0.532
1.320
0.882
0.662
1.374
0.753
0.697
bełchatowski
kutnowski
łaski
łęczycki
łowicki
łódzki wschodni
opoczyński
pabianicki
pajęczański
piotrkowski
poddębicki
radomszczański
rawski
sieradzki
skierniewicki
tomaszowski
wieluński
wieruszowski
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1.077
1.146
1.014
1.160
1.114
1.119
1.020
1.086
0.993
1.094
1.116
1.114
0.974
1.026
1.020
1.097
1.125
1.089
1.113
1.153
0.884
1.038
1.018
0.938
0.874
1.023
0.990
0.769
1.130
0.968
0.939
1.023
1.030
0.918
1.131
0.882
1.077
1.207
1.113
1.305
1.280
1.169
1.195
1.119
0.921
1.234
1.098
1.211
1.000
1.042
1.058
1.143
1.178
1.314
1.233
0.784
1.096
1.551
1.219
1.248
1.202
0.996
1.226
1.284
1.288
1.185
1.389
1.433
1.050
1.228
1.361
0.643
1.566
1.065
0.911
1.111
1.168
1.628
0.651
1.004
1.090
1.121
0.758
0.769
1.048
0.917
0.949
1.183
1.031
0.777
0.895
1.639
0.741
0.298
0.162
1.292
0.348
1.219
1.304
1.010
0.836
1.426
0.641
0.421
0.312
0.947
0.478
0.638
0.927
0.685
1.179
1.312
1.110
1.074
0.924
1.357
0.955
1.177
1.707
1.066
1.069
1.181
1.240
1.139
0.984
1.427
Page 254
1.013
0.730
2.458
1.233
1.219
1.657
1.006
0.879
3.916
1.496
1.477
1.829
1.593
0.772
1.524
1.134
0.622
0.651
1.030
0.814
0.991
0.914
1.231
1.214
1.832
0.470
1.076
0.934
0.558
1.396
1.052
0.644
1.072
0.921
1.194
1.199
0.738
1.088
1.739
0.869
1.140
1.352
0.884
1.322
0.739
0.481
0.644
1.827
0.647
0.714
1.358
0.796
1.265
1.469
1.035
0.788
2.423
0.773
0.928
1.207
0.686
1.141
0.863
0.830
0.904
0.956
0.734
1.032
1.248
External
1.043
1.251
0.994
0.738
1.069
1.082
1.397
0.865
1.017
0.732
1.545
0.891
0.872
1.390
0.881
0.785
0.738
0.770
1.200
1.716
1.408
0.559
0.686
1
District
zduńskowolski
zgierski
brzeziński
m. Łódź
m. Skierniewice
TERYT
1019
1020
1021
1061
1062
1063
Total
0.996
1.101
1.089
1.077
1.001
0.997
Cancer
0.990
1.066
0.849
0.960
0.830
1.071
CVD
0.998
1.103
1.380
0.985
1.092
1.071
bocheński
brzeski
chrzanowski
dąbrowski
gorlicki
krakowski
limanowski
miechowski
myślenicki
nowosądecki
nowotarski
olkuski
oświęcimski
proszowicki
suski
tarnowski
tatrzański
wadowicki
wielicki
m. Kraków
m. Nowy Sącz
m. Tarnów
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1261
1262
1263
1.008
1.042
0.999
0.863
0.975
1.009
0.972
0.968
1.064
0.976
0.959
0.958
0.924
1.029
1.079
0.960
1.047
0.985
1.032
0.838
0.924
0.935
0.882
0.761
1.029
0.867
0.779
1.002
1.000
0.858
0.979
0.988
0.872
0.866
1.002
1.047
1.054
0.959
1.101
1.006
1.014
0.910
1.035
0.978
1.069
1.232
1.062
0.865
1.095
1.075
0.976
1.010
1.229
0.969
1.027
0.950
0.925
0.941
1.177
1.005
1.098
1.062
1.158
0.862
0.885
0.987
1.099
1.011
1.000
0.970
1.022
0.886
1.131
1.224
0.870
1.027
0.918
1.168
1.129
2.122
1.006
1.042
0.728
0.865
0.473
0.616
0.701
0.773
0.873
0.748
0.657
0.748
0.886
0.775
0.522
1.044
1.066
0.764
0.770
0.904
0.722
0.668
0.801
0.785
0.455
0.611
0.973
0.818
0.893
0.619
1.256
1.014
0.822
1.188
0.902
0.790
0.965
0.464
0.524
1.204
0.883
0.814
0.577
0.344
0.905
1.171
0.661
0.675
0.530
0.662
1.149
0.633
1.097
1.144
0.886
0.518
0.817
1.225
0.825
1.186
1.144
1.179
0.933
1.206
0.840
1.226
0.906
0.606
1.251
1.186
1.311
0.640
0.669
0.844
białobrzeski
ciechanowski
garwoliński
gostyniński
grodziski
grójecki
kozienicki
legionowski
lipski
łosicki
makowski
miński
mławski
nowodworski
ostrołęcki
ostrowski
otwocki
piaseczyński
płocki
płoński
pruszkowski
przasnyski
przysuski
pułtuski
radomski
siedlecki
sierpecki
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1.055
1.080
0.991
1.112
1.042
1.016
0.985
0.967
1.013
0.971
1.082
1.008
1.093
1.078
1.053
0.986
0.891
0.984
1.117
1.107
0.868
1.186
0.974
1.058
1.070
0.970
1.082
0.801
1.124
0.772
1.176
1.148
0.831
0.903
1.035
0.762
0.870
1.044
1.001
1.206
1.255
1.044
0.940
0.867
0.935
1.120
1.066
0.966
1.179
0.648
1.146
0.944
0.807
1.176
1.143
0.977
1.137
0.969
1.018
1.121
0.968
0.929
1.104
1.048
1.151
1.014
1.038
0.984
0.938
1.047
0.883
0.954
1.053
1.176
0.825
1.240
1.145
1.022
1.122
0.998
1.050
0.995
1.867
0.850
1.297
1.053
1.203
1.269
1.213
1.123
0.971
1.095
1.403
1.531
1.272
1.993
1.114
0.675
1.183
1.548
0.978
0.935
1.234
1.232
1.350
1.256
1.331
1.223
1.351
0.786
0.850
0.564
0.927
1.144
0.697
1.077
1.201
1.015
0.547
1.104
1.251
1.317
0.984
0.928
1.239
1.119
0.745
0.969
0.892
0.788
0.960
0.684
0.885
1.160
0.968
1.568
0.311
0.921
2.061
0.752
0.667
0.841
0.526
1.054
0.549
0.424
0.479
0.764
0.534
1.054
0.911
1.218
0.803
1.307
0.763
0.559
0.743
0.845
1.384
1.406
0.858
0.957
1.366
1.866
1.306
1.360
1.317
1.106
1.899
1.200
1.973
1.502
1.254
1.270
1.262
1.272
0.989
0.886
0.831
1.202
1.510
1.743
0.979
1.771
0.851
1.074
1.279
1.088
0.850
Page 255
0.863
0.881
1.129
1.313
1.538
1.125
0.888
0.941
0.410
1.212
1.370
2.385
0.801
1.172
0.788
0.808
0.672
0.634
External
1.267
0.708
1.236
0.931
1.400
0.898
1
District
sochaczewski
sokołowski
szydłowiecki
warszawski zachodni
węgrowski
wołomiński
wyszkowski
zwoleński
żuromiński
żyrardowski
m. Ostrołęka
m. Płock
m. Radom
m. Siedlce
m. st. Warszawa
TERYT
1428
1429
1430
1432
1433
1434
1435
1436
1437
1438
1461
1462
1463
1464
1465
Total
1.113
0.952
1.074
0.891
0.945
1.062
1.037
1.061
1.008
1.132
1.009
1.045
0.979
0.883
0.794
Cancer
1.180
0.861
0.881
0.946
0.723
1.040
0.841
0.886
1.269
1.210
1.231
1.174
0.983
0.841
0.907
CVD
1.115
1.013
1.168
0.849
1.002
1.013
1.069
1.087
0.940
1.062
0.801
0.884
0.919
0.938
0.675
brzeski
głubczycki
kluczborski
krapkowicki
namysłowski
nyski
oleski
opolski
prudnicki
strzelecki
m. Opole
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1661
1.075
1.234
0.934
1.091
0.993
1.122
1.018
0.969
0.913
1.115
0.951
0.845
1.006
1.184
0.818
1.002
0.749
1.180
1.039
1.005
0.941
0.892
0.821
0.951
1.076
1.452
1.024
1.250
1.131
1.108
1.118
1.113
0.953
1.341
1.168
0.807
0.828
0.823
0.719
0.740
1.165
1.194
0.929
0.435
0.977
0.925
0.630
0.887
0.840
0.985
0.941
0.881
0.934
0.592
0.648
0.744
0.640
1.281
0.679
0.839
1.968
0.562
1.241
0.708
0.771
1.611
0.423
0.864
0.755
0.599
0.461
0.602
1.378
0.982
0.892
1.407
0.815
1.362
1.212
0.842
0.859
0.912
0.885
0.788
bieszczadzki
brzozowski
dębicki
jarosławski
jasielski
kolbuszowski
krośnieński
leżajski
lubaczowski
łańcucki
mielecki
niżański
przemyski
przeworski
rzeszowski
sanocki
stalowowolski
strzyżowski
tarnobrzeski
leski
m. Krosno
m. Przemyśl
m. Rzeszów
m. Tarnobrzeg
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1861
1862
1863
1864
1.150
1.008
0.986
1.001
1.007
0.931
0.929
0.947
1.030
0.908
0.856
0.989
1.031
0.962
1.028
0.951
0.924
0.985
1.012
0.936
0.852
0.909
1.001
0.775
0.869
1.088
1.058
1.041
0.915
0.992
0.911
0.929
0.919
0.957
0.781
0.941
0.910
0.923
0.890
0.868
0.845
0.840
0.991
0.978
1.039
0.959
1.078
1.040
0.900
0.927
1.194
0.919
1.134
1.205
1.037
1.038
0.870
0.987
1.185
1.049
0.724
1.171
1.075
1.045
1.280
1.114
0.930
1.054
0.923
0.997
0.754
0.831
1.053
0.791
1.002
1.437
1.288
0.631
0.461
0.967
0.836
0.958
0.642
0.810
0.711
1.021
0.387
0.684
0.977
0.700
0.849
0.792
0.783
0.966
0.704
0.992
0.612
0.559
0.450
0.478
1.377
1.008
0.494
0.859
1.156
0.584
0.657
0.603
0.536
0.688
0.617
0.661
1.114
0.661
0.656
0.747
1.053
0.644
0.561
0.743
1.087
0.841
0.827
0.560
0.731
0.958
1.349
0.447
0.980
1.415
0.357
1.547
1.338
0.833
0.759
1.134
1.289
2.307
0.963
0.276
0.658
1.200
0.860
2.439
0.370
1.099
1.138
1.291
0.449
0.386
0.553
0.936
0.823
0.772
0.841
0.974
0.870
1.246
0.578
1.270
0.991
0.992
0.955
0.925
1.029
0.783
0.770
0.798
1.151
0.580
0.782
0.734
0.887
0.941
0.257
augustowski
białostocki
bielski
2001
2002
2003
0.974
1.019
0.855
1.009
0.927
0.715
0.945
0.978
0.859
0.898
1.274
1.106
1.046
1.035
0.871
1.360
1.427
0.827
1.388
0.852
1.466
Page 256
1.386
1.087
0.637
1.355
0.416
0.310
1.442
0.658
1.035
0.959
0.924
0.908
0.990
1.303
0.757
1.351
1.104
1.236
1.707
0.848
0.629
1.562
1.060
1.383
1.108
0.569
0.413
1.492
1.323
0.973
1.631
1.268
0.436
1.322
1.506
0.975
0.970
1.215
1.725
0.818
0.801
0.726
0.948
0.997
0.865
External
1.260
1.226
1.162
1.242
1.419
1.533
1.501
0.742
0.614
0.934
1.821
1.043
0.733
0.673
0.844
1
District
grajewski
hajnowski
kolneński
łomżyński
moniecki
sejneński
siemiatycki
sokólski
suwalski
wysokomazowiecki
zambrowski
m. Białystok
m. Łomża
m. Suwałki
TERYT
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2061
2062
2063
Total
1.021
0.949
1.081
0.978
0.903
0.991
0.957
0.983
0.993
0.916
0.968
0.847
0.979
0.856
Cancer
1.087
0.910
0.882
0.967
0.916
1.348
0.904
0.902
1.118
0.795
0.985
0.966
1.214
0.935
CVD
0.961
0.884
0.998
0.857
0.812
0.764
0.899
0.968
0.938
0.906
0.942
0.757
0.825
0.762
bytowski
chojnicki
człuchowski
gdański
kartuski
kościerski
kwidzyński
lęborski
malborski
nowodworski
pucki
słupski
starogardzki
tczewski
wejherowski
sztumski
m. Gdańsk
m. Gdynia
m. Słupsk
m. Sopot
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2261
2262
2263
2264
1.111
1.041
1.063
1.025
0.987
0.980
1.197
1.138
1.156
1.289
1.078
1.105
1.058
1.076
0.991
1.351
0.881
0.839
0.928
0.738
1.067
1.226
1.276
1.075
1.109
1.155
1.343
1.440
1.052
1.564
1.252
1.304
1.047
1.249
1.108
1.674
1.051
1.079
1.006
1.144
0.974
0.939
0.892
0.925
0.915
0.865
1.098
0.945
1.129
1.112
0.997
0.835
0.920
0.989
0.754
1.419
0.736
0.725
0.785
0.591
1.483
1.268
1.011
1.238
0.923
1.402
1.263
1.150
0.962
1.270
0.871
1.145
1.702
1.124
1.317
0.733
0.881
0.678
0.671
0.469
1.506
0.811
1.093
1.305
1.183
0.840
2.063
1.491
1.553
1.646
1.029
1.440
1.127
0.925
1.355
1.063
1.030
0.802
1.114
0.793
1.513
1.005
2.277
0.953
1.093
1.072
0.728
0.975
3.001
1.344
0.714
2.337
0.823
0.642
1.429
0.917
1.149
0.907
2.173
0.636
1.026
1.095
1.005
1.391
0.870
0.641
1.495
1.137
0.903
0.756
1.540
1.169
1.496
0.984
0.987
0.633
0.825
0.667
0.655
0.574
będziński
bielski
cieszyński
częstochowski
gliwicki
kłobucki
lubliniecki
mikołowski
myszkowski
pszczyński
raciborski
rybnicki
tarnogórski
wodzisławski
zawierciański
żywiecki
m. Bielsko-Biała
m. Bytom
m. Chorzów
m. Częstochowa
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2461
2462
2463
2464
1.144
0.946
0.960
1.089
1.074
1.032
0.979
0.979
1.084
1.046
1.028
1.139
0.922
1.097
1.051
1.109
1.069
0.898
0.992
1.088
0.994
1.073
0.839
0.894
0.982
1.044
0.940
0.816
0.922
1.007
0.941
1.020
1.092
0.931
1.014
1.064
1.084
0.921
0.850
0.962
1.025
0.946
1.219
1.167
1.058
1.186
1.104
1.123
1.165
1.113
1.222
1.339
0.970
1.148
0.944
1.177
0.964
1.120
1.374
1.090
0.947
1.114
1.067
1.027
0.490
1.001
1.178
1.203
1.204
0.878
0.950
1.076
0.489
1.168
1.613
0.970
1.278
1.172
1.354
0.485
0.321
0.954
1.076
0.786
1.360
0.758
0.924
1.176
1.117
1.156
1.304
1.123
1.083
0.638
1.311
1.214
0.968
1.191
1.955
0.781
1.181
0.647
1.312
1.758
1.139
0.725
0.079
0.149
0.783
0.894
0.263
0.439
0.396
0.626
0.151
1.598
0.815
0.730
0.442
0.783
0.573
0.108
0.172
1.346
0.443
0.722
1.415
1.157
0.918
1.201
0.959
1.359
0.949
0.593
1.166
1.075
0.353
1.025
0.882
1.179
0.978
1.095
0.957
0.888
1.117
1.125
1.103
Page 257
1.091
0.663
0.754
1.300
1.034
0.961
1.979
1.056
1.825
1.444
0.545
1.498
1.281
0.985
1.044
1.053
1.047
0.994
1.353
0.934
0.705
1.213
1.046
0.729
0.811
0.697
1.179
1.377
0.623
0.899
1.069
0.888
0.544
0.693
0.977
0.933
0.684
0.784
0.877
0.793
1.022
0.507
External
1.221
0.723
0.817
1.297
0.777
1.144
1.115
1.172
1.092
1.270
1.404
0.856
0.991
0.733
1
District
m. Gliwice
m. Jaworzno
m. Katowice
m. Mysłowice
m. Ruda Śląska
m. Rybnik
m. Sosnowiec
m. Tychy
m. Zabrze
m. Żory
TERYT
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
Total
1.112
0.869
1.008
1.053
0.930
1.123
1.094
1.175
0.942
1.097
1.086
1.026
0.973
0.861
1.013
Cancer
1.101
0.943
1.056
1.165
0.975
1.138
0.998
1.085
0.894
1.296
1.109
0.940
1.132
0.838
1.151
CVD
1.146
0.797
0.958
1.097
0.919
1.175
1.270
1.221
0.973
1.044
1.075
1.152
0.932
0.779
1.027
buski
jędrzejowski
kazimierski
kielecki
konecki
opatowski
ostrowiecki
pińczowski
sandomierski
skarżyski
starachowicki
staszowski
włoszczowski
m. Kielce
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2661
0.968
0.996
1.184
1.066
1.088
1.092
1.046
1.045
0.993
1.027
1.010
1.016
0.986
0.840
0.893
0.866
1.045
0.963
0.960
0.991
1.027
0.864
0.919
0.900
0.942
0.930
0.750
0.817
0.871
1.023
1.196
1.017
1.138
1.146
1.150
1.126
1.041
1.157
1.001
1.080
1.113
0.777
1.142
1.182
1.649
1.427
1.246
0.979
0.598
0.762
0.630
0.813
1.189
0.760
0.782
0.952
0.689
0.794
0.729
0.980
1.200
0.607
0.882
1.017
1.307
0.940
1.024
0.837
0.692
0.972
2.504
0.773
1.238
1.227
0.612
1.123
0.475
1.680
0.799
0.467
0.549
1.193
0.550
1.091
0.930
1.127
0.718
1.347
1.143
1.577
1.009
0.975
0.977
1.176
0.908
0.911
0.814
0.961
bartoszycki
braniewski
działdowski
elbląski
ełcki
giżycki
iławski
kętrzyński
lidzbarski
mrągowski
nidzicki
nowomiejski
olecki
olsztyński
ostródzki
piski
szczycieński
gołdapski
węgorzewski
m. Elbląg
m. Olsztyn
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2861
2862
1.131
1.154
1.113
1.123
1.063
1.098
1.029
1.197
1.096
1.178
1.118
1.069
1.056
1.121
1.049
1.042
1.080
1.077
1.002
1.046
0.824
1.043
1.204
1.447
1.101
1.073
0.946
1.159
1.347
1.194
1.381
1.292
1.158
1.060
1.215
1.190
1.064
0.967
0.833
0.900
1.182
0.943
1.015
1.139
0.896
0.903
0.945
1.035
0.872
1.073
1.040
0.955
0.809
0.920
1.162
0.917
0.929
0.935
0.940
1.085
0.882
0.871
0.599
1.898
1.222
1.566
1.906
0.957
1.500
1.307
1.477
1.241
1.609
2.736
1.756
1.070
2.079
1.319
1.756
2.084
1.365
1.743
1.364
1.208
1.248
0.754
0.792
0.942
0.931
1.405
0.985
0.987
1.329
1.228
1.408
0.435
0.787
0.738
1.416
0.863
0.936
0.863
0.983
1.065
0.939
0.937
1.384
0.765
2.667
2.196
1.071
1.360
0.937
1.163
1.273
1.386
1.268
0.226
1.280
1.069
0.777
1.695
1.960
1.792
1.502
1.576
1.334
0.610
0.655
0.235
0.722
1.361
0.655
1.058
1.074
1.490
0.628
1.120
0.965
1.159
0.697
0.780
0.942
1.395
0.454
0.459
0.833
chodzieski
gnieźnieński
gostyński
grodziski
3001
3002
3003
3004
3005
1.044
1.090
1.029
1.052
1.215
0.980
1.179
1.113
1.080
0.996
0.954
0.969
0.971
1.010
1.461
1.223
0.898
0.676
1.045
0.922
0.815
0.616
1.183
1.192
1.599
1.593
2.085
0.840
0.854
0.381
1.162
1.418
1.233
1.434
1.178
Page 258
1.111
1.357
0.543
0.706
1.204
0.867
0.887
1.588
1.070
0.733
0.904
0.673
0.886
1.166
0.491
1.171
1.546
0.439
0.796
1.566
0.587
1.116
1.336
0.649
1.212
0.721
0.446
1.113
1.397
0.132
1.046
1.375
0.591
0.545
1.432
0.496
1.233
1.082
0.157
0.890
1.414
1.011
0.504
1.353
0.147
External
1.029
0.806
0.714
1.038
1.092
1.083
0.766
1.064
0.927
1.122
1.298
1.359
0.559
0.973
1.071
1
District
jarociński
kaliski
kępiński
kolski
koniński
kościański
krotoszyński
leszczyński
międzychodzki
nowotomyski
obornicki
ostrowski
ostrzeszowski
pilski
pleszewski
poznański
rawicki
słupecki
szamotulski
średzki
śremski
turecki
wągrowiecki
wolsztyński
wrzesiński
złotowski
m. Kalisz
m. Konin
m. Leszno
m. Poznań
TERYT
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3061
3062
3063
3064
Total
1.000
1.009
1.038
1.095
1.043
1.070
1.087
1.124
1.204
1.131
1.130
1.015
1.132
1.094
1.057
1.042
1.050
1.068
1.095
1.067
1.156
1.151
1.048
1.054
1.090
1.120
0.942
0.895
0.997
0.890
Cancer
1.044
0.947
1.054
1.095
1.210
1.054
0.981
1.185
0.843
1.083
1.179
1.180
0.927
1.196
1.006
1.171
1.087
1.130
1.313
0.932
1.329
1.133
0.948
0.937
1.107
1.150
1.012
0.999
1.104
1.004
CVD
1.059
0.889
1.025
1.260
0.963
1.006
1.137
1.087
1.208
1.124
1.042
0.918
1.290
1.011
1.135
1.010
1.068
0.990
1.010
1.126
0.994
1.157
0.865
1.131
1.081
1.089
0.930
0.828
0.911
0.837
białogardzki
choszczeński
drawski
goleniowski
gryficki
gryfiński
kamieński
kołobrzeski
koszaliński
myśliborski
policki
pyrzycki
sławieński
stargardzki
szczecinecki
świdwiński
wałecki
łobeski
m. Koszalin
m. Szczecin
m. Świnoujście
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
3261
3262
3263
1.201
1.124
1.180
1.071
1.153
1.071
1.075
0.978
1.273
1.135
1.026
1.125
1.074
1.046
1.220
1.105
1.177
1.147
0.870
0.967
1.052
1.034
1.106
1.053
1.014
1.028
1.117
0.995
1.106
1.329
1.019
1.030
1.209
1.235
1.076
1.046
1.106
1.232
0.960
1.057
1.036
0.988
1.388
1.115
1.288
1.099
1.163
1.166
1.114
0.833
1.253
1.212
0.986
1.051
1.100
1.032
1.312
1.155
1.199
1.159
0.823
0.952
1.143
1.186
1.810
0.951
1.140
1.369
0.890
0.900
0.585
1.153
1.388
1.317
0.985
0.668
1.163
0.831
1.061
0.994
1.599
0.634
0.744
0.701
1.720
0.786
1.068
1.346
0.999
0.591
1.178
1.086
0.811
1.279
1.149
1.595
0.997
1.027
1.666
1.623
1.676
1.402
1.194
1.106
1.162
0.345
0.632
0.718
0.923
2.028
0.444
1.053
2.556
1.835
0.796
0.846
1.607
0.737
0.901
1.528
0.692
0.373
0.762
0.618
0.841
1.637
1.107
0.757
1.760
1.025
0.693
1.153
0.724
0.388
0.948
1.062
1.222
1.179
1.297
1.068
1.137
0.964
2.228
1.944
0.577
0.976
0.756
1.000
1.000
1.000
1.000
1.000
1.000
1.000
Polska
Page 259
0.657
0.820
0.507
1.185
0.791
2.285
1.364
1.097
0.819
0.674
0.725
0.651
1.134
1.094
1.007
0.573
1.137
2.206
1.109
0.981
0.460
0.808
1.233
1.360
1.065
1.668
2.325
0.713
1.500
1.175
0.730
0.891
1.202
1.028
0.958
0.646
0.913
1.059
0.372
1.141
0.989
1.141
0.871
1.084
0.773
0.749
1.035
0.809
1.073
0.711
0.556
1.587
1.181
0.420
0.686
0.369
1.565
0.886
0.878
0.302
1.099
1.139
1.102
1.294
1.060
1.420
1.280
1.194
0.882
0.839
1.090
0.881
1.225
1.047
0.265
0.906
0.754
1.159
0.776
0.765
1.105
0.899
0.759
0.911
0.804
1.257
0.902
0.577
0.924
0.910
External
1.323
1.533
1.175
1.191
0.920
1.383
1.479
1.149
1.407
1.677
1.834
1.228
1.965
0.992
1.547
0.859
1.530
1.213
1.090
1.259
1.464
0.999
2.249
1.335
1.537
2.015
1.048
1.153
1.085
0.929
1
residence in 2006–2008, females of age 65 years and more
District
bolesławiecki
dzierżoniowski
głogowski
górowski
jaworski
jeleniogórski
kamiennogórski
kłodzki
legnicki
lubański
lubiński
lwówecki
milicki
oleśnicki
oławski
polkowicki
strzeliński
średzki
świdnicki
trzebnicki
wałbrzyski
wołowski
wrocławski
ząbkowicki
zgorzelecki
złotoryjski
m. Jelenia Góra
m. Legnica
m. Wrocław
TERYT
0201
0202
0203
0204
0205
0206
0207
0208
0209
0210
0211
0212
0213
0214
0215
0216
0217
0218
0219
0220
0221
0222
0223
0224
0225
0226
0261
0262
0264
Total
1.042
1.020
1.066
1.016
1.048
1.105
1.085
1.127
1.020
1.097
0.991
1.184
1.043
0.948
0.970
1.038
1.013
1.093
1.066
1.049
1.151
1.128
0.975
1.105
1.056
1.168
0.988
1.010
0.905
Cancer
1.258
1.046
1.184
0.856
0.961
1.194
1.077
1.129
1.049
0.859
1.010
0.820
0.859
0.926
0.977
1.032
1.043
0.942
1.204
1.224
1.171
1.146
1.031
1.115
1.087
1.220
1.156
1.210
1.054
CVD
1.030
1.067
1.027
1.136
1.166
1.083
1.066
1.198
1.089
1.300
1.016
1.197
1.215
1.067
1.037
1.082
1.216
1.208
1.067
1.136
1.226
1.259
1.028
1.173
1.142
1.234
0.876
0.962
0.935
aleksandrowski
brodnicki
bydgoski
chełmiński
grudziądzki
inowrocławski
lipnowski
mogileński
nakielski
radziejowski
rypiński
sępoleński
świecki
toruński
tucholski
wąbrzeski
włocławski
żniński
m. Bydgoszcz
m. Grudziądz
m. Toruń
m. Włocławek
0401
0402
0403
0404
0405
0406
0407
0408
0409
0410
0411
0412
0413
0414
0415
0416
0417
0418
0419
0461
0462
0463
0464
1.034
1.082
1.018
1.175
0.897
1.099
1.075
1.117
1.158
1.167
1.052
1.158
0.950
1.161
1.086
1.015
1.029
1.050
1.058
0.935
1.114
0.895
1.025
0.915
0.969
1.121
1.295
0.715
0.950
1.110
1.003
0.999
1.114
1.094
1.233
0.976
1.187
0.988
0.952
0.902
0.871
1.209
1.225
1.208
1.093
1.188
1.254
1.098
0.986
1.110
0.906
1.125
1.146
1.198
1.276
1.354
1.078
1.266
0.958
1.263
1.045
1.108
1.078
1.211
1.009
0.872
1.094
0.767
1.037
0.457
0.762
1.459
0.898
1.102
1.107
1.159
1.230
1.070
0.807
1.599
0.784
0.986
0.992
1.490
1.390
1.292
0.819
0.773
1.305
1.179
1.209
0.879
0.846
1.184
1.426
1.083
0.684
1.219
1.470
0.723
1.069
0.822
0.617
0.883
1.200
0.831
1.143
0.653
0.237
0.731
1.051
0.999
0.986
1.040
1.439
0.371
1.817
0.439
1.634
0.957
1.034
0.397
1.088
1.000
0.748
1.018
0.853
1.192
1.211
1.204
0.831
0.783
0.703
1.254
0.651
0.942
1.214
0.740
0.419
0.544
0.347
0.479
0.393
0.416
0.555
0.540
0.496
0.395
0.088
0.468
0.333
0.317
0.697
0.205
0.126
0.602
0.757
0.417
0.592
0.564
0.561
bialski
0601
1.072
0.725
1.231
0.508
0.855
1.012
1.593
Page 260
1.080
1.166
0.421
0.861
0.714
1.356
0.696
1.149
1.318
0.729
0.601
1.264
0.838
1.029
0.853
1.115
1.459
0.909
1.104
0.936
2.036
0.935
0.966
1.214
0.799
0.731
0.989
0.727
0.481
1.114
0.708
0.908
1.281
1.155
1.440
2.330
0.281
0.462
1.121
0.746
0.976
0.198
0.511
1.090
0.956
0.754
0.799
1.315
0.366
0.760
0.297
0.928
1.034
0.988
0.612
0.923
1.528
0.678
0.845
0.596
0.853
0.884
1.389
0.830
0.712
0.894
0.862
1.274
0.471
0.868
0.769
1.555
1.025
1.144
0.536
1.134
0.784
1.240
1.017
1.136
1.504
0.756
0.945
1.585
0.733
1.007
0.636
External
1.114
0.661
0.736
1.325
1.022
0.663
0.508
0.759
1.248
0.641
1.109
0.999
1.211
0.954
1.235
0.500
0.786
0.780
1.105
0.934
0.957
0.531
0.951
0.589
0.948
0.781
0.910
0.931
0.816
1
District
biłgorajski
chełmski
hrubieszowski
janowski
krasnostawski
kraśnicki
lubartowski
lubelski
łęczyński
łukowski
opolski
parczewski
puławski
radzyński
rycki
świdnicki
tomaszowski
włodawski
zamojski
m. Biała Podlaska
m. Chełm
m. Lublin
m. Zamość
TERYT
0602
0603
0604
0605
0606
0607
0608
0609
0610
0611
0612
0613
0614
0615
0616
0617
0618
0619
0620
0661
0662
0663
0664
Total
1.008
1.086
0.992
1.076
1.050
1.032
1.060
1.032
1.023
1.010
1.040
0.981
0.940
1.075
0.971
0.950
0.997
1.146
1.010
0.913
0.868
0.940
0.885
Cancer
0.688
0.779
0.707
0.665
0.604
0.859
0.822
0.588
0.709
0.736
0.615
0.800
0.831
0.715
0.745
0.719
0.686
0.754
0.754
0.990
0.740
0.895
0.889
CVD
1.030
1.170
0.882
1.246
1.157
1.188
1.209
1.241
1.158
1.182
1.258
1.086
0.943
1.158
1.161
1.082
1.065
1.344
0.955
0.956
0.940
0.961
0.851
gorzowski
krośnieński
międzyrzecki
nowosolski
słubicki
sulęciński
świebodziński
zielonogórski
żagański
żarski
wschowski
m. Zielona Góra
0801
0802
0803
0804
0805
0806
0807
0808
0809
0810
0811
0812
0861
0862
1.064
1.110
1.092
1.012
1.089
1.038
1.174
1.062
0.988
1.099
1.179
1.090
0.974
0.921
1.072
0.928
0.899
0.979
0.929
0.906
1.009
0.877
0.902
1.035
1.128
1.070
1.200
1.052
1.048
1.119
1.073
0.982
1.082
1.010
1.148
1.104
0.879
1.226
1.304
0.996
0.782
0.696
0.887
1.257
1.185
0.590
1.135
0.801
1.243
0.926
0.717
0.600
0.577
0.230
0.945
0.640
0.957
0.612
1.105
1.273
1.695
1.307
1.590
0.804
1.038
0.919
1.026
1.337
0.784
0.888
1.372
1.335
1.778
1.487
1.211
2.137
1.456
1.337
2.419
0.537
0.976
1.615
2.305
2.710
0.838
0.895
1.011
0.526
0.963
0.723
0.634
0.344
0.472
0.779
0.569
0.741
0.470
0.640
bełchatowski
kutnowski
łaski
łęczycki
łowicki
łódzki wschodni
opoczyński
pabianicki
pajęczański
piotrkowski
poddębicki
radomszczański
rawski
sieradzki
skierniewicki
tomaszowski
wieluński
wieruszowski
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1.049
1.102
1.025
1.197
1.005
1.099
1.102
1.049
1.000
1.114
1.209
1.039
1.009
1.034
0.980
1.012
1.024
1.149
1.042
0.938
0.764
0.979
0.882
0.948
0.681
0.854
0.865
0.818
0.939
0.769
0.879
0.998
0.831
0.919
0.893
0.855
1.118
1.301
1.127
1.422
1.211
1.163
1.391
1.083
1.098
1.244
1.270
1.182
1.048
1.069
1.150
1.068
1.149
1.373
1.271
0.683
0.976
0.899
0.689
1.293
0.511
1.142
1.066
0.928
1.130
0.823
1.582
1.002
0.848
1.014
0.795
0.469
1.052
0.798
1.375
1.297
1.020
1.151
1.092
1.314
0.747
1.336
1.079
0.694
1.140
1.194
0.908
0.894
0.982
1.117
0.659
0.543
0.694
0.231
0.136
0.918
0.347
0.941
0.770
0.872
1.196
1.170
0.427
0.703
0.285
0.679
0.382
0.670
0.799
0.835
0.821
1.023
0.381
0.881
1.291
1.470
0.655
1.314
2.015
0.912
1.321
1.546
0.958
0.807
1.010
1.010
Page 261
0.865
0.675
2.471
0.548
0.982
1.722
0.592
0.679
3.648
0.378
0.370
1.996
0.997
1.086
1.556
0.504
0.902
0.810
0.606
0.978
0.762
0.586
1.094
0.560
1.346
1.048
0.420
0.447
0.700
0.912
0.525
0.922
0.942
0.952
0.793
0.992
0.564
1.048
1.552
1.072
1.055
1.307
0.495
0.635
0.515
0.317
0.849
1.203
0.435
0.860
1.778
0.365
0.795
1.515
0.566
0.681
2.721
0.585
0.780
0.464
0.645
1.089
0.546
0.803
1.003
0.812
0.743
0.576
0.990
External
0.715
1.144
0.928
0.662
1.016
0.790
1.000
1.156
1.401
0.925
0.592
0.805
0.948
1.183
1.002
0.787
1.013
0.785
0.879
0.829
0.962
0.911
0.827
1
District
zduńskowolski
zgierski
brzeziński
m. Łódź
m. Skierniewice
TERYT
1019
1020
1021
1061
1062
1063
Total
1.088
1.066
1.215
1.056
1.031
1.054
Cancer
1.049
0.825
0.797
1.053
0.845
1.047
CVD
1.181
1.107
1.435
0.936
1.113
1.170
bocheński
brzeski
chrzanowski
dąbrowski
gorlicki
krakowski
limanowski
miechowski
myślenicki
nowosądecki
nowotarski
olkuski
oświęcimski
proszowicki
suski
tarnowski
tatrzański
wadowicki
wielicki
m. Kraków
m. Nowy Sącz
m. Tarnów
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1261
1262
1263
1.033
1.007
1.034
1.018
0.995
0.992
0.991
1.042
1.013
0.963
0.913
0.951
0.995
0.941
0.978
0.954
0.981
0.985
0.965
0.920
0.926
0.961
0.731
0.709
1.030
0.974
0.887
0.934
0.814
0.953
0.859
0.716
0.865
0.903
0.952
0.657
0.931
0.809
0.983
0.873
0.840
1.098
1.065
1.105
1.103
1.175
1.010
1.021
1.116
1.081
1.036
1.136
1.126
1.004
1.002
0.989
1.028
1.097
1.022
0.972
1.000
1.114
1.096
0.915
0.816
0.932
1.212
0.477
1.347
0.602
0.684
0.959
1.600
0.915
0.728
1.109
0.544
1.171
1.106
1.573
1.027
1.076
0.874
0.924
0.854
0.866
0.811
0.999
0.934
0.825
1.286
0.717
1.118
0.861
0.593
0.966
1.626
1.068
0.649
0.721
1.032
0.558
0.544
0.877
0.937
0.807
0.927
0.915
1.128
1.059
1.219
1.438
0.993
1.928
0.739
0.565
1.122
0.477
0.554
1.299
0.981
0.666
0.681
0.189
1.131
1.578
0.801
0.486
0.412
0.576
1.354
1.028
0.738
0.389
1.189
0.645
0.592
0.866
1.059
0.937
1.147
1.181
0.702
0.966
0.934
0.946
1.121
0.642
1.489
1.027
1.019
0.838
0.975
0.753
białobrzeski
ciechanowski
garwoliński
gostyniński
grodziski
grójecki
kozienicki
legionowski
lipski
łosicki
makowski
miński
mławski
nowodworski
ostrołęcki
ostrowski
otwocki
piaseczyński
płocki
płoński
pruszkowski
przasnyski
przysuski
pułtuski
radomski
siedlecki
sierpecki
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1.067
1.039
0.953
1.055
0.998
1.089
0.926
0.945
1.006
0.975
0.959
0.994
0.977
1.096
1.017
0.949
0.929
0.998
1.089
1.037
0.902
1.077
0.970
1.051
1.036
1.034
1.002
0.743
1.067
0.629
0.793
1.101
0.956
0.742
1.066
0.895
0.819
0.854
0.885
0.988
1.142
0.814
0.745
0.945
1.091
0.940
0.829
1.113
0.834
0.612
0.957
0.898
0.829
0.865
1.103
0.986
1.106
1.002
1.014
1.180
1.012
0.862
1.040
1.041
1.024
1.040
1.004
1.094
1.083
1.050
0.939
0.992
1.095
1.121
0.873
1.155
1.141
1.097
1.114
1.086
1.058
0.600
1.691
0.832
1.360
1.449
1.038
0.767
1.602
0.933
1.329
0.819
1.383
1.094
1.188
1.165
0.656
0.720
1.178
1.235
1.026
0.930
1.161
0.913
0.957
0.870
1.274
0.896
0.967
1.186
1.077
1.005
0.944
0.978
0.772
1.283
1.054
0.909
0.776
0.864
0.905
1.101
0.571
0.869
1.065
0.966
1.111
1.173
0.860
0.646
0.868
0.799
0.765
1.069
0.854
2.194
0.535
0.616
2.124
0.541
0.900
0.668
0.554
1.072
0.650
0.580
0.581
0.800
0.733
0.911
0.916
0.883
0.684
1.387
0.774
0.451
1.455
0.638
1.285
1.052
0.832
0.563
1.765
1.225
1.042
1.329
0.813
1.077
1.490
1.860
1.278
0.862
1.152
1.220
0.905
1.689
1.201
1.178
0.917
1.195
1.122
1.014
1.129
1.023
0.873
1.501
1.209
1.248
1.349
Page 262
1.460
0.892
0.507
1.193
1.446
1.093
0.568
1.005
1.128
1.585
1.358
1.815
0.897
1.269
0.785
1.063
0.842
0.374
External
0.734
1.299
1.884
0.944
0.872
0.921
1
District
sochaczewski
sokołowski
szydłowiecki
warszawski zachodni
węgrowski
wołomiński
wyszkowski
zwoleński
żuromiński
żyrardowski
m. Ostrołęka
m. Płock
m. Radom
m. Siedlce
m. st. Warszawa
TERYT
1428
1429
1430
1432
1433
1434
1435
1436
1437
1438
1461
1462
1463
1464
1465
Total
1.063
0.998
0.981
0.905
1.004
1.037
0.982
1.162
1.027
1.091
0.993
0.974
0.963
0.834
0.854
Cancer
0.897
0.779
0.646
1.065
0.901
0.987
0.901
0.818
1.103
1.101
0.929
1.132
1.006
0.965
1.090
CVD
1.184
1.046
1.073
0.839
1.029
1.036
1.000
1.116
1.097
1.115
0.796
0.875
0.896
0.859
0.725
brzeski
głubczycki
kluczborski
krapkowicki
namysłowski
nyski
oleski
opolski
prudnicki
strzelecki
m. Opole
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1661
0.958
1.075
0.946
1.029
1.036
0.998
1.058
1.025
0.955
1.064
1.011
0.917
0.843
1.088
0.981
0.895
0.872
1.039
1.038
1.002
0.982
0.895
0.765
1.186
0.990
1.190
0.970
1.100
1.131
0.946
1.209
1.105
0.988
1.214
1.208
0.874
0.555
0.661
0.645
0.809
0.745
0.889
0.731
0.254
0.983
0.885
0.944
0.964
0.768
0.970
0.567
0.914
0.914
0.598
0.512
0.716
0.834
0.683
0.805
0.759
1.746
0.548
1.055
1.131
0.702
1.919
0.356
1.275
0.497
0.758
0.422
0.531
0.639
1.255
0.826
0.890
0.810
0.855
0.880
1.194
0.777
0.668
0.395
0.702
bieszczadzki
brzozowski
dębicki
jarosławski
jasielski
kolbuszowski
krośnieński
leżajski
lubaczowski
łańcucki
mielecki
niżański
przemyski
przeworski
rzeszowski
sanocki
stalowowolski
strzyżowski
tarnobrzeski
leski
m. Krosno
m. Przemyśl
m. Rzeszów
m. Tarnobrzeg
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1861
1862
1863
1864
0.974
1.003
0.913
1.059
0.972
0.945
0.970
1.016
0.959
0.994
0.896
0.935
0.977
1.027
0.903
0.978
0.963
1.086
1.004
1.001
0.855
0.929
1.011
0.823
0.859
0.768
0.884
1.010
0.893
0.746
0.622
0.843
0.849
0.894
0.846
0.869
0.777
0.759
0.782
0.762
0.747
0.929
0.991
0.878
0.617
1.080
0.880
1.058
0.920
0.879
0.872
0.958
1.040
1.259
0.970
1.084
0.838
1.092
1.084
1.151
0.739
1.035
1.089
1.196
1.073
1.126
0.821
1.168
0.916
1.243
0.682
0.753
1.094
0.860
0.935
1.538
0.991
0.274
0.295
0.478
0.795
0.858
0.571
0.541
0.414
1.304
0.290
0.300
0.691
0.239
0.581
0.991
0.504
0.820
0.352
0.891
0.836
0.344
0.580
0.324
1.390
0.670
0.479
0.838
1.140
0.581
0.907
0.685
0.497
1.062
0.706
0.432
0.956
0.759
1.007
0.755
1.113
0.727
0.933
0.894
0.939
1.128
1.043
0.698
0.357
1.760
2.467
0.464
1.039
2.495
0.730
2.796
1.550
0.928
0.571
1.878
1.910
1.545
0.824
0.539
0.899
2.267
1.475
2.893
0.491
1.889
2.644
0.925
0.480
0.378
0.500
0.500
0.764
0.808
0.400
1.280
0.649
1.350
0.796
1.087
1.068
0.497
0.760
0.815
0.640
0.904
0.590
0.903
0.324
0.585
0.755
0.559
0.852
0.827
0.677
augustowski
białostocki
bielski
2001
2002
2003
0.928
0.961
0.930
0.962
0.850
0.825
0.848
0.871
0.960
0.805
1.292
0.869
0.846
0.895
0.717
1.692
1.889
1.011
0.829
1.149
1.632
Page 263
1.189
0.755
0.347
1.442
0.995
0.449
0.983
0.989
1.029
1.514
1.227
0.603
1.140
1.228
0.720
1.368
1.240
0.986
1.540
0.920
0.714
1.688
1.003
1.858
0.870
0.788
0.579
1.928
0.815
0.546
2.091
0.809
1.151
1.342
0.997
0.624
0.949
1.120
1.504
0.562
1.358
0.236
1.397
1.106
0.716
External
1.198
1.236
1.105
1.079
1.330
1.043
1.140
1.123
1.426
1.085
1.396
1.499
0.864
0.864
1.051
1
District
grajewski
hajnowski
kolneński
łomżyński
moniecki
sejneński
siemiatycki
sokólski
suwalski
wysokomazowiecki
zambrowski
m. Białystok
m. Łomża
m. Suwałki
TERYT
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2061
2062
2063
Total
0.921
0.987
0.995
0.913
0.884
1.015
0.967
0.939
0.973
0.964
0.967
0.830
0.906
0.901
Cancer
0.857
0.855
0.776
0.773
0.933
0.937
0.848
0.830
0.841
0.931
0.961
1.041
1.127
1.198
CVD
0.926
0.916
0.851
0.803
0.771
0.925
0.921
0.920
0.861
0.985
0.970
0.683
0.717
0.717
bytowski
chojnicki
człuchowski
gdański
kartuski
kościerski
kwidzyński
lęborski
malborski
nowodworski
pucki
słupski
starogardzki
tczewski
wejherowski
sztumski
m. Gdańsk
m. Gdynia
m. Słupsk
m. Sopot
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2261
2262
2263
2264
1.074
1.050
0.990
0.979
0.934
1.229
1.018
1.053
1.110
1.044
1.127
1.003
1.113
1.049
1.046
1.171
0.892
0.906
0.940
0.804
1.058
1.234
1.092
1.084
0.836
1.447
0.978
1.229
0.944
1.106
1.415
1.187
1.147
1.225
1.192
0.911
1.169
1.279
1.220
1.170
0.969
0.972
0.891
0.829
0.909
1.017
0.961
0.938
1.095
0.985
0.928
0.813
0.936
0.935
0.827
1.350
0.697
0.706
0.725
0.623
1.584
1.063
0.943
1.482
0.770
1.584
1.362
1.299
1.403
1.147
1.080
0.782
2.008
1.271
1.881
0.763
1.156
0.865
0.953
1.028
1.050
1.116
0.779
1.268
0.823
0.759
0.933
1.127
0.947
1.022
1.117
1.074
0.828
1.103
0.903
0.900
1.155
1.091
1.407
0.745
1.433
1.284
1.867
1.309
1.494
1.777
1.299
1.337
1.938
1.336
1.820
2.397
1.596
1.136
1.839
1.250
1.428
1.377
1.995
1.260
1.203
0.661
0.862
1.195
1.073
1.290
0.852
0.980
0.996
1.093
0.844
0.804
1.097
1.182
1.159
0.589
0.972
0.903
0.925
0.800
będziński
bielski
cieszyński
częstochowski
gliwicki
kłobucki
lubliniecki
mikołowski
myszkowski
pszczyński
raciborski
rybnicki
tarnogórski
wodzisławski
zawierciański
żywiecki
m. Bielsko-Biała
m. Bytom
m. Chorzów
m. Częstochowa
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2461
2462
2463
2464
1.109
1.036
1.075
1.082
1.065
1.028
1.068
1.073
1.077
1.078
1.080
1.107
1.012
1.107
1.087
1.139
1.097
0.906
1.000
1.147
1.040
1.144
0.920
0.953
0.900
1.099
0.792
0.949
0.828
0.849
1.154
1.034
1.031
1.019
1.147
1.256
1.042
0.810
0.955
1.075
1.166
1.037
1.185
1.196
1.135
1.249
1.066
1.201
1.198
1.184
1.197
1.213
0.993
1.095
1.068
1.145
0.982
1.170
1.311
0.995
0.924
1.112
1.116
0.783
0.756
1.429
0.719
0.972
0.639
0.792
1.469
0.971
0.731
1.016
1.599
0.956
1.119
1.516
1.216
0.673
0.605
0.887
1.529
1.031
1.233
0.948
0.775
1.078
1.154
0.951
0.696
1.292
0.655
0.819
1.234
1.238
0.776
1.207
1.370
1.216
1.171
0.721
1.299
1.528
1.098
0.539
0.046
0.084
0.468
0.830
0.460
0.674
0.427
1.206
0.022
1.844
0.828
0.769
0.160
0.743
0.822
0.056
0.032
1.204
0.393
0.423
1.220
1.061
1.455
1.073
1.382
1.103
0.751
0.786
0.986
0.912
1.404
1.647
0.945
1.022
1.070
1.218
1.205
1.071
1.222
1.579
1.009
Page 264
0.751
0.591
1.010
1.554
1.291
1.443
1.416
0.588
2.190
0.985
1.049
2.066
1.482
0.936
1.311
0.681
0.720
2.036
1.544
0.811
1.130
1.039
0.818
1.289
0.896
1.281
1.774
1.002
0.728
1.009
0.755
0.773
0.766
0.978
1.032
1.354
0.567
1.050
1.027
1.019
0.866
0.957
External
0.849
0.987
0.940
0.869
0.311
0.974
1.908
0.946
0.961
0.865
1.227
1.036
1.947
0.723
1
District
m. Gliwice
m. Jaworzno
m. Katowice
m. Mysłowice
m. Ruda Śląska
m. Rybnik
m. Sosnowiec
m. Tychy
m. Zabrze
m. Żory
TERYT
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
Total
1.068
0.922
1.072
1.025
1.012
1.167
1.066
1.241
1.003
1.234
1.104
1.068
1.026
0.874
1.019
Cancer
1.060
1.129
1.172
1.060
1.147
1.472
1.002
1.232
1.018
1.269
1.183
1.026
0.996
1.077
1.127
CVD
1.097
0.844
0.985
1.071
0.979
1.185
1.171
1.215
0.972
1.269
1.111
1.205
1.059
0.763
0.922
buski
jędrzejowski
kazimierski
kielecki
konecki
opatowski
ostrowiecki
pińczowski
sandomierski
skarżyski
starachowicki
staszowski
włoszczowski
m. Kielce
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2661
0.996
1.017
1.035
0.974
1.033
1.036
1.062
0.935
0.897
1.028
0.971
1.023
1.099
0.875
0.823
0.697
0.915
0.815
0.888
0.741
0.942
0.728
0.929
0.855
0.842
0.897
0.750
0.876
0.877
1.090
1.026
0.998
1.185
1.135
1.186
0.945
0.892
1.133
1.040
1.101
1.233
0.788
1.046
1.380
0.936
1.284
0.463
0.682
0.411
0.697
0.613
0.861
0.970
0.441
1.061
1.353
0.766
0.633
0.699
0.771
0.913
0.899
0.835
0.704
0.868
0.684
0.740
0.861
0.929
1.013
2.699
1.085
1.345
1.062
0.540
1.549
0.374
1.882
1.018
0.460
0.320
1.314
0.831
1.054
0.844
0.956
1.091
0.878
0.702
0.654
1.202
1.028
0.537
1.052
1.310
0.803
0.885
0.847
bartoszycki
braniewski
działdowski
elbląski
ełcki
giżycki
iławski
kętrzyński
lidzbarski
mrągowski
nidzicki
nowomiejski
olecki
olsztyński
ostródzki
piski
szczycieński
gołdapski
węgorzewski
m. Elbląg
m. Olsztyn
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2861
2862
1.055
1.080
1.068
1.064
0.942
0.963
0.960
1.012
1.041
1.016
1.079
0.961
1.080
1.034
0.928
0.960
0.978
1.022
1.043
0.976
0.805
0.992
0.870
1.355
0.932
0.954
0.916
0.878
1.074
1.176
1.043
1.107
1.074
0.818
1.013
0.901
1.001
0.821
1.069
1.016
1.239
1.041
0.992
1.163
0.974
1.012
0.849
0.952
0.954
0.946
1.057
0.943
0.975
0.802
1.259
0.936
0.893
0.924
0.936
0.895
0.963
0.836
0.646
1.736
1.539
1.393
1.571
1.268
1.812
1.419
2.116
1.046
1.426
2.700
1.410
1.316
2.264
1.606
1.531
1.490
1.161
1.556
1.679
1.630
1.784
1.538
0.718
0.769
0.835
0.887
0.799
0.700
1.455
0.967
1.012
0.902
0.433
0.642
0.859
0.579
0.853
0.696
0.978
0.800
0.696
0.688
0.598
1.036
1.721
1.576
0.832
1.197
0.997
0.649
1.174
1.269
1.415
0.330
1.382
0.888
0.934
1.784
2.116
1.535
1.070
1.104
0.377
0.500
0.380
0.492
0.450
0.395
0.580
0.664
0.304
0.557
0.441
0.806
0.703
0.591
0.595
0.475
0.540
0.378
0.874
0.466
0.552
chodzieski
gnieźnieński
gostyński
grodziski
3001
3002
3003
3004
3005
1.110
1.115
1.075
1.129
1.131
1.179
1.177
1.150
1.178
0.847
0.941
0.963
0.978
1.028
1.266
0.994
0.906
0.641
0.663
0.702
0.400
1.006
0.895
0.965
0.953
2.328
1.791
1.593
1.763
0.877
1.594
1.823
1.369
2.113
0.856
Page 265
1.349
1.121
0.466
0.984
1.157
0.722
1.220
1.242
0.635
0.743
1.058
0.614
1.144
1.354
0.457
0.777
1.329
0.279
0.729
1.058
0.360
1.132
1.805
0.858
1.216
1.107
0.494
1.317
1.895
0.353
1.290
1.240
0.454
0.425
1.089
0.440
1.119
1.084
0.360
0.918
1.179
0.773
1.364
1.428
0.502
External
1.164
1.043
1.579
1.074
1.354
1.767
0.905
1.321
1.424
0.939
1.186
1.220
0.928
0.931
1.502
1
District
jarociński
kaliski
kępiński
kolski
koniński
kościański
krotoszyński
leszczyński
międzychodzki
nowotomyski
obornicki
ostrowski
ostrzeszowski
pilski
pleszewski
poznański
rawicki
słupecki
szamotulski
średzki
śremski
turecki
wągrowiecki
wolsztyński
wrzesiński
złotowski
m. Kalisz
m. Konin
m. Leszno
m. Poznań
TERYT
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3061
3062
3063
3064
Total
1.087
1.053
1.058
1.019
0.953
1.000
1.172
1.070
1.203
1.081
1.195
0.972
1.145
1.042
1.206
1.084
1.142
1.072
1.110
1.061
1.219
1.059
1.073
1.198
1.044
1.015
0.961
0.861
0.973
0.947
Cancer
1.172
0.925
0.959
1.004
0.982
1.088
1.222
1.013
1.001
1.141
1.375
1.021
0.909
1.122
1.106
1.194
1.007
1.250
1.185
0.969
1.090
0.984
1.115
1.154
1.164
0.960
1.098
1.194
1.126
1.200
CVD
1.115
1.011
1.132
1.075
0.946
0.825
1.216
1.043
1.053
1.005
1.044
0.910
1.284
0.924
1.310
1.014
1.209
1.028
1.045
1.021
1.145
1.051
0.946
1.257
0.944
1.037
0.937
0.703
0.890
0.848
białogardzki
choszczeński
drawski
goleniowski
gryficki
gryfiński
kamieński
kołobrzeski
koszaliński
myśliborski
policki
pyrzycki
sławieński
stargardzki
szczecinecki
świdwiński
wałecki
łobeski
m. Koszalin
m. Szczecin
m. Świnoujście
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
3261
3262
3263
1.025
1.108
1.087
0.977
1.158
1.085
1.065
0.969
1.089
1.046
0.993
1.009
0.982
1.040
1.181
0.998
1.129
0.989
0.889
0.966
1.089
1.134
1.052
1.021
0.966
1.047
0.955
1.026
1.105
1.161
0.885
1.141
0.836
1.065
0.999
0.891
1.131
1.054
0.889
1.367
1.210
1.002
1.142
1.146
1.123
1.019
1.155
1.140
1.032
0.872
1.066
1.156
0.904
1.068
1.075
1.032
1.296
0.984
1.189
1.039
0.760
0.893
1.181
0.655
1.060
0.996
1.069
1.491
0.906
0.997
0.691
0.923
1.455
0.786
1.047
0.355
1.359
0.885
1.042
1.153
0.863
0.823
0.929
1.221
0.763
1.229
0.899
1.169
1.770
0.987
1.312
0.948
0.964
0.972
1.332
1.026
1.157
1.152
0.897
1.158
0.999
1.001
0.947
1.087
0.888
0.661
0.480
1.365
0.638
1.554
0.958
1.294
1.500
1.331
0.610
0.973
0.717
0.605
0.785
1.105
0.886
0.760
0.880
0.666
0.766
0.976
0.750
1.573
0.511
0.604
0.900
1.065
0.958
1.329
0.860
0.990
0.956
0.707
0.674
1.081
0.627
0.560
1.609
0.921
1.200
1.036
1.201
1.000
1.000
1.000
1.000
1.000
1.000
1.000
Polska
Page 266
0.479
0.943
0.723
0.809
1.026
2.088
0.755
0.756
0.517
0.442
1.267
0.538
0.624
0.835
0.965
0.661
1.196
2.006
0.640
0.981
0.491
0.520
1.100
1.108
0.516
1.844
2.674
0.553
1.352
1.311
0.596
0.806
1.212
0.772
0.920
0.535
0.342
1.054
0.276
0.967
0.974
1.364
0.642
1.215
0.673
0.778
1.224
1.042
0.777
1.302
0.512
1.266
0.844
0.619
0.654
0.959
1.972
0.778
1.334
0.578
1.262
0.934
1.625
0.727
1.120
1.338
0.965
0.937
0.864
0.689
1.664
0.544
1.196
1.317
0.703
0.543
0.772
1.143
0.771
0.896
0.918
0.721
0.841
0.857
0.520
1.080
1.109
0.873
1.013
0.673
External
1.699
0.870
2.209
1.398
0.955
2.113
1.902
2.129
2.490
1.913
2.846
2.137
1.303
1.554
2.209
1.734
1.975
1.469
1.293
1.475
1.783
1.548
2.722
1.176
1.742
2.013
1.061
1.514
1.106
1.463
Table 71. Infant mortality rates by age and district of residence in 2001–2003 and 2006–2008, per 1000
live births
District
TERYT
2001–2003
0-27 28+
Total
days days
2006–2008
0-27
28+
Total
days days
Total
change
(%)
bolesławiecki
0201
6.8
2.8
9.6
4.8
1.1
5.9
-38.7
dzierżoniowski
0202
6.1
3.8
9.9
4.6
1.4
6.1
-38.7
głogowski
0203
4.6
4.2
8.8
4.9
1.3
6.2
-30.1
górowski
0204
7.8
0.0
7.8
2.4
0.0
2.4
-69.0
jaworski
0205
5.9
5.1
11.0
3.3
1.3
4.6
-58.3
jeleniogórski
0206
7.4
1.9
9.3
2.8
2.8
5.5
-40.3
kamiennogórski
0207
6.3
2.8
9.1
3.6
2.2
5.8
-36.1
kłodzki
0208
6.5
2.6
9.1
4.3
1.8
6.0
-33.5
legnicki
0209
2.7
2.7
5.4
4.2
3.0
7.1
33.2
lubański
0210
6.9
3.8
10.6
4.9
1.2
6.1
-42.4
lubiński
0211
2.5
2.8
5.3
3.9
1.8
5.8
9.6
lwówecki
0212
3.8
3.0
6.8
5.1
4.4
9.5
39.7
milicki
0213
6.4
2.4
8.8
5.2
0.7
6.0
-32.3
oleśnicki
0214
6.2
4.2
10.4
5.8
1.8
7.6
-27.1
oławski
0215
7.4
1.1
8.5
5.3
4.8
10.1
19.3
polkowicki
0216
5.8
2.1
7.9
6.7
0.5
7.1
-9.2
strzeliński
0217
6.5
1.6
8.1
8.7
1.6
10.3
26.5
średzki
0218
3.7
6.6
10.3
5.7
1.3
7.0
-32.2
świdnicki
0219
5.0
2.9
7.9
5.3
2.4
7.7
-3.3
trzebnicki
0220
9.0
4.0
13.0
8.9
1.2
10.1
-22.4
wałbrzyski
0221
4.4
4.2
8.6
5.4
2.1
7.5
-13.0
wołowski
0222
6.2
4.6
10.8
3.7
1.5
5.2
-52.1
wrocławski
0223
9.2
2.0
11.2
7.4
1.5
8.8
-20.9
ząbkowicki
0224
11.1
2.7
13.8
6.6
2.2
8.8
-36.3
zgorzelecki
0225
7.2
3.0
10.2
6.7
1.9
8.5
-16.9
złotoryjski
0226
9.9
1.6
11.5
4.1
2.8
6.9
-39.9
m. Jelenia Góra
0261
7.8
3.1
10.9
4.3
1.4
5.8
-46.7
m. Legnica
m. Wrocław
0262
0264
3.8
6.8
3.8
2.9
7.5
9.7
4.3
5.4
3.3
1.4
7.7
6.8
2.2
-29.9
aleksandrowski
0401
6.7
1.2
7.9
4.1
3.0
7.1
-10.5
brodnicki
0402
6.4
3.6
9.9
3.6
1.1
4.6
-53.5
bydgoski
0403
4.5
3.8
8.2
5.9
2.1
8.0
-2.6
chełmiński
0404
6.6
7.8
14.4
4.6
3.4
8.0
-44.6
0405
5.8
2.6
8.3
4.1
0.7
4.8
-42.5
grudziądzki
0406
9.3
5.0
14.3
3.8
2.5
6.3
-55.9
inowrocławski
0407
6.1
1.7
7.8
3.9
1.7
5.6
-28.8
lipnowski
0408
5.7
2.6
8.3
5.8
3.7
9.5
15.5
mogileński
0409
5.1
2.9
7.9
4.0
1.3
5.3
-32.9
nakielski
0410
5.6
2.5
8.1
5.1
1.6
6.8
-16.4
radziejowski
0411
8.0
1.6
9.7
5.3
0.8
6.1
-37.1
rypiński
0412
4.7
0.7
5.4
4.9
1.8
6.7
24.9
sępoleński
0413
7.3
0.7
8.0
4.3
2.5
6.8
-15.1
Page 267
District
TERYT
2001–2003
0-27 28+
Total
days days
2006–2008
0-27
28+
Total
days days
Total
change
(%)
świecki
0414
7.3
1.6
8.9
5.4
2.1
7.5
-15.8
toruński
0415
3.7
2.4
6.1
3.8
1.2
5.0
-18.7
tucholski
0416
5.5
4.3
9.7
2.3
0.6
2.8
-70.8
wąbrzeski
0417
5.9
3.4
9.3
3.4
1.7
5.1
-45.1
włocławski
0418
5.5
2.4
7.8
6.5
0.8
7.3
-7.2
żniński
0419
4.9
1.3
6.3
3.0
0.4
3.5
-44.6
m. Bydgoszcz
0461
5.3
2.1
7.4
4.1
1.1
5.2
-29.7
m. Grudziądz
0462
3.7
3.3
7.0
5.5
1.6
7.1
2.4
m. Toruń
0463
4.8
3.3
8.1
2.3
2.0
4.3
-46.5
m. Włocławek
0464
7.0
1.5
8.6
3.1
1.9
5.0
-41.3
bialski
0601
4.5
1.9
6.4
2.4
1.3
3.7
-42.1
biłgorajski
0602
5.3
2.5
7.8
3.1
1.6
4.7
-39.5
chełmski
0603
3.1
4.4
7.4
5.2
1.6
6.9
-8.0
hrubieszowski
0604
3.4
2.9
6.3
3.0
3.0
6.0
-3.8
janowski
0605
8.3
3.8
12.2
6.1
1.4
7.5
-38.3
krasnostawski
0606
5.4
1.5
6.9
5.5
2.2
7.7
11.3
kraśnicki
0607
8.3
2.8
11.0
3.8
1.4
5.2
-52.7
lubartowski
0608
5.8
2.7
8.5
4.6
1.0
5.6
-33.6
lubelski
0609
5.5
1.8
7.3
6.6
1.7
8.4
15.1
łęczyński
0610
4.6
1.7
6.3
4.4
2.7
7.1
13.5
łukowski
0611
3.7
2.7
6.4
3.5
2.5
6.1
-5.0
opolski
0612
7.6
3.2
10.8
4.0
3.0
7.0
-35.3
parczewski
0613
4.3
1.7
6.0
4.5
0.9
5.4
-9.4
puławski
0614
4.7
1.2
5.8
3.6
2.1
5.7
-2.3
radzyński
0615
6.2
1.4
7.6
4.3
1.4
5.8
-24.0
rycki
0616
7.4
2.7
10.1
4.9
1.6
6.6
-34.8
świdnicki
0617
5.4
3.4
8.9
4.2
4.2
8.4
-5.5
tomaszowski
0618
4.3
1.4
5.8
3.8
1.9
5.7
-1.3
włodawski
0619
4.3
2.6
6.9
4.7
0.0
4.7
-31.6
zamojski
0620
4.6
2.7
7.3
5.6
2.8
8.4
14.4
m. Biała Podlaska
0661
5.9
3.0
8.9
4.0
2.9
6.9
-22.9
m. Chełm
0662
5.3
1.8
7.0
4.8
0.5
5.3
-24.6
m. Lublin
0663
6.9
2.6
9.5
4.4
2.2
6.5
-31.4
m. Zamość
0664
1.7
3.4
5.2
6.5
0.0
6.5
25.6
gorzowski
0801
5.0
4.0
9.0
3.7
0.9
4.6
-48.9
krośnieński
0802
4.7
4.1
8.8
1.7
1.7
3.3
-62.2
międzyrzecki
0803
3.7
2.4
6.1
4.3
1.6
5.9
-2.5
nowosolski
0804
6.4
3.0
9.4
5.2
1.7
6.9
-26.7
słubicki
0805
5.0
2.2
7.2
3.1
0.6
3.7
-49.2
0806
3.8
2.5
6.3
2.8
2.8
5.7
-9.8
sulęciński
0807
5.5
1.8
7.3
3.3
3.3
6.5
-11.0
świebodziński
0808
7.8
1.2
9.0
3.7
1.6
5.3
-40.5
zielonogórski
0809
4.4
1.6
6.0
3.8
2.1
5.9
-2.1
Page 268
District
TERYT
2001–2003
0-27 28+
Total
days days
2006–2008
0-27
28+
Total
days days
Total
change
(%)
żagański
0810
5.7
1.2
6.9
4.4
3.7
8.1
17.3
żarski
0811
3.8
4.8
8.6
4.3
0.3
4.6
-45.8
wschowski
0812
4.7
0.8
5.5
5.3
2.3
7.5
36.2
0861
6.7
1.3
7.9
6.3
2.4
8.7
8.9
m. Zielona Góra
0862
4.1
3.0
7.0
2.7
2.1
4.7
-32.9
bełchatowski
1001
4.7
1.3
6.0
2.8
0.3
3.0
-49.7
kutnowski
1002
2.7
2.3
5.1
5.5
2.5
8.0
58.3
łaski
1003
4.4
2.9
7.3
0.0
3.5
3.5
-52.2
łęczycki
1004
3.4
2.7
6.0
4.4
3.1
7.5
24.2
łowicki
1005
3.1
3.9
7.0
1.7
1.3
3.0
-57.6
łódzki wschodni
1006
8.1
2.5
10.6
3.2
1.6
4.8
-55.0
opoczyński
1007
7.3
1.1
8.4
1.8
1.8
3.7
-56.4
pabianicki
1008
6.8
1.9
8.7
3.1
2.2
5.3
-38.2
pajęczański
1009
3.9
0.7
4.6
5.0
0.6
5.7
22.8
piotrkowski
1010
4.6
2.1
6.7
3.7
0.7
4.4
-34.8
poddębicki
1011
6.9
2.6
9.5
3.3
0.0
3.3
-65.4
radomszczański
1012
5.4
2.3
7.7
5.1
1.1
6.2
-19.2
rawski
1013
4.1
3.4
7.6
3.2
0.0
3.2
-58.3
sieradzki
1014
3.4
2.0
5.5
2.5
0.5
3.0
-44.7
skierniewicki
1015
2.6
0.9
3.4
4.8
0.8
5.6
62.7
tomaszowski
1016
5.8
2.3
8.2
3.4
1.5
4.9
-40.0
wieluński
1017
2.7
0.9
3.5
4.2
1.3
5.4
53.6
wieruszowski
1018
3.5
2.1
5.7
2.2
0.7
2.9
-48.2
zduńskowolski
1019
3.1
3.1
6.2
5.2
0.5
5.7
-8.2
zgierski
1020
6.9
1.0
7.9
4.1
0.9
5.0
-37.0
brzeziński
1021
6.2
0.0
6.2
3.4
1.1
4.5
-26.7
m. Łódź
1061
6.3
2.5
8.8
4.0
2.1
6.1
-31.2
1062
3.6
2.7
6.4
5.0
2.5
7.6
18.6
m. Skierniewice
1063
6.2
0.8
7.0
3.8
3.2
7.0
0.5
bocheński
1201
4.5
1.8
6.4
3.3
0.8
4.2
-34.3
brzeski
1202
3.7
2.0
5.7
3.6
1.3
5.0
-13.5
chrzanowski
1203
4.2
3.6
7.7
4.6
2.6
7.1
-7.8
dąbrowski
1204
4.8
3.0
7.7
6.7
1.2
7.9
2.2
gorlicki
1205
3.1
2.5
5.6
3.5
0.8
4.3
-22.3
krakowski
1206
5.7
0.9
6.6
3.8
1.5
5.2
-20.1
limanowski
1207
6.0
1.9
7.9
4.8
2.2
7.0
-11.1
miechowski
1208
2.0
1.4
3.4
4.7
3.4
8.1
138.7
myślenicki
1209
6.9
1.2
8.1
2.6
0.7
3.3
-59.9
nowosądecki
1210
3.5
1.9
5.3
4.6
2.5
7.1
33.5
nowotarski
1211
3.3
1.7
5.0
3.0
2.8
5.8
15.3
olkuski
1212
7.5
2.2
9.6
5.5
1.5
7.1
-26.6
oświęcimski
1213
3.8
1.4
5.3
2.0
1.5
3.5
-34.2
proszowicki
1214
8.0
0.8
8.8
3.9
0.0
3.9
-55.7
Page 269
District
TERYT
2001–2003
0-27 28+
Total
days days
2006–2008
0-27
28+
Total
days days
Total
change
(%)
suski
1215
2.9
1.4
4.3
5.3
2.1
7.4
72.7
tarnowski
1216
3.9
1.7
5.6
3.6
2.1
5.7
0.2
tatrzański
1217
0.5
2.5
3.0
3.0
1.5
4.6
54.7
wadowicki
1218
5.3
1.6
6.9
4.6
2.7
7.2
4.6
wielicki
1219
6.6
1.3
7.9
3.7
0.6
4.2
-46.1
m. Kraków
1261
4.8
2.1
6.8
3.9
1.4
5.3
-22.4
m. Nowy Sącz
1262
3.9
2.7
6.7
5.1
0.7
5.8
-12.4
m. Tarnów
1263
5.3
1.3
6.6
3.0
0.7
3.7
-44.4
białobrzeski
1401
4.0
0.8
4.8
7.1
4.0
11.1
131.9
ciechanowski
1402
4.5
2.1
6.6
5.3
0.4
5.6
-14.8
garwoliński
1403
4.6
1.1
5.6
2.8
1.3
4.0
-28.3
gostyniński
1404
7.3
1.5
8.7
3.5
0.0
3.5
-59.6
grodziski
1405
5.6
1.5
7.1
2.0
1.6
3.6
-48.6
grójecki
1406
6.0
2.3
8.3
2.6
1.9
4.5
-45.7
kozienicki
1407
5.2
1.6
6.8
3.7
0.0
3.7
-46.0
legionowski
1408
4.7
1.7
6.4
3.1
1.6
4.7
-26.8
lipski
1409
2.9
2.9
5.7
7.1
0.0
7.1
23.0
łosicki
1410
4.8
3.9
8.7
2.0
3.0
5.1
-41.7
makowski
1411
3.1
3.1
6.2
3.6
0.0
3.6
-41.3
miński
1412
3.2
1.2
4.4
2.5
0.8
3.3
-25.5
mławski
1413
5.9
2.1
8.0
5.1
1.7
6.8
-15.9
nowodworski
1414
4.0
0.9
4.8
1.9
0.8
2.7
-44.1
ostrołęcki
1415
6.3
1.3
7.6
3.3
1.3
4.6
-38.9
ostrowski
1416
4.1
2.1
6.2
2.0
2.0
4.1
-34.2
otwocki
1417
3.2
1.0
4.2
3.5
1.3
4.8
14.7
piaseczyński
1418
5.4
0.9
6.3
3.0
2.1
5.1
-19.8
płocki
1419
3.0
1.8
4.8
5.9
3.5
9.5
95.2
płoński
1420
6.3
1.0
7.3
4.8
1.0
5.8
-19.9
pruszkowski
1421
4.0
0.8
4.8
3.8
0.2
4.0
-16.1
przasnyski
1422
5.5
2.5
7.9
2.1
2.1
4.1
-48.2
przysuski
1423
10.2
2.9
13.1
1.6
3.3
4.9
-62.8
pułtuski
1424
6.1
2.4
8.5
2.9
2.3
5.3
-38.4
radomski
1425
7.6
1.9
9.5
2.8
2.0
4.8
-49.4
siedlecki
1426
3.3
1.5
4.8
5.1
1.8
6.9
43.7
sierpecki
1427
5.5
0.6
6.1
2.3
2.3
4.6
-24.0
sochaczewski
1428
5.8
0.8
6.6
3.4
0.4
3.7
-43.3
sokołowski
1429
3.5
2.9
6.4
1.1
1.1
2.3
-64.3
szydłowiecki
1430
3.1
0.8
3.8
8.1
0.8
9.0
134.9
warszawski zachodni
1432
2.7
0.0
2.7
3.5
0.7
4.2
52.6
węgrowski
1433
6.9
2.8
9.7
2.7
2.2
4.9
-49.6
wołomiński
1434
3.6
3.2
6.8
2.6
1.6
4.1
-39.1
wyszkowski
1435
4.6
1.7
6.3
2.3
3.1
5.4
-13.6
zwoleński
1436
2.6
3.5
6.0
8.4
4.2
12.5
107.7
Page 270
District
TERYT
2001–2003
0-27 28+
Total
days days
2006–2008
0-27
28+
Total
days days
Total
change
(%)
żuromiński
1437
8.9
0.7
9.6
2.2
0.7
2.9
-69.3
żyrardowski
1438
3.7
3.7
7.4
3.3
1.6
4.9
-34.2
m. Ostrołęka
1461
6.9
1.3
8.1
4.8
0.6
5.4
-33.1
m. Płock
1462
9.2
1.5
10.6
4.8
1.0
5.9
-44.9
m. Radom
1463
6.3
2.6
8.9
4.2
0.9
5.1
-42.8
m. Siedlce
1464
5.7
0.9
6.6
1.2
2.4
3.6
-46.5
m. st. Warszawa
1465
4.7
1.9
6.6
3.8
1.1
4.8
-27.0
brzeski
1601
4.8
1.6
6.3
5.0
2.5
7.5
18.5
głubczycki
1602
2.2
0.7
3.0
0.7
3.0
3.7
25.5
1603
5.7
0.8
6.6
2.0
0.8
2.8
-57.0
kluczborski
1604
5.6
2.8
8.4
4.1
2.3
6.5
-22.7
krapkowicki
1605
4.3
1.2
5.6
6.8
1.2
8.0
43.8
namysłowski
1606
2.5
0.0
2.5
3.9
0.8
4.7
86.1
nyski
1607
3.7
1.3
5.1
5.1
1.6
6.7
32.6
oleski
1608
5.7
0.6
6.3
4.2
0.6
4.8
-22.9
opolski
1609
4.4
2.7
7.0
2.3
0.7
3.0
-57.6
prudnicki
1610
3.3
2.0
5.3
1.3
0.7
2.0
-63.0
strzelecki
1611
1.6
1.1
2.7
2.7
1.6
4.3
62.2
m. Opole
1661
4.8
2.6
7.3
3.8
2.9
6.7
-8.2
bieszczadzki
1801
4.4
1.5
5.8
6.0
1.5
7.5
29.1
brzozowski
1802
4.3
1.7
6.0
3.6
1.3
4.9
-17.9
dębicki
1803
2.8
1.4
4.3
4.5
1.7
6.2
45.3
jarosławski
1804
5.6
2.1
7.7
4.6
2.2
6.8
-10.9
jasielski
1805
3.3
1.1
4.4
6.0
0.9
6.9
55.5
kolbuszowski
1806
5.0
0.5
5.5
4.6
1.1
5.7
4.9
krośnieński
1807
6.1
2.6
8.8
4.7
0.8
5.6
-36.7
leżajski
1808
8.1
1.8
9.8
6.2
2.4
8.6
-13.0
lubaczowski
1809
2.7
1.1
3.8
5.3
0.0
5.3
38.6
łańcucki
1810
3.6
1.2
4.9
7.6
0.8
8.4
72.4
mielecki
1811
5.3
1.8
7.0
4.2
1.2
5.4
-22.9
niżański
1812
4.2
2.6
6.8
5.8
0.0
5.8
-15.1
przemyski
1813
5.3
1.2
6.5
2.6
1.3
3.8
-41.1
przeworski
1814
7.5
0.8
8.3
6.6
0.8
7.4
-10.9
1815
9.4
3.3
12.7
6.7
1.2
7.9
-37.7
rzeszowski
1816
7.2
3.1
10.3
3.2
1.9
5.1
-50.6
sanocki
1817
2.5
1.4
3.9
4.6
1.1
5.6
42.7
stalowowolski
1818
2.7
2.3
5.0
2.5
2.1
4.6
-8.2
strzyżowski
1819
7.1
1.5
8.6
3.7
0.5
4.2
-50.8
tarnobrzeski
1820
2.5
3.1
5.6
6.7
1.3
8.0
44.7
leski
1821
9.8
2.4
12.2
7.8
2.6
10.4
-14.5
m. Krosno
1861
2.5
2.5
4.9
3.8
0.8
4.6
-6.4
m. Przemyśl
1862
3.3
1.7
5.0
2.8
4.4
7.2
43.3
m. Rzeszów
1863
6.4
1.8
8.2
4.4
1.5
5.8
-28.9
Page 271
District
TERYT
2001–2003
0-27 28+
Total
days days
2006–2008
0-27
28+
Total
days days
Total
change
(%)
m. Tarnobrzeg
1864
11.3
1.5
12.9
3.8
2.3
6.0
-53.2
augustowski
2001
4.3
2.1
6.4
1.7
0.6
2.3
-64.5
białostocki
2002
3.0
1.1
4.0
4.0
1.6
5.5
36.9
bielski
2003
2.5
1.3
3.8
5.8
0.6
6.4
67.9
grajewski
2004
3.1
1.3
4.4
5.2
0.6
5.8
32.4
hajnowski
2005
0.9
2.6
3.5
0.9
1.9
2.8
-20.0
kolneński
2006
5.0
4.3
9.3
3.8
0.8
4.6
-50.2
łomżyński
2007
5.9
3.6
9.5
6.2
3.1
9.3
-2.2
moniecki
2008
8.3
4.1
12.4
1.8
2.6
4.4
-64.4
sejneński
2009
6.5
1.6
8.1
1.6
4.9
6.5
-20.3
siemiatycki
2010
5.4
3.1
8.4
3.6
1.8
5.3
-36.6
sokólski
2011
4.5
3.6
8.2
1.5
1.0
2.6
-68.5
suwalski
2012
2.3
2.3
4.6
1.6
2.4
3.9
-14.0
wysokomazowiecki
2013
3.3
2.2
5.5
1.6
0.5
2.1
-61.2
zambrowski
2014
4.4
2.2
6.5
2.2
3.6
5.7
-12.3
m. Białystok
2061
4.6
1.7
6.3
5.3
0.6
5.9
-6.7
m. Łomża
2062
7.8
1.2
9.0
6.3
1.7
8.1
-10.1
m. Suwałki
2063
6.3
1.0
7.3
5.6
3.3
8.9
21.2
bytowski
2201
5.4
2.9
8.2
6.6
1.0
7.6
-7.3
chojnicki
2202
2.5
1.3
3.8
3.3
1.7
5.0
31.6
człuchowski
2203
4.1
2.1
6.2
6.0
0.5
6.5
5.2
gdański
2204
1.6
3.6
5.2
3.4
1.8
5.2
1.0
kartuski
2205
2.0
0.7
2.7
3.4
1.6
5.0
84.2
kościerski
2206
2.3
2.3
4.6
3.4
0.7
4.1
-9.7
kwidzyński
2207
3.9
1.4
5.3
3.3
1.3
4.6
-12.8
lęborski
2208
1.4
2.3
3.6
6.0
1.7
7.7
112.2
malborski
2209
4.8
2.6
7.4
4.8
2.4
7.3
-2.0
nowodworski
2210
3.4
3.4
6.8
4.2
0.8
5.1
-25.3
pucki
2211
1.9
1.5
3.4
6.0
2.3
8.3
141.8
słupski
2212
6.6
0.9
7.6
5.0
2.1
7.1
-7.1
starogardzki
2213
4.9
1.2
6.1
4.5
1.9
6.4
5.6
tczewski
2214
3.9
1.6
5.5
5.3
1.2
6.5
18.4
wejherowski
2215
4.2
1.3
5.5
3.2
1.7
4.9
-9.7
sztumski
2216
1.4
1.4
2.8
2.0
1.3
3.3
20.0
m. Gdańsk
2261
16.1
2.6
18.7
4.3
1.5
5.8
-68.7
m. Gdynia
2262
1.6
2.2
3.8
4.3
1.1
5.4
42.8
m. Słupsk
2263
3.7
2.1
5.8
5.9
1.1
7.0
20.3
m. Sopot
2264
1.3
2.7
4.0
2.4
0.0
2.4
-41.0
będziński
2401
4.9
3.4
8.3
6.3
1.3
7.6
-8.9
bielski
2402
4.8
1.9
6.7
4.0
1.0
5.0
-25.4
cieszyński
2403
4.4
1.2
5.6
4.6
1.7
6.3
13.2
częstochowski
2404
6.4
2.9
9.3
5.0
1.4
6.4
-31.2
gliwicki
2405
5.5
4.8
10.2
5.2
1.2
6.4
-37.2
Page 272
District
TERYT
2001–2003
0-27 28+
Total
days days
2006–2008
0-27
28+
Total
days days
Total
change
(%)
kłobucki
2406
6.4
3.8
10.3
3.7
2.5
6.2
-39.6
lubliniecki
2407
6.2
2.4
8.5
3.8
0.5
4.2
-50.4
mikołowski
2408
4.7
2.6
7.3
4.5
1.4
5.9
-19.5
myszkowski
2409
6.0
4.0
9.9
2.6
4.7
7.3
-26.6
pszczyński
2410
6.9
3.1
10.0
5.5
2.5
8.0
-20.2
raciborski
2411
2.5
1.4
4.0
2.9
3.3
6.2
57.4
rybnicki
2412
4.6
2.1
6.7
3.8
2.9
6.7
0.6
tarnogórski
2413
5.6
2.5
8.1
3.4
1.1
4.5
-44.2
2414
6.9
1.3
8.2
5.2
2.3
7.6
-7.2
wodzisławski
2415
4.9
2.3
7.3
3.9
2.5
6.4
-12.3
zawierciański
2416
5.3
2.7
8.0
7.2
1.2
8.4
5.2
żywiecki
2417
6.1
2.0
8.1
3.1
2.3
5.3
-34.0
m. Bielsko-Biała
2461
5.4
2.5
7.9
2.9
0.8
3.7
-53.8
m. Bytom
2462
7.4
3.8
11.3
6.2
3.7
9.9
-11.9
m. Chorzów
2463
5.9
5.2
11.1
5.3
2.4
7.7
-30.4
m. Częstochowa
2464
5.3
2.4
7.6
4.2
1.6
5.8
-24.2
2465
5.7
2.0
7.8
4.0
2.0
6.0
-23.4
m. Gliwice
2466
5.4
2.2
7.6
3.7
1.3
5.0
-34.1
2467
6.1
2.1
8.2
6.3
2.0
8.3
1.2
m. Jaworzno
2468
3.8
3.4
7.1
2.7
4.3
7.0
-1.3
m. Katowice
2469
7.2
4.8
12.0
6.1
3.2
9.3
-22.3
m. Mysłowice
2470
5.7
4.2
9.9
3.6
3.6
7.2
-27.1
2471
6.1
1.4
7.4
4.4
1.9
6.2
-16.3
m. Ruda Śląska
2472
7.3
4.0
11.3
5.2
1.6
6.8
-39.3
m. Rybnik
2473
7.2
3.1
10.4
6.6
3.2
9.8
-5.7
2474
6.2
4.5
10.7
5.0
3.5
8.5
-20.6
m. Sosnowiec
2475
7.5
2.6
10.1
5.4
1.6
7.0
-30.4
2476
9.5
2.7
12.2
6.6
1.8
8.4
-31.0
m. Tychy
2477
2.9
0.9
3.8
5.2
2.5
7.7
100.1
m. Zabrze
2478
8.8
3.0
11.8
4.4
4.4
8.9
-24.5
m. Żory
2479
3.1
2.6
5.7
4.2
0.5
4.6
-18.4
buski
2601
4.4
2.4
6.8
4.0
2.5
6.5
-3.8
jędrzejowski
2602
4.9
1.9
6.7
1.5
1.8
3.3
-51.1
kazimierski
2603
4.4
3.3
7.6
2.2
1.1
3.3
-56.4
kielecki
2604
6.4
2.3
8.7
2.8
1.2
4.0
-54.2
konecki
2605
4.2
2.5
6.7
3.2
2.0
5.3
-21.9
opatowski
2606
6.0
1.2
7.2
3.3
1.3
4.6
-35.9
ostrowiecki
2607
5.0
2.7
7.7
3.7
1.4
5.1
-33.7
pińczowski
2608
9.1
3.3
12.4
1.8
0.9
2.7
-78.0
sandomierski
2609
7.5
2.1
9.6
3.1
0.9
4.0
-58.4
skarżyski
2610
5.8
1.6
7.4
4.6
1.0
5.7
-23.3
starachowicki
2611
6.5
1.9
8.4
3.7
1.9
5.6
-33.8
staszowski
2612
3.0
1.3
4.3
5.5
2.3
7.7
79.3
Page 273
District
TERYT
2001–2003
0-27 28+
Total
days days
2006–2008
0-27
28+
Total
days days
Total
change
(%)
włoszczowski
2613
4.0
0.7
4.7
2.7
2.7
5.5
16.1
m. Kielce
2661
4.8
1.6
6.4
3.4
1.9
5.2
-17.9
bartoszycki
2801
6.2
2.1
8.2
1.5
1.0
2.5
-69.4
braniewski
2802
5.4
3.4
8.8
6.7
2.2
8.9
0.8
działdowski
2803
3.9
2.2
6.0
3.8
0.4
4.2
-29.8
elbląski
2804
5.1
1.0
6.1
5.6
4.6
10.2
67.7
ełcki
2805
7.0
1.1
8.1
4.2
2.1
6.3
-22.6
giżycki
2806
4.1
1.2
5.3
5.0
0.6
5.6
4.9
iławski
2807
4.7
1.9
6.6
4.2
1.5
5.7
-13.9
kętrzyński
2808
5.6
2.0
7.7
4.4
2.0
6.4
-16.7
lidzbarski
2809
2.2
1.5
3.7
2.2
1.5
3.7
0.0
mrągowski
2810
3.8
0.6
4.4
3.6
2.4
5.9
33.9
nidzicki
2811
2.4
0.0
2.4
5.9
1.7
7.5
214.8
nowomiejski
2812
2.5
2.5
4.9
4.2
1.8
6.0
21.2
olecki
2813
5.0
2.5
7.5
2.4
1.6
4.1
-45.5
olsztyński
2814
4.4
2.0
6.4
2.8
1.3
4.1
-36.1
ostródzki
2815
3.3
0.9
4.2
2.5
0.8
3.3
-20.5
piski
2816
7.6
0.5
8.1
3.1
0.5
3.7
-55.0
szczycieński
2817
7.9
2.5
10.3
5.0
0.4
5.4
-47.6
gołdapski
2818
7.1
3.6
10.7
4.0
2.0
6.0
-43.6
węgorzewski
2819
5.9
4.5
10.4
5.9
1.5
7.4
-28.6
m. Elbląg
2861
3.2
2.4
5.6
2.3
2.5
4.8
-14.5
m. Olsztyn
2862
2.1
1.4
3.5
2.7
1.2
3.9
11.1
chodzieski
3001
3.4
1.3
4.7
6.7
1.2
7.9
67.5
3002
2.8
1.1
3.9
5.0
1.9
6.8
77.8
gnieźnieński
3003
6.8
2.3
9.1
4.6
0.6
5.2
-43.1
gostyński
3004
6.2
1.2
7.4
3.4
2.6
6.0
-18.2
grodziski
3005
4.8
1.2
5.9
3.7
3.2
6.9
16.4
jarociński
3006
5.9
2.3
8.2
4.9
0.4
5.3
-34.9
kaliski
3007
6.5
1.9
8.4
2.7
2.7
5.5
-35.4
kępiński
3008
6.9
1.2
8.1
3.1
1.6
4.7
-42.1
kolski
3009
4.1
2.2
6.3
2.8
1.7
4.5
-27.6
koniński
3010
3.6
1.9
5.6
3.9
1.1
5.0
-10.2
kościański
3011
3.8
0.8
4.7
4.7
0.0
4.7
1.0
krotoszyński
3012
5.7
2.4
8.1
1.9
1.9
3.8
-53.7
leszczyński
3013
6.4
0.0
6.4
2.6
4.2
6.9
6.4
międzychodzki
3014
5.1
0.8
5.9
6.0
2.3
8.3
40.4
nowotomyski
3015
6.3
1.3
7.5
5.8
2.2
8.0
6.9
obornicki
3016
6.2
1.5
7.7
3.7
0.5
4.1
-46.2
ostrowski
3017
3.6
1.5
5.1
4.3
1.6
5.9
14.8
ostrzeszowski
3018
6.6
0.6
7.2
4.2
0.5
4.7
-34.1
pilski
3019
4.7
2.8
7.4
4.2
1.3
5.5
-26.0
pleszewski
3020
3.4
1.0
4.4
5.4
1.3
6.7
53.8
Page 274
District
TERYT
2001–2003
0-27 28+
Total
days days
2006–2008
0-27
28+
Total
days days
Total
change
(%)
poznański
3021
4.4
1.3
5.7
4.7
1.9
6.6
16.9
rawicki
3022
1.5
1.5
3.0
4.1
0.0
4.1
33.0
słupecki
3023
5.4
0.5
6.0
2.6
1.5
4.1
-31.1
szamotulski
3024
6.4
1.5
7.9
3.8
1.3
5.1
-35.1
średzki
3025
5.3
1.2
6.5
5.2
3.1
8.3
26.9
śremski
3026
5.7
2.6
8.3
6.0
2.8
8.8
6.2
turecki
3027
4.6
1.5
6.1
5.3
2.8
8.1
32.8
wągrowiecki
3028
7.0
2.2
9.2
6.1
1.1
7.3
-20.9
wolsztyński
3029
3.1
1.0
4.2
1.9
2.9
4.8
15.0
wrzesiński
3030
6.1
1.3
7.5
2.2
1.9
4.1
-44.8
złotowski
3031
3.9
2.6
6.5
5.8
1.7
7.4
15.3
m. Kalisz
3061
6.0
2.5
8.5
3.9
0.7
4.6
-45.9
m. Konin
3062
8.6
1.0
9.5
3.0
2.2
5.2
-45.2
m. Leszno
3063
6.1
3.9
10.0
5.0
3.0
8.0
-20.4
m. Poznań
3064
4.6
1.5
6.1
5.4
1.8
7.2
18.9
białogardzki
3201
7.2
0.7
7.9
4.9
3.0
7.9
0.0
choszczeński
3202
3.8
3.2
7.0
2.4
1.8
4.2
-39.7
drawski
3203
3.3
3.3
6.6
8.6
2.7
11.2
71.1
goleniowski
3204
3.7
2.0
5.7
2.7
3.4
6.1
7.0
gryficki
3205
4.7
2.6
7.3
3.4
3.0
6.4
-12.0
gryfiński
3206
3.8
2.7
6.5
4.7
1.5
6.2
-5.1
kamieński
3207
7.8
2.8
10.7
4.8
1.4
6.2
-42.0
kołobrzeski
3208
6.7
2.9
9.5
3.1
1.8
4.9
-48.7
koszaliński
3209
7.5
4.0
11.4
3.4
1.0
4.3
-62.1
myśliborski
3210
3.4
3.9
7.4
6.5
1.8
8.3
12.7
policki
3211
5.0
1.7
6.7
4.1
1.4
5.4
-18.6
pyrzycki
3212
2.5
3.3
5.7
3.7
1.5
5.2
-9.2
sławieński
3213
7.6
2.2
9.8
2.1
3.1
5.2
-46.6
stargardzki
3214
5.6
3.1
8.7
4.1
2.2
6.2
-28.3
szczecinecki
3215
5.4
3.7
9.1
6.0
1.2
7.3
-20.6
świdwiński
3216
7.1
1.3
8.3
6.3
1.3
7.5
-9.5
wałecki
3217
3.2
2.1
5.4
3.4
1.7
5.1
-5.2
łobeski
3218
5.8
1.7
7.5
6.1
4.6
10.6
42.7
m. Koszalin
3261
6.1
2.0
8.2
4.0
1.1
5.1
-37.7
m. Szczecin
3262
5.5
2.2
7.7
4.0
2.2
6.1
-19.9
m. Świnoujście
3263
6.7
0.0
6.7
4.9
1.0
5.9
-11.7
5.2
2.2
7.4
4.2
1.7
5.9
-20.9
Polska
Page 275
Table 72. Life expectancy at birth (in years) of males and females in 2001–2003 and 2006–2008
Males
District
Females
TERYT
2001–2003
2006–2008
change
2001–2003
2006–2008
change
bolesławiecki
dzierżoniowski
głogowski
górowski
jaworski
jeleniogórski
kamiennogórski
kłodzki
legnicki
lubański
lubiński
lwówecki
milicki
oleśnicki
oławski
polkowicki
strzeliński
średzki
świdnicki
trzebnicki
wałbrzyski
wołowski
wrocławski
ząbkowicki
zgorzelecki
złotoryjski
m. Jelenia Góra
m. Legnica
m. Wrocław
0201
0202
0203
0204
0205
0206
0207
0208
0209
0210
0211
0212
0213
0214
0215
0216
0217
0218
0219
0220
0221
0222
0223
0224
0225
0226
0261
0262
0264
69.4
69.3
69.9
69.6
68.6
69.3
68.6
68.5
69.1
67.9
70.8
67.7
70.1
69.9
70.8
68.2
68.3
68.5
69.4
68.7
68.5
68.6
69.9
68.4
68.0
68.9
70.4
69.7
71.9
70.8
70.0
71.2
71.7
69.5
69.2
69.3
69.2
70.4
69.8
71.5
67.6
71.2
70.0
70.8
70.2
69.6
68.3
69.6
69.3
68.5
70.8
70.4
69.3
68.4
69.0
71.4
70.2
72.5
1.4
0.7
1.3
2.1
0.9
-0.2
0.7
0.7
1.3
1.9
0.7
-0.1
1.1
0.0
0.0
2.1
1.4
-0.2
0.2
0.6
0.0
2.2
0.5
0.9
0.4
0.1
1.0
0.6
0.6
78.0
77.7
78.6
77.8
78.1
77.1
78.3
77.0
76.9
77.8
78.7
77.7
79.5
78.4
79.1
78.4
78.8
77.9
77.9
77.9
77.0
78.5
78.7
76.9
77.4
77.4
78.1
77.5
79.5
80.0
79.2
79.3
79.2
79.0
78.4
78.2
78.3
78.9
78.9
80.0
78.2
79.5
80.1
79.8
79.2
78.9
78.4
78.6
78.9
77.4
78.3
79.6
78.5
78.4
78.3
79.3
78.8
80.4
1.9
1.5
0.7
1.4
0.8
1.3
-0.1
1.3
2.0
1.1
1.3
0.5
0.0
1.7
0.8
0.8
0.2
0.5
0.7
1.1
0.3
-0.2
0.9
1.6
1.0
0.9
1.2
1.2
0.9
aleksandrowski
brodnicki
bydgoski
chełmiński
grudziądzki
inowrocławski
lipnowski
mogileński
nakielski
radziejowski
rypiński
sępoleński
świecki
toruński
tucholski
wąbrzeski
włocławski
żniński
m. Bydgoszcz
m. Grudziądz
0401
0402
0403
0404
0405
0406
0407
0408
0409
0410
0411
0412
0413
0414
0415
0416
0417
0418
0419
0461
0462
68.9
69.9
70.4
68.9
70.9
68.4
69.8
68.3
69.0
69.6
69.3
69.1
70.8
69.9
69.6
69.6
70.0
69.1
69.3
71.5
69.4
68.9
70.7
71.3
68.9
70.8
69.7
69.8
68.2
70.1
69.5
69.7
69.8
71.6
70.8
70.2
70.9
70.3
68.4
71.0
72.4
69.9
0.1
0.8
0.9
0.0
-0.1
1.3
0.0
-0.1
1.1
-0.1
0.3
0.7
0.8
0.9
0.6
1.4
0.3
-0.6
1.7
0.9
0.5
77.1
78.1
78.7
75.6
79.0
76.6
78.3
77.0
76.9
77.7
78.4
78.8
79.4
76.8
77.3
78.0
78.7
77.7
78.3
79.1
77.5
78.9
78.8
79.1
78.2
80.6
79.0
79.0
78.3
78.8
78.2
79.1
78.9
80.1
78.0
79.1
79.7
78.8
79.2
79.9
80.0
78.4
1.8
0.7
0.5
2.5
1.6
2.3
0.7
1.2
1.9
0.5
0.7
0.2
0.7
1.2
1.8
1.7
0.2
1.5
1.6
0.9
0.9
Page 276
Males
District
Females
TERYT
2001–2003
2006–2008
change
2001–2003
2006–2008
change
m. Toruń
0463
71.6
72.4
0.8
79.5
80.6
1.1
m. Włocławek
0464
70.3
70.4
0.0
78.4
78.5
0.0
bialski
biłgorajski
chełmski
hrubieszowski
janowski
krasnostawski
kraśnicki
lubartowski
lubelski
łęczyński
łukowski
opolski
parczewski
puławski
radzyński
rycki
świdnicki
tomaszowski
włodawski
zamojski
m. Biała Podlaska
m. Chełm
m. Lublin
0601
0602
0603
0604
0605
0606
0607
0608
0609
0610
0611
0612
0613
0614
0615
0616
0617
0618
0619
0620
0661
0662
0663
68.5
70.6
66.5
67.7
70.7
68.3
70.2
68.8
69.0
69.9
69.4
68.5
69.1
69.9
69.8
68.6
70.1
69.9
67.3
69.5
69.8
70.1
71.1
68.9
71.4
66.5
68.5
71.2
69.7
71.1
69.2
69.3
69.0
69.7
69.3
69.8
70.7
69.7
69.6
70.6
70.5
68.7
69.6
71.3
70.7
71.9
0.4
0.8
0.0
0.9
0.5
1.4
0.9
0.4
0.4
-0.9
0.3
0.8
0.7
0.8
-0.1
1.0
0.5
0.6
1.4
0.1
1.6
0.6
0.8
78.1
79.7
78.0
78.8
79.0
79.5
78.7
78.9
79.1
77.9
79.7
78.7
78.9
79.8
78.4
78.4
79.8
79.8
78.3
79.4
78.0
79.2
79.4
79.8
81.1
79.0
80.3
80.5
79.6
80.6
80.2
80.0
80.1
80.4
79.6
80.0
80.9
79.2
80.2
80.6
80.7
79.4
80.6
80.7
81.8
80.2
1.6
1.4
1.0
1.6
1.4
0.1
1.9
1.3
1.0
2.2
0.8
0.9
1.1
1.1
0.8
1.8
0.8
0.9
1.2
1.2
2.7
2.6
0.8
m. Zamość
0664
71.7
72.8
1.1
81.0
81.8
0.9
gorzowski
krośnieński
międzyrzecki
nowosolski
słubicki
sulęciński
świebodziński
zielonogórski
żagański
żarski
wschowski
0801
0802
0803
0804
0805
0806
0807
0808
0809
0810
0811
0812
0861
69.2
68.0
70.3
70.2
69.4
69.2
68.9
68.1
70.2
68.2
67.7
70.1
71.7
70.5
69.4
69.7
71.1
68.4
69.5
68.7
69.8
70.1
69.1
69.3
70.3
72.2
1.3
1.4
-0.7
1.0
-1.0
0.3
-0.3
1.7
-0.1
0.9
1.7
0.3
0.6
78.2
77.7
77.9
77.9
78.9
78.9
78.9
78.0
78.3
77.6
77.2
79.4
79.0
78.9
79.4
79.1
78.8
77.9
78.8
78.3
78.9
79.9
78.5
78.3
78.5
79.5
0.7
1.7
1.2
0.9
-1.0
-0.1
-0.7
1.0
1.7
0.8
1.0
-0.9
0.5
m. Zielona Góra
0862
71.6
72.9
1.2
79.4
80.5
1.1
bełchatowski
kutnowski
łaski
łęczycki
łowicki
łódzki wschodni
opoczyński
pabianicki
pajęczański
piotrkowski
poddębicki
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
69.6
67.6
69.1
68.5
68.7
68.0
68.3
68.6
70.1
68.3
69.2
70.2
66.5
69.7
68.4
69.7
68.8
69.3
69.3
70.5
68.5
67.2
0.6
-1.1
0.6
-0.1
1.0
0.9
1.0
0.7
0.4
0.3
-2.0
78.9
77.6
78.4
77.7
78.3
76.9
78.6
77.5
79.6
77.9
77.3
80.3
77.4
80.1
78.1
79.7
78.4
79.3
78.5
80.2
78.7
77.8
1.4
-0.2
1.7
0.4
1.3
1.5
0.7
0.9
0.6
0.7
0.5
Page 277
Males
District
Females
TERYT
2001–2003
2006–2008
change
2001–2003
2006–2008
change
radomszczański
rawski
sieradzki
skierniewicki
tomaszowski
wieluński
wieruszowski
zduńskowolski
zgierski
brzeziński
m. Łódź
m. Skierniewice
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1061
1062
1063
68.6
69.4
69.5
69.0
67.8
70.2
68.6
69.6
68.4
66.9
67.7
68.7
69.9
69.2
70.0
70.5
69.4
67.3
69.5
69.4
69.1
68.4
67.0
68.0
69.1
71.4
0.6
0.6
1.0
0.4
-0.5
-0.7
0.8
-0.4
0.1
0.1
0.3
0.5
1.4
77.8
79.3
79.3
79.7
77.5
79.5
78.2
78.7
77.4
76.5
77.1
77.9
77.6
79.5
80.3
79.9
80.9
79.6
80.4
79.0
78.7
79.0
78.3
77.7
78.8
79.6
1.7
1.0
0.6
1.2
2.1
0.9
0.7
0.0
1.6
1.8
0.6
0.9
2.0
bocheński
brzeski
chrzanowski
dąbrowski
gorlicki
krakowski
limanowski
miechowski
myślenicki
nowosądecki
nowotarski
olkuski
oświęcimski
proszowicki
suski
tarnowski
tatrzański
wadowicki
wielicki
m. Kraków
m. Nowy Sącz
m. Tarnów
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1261
1262
1263
71.1
72.1
71.0
71.1
72.2
70.9
72.0
70.2
71.2
71.6
72.6
70.5
71.3
69.3
70.8
72.1
71.5
70.6
71.0
72.9
72.8
72.7
72.6
72.5
71.6
73.3
73.0
71.8
72.4
70.7
71.9
72.2
72.8
71.8
72.7
70.2
70.9
72.8
71.5
72.0
72.0
73.9
73.7
72.6
1.6
0.4
0.6
2.2
0.8
0.9
0.5
0.5
0.8
0.5
0.2
1.3
1.3
0.9
0.1
0.7
0.0
1.4
1.0
0.9
0.9
-0.1
79.6
79.5
79.0
79.5
79.2
79.2
79.6
80.5
78.3
79.7
79.5
78.8
79.3
79.1
78.9
79.8
80.4
79.3
78.5
79.6
79.7
78.8
80.2
80.8
79.9
80.3
80.8
80.7
80.6
79.6
80.8
81.0
81.2
80.7
80.5
81.2
80.6
81.2
80.2
80.1
80.8
80.9
81.3
80.5
0.6
1.3
0.9
0.8
1.6
1.4
1.0
-0.8
2.4
1.3
1.7
1.9
1.2
2.1
1.7
1.4
-0.2
0.8
2.3
1.3
1.6
1.7
białobrzeski
ciechanowski
garwoliński
gostyniński
grodziski
grójecki
kozienicki
legionowski
lipski
łosicki
makowski
miński
mławski
nowodworski
ostrołęcki
ostrowski
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
69.2
69.7
69.6
68.8
69.2
67.7
70.3
70.6
69.4
71.0
68.7
68.8
69.3
69.1
68.3
70.2
69.1
69.8
70.0
70.5
70.7
69.4
71.2
72.1
69.9
70.3
68.8
69.4
68.3
69.0
69.5
70.2
-0.1
0.1
0.3
1.7
1.5
1.7
0.8
1.5
0.5
-0.6
0.2
0.6
-1.0
-0.1
1.2
0.1
78.4
78.7
79.2
78.7
78.4
77.7
79.9
80.0
79.0
78.6
78.1
79.2
78.3
78.4
79.7
79.6
78.9
79.2
80.8
79.1
79.8
78.8
80.7
80.7
80.5
80.5
80.9
80.4
79.4
79.2
80.7
81.2
0.5
0.5
1.7
0.4
1.3
1.1
0.8
0.7
1.4
1.9
2.8
1.2
1.1
0.8
1.0
1.6
Page 278
Males
District
Females
TERYT
2001–2003
2006–2008
change
2001–2003
2006–2008
change
otwocki
piaseczyński
płocki
płoński
pruszkowski
przasnyski
przysuski
pułtuski
radomski
siedlecki
sierpecki
sochaczewski
sokołowski
szydłowiecki
warszawski zachodni
węgrowski
wołomiński
wyszkowski
zwoleński
żuromiński
żyrardowski
m. Ostrołęka
m. Płock
m. Radom
m. Siedlce
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1432
1433
1434
1435
1436
1437
1438
1461
1462
1463
1464
69.4
70.6
69.4
68.7
71.6
67.9
69.7
68.0
68.9
70.4
68.3
68.9
71.3
69.1
70.8
70.0
69.4
69.3
69.5
69.9
68.5
71.0
70.8
70.0
71.2
71.2
71.5
69.0
68.3
72.9
67.8
69.9
69.8
70.2
69.7
69.5
69.5
71.2
68.9
72.8
69.9
69.9
69.7
68.8
71.2
68.9
71.6
70.0
70.8
72.7
1.9
1.0
-0.4
-0.4
1.2
-0.1
0.1
1.7
1.2
-0.7
1.2
0.6
-0.1
-0.3
1.9
0.0
0.5
0.4
-0.7
1.3
0.5
0.6
-0.8
0.8
1.5
78.8
78.3
78.6
77.7
79.7
77.7
79.2
78.4
79.0
79.8
78.6
78.4
78.9
79.7
80.6
79.1
79.0
79.4
78.6
79.5
76.8
79.0
78.5
79.0
80.3
81.1
80.7
79.1
78.9
80.9
78.4
80.6
79.2
80.1
80.2
78.9
79.4
81.3
80.2
81.3
79.8
79.9
80.3
79.0
79.8
78.4
80.8
79.8
80.1
81.3
2.3
2.4
0.5
1.2
1.2
0.7
1.3
0.8
1.1
0.5
0.3
1.0
2.4
0.5
0.8
0.7
0.9
0.9
0.3
0.3
1.6
1.8
1.3
1.1
1.0
m. st. Warszawa
1465
72.4
74.0
1.6
79.6
81.2
1.5
brzeski
głubczycki
kluczborski
krapkowicki
namysłowski
nyski
oleski
opolski
prudnicki
strzelecki
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
70.2
69.7
70.4
70.2
71.7
69.6
70.1
70.7
72.1
70.3
72.0
70.4
69.5
72.7
70.5
72.1
70.8
71.4
72.4
73.9
70.4
72.4
0.2
-0.3
2.3
0.3
0.4
1.2
1.3
1.7
1.8
0.1
0.4
78.6
77.3
78.6
78.4
78.8
79.0
78.9
79.2
79.8
78.7
78.6
80.0
78.9
80.3
80.3
79.9
80.1
79.3
80.8
81.2
79.5
80.5
1.4
1.7
1.8
1.9
1.1
1.1
0.4
1.7
1.4
0.8
1.9
m. Opole
1661
73.0
74.5
1.5
80.1
80.9
0.8
bieszczadzki
brzozowski
dębicki
jarosławski
jasielski
kolbuszowski
krośnieński
leżajski
lubaczowski
łańcucki
mielecki
niżański
przemyski
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
72.1
72.5
72.5
70.5
71.1
72.5
71.1
71.1
71.6
71.7
72.4
70.1
69.2
71.1
72.8
72.7
71.8
72.2
73.6
72.8
72.7
71.0
73.0
74.2
71.7
71.9
-1.0
0.3
0.2
1.3
1.1
1.1
1.7
1.6
-0.6
1.3
1.9
1.6
2.7
81.0
78.7
79.7
78.9
79.9
80.9
80.0
79.9
80.5
79.7
79.7
80.0
79.2
80.1
81.0
81.3
79.8
80.4
81.4
81.0
80.7
81.4
80.6
81.2
81.7
80.6
-0.8
2.3
1.6
1.0
0.5
0.4
0.9
0.8
0.9
0.9
1.5
1.7
1.4
Page 279
Males
District
Females
TERYT
2001–2003
2006–2008
change
2001–2003
2006–2008
change
przeworski
rzeszowski
sanocki
stalowowolski
strzyżowski
tarnobrzeski
leski
m. Krosno
m. Przemyśl
m. Rzeszów
m. Tarnobrzeg
1814
1815
1816
1817
1818
1819
1820
1821
1861
1862
1863
1864
71.3
71.0
71.3
71.4
71.6
70.2
70.8
72.8
72.5
70.1
72.9
72.3
72.5
71.9
72.9
73.2
73.0
73.1
71.9
72.7
73.7
71.6
75.3
73.6
1.1
0.9
1.7
1.8
1.3
2.8
1.2
-0.1
1.2
1.5
2.4
1.4
79.5
79.2
79.4
79.7
79.7
78.5
80.2
80.4
79.9
77.7
80.0
79.8
80.9
81.5
81.3
81.3
80.2
81.3
81.2
82.5
80.9
80.0
82.1
81.5
1.4
2.3
1.9
1.6
0.5
2.8
1.0
2.1
1.0
2.3
2.1
1.7
augustowski
białostocki
bielski
grajewski
hajnowski
kolneński
łomżyński
moniecki
sejneński
siemiatycki
sokólski
suwalski
wysokomazowiecki
zambrowski
m. Białystok
m. Łomża
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2061
2062
70.0
69.9
70.8
70.3
67.9
70.0
70.5
69.9
70.2
69.8
68.5
69.9
71.3
70.9
71.7
72.4
71.6
70.6
71.3
70.0
69.7
70.7
70.7
72.5
69.5
70.2
69.7
70.2
71.5
71.6
73.6
72.3
1.6
0.6
0.4
-0.3
1.7
0.8
0.2
2.7
-0.6
0.5
1.2
0.3
0.2
0.7
1.9
-0.1
79.1
79.2
80.0
79.8
79.3
80.3
80.0
79.9
81.0
78.7
79.6
80.9
80.9
80.9
80.7
80.0
80.5
80.8
80.5
81.5
79.8
80.8
81.1
81.3
80.6
80.4
80.8
81.4
81.3
80.7
81.8
80.2
1.5
1.6
0.5
1.7
0.5
0.5
1.1
1.4
-0.4
1.7
1.2
0.5
0.4
-0.1
1.1
0.2
m. Suwałki
2063
70.8
72.4
1.5
79.3
80.5
1.2
bytowski
chojnicki
człuchowski
gdański
kartuski
kościerski
kwidzyński
lęborski
malborski
nowodworski
pucki
słupski
starogardzki
tczewski
wejherowski
sztumski
m. Gdańsk
m. Gdynia
m. Słupsk
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2261
2262
2263
70.6
71.6
70.2
70.5
71.2
71.4
69.6
70.6
69.0
68.8
71.1
68.6
70.3
69.9
71.0
69.1
71.0
73.7
71.3
71.4
72.0
70.6
71.5
72.6
72.7
70.0
69.5
69.8
69.1
71.3
69.7
70.8
70.6
71.9
68.4
72.8
73.9
70.8
0.8
0.5
0.4
1.0
1.4
1.3
0.5
-1.1
0.7
0.3
0.3
1.1
0.5
0.7
0.8
-0.6
1.8
0.2
-0.5
78.4
79.1
78.9
79.5
79.8
79.4
77.9
78.0
77.4
77.9
78.2
77.7
77.5
77.4
78.5
77.1
78.9
80.0
79.0
79.4
79.0
79.7
80.2
81.1
77.7
79.2
79.5
79.0
79.8
78.5
78.6
78.1
79.3
79.7
78.8
80.6
80.7
80.0
1.0
0.0
0.8
0.6
1.3
-1.7
1.3
1.5
1.5
2.0
0.3
0.9
0.5
1.9
1.2
1.7
1.7
0.7
1.0
m. Sopot
2264
74.2
74.5
0.2
79.8
82.1
2.3
będziński
bielski
2401
2402
69.0
70.9
68.6
73.0
-0.4
2.1
77.8
78.5
78.4
80.2
0.6
1.7
Page 280
Males
District
Females
TERYT
2001–2003
2006–2008
change
2001–2003
2006–2008
change
cieszyński
częstochowski
gliwicki
kłobucki
lubliniecki
mikołowski
myszkowski
pszczyński
raciborski
rybnicki
tarnogórski
wodzisławski
zawierciański
żywiecki
m. Bielsko-Biała
m. Bytom
m. Chorzów
m. Częstochowa
m. Gliwice
m. Jaworzno
m. Katowice
m. Mysłowice
m. Ruda Śląska
m. Rybnik
m. Sosnowiec
m. Tychy
m. Zabrze
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2461
2462
2463
2464
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
70.9
69.6
70.3
70.4
70.4
69.8
69.1
70.2
71.2
70.8
71.1
71.0
70.3
68.9
69.9
71.9
69.3
67.0
69.5
69.4
71.1
70.0
70.2
69.8
69.0
68.5
67.7
70.8
68.5
69.1
66.6
71.6
70.7
72.2
69.6
70.6
71.1
72.6
72.2
69.6
71.4
72.3
71.2
72.4
70.8
71.5
69.3
70.5
73.3
69.7
67.4
70.4
69.6
72.7
71.7
70.5
70.8
70.0
69.8
68.1
72.0
68.9
69.7
68.1
72.0
72.1
1.3
0.0
0.3
0.7
2.2
2.3
0.5
1.2
1.0
0.4
1.3
-0.1
1.3
0.5
0.5
1.4
0.4
0.3
0.9
0.2
1.5
1.7
0.3
0.9
0.9
1.3
0.4
1.2
0.5
0.6
1.5
0.4
1.4
78.6
78.5
77.9
79.1
78.3
77.5
77.6
78.7
79.1
78.6
78.6
78.4
78.6
77.5
78.4
79.2
77.0
75.6
78.6
78.1
78.8
77.7
77.8
77.3
77.7
77.1
75.7
78.3
76.1
77.2
75.5
78.0
77.8
79.6
79.4
79.0
79.9
80.0
79.3
79.0
79.7
79.4
78.7
79.8
79.0
79.3
78.1
79.5
81.1
78.0
77.1
79.1
78.8
79.8
79.0
79.1
78.4
77.8
78.9
76.5
79.4
76.3
78.0
77.6
79.5
80.0
1.0
0.9
1.1
0.8
1.7
1.8
1.3
1.0
0.3
0.0
1.2
0.7
0.6
0.6
1.1
1.9
1.0
1.5
0.5
0.7
1.0
1.4
1.3
1.1
0.1
1.8
0.9
1.0
0.2
0.7
2.1
1.5
2.2
m. Żory
2479
71.9
72.6
0.7
78.9
80.4
1.5
buski
jędrzejowski
kazimierski
kielecki
konecki
opatowski
ostrowiecki
pińczowski
sandomierski
skarżyski
starachowicki
staszowski
włoszczowski
m. Kielce
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2661
70.2
70.3
69.9
70.1
69.3
68.5
69.1
68.6
70.2
70.7
70.6
71.4
70.5
72.7
70.9
71.1
69.3
70.3
69.9
69.6
70.6
70.7
72.2
69.6
70.2
71.0
71.2
73.5
0.7
0.8
-0.6
0.2
0.6
1.1
1.5
2.1
2.1
-1.2
-0.4
-0.5
0.7
0.9
79.6
78.1
78.3
79.6
79.0
78.4
78.6
78.7
79.4
79.0
79.2
79.8
79.5
79.9
80.5
79.9
79.7
80.6
80.6
80.0
79.9
80.7
81.4
79.8
79.7
80.1
79.6
81.4
0.9
1.8
1.4
1.0
1.6
1.6
1.3
2.1
2.0
0.8
0.5
0.3
0.2
1.5
bartoszycki
braniewski
działdowski
2801
2802
2803
67.2
67.3
69.5
68.2
68.8
69.7
1.0
1.5
0.2
77.3
77.1
78.9
79.4
78.5
79.0
2.2
1.4
0.1
Page 281
Males
District
Females
TERYT
2001–2003
2006–2008
change
2001–2003
2006–2008
change
elbląski
ełcki
giżycki
iławski
kętrzyński
lidzbarski
mrągowski
nidzicki
nowomiejski
olecki
olsztyński
ostródzki
piski
szczycieński
gołdapski
węgorzewski
m. Elbląg
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2861
69.0
68.0
69.0
70.5
67.1
67.9
69.1
68.1
71.2
68.8
68.8
69.4
69.4
68.1
70.1
66.5
70.4
68.7
69.5
69.3
71.2
67.9
68.7
70.1
68.4
71.5
69.9
69.5
70.4
70.0
69.6
69.5
69.0
70.6
-0.3
1.4
0.3
0.7
0.8
0.8
1.0
0.3
0.3
1.1
0.7
1.0
0.6
1.5
-0.6
2.5
0.3
79.4
78.9
79.1
78.9
78.2
78.5
78.9
79.8
78.4
78.9
77.8
78.7
78.3
78.7
77.4
78.0
78.2
78.3
80.2
80.2
80.2
78.9
79.5
80.0
78.8
79.9
79.0
79.0
80.2
80.4
79.8
79.1
78.2
79.1
-1.0
1.4
1.1
1.3
0.7
1.0
1.1
-1.0
1.5
0.1
1.1
1.5
2.1
1.0
1.7
0.2
0.9
m. Olsztyn
2862
72.5
74.1
1.5
80.9
82.1
1.1
chodzieski
gnieźnieński
gostyński
grodziski
jarociński
kaliski
kępiński
kolski
koniński
kościański
krotoszyński
leszczyński
międzychodzki
nowotomyski
obornicki
ostrowski
ostrzeszowski
pilski
pleszewski
poznański
rawicki
słupecki
szamotulski
średzki
śremski
turecki
wągrowiecki
wolsztyński
wrzesiński
złotowski
m. Kalisz
m. Konin
3001
3002
3003
3004
3005
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3061
3062
69.7
69.8
70.0
71.6
69.7
70.4
69.4
70.2
68.6
70.1
70.3
70.3
71.9
69.2
69.6
69.3
70.8
68.4
70.2
69.3
70.5
69.8
70.6
69.4
71.2
70.4
69.3
70.0
71.1
70.0
70.2
69.4
71.1
71.0
70.9
71.3
71.7
71.1
71.9
70.4
72.0
70.0
71.1
71.6
70.9
72.0
70.2
70.6
69.9
71.6
70.6
70.5
71.1
72.1
71.6
70.9
71.2
70.5
71.1
69.8
70.8
71.7
70.6
70.5
71.2
73.1
1.3
1.0
1.3
0.2
1.4
1.5
1.0
1.8
1.4
1.0
1.3
0.7
0.1
1.1
1.1
0.6
0.8
2.2
0.3
1.8
1.6
1.9
0.3
1.8
-0.7
0.8
0.6
0.8
0.6
0.6
0.2
1.8
2.0
77.8
77.6
77.9
77.5
77.7
78.2
79.0
79.2
77.8
80.2
78.6
77.8
78.5
75.3
78.4
77.2
78.3
77.4
78.2
77.8
78.5
78.5
78.5
78.3
78.6
77.0
78.0
78.0
78.2
77.5
78.3
77.8
80.0
79.0
78.7
79.1
78.7
78.1
79.3
80.1
80.1
79.2
80.3
80.0
79.1
79.8
77.2
78.7
78.3
80.4
79.3
79.6
78.9
79.6
78.9
79.9
78.7
79.0
78.1
78.9
78.7
78.8
80.1
79.2
79.8
81.2
1.2
1.1
1.2
1.2
0.3
1.1
1.1
0.9
1.3
0.1
1.4
1.3
1.3
1.9
0.4
1.1
2.1
2.0
1.3
1.1
1.1
0.4
1.3
0.3
0.4
1.1
0.9
0.6
0.6
2.6
0.9
1.9
1.3
Page 282
Males
District
Females
TERYT
2001–2003
2006–2008
change
2001–2003
2006–2008
change
m. Leszno
3063
71.8
72.4
0.6
78.9
79.5
0.6
m. Poznań
3064
71.7
72.8
1.1
78.9
80.1
1.2
białogardzki
choszczeński
drawski
goleniowski
gryficki
gryfiński
kamieński
kołobrzeski
koszaliński
myśliborski
policki
pyrzycki
sławieński
stargardzki
szczecinecki
świdwiński
wałecki
łobeski
m. Koszalin
m. Szczecin
m. Świnoujście
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
3261
3262
3263
68.6
69.3
69.8
69.3
67.8
69.8
68.2
70.8
69.1
69.0
69.2
69.4
69.0
70.2
70.0
68.8
69.5
69.1
72.8
70.6
69.4
68.1
70.5
69.0
69.9
68.9
70.7
70.5
71.9
69.8
69.6
71.6
69.4
69.8
70.7
69.0
68.6
69.0
68.2
73.7
71.7
71.5
-0.6
1.2
-0.8
0.7
1.1
0.9
2.3
1.1
0.7
0.6
2.4
0.0
0.9
0.6
-0.9
-0.2
-0.4
-0.9
1.0
1.1
2.1
78.0
79.1
78.2
78.9
77.6
76.8
77.3
78.3
77.6
77.6
78.2
78.6
78.8
78.6
78.0
79.3
76.9
79.0
79.8
78.2
77.5
78.8
79.2
78.6
79.0
78.8
78.5
79.1
80.0
78.7
79.3
80.3
80.3
78.9
78.8
78.1
80.0
78.7
80.0
81.2
79.9
79.1
0.7
0.1
0.5
0.1
1.1
1.7
1.8
1.7
1.0
1.7
2.1
1.8
0.1
0.2
0.1
0.7
1.8
1.0
1.3
1.7
1.6
70.2
71.0
0.8
78.6
79.8
1.1
Polska
(TERYT -National Official Register of Territorial Division of the Country )
Page 283
4. Association of health status of districts population with socio-economic characteristic
of districts
Bogdan Wojtyniak, Daniel Rabczenko – National Institute of Public Health-National Institute
of Hygiene, Warsaw
Agnieszka Chłoń-Domińczak - Demography Unit, Institute of Statistics and Demography,
Warsaw School of Economics
In this Chapter results of statistical analysis of an association of health status of districts
population measured by standardised mortality ratios (SMRs) for selected main causes of
deaths, infant mortality rates and life expectancy at birth, with social and economic district
characteristics described in Chapter 2. Since SMR is a ratio type measure, and thus the same
value above or below 1 (which means, for instance, 20% above or 20% below national
average) should be taken into account as equally different from 1 but the sign, all analyses
were carried out after logarithmic transformation of each SMR. Infant mortality rate and life
expectancy were not transformed.
The analysis was carried out in three steps; in the first one, bivariate (pairwise) associations
between each of the health and socio-economic indicators were tested by applying Spearman
correlation coefficient (rho) and relative concentration index (RCI)39. The RCI summarizes
relative inequality in a given health parameter across the entire distribution of a given socioeconomic, district characteristic. It shows whether an elevated mortality level accumulates
faster amongst the districts with worse socio-economic situation (which is the case when the
index has a negative value). If all the values of an examined health variable are positive, RCI
varies between -1 and 1. However, if there are some negative values present, the limits of RCI
are not defined, and RCI interpretation is difficult. Since the analysed values of SMR
logarithm were positive as well as negative, negative values were changed to zeros, as advised
by Konigs et al1. Therefore, RCI calculated can be interpreted as a measure of concentration
of districts with unfavourable mortality situation.
At the second step, analysis was carried out by blocks – groups of socio-economic variables
as presented in Chapter 2. For each group we built a multivariate, linear regression model
weighted by district population, applying backward elimination procedure and retaining in the
39
Konings P, Harper S, Lynch J, Hosseinpoor AR, Berkvens B, Lorant V, Geckova A, Speybroeck N; Analysis
of socioeconomic health inequalities using the concentration index; Int J Public Health; Volume 55, Number 1,
71-74
Page 284
block model those variables that were statistically significant (p<.05) or were at the borderline
of significance (p<.1).
At the third step, we allowed all the variables that remained in the block models to enter the
pre-final model and, once again applying backward elimination according to the same rules,
we have built a final model that shows which variables contribute significantly to the
explanation of differences in district mortality level or life expectancy.
In the tables below, final model standardized regression coefficients and the values of
determination coefficient (R2) are presented. The former ones indicate the importance of
individual variables in explaining variability of the dependent variable across districts (health
indicator), while the latter one shows what proportion of a given health indicator variability is
explained by socio-economic variables in the model. For easy identification of whether the
variables show an expected association with mortality, those variables that have a negative
association (i.e. reduce mortality or increase life expectancy) are highlighted in green, and
those that have positive association are highlighted in red.
In tables 82–149 in Annex 4, for each mortality indicator and life expectancy, as well as for
each socio-economic variable, unweighted simple Spearman correlation coefficient (rho) is
presented, together with its significance level and relative concentration index (RCI). Regular,
not standardised regression coefficients are also presented (with their significance level), and
coefficients of determination (R2) for each block model and for the final model.
4.1. Overall mortality
All cause total population SMR (actually, its logarithmic transformation) was significantly
correlated with almost all factors taken into consideration (Table 82 in Annex 4). The
strongest bivariate association was observed in the case of lower secondary school literacy
exams results, the non-significant factors were old-age demographic dependency rate and the
share of employment in hazardous conditions.
All but two factors which were significantly associated with all causes SMR in individual
blocks models retained their statistical significance in the final regression model (Table 82).
This model explained 37% of variation in district mortality levels from all causes (SMRs). It
may be noticed that while the association of district budget revenue with mortality was
Page 285
inverse (negative) in the block model, it turned out to be positive in the final model, indicating
that, when controlling for other factors, district’s income does not necessarily contribute to the
reduction of total mortality risk. The opposite change in association is observed in the case of
the number of district residents per one physician – the positive (and expected) association in
the block model changes to the negative in the final model, which is difficult to explain.
Standardized regression coefficient of the number of households equipped with a bathroom,
which is higher than almost all of the others, indicates that conditions represented by this
variable have relatively stronger association with total mortality than those represented by
other variables (see Table 73).
Table 73 Standardized regression coefficients from the final multiple regression models for mortality due
to all causes for each age and sex group
Variables
feminization rate
population density
old-age demographic dependency rate
revenue of district budget per capita
share of employment in hazardous conditions
unemployment rate
local governments election turnout
share of households equipped with bathroom
number of inhabitants per 1 medical doctor
number of inhabitants per 1 health care institution
average lower secondary school exam results (literacy)
baccalaureate results - Polish language (basic level)
R2
Total
Total
Males
Females
-0.16
Total
0–64
Males
Females
0.35
0.28
0.15
0.17
0.11
0.16
0.10
0.21
-0.25
-0.49
-0.09
-0.09
-0.38
0.37
65+
Males
-0.27
-0.14
-0.22
-0.20
0.10
0.16
0.21
-0.57
-0.31
-0.51
Total
Females
-0.36
0.11
0.12
-0.93
-0.17
-0.65
-0.29
-0.57
-0.30
-0.49
-0.17
-0.69
-0.20
-0.34
-0.19
-0.33
-0.21
-0.29
0.19
-0.31
-0.55
-0.35
-0.15
-0.51
-0.33
-0.29
-0.38
0.40
0.30
0.34
0.35
0.40
0.38
0.42
0.25
The association of male and female total mortality with socio-economic variables taken into
account is somewhat different. In females, the strongest bivariate correlation is clearly
observed for the results of lower secondary school exams, while in males several variables are
associated with mortality with similar strength (Tables 83–90 in Annex 4). Final models of
mortality explain to a greater extent the variation in district male SMR (40%) than in female
SMR (30%) (Table 73). Three factors (households equipped with bathrooms, local
government election participation, lower secondary school exam results) were significant in
explaining variation in mortality of males as well as females, two factors (unemployment rate,
inhabitants per one physician) were significant in male model only, and two (employment in
agriculture and employment in hazardous conditions) were significant in explaining
differences only in female mortality.
Page 286
Models explaining mortality differentials in younger (below 65 years of age) and older (65
years and above) sub-populations show some similarities and discrepancies. In males and
females from both groups there is significant, negative association of district mortality level
with three factors: local government election participation, share of households equipped with
bathrooms, and lower secondary school exam results. Interestingly, population density and
old-age demographic dependency rate demonstrated some positive association with mortality
of the younger sub-population, and negative association with mortality of the elderly people.
Unemployment rate was significantly associated with mortality of males in younger and older
age groups.
4.2. Mortality from cancer
Cancer mortality was significantly correlated with most of the factors taken into consideration
(Tables 91–99 in Annex 4). However, only four factors that were significant in individual
blocks models retained their statistical significance in the final regression model for male
mortality, and only three factors remained significant in the female mortality model (Table
74).
Table 74. Standardized regression coefficients from the final multiple regression models for cancer
mortality for each age and sex group
Variables
feminization rate
population density
unemployment rate
R2
Total
Total
Males
Females
Total
0–64
Males
Females
-0.19
-0.12
Total
65+
Males
Females
0.19
-0.14
-0.12
-0.68
-0.54
-0.21
-0.36
-0.55
0.09
-0.48
0.36
0.31
-0.61
-0.13
-0.10
0.34
-0.42
-0.14
-0.44
-0.34
-0.45
-0.23
0.39
-0.26
0.24
-0.34
0.23
-0.66
-0.13
0.09
-0.51
-0.47
-0.49
-0.43
-0.35
0.26
0.21
0.25
0.38
0.29
Nevertheless, these models explained more than 30% of variation in district mortality from
cancer. There was only one variable, the share of employment in agriculture, which was
present in the models of male and female mortality and was negatively associated with
mortality level. This variable has the strongest association (largest standardized regression
Page 287
0.37
coefficient) with cancer mortality in both gender groups, and it is significant in younger and
older population. Another variable that is significantly (and negatively) associated with cancer
mortality of the younger and older population (except older women) is the average lower
secondary school exam results. The share of households equipped with a bathroom plays a
significant role in explaining differences in district mortality in older population and, rather
surprisingly, the association observed is positive. Overall, created models better describe
mortality differential of the elderly population than of the younger age group.
4.3. Mortality from circulatory system diseases
Mortality caused by cardiovascular diseases (CVD) was significantly correlated with most of
the factors taken into consideration (Tables 100–108 in Annex 4). Six factors which were
significant in individual blocks models retained their statistical significance in the final
regression model for male mortality, and only five factors remained significant in the female
mortality model (see Table 75). These models explained more than 20% of variation in
district mortality from CVD, and this proportion was lower than the one explained by cancer
mortality models. There were three variables that played a significant role in the models of
male as well as female mortality: population density and the share of households equipped
with a bathroom (the strongest association), and local government election participation. All
three variables were negatively associated with mortality level.
Models explaining CVD mortality differentials in younger (below 65 years of age) and older
(65 years and above) sub-populations show some similarities and discrepancies. The share of
households equipped with a bathroom is the only variable that is significantly associated with
district mortality level of males and females in both age groups. Local government election
participation and average lower secondary school examination results represent significant
factors in younger age group mortality model, while district budget revenue and population
density play a significant role in explaining mortality of the elderly population, the association
being negative. Overall, developed models better describe CVD mortality differential of the
elderly population (coefficients of determination R2 0.29, 0.23, 0.27 in total, males and
females, respectively) than of the younger age group (respective coefficients of determination
R2 0.17, 0.15, 0.21).
Page 288
Table 75. Standardized regression coefficients from the final multiple regression models for mortality due
to circulatory system diseases for each age and sex group
Total
Total
Males
Females
feminization rate
population density
unemployment rate
-0.16
-0.53
-0.17
-0.44
R2
0.29
Variables
Total
0–64
Males
Females
-0.20
-0.18
-0.58
Total
65+
Males
Females
-0.48
-0.50
-0.47
-0.24
-0.18
-0.19
0.14
0.19
0.15
-0.41
-0.16
-0.56
-0.23
-0.36
0.21
0.22
0.14
-0.38
0.10
-0.16
-0.62
0.26
-0.79
-0.28
-0.41
-0.19
-0.28
-0.19
-0.66
-0.27
-0.20
-0.33
0.17
0.15
0.21
-0.38
0.09
-0.38
0.11
-0.49
-0.17
-0.29
0.21
-0.52
0.29
0.23
0.27
4.4. Mortality from respiratory system diseases
Mortality from respiratory diseases was significantly associated with fewer variables than
cancer and CVD, although bivariate associations were often significant (Tables 109–117 in
Annex 4). Of the nine variables which were significant in individual blocks models of male
mortality, five retained their statistical significance in the final regression model, and only
three out of seven variables remained significant in the female mortality model (see Table 76).
to respiratory system diseases for each age and sex group
Variables
feminization rate
population density
unemployment rate
R2
Total
Total
Males
Females
0.41
0.34
0.32
0.16
0.23
0.20
-0.13
-0.13
-0.37
Total
0–64
Males
Females
0.46
0.16
-0.59
0.24
-0.15
-0.50
-0.37
-0.38
Total
65+
Males
Females
0.41
0.26
0.37
0.20
0.15
0.18
-0.36
-0.28
-0.18
-0.29
-0.26
-0.32
0.11
0.20
0.06
-0.16
-0.17
0.16
0.14
0.08
-0.27
-0.33
0.10
0.20
Explanatory power of the final model of female mortality was very low, since it explained
only 6% of the variation in district SMRs, while the model of male mortality explained 20%
Page 289
0.05
of district SMR differences. There were two variables which played significant role in the
models of male as well as female mortality: district budget revenue and district
unemployment rate. These variables were positively associated with mortality level.
The models explaining respiratory mortality differentials in younger (below 65 years of age)
and older (65 years and over) sub-populations show some similarities and differences.
Unemployment rate is a significant variable in both age groups, while district budget revenue
was important in older population. Share of households equipped with a bathroom and lower
secondary school exam results were important predictors of male mortality regardless of the
age group, however, they were insignificant predictors in female models.
Created models better explain district respiratory mortality differentials in the case of men
than women (only two variables retained their significance in younger and in older women
final models). However, there is no clear difference in the explained variation of district
SMRs in the elderly population and in the younger age group. Overall, differences in district
respiratory diseases SMR are explained to a lesser extent than the differences in SMRs for
cancer and CVD.
4.5. Mortality from digestive system diseases
Mortality from digestive system diseases was significantly associated with fewer variables
than cancer and CVD mortality ( Tables 118–126 in Annex4). Only three variables retained
their statistical significance in the final regression mortality model for males as well as
females (see Table 77). Two variables were significant in both gender- specific models: the
share of employment in agriculture and local government election participation. Both
variables were negatively associated with mortality from digestive diseases. The share of
households equipped with a bathrooms was a significant factor in models for total population
and for males, but not so for females.
Explanatory power of final regression model of female mortality was lower than that of the
male mortality model (respective R2: 28% and 16%).
Page 290
to digestive system diseases for each age and sex group
Variables
feminization rate
population density
unemployment rate
R2
Total
Total
Males
Females
Total
0–64
Males
Females
0.15
0.24
-1.05
-0.32
-1.09
-1.04
-0.15
-0.83
-0.15
-0.90
-0.13
-0.12
-0.72
-0.12
-0.78
-0.19
-0.11
-0.24
0.33
0.31
0.22
0.28
0.16
65+
Males
Females
-0.27
0.13
-0.24
-0.33
-0.13
-0.13
0.09
0.07
0.27
0.25
-1.06
0.29
Total
-0.48
The models explaining respiratory mortality differentials in younger (below 65 years of age)
and older (65 years and over) sub-populations are different. While the share of employment in
agriculture and local government election participation play a significant role in explaining
mortality differentials in both age groups, the share of households equipped with a bathroom,
average lower secondary school examination results, and old-age dependency rate were
significant factors in the younger population only. It may be noticed that, just like in the case
of respiratory diseases mortality models, in digestive diseases mortality models the share of
households equipped with a bathroom was an important predictor of male mortality in both
age groups, however, it was an insignificant predictor in female models.
Created models better explain the differences in district mortality from digestive system
diseases in the younger age group than in the elderly population, and in the younger
population the differences in district digestive diseases SMR are better explained by the
models than the differences in SMRs for respiratory diseases.
4.6. Mortality from ill-defined causes (symptoms, signs and abnormal clinical and
laboratory findings)
As mentioned in the previous chapter, ill-defined causes of deaths like symptoms, signs,
abnormal findings etc. without designation of any specific disease as a cause, are an indicator
of the quality of the system of assigning and coding cases of deaths. It is interesting to notice
that the level of district mortality due to ill-defined causes has a weak correlation with socioeconomic variables taken into account ( Tables 127–135 in Annex 4). The developed
Page 291
0.06
regression models explain only a small proportion of the differences in district SMRs (see
Table 78).
to ill-defined causes for each age and sex group
Variables
feminization rate
population density
unemployment rate
R2
Total
Total
Males
Females
0.20
0.40
Total
0–64
Males
Females
0.29
0.28
0.28
0.21
0.20
0.22
0.28
-0.14
0.22
0.24
0.25
0.25
0.16
-0.32
-0.21
-0.27
-0.41
-0.15
-0.54
0.22
-0.13
-0.55
0.25
-0.17
-0.44
-0.14
0.08
0.07
65+
Males
-0.14
0.11
0.11
0.10
-0.16
0.08
0.12
-0.20
0.04
It may be pointed out that in the total population and in the elderly group no variable was
significant jointly in male and female models. However, in the younger age group there were
several variables that revealed significant association consistently in males and in females.
For example, as it was observed in mortality models dedicated to specific diseases, in this
case as well the share of households equipped with bathrooms and local government election
participation play a significant role in explaining mortality differentials in both gender groups,
being negatively associated with mortality level. The variables: population density, district
budget revenue and unemployment rate, which were less significant in previous models, were
significantly associated with increased mortality from ill-defined causes of younger males and
females. It should be underlined, however, that despite the five/six factors in the younger age
group regression models, only 10-11% of the differences in district SMR are explained by
these models.
4.7. Mortality from the external causes of death
Analysis of the relationship between mortality caused by external causes and socio-economic
factors taken into account reveals two interesting phenomena. One is an almost non-existing
association of district female mortality with these factors, while the association of male
mortality is quite strong and the final regression model explains 39% of the variation in
district SMRs (Tables 136–144 in Annex 4, Table 79). The second interesting finding is that
Page 292
Females
0.15
0.11
-0.19
-0.18
0.07
Total
0.09
such relatively strong determination of male mortality differential by socio-economic factors
is observed only in the younger age group (41% of the explained variation), while in the
elderly men the factors taken into account are almost non-significant – the final model has
only two variables and explains only 3% of the differences in district SMRs.
to external causes for each age and sex group
Total
Total
Males
Females
Total
0–64
Males
Females
Total
65+
Males
feminization rate
population density
unemployment rate
average middle chool exam results (literacy)
-0.19
-0.15
-0.17
-0.25
-0.21
-0.21
-0.17
-0.22
-0.23
-0.19
0.16
0.14
R2
Variables
0.17
Females
0.13
-0.31
0.15
-0.30
0.14
-0.36
0.16
-0.33
-0.66
-0.53
-0.58
-0.60
-0.60
-0.51
-0.23
-0.18
-0.23
-0.19
-0.29
0.32
0.39
0.41
0.41
0.09
0.01
-0.26
-0.28
-0.13
-0.18
0.08
0.03
Similarly like in some of the models of mortality from other causes of deaths, among the
variables that were significantly associated with the lower mortality from the external causes
were employment in agriculture, share of households applied with bathrooms, gymnasium
examination results. Lower mortality is also associated with higher population density, and
interestingly in younger population with lower unemployment while in older age group with
higher unemployment.
4.8. Infant mortality
The association between district levels of infant mortality and socio-economic factors taken
into consideration is weak (Tables 145–147 in Annex 4). Only 10% of the differences
observed between district infant mortality rates (IMR) can be explained by the analysed
variables, even though six variable are in the final regression model (see Table 80). Three of
them are associated with IMR decrease, and three with IMR increase. Of the latter group, the
share of employment in hazardous conditions played a significant role in total IMR, neonatal
and post-neonatal models. It is interesting to note that when post-neonatal infant mortality
(infants 28 days old and above) was analyzed, and such mortality is more contingent on
Page 293
0.12
exogenous factors, the explained proportion of inter-district differences was only 2%, and
only three variables were significant: the one mentioned above, and local government election
participation and pre-school participation rate of children, which were associated with a
decrease in post-neonatal mortality.
Table 80. Standardized regression coefficients for infant mortality rate, total and in age groups
Variables
feminization rate
population density
unemployment rate
number of inhabitants per 1 health care
institution
average lower secondary school exam results
(literacy)
baccalaureate results - Polish language (basic
level)
R2
Total
0-27 days
0.28
0.16
0.18
0.19
0.20
28 days
and more
0.15
0.16
-0.18
-0.23
-0.32
-0.19
-0.18
0.10
0.08
0.02
4.9. Life expectancy at birth
The association between life expectancy in districts and socio-economic factors taken into
consideration is moderate, stronger in men than in women (Tables 148–149 in Annex4). As
much as 43% of the differences observed between district male life expectancy, and 36% of
differences in female life expectancy, can be explained by the analysed variables (see Table
81). More variables (7) played a significant role in the male regression model than in the
female one (4); however, three of them: the share of households equipped with bathrooms,
local governments election participation, and lower secondary school exam results, were
positively associated with life expectancy of both males and females. District unemployment
rate and, quite surprisingly, revenue of district budget per capita were the only variables that
were significantly associated with shorter life expectancy, and only in the case of males.
Page 294
Table 81. Standardized regression coefficients for life expectancy at birth of males and females
Variables
Males
feminization rate
population density
revenue of district budgets per capita
unemployment rate
number of inhabitants per 1 health care
institution
average lower secondary school exam results
(literacy)
baccalaureate results - Polish language (basic
level)
0.16
R2
Females
-0.21
-0.23
0.79
0.31
0.55
0.21
0.68
0.09
0.27
0.57
0.43
0.36
Summary
Regression models results show that social determinants are significantly associated with the
mortality outcomes of the district population. The analysis of the outcomes of final models for
standardized mortality ratios (SMRs) and life expectancy as dependent variables, with the use
of proposed groups of indicators representing demographic, economic and labour market,
social, access to health care and education-related factors as explanatory variables, confirms
that in some cases these indicators can explain a significant part of district variation in
mortality level and life expectancy.
Explanatory power is particularly strong in the case of overall mortality and life expectancy at
birth. Socio-economic determinants also explain some 30 to 40 percent of variation in the case
of cancer mortality and mortality from external causes in younger men. Selected indicators
also explain some 20 percent of variation in mortality due to cardiovascular diseases in total
population, and digestive system diseases in the younger age group of districts inhabitants.
However, indicators taken into consideration do not explain a meaningful portion of
differences in districts mortality from diseases of the respiratory system, diseases of the
digestive system in elderly population, or in infant mortality.
Page 295
The analysis of final models reveals that in the case of all groups of selected indicators, with
the exception of access to health care, there are some that play a significant role in explaining
mortality differentials more frequently than others. These include:
•
share of households equipped with a bathroom,
•
local government elections turnout,
•
lower secondary school exam results from humanities,
•
share of employment in agriculture,
•
population density,
•
unemployment rate.
On the other hand, high school (baccalaureate) exams never appear as explanatory variable in
the final models; access to health care indicators and library membership are also less
frequent.
There are three variables which are almost always associated with decreased mortality
outcomes in districts. These are: the share of employment in agriculture, local elections
turnout, and humanities exam results. Some hypotheses on those results can be proposed.
First, employment in agriculture, which also represents residence in rural areas, is related to
living and working in a less polluted physical environment, leading a life that is more
physically active, with better lifestyles of rural women who smoke tobacco less frequently
and drink less alcohol than urban women4041. Local elections turnout, as a proxy of social
activity, can indicate that in those districts where participation is higher people are more
active in various fields, and the social network, a well-known factor contributing to better
health, is stronger. Exam results (which are also correlated with the education structure, as
explained in the previous chapter) confirm positive impact of education on health and
mortality reduction.
Another variable which is associated with lower mortality level is the share of households
equipped with bathroom. Only in the case of cancer mortality for the elderly population of age
65 years and over, the association was reversed. Share of households equipped with bathroom
40
Globalny sondaż dotyczący używania tytoniu przez osoby dorosłe (GATS) Polska 2009-2010. Ministerstwo
Zdrowia, World Health Organization Regional Office for Europe, Warszawa 2010.
41
Sierosławski J. Substancje psychoaktywne – postawy i zachowania Polaków. W: Postawy i zachowania
Polaków wobec alkoholu i problemów alkoholowych. [Psychoactive substances: attitides and behaviour of Poles.
In: Attitudes and behavior of Poles related to alcohol and alcohol abuse.] Państwowa Agencja Rozwiązywania
Problemów Alkoholowych, Warszawa 2004, s. 33.
Page 296
is an indicator of living conditions – sanitary-hygienic situation and economic status
therefore, as could be expected, its higher level was associated with lower mortality from
several diseases and longer life expectancy.
Unemployment rate, as expected, is associated with increase in SMRs, especially in men, in
particular for overall mortality and mortality caused by respiratory diseases, which may be
related to overall stress as well as lifestyles of the unemployed. We do not have information
on the risk of death from respiratory diseases in the unemployed population however, as we
presented earlier, respiratory diseases mortality exhibits strong social gradient, which is also
very strong in the case of unemployment. Similar situation takes place in the case of mortality
from ill-defined conditions so there is coherence in these findings.
Surprisingly, the population density, revenue of local budgets per inhabitant, as well as share
of children in pre-school education seem to have a smaller and mixed impact on mortality
outcomes when other variables/factors are taken into account. Higher population density is
associated with lower overall mortality, which is due to its negative association with mortality
in elderly population especially due to the cardiovascular diseases and also negative
association with mortality from external causes in both the age groups. This may be a
reflection of lower mortality from CVD and external causes in the bigger towns than in
smaller communities42. On the other hand positive association with higher infant mortality
may at least partly result from high infant mortality in Śląskie towns.
Own revenue of districts budgets is associated with increased overall mortality, but looking
more thorough this association is present in the case of respiratory diseases and mortality
from external causes which may be related to higher mortality in regions with heavy industry
and pollution (such as Silesia region). On the other hand in districts with higher budget
revenue there was significantly lower CVD mortality in the elderly population which could
result from better health care and more common preventive programmes in those districts
however, we have no data to substantiate this presumption.
42
B. Wojtyniak: Zdrowie mieszkańców polskich miast. Wyniki analizy prezentowane na konferencji „Człowiek
i Miasto” organizowanej przez Biuro WHO w Polsce z okazji Światowego Dnia Zdrowia, Warszawa 7 kwietnia
2010.
Page 297
Higher participation in pre-school education has the expected direction of association in the
case of cancer mortality and infant mortality, which may again be linked to the overall
structure of education of population (as this indicator is strongly correlated with the share of
population with higher education, which is not used in the model). However, it is difficult to
explain why this association is observed in the case of these two health indicators only.
Finally, it should be stressed that the socio-economic variables taken into consideration in our
analysis are not narrow, very specific risk factors for a particular cause of death or other
health outcome. Each of them belongs to the up-stream level indicators and represents several
more specific factors more directly affecting population health. Therefore, the associations
observed should be interpreted with due caution.
Page 298
Annex 4
Table 82. Association between district SMR for all causes and socio-economic variables, total population,
all ages
Variable
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
2
p-value
R
0.14
Demographics
feminization rate
-0.336
0.00
-0.143
-6.29
0.00
population density
-0.377
0.00
-0.162
-0.02
0.00
-0.049
0.34
-0.014
-0.142
0.01
-0.042
-0.03
0.00
-0.052
0.31
-0.031
2.35
0.02
unemployment rate
0.390
0.00
0.209
5.29
0.00
0.167
0.00
0.042
-0.143
0.01
-0.036
0.121
0.02
0.034
-6.20
0.00
share of households applied with bathroom
-0.347
0.00
-0.176
-4.47
0.00
-0.304
0.00
-0.133
-1.75
0.00
0.254
0.00
0.104
0.03
0.00
0.02
0.123
0.02
0.054
average gymnasium exams results (literacy)
-0.462
0.00
-0.227
-32.99
0.00
0.26
-0.210
0.00
-0.103
population density
-0.02
0.02
0.37
0.02
0.02
unemployment rate
2.50
0.00
2.49
0.01
share of households applied with bathroom
-5.72
0.00
-5.44
0.00
-0.01
0.02
average gymnasium exams results (literacy)
-19.82
0.00
Economic and labour market situation
0.16
Social cohesion
0.19
Education
Final model
Page 299
Table 83. Association between district SMR for all causes and socio-economic variables, males, all ages
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.413
0.00
-0.204
-8.84
0.00
0.18
population density
-0.407
0.00
-0.199
-0.01
0.04
0.049
0.35
0.048
-0.198
0.00
-0.087
-0.04
0.00
-0.160
0.00
-0.105
unemployment rate
0.449
0.00
0.258
7.16
0.00
0.235
0.00
0.095
-0.175
0.00
-0.058
0.166
0.00
0.066
-6.97
0.00
share of households equipped with a bathroom
-0.442
0.00
-0.236
-6.50
0.00
-0.374
0.00
-0.185
-1.84
0.00
0.244
0.00
0.105
0.03
0.00
0.02
0.109
0.03
0.044
average lower secondary school exams results
(literacy)
-0.400
0.00
-0.196
-37.30
0.00
0.21
-0.226
0.00
-0.119
unemployment rate
3.97
0.00
0.40
-6.65
0.00
-5.43
0.00
(literacy)
-0.01
0.02
-19.75
0.00
Variable
2
Demographics
0.21
Social cohesion
0.24
Education
Final model
Page 300
Table 84. Association between district SMR for all causes and socio-economic variables, females, all ages
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.097
0.06
-0.010
-4.67
0.00
0.04
population density
-0.178
0.00
-0.048
-0.176
0.00
-0.111
-3.33
0.00
0.028
0.59
0.065
-0.03
0.00
0.142
0.01
0.106
3.30
0.00
unemployment rate
0.223
0.00
0.109
3.34
0.00
-0.043
0.41
-0.091
-0.61
0.03
-0.032
0.53
0.026
-0.036
0.49
-0.073
-6.03
0.00
-0.102
0.05
-0.036
-2.38
0.00
-0.070
0.18
0.002
-1.23
0.00
0.161
0.00
0.044
0.01
0.02
0.00
0.090
0.08
0.034
(literacy)
-0.426
0.00
-0.254
-25.13
0.00
0.19
-0.120
0.02
-0.047
-2.28
0.00
0.30
2.04
0.03
-6.49
0.00
(literacy)
-2.63
0.00
-25.36
0.00
Variable
2
Demographics
0.07
Social cohesion
0.04
Education
Final model
Page 301
aged 0–64 years
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.379
0.00
-0.177
-12.73
0.00
0.13
population density
-0.330
0.00
-0.122
0.03
0.00
0.161
0.00
0.170
-0.103
0.05
-0.043
-0.154
0.00
-0.111
unemployment rate
0.376
0.00
0.216
0.149
0.00
0.048
Variable
2
Demographics
0.11
9.53
0.00
Social cohesion
-0.171
0.00
-0.058
0.103
0.05
0.009
-10.24
0.00
0.20
-0.428
0.00
-0.283
-10.97
0.00
-0.303
0.00
-0.156
0.152
0.00
0.040
0.040
0.44
-0.016
(literacy)
-0.355
0.00
-0.160
-34.34
0.00
0.13
-0.213
0.00
-0.081
population density
0.05
0.00
0.34
unemployment rate
3.82
0.00
-9.93
0.00
(literacy)
-7.93
0.00
-28.53
0.00
Education
Final model
Page 302
Table 86. Association between district SMR for all causes and socio-economic variables, males, aged 0–64
years
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.444
0.00
-0.243
-11.60
0.00
0.19
population density
-0.338
0.00
-0.154
0.269
0.00
0.243
7.09
0.00
-0.186
0.00
-0.118
-0.253
0.00
-0.197
unemployment rate
0.357
0.00
0.199
0.243
0.00
0.138
Variable
2
Demographics
0.12
11.00
0.00
Social cohesion
-0.198
0.00
-0.079
0.175
0.00
0.072
-9.70
0.00
0.28
-0.524
0.00
-0.357
-13.14
0.00
-0.346
0.00
-0.196
0.145
0.00
0.045
0.039
0.44
-0.015
(literacy)
-0.275
0.00
-0.119
-34.39
0.00
0.09
-0.197
0.00
-0.083
6.38
0.00
0.35
unemployment rate
5.34
0.00
-9.27
0.00
(literacy)
-8.96
0.00
-13.16
0.01
Education
Final model
Page 303
Table 87. Association between district SMR for all causes and socio-economic variables, females, aged 0–
64 years
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
0.097
0.06
0.111
-4.94
0.01
0.06
population density
0.017
0.74
0.114
0.07
0.00
-0.182
0.00
-0.014
-5.28
0.03
0.331
0.00
0.236
0.236
0.00
0.175
unemployment rate
0.166
0.00
0.107
7.20
0.00
-0.344
0.00
-0.280
-3.97
0.00
Variable
2
Demographics
feminization rate
0.18
Social cohesion
0.040
0.44
0.037
-0.275
0.00
-0.244
-13.17
0.00
0.09
0.121
0.02
-0.009
-3.41
0.01
0.106
0.04
0.087
1.10
0.04
-0.042
0.41
-0.116
-0.04
0.00
0.00
-0.021
0.69
-0.061
(literacy)
-0.308
0.00
-0.188
-15.68
0.00
0.07
-0.063
0.22
-0.018
population density
0.05
0.00
0.40
-7.24
0.00
-13.22
0.00
(literacy)
-5.11
0.00
-46.13
0.00
Education
Final model
Page 304
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.225
0.00
-0.089
-4.39
0.00
0.25
population density
-0.339
0.00
-0.163
-0.03
0.00
-0.228
0.00
-0.147
-3.86
0.00
-0.135
0.01
-0.038
-0.04
0.00
0.053
0.30
0.043
3.11
0.00
unemployment rate
0.293
0.00
0.138
3.50
0.00
0.147
0.00
0.037
Variable
2
Demographics
0.17
Social cohesion
-0.100
0.05
-0.023
0.112
0.03
0.038
-4.22
0.00
0.14
-0.196
0.00
-0.062
-1.46
0.01
-0.234
0.00
-0.083
-2.53
0.00
0.302
0.00
0.139
0.03
0.00
0.03
0.168
0.00
0.089
(literacy)
-0.455
0.00
-0.239
-32.46
0.00
0.27
-0.159
0.00
-0.085
-3.27
0.00
0.38
population density
-0.02
0.00
2.08
0.01
-3.50
0.00
(literacy)
-3.17
0.00
-15.79
0.00
Education
Final model
Page 305
Table 89. Association between district SMR for all causes and socio-economic variables, males, aged 65
years and over
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.282
0.00
-0.132
-6.16
0.00
0.30
population density
-0.418
0.00
-0.231
-0.03
0.00
-0.256
0.00
-0.168
-6.03
0.00
-0.156
0.00
-0.051
-0.05
0.00
0.009
0.86
0.014
2.59
0.01
unemployment rate
0.409
0.00
0.221
5.77
0.00
0.176
0.00
0.059
Variable
2
Demographics
0.23
Social cohesion
-0.130
0.01
-0.046
0.118
0.02
0.051
-4.80
0.00
0.18
-0.220
0.00
-0.070
-1.47
0.04
-0.315
0.00
-0.138
-3.27
0.00
0.331
0.00
0.172
0.04
0.00
0.05
0.175
0.00
0.101
(literacy)
-0.470
0.00
-0.253
-39.81
0.00
0.28
-0.209
0.00
-0.139
-5.55
0.00
0.42
population density
-0.02
0.00
unemployment rate
2.02
0.01
-4.02
0.00
(literacy)
-3.68
0.00
-16.43
0.00
Education
Final model
Page 306
Table 90. Association between district SMR for all causes and socio-economic variables, females, aged 65
years and over
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.141
0.01
-0.041
-3.08
0.00
0.12
population density
-0.212
0.00
-0.088
-0.03
0.00
-0.164
0.00
-0.123
-2.31
0.03
-0.086
0.09
-0.007
-0.04
0.00
0.100
0.05
0.082
3.38
0.00
unemployment rate
0.195
0.00
0.076
2.43
0.00
0.072
0.16
-0.011
Variable
2
Demographics
0.09
Social cohesion
-0.046
0.37
0.013
0.060
0.24
-0.002
-4.23
0.00
0.05
-0.152
0.00
-0.043
-1.74
0.01
-0.112
0.03
-0.016
-1.78
0.00
0.222
0.00
0.085
0.02
0.00
0.01
0.121
0.02
0.055
(literacy)
-0.386
0.00
-0.229
-26.81
0.00
0.18
-0.118
0.02
-0.046
population density
-0.03
0.00
0.25
2.31
0.01
0.76
0.03
Education
Final model
Page 307
Table 91. Association between district SMR for malignant neoplasms and socio-economic variables, total
population, all ages
Spearman
correlation
coefficient
p-value
RCI
feminization rate
0.157
0.00
0.047
population density
-0.022
0.67
-0.062
0.02
0.00
-0.410
0.00
-0.280
-11.22
0.00
0.330
0.00
0.133
0.264
0.00
0.134
3.75
0.01
unemployment rate
0.129
0.01
0.143
4.55
0.00
-0.308
0.00
-0.138
-1.91
0.00
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
0.16
0.15
Social cohesion
0.023
0.66
0.010
-0.29
0.02
-0.245
0.00
-0.103
-4.05
0.00
0.286
0.00
0.119
6.32
0.00
0.075
0.15
-0.034
-1.75
0.00
0.048
0.36
0.074
0.090
0.08
0.055
(literacy)
-0.388
0.00
-0.309
-21.37
0.00
-0.023
0.65
-0.038
5.90
0.02
unemployment rate
-2.09
0.04
-3.52
0.00
(literacy)
-1.08
0.02
-32.40
0.00
0.21
Education
0.14
Final model
Page 308
0.36
Table 92. Association between district SMR for malignant neoplasms and socio-economic variables, males,
all ages
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.034
0.51
-0.076
-3.21
0.00
0.12
population density
-0.180
0.00
-0.196
-0.359
0.00
-0.268
-11.72
0.00
0.139
0.01
0.022
-0.02
0.04
0.131
0.01
0.044
2.99
0.05
unemployment rate
0.268
0.00
0.243
6.32
0.00
-0.104
0.04
-0.015
-1.16
0.00
-0.066
0.20
-0.039
-0.109
0.03
-0.017
-3.67
0.00
0.097
0.06
0.029
5.03
0.00
-0.116
0.02
-0.148
-3.40
0.00
0.177
0.00
0.170
0.159
0.00
0.115
0.01
0.00
(literacy)
-0.458
0.00
-0.364
-31.36
0.00
0.20
-0.094
0.07
-0.099
-2.93
0.00
0.31
-1.99
0.00
(literacy)
0.01
0.04
-30.29
0.00
Variable
2
Demographics
0.10
Social cohesion
0.15
0.02
Education
Final model
Page 309
Table 93. Association between district SMR for malignant neoplasms and socio-economic variables,
females, all ages
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
0.374
0.00
0.224
3.00
0.05
0.23
population density
0.212
0.00
0.159
0.05
0.00
-0.380
0.00
-0.206
-11.80
0.00
0.521
0.00
0.298
0.376
0.00
0.241
unemployment rate
-0.048
0.35
-0.028
-0.529
0.00
-0.323
-4.17
0.00
Variable
2
Demographics
feminization rate
0.30
Social cohesion
0.141
0.01
0.102
-0.31
0.03
-0.384
0.00
-0.236
-5.72
0.00
0.462
0.00
0.235
8.08
0.00
0.321
0.00
0.167
-0.148
0.00
-0.111
-0.05
0.00
-0.045
0.38
-0.064
(literacy)
-0.197
0.00
-0.154
0.064
0.21
0.041
0.30
0.01
Education
0.00
7.39
0.02
-4.17
0.00
-0.39
0.00
-2.58
0.05
Final model
Page 310
0.34
population, aged 0–64 years
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.094
0.07
-0.073
-4.01
0.00
0.05
population density
-0.181
0.00
-0.150
-0.210
0.00
-0.149
-6.40
0.00
0.094
0.07
0.007
0.096
0.06
0.032
3.63
0.03
unemployment rate
0.238
0.00
0.225
7.16
0.00
-0.079
0.13
-0.026
-0.81
0.03
-6.26
0.00
-1.85
0.00
Variable
2
Demographics
0.07
Social cohesion
-0.086
0.09
-0.037
-0.086
0.09
-0.063
0.04
-0.028
0.58
-0.052
-0.091
0.08
-0.120
0.185
0.00
0.156
0.140
0.01
0.093
0.01
0.01
(literacy)
-0.470
0.00
-0.343
-32.20
0.00
0.20
-0.115
0.03
-0.084
feminization rate
-3.87
0.01
0.26
-2.59
0.00
(literacy)
0.01
0.04
-36.20
0.00
0.03
Education
Final model
Page 311
aged 0–64 years
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.203
0.00
-0.157
-7.89
0.00
0.05
population density
-0.272
0.00
-0.210
-0.105
0.04
-0.079
-6.71
0.00
-0.046
0.38
-0.078
-0.03
0.00
-0.015
0.77
-0.037
3.61
0.03
unemployment rate
0.267
0.00
0.231
6.46
0.00
0.087
0.09
0.072
-5.66
0.00
Variable
2
Demographics
0.06
Social cohesion
-0.142
0.01
-0.078
0.034
0.51
0.004
0.06
-0.163
0.00
-0.118
-0.197
0.00
-0.179
-3.13
0.00
0.290
0.00
0.218
0.04
0.00
0.05
0.191
0.00
0.118
(literacy)
-0.464
0.00
-0.342
-41.17
0.00
0.20
-0.145
0.00
-0.082
feminization rate
-2.65
0.04
0.21
(literacy)
-3.35
0.01
-36.89
0.00
Education
Final model
Page 312
females, aged 0–64 years
Spearman
correlation
coefficient
p-value
RCI
0.120
0.02
0.050
population density
0.054
0.29
-0.021
0.04
0.00
-0.247
0.00
-0.146
-8.94
0.00
0.292
0.00
0.126
0.217
0.00
0.099
unemployment rate
0.083
0.11
0.105
5.28
0.00
-0.314
0.00
-0.136
-3.50
0.00
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
feminization rate
0.08
0.13
Social cohesion
0.019
0.71
0.002
-0.36
0.05
-0.248
0.00
-0.134
-7.04
0.00
0.08
0.180
0.00
0.034
3.24
0.00
0.132
0.01
-0.005
-0.040
0.44
0.022
-0.03
0.00
0.00
0.020
0.70
0.020
(literacy)
-0.287
0.00
-0.243
-13.68
0.00
0.07
-0.018
0.73
-0.062
population density
0.03
0.01
0.25
-4.98
0.00
(literacy)
-4.27
0.01
-42.29
0.00
Education
Final model
Page 313
population, aged 65 years and over
Spearman
correlation
coefficient
p-value
RCI
0.285
0.00
0.135
population density
0.097
0.06
0.025
0.03
0.00
-0.469
0.00
-0.333
-14.93
0.00
0.432
0.00
0.225
0.321
0.00
0.188
4.07
0.01
unemployment rate
0.017
0.74
0.051
2.99
0.00
-0.401
0.00
-0.208
-2.49
0.00
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
feminization rate
0.22
0.20
Social cohesion
0.095
0.06
0.051
-0.27
0.03
-0.297
0.00
-0.132
-2.95
0.01
0.31
0.440
0.00
0.240
9.46
0.00
0.180
0.00
0.053
-1.81
0.00
-0.049
0.34
0.014
-0.02
0.02
0.00
0.036
0.49
0.035
(literacy)
-0.245
0.00
-0.228
-14.04
0.00
0.07
0.041
0.43
-0.014
7.20
0.01
unemployment rate
-2.74
0.01
-2.51
0.00
-1.52
0.00
(literacy)
5.48
0.00
-27.76
0.00
Education
Final model
Page 314
0.38
Spearman
correlation
coefficient
p-value
RCI
feminization rate
0.072
0.16
-0.025
population density
-0.084
0.10
-0.149
-0.448
0.00
-0.339
0.231
0.00
0.089
0.200
0.00
0.082
3.76
0.02
unemployment rate
0.197
0.00
0.192
6.91
0.00
-0.200
0.00
-0.063
-1.36
0.00
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
0.18
-15.27
0.00
0.11
Social cohesion
0.002
0.96
-0.015
-0.171
0.00
-0.021
-2.39
0.05
0.22
0.242
0.00
0.114
8.71
0.00
-0.036
0.49
-0.096
-3.81
0.00
0.079
0.13
0.112
0.097
0.06
0.094
0.01
0.04
(literacy)
-0.340
0.00
-0.311
-25.87
0.00
0.11
-0.054
0.29
-0.101
-4.67
0.02
0.29
-2.05
0.00
-2.06
0.00
(literacy)
3.58
0.01
-24.02
0.00
0.01
Education
Final model
Page 315
females aged 65 years and over
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
0.460
0.00
0.303
6.20
0.00
0.28
population density
0.293
0.00
0.236
0.05
0.00
-0.386
0.00
-0.234
-12.99
0.00
0.577
0.00
0.366
0.402
0.00
0.280
unemployment rate
-0.131
0.01
-0.105
-0.576
0.00
-0.384
0.185
0.00
0.146
-0.407
0.00
-0.253
-4.98
0.00
0.557
0.00
0.341
10.07
0.00
0.395
0.00
0.241
-0.207
0.00
-0.148
-0.06
0.00
-0.075
0.14
-0.085
(literacy)
-0.086
0.10
-0.067
0.119
0.02
0.098
Variable
2
Demographics
0.33
-4.91
0.00
Social cohesion
0.35
0.02
Education
0.01
11.33
0.00
-3.45
0.00
4.26
0.00
Final model
Page 316
0.37
Table 100. Association between district SMR for circulatory system diseases and socio-economic variables,
total population, all ages
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.258
0.00
-0.122
-7.41
0.00
0.17
population density
-0.204
0.00
-0.100
-0.06
0.00
0.136
0.01
0.091
-0.265
0.00
-0.131
-0.07
0.00
-0.043
0.40
-0.025
4.61
0.01
unemployment rate
0.224
0.00
0.116
3.84
0.00
0.261
0.00
0.119
1.13
0.01
Variable
2
Demographics
0.16
Social cohesion
-0.106
0.04
-0.047
0.174
0.00
0.082
-5.99
0.00
0.16
-0.385
0.00
-0.209
-6.75
0.00
-0.207
0.00
-0.096
-2.78
0.00
0.230
0.00
0.110
0.05
0.00
0.01
0.073
0.16
0.026
(literacy)
-0.184
0.00
-0.088
-40.84
0.00
0.07
-0.183
0.00
-0.085
feminization rate
-3.90
0.01
0.29
population density
-0.08
0.00
-3.06
0.00
-10.27
0.00
-4.53
0.00
Education
Final model
Page 317
males, all ages
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.271
0.00
-0.136
-8.76
0.00
0.15
population density
-0.224
0.00
-0.123
-0.05
0.00
0.132
0.01
0.083
-0.234
0.00
-0.116
-0.09
0.00
-0.077
0.14
-0.044
unemployment rate
0.253
0.00
0.149
4.40
0.00
0.236
0.00
0.104
Variable
2
Demographics
0.14
Social cohesion
-0.112
0.03
-0.048
0.160
0.00
0.076
-6.96
0.00
0.14
-0.373
0.00
-0.208
-7.17
0.00
-0.214
0.00
-0.107
-2.81
0.00
0.211
0.00
0.104
0.05
0.00
0.01
0.051
0.32
0.013
(literacy)
-0.177
0.00
-0.089
-42.05
0.00
0.06
-0.188
0.00
-0.105
feminization rate
-4.25
0.01
0.22
population density
-0.07
0.00
unemployment rate
3.87
0.00
1.61
0.01
-6.83
0.00
-6.90
0.00
Education
Final model
Page 318
females, all ages
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.214
0.00
-0.102
-6.14
0.00
0.14
population density
-0.155
0.00
-0.070
-0.06
0.00
0.127
0.01
0.091
-0.255
0.00
-0.132
-0.07
0.00
-0.006
0.91
-0.004
4.93
0.01
unemployment rate
0.185
0.00
0.094
3.44
0.01
0.241
0.00
0.116
1.17
0.01
Variable
2
Demographics
0.13
Social cohesion
-0.085
0.10
-0.033
0.36
0.03
0.154
0.00
0.071
-5.50
0.00
0.13
-0.359
0.00
-0.199
-7.07
0.00
-0.170
0.00
-0.075
-2.68
0.00
0.218
0.00
0.099
0.05
0.00
0.01
0.083
0.11
0.031
(literacy)
-0.175
0.00
-0.091
-37.66
0.00
0.06
-0.163
0.00
-0.069
population density
-0.09
0.00
0.26
-2.79
0.00
0.32
0.02
-11.41
0.00
-4.52
0.00
Education
Final model
Page 319
total population, aged 0–64
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.241
0.00
-0.124
-9.49
0.00
0.06
population density
-0.206
0.00
-0.081
-0.03
0.03
0.178
0.00
0.155
7.07
0.01
-0.123
0.02
-0.050
-0.04
0.00
-0.058
0.26
-0.037
unemployment rate
0.223
0.00
0.130
7.05
0.00
0.134
0.01
0.040
Variable
2
Demographics
0.05
Social cohesion
-0.125
0.02
-0.047
0.087
0.09
0.027
-9.74
0.00
0.11
-0.335
0.00
-0.239
-9.93
0.00
-0.189
0.00
-0.095
-1.41
0.02
0.120
0.02
0.042
-0.015
0.77
-0.030
(literacy)
-0.247
0.00
-0.105
-43.08
0.00
0.07
-0.183
0.00
-0.089
feminization rate
-6.00
0.01
0.17
0.04
0.01
-9.32
0.00
(literacy)
-9.97
0.00
-28.61
0.00
Education
Final model
Page 320
males, aged 0–64
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.272
0.00
-0.161
-10.18
0.00
0.09
population density
-0.185
0.00
-0.083
-0.03
0.04
0.237
0.00
0.203
9.45
0.00
-0.152
0.00
-0.090
-0.04
0.00
-0.118
0.02
-0.089
unemployment rate
0.207
0.00
0.127
6.28
0.00
0.169
0.00
0.082
Variable
2
Demographics
0.05
Social cohesion
-0.120
0.02
-0.054
0.130
0.01
0.064
-7.41
0.00
0.13
-0.372
0.00
-0.278
-12.23
0.00
-0.194
0.00
-0.108
0.093
0.07
0.032
-0.043
0.41
-0.043
(literacy)
-0.181
0.00
-0.077
-36.99
0.00
0.04
-0.150
0.00
-0.075
feminization rate
-5.51
0.01
0.15
7.13
0.01
-6.31
0.00
(literacy)
-6.78
0.00
-21.57
0.00
Education
Final model
Page 321
females, aged 0–64
Spearman
correlation
coefficient
p-value
RCI
feminization rate
0.071
0.17
0.038
population density
-0.005
0.93
0.005
-0.038
0.46
0.021
0.149
0.00
0.097
0.192
0.00
0.108
8.00
0.01
unemployment rate
0.104
0.04
0.101
9.51
0.00
-0.187
0.00
-0.129
-2.56
0.00
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
0.06
Social cohesion
-0.014
0.79
0.003
-0.182
0.00
-0.127
-17.17
0.00
0.05
0.007
0.90
-0.076
-6.12
0.00
0.055
0.29
0.005
0.027
0.60
-0.002
-0.001
0.99
0.010
(literacy)
-0.215
0.00
-0.152
-30.01
0.00
0.04
-0.114
0.03
-0.094
-8.96
0.00
0.21
-18.55
0.00
(literacy)
-8.50
0.00
-43.93
0.00
Education
Final model
Page 322
total population, aged 65 years and over
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.244
0.00
-0.118
-6.43
0.00
0.19
population density
-0.195
0.00
-0.098
-0.07
0.00
0.109
0.03
0.072
-0.289
0.00
-0.153
-0.08
0.00
-0.035
0.49
-0.023
4.85
0.01
unemployment rate
0.203
0.00
0.098
3.08
0.01
0.283
0.00
0.141
1.31
0.00
Variable
2
Demographics
0.17
Social cohesion
-0.101
0.05
-0.046
0.34
0.03
0.182
0.00
0.095
-5.08
0.00
0.14
-0.364
0.00
-0.193
-6.27
0.00
-0.197
0.00
-0.090
-3.32
0.00
0.251
0.00
0.120
0.06
0.00
0.02
0.093
0.07
0.043
(literacy)
-0.162
0.00
-0.076
-40.21
0.00
0.06
-0.166
0.00
-0.080
population density
-0.07
0.00
0.29
-0.04
0.00
-2.88
0.00
0.31
0.02
-9.04
0.00
Education
Final model
Page 323
males, aged 65 years and over
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.235
0.00
-0.117
-7.00
0.00
0.18
population density
-0.232
0.00
-0.138
-0.07
0.00
0.037
0.47
0.016
-0.247
0.00
-0.129
-0.10
0.00
-0.032
0.53
-0.019
unemployment rate
0.243
0.00
0.134
3.71
0.00
0.243
0.00
0.117
Variable
2
Demographics
0.15
Social cohesion
-0.102
0.05
-0.048
0.158
0.00
0.078
-6.42
0.00
0.10
-0.307
0.00
-0.153
-5.43
0.00
-0.197
0.00
-0.094
-3.55
0.00
0.256
0.00
0.136
0.06
0.00
0.02
0.093
0.07
0.049
(literacy)
-0.169
0.00
-0.096
-43.86
0.00
0.06
-0.184
0.00
-0.117
population density
-0.08
0.00
0.23
-0.03
0.02
unemployment rate
3.68
0.00
1.68
0.01
-5.65
0.00
-5.14
0.00
Education
Final model
Page 324
females, aged 65 years and over
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.237
0.00
-0.117
-5.16
0.00
0.17
population density
-0.165
0.00
-0.076
-0.08
0.00
0.140
0.01
0.096
3.85
0.04
-0.294
0.00
-0.160
-0.07
0.00
-0.032
0.54
-0.021
4.68
0.01
unemployment rate
0.180
0.00
0.089
3.03
0.02
0.285
0.00
0.145
1.47
0.00
Variable
2
Demographics
0.15
Social cohesion
-0.090
0.08
-0.040
0.40
0.01
0.183
0.00
0.096
-4.40
0.00
0.14
-0.381
0.00
-0.209
-6.92
0.00
-0.185
0.00
-0.085
-2.91
0.00
0.235
0.00
0.108
0.05
0.00
0.01
0.092
0.07
0.038
(literacy)
-0.159
0.00
-0.076
-37.89
0.00
0.05
-0.152
0.00
-0.060
population density
-0.07
0.00
0.27
-0.03
0.00
-2.88
0.00
0.38
0.01
-9.81
0.00
Education
Final model
Page 325
Table 109. Association between district SMR for respiratory system diseases and socio-economic
variables, total population, all ages
Spearman
correlation
coefficient
p-value
RCI
feminization rate
-0.207
0.00
-0.212
population density
-0.238
0.00
-0.241
-0.011
0.83
-0.084
-0.052
0.31
-0.101
-0.136
0.01
-0.177
unemployment rate
0.210
0.00
0.226
0.111
0.03
0.115
Variable
Regression
coefficient
(x 1000)
p-value
R
0.06
0.00
0.05
14.74
0.00
2
Demographics
Social cohesion
-0.161
0.00
-0.136
0.058
0.26
0.029
-0.175
0.00
-0.146
-0.271
0.00
-0.265
0.092
0.07
0.076
0.063
0.22
0.056
(literacy)
-0.228
0.00
-0.219
-0.016
0.76
-0.006
0.06
-1.93
0.01
-36.48
0.00
0.06
0.12
0.00
0.11
unemployment rate
7.19
0.02
(literacy)
-2.50
0.03
-40.13
0.00
Education
Final model
Page 326
variables, males, all ages
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.314
0.00
-0.283
-12.04
0.00
0.07
population density
-0.323
0.00
-0.289
0.037
0.47
-0.014
-0.200
0.00
-0.187
0.05
0.03
0.14
-0.169
0.00
-0.169
unemployment rate
0.301
0.00
0.268
17.69
0.00
0.250
0.00
0.200
2.75
0.00
Variable
2
Demographics
Social cohesion
-0.191
0.00
-0.151
0.131
0.01
0.076
-6.26
0.03
0.15
-0.317
0.00
-0.233
-7.73
0.00
-0.379
0.00
-0.329
-3.14
0.00
0.173
0.00
0.135
0.06
0.00
0.02
0.099
0.05
0.086
(literacy)
-0.302
0.00
-0.246
-63.24
0.00
0.11
-0.081
0.12
-0.043
0.10
0.00
0.20
unemployment rate
10.57
0.00
-12.38
0.00
(literacy)
-7.01
0.01
-51.05
0.00
Education
Final model
Page 327
variables, females, all ages
Spearman
correlation
coefficient
p-value
RCI
feminization rate
0.001
0.98
-0.061
population density
-0.053
0.31
-0.099
-0.107
0.04
-0.164
0.198
0.00
0.066
-0.039
0.44
-0.124
unemployment rate
0.069
0.18
-0.148
0.00
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
0.00
0.08
0.00
0.12
0.00
0.144
11.61
0.00
-0.066
-2.28
0.04
0.06
Social cohesion
-0.069
0.18
-0.079
-0.103
0.04
-0.048
0.089
0.08
0.020
-0.031
0.55
-0.102
-0.059
0.25
-0.043
-0.010
0.84
-0.014
(literacy)
-0.093
0.07
-0.135
0.068
0.18
0.067
0.01
7.72
0.00
-0.06
0.02
0.00
Education
0.00
15.54
0.04
0.12
0.00
unemployment rate
11.61
0.00
-2.28
0.04
Final model
Page 328
0.06
variables, total population, aged 0–64
Spearman
correlation
coefficient
p-value
RCI
feminization rate
-0.083
0.10
-0.137
population density
-0.098
0.06
-0.136
0.027
0.60
0.009
0.075
0.15
0.036
-0.018
0.72
-0.078
unemployment rate
0.272
0.00
0.289
17.71
0.00
-0.059
0.25
-0.045
-4.00
0.00
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
0.08
Social cohesion
-0.090
0.08
-0.046
-0.098
0.06
-0.114
-19.11
0.00
0.04
-0.095
0.07
-0.098
-8.24
0.00
-0.088
0.09
-0.115
0.061
0.24
0.017
0.005
0.92
0.009
(literacy)
-0.224
0.00
-0.210
-36.88
0.00
0.04
-0.074
0.15
-0.054
unemployment rate
9.22
0.01
0.16
-9.90
0.00
-20.53
0.00
(literacy)
-9.52
0.01
-31.76
0.01
Education
Final model
Page 329
variables, males, aged 0–64
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.114
0.03
-0.155
-10.96
0.01
0.02
population density
-0.095
0.06
-0.124
0.08
0.00
0.104
0.04
0.092
0.016
0.76
-0.006
0.08
0.00
-0.064
0.21
-0.088
unemployment rate
0.232
0.00
0.259
18.00
0.00
-0.005
0.92
-0.011
Variable
2
Demographics
0.05
Social cohesion
-0.114
0.03
-0.058
-0.069
0.18
-0.095
-19.80
0.00
0.05
-0.148
0.00
-0.156
-10.88
0.00
-0.102
0.05
-0.116
0.064
0.21
0.022
0.013
0.81
0.022
(literacy)
-0.180
0.00
-0.181
-32.15
0.00
0.02
-0.060
0.24
-0.064
0.18
0.00
0.14
unemployment rate
14.41
0.00
-16.60
0.00
(literacy)
-24.93
0.00
-33.78
0.01
Education
Final model
Page 330
variables, females, aged 0–64
Spearman
correlation
coefficient
p-value
RCI
0.076
0.14
-0.056
population density
0.023
0.66
-0.109
0.14
0.00
-0.109
0.03
-0.140
-22.81
0.01
0.222
0.00
0.090
0.091
0.08
0.008
unemployment rate
0.169
0.00
0.256
17.30
0.00
-0.210
0.00
-0.115
-10.14
0.00
-25.86
0.00
unemployment rate
17.30
0.00
-10.14
0.00
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
feminization rate
0.04
0.08
Social cohesion
0.024
0.64
0.006
-0.192
0.00
-0.097
0.108
0.04
0.027
0.069
0.18
-0.064
-0.032
0.53
0.013
-0.013
0.80
-0.056
(literacy)
-0.120
0.02
-0.254
-0.009
0.86
-0.077
0.04
Education
Final model
Page 331
0.08
variables, total population, aged 65 years and over
Spearman
correlation
coefficient
p-value
RCI
feminization rate
-0.215
0.00
-0.202
population density
-0.261
0.00
-0.233
-0.040
0.44
-0.097
-0.084
0.10
-0.114
-0.155
0.00
-0.179
unemployment rate
0.181
0.00
0.148
Variable
Regression
coefficient
(x 1000)
p-value
R
0.09
0.00
0.06
0.182
13.23
0.00
0.00
0.132
2.31
0.01
2
Demographics
Social cohesion
-0.173
0.00
-0.140
0.094
0.07
0.059
-0.165
0.00
-0.142
-0.294
0.00
-0.264
0.102
0.05
0.075
0.080
0.12
0.057
(literacy)
-0.218
0.00
-0.199
-0.001
0.99
0.008
0.07
-2.24
0.00
-35.45
0.00
0.05
0.13
0.00
0.10
(literacy)
-4.14
0.00
-44.27
0.00
Education
Final model
Page 332
variables, males, aged 65 years and over
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.339
0.00
-0.278
-8.14
0.02
0.11
population density
-0.381
0.00
-0.294
-0.05
0.01
-0.021
0.68
-0.050
-0.250
0.00
-0.204
0.05
0.02
-0.186
0.00
-0.159
unemployment rate
0.286
0.00
0.232
18.07
0.00
0.312
0.00
0.224
4.04
0.00
Variable
2
Demographics
0.15
Social cohesion
-0.201
0.00
-0.152
0.195
0.00
0.115
0.16
-0.315
0.00
-0.224
-5.19
0.04
-0.427
0.00
-0.339
-4.29
0.00
0.206
0.00
0.148
0.08
0.00
0.03
0.129
0.01
0.092
(literacy)
-0.317
0.00
-0.245
-70.98
0.00
0.11
-0.082
0.11
-0.038
0.08
0.00
0.20
unemployment rate
9.94
0.00
-10.14
0.00
-55.94
0.00
Education
Final model
(literacy)
Page 333
variables, females, aged 65 years and over
Spearman
correlation
coefficient
p-value
RCI
feminization rate
-0.012
0.81
-0.052
population density
-0.067
0.20
-0.087
-0.097
0.06
-0.160
0.161
0.00
0.065
-0.074
0.15
-0.121
unemployment rate
0.043
0.41
0.109
-0.115
0.02
-0.062
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
0.00
0.09
0.00
0.15
0.00
9.06
0.02
0.04
Social cohesion
-0.080
0.12
-0.085
-0.075
0.15
-0.036
0.077
0.13
0.023
-0.051
0.32
-0.092
-0.053
0.31
-0.047
0.003
0.95
-0.013
(literacy)
-0.075
0.15
-0.105
0.086
0.09
0.081
0.00
7.82
0.00
-0.06
0.02
0.00
Education
0.00
19.68
0.02
0.15
0.00
unemployment rate
9.06
0.02
Final model
Page 334
0.05
Table 118. Association between district SMR for digestive system diseases and socio-economic variables,
total population, all ages
Spearman
correlation
coefficient
p-value
RCI
0.142
0.01
0.215
population density
0.205
0.00
0.333
-0.023
0.65
0.091
0.419
0.00
0.365
0.174
0.00
0.229
unemployment rate
-0.082
0.11
-0.033
-0.399
0.00
-0.431
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
feminization rate
0.09
0.08
0.00
0.15
-4.76
0.00
Social cohesion
0.129
0.01
0.169
-0.221
0.00
-0.355
-14.23
0.00
0.11
0.153
0.00
0.061
-8.09
0.00
0.273
0.00
0.319
4.47
0.00
-0.214
0.00
-0.294
-0.07
0.00
0.03
-0.144
0.01
-0.173
(literacy)
0.024
0.65
0.018
0.029
0.58
0.040
-11.47
0.00
0.29
-22.12
0.00
-6.43
0.00
Education
Final model
Page 335
males, all ages
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
0.086
0.09
0.151
-6.39
0.02
0.08
population density
0.198
0.00
0.257
0.11
0.00
0.065
0.21
0.103
0.348
0.00
0.281
0.126
0.01
0.154
unemployment rate
-0.030
0.56
0.038
5.28
0.01
-0.346
0.00
-0.345
-5.18
0.00
Variable
2
Demographics
0.11
Social cohesion
0.103
0.05
0.140
-0.210
0.00
-0.301
-15.74
0.00
0.10
0.082
0.11
-0.006
-11.37
0.00
0.242
0.00
0.241
5.08
0.00
-0.222
0.00
-0.237
-0.08
0.00
0.03
-0.146
0.00
-0.149
(literacy)
0.056
0.28
0.018
-0.003
0.95
0.016
-12.70
0.00
0.28
-26.70
0.00
-7.16
0.00
Education
Final model
Page 336
females, all ages
Spearman
correlation
coefficient
p-value
RCI
0.241
0.00
0.222
population density
0.256
0.00
0.287
-0.121
0.02
0.006
0.459
0.00
0.382
0.228
0.00
0.262
unemployment rate
-0.176
0.00
-0.098
-0.429
0.00
-0.439
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
feminization rate
0.09
0.11
0.00
0.16
-6.05
0.00
-11.63
0.00
Social cohesion
0.177
0.00
0.141
-0.247
0.00
-0.318
0.10
0.255
0.00
0.152
0.317
0.00
0.313
3.65
0.00
-0.200
0.00
-0.263
-0.08
0.00
-0.118
0.02
-0.173
(literacy)
0.033
0.52
-0.017
0.099
0.05
0.054
0.02
Education
0.00
15.16
0.01
population density
0.04
0.01
-3.92
0.00
-6.32
0.01
Final model
Page 337
0.16
total population, aged 0–64
Spearman
correlation
coefficient
p-value
RCI
feminization rate
-0.083
0.10
-0.137
population density
-0.098
0.06
-0.136
0.027
0.60
0.009
0.075
0.15
0.036
-0.018
0.72
-0.078
unemployment rate
0.272
0.00
0.289
17.71
0.00
-0.059
0.25
-0.045
-4.00
0.00
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
0.08
Social cohesion
-0.090
0.08
-0.046
-0.098
0.06
-0.114
-19.11
0.00
0.04
-0.095
0.07
-0.098
-8.24
0.00
-0.088
0.09
-0.115
0.061
0.24
0.017
0.005
0.92
0.009
(literacy)
-0.224
0.00
-0.210
-36.88
0.00
0.04
-0.074
0.15
-0.054
unemployment rate
9.22
0.01
0.16
-9.90
0.00
-20.53
0.00
(literacy)
-9.52
0.01
-31.76
0.01
Education
Final model
Page 338
males, aged 0–64
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.114
0.03
-0.155
-10.96
0.01
0.02
population density
-0.095
0.06
-0.124
0.08
0.00
0.104
0.04
0.092
0.016
0.76
-0.006
0.08
0.00
-0.064
0.21
-0.088
unemployment rate
0.232
0.00
0.259
18.00
0.00
-0.005
0.92
-0.011
Variable
2
Demographics
0.05
Social cohesion
-0.114
0.03
-0.058
-0.069
0.18
-0.095
-19.80
0.00
0.05
-0.148
0.00
-0.156
-10.88
0.00
-0.102
0.05
-0.116
0.064
0.21
0.022
0.013
0.81
0.022
(literacy)
-0.180
0.00
-0.181
-32.15
0.00
0.02
-0.060
0.24
-0.064
0.18
0.00
0.14
unemployment rate
14.41
0.00
-16.60
0.00
(literacy)
-24.93
0.00
-33.78
0.01
Education
Final model
Page 339
females, aged 0–64
Spearman
correlation
coefficient
p-value
RCI
0.076
0.14
-0.056
population density
0.023
0.66
-0.109
0.14
0.00
-0.109
0.03
-0.140
-22.81
0.01
0.222
0.00
0.090
0.091
0.08
0.008
unemployment rate
0.169
0.00
0.256
17.30
0.00
-0.210
0.00
-0.115
-10.14
0.00
-25.86
0.00
unemployment rate
17.30
0.00
-10.14
0.00
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
feminization rate
0.04
0.08
Social cohesion
0.024
0.64
0.006
-0.192
0.00
-0.097
0.108
0.04
0.027
0.069
0.18
-0.064
-0.032
0.53
0.013
-0.013
0.80
-0.056
(literacy)
-0.120
0.02
-0.254
-0.009
0.86
-0.077
0.04
Education
Final model
Page 340
0.08
total population, aged 65 years and over
Spearman
correlation
coefficient
p-value
RCI
0.169
0.00
0.165
population density
0.098
0.06
0.091
0.06
0.00
-0.220
0.00
-0.198
-8.40
0.00
0.370
0.00
0.304
0.183
0.00
0.204
unemployment rate
-0.101
0.05
-0.048
-0.329
0.00
-0.312
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
feminization rate
0.07
0.09
-3.40
0.00
-8.71
0.00
Social cohesion
0.075
0.14
0.060
-0.162
0.00
-0.174
0.04
0.228
0.00
0.200
0.216
0.00
0.201
1.30
0.02
-0.113
0.03
-0.095
-0.03
0.01
0.00
-0.110
0.03
-0.122
(literacy)
-0.018
0.73
-0.062
0.051
0.32
0.034
-2.70
0.00
0.09
-4.93
0.03
Education
Final model
Page 341
males, aged 65 years and over
Spearman
correlation
coefficient
p-value
RCI
0.110
0.03
0.087
population density
0.036
0.48
0.009
0.05
0.00
-0.174
0.00
-0.179
-10.45
0.01
0.290
0.00
0.221
0.149
0.00
0.161
unemployment rate
-0.003
0.95
0.054
5.94
0.01
-0.269
0.00
-0.224
-3.99
0.00
-11.85
0.00
unemployment rate
5.46
0.02
-3.03
0.00
-6.32
0.03
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
feminization rate
0.04
0.07
Social cohesion
-0.002
0.97
0.009
-0.158
0.00
-0.107
0.161
0.00
0.138
0.152
0.00
0.122
-0.079
0.12
-0.062
-0.101
0.05
-0.088
(literacy)
-0.071
0.17
-0.098
-0.020
0.70
-0.013
0.03
Education
Final model
Page 342
0.07
females, aged 65 years and over
Spearman
correlation
coefficient
p-value
RCI
0.172
0.00
0.126
population density
0.147
0.00
0.083
-0.152
0.00
-0.157
0.330
0.00
0.244
0.164
0.00
0.154
unemployment rate
-0.159
0.00
-0.076
-0.288
0.00
-0.255
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
feminization rate
0.03
0.07
0.00
0.06
-3.86
0.00
-7.39
0.00
Social cohesion
0.138
0.01
0.088
-0.137
0.01
-0.142
0.03
0.194
0.00
0.153
0.216
0.00
0.172
2.30
0.00
-0.099
0.05
-0.099
-0.04
0.01
-0.094
0.07
-0.109
(literacy)
0.054
0.30
-0.057
0.111
0.03
0.043
0.00
Education
0.01
12.80
0.01
-3.86
0.00
Final model
Page 343
0.06
Table 127. Association between district SMR for ill-defined causes and socio-economic variables, total
Spearman
correlation
coefficient
p-value
RCI
feminization rate
-0.164
0.00
-0.176
population density
-0.193
0.00
-0.162
0.029
0.58
0.076
-0.096
0.06
-0.151
unemployment rate
0.196
0.108
Variable
Regression
coefficient
(x 1000)
p-value
R
-0.120
0.07
0.03
0.05
0.00
-0.126
-18.89
0.00
0.00
0.127
16.82
0.00
0.04
0.110
2
Demographics
Social cohesion
0.000
1.00
0.010
0.110
0.03
0.075
-11.80
0.02
0.03
-0.159
0.00
-0.158
-12.58
0.00
-0.168
0.00
-0.144
0.062
0.23
0.081
0.158
0.00
0.117
(literacy)
-0.156
0.00
-0.084
-0.128
0.01
-0.090
Education
0.02
-21.67
0.04
0.15
0.00
unemployment rate
17.24
0.00
-16.39
0.01
-15.50
0.00
-19.04
0.00
Final model
Page 344
0.07
Table 128. Association between district SMR for ill-defined causes and socio-economic variables, males,
all ages
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.146
0.00
-0.164
-13.54
0.02
0.01
population density
-0.121
0.02
-0.080
0.16
0.00
0.090
0.08
0.106
-0.026
0.61
-0.069
0.12
0.00
-0.151
0.00
-0.154
-22.63
0.00
unemployment rate
0.165
0.00
0.108
15.63
0.00
0.074
0.15
0.078
Variable
2
Demographics
0.04
Social cohesion
-0.011
0.83
0.038
0.092
0.07
0.048
-10.85
0.05
0.04
-0.173
0.00
-0.166
-26.53
0.00
-0.123
0.02
-0.102
7.98
0.00
0.042
0.42
0.024
0.125
0.01
0.084
(literacy)
-0.067
0.20
0.011
-0.092
0.07
-0.042
feminization rate
16.83
0.01
population density
0.21
0.00
unemployment rate
20.63
0.00
-25.33
0.00
Education
Final model
Page 345
0.08
Table 129. Association between district SMR for ill-defined causes and socio-economic variables, females,
all ages
Spearman
correlation
coefficient
p-value
RCI
feminization rate
-0.105
0.04
-0.126
population density
-0.234
0.00
-0.195
-0.075
0.14
0.012
-0.119
0.02
-0.142
-0.095
0.07
-0.060
-14.88
0.03
unemployment rate
0.190
0.00
0.103
14.93
0.00
0.088
0.09
0.097
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
0.04
Social cohesion
-0.011
0.83
0.007
0.072
0.16
0.062
0.00
-0.069
0.18
-0.089
-0.168
0.00
-0.139
0.086
0.10
0.066
0.178
0.00
0.102
0.04
0.01
(literacy)
-0.248
0.00
-0.167
-54.80
0.00
0.05
-0.141
0.01
-0.096
-19.06
0.01
0.07
-55.17
0.00
-6.65
0.05
0.02
Education
Final model
(literacy)
Page 346
population, aged 0–64
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.132
0.01
-0.172
-20.45
0.00
0.03
population density
-0.058
0.26
-0.035
0.25
0.00
0.118
0.02
0.174
0.050
0.33
-0.065
0.18
0.00
-0.114
0.03
-0.180
-23.26
0.00
unemployment rate
0.143
0.01
0.106
16.99
0.01
0.015
0.77
0.086
Variable
2
Demographics
0.04
Social cohesion
-0.010
0.85
0.058
0.038
0.46
0.036
-15.51
0.01
0.05
-0.150
0.00
-0.218
-37.28
0.00
-0.067
0.19
-0.099
13.45
0.00
-0.008
0.87
-0.012
0.091
0.08
0.045
(literacy)
-0.011
0.83
0.063
40.78
0.02
0.00
-0.071
0.17
0.012
population density
0.17
0.00
0.11
0.14
0.03
unemployment rate
24.55
0.00
6.68
0.03
-38.79
0.00
-17.54
0.01
Education
Final model
Page 347
aged 0–64
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.144
0.00
-0.178
-21.05
0.00
0.03
population density
-0.054
0.29
-0.015
0.25
0.00
0.142
0.01
0.195
0.036
0.49
-0.071
0.18
0.00
-0.137
0.01
-0.196
-27.69
0.00
unemployment rate
0.137
0.01
0.086
16.60
0.01
0.032
0.53
0.093
Variable
2
Demographics
0.04
Social cohesion
-0.021
0.69
0.069
0.052
0.31
0.045
-13.89
0.03
0.06
-0.172
0.00
-0.229
-39.60
0.00
-0.073
0.16
-0.091
14.31
0.00
-0.012
0.81
-0.030
0.080
0.12
0.037
(literacy)
0.013
0.80
0.091
46.65
0.01
0.00
-0.061
0.24
0.024
population density
0.18
0.00
0.11
0.13
0.04
unemployment rate
25.02
0.00
7.79
0.01
-40.92
0.00
-15.38
0.03
Education
Final model
Page 348
aged 0–64
Spearman
correlation
coefficient
p-value
RCI
0.065
0.21
0.023
population density
0.066
0.20
0.037
-0.040
0.43
0.052
0.224
0.00
0.134
0.069
0.18
-0.019
unemployment rate
0.089
0.08
-0.202
0.00
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
feminization rate
0.04
0.28
0.00
0.20
0.00
0.126
16.83
0.02
-0.130
-6.27
0.00
0.07
Social cohesion
0.118
0.02
0.073
-0.133
0.01
-0.146
-25.46
0.00
0.03
0.097
0.06
-0.017
-22.91
0.00
0.101
0.05
0.022
14.58
0.00
-0.081
0.11
-0.079
-0.17
0.00
0.00
0.069
0.18
-0.015
(literacy)
-0.011
0.83
-0.008
69.58
0.00
0.00
-0.026
0.61
0.002
population density
0.20
0.00
0.10
0.16
0.02
unemployment rate
18.33
0.01
-10.71
0.00
-37.05
0.00
-22.23
0.01
Education
Final model
Page 349
Spearman
correlation
coefficient
p-value
RCI
feminization rate
-0.116
0.02
-0.121
population density
-0.251
0.00
-0.201
-0.093
0.07
-0.022
-0.137
0.01
-0.137
-0.108
0.04
-0.057
-14.93
0.04
unemployment rate
0.189
0.00
0.098
17.31
0.00
0.114
0.03
0.100
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
0.04
Social cohesion
-0.030
0.56
-0.001
0.108
0.04
0.078
0.03
-0.081
0.12
-0.073
-0.190
0.00
-0.141
0.118
0.02
0.094
0.197
0.00
0.123
0.05
0.00
(literacy)
-0.253
0.00
-0.166
-66.05
0.00
0.05
-0.145
0.00
-0.105
-19.61
0.00
0.08
0.04
0.03
-58.85
0.00
-4.00
0.01
0.03
Education
Final model
(literacy)
Page 350
Spearman
correlation
coefficient
p-value
RCI
feminization rate
-0.097
0.06
-0.121
population density
-0.196
0.00
-0.188
-0.069
0.18
-0.043
-0.091
0.08
-0.079
-0.106
0.04
-0.071
unemployment rate
0.171
0.00
0.133
0.090
0.08
0.070
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
0.03
13.27
0.00
Social cohesion
-0.009
0.87
-0.017
0.110
0.03
0.076
-0.092
0.08
-0.059
-0.155
0.00
-0.127
0.109
0.03
0.122
0.186
0.00
0.139
(literacy)
-0.186
0.00
-0.149
-0.128
0.01
-0.124
0.00
-6.87
0.04
0.02
0.04
0.02
Education
0.02
-29.84
0.01
13.27
0.00
Final model
unemployment rate
Page 351
0.04
Spearman
correlation
coefficient
p-value
RCI
feminization rate
-0.121
0.02
-0.116
population density
-0.270
0.00
-0.195
-0.094
0.07
-0.015
-0.164
0.00
-0.154
-0.106
0.04
-0.053
-19.50
0.02
unemployment rate
0.191
0.00
0.085
20.95
0.00
0.129
0.01
0.105
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
0.00
-19.49
0.03
0.05
Social cohesion
-0.040
0.44
0.008
0.105
0.04
0.068
0.03
-0.081
0.11
-0.075
-0.198
0.00
-0.139
0.124
0.02
0.068
0.187
0.00
0.098
0.07
0.00
(literacy)
-0.269
0.00
-0.176
-82.68
0.00
0.06
-0.151
0.00
-0.095
-25.18
0.00
0.09
0.05
0.02
-74.11
0.00
-5.36
0.00
0.03
Education
Final model
(literacy)
Page 352
Table 136. Association between district SMR for external causes and socio-economic variables, total
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.456
0.00
-0.314
-15.72
0.00
0.24
population density
-0.490
0.00
-0.313
-0.05
0.00
0.096
0.06
0.156
-0.271
0.00
-0.206
-0.242
0.00
-0.213
6.08
0.02
unemployment rate
0.253
0.00
0.111
11.07
0.00
0.388
0.00
0.294
4.30
0.00
Variable
2
Demographics
0.19
Social cohesion
-0.335
0.00
-0.211
0.309
0.00
0.224
0.26
-0.478
0.00
-0.347
-9.74
0.00
-0.403
0.00
-0.281
-3.21
0.00
0.262
0.00
0.143
0.07
0.00
0.01
0.066
0.20
0.005
(literacy)
-0.303
0.00
-0.137
-59.17
0.00
0.11
-0.205
0.00
-0.093
-9.51
0.04
feminization rate
-6.61
0.01
population density
-0.03
0.02
-3.37
0.00
(literacy)
-14.08
0.00
-28.98
0.00
Education
Final model
Page 353
0.32
Table 137. Association between district SMR for external causes and socio-economic variables, males, all
ages
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.487
0.00
-0.334
-18.02
0.00
0.28
population density
-0.526
0.00
-0.339
-0.06
0.00
0.133
0.01
0.169
-0.331
0.00
-0.234
-0.03
0.04
-0.305
0.00
-0.252
unemployment rate
0.339
0.00
0.172
13.30
0.00
0.430
0.00
0.313
3.69
0.00
Variable
2
Demographics
0.26
Social cohesion
-0.348
0.00
-0.222
0.332
0.00
0.230
-4.19
0.04
0.33
-0.533
0.00
-0.364
-12.12
0.00
-0.471
0.00
-0.320
-4.41
0.00
0.262
0.00
0.138
0.08
0.00
0.01
0.082
0.11
0.012
(literacy)
-0.312
0.00
-0.147
-67.67
0.00
0.13
-0.235
0.00
-0.124
-13.74
0.01
feminization rate
-6.87
0.01
population density
-0.06
0.00
0.04
0.01
unemployment rate
6.10
0.00
-3.62
0.00
(literacy)
-17.32
0.00
-24.93
0.00
Education
Final model
Page 354
0.39
Table 138. Association between district SMR for external causes and socio-economic variables, females, all
ages
Spearman
correlation
coefficient
p-value
RCI
-0.025
0.63
-0.021
population density
0.016
0.76
-0.021
-0.040
0.44
-0.063
0.173
0.00
0.054
0.098
0.06
0.080
unemployment rate
-0.185
0.00
-0.142
-0.061
0.24
0.012
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
feminization rate
0.01
7.78
0.01
7.78
0.01
Social cohesion
-0.056
0.28
-0.021
0.006
0.90
-0.004
-0.002
0.96
-0.029
0.104
0.04
0.072
0.031
0.55
0.078
-0.065
0.21
0.006
(literacy)
-0.012
0.81
-0.057
0.063
0.22
0.106
Education
Final model
Page 355
0.01
population, aged 0–64
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.499
0.00
-0.346
-20.45
0.00
0.28
population density
-0.547
0.00
-0.346
-0.05
0.00
0.136
0.01
0.185
-0.318
0.00
-0.229
-0.04
0.02
-0.302
0.00
-0.256
unemployment rate
0.361
0.00
0.181
14.65
0.00
0.424
0.00
0.315
3.46
0.00
Variable
2
Demographics
0.27
Social cohesion
-0.359
0.00
-0.221
0.314
0.00
0.222
-6.56
0.00
0.33
-0.540
0.00
-0.378
-12.68
0.00
-0.498
0.00
-0.335
-5.02
0.00
0.274
0.00
0.137
0.09
0.00
0.02
0.084
0.10
-0.003
(literacy)
-0.352
0.00
-0.166
-75.97
0.00
0.15
-0.238
0.00
-0.122
-13.08
0.01
feminization rate
-8.77
0.00
population density
-0.06
0.00
0.04
0.02
unemployment rate
5.89
0.01
-4.58
0.00
(literacy)
-19.04
0.00
-33.47
0.00
Education
Final model
Page 356
0.41
Table 140. Association between district SMR for external causes and socio-economic variables, males,
aged 0–64
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.501
0.00
-0.351
-20.39
0.00
0.29
population density
-0.545
0.00
-0.351
-0.06
0.00
0.155
0.00
0.196
-0.345
0.00
-0.249
-0.04
0.02
-0.325
0.00
-0.276
unemployment rate
0.372
0.00
0.191
15.25
0.00
0.440
0.00
0.330
3.81
0.00
Variable
2
Demographics
0.29
Social cohesion
-0.355
0.00
-0.222
0.330
0.00
0.237
-6.02
0.01
0.35
-0.553
0.00
-0.387
-13.36
0.00
-0.507
0.00
-0.349
-5.18
0.00
0.263
0.00
0.135
0.09
0.00
0.02
0.092
0.07
0.006
(literacy)
-0.330
0.00
-0.162
-75.88
0.00
0.14
-0.246
0.00
-0.138
-14.97
0.01
feminization rate
-7.28
0.01
population density
-0.06
0.00
0.04
0.03
unemployment rate
7.28
0.00
-4.36
0.00
(literacy)
-19.81
0.00
-28.42
0.00
Education
Final model
Page 357
0.41
Table 141. Association between district SMR for external causes and socio-economic variables, females,
aged 0–64
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.077
0.13
-0.088
-5.30
0.03
0.00
population density
-0.110
0.03
-0.104
-0.062
0.23
0.005
0.161
0.00
0.067
0.045
0.38
0.004
unemployment rate
0.062
0.23
0.081
8.60
0.00
-0.076
0.14
0.006
-2.51
0.00
Variable
2
Demographics
0.01
Social cohesion
-0.099
0.05
-0.089
-0.054
0.29
-0.032
-11.84
0.00
0.01
-0.065
0.21
-0.115
-5.69
0.00
-0.023
0.65
-0.049
0.036
0.48
0.020
-0.061
0.24
-0.084
(literacy)
-0.165
0.00
-0.117
-33.79
0.00
0.02
-0.006
0.91
0.047
-9.55
0.00
0.09
(literacy)
-18.06
0.00
-48.50
0.00
Education
Final model
Page 358
Spearman
correlation
coefficient
p-value
RCI
feminization rate
-0.081
0.11
-0.076
population density
-0.009
0.86
-0.070
0.007
0.89
-0.127
-0.004
0.94
-0.042
-0.007
0.90
0.036
8.06
0.03
unemployment rate
-0.220
0.00
-0.153
-7.23
0.00
0.094
0.07
0.087
3.51
0.00
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
0.00
-0.04
0.00
0.06
Social cohesion
-0.120
0.02
-0.024
0.138
0.01
0.081
-0.086
0.09
-0.024
0.043
0.41
0.044
0.090
0.08
0.121
0.001
0.98
0.042
(literacy)
0.060
0.24
-0.044
0.019
0.72
0.066
0.01
-4.72
0.00
population density
-0.06
0.00
unemployment rate
-11.07
0.00
-4.23
0.03
Education
Final model
Page 359
0.08
Table 143. Association between district SMR for external causes and socio-economic variables, males,
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
-0.154
0.00
-0.155
-7.75
0.02
0.02
population density
-0.146
0.00
-0.168
-0.05
0.02
-0.024
0.65
-0.084
-0.105
0.04
-0.077
-0.060
0.24
-0.056
unemployment rate
-0.012
0.82
0.038
0.163
0.00
0.119
Variable
2
Demographics
0.01
3.71
0.00
Social cohesion
-0.171
0.00
-0.104
0.159
0.00
0.117
-0.175
0.00
-0.099
-0.078
0.13
-0.079
0.137
0.01
0.107
0.005
0.92
0.038
(literacy)
-0.058
0.26
-0.085
-0.050
0.34
-0.027
0.03
-9.90
0.00
-35.32
0.00
0.00
population density
-0.06
0.00
0.03
-6.35
0.00
Education
Final model
Page 360
Table 144. Association between district SMR for external causes and socio-economic variables, females,
Spearman
correlation
coefficient
p-value
RCI
feminization rate
0.024
0.64
0.023
population density
0.141
0.01
0.006
0.057
0.27
-0.172
0.099
0.05
0.020
0.042
0.42
0.141
unemployment rate
-0.325
0.00
-0.237
-0.018
0.73
0.008
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
0.11
-17.25
0.00
Social cohesion
0.005
0.92
0.058
0.052
0.32
-0.004
0.05
0.029
0.57
0.083
-6.45
0.05
0.171
0.00
0.145
4.74
0.00
0.005
0.92
0.108
-0.004
0.94
0.052
(literacy)
0.160
0.00
-0.046
24.40
0.02
0.02
0.090
0.08
0.098
-17.25
0.00
0.12
Education
Final model
unemployment rate
Page 361
Table 145. Association between district infant mortality rate and socio-economic variables
Spearman
correlation
coefficient
p-value
RCI
0.116
0.02
-0.283
population density
0.102
0.05
-0.276
0.17
0.05
-0.160
0.00
0.105
-63.98
0.01
0.106
0.04
-0.207
0.188
0.00
-0.207
101.25
0.00
unemployment rate
0.117
0.02
0.298
35.88
0.01
-0.186
0.00
0.241
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
feminization rate
0.04
0.06
Social cohesion
0.191
0.00
-0.235
5.44
0.00
-0.138
0.01
0.246
-82.73
0.00
0.04
0.124
0.02
-0.287
0.011
0.83
-0.385
-16.17
0.00
-0.002
0.98
0.325
-0.014
0.78
0.303
(literacy)
-0.096
0.06
-0.366
-108.29
0.01
0.01
-0.008
0.88
-0.225
population density
0.44
0.00
0.10
68.80
0.00
-24.13
0.00
5.39
0.00
-53.39
0.00
-158.00
0.01
Education
Final model
(literacy)
Page 362
Table 146. Association between districts infant neonatal (0-27 days) mortality rate and socio-economic
variables
Spearman
correlation
coefficient
p-value
RCI
feminization rate
0.078
0.13
0.005
population density
0.086
0.09
-0.134
0.17
0.02
-0.164
0.00
-0.086
-53.41
0.00
0.094
0.07
-0.014
0.140
0.01
-0.024
42.53
0.02
unemployment rate
0.124
0.02
0.217
33.66
0.00
-0.162
0.00
0.063
-8.54
0.02
0.209
0.00
-0.116
3.81
0.02
-0.112
0.03
0.120
-39.37
0.00
0.123
0.02
-0.059
0.008
0.87
-0.183
-7.46
0.05
0.000
0.99
0.073
-0.014
0.79
0.167
(literacy)
-0.067
0.19
-0.203
0.060
0.24
-0.125
population density
0.21
0.01
unemployment rate
41.44
0.00
58.84
0.00
Variable
Regression
coefficient
(x 1000)
p-value
2
R
Demographics
0.04
0.05
Social cohesion
0.03
Education
Final model
Page 363
0.08
Table 147. Association between districts infant postneonatal (28–365 days) mortality rate and socioeconomic variables
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
0.052
0.31
0.004
population density
0.049
0.35
-0.015
-0.042
0.42
0.009
0.046
0.37
0.001
-0.13
0.00
0.01
0.118
0.02
0.073
43.03
0.00
unemployment rate
-0.003
0.96
0.068
-0.084
0.10
-0.037
0.027
0.60
0.006
-0.075
0.15
-0.064
0.021
0.69
-0.024
-0.009
0.87
-0.035
-0.012
0.82
0.058
-0.016
0.75
0.024
(literacy)
-0.074
0.15
-0.113
-0.053
0.31
-0.145
Variable
2
Demographics
Social cohesion
0.01
-43.91
0.00
-7.02
0.00
-61.14
0.01
0.01
30.92
0.01
0.02
-7.73
0.00
-36.58
0.00
Education
Final model
Page 364
Table 148. Association between districts life expectancy and socio-economic variables, males
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
0.444
0.00
0.425
150.59
0.00
0.20
population density
0.421
0.00
0.443
-0.111
0.03
0.039
0.209
0.00
0.289
0.45
0.00
0.20
0.214
0.00
0.096
unemployment rate
-0.437
0.00
-0.387
-106.42
0.00
-0.260
0.00
-0.349
0.196
0.00
0.320
-0.186
0.00
-0.122
101.88
0.00
0.482
0.00
0.457
109.26
0.00
0.397
0.00
0.420
20.56
0.00
-0.236
0.00
-0.317
-0.40
0.00
0.02
-0.091
0.08
-0.213
(literacy)
0.375
0.00
0.446
508.65
0.00
0.18
0.243
0.00
0.186
0.43
Variable
2
Demographics
Social cohesion
0.27
Education
Final model
feminization rate
42.03
0.01
-0.38
0.00
unemployment rate
-62.97
0.00
109.79
0.00
97.30
0.00
(literacy)
0.19
0.02
255.70
0.00
Page 365
Table 149. Association between districts life expectancy and socio-economic variables, females
Spearman
correlation
coefficient
p-value
RCI
Regression
coefficient
(x 1000)
p-value
R
feminization rate
0.035
0.50
0.074
36.87
0.00
0.04
population density
0.126
0.01
0.096
0.223
0.00
0.095
33.04
0.02
-0.153
0.00
-0.030
0.29
0.00
-0.193
0.00
-0.092
-34.12
0.01
unemployment rate
-0.216
0.00
-0.105
-47.26
0.00
0.163
0.00
0.023
15.12
0.00
Variable
2
Demographics
0.10
Social cohesion
0.003
0.95
0.068
0.138
0.01
0.069
87.71
0.00
0.05
0.006
0.90
0.053
25.79
0.00
0.014
0.79
0.030
10.24
0.00
-0.105
0.04
-0.101
-0.056
0.28
-0.051
(literacy)
0.428
0.00
0.234
270.29
0.00
0.17
0.105
0.04
0.053
39.87
0.00
0.36
84.89
0.00
(literacy)
41.50
0.00
329.01
0.00
Education
Final model
Page 366
Page 367

Analysis of health and socio-economic characteristics of district level

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