The Dartmouth Atlas of Musculoskeletal Health Care

Transcription

The Dartmouth Atlas
of Musculoskeletal
Health Care
The Center for the Evaluative Clinical Sciences
Dartmouth Medical School
The Center for Outcomes Research and Evaluation
Maine Medical Center
The views expressed in this publication are strictly those of the authors and do not necessarily represent
official positions of the American Hospital Association.
Library of Congress Cataloging-in-Publication Data
Dartmouth Medical School. Center for the Evaluative Clinical Sciences.
The Dartmouth Atlas of Musculoskeletal Health Care / The Center for the Evaluative Clinical
Sciences, Dartmouth Medical School.
ISBN 1-55648-289-2
1. Medical care—United States—Marketing—Maps. 2. Health facilities—United States—
Statistics. I. Title.
Catalog no. 044600
© 2000 The Trustees of Dartmouth College
All rights reserved. The reproduction or use of this book in any form or in any information storage or
retrieval system is forbidden without the express written permission of the publisher.
Printed in the USA
The Dartmouth Atlas of Musculoskeletal Health Care
James N. Weinstein, D.O., M.S., Principal Investigator
John D. Birkmeyer, M.D., Editor, Specialty Care Series
Dartmouth Atlas of Musculoskeletal Health Care Working Group
William A. Abdu, M.D.
Nancy O’C. Birkmeyer, Ph.D.
Kristen K. Bronner, M.A.
Megan McAndrew Cooper, M.B.A, M.S.
Jon D. Lurie, M.D., M.S.
Sandra M. Sharp, S.M.
Tamara A. Shawver, M.A.
Andrea E. Siewers, M.P.H.
Clinical Consultants
Philip M. Bernini, M.D.
Michael B. Sparks, M.D.
Scott Sporer, M.D.
Simone Topal, M.D.
The Dartmouth Atlas of Health Care in the United States
John E. Wennberg, M.D., M.P.H., Principal Investigator and Series Editor
Megan McAndrew Cooper, M.B.A., M.S., Editor
The Dartmouth Atlas of Health Care Working Group
Kristen K. Bronner, M.A.
Thomas A. Bubolz, Ph.D.
Elliott S. Fisher, M.D., M.P.H.
David C. Goodman, M.D., M.S.
James F. Poage, Ph.D.
Sandra M. Sharp, S.M.
Jonathan S. Skinner, Ph.D.
Therese A. Stukel, Ph.D.
David E. Wennberg, M.D., M.P.H.
Atlas design and print production
Jonathan Sa’adah and Elizabeth Adams
Intermedia Communications
The Dartmouth Atlas of Musculoskeletal Health Care was made possible by a grant from
The American Academy of Orthopaedic Surgeons
The research on which the Dartmouth Atlas of Health Care series is based
was made possible by a grant from
The Robert Wood Johnson Foundation
The Center for the Evaluative Clinical Sciences
Dartmouth Medical School
Hanover, New Hampshire 03756
(603) 650-1820
http://www.dartmouth.edu/~atlas/
Published by AHA Press, a division of Health Forum, Inc.
Chicago, Illinois
CONTENTS
VII
Table of Contents
Preface ................................................................................................................................................. XV
Introduction ...................................................................................................................................... XVII
The Orthopaedic Surgery Workforce ..................................................................................................... 1
The Orthopaedic Surgery Workforce .......................................................................................................................... 2
Trends in the Physician Workforce ......................................................................................................... 3
Orthopaedic Surgeons ................................................................................................................................................ 4
Geographic Variation in the Distribution of the Physician Workforce ......................................................................... 6
Orthopaedic Surgeons, Defined by Self-Designation .................................................................................................. 8
Orthopaedic Surgeons, Defined by Procedures Performed in Medicare Patients ........................................................ 10
How Many Physicians Are Enough? ......................................................................................................................... 12
Orthopaedic Surgery Workforce Benchmarks ........................................................................................................... 13
Projections of the Future Supply of Orthopaedic Surgeons ....................................................................................... 14
Neurosurgeons, Defined by Self-Designation ........................................................................................................... 16
Chapter One Table ................................................................................................................................................... 19
Conditions of the Spine ....................................................................................................................... 27
Spine Problems ......................................................................................................................................................... 28
Rates of Spine Surgery .............................................................................................................................................. 30
Cervical Spine Surgery .............................................................................................................................................. 32
Use of Discectomy Procedures of the Lumbar Spine ................................................................................................. 34
Lumbar Decompression Procedures for Spinal Stenosis ............................................................................................ 36
Overall Use of Fusion Procedures ............................................................................................................................. 38
Use of Fusion with Surgery for Lumbar Spinal Stenosis ............................................................................................ 40
The Surgical Signature in Spine Surgery ................................................................................................................... 42
The Relationship Between Surgery and Diagnostic Imaging ..................................................................................... 45
Who Performs Spine Surgery? .................................................................................................................................. 46
Chapter Two Table .................................................................................................................................................... 51
Degenerative Joint Disease and Other Conditions ............................................................................... 59
Overview .................................................................................................................................................................. 60
Knee Arthroscopy ..................................................................................................................................................... 62
Shoulder Arthroscopy ............................................................................................................................................... 64
Total Joint Replacement (Hip, Knee, and Shoulder) ................................................................................................. 66
Total Hip Replacement and Revision ........................................................................................................................ 68
VIII
THE DARTMOUTH ATLAS OF MUSCULOSKELETAL HEALTH CARE
Total Knee Replacement ........................................................................................................................................... 70
Shoulder Replacement and Reconstruction ............................................................................................................... 72
Decision Making in Joint Replacement .................................................................................................................... 74
Discharge to Nursing Homes After Joint Replacement ............................................................................................. 78
Surgical Treatment of Carpal Tunnel Syndrome ........................................................................................................ 80
Bunion Surgery ........................................................................................................................................................ 82
Lower Extremity Amputation ................................................................................................................................... 84
Chapter Three Table ................................................................................................................................................. 87
Fractures .............................................................................................................................................. 95
Overview .................................................................................................................................................................. 96
Surgical Treatment of Fractures ................................................................................................................................. 98
Hip Fractures .......................................................................................................................................................... 100
Femur Fractures ...................................................................................................................................................... 102
Lower Leg Fractures ................................................................................................................................................ 104
Surgical Treatment of Lower Leg Fractures .............................................................................................................. 106
Ankle Fractures ....................................................................................................................................................... 108
Surgical Treatment of Ankle Fractures ..................................................................................................................... 110
Proximal Humerus Fractures .................................................................................................................................. 112
Surgical Treatment of Proximal Humerus Fractures ................................................................................................. 114
Humeral Shaft and Distal Humerus Fractures ......................................................................................................... 116
Surgical Treatment of Humeral Shaft and Distal Humerus Fractures ....................................................................... 118
Proximal Forearm and Shaft Fractures .................................................................................................................... 120
Wrist Fractures ....................................................................................................................................................... 122
Surgical Treatment of Wrist Fractures ...................................................................................................................... 124
Explaining Variation in Fracture Incidence and Treatment ...................................................................................... 126
Chapter Four Table ................................................................................................................................................. 129
Conclusions ....................................................................................................................................... 137
The Orthopaedic Workforce ................................................................................................................................... 138
Increasing Use of Musculoskeletal Procedures ......................................................................................................... 139
Variation in the Use of Musculoskeletal Procedures ................................................................................................ 140
Which Rate is Right? .............................................................................................................................................. 142
Appendix on Methods ....................................................................................................................... 143
Appendix on the Geography of Health Care in the United States ...................................................... 179
Endnote ............................................................................................................................................. 201
MAPS
IX
Maps
Map 1.1.
The Physician Workforce Active in Patient Care (1996) ........................................................................................ 7
Map 1.2.
Self-Designated Orthopaedic Surgeons (1996) ...................................................................................................... 9
Map 1.3.
Orthopaedic Surgeons Performing Procedures on Medicare Patients (1996) ........................................................ 11
Map 1.4.
Self-Designated Neurosurgeons (1996) ............................................................................................................... 17
Map 2.1.
Spine Surgery (1996-97) ..................................................................................................................................... 31
Map 2.2.
Cervical Spine Surgery (1996-97) ....................................................................................................................... 33
Map 2.3.
Lumbar Discectomy (1996-97) ........................................................................................................................... 35
Map 2.4.
Lumbar Decompression Surgery (1996-97) ........................................................................................................ 37
Map 2.5.
Spinal Fusion (1996-97) ..................................................................................................................................... 39
Map 2.6.
Use of Fusion with Surgery for Lumbar Spinal Stenosis (1996-97) ...................................................................... 41
Map 2.7.
Variation in Spine Surgery Rates in Contiguous California Hospital Referral Regions (1996-97) ........................ 43
Map 2.8.
Proportion of Spine Surgery Performed by Neurosurgeons (1996) ...................................................................... 47
Map 3.1.
Knee Arthroscopy (1996-1997)........................................................................................................................... 63
Map 3.2.
Shoulder Arthroscopy (1996-1997) ..................................................................................................................... 65
Map 3.3.
Total Joint Replacement (1996-1997) ................................................................................................................. 67
Map 3.4.
Total Hip Replacement (1996-1997) .................................................................................................................. 69
Map 3.5.
Total Knee Replacement (1996-1997) ................................................................................................................. 71
Map 3.6.
Shoulder Replacement and Reconstruction (1996-1997) .................................................................................... 73
Map 3.7.
Proportion of Hip Replacement Patients Discharged to Nursing Homes (1996-1997) ........................................ 79
Map 3.8.
Carpal Tunnel Surgery (1997) ............................................................................................................................. 81
Map 3.9.
Bunion Surgery (1996-1997) .............................................................................................................................. 83
Map 3.10. Major Amputation (1996-1997) ......................................................................................................................... 85
Map 4.1.
Hip Fractures (1996-97) ................................................................................................................................... 101
Map 4.2.
Femur Fractures (1996-97) ............................................................................................................................... 103
Map 4.3.
Lower Leg Fractures (1996-97) ......................................................................................................................... 105
Map 4.4.
Lower Leg Fractures Treated with Surgery (1996-97) ........................................................................................ 107
Map 4.5.
Ankle Fractures (1996-97) ................................................................................................................................ 109
X
Map 4.6.
Proportion of Ankle Fractures Treated with Surgery (1996-97) .......................................................................... 111
Map 4.7.
Proximal Humerus Fractures (1996-97) ............................................................................................................ 113
Map 4.8.
Proximal Humerus Fractures Treated with Surgery (1996-97) ........................................................................... 115
Map 4.9.
Humeral Shaft and Distal Humerus Fractures (1996-97) .................................................................................. 117
Map 4.10. Proportion of Humeral Shaft and Distal Humerus Fractures Repaired Surgically (1996-97) .............................. 119
Map 4.11. Forearm Fractures (1996) .................................................................................................................................. 121
Map 4.12. Wrist Fractures (1996) ...................................................................................................................................... 122
Map 4.13. Wrist Fractures Treated with Surgery (1996) ...................................................................................................... 125
Map A.
ZIP Codes Assigned to the Windsor, Vermont Hospital Service Area ................................................................ 183
Map B.
Hospital Service Areas According to the Number of Acute Care Hospitals ........................................................ 185
Map C.
Hospital Service Areas Assigned to the Evansville, Indiana, Hospital Referral Region ........................................ 187
Map D.
New England Hospital Referral Regions ............................................................................................................ 189
Map E.
Northeast Hospital Referral Regions ................................................................................................................. 190
Map F.
South Atlantic Hospital Referral Regions .......................................................................................................... 191
Map G.
Southeast Hospital Referral Regions .................................................................................................................. 192
Map H.
South Central Hospital Referral Regions ........................................................................................................... 193
Map I.
Southwest Hospital Referral Regions ................................................................................................................. 194
Map J.
Great Lakes Hospital Referral Regions .............................................................................................................. 195
Map K.
Upper Midwest Hospital Referral Regions ......................................................................................................... 196
Map L.
Rocky Mountains Hospital Referral Regions ..................................................................................................... 197
Map M.
Pacific Northwest Hospital Referral Regions ..................................................................................................... 198
Map N.
Pacific Coast Hospital Referral Regions ............................................................................................................. 199
FIGURES
XI
Figures
Figure I.1.
Profiles of Surgical Variation for Ten Common Surgical Procedures (1995-96) .................................................... XX
Figure 1.1. Growth in the Physician Workforce in the United States (1970 - 1995) ............................................................... 3
Figure 1.2. Clinically Active Physicians Specializing in Orthopaedic Surgery and Neurosurgery (1975 - 1995) ...................... 4
Figure 1.3. Relative Growth in the Supply of Clinically Active Physicians (1975-1995) ......................................................... 5
Figure 1.4. Physicians Allocated to Hospital Referral Regions (1996) ..................................................................................... 6
Figure 1.5. Self-Designated Orthopaedic Surgeons Allocated to Hospital Referral Regions (1996) ......................................... 8
Figure 1.6. Orthopaedic Surgeons Performing Procedures on Medicare Patients, Allocated to Hospital Referral Regions 1996) .... 10
Figure 1.7. Excess (or Deficit) Supply of Orthopaedic Surgeons per 100,000 Residents, Compared to Benchmark
Hospital Referral Regions (1996) ....................................................................................................................... 13
Figure 1.8. Projected Supply of Orthopaedic Surgeons Adjusted for Population and Workforce Demographic Changes
(1995-2020) ....................................................................................................................................................... 15
Figure 1.9. Projected Supply of Orthopaedic Surgeons Compared to High and Low Benchmarks (1995-2020) ................... 15
Figure 1.10. Self-Designated Neurosurgeons Allocated to Hospital Referral Regions (1996) .................................................. 16
Figure 2.1. Types of Surgical Procedures Performed for Medicare Enrollees with Conditions of the Spine (1996-97) ........... 28
Figure 2.2. Increase in Rates of Spine Surgery Among Medicare Enrollees (1988-1997) ....................................................... 29
Figure 2.3. Spine Surgery (1996-97) .................................................................................................................................... 30
Figure 2.4. Cervical Spine Surgery (1996-97) ....................................................................................................................... 32
Figure 2.5. Lumbar Discectomy (1996-97) .......................................................................................................................... 34
Figure 2.6. Lumbar Decompression Surgery (1996-97) ........................................................................................................ 36
Figure 2.7. Spinal Fusion (1996-97) ..................................................................................................................................... 38
Figure 2.8. Use of Fusion with Surgery for Lumbar Spinal Stenosis (1996-97) ..................................................................... 40
Figure 2.9. The Surgical Signature of Spine Surgery in Eight California Hospital Referral Regions (1996-97) ...................... 44
Figure 2.10. The Relationship Between Spinal CT/MRI and Spine Surgery Rates (1996-97) ................................................. 45
Figure 2.11. Proportion of Overall Spine Surgery, Lumbar Discectomy, Lumbar Decompression and
Cervical Spine Surgery Performed by Orthopaedists and Neurosurgeons (1996) ................................................ 48
Figure 2.12. Use of Fusion (Uninstrumented and With Hardware) by Orthopaedists and Neurosurgeons
in Spine Surgery, by Indication (1996) ............................................................................................................... 49
Figure 3.1. Joint Replacement Procedures (1996-97) ........................................................................................................... 60
Figure 3.2. Growth in Rates of Joint Replacement (1988-1997) .......................................................................................... 61
XII
Figure 3.3. Knee Arthroscopy (1996-1997) .......................................................................................................................... 62
Figure 3.4. Shoulder Arthroscopy (1996-1997) .................................................................................................................... 64
Figure 3.5. Total Joint Replacement (1996-1997) ............................................................................................................... 66
Figure 3.6. Total Hip Replacement (1996-1997) ................................................................................................................. 68
Figure 3.7. Proportion of Total Hip Procedures That Were Primary and Revisions Over the Five-Year Period 1993-1997 .... 68
Figure 3.8. Total Knee Replacement (1996-1997) ............................................................................................................... 70
Figure 3.9. Proportion of Total Knee Replacements That Were Revisions Over the Five-Year Period 1993-1997 .................. 70
Figure 3.10. Shoulder Replacement and Reconstruction (1996-1997) .................................................................................... 72
Figure 3.11. The Association Between Rates of Total Hip Replacement and Total Knee Replacement (1996-97) ................... 75
Figure 3.12. Hip Replacement by Age, Sex, and Race (1996) ................................................................................................. 76
Figure 3.13. Knee Replacement by Age, Sex, and Race (1996) ............................................................................................... 76
Figure 3.14. Proportion of Hip Replacement Patients Discharged to Nursing Homes (1996-1997) ....................................... 78
Figure 3.15. Discharge to Nursing Homes After Total Joint Replacement .............................................................................. 78
Figure 3.16. Carpal Tunnel Surgery (1997) ............................................................................................................................ 80
Figure 3.17. Proportion of Carpal Tunnel Procedures Performed by Orthopaedists, Plastic Surgeons, Neurosurgeons,
and General Surgeons (1996) ............................................................................................................................. 80
Figure 3.18. Bunion Surgery (1996-1997) ............................................................................................................................. 82
Figure 3.19. Major Amputation (1996-1997) ........................................................................................................................ 84
Figure 3.20. Proportion of Major Amputation Performed by Orthopaedists, Vascular Surgeons, General Surgeons,
and Cardiothoracic Surgeons (1996) .................................................................................................................. 84
Figure 4.1. Distribution of Fracture Types (1996) ................................................................................................................ 96
Figure 4.2. Profiles of Variation in the Incidence of Eight Fractures (1996) .......................................................................... 97
Figure 4.3. Profiles of Variation in the Use of Surgical Treatment for Eight Fractures (1996-97) ........................................... 99
Figure 4.4. Hip Fractures (1996-97) ................................................................................................................................... 100
Figure 4.5. Femur Fractures (1996-97) ............................................................................................................................... 102
Figure 4.6. Lower Leg Fractures (1996-97) ......................................................................................................................... 104
Figure 4.7. Proportion of Lower Leg Fractures Treated with Surgery (1996-97) .................................................................. 106
Figure 4.8. Ankle Fractures (1996-97) ................................................................................................................................ 108
Figure 4.9. Proportion of Ankle Fractures Treated with Surgery (1996-97) ......................................................................... 110
FIGURES
XIII
Figure 4.10. Proximal Humerus Fractures (1996-97) .......................................................................................................... 112
Figure 4.11. Proportion of Proximal Humerus Fractures Treated with Surgery (1996-97) ..................................................... 114
Figure 4.12. Humeral Shaft and Distal Humerus Fractures (1996-97) ................................................................................. 116
Figure 4.13. Proportion of Humeral Shaft and Distal Humerus Fractures Treated with Surgery (1996-97) ........................... 118
Figure 4.14. Forearm Fractures (1996) ................................................................................................................................. 120
Figure 4.15. Wrist Fractures (1996) ...................................................................................................................................... 122
Figure 4.16. Proportion of Wrist Fractures Treated with Surgery (1996) ............................................................................... 124
Figure 5.1. Profiles of Variation in the Use of Surgical Treatment (1996-97) ...................................................................... 141
Figure 2.2. Hospital Employees Allocated to Hospital Referral Regions (1995) .................................................................. 177
Figure 3.5. The Association Between Hospital Beds per 1,000 Residents and Age, Sex, Race and Illness Adjusted
Hospitalization Rates for Medical Conditions per 1,000 Medicare Enrollees .................................................... 176
Figure A3.
Cumulative Percentage of Population of the United States According to the Hospital Service Area
Localization Index (1992-93) ............................................................................................................................ 184
XIV
Tables
Table I.1.
Quantitative Measures of Variability of Ten Common Surgical Procedures by
Hospital Referral Region (1995-96) .................................................................................................... XXI
Table I.2.
Trade-offs, Risks and Benefits of Treatment Options for Selected Conditions ................................... XXIII
Chapter 1 Table.
The Orthopaedic Surgery Workforce .................................................................................................. 19
Chapter Two Table.
Surgery for Conditions of the Spine .................................................................................................... 51
Table 3.1.
Outcomes After Total Joint Replacement ............................................................................................ 77
Chapter Three Table.
Surgery for Degenerative Joint Disease ................................................................................................ 87
Table 4.1.
Quantitative Measures of Variability in the Incidence of Eight Fractures (1996) .................................. 97
Table 4.2.
Use of Surgical Treatment for Eight Fractures (1996-97) ..................................................................... 99
Chapter Four Table.
Rates and Proportion of Fractures Treated Surgically ......................................................................... 129
Table 5.1.
Use of Surgical Treatment (1996-97) ................................................................................................ 143
Appendix Table 1.
Files Used in the Atlas ....................................................................................................................... 144
Appendix Table 2.
Procedure-based Workload of Orthopaedic Surgeons in Medicare (1996). ........................................ 150
Appendix Table 3.
Codes Used to Identify Specialist Groups .......................................................................................... 151
Appendix Table 4.
Codes Used to Identify Procedures .................................................................................................... 153
Preface
Musculoskeletal disease is a major cause of disability, and requires substantial health care expenditure in the
United States. It affects all ages, but the Medicare population represents many unique challenges that are increasing in frequency as our population ages. Progress in the treatment of musculoskeletal disease has been
substantial during the past fifty years, as surgical techniques have been perfected. More recently, there has been
the introduction of less invasive techniques, new biologics to aid in the injury and repair processes, along with
continuing development of new arthritis medications and devices that allow for natural bone and soft tissue
ingrowth. These modalities, combined with better overall medical management, now provide successful treatment options for elderly patients with musculoskeletal disease.
Despite these advances, many questions remain concerning the optimal treatment of musculoskeletal disease.
In fact, little is known about the demographics of musculoskeletal health care across our country. As we
attempt to refine our treatment of musculoskeletal injury and disease, the variations in treatment patterns that
are evident in the delivery of care are important to understand, particularly as this understanding can lead to
an improvement in the care of musculoskeletal injuries and disease.
Clearly, variation exists and, thanks to publications like the Dartmouth Atlas series, a great deal of that variation is documented. In many ways, that is the easy part. The hard part — the part in which physicians must
play an integral role — is interpreting the variation, determining whether and where the variation is inappropriate, and then making changes.
A starting point should be the fact that, although variation patterns are extremely interesting, they mean very
little in and of themselves. They must be interpreted in relation to numerous factors. The next parameter to
be considered is patient expectations. Third-party payers, the federal government, the media, patients, and
physicians all need to realize that some variation is both reasonable and to be expected. Medicine is, after all,
an art as well as a science. Another extremely important aspect is the individual physician’s judgment about
what is best for each patient, who is likely to have a unique constellation of symptoms as well as personal
preferences about his or her care.
In short, there are legitimate variations in the practice of medicine and, specifically, in the utilization of
procedures. Physician judgment is likely to be influenced by a variety of factors. The Atlas data reveals trends
that imply that certain areas tend to apply different treatment strategies in the care of various musculoskeletal
conditions. Certainly, these trends affect physician judgment, but so do even more entrenched cultural elements —
many of which are not explicitly studied during research on practice variation. Some geographic areas may
be more heavily populated by patients who are medically savvy and likely to demand the newest, most
innovative, and perhaps most recently publicized treatment. Other areas might have more traditional
populations, where age, religion, gender, or other factors might influence the patients or their families to resist
newer therapies. We must take an active role in the discussion about variations in practice patterns. We must
involve patients in a shared decision-making process. To be involved in decision-making, patients or health
care consumers need knowledge about their own health and diseases. We must find ways to educate the
patient because an educated patient is empowered and better equipped to share in the decision making
process, which often leads to more cost effective delivery of healthcare.
The American Academy of Orthopaedic Surgeons and the Robert Wood Johnson Foundation are pleased to
support this effort by the Center for the Evaluative Clinical Sciences at Dartmouth Medical School.
S. Terry Canale, M.D.
President,
American Academy of Orthopaedic Surgeons
James N. Weinstein, D.O., M.S.
Principal Investigator
Dartmouth Atlas of Musculoskeletal Health Care
Introduction
XVIII
The Geography of Health Care in the United States
The tools used to measure and explore variation in this edition of the Atlas will be
familiar to most readers. We have again based our measurements on the experience
of populations — how health care is used by defined populations, rather than the
physical location of health care resources. This methodology, which is generally
known as small area analysis, is at the core of our work. Readers who are unfamiliar with the strategies of studying population-based rates of resource distribution
and utilization are urged to read the Appendix on Methods.
The first task of the Atlas project, undertaken in 1993, was to establish the geographic boundaries of naturally-occurring health care markets in the United States.
Based on a study of where Medicare patients were hospitalized, 3,436 geographic
hospital service areas were defined. The hospital service areas were then grouped into
306 hospital referral regions on the basis of where Medicare patients were hospitalized for major cardiovascular surgical procedures and neurosurgery. One important
finding was that most hospital service areas and hospital referral regions, as defined
by where patients actually receive their care, correspond poorly to political configurations, such as counties, which have traditionally been used to measure health care
resources and utilization.
Hospital referral regions essentially define the local markets for cardiovascular interventions. Because referral patterns for cardiac surgery and (non-cardiac) major
vascular surgery are generally very similar, the hospital referral region grouping is of
particular importance for this Atlas. The Appendix on the Geography of Medical
Care in the United States, which is reprinted from the first edition of the Atlas,
contains a series of maps that detail each hospital referral region in the United States
and describes more fully how they were created.
Growth in the Specialist Physician Workforce
Although the number of all types of physicians has increased in the last 20 years,
growth in the supply of surgeons and other specialists in the United States has been
particularly dramatic. Between 1970 and 1995, the total number of specialist phy-
INTRODUCTION
sicians more than doubled, from 311,00 to 625,000. Because growth in the physician workforce substantially exceeds growth in the population of the United States,
many predict a serious oversupply of specialists.
In Chapter One, we consider possible futures for the orthopaedic workforce. We
first review the current supply of different types of specialists providing musculoskeletal health care and consider how the current distribution of orthopaedists could
change over time. Using a simulation model, we also account for potential increases
in the “need” for musculoskeletal health care as the population grows and ages. We
also present the neurosurgical workforce as it relates to the overlap in spine surgery
(Chapter Two).
Geographic Variation in the Use of Surgical Procedures
Previous editions of the Atlas have demonstrated wide variation in surgery rates
among the 306 hospital referral regions of the United States. While geographic
variation in the use of surgery has long been recognized, not all surgical procedures
are equally variable. For example, rates of colon resection, like rates of hospitalization for hip fracture, vary only slightly between regions. Other procedures, such as
radical prostatectomy for prostate cancer, are highly variable, depending on region.
Figure I.1 shows geographic variation “profiles” of ten common surgical procedures;
Table I.1 reports the corresponding quantitative measures of variability. The
procedures are ranked from low to high, according to the systematic component of
variation. The systematic component of variation for coronary artery bypass
grafting, a high variation procedure, is more than twice that of colectomy; and
percutaneous transluminal coronary angioplasty is more than twice as variable as
coronary artery bypass surgery. The increases in variability from low to high and
from high to very high are statistically and clinically significant.
Table I.1 also reports the extremal ratio, or the ratio of highest to lowest rates
among the 306 hospital referral regions. The extremal ratio of hip fracture repair
is 2.0. For high variation procedures, the extremal ratios are 3.5 to 5.2 times
greater in the highest region compared to the lowest. For very high variation
XIX
XX
procedures, the ratios are between six and ten times greater in the highest
compared to the lowest region.
Why Procedures Vary to Different Degrees
Although regional variation in health care is ubiquitous, not all surgical procedures
vary to the same degree. Procedures that are not very variable are generally performed
for clinical conditions in which treatment is constrained to a single clinical approach.
For example, there is wide consensus that surgery is the primary treatment for both
hip fracture and colorectal cancer. The geographic variation in the use of surgery for
these two conditions is largely due to variations in illness rates — for example,
colorectal cancer is slightly more common among residents of the Mountain States
and parts of the Southeast than among residents of other parts of the country.
Figure I.1 Profiles of Surgical Variation for Ten Common Surgical Procedures (1995-96)
Hip fracture repair is the least variable; radical prostatectomy for cancer of the prostate is the most variable. Each point represents the procedure rate
in each of the 306 hospital referral regions, relative to the United States average.
INTRODUCTION
XXI
TABLE I.1. QUANTITATIVE MEASURES OF VARIABILITY OF TEN COMMON SURGICAL PROCEDURES BY HOSPITAL REFERRAL REGION
(1995-96)
Hi
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Index of Variation
Systematic Component of Variation (SCV)
Ratio to SCV of surgical repair of hip fracture
10.3
15.7
26.5
38.0
61.8
88.0
95.6
102.0
104.8
130.3
1.0
1.5
2.6
3.7
6.0
8.6
9.3
9.9
10.2
12.7
Range of Variation
Extremal Ratio (highest to lowest region)
2.0
2.2
2.7
3.7
4.5
5.7
7.7
6.9
9.0
9.4
Interquartile Ratio (75th to 25th percentile region)
1.2
1.2
1.3
1.3
1.4
1.5
1.5
1.5
1.5
1.6
Rates more than 25% below the national average
1
10
10
19
40
41
54
61
80
67
Rates 30% or more above the national average
0
1
19
21
46
63
53
54
29
62
Number of Regions with High and Low Rates
The amount of regional variation for most procedures, however, is too large to attribute to chance or variation in illness rates; the rates of surgery described in Table I.1
and Figure I.1 have been adjusted for regional differences in illness rates, but still vary
substantially. Variation in the rates of the use of these procedures reflects variation in
practice style and how physicians diagnose and treat common clinical conditions.
■ Variation in diagnostic intensity. Surgery rates might vary because physicians in
different regions vary in how aggressively they look for surgically treatable disease. For
example, because early-stage prostate cancer frequently has no symptoms, the diagnosis is increasingly being made through a screening test for prostate-specific antigen.
There is a great deal of regional variation in the frequency of use of this controversial
screening test; as a result, there is also variation in the rate at which men are diagnosed
(screening more men means that more men are diagnosed with early-stage disease) and
variation in how often men undergo surgery (where more men are diagnosed with
early-stage disease, more undergo surgical treatment for the condition).
■ Problems with medical science. For some procedures, regional variation in the
use of surgery is due to gaps in medical science and professional uncertainty about
the implications of alternative treatments. For example, variation in rates of radical
prostatectomy might be partly attributable to the lack of controlled clinical trials
comparing the risks and benefits of surgery, radiation therapy, and watchful waiting.
XXII
For other procedures, even the best clinical trials are often not sufficient to eliminate
variation in procedure rates: physicians vary in how they interpret and apply
findings from the carefully controlled settings of clinical trials to decision making
for individual patients in other settings.
■ Failure to incorporate patient preferences into treatment decisions. Although
medical science is necessary for quantifying risks and benefits, some of the trade-offs
involved in surgical decisions can only be assessed by patients. For example, the
major risks of radical prostatectomy are urinary incontinence and impotence. Only
patients themselves can weigh the importance of these side effects against the potential benefits of surgically removing the prostate cancer. Table I.2 lists the treatment
options available to patients and the clinical tradeoffs patients face in terms of the
risks and benefits for the nine conditions for which the procedures in Figure I.1 are
commonly performed.
In Chapters Two through Four, we describe how these factors are reflected in geographic variation in the use of common musculoskeletal procedures.
Communicating With Us About the Atlas
The Atlas internet home page contains Atlas information, including a summary of
Dartmouth-related research and electronic copies of some hard-to-find references.
Please send us your comments on the Atlas, particularly suggestions on how to
improve it in the future.
We are at http://www.dartmouth.edu/~atlas.
INTRODUCTION XXIII
TABLE I.2 TRADE-OFFS, RISKS AND BENEFITS OF TREATMENT OPTIONS FOR SELECTED CONDITIONS
Clinical Condition
Treatment Options
Trade-Offs Among Alternatives
Hip fracture
Surgical repair
No alternatives
Colorectal cancer
Colectomy
No alternatives
Chronic cholecystitis
(intermittent abdominal pain from
gallstones)
Watchful waiting
Avoids surgery, but carries a risk of a later serious attack (acute
cholecystitis) and the need for urgent, open surgery
Cholecystectomy (usually
laparoscopic rather than open
surgery)
Very effective, but there are small risks of serious complications
Medical treatment
Avoids the downsides of interventions, but is less effective at improving
symptoms and some patients have shorter survival
Angioplasty
Lower procedure risks than surgery, but symptom relief is not as long
lasting
Bypass surgery
Effective and durable in relieving symptoms, but there are significant risks
of mortality and disability, including stroke
Medical treatment
Low risk, but not very effective in relieving symptoms
Hip replacement
Very effective, but there are modest risks of mortality and complications, as
well as a long recovery period
Medical treatment, exercise
Low risk, but only modestly effective
Angioplasty
Effective at improving symptoms, but there are risks of complications and
subsequent interventions are often necessary
Bypass surgery
Very effective and durable, but there are significant risks of complications
and death
Aspirin
Lower short-term risks, but higher risks of stroke over the long term
Carotid endarterectomy
Reduces overall stroke risks, but there are significant risks of mortality and
of perioperative stroke
Medical treatment, chiropractic,
other
Symptoms often resolve without surgery, but might not
Back surgery
Frequently relieves symptoms, but has complication risks and is not always
effective
Watchful waiting
Many prostate cancers never progress to affect quality of life or survival,
but some do
Radiation (conventional or
implant seeds)
Shrinks or eliminates cancer in the prostate, but there are risks of side
effects
Radical prostatectomy
Removes prostate cancer entirely, but there are substantial risks of
incontinence and impotence
Chronic stable angina
(chest pain or other symptoms from
coronary artery disease)
Hip osteoarthritis
Claudication
(exertional leg pain from peripheral
vascular disease)
Carotid stenosis
(stroke risk from narrowing of carotid
artery)
Herniated disc or Spinal Stenosis
(causing back pain or other
symptoms)
Early-stage prostate cancer
CHAPTER ONE
The Orthopaedic Surgery
Workforce
2
The Orthopaedic Surgery Workforce
The number of physicians in the United States has increased dramatically in the last
20 years. Although one aim of policies in medical education was to deploy more
physicians to “underserved” areas, the growth in the supply of physicians clinically
active in orthopaedic surgery has not resulted in a particularly uniform national distribution. The supply of orthopaedic surgeons in 1996 varied by a factor of about
four from the lowest-rate region to the highest.
We do not know what the optimal supply of physicians is. However, the current
supply of physicians in a region that has a stable, efficiently-sized workforce that
delivers high quality health care provides one basis for comparison, and allows us
to ask and answer questions about how many more (or fewer) physicians would be
needed in the United States in order to uniformly duplicate the level of supply of
a particular hospital referral region or health maintenance organization.
If current training levels continue over the next 20 years, the supply of orthopaedic
surgeons will grow substantially. These changes in supply, however, are unlikely to
resolve the issues raised by the substantial variation among geographic regions and
medical markets. In order to make rational workforce planning decisions, it is
necessary to better understand the relationship between the physician workforce
supply and the quality of care.
Orthopaedic surgeons were identified through two complementary approaches —
a procedure-based analysis (based on procedures for which they submitted claims
to the Medicare program) and on self-designated specialty (how they identified
themselves to both the American Medical Association and the Medicare program).
Only clinically active physicians were included in the analyses. For the procedurebased analysis, physicians were considered “clinically active” if they met a minimum
threshold of clinical activity, measured in relative value units. For the analysis based
on self-designation, physicians were considered clinically active if they reported
providing clinical care more than 50% of the time. The population count is the
Claritas® estimate for 1996. The estimates of physicians allocated to populations
THE ORTHOPAEDIC SURGERY WORKFORCE
take into account patient migration across the boundaries of hospital referral
regions, and have been adjusted for age and sex differences in the populations (see
the Appendix on Methods).
Trends in the Physician Workforce
During the past 25 years, there has been rapid growth of the physician workforce in
the United States, due in large part to federal policies aimed at remediating a perceived
physician shortage in the 1950s and 1960s. These policies increased the number of
United States medical school graduates and expanded opportunities for international
medical graduates to train and practice in the United States. Between 1970 and 1995,
the total number of physicians in the United States increased from 311,000 to
625,000. During the same period, the number of specialist physicians, both medical
and surgical, increased even more dramatically, from 195,000 to 417,000. By 1996
about two-thirds of the physicians in the workforce were specialists.
Figure 1.1. Growth in the Physician Workforce in the United States (1970 - 1995)
3
4
Orthopaedic Surgeons
As with the physician workforce as a whole, the number of clinically active physicians specializing in orthopaedic surgery has grown rapidly in the last two decades.
Between 1985 and 1990, the orthopaedic workforce increased about 13%, from
about 16,600 to more than 18,700. By 1995 the orthopaedic workforce had increased about 29% from its 1985 level. As a point of reference, over the same time
period the total number of specialists increased by 33% and the number of primary
care physicians increased by about 25%.
Figure 1.2. Clinically Active Physicians Specializing in Orthopaedic Surgery and Neurosurgery (1975 - 1995)
Figure 1.3. Relative Growth in the Supply of Clinically Active Physicians (1975-1995)
5
6
Geographic Variation in the Distribution of the Physician Workforce
While the overall number of physicians increased steadily, the distribution of the
workforce varied by a factor of three among hospital referral regions. For example,
in 1996 there were an average of 189 clinically active physicians per 100,000 residents of the United States, but some hospital referral regions had about 300
physicians per 100,000 residents, while one had fewer than 90 physicians per
100,000 residents.
Among the hospital referral regions with the highest total numbers of active physicians per 100,000 residents in 1996 were White Plains, New York (333.5);
Hackensack, New Jersey (299.6); Royal Oak, Michigan (288.5) and San Francisco
(282.2).
Physicians per 100,000 Residents
The McAllen, Texas hospital referral region had the lowest supply of physicians in
the United States (88.2). Other regions with supplies of physicians substantially
lower than the national average included Provo, Utah (131.5); San Bernardino,
California (144.7); Wichita, Kansas (147.5) and Dayton, Ohio (147.7).
Figure 1.4. Physicians Allocated to Hospital Referral Regions (1996)
The number of physicians in active practice per 100,000 residents, after adjusting
for differences in age and sex of local populations, ranged from fewer than 90 to
more than 330. Each point represents one of the 306 hospital referral regions in the
United States.
Map 1.1. The Physician Workforce Active in Patient Care (1996)
In general, the physician workforce was concentrated in the urban areas of
the East and West coasts; however in some places regions with high supplies
of physicians were contiguous with areas that had much lower supplies.
San Francisco
Chicago
New York
Washington-Baltimore
Detroit
7
8
Orthopaedic Surgeons, Defined by Self-Designation
To describe the current workforce in musculoskeletal health care, we defined orthopaedic surgeons in two ways — by 1)self-designation and 2)clinical activity in
Medicare patients. For the former, orthopaedic surgeons were identified from the
American Medical Association and American Osteopathic Association files as physicians who designated themselves as clinically active orthopaedists. This approach
might overestimate the orthopaedic surgery workforce supply, as it includes some
physicians without specific orthopaedic surgery training.
In 1996 there were 18,622 self-designated orthopaedic surgeons in United States.
Among the hospital referral regions where the rates of self-designated orthopaedic
surgeons were higher than the United States average of 7.1 per 100,000 residents
were Napa, California (12.0); Bridgeport, Connecticut (10.4); Evanston, Illinois
(9.8); White Plains, New York (9.7) and Providence, Rhode Island (9.3).
Self-Designated Orthopaedic Surgeons per
100,000 Residents
Hospital referral regions where rates were lower than the United States average included the Bronx, New York (3.9); Ann Arbor, Michigan (4.9); Memphis,
Tennessee (5.0); Lexington, Kentucky (5.1) and Las Vegas, Nevada (5.2).
Figure 1.5. Self-Designated Orthopaedic Surgeons Allocated to Hospital
Referral Regions (1996)
The supplies of orthopaedic surgeons varied by a factor of four, from 2.8 per 100,000
residents to 12.1, after adjusting for differences in population age and sex. Each point
represents one of the 306 hospital referral regions in the United States.
Map 1.2. Self-Designated Orthopaedic Surgeons (1996)
Thirty-three hospital referral regions had supplies of orthopaedic surgeons
at least 30% above the national average. Thirty-seven regions had rates more
than 25% below the average.
San Francisco
Chicago
New York
Detroit
9
10
Orthopaedic Surgeons, Defined by Procedures Performed in
Medicare Patients
In our second approach to estimating the orthopaedic workforce, we identified
those orthopaedic surgeons who performed a threshold number of orthopaedic
procedures on Medicare patients in 1996. This approach underestimates to some
degree the orthopaedic surgery workforce supply, as it excludes physicians who
perform no operative care and those who do not care for Medicare patients in feefor-service arrangements. In 1996 there were 16,339 clinically active orthopaedic
surgeons in the United States performing procedures for Medicare patients (12%
fewer than the 18,622 self-designated orthopaedists).
Their geographic distribution varied nearly four-fold, from a low of 3.2 orthopaedic
surgeons per 100,000 residents of the Harlingen, Texas hospital referral region, to a
high of 11.9 per 100,000 residents of the hospital referral region in Casper, Wyoming.
Orthopaedic Surgeons per 1000,000 Residents
Among the hospital referral regions where the rates of orthopaedic surgeons were
higher than the United States average of 6.2 per 100,000 residents were Sun City,
Arizona (11.8); Billings, Montana (9.7); Portland,
Maine (8.9); Fort Lauderdale, Florida (8.4) and
Manchester, New Hampshire (8.3). Hospital referral
regions where rates were lower than the United States
average included the Bronx, New York (3.8); Flint,
Michigan (4.2); San Bernardino, California (4.4);
Chicago (4.5); and Fresno, California (4.6).
Figure 1.6. Orthopaedic Surgeons Performing Procedures on
Medicare Patients, Allocated to Hospital Referral Regions (1996)
Adjusted rates of clinically active orthopaedic surgeons varied by a factor of
nearly four, from 3.2 per 100,000 residents to 11.9. Each point represents
one of the 306 hospital referral regions in the United States. Clinical activity
was determined by procedures performed for Medicare patients.
Map 1.3. Orthopaedic Surgeons Performing Procedures on Medicare
Patients (1996)
Thirty-one hospital referral regions had supplies of orthopaedic surgeons at
least 30% higher than the national average; 13 regions had rates more than
25% below the average.
San Francisco
Chicago
New York
Detroit
11
12
How Many Physicians Are Enough?
The large variation in the per capita supply of physicians, coupled with growing
concern about a physician surplus, raises the question, What is the right number of
physicians? How many generalists are needed, and how many specialists and
subspecialists, in order to produce an ideal outcome in terms of population health
and longevity?
The problem is that making such needs-based estimates would require reliable information about the relationship between physician supply, the delivery of health
care services, and health outcomes — information that simply does not exist. Furthermore, rapid changes in technology and the inevitable failure of outcomes
research to keep pace with innovation mean that it is virtually impossible to establish this knowledge base.
The current utilization of physician services is sometimes proposed as a proxy for
“need.” This assumes that utilization is driven exclusively by patient demand for services, and that the current utilization of care is the “right” rate. Projections of need
based on current rates being “right” ignore the wide variations in both the supplies
of physicians and in practice styles; they simply perpetuate the status quo.
A third method, benchmarking, provides a pragmatic alternative for estimating the
size of an adequate and reasonable workforce. Benchmarking uses the actual deployment of the physician workforce in health plans or geographic areas to estimate the
surplus or shortfall of workforces that employ either more or fewer doctors per
100,000 residents or enrollees. The benchmarks used in the following comparisons
were chosen because they represent stable, efficiently sized workforces that deliver
an acceptable quality of service.
Orthopaedic Surgery Workforce Benchmarks
Figure 1.7 compares the supply of orthopaedic surgeons per 100,000 residents in
several hospital referral regions. The Chicago hospital referral region appears to be
a benchmark with efficient deployment of orthopaedic surgeons. If the supply of
orthopaedic surgeons in Chicago prevailed in other hospital referral regions, there
would be a surplus of more than 4,600 orthopaedic surgeons in the United States.
Using regions where the supplies of orthopaedic surgeons are relatively high as
benchmarks provides a different answer to the question of whether there are too
many or not enough orthopaedic surgeons in the United States. In order to raise the
supply of orthopaedic surgeons in every hospital referral region in the country to the
level of supply in the Sun City, Arizona hospital referral region, the United States
would need more than 14,600 additional orthopaedic surgeons.
Figure 1.7. Excess (or Deficit) Supply of Orthopaedic Surgeons per 100,000 Residents, Compared to
Benchmark Hospital Referral Regions (1996)
13
14
Projections of the Future Supply of Orthopaedic Surgeons
The challenge in physician workforce planning is to understand not only the current relationship between the supply of physicians and the health care needs of the
population, but also how these will change in the future. The physician workforce
active in patient care is determined by the number of new physicians entering the
workforce, the number of physicians who retire or die, and the number involved in
activities other than clinical care, such as teaching, research, and administration.
In this section we model the future supply of orthopaedic surgeons based on the
current supply, the current number of trainees, the proportion of trainees expected
to be active in patient care, and physician death and retirement rates from the
Bureau of Health Professions. The projections account for growth in the population
using United States Census population projections.
One of the major predictable changes that will affect the “need” for physicians in
the future is the disproportionate growth in the elderly population. In order to
account for these changes, the projections are adjusted for the age and sex distributions of future populations — the same adjustments used to adjust the estimates of
the size of the current workforce. The projections are also adjusted for changing demographics of the physician workforce using age and sex specific rates of hours worked per
week, according to the Bureau of Health Professions.
Approximately 615 physicians complete postgraduate medical training in orthopaedic
surgery each year and enter the workforce in the United States. About 595 (97%) of
these physicians are expected to become clinically active in orthopaedic surgery in the
United States. Assuming that this level remains constant, the orthopaedic surgery
workforce is expected to grow substantially over the next 25 years. The per capita
supply of orthopaedic surgeons is expected to peak in about ten years, before slowly
decreasing as the workforce stabilizes while the population continues to grow.
The magnitude of expected growth depends upon whether self-designation or clinical activity in Medicare patients is used to estimate current workforce supply.
15
Estimates based on these two approaches differ by about 12%. However, as time
passes the number of new trainees increasingly determines the size of the workforce.
The two projections converge on a projected supply of approximately 6.7 orthopaedic surgeons per 100,000 in the year 2020. The change in the size of the
workforce over time pales in comparison to the huge disparities in supply that currently exist between geographic regions.
Figure 1.8. Projected Supply of
Orthopaedic Surgeons Adjusted for
Population and Workforce Demographic
Changes (1995-2020)
Figure 1.9. Projected Supply of
Orthopaedic Surgeons Compared to High
and Low Benchmarks (1995-2020)
16
Neurosurgeons, Defined by Self-Designation
Because neurosurgeons perform the majority of spine surgeries in the Medicare population (see Chapter Two), we also examined the neurosurgery workforce. Neurosurgeons
were identified from the American Medical Association and American Osteopathic
Association files as physicians who designated themselves as clinically active
neurosurgeons. This approach might overestimate the neurosurgery workforce supply,
as it includes some physicians without specific neurosurgery training.
In 1996 there were 3,799 self-designated neurosurgeons in United States. Among
the hospital referral regions where the rates of self-designated neurosurgeons were
higher than the United States average of 1.4 per 100,000 residents were Great Falls,
Montana (3.3); Owensboro, Kentucky (3.3); Bridgeport, Connecticut (3.2); Bend,
Oregon (2.9) and Terre Haute, Indiana (2.8).
Self-Designated Neurosurgeons per
100,000 Residents
Hospital referral regions where rates of neurosurgeons per 100,000 residents were
lower than the United States average included Temple, Texas (0.3); Dothan, Alabama
(0.6); Paterson, New Jersey (0.6); McAllen, Texas (0.6) and Akron, Ohio (0.7).
Figure 1.10. Self-Designated Neurosurgeons Allocated to Hospital Referral
Regions (1996)
The supply of neurosurgeons varied by a factor of more than ten, from 0.3 per
100,000 residents to 3.3, after adjusting for differences in population age and sex.
Each point represents one of the 306 hospital referral regions in the United States.
Map 1.4. Self-Designated Neurosurgeons (1996)
Forty-five hospital referral regions had supplies of neurosurgeons at least
30% higher than the national average. Fifty-four regions had rates more
than 25% below the average.
San Francisco
Chicago
New York
Detroit
17
18
Chapter One
Table Notes
Measures of workforce supply are expressed as clinically active full-time equivalents
(FTEs) per 100,000 residents. All rates are adjusted for age and sex.
See the Appendix on Methods for details on the methods used for classifying
physician specialty, determining clinical activity, allocating physicians to geographic
areas, and adjusting rates.
CHAPTER ONE TABLE
The Orthopaedic Surgery Workforce (1996)
Ho
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Alabama
Birmingham
2,067,166
157.5
6.3
6.5
Dothan
341,377
145.6
6.5
5.7
1.5
0.6
Huntsville
512,335
145.2
6.2
6.2
1.6
Mobile
735,828
164.1
7.6
6.7
1.6
Montgomery
420,205
142.7
6.4
5.8
1.5
Tuscaloosa
233,184
156.4
6.0
5.6
1.5
615,424
186.9
10.1
7.4
1.0
Alaska
Anchorage
Arizona
Mesa
759,053
151.2
6.0
5.6
0.9
2,230,182
177.9
7.3
6.7
1.3
Sun City
162,537
201.0
12.1
11.8
1.9
Tucson
935,802
179.9
6.1
5.2
1.6
Phoenix
Arkansas
Fort Smith
316,211
145.9
5.3
5.5
1.1
Jonesboro
207,464
133.3
5.6
5.6
1.6
Little Rock
1,378,605
163.9
6.9
5.9
1.8
Springdale
344,873
146.0
5.7
6.4
1.3
Texarkana
251,418
137.7
5.7
5.0
1.2
1.5
California
Orange County
2,732,562
211.8
9.1
5.9
Bakersfield
841,548
145.9
5.6
5.2
1.1
Chico
263,211
171.9
9.2
8.9
1.6
1.2
Contra Costa County
862,123
214.9
8.6
5.6
Fresno
974,617
152.4
5.0
4.6
1.4
9,230,785
197.6
7.2
4.7
1.5
Modesto
717,600
148.9
6.2
5.7
1.4
Napa
250,705
243.9
12.0
9.5
1.8
1,348,508
223.9
8.2
4.6
1.4
Los Angeles
Alameda County
Palm Spa/Rancho Mir
248,351
197.5
10.0
7.5
1.7
Redding
314,477
185.8
10.3
10.6
2.2
1,987,776
184.2
8.0
6.1
1.4
337,282
192.5
8.9
7.4
1.8
San Bernardino
2,306,438
144.7
5.8
4.4
1.0
San Diego
3,006,551
194.1
8.7
5.1
1.4
Sacramento
Salinas
San Francisco
1,323,898
282.2
9.1
6.0
2.2
San Jose
1,525,072
196.2
7.8
5.4
1.5
San Luis Obispo
213,259
227.7
11.0
7.5
2.5
San Mateo County
761,040
234.7
8.6
5.3
2.0
Santa Barbara
393,410
215.2
10.6
7.5
1.8
19
20
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Santa Cruz
245,459
223.6
9.3
6.3
1.7
Santa Rosa
419,080
228.2
10.5
7.3
1.6
Stockton
452,439
149.8
7.1
6.6
1.2
Ventura
744,436
203.5
9.6
6.6
1.9
Boulder
238,607
231.8
9.0
10.0
1.6
Colorado Springs
643,355
174.9
8.9
8.0
1.2
2,124,949
203.2
8.6
6.2
1.5
Colorado
Denver
Fort Collins
261,453
169.4
9.4
8.1
2.0
Grand Junction
237,226
182.1
11.8
9.5
1.5
Greeley
257,786
169.4
9.0
10.4
1.0
Pueblo
140,345
193.4
10.4
8.3
2.0
Connecticut
Bridgeport
627,917
258.8
10.4
9.1
3.2
Hartford
1,384,445
217.8
9.3
7.8
1.5
New Haven
1,352,454
236.8
9.3
7.8
2.0
672,137
186.9
6.6
5.9
1.9
2,254,795
268.6
8.6
6.7
2.0
Bradenton
210,696
163.4
6.0
6.3
1.2
Clearwater
468,567
190.7
7.7
7.5
1.4
2,147,234
215.4
9.8
8.4
1.7
Delaware
Wilmington
District of Columbia
Washington
Florida
Fort Lauderdale
Fort Myers
717,985
171.4
7.0
6.4
2.2
Gainesville
445,145
170.1
5.6
5.8
1.6
Hudson
Jacksonville
Lakeland
Miami
Ocala
Orlando
310,353
166.2
7.4
6.7
1.5
1,240,525
181.0
6.2
5.4
1.6
302,262
146.7
5.7
5.4
1.2
2,513,109
229.7
7.6
5.4
1.7
341,901
147.1
5.9
7.1
1.5
2,535,044
162.8
7.9
6.7
1.2
Ormond Beach
290,820
160.2
9.9
7.0
1.1
Panama City
179,736
148.2
7.5
6.0
1.2
Pensacola
647,155
174.3
7.0
5.9
1.4
Sarasota
339,490
205.4
10.2
8.5
2.0
St. Petersburg
402,889
202.9
8.2
7.9
1.6
Tallahassee
672,896
155.8
5.3
5.2
1.8
Tampa
933,943
181.4
7.2
6.0
1.4
Georgia
Albany
208,867
124.1
5.0
6.8
1.8
Atlanta
4,200,842
173.0
6.6
6.2
1.2
Augusta
594,919
179.0
5.5
5.5
2.0
Columbus
316,092
144.7
8.4
5.3
1.0
Macon
633,839
167.5
6.3
6.1
1.4
Rome
231,352
159.9
6.3
6.2
1.5
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1.1
1,190,170
208.7
6.9
4.5
0.9
Boise
605,996
156.8
8.9
8.0
1.7
Idaho Falls
181,481
127.6
7.2
7.6
2.1
Aurora
200,393
136.9
7.1
5.6
2.3
Blue Island
855,979
184.7
6.4
5.9
1.3
2,590,942
225.4
5.4
4.5
1.7
Elgin
617,504
150.7
6.4
5.4
1.4
Evanston
908,751
276.1
9.8
8.2
1.5
Hinsdale
394,729
253.5
6.3
5.1
2.2
Joliet
461,271
166.6
6.1
5.7
1.6
Hawaii
Honolulu
Idaho
Illinois
Chicago
Melrose Park
1,261,491
223.2
7.2
6.3
1.7
Peoria
606,294
148.0
5.3
5.7
0.9
Rockford
655,790
154.9
6.1
6.7
1.6
Springfield
828,552
140.3
4.8
6.7
1.4
Urbana
425,820
159.5
4.9
4.9
1.0
Bloomington
174,433
143.7
4.5
5.3
1.3
Indiana
Evansville
658,585
143.4
5.6
6.9
1.5
Fort Wayne
791,565
131.5
5.2
5.8
1.0
Gary
Indianapolis
498,010
146.0
6.9
6.2
1.8
2,448,580
170.4
6.7
6.7
1.2
Lafayette
208,245
141.8
5.5
4.2
1.1
Muncie
169,763
160.8
6.4
6.6
1.7
Munster
306,380
159.6
5.4
6.5
1.0
South Bend
640,771
147.0
5.4
5.8
1.0
Terre Haute
179,192
153.2
6.2
7.2
2.8
Cedar Rapids
263,391
143.3
7.3
6.2
1.3
Davenport
496,950
155.0
6.4
5.8
1.5
Des Moines
955,106
155.9
6.2
7.0
0.9
Dubuque
148,398
152.4
8.0
8.2
1.0
Iowa City
318,164
174.0
5.9
6.5
1.6
Mason City
141,892
147.0
5.9
5.4
1.1
Sioux City
260,316
127.7
4.9
7.2
1.6
Waterloo
206,613
149.5
5.4
4.9
0.9
Iowa
Kansas
Topeka
432,709
154.0
5.7
6.1
0.8
Wichita
1,196,236
147.5
5.8
6.0
0.7
Covington
339,291
159.1
6.1
5.2
1.5
Lexington
1,359,503
153.0
5.1
5.3
0.7
Louisville
1,527,661
176.0
6.3
5.9
1.5
Kentucky
21
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Owensboro
135,989
134.8
7.2
6.5
3.3
Paducah
354,665
141.5
5.8
6.0
1.8
Alexandria
274,229
164.1
6.6
5.7
1.4
Baton Rouge
775,757
152.5
5.1
5.1
1.5
Houma
251,883
132.2
6.5
7.3
1.3
Louisiana
Lafayette
560,865
141.3
5.6
5.3
1.1
Lake Charles
255,156
138.7
5.9
4.4
1.0
Metairie
416,838
251.8
9.0
7.5
2.0
Monroe
271,786
137.4
6.0
5.4
1.2
New Orleans
834,289
220.4
9.7
7.3
2.6
Shreveport
657,767
157.4
6.9
7.0
1.6
Slidell
160,715
164.9
7.6
7.0
2.4
Maine
Bangor
397,915
170.3
8.8
8.4
1.3
Portland
970,466
200.8
9.7
8.9
1.6
Baltimore
2,309,251
250.3
8.5
7.9
2.1
Salisbury
330,541
189.1
7.7
7.0
1.3
Takoma Park
818,509
277.8
7.6
7.0
1.4
1.8
Maryland
Massachusetts
Boston
4,456,609
260.4
8.7
6.7
Springfield
718,474
202.0
7.4
6.7
1.3
Worcester
721,916
215.6
7.7
6.5
1.1
Ann Arbor
1,263,300
189.7
4.9
5.6
1.1
Dearborn
517,047
174.0
5.9
5.8
1.4
1,874,979
175.8
5.7
4.9
1.3
Michigan
Detroit
Flint
564,745
163.8
4.1
4.2
0.7
1,022,322
154.1
6.4
6.0
1.3
Kalamazoo
638,376
166.8
6.4
6.6
1.4
Lansing
655,609
174.9
6.3
5.4
1.0
Marquette
204,947
153.0
6.2
6.8
1.7
Muskegon
253,057
155.0
7.4
6.1
1.0
Petoskey
162,989
168.0
8.3
7.6
1.2
Pontiac
430,414
252.5
6.0
4.9
1.8
Royal Oak
667,417
288.5
7.8
5.6
1.1
Saginaw
644,015
155.7
5.1
5.3
1.7
Grand Rapids
St. Joseph
147,378
164.1
5.9
5.3
1.1
Traverse City
192,826
180.1
7.4
8.0
1.2
Minnesota
Duluth
333,442
167.0
6.8
6.2
1.5
2,761,315
169.7
6.8
6.4
1.0
Rochester
373,148
205.0
6.3
7.4
1.4
St. Cloud
215,944
150.3
6.1
6.7
1.1
St. Paul
897,880
188.4
7.8
5.6
1.0
Minneapolis
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Mississippi
Gulfport
190,269
173.7
7.4
6.0
Hattiesburg
269,497
137.7
6.2
5.1
2.4
1.4
Jackson
1,008,214
149.0
5.3
5.2
2.1
Meridian
196,424
140.1
4.8
5.6
2.0
Oxford
131,140
139.7
5.5
5.0
1.9
Tupelo
367,620
123.3
4.0
4.9
1.8
Missouri
Cape Girardeau
256,947
136.8
4.7
6.5
1.4
Columbia
614,064
157.3
6.6
7.6
0.8
Joplin
323,324
150.2
6.1
6.2
1.5
2,115,460
180.1
6.4
6.4
1.2
685,835
144.3
5.7
6.5
1.0
3,202,811
182.3
6.9
6.6
1.5
Billings
500,410
177.7
9.3
9.7
1.1
Great Falls
151,554
175.4
8.6
9.9
3.3
Missoula
322,927
191.7
10.9
11.2
2.6
Lincoln
527,095
134.4
5.3
6.5
1.1
Omaha
1,151,585
160.2
6.2
6.9
1.1
Kansas City
Springfield
St. Louis
Montana
Nebraska
Nevada
Las Vegas
1,039,539
148.8
5.2
5.2
1.1
539,845
179.9
8.3
9.5
1.5
Lebanon
374,665
200.8
7.7
8.7
1.0
Manchester
747,835
186.3
9.4
8.3
1.3
Camden
2,544,746
218.8
7.9
6.8
1.3
Hackensack
1,142,994
299.6
8.6
7.1
1.4
930,015
249.2
9.4
8.1
1.1
Reno
New Hampshire
New Jersey
Morristown
New Brunswick
883,173
236.4
7.9
6.2
1.2
1,450,943
216.6
7.3
6.0
1.2
Paterson
378,389
189.2
7.0
6.8
0.6
Ridgewood
386,390
263.2
9.4
7.0
1.6
1,384,541
194.6
8.6
6.5
1.4
1,749,451
201.4
7.4
6.6
1.5
378,203
172.7
7.4
6.8
1.4
Bronx
1,205,120
201.1
3.9
3.8
1.0
Buffalo
1,445,723
189.9
5.9
5.3
1.9
Newark
New Mexico
Albuquerque
New York
Albany
Binghamton
Elmira
East Long Island
345,998
185.7
7.8
7.2
1.8
4,303,545
273.7
8.2
6.2
1.3
Manhattan
4,574,772
260.5
5.6
4.8
1.4
Rochester
1,274,455
195.6
7.2
6.8
1.1
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Syracuse
1,091,054
173.2
8.1
7.0
1.3
White Plains
1,063,794
333.5
9.7
7.3
1.8
Asheville
522,456
181.8
9.6
8.3
1.1
Charlotte
1,689,258
158.0
6.9
7.0
1.1
Durham
1.1
North Carolina
1,112,805
167.0
6.2
6.4
Greensboro
503,135
160.6
6.8
7.8
1.9
Greenville
721,867
155.7
6.3
5.8
0.9
Hickory
250,356
137.1
6.1
6.6
2.2
Raleigh
1,416,777
157.5
5.8
5.0
1.3
Wilmington
315,710
164.7
6.7
7.0
1.6
Winston-Salem
931,839
147.3
5.1
5.9
1.1
Bismarck
203,420
154.5
5.2
7.2
2.1
Fargo Moorhead
481,267
138.7
4.9
7.1
1.3
Grand Forks
178,360
147.2
4.5
5.1
1.5
Minot
124,656
170.8
8.5
9.0
2.7
Akron
682,339
182.0
8.3
6.9
0.7
Canton
621,016
148.6
6.5
6.5
1.1
1,576,226
192.8
7.5
6.7
2.0
North Dakota
Ohio
Cincinnati
Cleveland
2,115,071
210.2
7.5
6.6
1.6
Columbus
2,661,834
158.2
6.2
5.8
1.1
Dayton
1,118,493
147.7
5.2
4.8
1.6
Elyria
246,230
162.1
6.8
6.1
1.4
Kettering
376,920
210.5
8.1
5.3
1.5
Toledo
993,905
179.6
5.9
5.5
1.4
Youngstown
696,849
175.6
6.2
6.2
1.3
Oklahoma
Lawton
199,870
154.1
6.3
4.3
1.7
Oklahoma City
1,624,681
160.9
6.4
6.0
1.3
Tulsa
1,198,154
161.9
6.3
6.3
1.9
Bend
139,460
171.1
7.3
11.6
2.9
Eugene
643,098
179.1
8.3
8.3
2.0
Oregon
Medford
380,925
166.7
10.2
9.0
1.6
Portland
2,117,067
190.4
7.5
6.0
1.8
254,854
169.3
7.5
8.4
2.0
1.5
Salem
Pennsylvania
Allentown
1,046,197
180.7
7.1
6.9
Altoona
302,509
148.4
6.7
6.3
1.0
Danville
548,307
170.3
6.2
7.0
1.2
Erie
739,828
159.8
7.9
6.8
1.7
Harrisburg
923,527
172.5
7.0
6.9
1.4
Johnstown
238,917
178.2
6.4
5.9
2.1
Lancaster
563,618
157.8
6.5
6.2
1.3
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Philadelphia
3,913,956
263.5
8.2
6.2
1.7
Pittsburgh
3,057,775
191.9
6.6
5.8
1.8
Reading
525,543
166.0
6.6
6.7
1.1
Sayre
196,822
158.3
6.5
6.8
1.2
Scranton
299,324
190.2
6.4
7.2
2.4
Wilkes-Barre
255,080
200.1
6.4
5.4
1.9
York
359,300
161.7
5.7
6.0
1.5
1,151,437
209.6
9.3
7.2
2.1
Rhode Island
Providence
South Carolina
Charleston
767,555
180.0
6.7
6.7
1.6
Columbia
1,036,171
163.4
5.2
5.4
1.0
Florence
355,153
132.7
5.0
5.4
0.8
Greenville
724,640
164.9
8.1
7.1
1.4
Spartanburg
321,110
146.2
7.2
6.7
1.8
Rapid City
194,449
172.0
6.1
5.8
1.6
Sioux Falls
715,918
149.6
6.0
8.1
0.8
South Dakota
Tennessee
Chattanooga
587,633
161.2
7.2
6.3
1.3
Jackson
296,452
135.0
5.3
5.5
1.6
Johnson City
228,757
189.9
9.5
7.2
1.2
Kingsport
475,243
160.8
6.6
7.4
0.9
Knoxville
1,147,521
164.7
6.5
7.0
1.5
Memphis
1,656,593
149.9
5.0
4.7
2.4
Nashville
2,132,830
168.4
6.9
6.6
1.4
Abilene
279,801
154.0
6.2
7.2
1.8
Amarillo
396,166
152.0
5.7
5.9
1.2
1,014,387
179.3
6.4
6.4
1.5
Beaumont
444,993
162.8
6.4
5.7
1.4
Bryan
198,132
145.3
5.4
4.8
1.2
Corpus Christi
527,470
156.8
7.1
6.3
1.1
3,350,616
168.3
7.0
5.7
1.2
Texas
Austin
Dallas
El Paso
Fort Worth
Harlingen
927,960
141.7
6.1
4.8
2.1
1,543,710
152.8
6.5
5.3
1.5
1.2
433,491
100.4
2.9
3.2
4,654,165
171.3
6.0
5.1
1.2
178,258
138.0
5.3
6.2
1.2
Lubbock
654,516
153.0
7.2
7.6
1.3
Mcallen
419,177
88.2
2.8
3.8
0.6
Odessa
318,045
124.3
5.3
5.2
1.5
San Angelo
154,120
156.5
7.4
6.3
1.4
San Antonio
2,005,038
182.7
6.4
4.9
1.7
Houston
Longview
Temple
369,253
124.1
4.5
4.6
0.3
Tyler
452,122
164.1
6.5
6.2
1.5
25
26
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Ph Res
Se Sur esid
Se Neu 00,0
R
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Victoria
140,251
156.7
6.1
5.6
2.0
Waco
292,850
151.4
5.4
5.0
1.9
Wichita Falls
196,171
172.2
7.3
5.9
0.8
Ogden
343,189
137.1
7.2
7.8
1.2
Provo
352,423
131.5
6.7
6.1
1.3
1,553,931
155.6
7.5
7.9
1.4
617,328
192.7
6.6
6.7
2.0
Utah
Salt Lake City
Vermont
Burlington
Virginia
Arlington
1,653,868
208.3
8.2
6.6
1.2
Charlottesville
462,687
188.2
6.5
7.1
1.3
Lynchburg
220,257
145.6
6.3
7.0
1.6
Newport News
511,940
181.0
6.4
6.0
1.4
Norfolk
1,194,664
192.7
6.8
6.0
1.6
Richmond
1,356,790
174.7
7.3
6.6
1.2
Roanoke
669,247
177.1
6.1
5.9
1.3
Winchester
323,755
159.1
7.7
7.0
0.9
Washington
Everett
506,893
173.3
8.6
6.7
0.9
Olympia
313,358
170.7
7.2
7.2
1.6
Seattle
2,323,430
219.3
8.8
6.7
1.6
Spokane
1,222,904
172.2
7.7
7.3
1.7
Tacoma
654,628
179.7
8.5
6.4
1.3
Yakima
254,946
161.4
7.3
7.0
1.9
West Virginia
Charleston
868,182
167.0
5.0
5.4
1.1
Huntington
355,646
167.3
5.1
4.7
1.6
Morgantown
386,430
174.1
6.5
5.7
1.0
Appleton
282,642
142.4
5.8
6.9
1.8
Green Bay
471,256
143.0
6.5
6.4
1.4
La Crosse
332,104
159.2
6.5
6.2
1.4
Madison
935,588
168.7
6.8
7.2
1.4
Marshfield
356,526
174.5
7.3
8.2
0.8
Milwaukee
2,405,169
190.3
8.1
7.0
1.4
Wisconsin
Neenah
212,358
163.7
8.6
7.5
1.3
Wausau
179,319
168.4
6.9
7.9
1.1
170,887
177.3
10.3
11.9
1.8
262,306,124
188.9
7.1
6.2
1.4
Wyoming
Casper
United States
CHAPTER TWO
Conditions of the Spine
28
Spine Problems
Spine problems are some of the most costly and prevalent health problems in the
industrialized world. Up to 80% of Americans report having low back pain at some
point in their lives. In most cases of back and neck pain, the symptoms are self-limited
and precise causes are never established.
There remains significant variation in the use of surgery for many spine-related
problems. Most spine surgery is for the treatment of two conditions:
■ Herniated disc: gelatinous discs serve as “cushions” between the bony vertebrae of
the spine. When discs herniate (extrude beyond the vertebrae), they can cause pain or
neurological deficits from irritation or compression of nerve roots exiting the spine.
■ Spinal stenosis: a narrowing of the bony spinal canal due to thickening of the ligaments and spine joints, which can cause pain or neurological deficits from entrapment
of nerves exiting the spinal cord.
These conditions can affect either the neck (cervical
spine) or, more frequently, the lower back (lumbar spine).
Surgical procedures for herniated discs and spinal
stenosis accounted for 83% of the more than 188,000
spine surgeries done in Medicare patients in 1996-97.
There were approximately 39,000 discectomies for
herniated disc, 90,000 decompressions for spinal
stenosis at the lumbar level, and 27,000 surgical procedures (for either herniated disc or spinal stenosis) on
the cervical spine (Figure 2.1). The remaining 32,000
procedures were for other conditions of the spine.
Figure 2.1. Types of Surgical Procedures Performed for
Medicare Enrollees with Conditions of the Spine (1996-97)
The numbers in parentheses reflect the number of patients undergoing these procedures.
CONDITIONS OF THE SPINE
Overall, spine surgery rates increased by 57% over the ten-year period from 1988 to
1997, from 2.1 to 3.4 per 1,000 Medicare enrollees (Figure 2.2).
In this chapter, we focus on variations in the use of spine surgery in patients with disc
herniation and spinal stenosis of the neck or lower back. We also describe variation in
the use of fusion — with and without the use of spinal fixation devices — among
Medicare enrollees undergoing spine surgery. We explore the relationship between
spine surgery rates and the regional use of diagnostic imaging of the spine. Finally, we
examine workforce issues specific to spine surgery and describe the relative roles of
orthopaedic surgeons and neurosurgeons in different hospital referral regions.
Figure 2.2. Increase in Rates of Spine Surgery Among Medicare Enrollees
(1988-1997)
Overall surgery rates increased by 57% between 1988 and 1997.
29
30
Rates of Spine Surgery
As described in earlier editions of the Atlas, rates of spine surgery vary more than
almost any other common inpatient procedure. In 1996-97, rates of spine surgery
varied by a factor of almost six, from 1.4 per 1,000 Medicare enrollees in the Bronx,
New York hospital referral region to 8.6 per 1,000 among Medicare residents of the
Bend, Oregon hospital referral region.
Among the hospital referral regions where rates of spine surgery were substantially
higher than the United States average of 3.4 per 1,000 Medicare enrollees were
Boise, Idaho (7.6); Santa Barbara, California (7.0); Fort Myers, Florida (6.3); Savannah, Georgia (6.0) and Tucson, Arizona (5.4).
Spine Surgeries per 1,000 Medicare Enrollees
Among the regions where rates of spine surgery per 1,000 Medicare enrollees were
lower than average were Manhattan (1.6); Charleston, West Virginia (1.7); Honolulu (2.1); Lebanon, New Hampshire (2.1) and Chicago (2.1).
Figure 2.3. Spine Surgery (1996-97)
Rates of spine surgery varied by a factor of six, from 1.4 per 1,000 Medicare
enrollees to 8.6, after adjusting for differences in population age, sex, and race.
Map 2.1. Spine Surgery (1996-97)
In 59 hospital referral regions, rates of spine surgery were at least 30%
higher than the national average. In 49 hospital referral regions, rates were
more than 25% below the average.
San Francisco
Chicago
New York
Detroit
31
32
Cervical Spine Surgery
Cervical spine procedures accounted for 14% of the spine surgery performed in the
Medicare population in 1996-97. These procedures include both discectomies (removal of herniated discs or disc fragments) and decompression procedures for spinal
stenosis. Cervical spine surgery can be performed with or without fusion, in which
bone or hardware is inserted to prevent motion between spinal segments. Rates of
cervical spine surgery ranged from 0.16 to 1.72 per 1,000 Medicare enrollees.
Among the hospital referral regions where rates of cervical spine surgery were substantially higher than the United States average of 0.49 per 1,000 Medicare enrollees
were Amarillo, Texas (1.19); Boise, Idaho (1.19); Macon, Georgia (1.14); Savannah,
Georgia (1.12) and Santa Barbara, California (1.04).
Cervical Spine Surgeries per 1,000 Medicare Enrollees
In other regions, rates of cervical spine surgery per 1,000 Medicare enrollees were
lower than the average, including Charleston, West Virginia (0.18); Paterson, New
Jersey (0.18); Spartanburg, South Carolina (0.20); Newark, New Jersey (0.20) and
Worcester, Massachusetts (0.21).
Figure 2.4. Cervical Spine Surgery (1996-97)
Rates of cervical spine surgery varied by a factor of more than ten, from 0.16 per
1,000 Medicare enrollees to 1.72, after adjusting for differences in population age,
sex, and race. Each point represents one of the 306 hospital referral regions in the
United States.
Map 2.2. Cervical Spine Surgery (1996-97)
In 74 hospital referral regions, rates of cervical spine surgery were at least
30% higher than the national average. In 75 hospital referral regions, rates
were more than 25% below the average.
San Francisco
Chicago
New York
Detroit
33
34
Use of Discectomy Procedures of the Lumbar Spine
Lumbar Discectomies per 1,000 Medicare Enrollees
The vast majority (95%) of herniated discs occur in the lower back. Although
patients with this condition can have back pain alone, many experience pain along
the back of the thigh, leg, and foot, a pain pattern known as sciatica. Some patients
also note leg numbness and weakness. Although symptoms of herniated lumbar
discs will most often resolve without intervention, many patients undergo surgery
for this condition. Surgical treatment for herniated disc involves removal of the protruding portion of the affected disc (discectomy). Discectomy also frequently
requires partial or complete removal of part of the vertebrae known as the lamina
(laminotomy or laminectomy).
In 1996-97, rates of lumbar discectomy among Medicare enrollees ranged from 0.14
per 1,000 enrollees in the Paterson, New Jersey hospital referral region to 2.20 per
1,000 among Medicare residents of the Rapid City, South Dakota hospital referral
region. Among the hospital referral regions where rates of lumbar discectomy were
substantially higher than the United States average of 0.72 per 1,000 Medicare enrollees were Longview, Texas (1.85); Montgomery, Alabama (1.78); Casper, Wyoming
(1.74); Provo, Utah (1.71) and Newport News, Virginia (1.60). Among the regions
where rates of lumbar discectomy per 1,000 Medicare enrollees were lower than average were the Bronx, New York
(0.16); McAllen, Texas (0.19); Newark, New Jersey (0.20);
East Long Island, New York (0.24) and Honolulu (0.26).
Figure 2.5. Lumbar Discectomy (1996-97)
Rates of lumbar discectomy varied by a factor of 15, from 0.14 per 1,000
Medicare enrollees to 2.20, after adjusting for differences in population age, sex,
and race. Each point represents one of the 306 hospital referral regions in the
United States.
Map 2.3. Lumbar Discectomy (1996-97)
Seventy-five regions had rates at least 30% higher than the national average.
Sixty-four regions had rates more than 25% below the national average.
San Francisco
Chicago
New York
Detroit
35
36
Lumbar Decompression Procedures for Spinal Stenosis
Unlike herniated discs, spinal stenosis primarily affects the elderly. Patients with spinal
stenosis can experience sciatica symptoms similar to those produced by herniated discs,
but the condition can also cause leg pain with walking that resolves at rest. Because
they mimic the symptoms of peripheral vascular disease, these symptoms are often
called “pseudoclaudication.” Patients with bothersome or progressive symptoms often
undergo surgical treatment, which involves removal of bony or soft tissue elements
that are compressing the spinal nerve roots (decompressive laminectomy). In 1996-97,
rates of lumbar decompression ranged from 0.65 per to 4.43 per 1,000 Medicare
enrollees.
Rates of decompression surgery were substantially higher than the United States
average of 1.63 per 1,000 Medicare enrollees in Bend, Oregon (4.43); Santa Barbara, California (3.94); Fort Collins, Colorado (3.75); Boise, Idaho (3.73); Casper,
Wyoming (3.41) and Tacoma, Washington (3.32).
Lumbar Decompression Surgery
per 1,000 Medicare Enrollees
Among the regions where rates of lumbar decompression surgery per 1,000 Medicare enrollees were lower than average were Charleston, West Virginia (0.65);
Harlingen, Texas (0.69); the Bronx, New York (0.73);
Manhattan (0.74); San Jose, California (0.75) and
Spartanburg, South Carolina (0.75).
Figure 2.6. Lumbar Decompression Surgery (1996-97)
Rates of lumbar decompression varied by a factor of almost seven, from 0.65
per 1,000 Medicare enrollees to 4.43, after adjusting for differences in
population age, sex, and race. Each point represents one of the 306 hospital
referral regions in the United States.
Map 2.4. Lumbar Decompression Surgery (1996-97)
Sixty-eight hospital referral regions had rates at least 30% higher than the
national average. Fifty-nine regions had rates more than 25% below the
national average.
San Francisco
Chicago
New York
Detroit
37
38
Overall Use of Fusion Procedures
Sometimes physicians suspect that spinal instability is a contributing factor in back
or neck pain. In such cases, spine surgery can include the use of bone or implanted
hardware to fuse together two or more vertebrae, preventing motion within those
spinal segments. The indications for this procedure, known as “fusion,” are not
firmly established, and the use of spinal fusion displays particularly wide variation
among geographic areas. The proportion of patients undergoing spine surgery who
received spinal fusion increased from 23% in 1993 to 29% in 1997. During the
same period, the proportion of patients undergoing fusion who received hardware
fixation devices rose from 50% to 60%.
Among the hospital referral regions where rates of spinal fusion were substantially
Provo, Utah (3.05); Fort Collins, Colorado (2.64); San Luis Obispo, California
(2.40); Columbus, Georgia (2.36); Montgomery, Alabama (2.35) and Santa Barbara,
California (2.28).
Spinal Fusion Procedures per 1,000 Medicare
Enrollees
Among the regions where rates of spinal fusion per 1,000 Medicare enrollees were
lower than average were Worcester, Massachusetts (0.31); Harlingen, Texas (0.32);
Aurora, Illinois (0.33); Charleston, West Virginia
(0.34); Grand Forks, North Dakota (0.34) and Paterson, New Jersey (0.35).
Figure 2.7. Spinal Fusion (1996-97)
Rates of spinal fusion varied by a factor of almost ten, from 0.3 to 3.0 per
1,000 Medicare enrollees, after adjusting for differences in population age,
sex, and race. Each point represents one of the 306 hospital referral regions in
the United States.
Map 2.5. Spinal Fusion (1996-97)
Seventy-one regions had rates at least 30% higher than the national average.
Ninety-four regions had rates more than 25% below the national average.
San Francisco
Chicago
New York
Detroit
39
40
Use of Fusion with Surgery for Lumbar Spinal Stenosis
The use of fusion also varies markedly among patients being treated for the same
spine condition. Fusion is most commonly used in conjunction with decompression
surgery (laminectomy) for lumbar spinal stenosis. Among Medicare patients undergoing surgery for this condition in 1996-97, the use of fusion varied from 4% of
operations to 56%.
Among the hospital referral regions where the proportion of lumbar decompression
surgery using fusion was substantially higher than the United States average of
23.2% were Columbia, Missouri (56.2%); Sioux City, Iowa (49.7%); Orlando,
Florida (47.6%); Idaho Falls, Idaho (46.8%); Columbus, Georgia (43.5%) and
Muncie, Indiana (42.5%).
Percent of Surgery for
Lumbar Spinal Stenosis Using Fusion
Use of fusion was lower than average in Shreveport, Louisiana (4.4%); Spokane,
Washington (7.1%); Worcester, Massachusetts (7.2%); Bend, Oregon (7.7%); Portland, Oregon (7.8%) and Alameda County, California (8.2%).
Figure 2.8. Use of Fusion with Surgery for Lumbar Spinal Stenosis (1996-97)
The proportion of patients undergoing fusion with surgery for lumbar spinal
stenosis varied from less than 5% to almost 60%. Each point represents one of the
306 hospital referral regions in the United States.
Map 2.6. Use of Fusion with Surgery for Lumbar Spinal Stenosis (1996-97)
Fusion was used in at least 40% of lumbar decompression procedures in
twelve hospital referral regions. In ten regions, fusion was used in less than
10% of operations.
San Francisco
Chicago
New York
Detroit
41
42
The Surgical Signature in Spine Surgery
It is unlikely that the large degree of regional variation in the use of spine surgery
reflects regional differences in the incidence of disease. For example, there is no
evidence to suggest that residents of the Rocky Mountain West, which has particularly high overall rates of spine surgery, have a higher incidence of spinal disc
herniation or stenosis. Instead, regional variation in surgery reflects differences in
physician practice style — physicians in some regions of the United States are simply
more inclined to recommend surgery in patients with surgically treatable conditions
of the spine.
However, variation in physicians’ practice styles is also apparent on a more local
level. Even among neighboring regions (with presumably very similar populations),
the likelihood that patients will undergo particular surgical procedures of the spine
is remarkably variable. Because the differences in rates tend to persist from year to
year, communities become recognizable by their “surgical signatures.”
Surgical signatures reflect the practice patterns of individual physicians and the local
medical culture, rather than differences in need or physician supply. Neighboring
regions with similar demographics and about the same per capita numbers of spine
surgeons can have very different signatures for spine surgery. The eight California
hospital referral regions bounded on the north by the San Francisco and Stockton
regions and, on the south, by the Salinas and Fresno regions, provide a good example
of this phenomenon. Overall rates of spine surgery among these regions varied considerably, from 2.2 per 1,000 Medicare enrollees in San Jose to 4.6 per 1,000 in
Salinas.
Map 2.7. Variation in Spine Surgery Rates in Contiguous California
Hospital Referral Regions (1996-97)
Rates of spine surgery per 1,000 Medicare enrollees in the San Jose (2.2),
Contra Costa (2.5), and San Mateo (2.3) hospital referral regions were
substantially lower than the national average. The rate of surgery in the Salinas
hospital referral region (4.6) was substantially higher than the national average
and twice as high as in neighboring San Jose, Contra Costa, and San Mateo.
43
44
Figure 2.9. The Surgical Signature of Spine Surgery in Eight California Hospital Referral Regions (1996-97)
Patterns of spine surgery — the “surgical signatures” of hospital referral regions — varied in idiosyncratic ways. This graph
compares rates of three kinds of spine surgery in eight California hospital referral regions to the national average. The rate
of lumbar discectomy was 41% higher than the national average in the Stockton, California hospital referral region, but
the rate of decompression for lumbar stenosis was 7% lower than the average. By contrast, the rate of lumbar discectomy
in Fresno was 39% below the average, but the rate of cervical spine surgery was 9% higher than the national average.
In San Jose, the rate of decompression surgery was 54% below the average, but the rate of lumbar discectomy was only
2% below the average.
45
The Relationship Between Surgery and Diagnostic Imaging
Variation in rates of spine surgery could be a result of physicians in some regions
looking harder for spine conditions than physicians elsewhere. The decision to use
diagnostic imaging for patients with spine pain is controversial, because between
one-third and half of patients without any symptoms of back or neck pain have
intervertebral disk herniations on MRI scans. Since there is no consensus among
physicians about which patients should undergo imaging tests, imaging rates vary
widely among hospital referral regions. In 1996-97, rates of spinal CT/MRI varied
from fewer than 14 per 1,000 Medicare enrollees to more than 75.
Spine Surgeries per 1,000 Medicare Enrollees
Spinal CT/MRI rates were correlated with rates of spine surgery. Variability in the
use of these imaging tests explained about 22% of the variability in regional rates
of spine surgery (Figure 2.10). As with prostate cancer and other diseases which can
be detected even in the absence of symptoms, the more carefully physicians look for
surgically treatable spinal disease, the more they find, and the more likely it is that
these patients will undergo surgery.
Figure 2.10. The Relationship Between Spinal CT/MRI and
Spine Surgery Rates (1996-97)
Rates of spinal CT/MRI were correlated with the likelihood that
Medicare enrollees would undergo spine surgery (R2 = 0.22).
CT/MRI per 1,000 Medicare Enrollees
46
Who Performs Spine Surgery?
Spine surgery is performed by both orthopaedic surgeons and neurosurgeons.
Among surgeons who performed spine surgery in Medicare enrollees in 1996, 3,011
were orthopaedic surgeons and 2,934 were neurosurgeons. Overall, neurosurgeons
performed 64% of all spine surgery among Medicare enrollees, compared to 36%
by orthopaedists. However, the proportion of spine surgery performed by
neurosurgeons varied markedly among hospital referral regions, from 19% in
Akron, Ohio to 99% in Rapid City, South Dakota.
The proportion of spine surgery performed by neurosurgeons was above 95% in
nine regions, including Owensboro, Kentucky (98%); Bangor, Maine (97%); Bismarck, North Dakota (97%); Wilkes-Barre, Pennsylvania (96%) and Lafayette,
Indiana (95%). Neurosurgeons were much less likely to perform spine surgery in
Columbia, Missouri (22%); York, Pennsylvania (26%); Everett, Washington (27%);
Chattanooga, Tennessee (29%) and Boulder, Colorado (30%).
Map 2.8. Proportion of Spine Surgery Performed by Neurosurgeons
(1996)
In fifty-one hospital referral regions at least 80% of spine surgery was
performed by neurosurgeons; in another fifty-one regions less than 50% was
performed by neurosurgeons.
San Francisco
Chicago
New York
Detroit
47
Proportion of Procedures Performed by Orthopaedic
Surgeons and Neurosurgeons
48
Figure 2.11. Proportion of Overall Spine Surgery, Lumbar Discectomy, Lumbar Decompression and
Cervical Spine Surgery Performed by Orthopaedists and Neurosurgeons (1996)
The relative contributions of orthopaedists and neurosurgeons also varied widely
according to the kind of procedure. While neurosurgeons performed 85% of surgical procedures on the cervical spine, they performed only 59% of decompressions
for lumbar stenosis (Figure 2.11).
Proportion of Surgery Using No Fusion, Uninstrumented
Fusion, and Fusion With Hardware
Figure 2.12. Use of Fusion (Uninstrumented and With Hardware) by Orthopaedists and Neurosurgeons in
Spine Surgery, by Indication (1996)
Neurosurgeons and orthopaedic surgeons were quite different in their use of fusion for
some types of spine surgery (Figure 2.12). While both performed uninstrumented
fusions in about a third of cervical procedures, orthopaedic surgeons were much more
likely to perform fusion during lumbar procedures and were much more likely to
perform instrumented fusion during both cervical and lumbar procedures.
49
50
Chapter Two
Table Notes
Most rates of procedures performed to treat conditions of the spine in 1996-97 are
expressed as rates per 1,000 Medicare enrollees and are adjusted for age, sex and
race. Specialty market share (based on 1996 data alone), and proportions of
decompressions for spinal stenosis that included fusion, are expressed as simple
proportions. Rates were determined from Medicare Part B (physician) claims, and
exclude Medicare enrollees who were members of risk-bearing health maintenance
organizations.
See the Appendix on Methods for details on codes used to identify procedures,
adjustment methods, and methods used to calculate proportions.
51
CHAPTER TWO TABLE
Surgery for Conditions of the Spine
by Hospital Referral Regions (1996-97)
Ho
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fe
Re
rra
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io
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Po
re 7)
a
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ed
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ar
ba car
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rg
for clud
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00
er lees
dic
00
u
r y llee
um edi
s
p
e
s
0
e
,1 0
n
ae
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l
,
n I
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r y euro
my nro
r 1 es
ur Enro
0 M 97)
sio that
for 00 7)
e
e
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0
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s
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t
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r N
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1,0 9 6
pr sis 97)
sio 1, 6Su O 6
ine are
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isc ica
er 1 9
e by 199
e db
sio nr
es er 99
om no 6rg En
Sp ic
pr is p s (1
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Fu re E 7)
r D Med 7)
pin ed s (
ec Ste 199
pin r me
al Med 7 )
Su are 7 )
l
R
a
m
c
S
S
D
i
s
(
e
m
n
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e
/M o l l
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r v 0 6-9
ina ic 6-9
of for eo
of rfo 6)
ine ic 6 - 9
of nal n
mb 0 6 - 9
CT E n r
De Ste nro
% Per urg
Lu 1,00 1 9 9
Sp Med 199
% Pe 199
Ce 1 , 0 0 1 9 9
% Spi usio
Sp Med 1 9 9
E
S
(
(
(
(
F
(
Alabama
Birmingham
512,494
4.89
0.89
1.17
2.06
1.39
23.9
37.3
69.7
91,156
4.30
0.91
0.63
2.14
1.78
32.4
28.7
37.1
62.9
Huntsville
110,114
5.27
1.01
1.52
1.86
1.20
14.1
31.8
75.0
25.0
Mobile
Dothan
30.3
156,629
3.27
0.48
0.89
1.38
0.71
12.7
35.3
67.7
32.3
Montgomery
96,196
6.50
1.72
1.78
1.99
2.35
25.3
48.1
73.9
26.1
Tuscaloosa
56,676
5.02
0.94
1.13
1.93
1.66
21.4
49.5
62.5
37.5
55,021
3.22
0.45
0.54
1.60
1.14
30.9
23.1
36.4
63.6
Alaska
Anchorage
Arizona
Mesa
100,972
4.33
0.31
0.56
2.60
1.22
23.7
33.0
63.3
36.7
Phoenix
348,192
3.83
0.51
0.67
1.94
1.41
27.9
32.7
55.9
44.1
Sun City
90,946
4.26
0.34
0.92
2.28
1.10
21.1
34.3
62.9
37.1
Tucson
134,697
5.36
0.75
0.86
2.91
1.72
20.1
35.2
77.6
22.4
10.0
22.6
Arkansas
Fort Smith
86,809
2.97
0.38
0.97
1.23
0.53
Jonesboro
60,650
4.13
0.68
1.22
1.54
0.63
Little Rock
372,224
4.04
0.66
1.05
1.63
1.27
23.3
20.2
29.5
77.4
36.6
95.0
5.0
31.9
71.6
28.5
32.7
81.3
18.7
42.3
71.2
28.8
Springdale
93,352
3.04
0.47
0.90
1.16
0.87
Texarkana
68,023
4.40
0.77
0.72
2.31
0.49
Orange County
266,228
3.28
0.52
0.32
1.82
1.16
28.5
36.8
46.8
53.2
Bakersfield
112,711
3.59
0.68
0.65
1.54
1.11
16.2
24.4
63.0
37.0
California
Chico
69,073
4.69
0.77
0.55
2.28
1.41
20.6
34.1
67.3
32.7
Contra Costa County
100,388
2.48
0.32
0.36
1.18
0.83
24.8
20.7
34.3
65.7
Fresno
144,032
3.13
0.53
0.44
1.53
0.93
23.1
21.8
71.5
28.5
Los Angeles
972,263
3.23
0.47
0.56
1.52
0.76
17.1
35.9
53.5
46.5
Modesto
105,228
3.13
0.40
0.52
1.70
0.68
9.2
21.2
53.5
46.6
64,570
3.66
0.37
0.74
1.55
0.93
13.5
23.3
79.2
20.8
169,920
2.83
0.41
0.45
1.44
0.53
8.2
20.2
76.2
23.8
Palm Spa/Rancho Mir
57,440
5.83
0.64
1.33
2.45
1.82
23.6
75.2
47.7
52.3
Redding
84,868
4.59
0.68
0.67
2.21
1.56
20.7
35.5
63.2
36.8
292,139
3.29
0.40
0.60
1.42
1.01
20.3
24.0
58.0
42.0
25.8
Napa
Alameda County
Sacramento
Salinas
62,442
4.58
0.68
0.91
1.92
1.15
12.1
27.7
74.2
San Bernardino
165,215
4.37
0.87
0.74
1.85
1.92
35.1
32.6
60.8
39.2
San Diego
315,936
4.24
0.57
0.68
2.02
1.77
37.2
29.2
49.3
50.7
San Francisco
203,727
2.88
0.48
0.66
1.18
0.72
13.6
20.0
52.3
47.7
San Jose
163,058
2.24
0.26
0.71
0.75
0.41
13.5
17.7
74.0
26.0
San Luis Obispo
40,051
5.45
(1.15)
0.77
2.60
2.40
32.1
49.9
70.7
29.3
San Mateo County
97,217
2.33
0.53
0.62
0.84
0.59
13.3
18.9
71.0
29.0
Santa Barbara
59,737
7.01
1.04
0.74
3.94
2.28
20.2
41.2
64.0
36.0
52
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Santa Cruz
40,158
4.24
(0.44)
0.81
2.07
0.58
25.6
81.8
18.2
Santa Rosa
65,118
2.92
0.46
0.59
1.13
1.21
24.0
22.0
64.1
35.9
Stockton
68,724
3.81
0.56
1.01
1.51
0.64
10.4
17.1
90.0
10.0
Ventura
81,849
5.46
0.83
1.03
2.21
1.66
15.5
39.3
73.8
26.2
70.5
Colorado
Boulder
26,915
4.87
(0.66)
(0.55)
3.02
(1.62)
19.5
30.4
29.5
Colorado Springs
108,377
3.17
0.27
0.74
1.61
1.28
41.4
28.1
72.7
27.3
Denver
260,204
4.37
0.53
0.66
2.35
1.88
37.5
23.8
46.6
53.4
39.2
Fort Collins
49,429
6.99
(0.76)
1.35
3.75
2.64
32.1
37.9
60.8
Grand Junction
61,036
1.86
0.24
0.37
0.97
0.62
19.4
13.6
58.8
41.2
Greeley
58,804
5.96
0.46
1.40
2.96
1.57
20.6
23.9
54.1
45.9
Pueblo
32,543
4.89
(0.43)
(0.62)
2.54
2.09
42.4
34.4
52.2
47.8
21.3
Connecticut
Bridgeport
157,925
3.46
0.44
0.58
1.87
0.81
15.9
31.2
78.7
Hartford
360,958
2.61
0.34
0.60
1.20
0.62
22.7
22.4
69.5
30.5
New Haven
331,229
2.45
0.35
0.59
1.13
0.72
28.2
28.5
68.4
31.6
140,097
2.81
0.38
0.81
1.18
0.69
15.9
25.9
70.0
30.0
399,140
3.92
0.52
0.67
2.06
1.06
22.8
35.8
71.6
28.4
Bradenton
92,103
5.23
0.82
0.92
2.68
2.16
41.0
35.2
84.0
16.0
Clearwater
159,414
4.58
0.61
0.41
2.93
1.33
21.8
35.5
44.4
55.6
Fort Lauderdale
583,034
3.98
0.59
0.88
1.88
1.55
40.6
48.5
53.2
46.8
Fort Myers
326,788
6.29
0.75
1.60
3.01
1.07
12.0
39.1
89.3
10.7
Gainesville
96,428
3.32
0.60
0.78
1.24
1.05
14.3
33.2
72.9
27.1
Hudson
146,135
4.48
0.72
0.59
2.41
1.27
17.0
45.3
59.2
40.8
Jacksonville
211,676
3.36
0.53
0.66
1.65
0.80
12.6
39.4
83.7
16.4
84,575
3.80
0.36
0.89
2.00
0.70
12.1
25.1
83.9
16.1
393,275
2.28
0.34
0.38
0.99
0.72
27.3
42.8
66.7
33.3
Delaware
Wilmington
Washington
Florida
Lakeland
Miami
Ocala
167,287
4.83
0.99
0.60
2.66
1.60
16.9
37.9
78.8
21.2
Orlando
689,170
3.83
0.50
0.85
1.85
1.71
47.6
34.7
45.9
54.1
Ormond Beach
89,977
3.52
0.56
0.75
1.67
1.25
26.3
32.5
54.8
45.2
Panama City
45,885
3.90
(0.59)
0.60
2.16
1.47
30.7
34.0
50.0
50.0
Pensacola
155,577
4.83
0.93
0.86
2.55
1.45
18.7
36.7
67.2
32.8
Sarasota
185,449
4.92
0.54
0.84
2.82
1.77
34.0
39.1
58.4
41.6
St. Petersburg
117,248
4.03
0.52
0.66
2.24
1.05
16.3
36.5
46.3
53.7
Tallahassee
143,281
3.97
0.58
0.99
1.68
1.40
28.5
32.7
61.7
38.3
Tampa
166,552
3.84
0.58
0.74
1.73
1.41
35.3
38.9
58.1
41.9
36.8
Georgia
Albany
43,737
4.20
(1.18)
0.92
1.38
2.04
30.8
22.4
63.2
Atlanta
709,776
3.16
0.53
0.62
1.34
1.13
26.4
28.1
67.5
32.5
Augusta
124,998
4.63
0.74
1.02
2.13
1.43
19.9
27.3
71.8
28.2
69.6
Columbus
67,186
5.06
0.87
0.77
2.44
2.36
43.5
44.1
30.4
Macon
143,726
4.45
1.14
0.96
1.51
2.25
35.7
35.5
53.6
46.4
Rome
61,084
3.20
0.55
0.81
1.26
0.59
28.2
84.1
15.9
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144,069
6.02
1.12
1.56
2.25
1.78
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40.3
58.8
41.2
172,137
2.11
0.30
0.26
1.13
0.63
19.7
16.2
61.9
38.2
138,789
7.59
1.19
1.35
3.73
2.15
22.6
36.7
54.2
45.8
33,686
5.43
(0.46)
1.18
3.06
2.23
46.8
44.5
74.7
25.3
18.5
95.2
4.8
30.2
36.4
52.7
47.3
Hawaii
Honolulu
Idaho
Boise
Idaho Falls
Illinois
Aurora
33,076
3.26
(0.51)
(0.35)
2.04
0.33
Blue Island
177,705
2.77
0.42
0.54
1.30
0.87
Chicago
418,751
2.14
0.34
0.33
1.01
0.69
28.5
27.8
56.9
43.1
82,955
3.43
0.54
0.71
1.59
0.85
13.2
25.8
72.4
27.6
Elgin
Evanston
211,299
3.61
0.46
0.73
1.83
1.02
28.3
34.7
50.9
49.1
Hinsdale
62,193
3.77
0.46
0.79
1.81
0.89
28.4
37.5
63.5
36.5
Joliet
Melrose Park
98,864
3.55
0.53
0.99
1.49
0.84
15.3
40.4
78.3
21.7
247,020
2.92
0.33
0.71
1.40
0.61
16.4
28.1
71.0
29.0
Peoria
187,488
2.74
0.24
1.01
1.13
0.50
17.5
22.8
64.5
35.5
Rockford
169,843
2.75
0.40
0.38
1.44
0.66
17.1
28.2
77.8
22.2
Springfield
251,275
3.56
0.40
0.84
1.89
0.75
15.5
28.2
71.1
28.9
Urbana
108,324
3.04
0.33
0.88
1.27
0.61
9.3
30.6
35.9
64.2
38,114
4.63
(0.32)
1.18
2.44
1.15
18.9
29.1
79.3
20.7
Evansville
193,076
3.84
0.45
1.07
1.77
0.61
10.0
27.3
79.0
21.1
Fort Wayne
194,325
5.26
0.72
0.92
3.11
1.47
30.7
29.6
54.1
45.9
Gary
112,108
3.85
0.48
0.69
1.66
1.18
30.4
42.2
48.6
51.4
Indianapolis
17.8
27.6
59.2
40.9
20.0
95.5
4.6
23.2
50.0
50.0
Bloomington
Indiana
562,927
3.18
0.43
0.73
1.52
0.78
Lafayette
44,725
3.10
(0.25)
0.54
1.96
0.48
Muncie
44,983
3.52
(0.29)
0.79
1.89
1.15
Munster
42.5
78,111
2.81
0.46
0.67
1.00
0.68
25.9
37.0
66.7
33.3
South Bend
163,476
2.91
0.44
0.68
1.39
0.83
36.5
27.4
51.9
48.1
Terre Haute
52,008
4.13
(0.77)
1.48
1.38
0.53
27.4
93.9
6.1
28.8
Iowa
Cedar Rapids
53
70,095
3.91
0.50
1.24
1.71
0.82
24.4
22.5
71.2
Davenport
137,016
2.54
0.37
0.57
1.25
0.55
17.6
23.6
74.9
25.2
Des Moines
273,014
3.14
0.39
0.76
1.52
0.83
25.4
22.8
59.2
40.9
Dubuque
43,202
2.74
(0.31)
0.62
1.46
0.68
21.5
22.2
31.3
68.8
Iowa City
82,274
2.78
0.48
0.43
1.38
0.81
23.3
27.9
48.6
51.4
Mason City
52,362
4.39
(0.42)
1.25
2.18
0.70
21.6
87.9
12.1
Sioux City
78,286
4.34
0.54
0.69
2.31
2.03
24.0
49.7
50.3
Waterloo
62,652
2.71
0.33
0.71
1.34
0.54
16.0
60.2
39.8
49.7
Kansas
Topeka
108,659
2.50
0.35
0.70
1.09
0.56
21.8
24.3
66.4
33.6
Wichita
345,977
3.83
0.47
0.58
2.10
1.08
21.7
30.6
46.0
54.0
Kentucky
Covington
68,244
3.07
0.38
0.57
1.49
0.52
19.3
80.7
19.3
Lexington
305,383
2.04
0.24
0.52
1.00
0.51
20.1
20.6
80.5
19.5
Louisville
363,643
3.05
0.45
0.64
1.42
0.99
23.8
28.2
67.0
33.0
54
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36,224
5.97
(0.85)
1.10
3.28
0.98
112,083
3.47
0.66
0.99
1.22
1.40
41.7
36.7
97.9
2.1
29.8
61.9
38.1
Louisiana
Alexandria
Baton Rouge
Houma
67,615
4.03
0.76
1.13
1.65
1.11
11.4
36.4
93.7
6.4
110,899
2.11
0.45
0.51
0.82
0.65
29.3
26.7
86.1
13.9
36.5
44,008
3.92
(0.85)
1.37
1.11
1.20
26.9
63.5
119,614
2.55
0.47
0.62
1.10
0.58
21.0
34.1
74.5
25.6
Lake Charles
52,954
4.07
0.50
0.94
2.02
0.84
18.3
30.5
56.3
43.7
Metairie
77,485
2.86
0.49
0.88
0.98
0.83
17.1
27.3
74.1
25.9
Monroe
67,383
3.66
0.72
1.00
1.53
0.93
13.8
41.3
59.3
40.7
New Orleans
139,937
2.24
0.40
0.52
0.85
0.74
28.2
30.2
68.6
31.5
Shreveport
165,575
4.34
0.72
0.89
2.15
0.94
4.4
26.9
67.1
32.9
30,442
4.76
(1.08)
(1.65)
1.45
1.22
42.3
75.0
25.0
Lafayette
Slidell
Maine
Bangor
109,864
2.31
0.35
0.40
1.21
0.38
22.0
97.0
3.0
Portland
256,587
3.39
0.56
0.75
1.57
0.68
14.8
27.4
80.9
19.1
Baltimore
494,344
4.06
0.66
0.91
1.73
1.34
35.0
28.9
65.0
35.0
Salisbury
100,285
2.29
0.33
0.50
1.11
0.85
34.5
22.5
53.3
46.7
Takoma Park
121,878
4.17
0.49
0.61
2.21
1.30
27.6
33.8
63.3
36.7
Maryland
Massachusetts
Boston
958,947
2.53
0.35
0.63
1.14
0.47
12.7
27.7
73.4
26.6
Springfield
177,284
2.61
0.32
0.48
1.48
0.61
25.4
24.8
70.0
30.0
Worcester
119,864
2.70
0.21
0.55
1.49
0.31
7.2
21.3
83.9
16.1
Ann Arbor
261,026
3.26
0.39
0.57
1.77
0.98
26.6
28.1
66.4
33.6
Dearborn
141,149
2.93
0.32
0.50
1.55
0.80
24.6
28.1
70.8
29.2
Detroit
441,063
3.21
0.47
0.69
1.48
1.09
25.1
33.6
72.8
27.2
Flint
117,281
4.87
0.68
0.84
2.70
1.57
26.6
41.2
83.3
16.7
Grand Rapids
225,955
4.56
0.72
0.98
2.43
1.38
23.1
27.6
75.4
24.6
Kalamazoo
153,631
4.28
0.45
0.73
2.55
1.23
26.3
35.6
76.3
23.7
Lansing
125,596
4.14
0.46
1.32
1.72
0.82
13.5
32.1
73.7
26.3
Marquette
64,706
3.29
0.31
0.67
1.78
1.15
25.8
22.1
57.3
42.7
Muskegon
68,935
4.36
0.65
0.96
2.10
1.11
12.8
31.5
76.3
23.7
Petoskey
50,319
4.63
(0.65)
0.75
2.46
0.89
17.7
32.8
89.7
10.3
Pontiac
72,683
4.40
0.57
1.14
2.00
1.20
27.7
32.2
77.9
22.1
Royal Oak
160,221
3.73
0.51
0.51
2.05
1.45
40.1
38.6
47.2
52.8
Saginaw
190,701
4.75
0.75
0.73
2.66
1.54
31.2
33.3
71.7
28.3
Michigan
St. Joseph
39,098
3.32
(0.30)
0.83
1.76
0.82
25.0
27.8
75.4
24.6
Traverse City
64,324
3.60
0.46
0.40
2.34
1.15
31.4
29.7
61.3
38.7
Minnesota
Duluth
106,639
2.47
0.25
0.35
1.53
0.48
14.3
14.4
82.4
17.6
Minneapolis
554,141
3.03
0.31
0.66
1.51
0.90
28.0
23.1
51.4
48.6
Rochester
109,813
2.58
0.30
0.58
1.09
0.63
17.2
20.7
72.1
27.9
St. Cloud
50,683
3.11
(0.28)
0.50
1.96
0.74
20.6
24.7
40.3
59.7
St. Paul
146,928
3.47
0.31
1.15
1.44
0.76
22.8
28.4
56.4
43.6
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Mississippi
Gulfport
37,942
3.62
(0.91)
0.82
1.50
1.32
26.3
32.1
74.0
26.0
Hattiesburg
63,256
2.82
0.48
0.60
1.18
0.76
18.6
36.6
78.9
21.1
Jackson
232,871
3.34
0.58
0.82
1.40
0.75
16.2
34.8
68.2
31.8
Meridian
52,837
3.72
(0.45)
1.34
1.32
0.57
24.5
90.2
9.8
Oxford
33,823
4.62
(0.64)
2.75
0.86
44.7
91.4
8.6
Tupelo
89,351
3.26
0.93
1.28
1.12
30.6
86.6
13.5
0.56
32.4
Missouri
Cape Girardeau
75,339
4.51
0.48
1.25
2.28
1.19
23.8
29.8
63.9
36.1
Columbia
174,601
4.14
0.58
0.73
2.23
2.17
56.2
32.2
22.1
78.0
Joplin
102,279
3.41
0.49
0.64
1.79
0.76
15.3
33.6
79.9
20.1
Kansas City
448,003
3.30
0.41
0.82
1.48
0.79
19.1
33.8
62.3
37.7
Springfield
216,274
3.23
0.44
0.90
1.40
0.85
29.2
25.3
47.4
52.6
St. Louis
739,963
3.57
0.51
0.80
1.52
1.33
40.0
26.9
63.0
37.0
21.2
25.1
37.4
62.6
28.7
82.6
17.4
Montana
Billings
123,836
4.98
0.66
0.48
2.92
1.25
Great Falls
39,696
4.71
(0.78)
0.84
2.20
1.03
Missoula
84,932
4.33
0.68
0.93
1.99
1.29
22.0
25.7
64.1
35.9
Lincoln
153,839
3.81
0.46
0.73
2.04
1.29
30.8
21.0
35.4
64.6
Omaha
291,816
4.14
0.52
0.89
2.04
1.07
19.9
28.3
66.5
33.5
Nebraska
Nevada
Las Vegas
155,466
4.24
0.61
0.77
2.11
1.76
38.4
41.2
44.3
55.7
Reno
111,501
4.03
0.54
0.83
1.85
1.41
25.9
33.2
49.6
50.5
New Hampshire
Lebanon
106,147
2.12
0.16
0.37
1.38
0.47
20.3
19.0
42.3
57.7
Manchester
163,580
2.69
0.43
0.62
1.12
0.55
17.9
22.2
62.2
37.8
39.9
New Jersey
Camden
632,500
2.28
0.37
0.39
1.17
0.62
22.5
30.0
60.1
Hackensack
293,936
2.17
0.23
0.28
1.29
0.52
16.4
33.9
65.3
34.7
Morristown
201,609
2.32
0.26
0.30
1.39
0.60
18.6
28.4
49.3
50.7
New Brunswick
181,188
2.23
0.34
0.29
1.25
0.57
16.2
31.7
51.0
49.0
Newark
321,937
1.51
0.20
0.20
0.77
0.39
18.0
31.9
71.4
28.6
Paterson
77,333
1.87
0.18
0.14
1.15
0.35
34.8
60.6
39.4
Ridgewood
82,131
2.45
0.27
0.32
1.51
0.58
15.1
33.1
78.8
21.2
216,388
3.46
0.47
0.45
1.99
0.87
15.9
26.0
67.9
32.1
New Mexico
Albuquerque
55
New York
Albany
456,704
2.37
0.27
0.56
1.14
0.53
21.5
24.5
69.2
30.8
Binghamton
106,086
3.02
0.31
1.03
1.27
0.95
31.7
26.7
74.2
25.8
Bronx
187,753
1.45
0.28
0.16
0.73
0.43
28.0
28.1
64.0
36.0
Buffalo
374,703
2.50
0.33
0.31
1.37
0.79
26.4
24.9
65.5
34.5
Elmira
106,968
1.91
0.32
0.37
0.81
0.62
34.8
29.7
63.3
36.7
East Long Island
880,064
1.77
0.24
0.24
0.89
0.50
23.7
32.9
62.2
37.8
Manhattan
859,852
1.64
0.23
0.28
0.74
0.50
25.4
35.9
63.9
36.1
Rochester
276,081
2.29
0.23
0.58
1.03
0.47
17.6
16.7
69.5
30.5
56
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Syracuse
262,651
1.92
0.27
0.32
1.01
0.46
19.9
23.9
43.4
56.6
White Plains
226,460
2.59
0.34
0.46
1.31
0.52
11.6
36.4
70.8
29.2
Asheville
184,420
4.00
0.68
1.56
1.21
1.08
33.9
29.5
44.7
55.3
Charlotte
386,048
4.33
0.62
1.25
1.71
1.13
23.8
29.8
51.3
48.8
North Carolina
Durham
288,207
3.27
0.44
0.77
1.56
0.77
17.4
25.7
65.1
34.9
Greensboro
126,165
4.63
0.69
1.17
2.06
1.25
16.7
31.8
58.3
41.7
Greenville
21.4
168,005
3.85
0.71
1.18
1.41
0.79
28.7
24.2
78.6
Hickory
62,917
4.01
0.68
1.11
1.66
0.88
18.5
26.6
55.1
44.9
Raleigh
269,625
4.07
0.67
0.91
1.75
0.80
17.2
30.1
77.8
22.2
Wilmington
Winston-Salem
84,924
3.82
0.66
0.63
1.90
1.23
22.7
32.4
55.4
44.6
242,406
3.71
0.50
0.90
1.60
0.86
15.1
26.8
72.0
28.0
26.4
96.9
3.1
16.9
21.0
74.4
25.7
North Dakota
Bismarck
62,096
4.22
0.50
1.31
1.67
0.61
141,367
3.24
0.25
1.01
1.39
0.68
Grand Forks
47,069
2.97
1.19
1.23
0.34
24.9
77.1
22.9
Minot
38,653
3.96
(0.54)
1.18
1.63
0.74
38.9
90.8
9.2
Fargo Moorhead
Ohio
Akron
166,925
3.72
0.32
0.49
2.48
1.26
30.7
27.2
19.5
80.6
Canton
168,884
3.12
0.49
0.66
1.44
0.76
22.2
23.6
51.3
48.7
28.6
Cincinnati
342,847
3.70
0.53
0.85
1.66
1.01
18.5
23.7
71.4
Cleveland
514,477
3.16
0.45
0.63
1.50
1.02
26.9
29.3
62.0
38.0
Columbus
589,133
2.74
0.35
0.52
1.38
0.98
34.3
19.0
69.0
31.0
Dayton
270,065
3.59
0.57
1.12
1.38
1.03
23.7
28.1
82.7
17.4
Elyria
57,518
3.87
0.44
1.43
1.48
0.77
28.4
29.3
86.8
13.2
Kettering
91,048
3.49
0.41
1.20
1.40
0.91
22.9
26.6
85.3
14.7
Toledo
236,591
3.77
0.68
0.63
2.00
1.02
18.8
28.8
75.0
25.0
Youngstown
216,042
2.51
0.41
0.55
1.21
0.70
24.2
24.9
67.7
32.3
32.1
Oklahoma
Lawton
47,560
3.39
(0.54)
1.01
1.31
0.71
27.9
68.0
Oklahoma City
389,841
4.12
0.56
0.77
2.14
1.02
17.9
28.8
68.1
31.9
Tulsa
271,618
4.44
0.63
0.72
2.28
1.57
29.8
30.6
63.1
36.9
Oregon
Bend
41,180
8.56
(0.91)
1.59
4.43
1.54
7.7
32.9
74.9
25.2
Eugene
153,816
4.58
0.65
1.19
2.11
1.09
16.8
22.0
68.6
31.4
Medford
113,111
5.34
0.77
1.11
2.81
1.31
19.6
20.4
65.6
34.4
Portland
282,862
4.79
0.65
1.24
2.11
0.76
7.8
23.8
83.4
16.6
50,048
3.54
(0.44)
0.67
2.00
1.12
26.2
15.3
83.5
16.5
20.2
Salem
Pennsylvania
Allentown
279,344
2.73
0.35
0.47
1.52
0.45
10.9
32.7
79.8
Altoona
85,570
2.68
0.25
0.49
1.49
0.82
30.1
22.9
51.5
48.5
Danville
122,884
4.03
0.51
1.01
1.92
0.77
20.6
22.7
65.7
34.4
Erie
213,144
3.28
0.50
0.69
1.65
0.86
16.7
26.4
75.4
24.6
Harrisburg
234,150
3.63
0.52
0.56
1.99
1.34
37.0
29.4
45.3
54.7
Johnstown
80,983
3.07
0.34
0.51
1.65
0.63
17.1
27.6
75.2
24.8
Lancaster
130,521
5.12
0.66
0.70
3.10
0.87
14.7
32.1
75.9
24.1
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Philadelphia
761,844
2.69
0.38
0.43
1.39
0.62
15.8
34.9
65.0
35.0
Pittsburgh
856,834
3.61
0.42
0.74
1.82
0.92
21.3
31.6
75.1
24.9
Reading
147,068
2.80
0.36
0.67
1.40
0.71
11.8
27.7
60.3
39.7
53,139
2.72
(0.49)
0.61
1.22
0.46
28.3
93.2
6.9
100,067
2.45
0.30
0.43
1.39
0.44
11.1
27.6
73.6
26.5
Wilkes-Barre
80,181
2.68
0.48
0.30
1.50
0.99
32.3
42.5
95.5
4.5
York
91,191
3.02
0.46
0.42
1.76
0.98
29.5
27.3
25.7
74.3
272,795
2.27
0.31
0.33
1.24
0.53
15.4
22.9
62.7
37.3
Sayre
Scranton
Rhode Island
Providence
South Carolina
Charleston
167,673
3.99
0.80
0.88
1.57
1.49
21.2
34.4
68.5
31.5
Columbia
223,053
2.82
0.54
0.56
1.30
0.94
33.0
27.6
61.4
38.6
33.0
Florence
79,967
3.18
0.54
0.62
1.54
1.21
33.0
34.9
67.0
Greenville
176,797
3.52
0.58
0.82
1.68
0.73
12.5
27.4
61.3
38.7
84,989
2.01
0.20
0.64
0.75
0.45
25.8
22.9
62.0
38.0
Spartanburg
South Dakota
Rapid City
44,470
6.73
(0.45)
2.20
2.74
1.96
22.2
31.0
99.4
0.6
Sioux Falls
229,172
3.88
0.33
1.47
1.37
1.31
29.3
28.2
44.8
55.2
71.3
Tennessee
Chattanooga
150,251
3.27
0.43
0.64
1.56
1.18
27.8
25.7
28.7
Jackson
90,862
2.89
0.36
1.03
1.06
0.61
14.0
24.1
65.7
34.4
Johnson City
61,042
1.71
0.26
0.37
0.76
0.56
31.3
16.3
56.6
43.4
Kingsport
134,376
2.13
0.29
0.60
0.90
0.44
22.8
21.2
54.4
45.6
Knoxville
306,297
2.42
0.39
0.60
1.12
0.71
25.2
23.9
52.0
48.0
Memphis
355,268
2.92
0.40
1.02
1.11
0.57
13.9
33.2
90.3
9.7
Nashville
479,948
4.13
0.63
0.76
2.18
1.23
25.0
35.3
66.6
33.4
Texas
Abilene
86,747
3.50
0.62
0.86
1.45
0.98
23.4
34.8
56.1
43.9
Amarillo
102,436
5.32
1.19
1.23
1.93
1.74
27.1
29.9
65.0
35.0
Austin
155,017
4.12
0.52
1.12
1.81
1.11
19.5
30.1
50.0
50.0
Beaumont
110,830
3.77
0.58
0.67
1.90
1.21
27.6
35.7
73.1
26.9
20.0
Bryan
37,121
3.60
(0.38)
(0.62)
2.07
0.47
23.5
80.0
Corpus Christi
95,484
3.13
0.36
0.62
1.34
0.98
19.7
33.9
58.7
41.4
Dallas
541,927
3.60
0.59
0.81
1.56
1.15
28.6
35.4
57.7
42.3
El Paso
166,767
2.78
0.49
0.64
1.13
0.88
32.2
23.7
68.7
31.3
Fort Worth
228,615
3.87
0.69
0.98
1.54
1.30
25.3
32.9
73.6
26.4
23.1
87.9
12.1
14.4
32.0
63.9
36.1
17.4
Harlingen
84,691
1.61
0.29
0.29
0.69
0.32
Houston
615,858
3.55
0.57
0.76
1.60
0.75
Longview
47,038
5.03
(0.89)
1.85
1.58
1.25
46.3
82.7
Lubbock
152,404
3.15
0.59
0.50
1.63
0.97
24.5
31.0
42.3
57.7
Mcallen
71,206
1.73
0.26
0.19
0.92
0.38
17.1
25.3
70.2
29.8
Odessa
65,407
3.14
0.61
0.64
1.35
0.80
19.8
32.8
84.4
15.6
San Angelo
42,104
5.21
(1.01)
0.55
3.26
1.58
24.3
33.7
44.3
55.7
San Antonio
322,124
3.09
0.49
0.66
1.39
1.23
32.1
32.8
56.9
43.1
61,627
1.61
0.31
0.80
0.57
38.8
16.9
37.2
62.8
137,277
3.39
1.05
1.22
0.70
41.9
80.4
19.6
Temple
Tyler
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0.56
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Victoria
38,792
3.64
(0.56)
0.66
1.90
1.11
18.9
38.7
85.9
Waco
81,736
4.37
0.81
0.98
2.08
1.72
34.5
26.8
93.6
14.1
6.4
Wichita Falls
56,709
3.81
0.62
0.71
1.96
0.93
12.5
33.8
67.7
32.4
Ogden
54,866
4.32
0.55
1.07
1.87
1.94
25.9
20.7
45.1
54.9
Provo
51,575
6.95
(1.33)
1.71
2.77
3.05
35.6
36.2
82.7
17.3
259,354
4.67
0.74
0.99
2.20
1.54
27.1
28.6
61.9
38.1
139,146
2.07
0.22
0.52
1.08
0.55
26.9
20.2
45.9
54.1
Utah
Salt Lake City
Vermont
Burlington
Virginia
Arlington
209,501
3.36
0.40
0.66
1.56
1.13
34.9
30.9
53.8
46.2
Charlottesville
118,182
3.32
0.45
0.67
1.62
0.88
23.4
26.6
50.5
49.5
Lynchburg
62,710
3.48
0.50
0.71
1.71
0.49
12.6
31.1
57.9
42.1
Newport News
99,649
5.87
0.85
1.60
2.95
1.54
26.3
31.9
86.1
13.9
Norfolk
217,774
4.41
0.69
0.84
2.19
1.25
27.3
30.2
75.6
24.4
Richmond
306,150
4.30
0.66
0.75
2.27
1.74
39.5
33.7
44.4
55.6
Roanoke
189,533
2.82
0.30
0.93
1.14
0.43
9.1
21.5
86.1
13.9
79,370
3.56
0.49
0.36
1.99
1.20
25.0
26.7
43.8
56.3
Winchester
Washington
Everett
72,618
4.11
0.49
0.78
2.15
1.11
16.6
29.0
26.7
73.3
Olympia
60,845
4.46
0.43
0.43
2.88
0.96
11.5
24.1
38.5
61.5
Seattle
378,233
4.21
0.49
0.72
2.23
1.08
17.0
25.9
49.7
50.3
Spokane
269,710
4.70
0.55
0.83
2.52
0.89
7.1
28.4
74.3
25.7
Tacoma
102,183
5.25
0.42
0.33
3.32
1.16
10.0
28.7
40.4
59.6
Yakima
55,663
3.45
0.36
0.50
1.89
0.92
21.1
26.0
50.6
49.4
23.4
West Virginia
Charleston
249,276
1.65
0.18
0.49
0.65
0.34
22.9
15.3
76.6
Huntington
98,963
2.79
0.40
0.59
1.37
0.55
12.7
26.3
95.3
4.7
Morgantown
109,926
2.31
0.39
0.45
1.04
0.48
9.3
28.8
65.0
35.0
49.6
Wisconsin
Appleton
76,638
3.38
0.41
0.80
1.69
1.06
24.6
24.0
50.4
Green Bay
130,120
4.96
0.54
1.58
1.98
1.01
20.3
31.8
75.1
24.9
La Crosse
93,929
2.42
0.29
0.69
0.94
0.51
16.0
83.7
16.3
Madison
221,656
2.41
0.23
0.57
1.00
1.02
27.6
19.0
38.7
61.3
Marshfield
107,048
2.90
0.44
0.59
1.39
1.13
33.8
27.1
44.4
55.6
Milwaukee
551,015
3.21
0.43
0.54
1.50
1.21
29.1
29.7
38.4
61.6
Neenah
59,869
3.13
0.54
0.75
1.38
1.24
35.3
22.4
46.8
53.3
Wausau
53,502
3.06
0.50
0.68
1.42
1.32
35.4
27.7
44.9
55.1
44,079
7.45
(1.00)
1.74
3.41
2.05
22.8
33.3
78.3
21.7
55,013,603
3.42
0.49
0.72
1.63
0.98
23.2
29.8
64.1
35.9
Wyoming
Casper
United States
CHAPTER THREE
Degenerative Joint Disease
and Other Conditions
60
Overview
More than 40 million Americans suffer from arthritis, which can cause chronic
joint pain, stiffness and swelling, and can limit daily activity. A small minority of
patients have rheumatoid arthritis — joint inflammation caused by an immunological condition — which can affect any age group. Most patients, however, have
osteoarthritis, which is caused by long-term wear and tear on the joints and is most
prevalent in the elderly. For most patients with mild osteoarthritis, treatment consists of exercise, weight loss, and over-the-counter pain relievers.
Patients who are more limited by their joint disease often undergo surgical treatment, including arthroscopy, in which a pencil-sized telescope is inserted surgically
into the painful joint. In addition to grading the severity of joint degeneration,
arthroscopic procedures can repair or remove damaged cartilage (the lining inside
the joint) or ligament injuries. Arthroscopy is most commonly performed for knee
and shoulder problems. In 1996-97, Medicare patients underwent approximately
165,000 arthroscopies for knee conditions and 27,000 arthroscopies for shoulder
conditions.
Figure 3.1. Joint Replacement Procedures (1996-97)
The numbers in parentheses reflect the number of patients
undergoing these procedures.
DEGENERATIVE JOINT DISEASE AND OTHER CONDITIONS
61
Patients with severe pain or limitations from joint degeneration are treated by joint
replacement, or arthroplasty, which involves removing the diseased joint and replacing it with a low-friction prosthetic joint made of metal or plastic. Among the most
common procedures in the elderly, total joint replacements were performed on
562,600 Medicare patients in 1996-97. Joint replacement is most commonly performed for arthritis of the hip and knee, but it is also used for shoulder conditions
(Figure 3.1). The use of joint arthroplasty increased more than 100% between 1988
and 1997, from 4.9 per 1,000 Medicare enrollees to 9.9 (Figure 3.2).
This chapter examines geographic variation in the use of arthroscopy and total joint
replacement in the Medicare population, and outcomes of joint replacement
surgery. The chapter concludes with a discussion of variation in the use of surgery
for other common conditions, including carpal tunnel surgery, surgery for bunions,
and lower extremity amputations to treat peripheral vascular disease.
Figure 3.2. Growth in Rates of Joint
Replacement (1988-1997)
Joint replacement surgery rates increased by
101% between 1988 and 1997. The use of
shoulder replacement increased from .35 to .79
per 1,000 enrollees (126%); knee replacement
rates increased from 2.7 to 5.9 per 1,000
enrollees (120%).
62
Knee Arthroscopy
Knee arthroscopy is often used for patients with symptoms of knee osteoarthritis refractory to medications and other conservative measures. Under general or regional
anesthesia, a small telescope is inserted into the knee joint, allowing inspection of
all surfaces of the joint. Arthroscopy is used for both diagnosis and treatment; loose
fragments of joint cartilage causing pain from impingement can be removed directly
through the arthroscope. In 1996-97, knee arthroscopy rates varied by a factor of
more than seven, from 0.9 to 7.1.
Among the hospital referral regions where rates of knee arthroscopy were higher
than the United States average of 3.0 per 1,000 Medicare enrollees were Palm
Springs/Rancho Mirage, California (7.1); Boulder, Colorado (5.2); Orange County,
California (5.0); Fort Myers, Florida (4.8); Fort Lauderdale, Florida (4.4) and Lansing, Michigan (4.2).
Knee Arthroscopy per 1,000 Medicare Enrollees
Among the hospital referral regions where rates were lower than the United States
average were Waco, Texas (0.9); Lafayette, Louisiana (1.1); Elmira, New York (1.3);
Scranton, Pennsylvania (1.6); Urbana, Illinois (1.8) and Memphis, Tennessee (2.0).
Figure 3.3. Knee Arthroscopy (1996-1997)
Rates of knee arthroscopy varied by a factor of about seven, from 0.9 per 1,000
Medicare enrollees to 7.1, after adjusting for differences in population age, sex, and race.
Map 3.1. Knee Arthroscopy (1996-1997)
Forty regions had rates at least 30% higher than the national average. Fifty
regions had rates more than 25% below the national average.
San Francisco
Chicago
New York
Detroit
63
64
Shoulder Arthroscopy
Shoulder arthroscopy involves inserting a telescope into the shoulder joint via two
small incisions, usually under general anesthesia. This procedure is often used as a
diagnostic test to identify cartilage tears and other problems not well seen by computed tomography (CT) or magnetic resonance imaging (MRI) scans. As with the
knee, shoulder arthroscopy can be also used to remove loose cartilage and/or tears
causing impingement. In 1996-97, rates of shoulder arthroscopy varied by a factor
of 18, from 0.1 per 1,000 Medicare enrollees to 1.8.
Among the hospital referral regions where rates of shoulder arthroscopy were higher
than the United States average of 0.5 per 1,000 Medicare enrollees were Fort
Collins, Colorado (1.8); Sun City, Arizona (1.7); Palm Springs/Rancho Mirage,
California (1.5); Tallahassee, Florida (1.2); Orlando, Florida (1.1) and Phoenix,
Arizona (0.9).
Shoulder Arthroscopy per 1,000 Medicare Enrollees
average were Morgantown, West Virginia (0.1); Charleston, West Virginia (0.1);
Louisville, Kentucky (0.1); Albany, New York (0.2); Baton Rouge, Louisiana (0.2);
and Pittsburgh (0.3).
Figure 3.4. Shoulder Arthroscopy (1996-1997)
Rates of shoulder arthroscopy varied by a factor of 18, from 0.1 per 1,000 Medicare
enrollees to 1.8, after adjusting for differences in population age, sex, and race. Each
point represents one of the 306 hospital referral regions in the United States.
Map 3.2. Shoulder Arthroscopy (1996-1997)
Seventy-eight hospital referral regions had rates at least 30% higher than the
national average. One hundred eleven regions had rates more than 25%
below the national average.
San Francisco
Chicago
New York
Detroit
65
66
Total Joint Replacement (Hip, Knee, and Shoulder)
Total joint replacement procedures are among the most common operations performed in the Medicare population. There is little disagreement that joint
replacement is the most effective treatment for patients with severe degeneration of
the hip, knee, or shoulder. Replacement eliminates joint-related pain in most
patients and allows them to return to their usual levels of activity. Unfortunately,
joint replacement is a major surgical procedure that carries risks of mortality and
other complications. In addition, many prosthetic joints wear out over time, requiring additional surgical interventions. Patients considering joint replacement must
consider the trade-offs between the risks and benefits of the procedure. In 1996-97,
decision making about the use of total joint replacement varied widely across the
United States. Total joint replacement rates varied by a factor of more than four,
from 4.1 per 1,000 Medicare enrollees to 18.0.
Total Joint Replacement per 1,000
Medicare Enrollees
Among the hospital referral regions where rates of total joint replacement were substantially higher than the United States average of 10.2 per 1,000 Medicare enrollees
were Sioux City, Iowa (18.0); Boise, Idaho (17.7); Provo, Utah (17.5); Sioux Falls,
South Dakota (15.9); Des Moines, Iowa (14.1) and Wichita, Kansas (13.8).
Among the hospital referral regions where rates were
lower than average were Honolulu (4.1); Manhattan
(5.5); Lexington, Kentucky (6.2); Alameda County,
California (6.5); Hackensack, New Jersey (6.8); and
Miami (7.0).
Figure 3.5. Total Joint Replacement (1996-1997)
Rates of total joint replacement varied from 4.1 per 1,000 Medicare enrollees
to 18.0, after adjusting for differences in population age, sex, and race. Each
point represents one of the 306 hospital referral regions in the United States.
Map 3.3. Total Joint Replacement (1996-1997)
Forty-five regions had rates at least 30% higher than the national average.
Twenty-six regions had rates more than 25% below the national average.
San Francisco
Chicago
New York
Detroit
67
68
Total Hip Replacement and Revision
Total hip replacement involves replacing both the ball (femoral head) and socket
(acetabulum) of the hip joint. In 1996-97, rates of total hip replacement varied by a
factor of nearly five, from 1.1 per 1,000 enrollees to 5.4. Because joint prostheses can
become infected or dislocate, and the bones into which they are implanted can loosen
or fracture, many patients undergoing hip replacement ultimately require additional
procedures (revisions). In 1996-97, revisions accounted for approximately 17% of all
total hip replacements. Despite improvements in surgical techniques and the prostheses
themselves, revision rates do not seem to be declining over time (Figure 3.7).
Among the hospital referral regions where rates of total hip replacement were higher
than the United States average of 3.0 per 1,000 Medicare enrollees were Provo, Utah
(5.4); Sioux City, Iowa (5.4); Billings, Montana (5.0); Boise, Idaho (4.9); Salt Lake City
(4.7) and Sioux Falls, South Dakota (4.6).
Total Hip Replacement per 1,000 Medicare Enrollees
average were Honolulu (1.1); Baton Rouge, Louisiana (1.6); Lafayette, Louisiana (1.8);
New Orleans (1.8); the Bronx, New York (1.8) and San Antonio, Texas (1.9).
Figure 3.6. Total Hip Replacement (1996-1997)
Rates of total hip replacement varied by a factor of five, from 1.1
per 1,000 Medicare enrollees to 5.4, after adjusting for
differences in population age, sex, and race. Each point represents
one of the 306 hospital referral regions in the United States.
Figure 3.7. Proportion of Total Hip Procedures That Were Primary
and Revisions Over the Five-Year Period (1993-1997)
The rates of hip replacement increased 21% over this 5-year period. The
proportion of procedures that were revisions remained between 16.8%
and 17.7%.
Map 3.4. Total Hip Replacement (1996-1997)
Forty-six regions had rates at least 30% higher than the national average.
Thirty-six regions had rates more than 25% below the national average.
San Francisco
Chicago
New York
Detroit
69
70
Total Knee Replacement
Total knee replacement involves removing the lower end of the femur and upper
end of the tibia and inserting a prosthetic joint. In 1996-97, rates of total knee replacement varied by a factor of more than four, from 2.2 per 1,000 Medicare
enrollees to 10.8. Many patients undergoing knee replacement later require revision
procedures because of infection, loosening, or other problems. Revision procedures
account for approximately 8% of all of total knee replacements. Revision rates have
been relatively constant over time (Figure 3.9).
Among the hospital referral regions where rates of total knee replacement were
Sioux City, Iowa (10.8); Lubbock, Texas (10.3); Cedar Rapids, Iowa (9.6); Green
Bay, Wisconsin (9.3); Omaha, Nebraska (8.5) and Grand Rapids, Michigan (7.7).
Total Knee Replacement per 1,000
Medicare Enrollees
average were Honolulu (2.2); Newark, New Jersey (2.7); the Bronx, New York (2.7);
San Francisco (3.0); Lexington, Kentucky (3.4) and Miami (3.9).
Figure 3.8. Total Knee Replacement (1996-1997)
Rates of total knee replacement varied by a factor of almost
five, from 2.2 per 1,000 Medicare enrollees to 10.8, after
adjusting for differences in population age, sex, and race. Each
point represents one of the 306 hospital referral regions in the
United States.
Figure 3.9. Proportion of Total Knee Replacements That Were
Revisions Over the Five-Year Period (1993-1997)
Rates of knee replacement increased 36% over this 5-year period. The
proportion of revision to primary replacement remained between 7.5%
and 8.3%.
Map 3.5. Total Knee Replacement (1996-1997)
Forty-seven regions had rates at least 30% higher than the national average.
Thirty-three regions had rates more than 25% below the national average.
San Francisco
Chicago
New York
Detroit
71
72
Shoulder Replacement and Reconstruction
The shoulder joint is shaped like a ball (humeral head) and socket (glenoid). Shoulder replacement usually consists of replacing the humeral head, with or without
“resurfacing” of the glenoid (total shoulder). This procedure is most commonly performed for chronic pain from joint degeneration, although it is sometimes used for
treating fractures extending into the shoulder joint. Shoulder reconstruction, which
consists of several related procedures, is most commonly performed for rotator cuff
injuries. In 1996-97, rates of shoulder replacement and reconstruction (combined)
varied by a factor of more than seven, from 0.5 per 1,000 Medicare enrollees to 3.7.
Among the hospital referral regions where rates of shoulder replacement/reconstruction were higher than the United States average of 1.4 per 1,000 Medicare enrollees
were Bend, Oregon (3.7); Provo, Utah (3.6); Boise, Idaho (3.1); Casper, Wyoming
(2.6); Reno, Nevada (2.2) and Denver, Colorado (2.1).
Shoulder Replacement/Reconstruction per 1,000
Medicare Enrollees
average were Waco, Texas (0.5); Newark, New Jersey (0.6); Lexington, Kentucky (0.6);
Memphis, Tennessee (0.7); Buffalo, New York (0.8) and Albany, New York (0.9).
Figure 3.10. Shoulder Replacement and Reconstruction (1996-1997)
Rates of shoulder replacement/reconstruction varied by a factor of seven, from
0.5 per 1,000 Medicare enrollees to 3.7, after adjusting for differences in
population age, sex, and race. Each point represents one of the 306 hospital
Map 3.6. Shoulder Replacement and Reconstruction (1996-1997)
Sixty-five regions had rates at least 30% higher than the national average.
Sixty-five regions had rates more than 25% below the national average.
San Francisco
Chicago
New York
Detroit
73
74
Decision Making in Joint Replacement
Why do rates of joint replacement vary so widely among geographic regions? Variations in the treatment of some conditions reflect geographic differences in the
incidence of disease, but there is no evidence that there are significant regional differences in the prevalence of osteoarthritis and degenerative joint disease. Variations
in treatment of conditions which are not themselves variable reflect regional differences in how hard surgeons look for surgically treatable disease; for example,
regional rates of carotid endarterectomy are strongly correlated with regional rates
of carotid ultrasound (a test necessary for identifying patients with disease but no
symptoms). However, the diagnosis of degenerative joint disease is made on clinical
grounds and usually does not require special imaging tests. Regional variation in diagnostic intensity is an unlikely explanation for variation in rates of joint replacement.
The principal reason for variation in the use of joint replacement is probably simpler: physicians in different regions have different practice styles. Although all
surgeons consider the same basic trade-offs between risks and benefits in making
recommendations about joint replacement, regional variation in surgery rates suggests that they frequently come to different conclusions. Of two patients with
similar symptoms and disease severity, the patient in Omaha is more likely to get
total knee replacement than the one in Hackensack.
75
Knee Replacement per 1,000 Enrollees
In some regions, there are idiosyncrasies in the use of joint replacement. For example,
in Harlingen, Texas, the rate of total hip replacement is among the lowest in the country, while the rate of total knee replacement is among the highest. More commonly,
however, rates of these two procedures are correlated: regions with high rates of hip
replacement often have high rates of knee replacement (R2 = 0.35) (Figure 3.11). This
suggests that regions have different “signatures,” which reflect their surgeons’ level of
enthusiasm for surgical intervention in patients with degenerative joint disease.
Figure 3.11. The Association Between Rates of Total Hip
Replacement and Total Knee Replacement (1996-97)
Each point represents one of the 306 hospital referral regions.
Rates are adjusted for age, sex, and race (R 2 = 0.35).
Hip Replacement per 1,000 Enrollees
76
Figure 3.12. Hip Replacement by Age, Sex, and Race (1996)
Hip replacement rates by age, sex, and race. Surgery rates were higher in women than
in men, and higher in non-blacks than in blacks.
Figure 3.13. Knee Replacement by Age, Sex, and Race (1996)
Knee replacement rates by age, sex, and race. Surgery rates were higher in women than
in men, and higher in non-blacks than in blacks.
Physicians also disagree about when to intervene surgically. Although there is little
evidence that the prevalence of degenerative joint disease and symptom severity differs by sex and race, non-black women are much more likely to undergo hip or knee
replacement than men or blacks (Figures 3.12, 3.13). Although our data cannot
identify the underlying reasons for these differences, recent studies suggest that differences in surgery rates by sex and race reflect orthopaedic surgeons’ opinions about
or enthusiasm for these procedures.
Regional variation in the use of joint replacement might reflect physician
disagreement about the risks and benefits of the procedure. Non-randomized
trials and several retrospective studies have found that joint replacement surgery
is one of the most valued procedures for patients with severe arthritis. Surgical
mortality rates for total hip and total knee replacement are relatively low (1% and
0.5%, respectively), prolonged hospitalizations, discharge to nursing homes, and
readmissions are common (Table 3.1).
Table 3.1. Outcomes After Total Joint Replacement
Outcomes
THA 1°
TKA 1°
Length of stay (days)
5.33
5.02
Prolonged hospitalization (more than 10 days)
5.3%
3.8%
30-day mortality
0.97%
0.48%
Discharge to skilled nursing facility
33.3%
29.2%
Re-hospitalization w/in 3 months
15.8%
12.5%
Revision w/in 2 years
2.7%
2.1%
Another primary replacement w/in 2 years
8.0%
12.0%
Dislocations at 1 year
2.3%
77
78
Discharge to Nursing Homes After Joint Replacement
Discharge to Nursing Home After Knee
Replacement (%)
Proportion of Hip Replacement Patients Discharged
to Nursing Homes
Following joint replacement, many patients are discharged to nursing homes. In
1996-97, 33% of Medicare enrollees who had had total hip replacements, and 29%
of those who had had total knee replacements, were discharged to nursing homes.
A few patients go to nursing homes because they have surgical complications resulting in disability; most, however, go for short-term rehabilitation. This approach is
substantially more expensive than having patients recover at home and receive
physical therapy on an outpatient basis. In 1996-97, the proportion of total hip
replacement patients discharged directly to nursing homes varied by a factor of
more than 18, from 4.7% to 85.2%. The geographic patterns of discharges to nursing homes after knee replacement were nearly identical. The correlation between
regional nursing home utilization rates with total knee and total hip replacement
rates was very strong (R2 = 0.92) (Figure 3.15).
Figure 3.14. Proportion of Hip Replacement
Patients Discharged to Nursing Homes (19961997)
The proportion of Medicare patients discharged to
nursing home after hip replacement varied by a
factor of eighteen, from 4.7% to 85.2%. Each
point represents one of the 306 hospital referral
regions in the United States.
Discharge to Nursing Home After Hip Replacement (%)
Figure 3.15. Discharge to Nursing Homes
After Total Joint Replacement
Regional practices of discharging patients to
nursing homes with total hip and total knee
replacement were tightly correlated (R 2 = 0.92)
Map 3.7. Proportion of Hip Replacement Patients Discharged to Nursing
Homes (1996-1997)
Fifty-two regions had rates of discharge to nursing homes of 50% or more.
Seventy-six regions had rates of less than 20%.
San Francisco
Chicago
New York
Detroit
79
80
Surgical Treatment of Carpal Tunnel Syndrome
Carpal tunnel syndrome is caused by compression of the median nerve at the wrist.
Symptoms include numbness in the median nerve distribution, or the entire hand,
and in the latter stages, weakness involving the thumb, index and middle fingers.
Patients with mild symptoms of carpal tunnel syndrome are often managed nonoperatively, with injections of steroids along with local anesthetics, and sometimes
wrist-splinting. Patients with symptoms refractory to these measures often undergo
surgical release. Surgery involves relieving the pressure on the median nerve by opening up the narrow canal containing the nerve at the wrist.
Carpal Tunnel Procedures per 1,000
Medicare Enrollees
Of carpal tunnel release procedures performed on Medicare enrollees in 1996,
76.7% were performed by orthopaedic surgeons, 8.4% by neurosurgeons, 8.2% by
plastic surgeons, and 4.3% by general surgeons (Figure 3.17).
Figure 3.16. Carpal Tunnel Surgery (1997)
Surgery rates varied by a factor of 5.5, from 0.9 to
5.2 per 1,000 Medicare enrollees, after adjusting
for differences in population age, sex, and race.
Each point represents one of the 306 hospital
Figure 3.17. Proportion of Carpal Tunnel Procedures
Performed by Orthopaedists, Plastic Surgeons,
Neurosurgeons, and General Surgeons (1996)
Numbers of Medicare enrollees undergoing procedures by
specialist type are in parentheses.
Map 3.8. Carpal Tunnel Surgery (1997)
Sixty-seven regions had rates at least 30% higher than the national average.
Sixty regions had rates more than 25% below the national average.
San Francisco
Chicago
New York
Detroit
81
82
Bunion Surgery
Bunions are bony outgrowths that most commonly develop at the base of the great
toe. The growth causes deformity of the foot and other toes and, in some people,
pain. Symptoms can be treated with special orthopaedic shoes and careful foot
hygiene. Many patients with severe symptoms or deformities opt for surgery, which
involves removing the bony outgrowth and realigning the toes.
Of bunion procedures performed on Medicare enrollees in 1996, 25% were performed by orthopaedic surgeons and 75% were performed by podiatrists.
Among the hospital referral regions where rates of bunion surgery were higher than
the United States average of 1.2 per 1,000 Medicare enrollees were Ocala, Florida
(2.5); Sun City, Arizona (2.5); Chico, California (2.4); Ventura, California (2.3) and
Mesa, Arizona (2.2).
Bunion Surgery per 1,000 Medicare Enrollees
average were Honolulu (0.3); Lexington, Kentucky (0.4); Anchorage, Alaska (0.5);
Huntington, West Virginia (0.6) and Burlington, Vermont (0.6).
Figure 3.18. Bunion Surgery (1996-1997)
Bunion surgery rates varied by a factor of 7.8, from 0.3 to 2.5 per 1,000 enrollees,
after adjusting for differences in population age, sex, and race. Each point represents
Map 3.9. Bunion Surgery (1996-1997)
Forty-one regions had rates at least 30% higher than the national average.
Sixty-three regions had rates more than 25% below the national average.
San Francisco
Chicago
New York
Detroit
83
84
Lower Extremity Amputation
Although sometimes necessary because of trauma or severe infection, most lower
extremity amputation surgery is performed in patients with peripheral vascular
disease — poor circulation in the legs caused by atherosclerosis. Patients with
peripheral vascular disease can have muscle pain with walking or recurrent infections
and foot sores. Early forms of peripheral vascular disease are treated with risk factor
modifications, including smoking cessation, diabetes control, and exercise. More
severe forms often require angioplasty or bypass surgery to improve blood flow to
the legs. When these treatments fail, amputation is the last resort.
Major Amputation per 1,000 Medicare Enrollees
Of major leg amputations performed on Medicare enrollees in 1996, 51% were performed by general surgeons, 19% by vascular surgeons, 18% by orthopaedic
surgeons, and 12% by cardiothoracic surgeons (Figure 3.20).
Figure 3.19. Major Amputation (1996-1997)
Rates of major amputation ranged from 0.4 to 4.4
per 1,000 Medicare enrollees, after adjustments for
differences in the age, sex, and race of local
populations. Each point represents one of the 306
hospital referral regions in the United States.
Figure 3.20. Proportion of Major Amputation
Performed by Orthopaedists, Vascular Surgeons,
General Surgeons, and Cardiothoracic Surgeons
(1996)
Numbers of Medicare enrollees undergoing procedures by
each type of specialist are in parentheses.
Map 3.10. Major Amputation (1996-1997)
Rates of major amputation were higher in the Southern United States than
in the rest of the country. Thirty-nine regions had rates at least 30% higher
than the national average. Sixty-three regions had rates at least 25% lower
than average.
San Francisco
Chicago
New York
Detroit
85
86
Chapter Three
Table Notes
Rates of joint surgery and miscellaneous musculoskeletal procedures are expressed
as rates per 1,000 Medicare enrollees and are adjusted for age, sex, and race, with the
exception of nursing home discharges after hip and knee replacement, which are
expressed as crude proportions. Rates were determined from Medicare Part B
(physician) claims, and exclude Medicare enrollees who were members of riskbearing health maintenance organizations.
adjustment methods, and methods to calculate proportions.
87
CHAPTER THREE TABLE
Surgery for Degenerative Joint Disease by Hospital Referral Regions (1996-97)
Ho
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t/ 00 96-9
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00 996
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ty lees
se 0 997
,
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pla nro
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Nu Kn -97
Nu Hip -97
co nro
pla nro
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n Enr
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ip ed 7)
ne ed 7)
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A
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d
d
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7
l
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M
l
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M
M
p
l
s
a
s
s
r
s
l
l
l
r ce ca
jo 0 6-9
nio ic
ou on ca
Di e op
ee 0 6 - 9
ou 0 6 - 9
Di e op
ta 0 6-9
ta 0 6 - 9
ta 0 6-9
Ca Pro edi
% Hom r thr
Bu Med
Ma 1,00 199
To 1,00 199
Sh Rec edi
% Hom r thr
Kn 1,00 1 9 9
To 1,00 199
Sh 1,00 1 9 9
To 1,00 199
M
A
(
(
M
A
(
(
(
(
Alabama
Birmingham
512,494
3.3
1.1
9.5
2.6
5.6
1.3
30.8
28.1
2.5
1.0
91,156
2.6
0.9
10.3
2.7
6.3
1.4
15.0
11.4
2.1
0.7
1.7
Huntsville
110,114
3.4
0.4
9.3
2.5
5.4
1.4
5.8
7.2
2.3
0.9
2.0
Mobile
Dothan
1.9
156,629
2.8
0.6
10.5
2.6
6.1
1.7
10.6
6.6
2.0
0.9
1.8
Montgomery
96,196
4.0
0.6
8.6
2.4
4.8
1.3
9.4
8.8
2.5
0.7
1.9
Tuscaloosa
56,676
4.4
(1.0)
10.8
2.9
4.7
3.3
7.0
3.1
1.1
2.4
55,021
3.4
0.6
9.8
3.0
4.9
1.8
9.1
2.4
0.5
1.5
Alaska
Anchorage
14.3
Arizona
Mesa
100,972
3.3
1.1
13.7
4.0
7.6
2.1
54.6
46.4
2.0
2.2
0.8
Phoenix
348,192
3.9
0.9
10.5
3.3
5.7
1.6
48.5
43.1
2.0
1.6
1.4
Sun City
90,946
3.1
1.7
12.2
3.8
6.9
1.4
62.1
66.7
1.2
2.5
0.5
Tucson
134,697
2.4
0.2
10.8
3.2
5.6
1.9
51.7
53.7
2.5
1.5
1.3
0.2
Arkansas
Fort Smith
86,809
2.4
Jonesboro
60,650
3.2
7.9
2.1
4.7
1.1
50.7
48.2
1.9
1.2
2.3
9.3
2.4
5.4
1.5
25.0
17.2
1.8
1.2
2.1
Little Rock
372,224
3.3
0.3
10.0
2.7
5.9
1.4
37.7
32.5
Springdale
93,352
3.7
0.5
9.0
2.9
4.8
1.3
34.9
34.5
2.2
1.1
1.9
2.3
1.3
Texarkana
68,023
2.3
8.1
2.0
4.9
1.2
46.5
29.2
1.9
2.1
1.3
2.2
Orange County
266,228
5.0
0.9
9.8
3.3
4.7
1.8
56.2
Bakersfield
112,711
3.3
0.7
9.2
2.7
5.1
1.4
46.0
56.6
1.4
2.0
1.2
50.5
2.1
1.9
69,073
3.7
0.5
11.6
3.6
6.1
1.9
2.4
37.9
29.2
2.7
2.4
Contra Costa County
100,388
2.8
0.6
7.8
2.7
4.1
1.0
1.1
58.8
55.6
1.3
1.2
1.2
Fresno
144,032
4.0
0.9
9.8
2.7
Los Angeles
972,263
4.0
0.6
8.2
2.7
5.5
1.7
42.5
34.5
2.3
1.8
1.8
4.2
1.3
45.3
46.5
1.6
1.9
1.5
Modesto
105,228
3.2
0.4
10.9
64,570
3.6
0.8
10.9
2.9
6.3
1.6
56.7
45.2
2.1
1.2
1.9
3.6
5.4
1.8
51.4
46.0
2.0
1.2
1.4
California
Chico
Napa
Alameda County
169,920
2.4
0.4
6.5
2.2
3.4
0.9
63.8
60.1
1.3
1.3
1.2
Palm Spa/Rancho Mir
57,440
7.1
1.5
14.9
4.9
6.7
3.3
78.4
79.4
2.9
1.5
0.9
Redding
84,868
4.2
0.9
12.4
3.6
6.5
2.3
36.6
33.0
3.0
2.0
1.0
292,139
3.3
0.9
9.1
3.0
4.2
1.9
56.8
46.7
2.3
1.2
1.2
1.8
Sacramento
Salinas
62,442
5.5
0.7
11.1
4.0
5.6
1.4
49.1
40.0
1.7
1.7
San Bernardino
165,215
3.2
0.7
9.6
2.7
5.1
1.9
57.3
51.9
2.0
2.2
2.0
San Diego
315,936
3.8
0.9
9.9
3.0
5.0
1.8
53.0
43.4
1.4
1.5
1.4
San Francisco
203,727
2.5
0.8
6.9
2.6
3.0
1.2
58.4
53.5
1.0
1.1
0.9
San Jose
163,058
3.2
0.5
6.7
2.5
3.1
1.1
67.3
55.4
1.0
1.3
1.1
San Luis Obispo
40,051
4.7
(1.0)
11.3
3.7
5.6
1.9
44.3
34.6
3.3
1.4
1.6
San Mateo County
97,217
3.1
0.6
7.3
2.8
3.2
1.4
68.0
58.4
1.6
1.0
1.1
Santa Barbara
59,737
5.9
1.3
12.7
4.2
5.8
2.7
68.7
64.9
2.5
1.9
1.1
88
Ho
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t/ 00 96-9
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ty lees
se 0 997
,0 199
t
n
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s
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s
s
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pe oll
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Nu Kn -97
Nu Hip -97
co nro
pla nro
las nro
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n Enr
p
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s
o
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6
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th
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pla n llee
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r th re
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th re
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Ar re
ut car
rg nro
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Re ctio ro
Ar ica
rg llow (1
un s nr
t A ica
rg llow (1
e ica
Ar ica
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Su re E
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ha Fo asty
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ip ed 7)
ne ed 7)
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A
a
c
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d
d
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7
l
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M
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M
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a
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s
s
r
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r ce ca
jo 0 6-9
nio ic
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Di e op
ee 0 6 - 9
ou 0 6 - 9
Di e op
ta 0 6-9
ta 0 6 - 9
ta 0 6-9
Ca Pro edi
% Hom r thr
Ma 1,00 199
Bu Med
To 1,00 199
Sh Rec edi
% Hom r thr
Kn 1,00 1 9 9
To 1,00 199
Sh 1,00 1 9 9
To 1,00 199
M
A
(
(
M
A
(
(
(
(
Santa Cruz
40,158
2.8
(1.5)
9.0
3.3
4.6
1.0
55.7
55.6
1.7
1.8
1.3
Santa Rosa
65,118
3.0
0.6
9.3
3.3
4.2
1.7
85.2
77.0
2.2
1.5
1.1
Stockton
68,724
1.8
0.7
7.4
2.6
3.5
1.3
61.5
45.4
2.2
1.0
1.7
Ventura
81,849
5.1
1.1
10.3
3.6
4.6
2.1
42.6
35.2
2.0
2.3
1.7
Colorado
Boulder
26,915
5.2
(0.5)
12.4
4.1
6.5
1.7
22.8
13.5
2.0
0.9
0.9
Colorado Springs
108,377
3.3
0.7
12.3
3.8
6.8
1.8
47.3
38.0
2.0
1.2
0.9
Denver
1.0
260,204
3.5
0.7
13.2
4.3
6.7
2.1
17.3
14.1
2.4
1.2
Fort Collins
49,429
4.8
1.8
15.1
4.7
7.2
3.1
15.2
10.1
2.3
1.6
0.7
Grand Junction
61,036
2.5
0.6
9.4
2.6
5.1
1.7
36.6
24.0
1.5
1.0
0.4
Greeley
58,804
3.5
1.0
16.1
4.3
8.4
3.3
16.1
18.3
2.8
1.7
1.1
Pueblo
32,543
2.3
(0.5)
12.7
3.7
7.2
1.8
48.1
47.5
1.7
1.3
1.7
Connecticut
Bridgeport
157,925
3.1
0.5
8.8
3.0
4.5
1.2
41.0
40.5
1.7
0.9
1.2
Hartford
360,958
2.8
0.4
9.3
3.2
4.8
1.4
65.4
59.2
2.8
1.2
1.5
New Haven
331,229
3.2
0.3
9.4
3.1
4.6
1.7
71.0
72.5
2.2
1.3
1.4
140,097
3.5
0.8
9.9
3.0
5.3
1.6
20.6
11.6
3.6
1.6
1.5
399,140
3.5
0.5
10.2
3.2
5.5
1.5
48.2
42.7
2.1
1.3
1.6
Bradenton
92,103
3.1
0.7
11.5
3.6
6.4
1.5
24.8
20.2
2.6
2.0
1.4
Clearwater
159,414
4.9
1.0
12.4
3.6
6.0
2.7
59.0
45.2
2.7
2.1
1.4
Fort Lauderdale
583,034
4.4
0.9
10.2
3.7
4.7
1.7
28.3
26.6
2.3
1.5
1.1
Fort Myers
326,788
4.8
1.2
14.7
4.1
8.0
2.5
14.0
15.1
2.9
2.5
0.9
Gainesville
96,428
3.0
0.5
9.4
2.4
5.4
1.6
40.7
42.1
2.8
1.5
1.7
Hudson
146,135
4.5
1.4
12.0
3.1
6.5
2.3
49.4
44.8
3.4
2.1
1.5
Jacksonville
211,676
3.6
0.8
9.4
2.7
5.2
1.6
41.1
35.3
2.0
1.5
2.1
84,575
1.7
0.4
12.4
3.3
7.6
1.6
40.2
41.0
2.8
1.6
1.8
Miami
393,275
3.0
0.5
7.0
2.2
3.9
0.9
14.3
15.1
1.5
1.7
1.7
Ocala
167,287
3.0
0.8
11.0
3.3
6.4
1.2
53.4
44.8
2.9
2.5
1.6
Orlando
689,170
3.6
1.1
11.3
3.3
6.2
1.8
35.3
32.2
3.3
1.5
1.5
Ormond Beach
89,977
4.7
1.5
10.9
3.8
5.5
1.7
67.2
61.1
2.3
1.4
1.3
Panama City
45,885
3.3
(1.7)
12.0
2.7
6.6
2.6
18.4
22.1
1.8
1.4
2.0
Delaware
Wilmington
Washington
Florida
Lakeland
Pensacola
155,577
3.6
0.8
11.5
2.6
7.0
1.9
28.8
23.2
2.2
1.3
1.9
Sarasota
185,449
4.3
0.9
12.0
3.9
6.5
1.6
36.5
36.6
2.6
2.0
1.0
St. Petersburg
117,248
3.6
1.1
10.1
3.3
5.1
1.7
44.2
32.3
2.6
1.5
1.6
Tallahassee
143,281
3.7
1.2
10.0
2.6
6.1
1.3
23.7
20.4
2.3
1.4
1.8
Tampa
166,552
3.5
0.5
9.7
2.8
5.5
1.4
53.9
49.2
2.9
1.2
1.5
1.6
Georgia
Albany
43,737
2.5
(1.0)
11.8
3.2
6.7
1.9
1.8
1.1
Atlanta
709,776
3.0
0.6
9.1
2.8
4.9
1.4
24.3
18.9
1.6
1.1
1.6
Augusta
124,998
3.1
0.4
9.0
2.2
5.4
1.4
6.6
8.8
2.5
1.1
1.9
7.8
2.0
0.7
2.3
2.4
1.3
2.3
1.3
0.9
2.0
Columbus
67,186
3.1
0.7
9.9
2.3
5.5
2.1
14.4
Macon
143,726
2.2
0.3
10.0
2.5
6.3
1.2
6.1
Rome
61,084
2.5
0.3
8.3
2.2
5.0
1.1
5.6
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144,069
4.5
1.1
11.3
3.0
6.4
2.0
19.4
13.5
2.8
1.6
1.9
172,137
2.9
0.6
4.1
1.1
2.2
0.8
19.1
16.8
1.3
0.3
1.3
138,789
4.3
1.2
17.7
4.9
9.7
3.1
40.2
36.4
3.6
1.8
0.8
33,686
4.8
(0.3)
16.1
5.2
8.4
2.4
22.5
13.1
3.8
1.7
0.5
Hawaii
Honolulu
Idaho
Boise
Idaho Falls
Illinois
Aurora
33,076
3.0
Blue Island
177,705
3.0
Chicago
418,751
2.3
82,955
2.4
Elgin
10.1
3.4
5.6
1.1
35.3
41.5
2.4
1.1
1.3
9.7
3.1
5.6
1.0
39.4
30.6
2.2
1.3
1.9
0.2
7.8
2.6
4.2
0.9
30.0
26.3
1.6
1.7
1.4
0.5
11.7
3.5
6.7
1.4
46.0
45.8
2.7
1.3
1.4
0.2
Evanston
211,299
3.5
0.3
10.7
3.9
5.7
1.1
63.9
67.5
2.1
1.2
1.2
Hinsdale
62,193
3.1
0.5
10.1
3.6
5.3
1.1
27.1
29.0
2.1
1.3
1.5
Joliet
98,864
3.2
0.3
12.0
3.3
7.4
1.3
23.1
14.5
2.9
1.2
1.7
Melrose Park
247,020
2.9
0.3
10.4
3.3
5.9
1.2
36.8
38.5
1.9
1.4
1.6
Peoria
187,488
2.5
0.2
11.8
3.7
6.8
1.3
64.8
65.9
3.3
1.1
1.4
Rockford
169,843
2.3
0.2
12.7
4.0
7.5
1.2
52.9
54.1
3.1
1.3
1.3
Springfield
251,275
2.4
0.3
12.1
3.3
7.6
1.2
48.5
43.4
3.8
1.4
1.6
Urbana
108,324
1.8
0.1
9.7
3.0
5.8
0.9
42.3
40.9
3.0
0.9
1.5
38,114
2.0
11.2
3.7
6.5
0.9
46.3
45.2
2.6
1.2
1.2
Bloomington
Indiana
Evansville
193,076
2.4
0.3
10.0
2.6
5.9
1.5
30.4
27.7
2.5
1.0
1.7
Fort Wayne
194,325
2.4
0.3
13.9
4.1
8.3
1.6
50.1
47.1
3.2
1.4
1.5
Gary
112,108
3.1
0.3
11.0
3.5
6.3
1.2
11.5
11.7
2.8
1.3
1.6
Indianapolis
562,927
2.7
0.5
10.8
3.3
5.8
1.7
33.5
28.4
2.8
1.4
1.5
Lafayette
44,725
2.6
(0.3)
8.8
2.5
4.4
1.9
34.5
27.9
2.7
1.0
1.3
Muncie
44,983
1.6
10.9
2.9
6.5
1.5
23.1
15.8
1.8
1.0
1.7
Munster
78,111
3.1
10.0
3.1
6.0
0.9
22.8
18.6
2.4
1.3
2.0
South Bend
163,476
2.2
0.4
13.3
3.6
7.8
1.9
14.1
10.3
3.6
1.2
1.5
Terre Haute
52,008
2.4
(0.3)
9.4
2.9
5.0
1.5
30.0
24.4
2.9
0.9
1.9
Iowa
Cedar Rapids
70,095
2.3
0.3
15.7
4.2
9.6
1.9
82.5
85.5
3.7
1.1
1.3
Davenport
137,016
2.1
0.2
12.7
3.0
7.9
1.8
61.7
63.1
2.7
0.8
1.3
Des Moines
273,014
2.2
0.3
14.1
3.9
8.5
1.7
44.9
42.3
3.5
1.3
1.0
Dubuque
43,202
3.3
(0.5)
10.1
2.6
5.9
1.6
81.7
74.6
2.5
0.6
1.1
Iowa City
82,274
2.1
0.2
13.6
4.0
8.1
1.6
48.8
50.1
3.0
1.2
1.1
Mason City
52,362
1.7
(0.3)
15.0
4.7
9.1
1.2
48.3
50.7
2.6
1.0
1.1
0.4
18.0
5.4
10.8
1.9
62.0
65.3
3.9
1.3
0.8
13.7
4.6
8.2
0.9
44.4
38.1
3.4
1.1
1.1
Sioux City
78,286
3.4
Waterloo
62,652
3.5
Kansas
Topeka
108,659
2.6
0.2
14.6
4.0
8.8
1.8
15.2
11.7
2.8
1.0
1.0
Wichita
345,977
2.6
0.7
13.8
3.8
8.4
1.6
46.3
43.5
3.3
1.5
1.2
Kentucky
Covington
68,244
2.0
0.2
9.4
2.8
5.5
1.1
55.4
32.9
3.1
1.2
2.1
Lexington
305,383
1.3
0.1
6.2
2.1
3.4
0.6
29.6
26.0
1.3
0.4
1.6
Louisville
363,643
2.4
0.1
9.7
2.9
5.5
1.3
44.3
42.6
1.6
0.8
1.8
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2.0
112,083
1.8
0.2
9.0
2.7
4.9
1.4
20.3
8.2
1.4
0.9
1.6
9.4
2.6
6.0
0.9
43.5
44.6
2.2
0.6
1.3
Louisiana
Alexandria
67,615
1.6
0.2
8.4
1.8
5.5
1.0
18.4
9.3
2.8
0.4
2.6
110,899
2.0
0.2
8.4
1.6
5.6
1.1
23.0
14.1
2.4
0.8
2.2
44,008
2.9
(0.2)
9.8
1.5
7.0
1.3
31.4
22.7
2.5
0.5
2.4
119,614
1.1
0.3
8.7
1.8
5.9
1.0
48.2
32.9
2.1
1.1
2.6
Lake Charles
52,954
2.6
(0.6)
10.7
2.3
7.5
0.9
11.6
2.8
1.7
2.2
Metairie
77,485
2.7
0.6
8.9
1.8
6.0
1.2
58.0
46.2
3.4
1.1
2.5
Monroe
67,383
3.0
0.5
10.3
2.5
6.1
1.8
46.0
46.3
2.8
0.7
2.0
New Orleans
139,937
2.4
0.5
8.0
1.8
5.1
1.1
51.2
46.9
2.6
1.1
2.2
Shreveport
165,575
2.1
0.5
8.9
2.4
5.3
1.1
29.4
34.8
2.7
1.0
2.1
30,442
3.7
(1.0)
9.9
2.0
6.2
1.6
48.6
27.3
2.7
0.7
2.1
Baton Rouge
Houma
Lafayette
Slidell
Maine
Bangor
109,864
3.2
0.2
11.4
3.5
6.2
1.7
47.3
39.5
2.8
0.7
1.3
Portland
256,587
2.6
0.3
10.4
3.2
5.7
1.5
32.6
31.4
2.9
0.9
1.5
Maryland
Baltimore
494,344
3.9
0.7
11.5
3.0
6.8
1.6
33.5
27.2
2.7
1.4
1.8
Salisbury
100,285
2.6
0.5
11.0
3.0
6.2
1.8
24.5
18.0
2.7
0.8
1.7
Takoma Park
121,878
3.7
0.8
9.7
3.1
5.1
1.5
65.0
51.0
2.1
1.6
1.2
Boston
958,947
3.3
0.3
8.9
3.1
4.7
1.1
51.1
52.5
2.4
0.8
1.5
Springfield
177,284
2.7
0.3
7.8
2.7
4.2
0.9
50.1
45.2
2.3
0.6
1.6
Worcester
119,864
2.6
0.4
8.3
3.0
4.3
1.0
32.8
24.8
2.0
0.9
2.1
Ann Arbor
261,026
2.6
0.3
12.2
3.6
7.1
1.4
18.2
10.5
2.3
1.4
1.4
Dearborn
141,149
3.2
0.2
10.0
3.1
5.9
0.9
7.3
6.7
2.2
1.3
1.5
Detroit
441,063
2.5
0.2
9.6
2.9
5.7
1.0
7.8
5.3
2.0
1.4
1.2
Flint
117,281
2.5
0.2
12.3
4.0
7.3
1.0
4.7
2.3
2.2
1.3
1.5
Grand Rapids
225,955
3.8
0.6
13.3
3.8
7.7
1.9
13.8
8.9
3.9
1.6
1.5
Kalamazoo
153,631
2.9
0.4
12.2
3.9
6.7
1.5
15.5
8.3
3.9
1.1
1.3
Lansing
125,596
4.2
1.1
13.9
4.2
7.9
1.8
9.3
8.0
3.9
1.3
1.8
Marquette
64,706
2.6
0.6
11.5
2.8
6.7
2.1
9.6
6.6
4.1
1.4
1.9
Muskegon
68,935
4.1
0.6
15.1
4.4
9.0
1.8
12.5
5.2
3.7
2.1
1.8
Petoskey
50,319
3.3
0.5
14.0
3.9
8.1
2.0
27.3
26.4
4.6
1.0
1.1
Pontiac
72,683
3.1
0.4
11.2
3.4
6.4
1.3
16.6
10.2
2.1
1.4
1.4
Royal Oak
160,221
2.8
0.4
10.0
3.2
5.8
1.0
37.5
23.3
2.2
1.4
1.1
Saginaw
190,701
3.2
0.4
14.8
4.4
8.5
1.9
8.2
5.2
4.4
1.2
1.6
12.2
4.1
6.8
1.3
3.4
1.6
1.5
0.7
12.3
3.6
6.5
2.1
15.2
8.8
5.2
1.1
1.0
Massachusetts
Michigan
St. Joseph
39,098
4.0
Traverse City
64,324
3.4
Minnesota
Duluth
106,639
2.3
0.2
10.6
3.4
5.7
1.4
37.7
22.1
3.5
0.7
1.8
Minneapolis
554,141
2.8
0.3
12.0
3.7
6.6
1.6
36.0
30.4
3.0
0.9
1.1
Rochester
109,813
2.2
0.2
13.6
4.6
7.3
1.7
21.5
18.8
3.3
1.4
1.2
St. Cloud
50,683
3.0
(0.2)
14.9
4.9
8.7
1.4
22.1
18.8
4.0
1.3
1.4
St. Paul
146,928
3.6
0.7
12.9
3.7
7.0
2.2
44.8
43.4
2.5
1.0
1.2
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Mississippi
Gulfport
37,942
1.5
(0.3)
10.1
2.6
6.2
1.2
39.7
37.9
2.0
1.1
Hattiesburg
63,256
3.2
0.5
11.6
2.5
7.2
1.9
49.5
57.7
2.3
0.6
2.2
Jackson
232,871
3.1
0.9
9.5
2.2
5.5
1.8
47.1
46.5
1.8
0.7
1.6
Meridian
52,837
4.4
(0.3)
10.2
2.4
6.1
1.7
48.7
Oxford
33,823
1.7
8.2
2.5
4.9
0.8
Tupelo
89,351
1.2
8.8
2.3
5.4
1.0
0.2
29.9
2.1
56.3
3.0
0.5
2.2
21.8
3.1
0.5
1.5
27.5
1.8
0.6
2.3
Missouri
Cape Girardeau
75,339
2.2
0.4
10.3
2.6
6.4
1.3
28.9
20.9
1.7
0.9
1.8
Columbia
174,601
3.7
0.6
13.0
3.6
7.4
2.0
30.3
31.1
2.9
1.0
1.3
Joplin
102,279
2.8
0.5
12.9
3.0
8.1
1.9
42.7
40.8
4.3
1.8
1.4
Kansas City
448,003
3.0
0.4
12.4
3.2
7.4
1.8
34.4
31.2
3.0
1.5
1.3
Springfield
216,274
2.6
0.6
10.8
2.7
6.4
1.7
37.1
37.2
2.6
1.1
1.1
St. Louis
739,963
3.5
0.5
11.3
2.8
7.1
1.4
40.0
37.9
2.8
1.4
1.6
1.2
Montana
Billings
123,836
3.4
0.4
14.5
5.0
7.2
2.2
34.2
38.0
3.6
1.6
Great Falls
39,696
3.5
(0.3)
15.7
5.3
7.7
2.7
46.9
34.3
2.8
1.4
1.0
Missoula
84,932
3.1
0.7
13.1
4.6
6.6
1.9
38.4
31.6
3.4
1.2
1.0
Lincoln
153,839
2.8
0.5
15.9
4.0
9.9
2.0
30.3
28.5
3.9
1.5
1.0
Omaha
291,816
2.5
0.7
14.1
3.9
8.5
1.7
48.1
39.2
3.8
1.4
1.3
Nebraska
Nevada
Las Vegas
155,466
3.6
1.1
8.6
3.0
4.2
1.3
32.9
29.5
1.7
1.1
1.2
Reno
111,501
4.1
0.9
10.5
3.4
4.8
2.2
28.5
19.3
2.9
1.6
1.1
New Hampshire
Lebanon
106,147
2.7
0.2
10.2
3.6
5.3
1.3
9.3
6.0
2.6
0.7
1.5
Manchester
163,580
3.6
0.6
10.0
3.4
5.5
1.1
20.3
15.4
2.8
1.0
1.2
1.7
New Jersey
Camden
632,500
2.2
0.5
8.4
2.5
4.7
1.1
22.3
19.5
2.3
1.1
Hackensack
293,936
3.4
0.4
6.8
2.3
3.4
1.1
27.4
24.8
1.5
1.4
1.4
Morristown
201,609
3.2
0.4
7.7
2.9
3.7
1.0
7.2
5.1
2.1
1.1
1.3
New Brunswick
181,188
2.5
0.4
6.9
2.6
3.5
0.8
11.8
12.0
1.7
0.9
1.4
Newark
321,937
2.7
0.3
5.4
2.1
2.7
0.6
21.9
20.8
1.6
1.1
1.4
Paterson
77,333
2.7
6.7
2.1
3.8
0.8
23.2
14.9
1.3
1.0
1.4
Ridgewood
82,131
3.7
0.4
7.5
2.9
3.3
1.3
13.6
7.0
2.1
1.2
1.4
216,388
2.9
0.6
9.1
2.8
4.9
1.4
45.3
36.2
1.7
1.0
1.6
New Mexico
Albuquerque
New York
Albany
456,704
2.5
0.2
8.5
3.2
4.4
0.9
13.3
9.8
2.9
1.1
1.2
Binghamton
106,086
2.9
0.4
9.2
3.0
5.3
1.0
10.6
5.9
3.0
1.6
1.0
Bronx
187,753
1.9
0.2
5.3
1.8
2.7
0.7
22.6
15.8
0.9
1.5
2.0
Buffalo
374,703
2.2
0.2
9.2
3.1
5.2
0.8
21.5
18.9
1.9
1.1
1.6
Elmira
106,968
1.3
0.2
8.9
2.6
5.6
0.7
7.8
4.9
3.1
1.0
1.8
East Long Island
880,064
2.5
0.3
6.5
2.5
3.1
0.8
14.4
12.5
1.4
0.9
1.5
Manhattan
859,852
2.3
0.3
5.5
2.0
2.8
0.7
10.7
7.4
1.2
1.1
1.6
Rochester
276,081
2.0
0.2
8.1
2.8
4.3
1.0
21.9
16.8
2.4
1.0
1.3
92
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Syracuse
262,651
2.6
0.3
10.7
3.5
6.1
1.1
28.3
24.0
3.3
1.0
1.4
White Plains
226,460
3.2
0.5
7.8
3.1
3.4
1.3
12.3
10.1
1.6
1.1
1.3
Asheville
184,420
3.2
0.5
9.8
3.2
4.8
1.8
43.5
37.5
1.5
0.8
1.4
Charlotte
386,048
2.1
0.3
9.7
2.6
5.3
1.8
34.5
32.7
1.9
0.9
2.1
Durham
288,207
2.5
0.4
10.0
3.0
5.5
1.5
21.6
21.3
2.0
1.0
1.8
Greensboro
126,165
4.1
0.9
10.2
2.7
4.9
2.6
17.5
14.9
1.9
1.3
1.9
Greenville
North Carolina
168,005
2.5
0.3
10.0
2.8
5.7
1.5
10.8
7.5
2.3
1.0
1.5
Hickory
62,917
1.5
0.5
8.8
2.9
4.4
1.5
29.8
37.6
2.3
0.9
2.1
Raleigh
269,625
2.3
0.4
9.6
2.8
5.5
1.3
20.1
17.3
1.9
0.9
2.1
84,924
3.3
0.5
8.7
2.6
5.1
1.0
12.5
16.9
2.2
1.1
1.9
242,406
2.9
0.7
8.5
2.6
4.6
1.3
33.5
29.3
2.0
0.9
2.1
Wilmington
Winston-Salem
North Dakota
Bismarck
Fargo Moorhead
62,096
4.1
0.5
15.9
4.4
9.2
2.4
45.0
33.5
4.4
1.7
1.5
141,367
2.8
0.6
13.9
4.5
7.9
1.6
32.7
31.8
2.9
1.0
1.5
12.2
3.9
6.5
1.8
12.4
10.7
3.5
1.1
1.2
12.6
4.1
7.3
1.2
11.1
9.0
3.7
1.0
1.1
1.4
Grand Forks
47,069
2.4
Minot
38,653
2.7
(0.4)
Ohio
Akron
166,925
3.8
0.5
12.5
3.4
7.2
1.9
13.5
9.9
3.6
1.0
Canton
168,884
2.7
0.3
11.3
3.2
6.6
1.4
19.6
13.7
3.2
0.9
1.5
Cincinnati
342,847
3.5
0.4
10.7
3.0
5.7
1.9
39.9
35.2
3.3
1.1
1.9
Cleveland
514,477
2.9
0.4
10.5
3.3
6.0
1.2
49.7
50.8
3.3
1.2
1.6
Columbus
589,133
2.4
0.3
10.1
3.1
5.6
1.3
37.4
28.5
3.2
1.0
1.7
Dayton
270,065
2.8
0.4
11.4
3.5
6.7
1.2
39.8
40.3
3.2
0.9
1.7
Elyria
57,518
3.4
0.4
12.0
3.1
7.1
1.8
50.8
53.0
3.1
1.4
1.8
Kettering
91,048
2.7
0.5
10.1
3.1
5.7
1.3
21.9
21.2
3.0
0.8
1.6
Toledo
236,591
2.9
0.2
13.0
3.8
7.4
1.8
52.3
43.6
3.0
1.6
1.6
Youngstown
216,042
3.1
0.4
11.1
3.1
6.6
1.4
25.8
23.8
2.8
0.9
1.7
Oklahoma
Lawton
47,560
3.3
(0.2)
10.6
2.5
6.9
1.2
23.2
23.5
1.6
1.2
1.7
Oklahoma City
389,841
3.0
0.5
10.6
2.6
6.4
1.6
18.9
17.3
2.3
1.8
1.6
Tulsa
271,618
3.0
0.4
10.5
2.7
6.3
1.4
35.8
31.4
1.9
1.2
1.7
Oregon
Bend
41,180
5.2
(0.8)
15.7
5.0
6.9
3.7
28.9
15.6
4.1
1.0
0.9
Eugene
153,816
3.2
0.2
8.8
2.9
4.5
1.4
31.4
21.4
2.1
0.7
0.7
Medford
113,111
3.0
0.5
10.3
3.2
5.7
1.4
42.4
26.5
3.2
1.4
1.0
Portland
282,862
3.1
0.5
9.7
3.3
4.8
1.6
42.8
27.9
3.2
1.2
1.0
50,048
2.2
7.8
2.7
4.1
1.0
39.1
27.6
3.0
0.9
0.4
1.9
Salem
Pennsylvania
Allentown
279,344
3.2
0.4
10.0
3.0
5.9
1.1
26.8
27.8
3.6
1.4
Altoona
85,570
1.6
0.1
9.6
2.7
5.9
1.1
15.8
15.3
3.4
1.0
1.7
Danville
122,884
2.3
0.3
11.1
3.0
6.8
1.3
16.7
12.5
3.8
0.9
2.3
Erie
213,144
3.0
0.3
10.7
3.2
6.1
1.4
21.7
16.1
3.3
1.0
1.9
Harrisburg
234,150
3.3
0.2
11.0
2.8
6.4
1.8
13.1
6.8
3.6
1.1
1.8
Johnstown
80,983
2.7
0.4
8.5
2.4
5.1
1.0
30.7
21.0
3.5
0.9
2.0
Lancaster
130,521
3.1
0.3
9.6
3.1
5.0
1.4
18.2
13.3
4.0
1.2
2.0
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Philadelphia
761,844
3.1
0.6
9.8
2.9
5.6
1.3
22.9
21.6
2.3
1.1
1.7
Pittsburgh
856,834
2.4
0.3
10.3
2.9
6.4
1.0
30.9
26.6
3.3
1.1
1.7
Reading
147,068
3.3
0.4
10.6
2.6
6.8
1.2
25.6
19.2
3.6
1.6
2.1
53,139
2.3
1.1
11.0
3.3
7.1
0.6
11.7
7.0
2.7
0.9
2.0
13.1
11.3
2.0
0.9
1.9
4.6
2.3
1.4
1.9
Sayre
Scranton
100,067
1.6
0.2
8.7
2.4
5.5
0.8
Wilkes-Barre
80,181
2.0
0.3
8.9
2.4
5.8
0.8
York
91,191
1.8
0.1
9.3
2.9
4.8
1.5
18.9
11.1
3.3
1.3
1.6
272,795
2.5
0.4
9.1
2.8
5.2
1.1
26.0
22.6
3.0
1.4
1.7
Rhode Island
Providence
South Carolina
Charleston
167,673
3.3
0.6
10.5
2.9
6.0
1.6
33.2
28.2
2.9
1.3
2.1
Columbia
223,053
2.4
0.6
9.2
2.4
5.2
1.6
33.5
30.6
2.5
0.7
2.1
Florence
79,967
4.4
0.4
8.6
2.1
5.5
0.9
13.1
11.3
1.6
1.0
2.4
Greenville
176,797
3.8
0.4
9.4
2.7
5.4
1.3
31.5
29.6
1.8
0.8
1.9
84,989
3.5
0.6
9.4
2.7
5.2
1.5
9.4
9.4
2.4
0.5
1.9
Rapid City
44,470
2.6
(0.5)
14.1
4.1
8.6
1.3
5.3
4.6
1.8
1.4
Sioux Falls
229,172
3.2
0.2
15.9
4.6
9.3
1.9
15.9
13.7
2.9
1.7
1.2
Spartanburg
South Dakota
Tennessee
Chattanooga
150,251
3.2
0.4
9.5
2.7
5.2
1.6
24.5
19.7
2.2
0.7
2.0
Jackson
90,862
1.7
0.5
7.6
2.3
4.5
0.8
25.7
20.6
1.8
0.6
1.7
Johnson City
61,042
2.5
0.3
6.7
2.3
3.5
1.0
26.4
16.9
1.1
0.5
2.3
134,376
2.1
0.1
6.2
2.3
2.8
1.1
43.0
43.4
1.9
0.4
2.1
Kingsport
Knoxville
306,297
2.3
0.5
7.5
2.2
4.2
1.1
22.9
16.4
1.5
1.0
1.9
Memphis
355,268
2.0
0.3
7.2
2.5
4.0
0.7
14.8
17.6
1.4
1.0
1.7
Nashville
479,948
2.9
0.6
8.9
2.6
5.2
1.1
33.2
28.4
1.8
0.8
1.7
1.8
Texas
Abilene
86,747
2.2
0.4
9.5
2.6
6.3
0.7
54.9
49.2
2.5
1.1
Amarillo
102,436
1.9
0.2
15.2
3.7
9.6
1.9
72.2
72.9
2.3
1.3
1.1
Austin
155,017
3.1
0.8
11.0
2.3
7.1
1.5
31.5
25.7
1.7
1.4
1.8
Beaumont
2.1
110,830
3.8
1.1
9.9
2.1
6.6
1.2
17.3
6.0
2.6
1.8
Bryan
37,121
2.3
(0.8)
9.3
1.9
6.0
1.4
43.8
49.1
3.1
1.1
2.0
Corpus Christi
95,484
3.6
0.6
11.1
2.2
7.4
1.5
12.7
15.6
2.5
1.5
4.4
Dallas
541,927
3.2
0.3
9.2
2.5
5.4
1.4
37.9
33.4
1.4
1.2
1.7
El Paso
166,767
3.1
0.4
8.5
2.1
5.6
0.9
17.2
11.6
1.4
1.5
1.6
Fort Worth
228,615
3.3
0.3
9.6
2.4
5.9
1.3
34.1
25.1
2.1
1.2
2.1
Harlingen
84,691
1.3
0.6
10.9
1.9
8.4
0.6
11.8
9.0
1.7
1.2
3.4
Houston
615,858
3.4
0.7
9.4
2.3
5.6
1.4
16.8
14.4
2.2
1.5
2.0
Longview
47,038
2.4
(0.4)
10.7
2.7
6.7
1.3
19.2
14.0
2.1
1.2
2.4
Lubbock
152,404
2.2
0.2
15.6
3.6
10.3
1.8
9.1
6.4
2.3
1.4
2.0
Mcallen
71,206
2.8
0.3
10.1
1.7
7.4
1.0
30.1
20.6
1.5
1.2
3.7
1.9
Odessa
65,407
2.4
0.3
10.4
2.5
7.1
0.8
14.1
4.1
1.8
1.0
San Angelo
42,104
3.2
(1.0)
13.4
2.3
7.6
3.4
23.2
13.7
2.5
1.0
2.2
San Antonio
322,124
2.2
0.4
9.6
1.9
6.6
1.2
33.8
32.9
2.0
1.1
3.1
61,627
1.2
0.3
6.7
1.7
4.2
0.9
52.0
49.4
1.1
0.8
1.8
137,277
3.8
0.5
11.2
2.7
6.2
2.3
48.9
52.4
2.5
1.0
2.1
Temple
Tyler
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(
(
(
(
Victoria
38,792
4.3
11.1
2.0
7.8
1.4
42.6
31.1
3.6
1.2
Waco
81,736
0.9
8.4
2.0
5.8
0.5
38.2
24.6
1.7
0.6
2.5
1.8
Wichita Falls
56,709
2.9
11.9
2.6
8.0
1.3
57.1
50.2
2.2
1.1
1.7
Ogden
54,866
4.2
0.7
14.9
4.2
7.8
2.8
85.1
80.8
3.6
1.9
0.8
Provo
51,575
7.0
1.4
17.5
5.4
8.5
3.6
67.6
62.6
5.1
1.8
0.7
259,354
3.8
0.8
15.4
4.7
7.7
3.0
65.4
61.7
3.9
1.7
0.7
139,146
2.8
0.2
10.1
3.8
5.3
1.0
13.5
8.0
3.8
0.6
1.6
1.2
Utah
Salt Lake City
Vermont
Burlington
Virginia
Arlington
209,501
3.6
0.7
9.7
3.4
4.9
1.3
27.4
24.1
2.1
1.1
Charlottesville
118,182
4.3
0.3
11.1
3.0
6.1
2.0
41.7
25.0
3.1
1.0
1.5
62,710
2.6
0.5
9.6
2.4
5.2
2.0
55.8
48.6
2.4
0.7
1.9
1.7
Lynchburg
Newport News
99,649
4.3
1.1
10.7
2.9
5.7
2.0
5.6
3.5
2.2
1.4
Norfolk
217,774
2.3
0.5
10.3
2.6
5.8
1.9
27.0
24.6
2.5
1.3
1.5
Richmond
306,150
3.2
0.8
11.4
3.3
5.8
2.3
8.2
5.4
2.6
1.5
1.6
Roanoke
189,533
2.3
0.3
7.6
2.4
4.4
0.8
31.1
23.0
2.8
0.7
1.4
79,370
2.5
0.5
10.1
2.8
6.1
1.2
22.9
14.6
2.9
0.8
2.1
Winchester
Washington
Everett
72,618
3.6
0.3
11.3
4.0
5.2
2.0
30.8
20.3
2.3
1.0
1.6
Olympia
60,845
3.0
0.5
13.5
4.4
6.7
2.4
54.4
36.0
3.6
1.1
1.7
Seattle
378,233
3.5
0.3
11.5
4.1
5.1
2.3
41.4
31.6
2.4
1.1
1.2
Spokane
269,710
4.0
0.6
12.7
4.0
6.3
2.4
28.8
22.5
4.0
1.8
1.2
Tacoma
102,183
4.1
0.3
12.9
4.1
6.3
2.5
36.6
30.1
3.6
1.3
0.9
Yakima
55,663
3.8
1.1
11.8
3.6
5.6
2.6
28.7
27.0
3.3
0.9
1.6
West Virginia
Charleston
249,276
2.0
0.1
6.6
1.9
3.9
0.8
27.0
31.8
2.2
0.6
1.5
Huntington
98,963
1.7
0.2
7.1
2.3
4.2
0.7
19.4
18.0
2.2
0.6
1.7
Morgantown
109,926
1.9
0.1
9.8
2.6
6.1
1.0
51.8
46.4
3.3
0.6
1.8
76,638
4.3
0.9
15.2
4.2
9.2
1.9
25.8
13.7
4.7
1.2
1.9
130,120
3.0
0.2
15.8
4.4
9.3
2.1
16.3
8.7
4.2
1.5
1.7
Wisconsin
Appleton
Green Bay
La Crosse
93,929
1.6
0.2
11.5
3.6
7.2
0.8
19.4
13.4
3.4
0.9
1.2
Madison
221,656
3.0
0.3
11.5
3.6
6.5
1.4
27.6
20.7
2.8
1.1
1.5
Marshfield
107,048
3.0
0.4
13.6
4.2
8.0
1.4
23.8
15.5
4.2
1.0
1.8
Milwaukee
551,015
2.6
0.4
12.3
3.8
7.3
1.2
21.9
22.5
3.1
1.3
1.8
Neenah
59,869
3.7
1.4
15.8
3.7
10.1
2.1
16.6
13.7
4.2
1.3
2.0
Wausau
53,502
2.9
0.5
14.6
5.0
8.1
1.4
29.1
16.8
4.3
0.9
1.2
44,079
3.5
(1.8)
13.2
3.9
6.6
2.6
22.5
19.2
3.1
1.3
1.3
55,013,603
3.0
0.5
10.2
3.0
5.7
1.4
33.3
29.2
2.5
1.2
1.6
Wyoming
Casper
United States
CHAPTER FOUR
Fractures
96
Overview
Fractures — broken bones — are usually the result of traumas. Patients of all ages
have fractures from serious injuries, such as life-threatening motor vehicle accidents.
Fractures can also be caused by simple falls, particularly in patients at high risk for
fracture because of osteoporosis. Osteoporosis, a condition characterized by decreased bone density, is most prevalent in the elderly and occurs more commonly
in women than in men; among those aged 65 to 99, women are four times more
likely to have bone fractures than men.
In the Medicare population, fractures are the most common musculoskeletal condition requiring hospitalization. More than 450,000 Medicare patients sustained
fractures in 1996. Among fracture types, hip fractures (femoral neck and intertrochanteric) were the most common (45%), followed by fractures of the wrist (20%); ankle
(10%); proximal humerus (9%); forearm (5%); femur (4%); distal humerus/shaft
(4%); and proximal tibia/shaft (4%) (Figure 4.1).
There are important geographic differences in the
incidence of some fractures. For example, rates of
distal humerus/humeral shaft fractures in Medicare
patients varied by a factor of almost five among the
306 hospital referral regions (Table 4.1). By contrast, there was little geographic difference in rates
of hip fracture (Figure 4.2).
Figure 4.1. Distribution of Fracture Types (1996)
The relative frequency of eight different types of fractures in Medicare
patients. The number of Medicare enrollees with each fracture is in
parentheses.
Ratio of Fracture Rates to the United States Average
FRACTURES
97
Figure 4.2. Profiles of Variation
in the Incidence of Eight
Fractures (1996)
The incidence of hip fracture was
the least variable; proximal
humerus fracture was the most
variable. Each point represents
fracture incidence in one of the
306 hospital referral regions,
relative to the United States
average.
Type of Fracture
Humeral
Shaft/Distal
Humerus
Forearm
Tibia
Ankle
Proximal
Humerus
28.2
29.7
32.1
40.5
2.1
2.2
2.4
3.0
4.6
6.0
5.6
5.7
13.2
1.4
1.5
1.5
1.7
2.0
61
45
63
44
84
110
29
19
25
28
52
57
Hip
Femur
Wrist
13.6
21.7
25.1
26.4
1.0
1.6
1.8
1.9
2.2
3.5
3.8
1.2
1.3
1.5
8
27
6
17
Index of Variation
Coefficient of Variation (CV)
Ratio to CV of treatment of hip fracture
Range of Variation
Table 4.1. Quantitative Measures of Variability in the Incidence of Eight Fractures (1996)
The coefficient of variation of hip fracture, the least variable kind of fracture, was 13.6; the coefficient of variation of proximal humerus fracture was
40.5, or almost three times higher. The extremal ratio — the ratio of the highest rate to the lowest — was 2.2 for hip fracture, 6.0 for forearm fracture,
and 13.2 for proximal humerus fracture. Another measure of variation is the number of hospital referral regions with rates more than 25% below the
national average or at least 30% above the national average. Again, the number of regions with rates of hip fractures at either end of the distribution
was much smaller than the number of regions with rates of forearm, tibia, ankle and proximal humerus fracture substantially different from the
national average.
98
Surgical Treatment of Fractures
Fractures can be treated with or without surgery. For most types of fractures, surgical treatment involves stabilizing or re-approximating the broken bone or bones
with rods, metal plates or screws. In some instances (e.g., hip fracture), surgical
treatment can involve joint replacement. With non-surgical therapy, patients can be
treated with a sling (e.g., humerus fracture), a splint or cast (e.g., wrist or ankle fracture), or, in some instances, bed rest and traction (e.g., femur fracture).
A patient’s chance of receiving surgery is determined primarily by which bone is
fractured (Table 4.2). Among Medicare patients who had hip fractures in 1996-97,
for example, the overwhelming majority (98%) underwent surgical treatment. In
contrast, among patients with proximal humerus fracture, almost all (86%) received
non-surgical treatment. For fractures often treated either way (e.g., ankle) (37%
operative treatment), fracture severity and other clinical variables influence the likelihood of undergoing surgery. For example, complex or displaced (widely separated)
fractures are more likely to be treated surgically, while simple, non-displaced fractures are usually treated without surgery. “Open” fractures (in which the skin is
broken) at any location are almost always treated surgically.
However, the likelihood of receiving surgery for specific types of fractures also depends on geography. Statistical measures of variation in how various fractures are
treated — with or without surgery — are given in Table 4.2.
Proportion of Fractures Treated Surgically
FRACTURES
99
Figure 4.3. Profiles of Variation
in the Use of Surgical Treatment
for Eight Fractures (1996-97)
The use of surgery for hip fracture
was least variable; nearly all hip
fractures were treated surgically.
The range of variation in surgical
treatment of other kinds of
fractures was far greater; surgery
for proximal humerus fracture was
the most variable. Each point
represents the proportion of
fracture patients undergoing
surgery in each of the 306 hospital
referral regions.
% of Fractures Treated Surgically
Hip
Femur
Humeral
Shaft/Distal
Humerus
Ankle
Forearm
(1996)
Tibia
Wrist
(1996)
Proximal
Humerus
Index of Variation
1.2
12.8
28.9
30.6
33.2
35.8
45.0
50.9
Ratio to CV of surgical treatment of hip fracture
1.0
11.1
24.9
26.3
28.6
30.8
38.8
43.8
1.1
2.1
5.3
3.7
10.0
6.7
12.9
21.5
1.0
1.2
1.5
1.6
1.5
1.7
1.8
1.9
Range of Variation
0
2
33
34
53
52
79
63
0
5
64
90
70
96
92
105
Table 4.2. Use of Surgical Treatment for Eight Fractures (1996-97)
There is little variation in how hip fractures are treated; virtually all fractures are treated surgically (coefficient of variation = 1.2). By contrast, there
is substantial variation in the likelihood that proximal humerus fractures will be treated surgically (coefficient of variation = 50.9). The extremal ratio
— the proportion of fractures treated surgically in the region with the highest proportion, compared to the region with the lowest proportion — varies
from 1.1 for hip fracture to 21.5 for proximal humerus fractures.
100
Hip Fractures
Hip fractures (femoral neck and intertrochanteric fractures), which most often result
from simple falls, are among the most common and most devastating fractures in the
elderly. The incidence of hip fracture increases with age and is highest in white
women, probably because of the higher prevalence of osteoporosis in this group. Most
patients with hip fractures undergo surgery, either total joint replacement or internal
fixation with screws and plates. In 1996-97, approximately 420,000 Medicare patients
suffered hip fractures, of whom 98% were treated surgically. Mortality rates were very
high in these patients: 7% at 30 days and 25% at one year. Mortality rates were considerably higher in the smaller number treated non-operatively (17% at 30 days, 39%
at one year). In 1996-97, hip fracture rates varied by a factor of two, from less than
5.0 per 1,000 Medicare enrollees to more than 10.5.
Hip Fractures per 1,000 Medicare Enrollees
Among the hospital referral regions where hip fracture rates were substantially higher
than the United States average of 7.7 per 1,000 Medicare enrollees were Rome, Georgia (10.7); Lubbock, Texas (10.0); Nashville, Tennessee (9.5); Winston-Salem, North
Carolina (9.5); Chattanooga, Tennessee (9.2) and Cincinnati (9.1).
Among hospital referral regions where hip fracture rates
per 1,000 Medicare enrollees were lower than average
were Honolulu (4.9); San Francisco (5.6); Eugene,
Oregon (5.9); San Jose, California (6.0); Newark, New
Jersey (6.0) and Manhattan (6.3).
Figure 4.4. Hip Fractures (1996-97)
Rates of hip fracture (femoral neck and intertrochanteric fracture) varied by
a factor of two, from 4.9 per 1,000 Medicare enrollees to 10.7, after
adjusting for differences in population age, sex and race. Each point represents
FRACTURES
Map 4.1. Hip Fractures (1996-97)
Four hospital referral regions had rates of hip fracture at least 30% higher
than the national average. Six regions had rates more than 25% below the
national average.
San Francisco
Chicago
New York
Detroit
101
102
Femur Fractures
Femur fractures (including the subtrochanteric region and shaft of the femur) can
be the result of major traumas, such as motor vehicle accidents, or simple falls in
elderly patients predisposed to fracture because of osteoporosis. Patients with prosthetic hip joints or metastatic disease (cancer spread to bone) are also at greatly
increased risk of femur fracture. Some patients receive non-surgical treatment —
bed rest with skeletal traction. Most, however, undergo surgical repair, which usually involves insertion of a metal rod in the femoral shaft with or without fixation
with screws or, occasionally, plates and screws. In 1996-97, 40,000 Medicare patients
sustained femur fractures, 73% of whom underwent surgery. In 1996-97, rates of femur fracture varied by a factor of four, from 0.3 per 1,000 Medicare enrollees to 1.2.
Femur Fractures per 1,000 Medicare Enrollees
Rates of femur fracture were substantially higher than the United States average of
0.7 per 1,000 Medicare enrollees among residents of the hospital referral regions in
Tuscaloosa, Alabama (1.2); Lubbock, Texas (1.1); Cincinnati (1.0); Toledo, Ohio
(1.0); Atlanta (1.0); Kansas City, Missouri (0.9) and Philadelphia (0.9).
Among the hospital referral regions where rates were
lower than average were Salem, Oregon (0.3); San Francisco (0.4); Lebanon, New Hampshire (0.4); Boise, Idaho
(0.5); Sacramento, California (0.5) and New Brunswick,
New Jersey (0.5).
Figure 4.5. Femur Fractures (1996-97)
Rates of femur fracture (including the subtrochanteric region and shaft of the
femur) varied by a factor of four, from 0.3 per 1,000 Medicare enrollees to
1.2, after adjusting for differences in population age, sex and race. Each point
FRACTURES
Map 4.2. Femur Fractures (1996-97)
Thirteen regions had rates at least 30% higher than the national average.
Thirty-one regions had rates more than 25% below the national average.
San Francisco
Chicago
New York
Detroit
103
104
Lower Leg Fractures
Fractures of the lower leg (proximal tibia and tibia shaft) can occur by many different
mechanisms: overuse (i.e., “stress fractures”); seemingly minor twisting injuries; motor
vehicle accidents and other major traumas (which often cause complex fractures); or
fracture through diseased bone (for example, osteoporosis or malignancy). The diagnosis is generally made by clinical findings and plain X-rays. Lower leg fractures are
often associated with protracted recovery periods and patients often require assistance
with mobility and other daily activities. Although long-term sequelae are uncommon,
patients with fractures extending into the knee joint risk premature arthritis and loss
of motion. In 1996-97, rates of lower leg fracture varied by a factor of more than five,
Lower Leg Fractures per 1,000 Medicare Enrollees
Among the hospital referral regions where rates of lower leg fracture were higher than
the United States average of 0.6 per 1,000 Medicare enrollees were Spartanburg, South
Carolina (1.1); Cincinnati (1.1); Toledo, Ohio (1.0); Philadelphia (1.0); Pittsburgh
(0.9) and Birmingham, Alabama (0.9).
Among the hospital referral regions where rates of proximal tibia and tibia shaft fracture were lower than average
were Honolulu (0.2); Sun City, Arizona (0.2); San Francisco (0.2); Eugene, Oregon (0.3); Seattle (0.3) and
Orange County, California (0.3).
Figure 4.6. Lower Leg Fractures (1996-97)
Rates varied by a factor of more than five, from 0.2 per 1,000 Medicare
enrollees to 1.1, after adjusting for differences in population age, sex and
race. Each point represents one of the 306 hospital referral regions in the
United States.
FRACTURES
Map 4.3. Lower Leg Fractures (1996-97)
Thirty-four regions had rates at least 30% higher than the national average.
Sixty-one regions had rates more than 25% below the national average.
San Francisco
Chicago
New York
Detroit
105
106
Surgical Treatment of Lower Leg Fractures
Most patients with lower leg (proximal tibia and tibia shaft) fractures are treated
non-operatively, with cast immobilization. Surgery for lower leg fracture, which
involves stabilizing the tibia with plates and screws, is recommended for patients
with open fractures (through the skin) and frequently for patients with displaced or
comminuted (multiple fragments) fractures. Surgery is also more likely to be performed in patients with major trauma and severe injuries to other bones or organs.
In 1996-97, 30% of Medicare patients with lower leg fractures underwent surgical
repair. However, the use of surgery for lower leg fractures varied widely across
hospital referral regions. In 1996-97, the proportion of Medicare patients treated
with surgery ranged from 13.7% to 69.4%.
Proportion of Tibia Fractures Treated Surgically
Among the hospital referral regions where the proportion of surgical repair was
higher than the United States average of 30.3% of all tibia fractures were Duluth,
Minnesota (69.4%); San Francisco (68.6%); Seattle (58.3%); Billings, Montana
(56.7%); Phoenix, Arizona (53.3%); Los Angeles (45.3%) and Houston (41.7%).
Among the hospital referral regions where the proportion of surgical repair of tibia fracture was lower than
average were Buffalo, New York (13.7%); Rochester,
New York (14.9%); Syracuse, New York (16.2%);
Washington, D.C. (17.4%); Allentown, Pennsylvania
(19.5%) and Fort Lauderdale, Florida (21.7%).
Figure 4.7. Proportion of Lower Leg Fractures Treated with Surgery
(1996-97)
The proportion of patients with lower leg fractures undergoing surgery varied
from 13.7% to 69.4%. Each point represents one of the 306 hospital referral
FRACTURES
Map 4.4. Lower Leg Fractures Treated with Surgery (1996-97)
In 31 regions, the proportion of fractures treated surgically was at least
50%. In 22 regions, the proportion was less than 20%.
San Francisco
Chicago
New York
Detroit
107
108
Ankle Fractures
Ankle fractures are among the most common fractures in the elderly. Ankle fractures
generally occur with seemingly minor injuries, such as twisting or stepping off a
curb awkwardly. As with other fractures strongly linked to osteoporosis, ankle fractures are more common in women than in men and more common in whites than
in non-whites. In 1996-97, rates of ankle fracture varied by a factor of almost six,
Among the hospital referral regions where rates of ankle fracture were higher than
the United States average of 1.7 per 1,000 Medicare enrollees were Danville, Pennsylvania (3.1); Altoona, Pennsylvania (2.9); Reading, Pennsylvania (2.8); Allentown,
Pennsylvania (2.7); Birmingham, Alabama (2.5); Ann Arbor, Michigan (2.5) and
Cincinnati (2.4).
Ankle Fractures per 1,000 Medicare Enrollees
Among the hospital referral regions where rates were lower than average were
Honolulu (0.5); San Jose, California (0.7); New Orleans (0.8); Los Angeles (0.8);
Sacramento, California (0.8) and Phoenix, Arizona (0.9).
Figure 4.8. Ankle Fractures (1996-97)
Rates of ankle fracture varied by a factor of six, from 0.5 per 1,000 Medicare
enrollees to 3.1, after adjusting for differences in population age, sex and race.
FRACTURES
Map 4.5. Ankle Fractures (1996-97)
Forty-six regions had rates at least 30% higher than the national average.
Seventy-nine regions had rates more than 25% below the national average.
San Francisco
Chicago
New York
Detroit
109
110
Surgical Treatment of Ankle Fractures
Patients with relatively minor, displaced fractures of the ankle are usually treated
with casts or splint immobilization, and most surgeons believe that severe, comminuted fractures are best treated with surgical repair. There is considerable
controversy about optimal treatment for patients with fractures between these two
extremes. Many surgeons argue that surgery promotes early restoration of the joint
surface and early motion, which they believe contributes to more favorable function
long-term. Others argue that surgery is unnecessary and that most patients do well
without it. This disagreement is apparent in the wide regional variation in the use of
surgery for ankle fractures. In 1996-97, the proportion of surgical repair of ankle fracture varied by a factor of more than three, from 20.8% of all ankle fractures to 77.1%.
Proportion of Ankle Fractures Treated with Surgery
Among the hospital referral regions where the proportion of surgical repair of ankle
fracture was higher than the United States average of 37.2% were Chico, California
(77.1%); Tacoma, Washington (71.4%); Spokane, Washington (70.9%); Eugene,
Oregon (69.7%); Little Rock, Arkansas (68.1%) and Phoenix, Arizona (67.9%).
Among the hospital referral regions where the proportion of ankle fractures treated
surgically was lower than average were Altoona, Pennsylvania (20.8%); Buffalo, New
York (21.9%); Charleston, South Carolina (23.1%); White Plains, New York
(23.4%); Philadelphia (24.6%); Detroit (24.7%) and
Albany, New York (25.6%).
Figure 4.9. Proportion of Ankle Fractures Treated with Surgery
(1996-97)
Proportions of surgical repair of ankle fracture varied from 20.8% to
77.1%. Each point represents one of the 306 hospital referral regions in the
United States.
FRACTURES
Map 4.6. Proportion of Ankle Fractures Treated with Surgery (1996-97)
In 32 regions, at least 60% of ankle fractures were treated surgically. In 50
regions the proportion was less than 30%.
San Francisco
Chicago
New York
Detroit
111
112
Proximal Humerus Fractures
Because they occur predominantly in patients with osteoporosis, proximal humerus fractures happen almost exclusively to the elderly. These fractures of the
upper arm just below the shoulder joint are usually the result of falls. Patients recovering from proximal humerus fractures commonly experience shoulder
stiffness with loss of motion or painful motion, often long-term. In 1996-97,
rates of proximal humerus fracture varied by a factor of eleven, from 0.3 per 1,000
Medicare enrollees to 3.4.
Among the hospital referral regions where rates of proximal humerus fracture were
Covington, Kentucky (3.4); Allentown, Pennsylvania (2.8); Philadelphia (2.8);
Harrisburg, Pennsylvania (2.5); Columbia, South Carolina (2.5); Cincinnati (2.4)
and Baltimore (2.3).
Proximal Humerus Fractures per 1,000 Medicare
Enrollees
Among the hospital referral regions where rates of proximal humerus fracture were
lower than average were Jackson, Tennessee (0.3); Honolulu (0.4); Tacoma, Washington (0.5); San Jose, California (0.6); Spokane, Washington (0.6); San Francisco
(0.7) and San Bernardino, California (0.8).
Figure 4.10. Proximal Humerus Fractures (1996-97)
Rates of proximal humerus fracture varied by a factor of eleven, from 0.3 per
1,000 Medicare enrollees to 3.4, after adjusting for differences in population
age, sex and race. Each point represents one of the 306 hospital referral regions
in the United States.
FRACTURES
Map 4.7. Proximal Humerus Fractures (1996-97)
Fifty-six regions had rates at least 30% higher than the national average.
One hundred eleven regions had rates more than 25% below the national
average.
San Francisco
Chicago
New York
Detroit
113
114
Surgical Treatment of Proximal Humerus Fractures
The large majority of patients with proximal humerus fractures are treated without
surgery, generally with a sling that immobilizes the arm and shoulder. Although
there is disagreement about indications for surgical repair, surgery is most often
performed for patients with displaced (separated) fractures, in the hope that better
anatomic alignment will improve long-term shoulder function. In 1996-97, the
proportion of surgical repair of proximal humerus fracture varied by a factor of almost ten, from 6.4% of all proximal humerus fractures to 60.0%.
Among the hospital referral regions where the proportion of surgical repair of proximal humerus fracture was higher than the United States average of 14.3% were
Tacoma, Washington (60.0%); Little Rock, Arkansas (34.8%); Spokane, Washington
(33.3%); Phoenix, Arizona (29.8%); San Diego (27.5%) and Minneapolis (22.9%).
Proportion of Proximal Humerus Fractures Treated
with Surgery
Among hospital referral regions where the proportion of proximal humerus fractures
treated surgically was lower than average were Takoma Park, Maryland (6.4%); Detroit (8.5%); Buffalo, New York (8.8%); East Long Island, New York (8.9%);
Cleveland (9.3%) and Milwaukee (9.8%).
Figure 4.11. Proportion of Proximal Humerus Fractures Treated with
Surgery (1996-97)
Proportions of surgical repair of proximal humerus fracture varied by a factor of
nearly ten, from 6.4% to 60.0%. Each point represents one of the 306 hospital
FRACTURES
Map 4.8. Proximal Humerus Fractures Treated with Surgery (1996-97)
In eight regions, the proportion of fractures treated surgically was 40% or
more. In 35 regions, the proportion treated surgically was less than 10%.
San Francisco
Chicago
New York
Detroit
115
116
Humeral Shaft and Distal Humerus Fractures
Fractures of the humeral shaft or distal humerus (upper arm) usually result from
falls, although they can also occur from a direct blow to the arm. In 1996-97, there
were 35,000 fractures of the humeral shaft or distal humerus among Medicare enrollees, a rate of 0.6 per 1,000 enrollees. Rates of humeral shaft and distal humerus
fracture varied by a factor of almost six among hospital referral regions.
Among the hospital referral regions where rates of humeral shaft and distal humerus
fracture were higher than the United States average of 0.6 per 1,000 Medicare enrollees were Hinsdale, Illinois (1.4); Takoma Park, Maryland (1.2); Corpus Christi, Texas
(1.1); Tuscaloosa, Alabama (1.1); Norfolk, Virginia (0.9) and Philadelphia (0.9).
Distal Humerus/Humeral Shaft Fractures per
1,000 Enrollees
Among the hospital referral regions where rates of humeral shaft and distal humerus
fracture were lower than average were San Angelo, Texas (0.3); Eugene, Oregon (0.3);
San Francisco (0.3); Portland, Oregon (0.3); Seattle (0.3) and Tucson, Arizona (0.4).
Figure 4.12. Humeral Shaft and Distal Humerus Fractures (1996-97)
Rates of humeral shaft and distal humerus fracture varied from 0.3 per 1,000
Medicare enrollees to 1.4, after adjusting for differences in population age, sex
United States.
FRACTURES
Map 4.9. Humeral Shaft and Distal Humerus Fractures (1996-97)
Twenty-four regions had rates at least 30% higher than the national average.
Sixty-five regions had rates more than 25% below the national average.
San Francisco
Chicago
New York
Detroit
117
118
Surgical Treatment of Humeral Shaft and Distal Humerus Fractures
Most fractures of the humeral shaft or distal humerus can be treated non-operatively,
with a coaptation splint or humeral brace. Immobilization is required for at least
two months. Surgical treatment is recommended for patients with associated nerve
or vascular injuries and those with open or complex fractures. Surgery is also used
when non-operative treatment fails to achieve adequate fracture alignment. In
1996-97, 38.7% of Medicare patients with fractures of the humeral shaft and distal humerus underwent surgical intervention. The proportion of surgical repair of
humeral shaft and distal humerus fracture varied by a factor of almost four, from
18.6% of all proximal humerus fractures to 70.1%.
Proportion of Humeral Shaft and Distal Humerus
Fractures Treated with Surgery
Among the hospital referral regions where surgery rates for humeral shaft and distal humerus fracture were higher than the United States average were San Francisco
(70.1%); Seattle (64.1%); Little Rock, Arkansas (63.6%); Los Angeles (55.3%);
Knoxville, Tennessee (53.2%) and Peoria, Illinois (50.9%).
Among the hospital referral regions where the proportion of humeral shaft and distal humerus fractures treated surgically was lower than average were Paterson, New
Jersey (18.6%); Hartford, Connecticut (22.9%); Buffalo, New York (25.6%); Evanston, Illinois (27.0%);
Milwaukee (27.3%); Washington, D.C. (27.7%) and
East Long Island, New York (28.7%).
Figure 4.13. Proportion of Humeral Shaft and Distal Humerus
Fractures Treated with Surgery (1996-97)
Proportions of surgical repair of humeral shaft and distal humerus fracture
varied by a factor of nearly four, from 18.6% to 70.1%. Each point
FRACTURES
Map 4.10. Proportion of Humeral Shaft and Distal Humerus Fractures
Repaired Surgically (1996-97)
In 17 hospital referral regions at least 60% of humeral shaft and distal
humerus fractures were treated surgically. In 30 regions the proportion was
less than 30%.
San Francisco
Chicago
New York
Detroit
119
120
Proximal Forearm and Shaft Fractures
Fractures of the forearm (proximal forearm or shafts) usually result from falls onto
outstretched arms or from direct blows. Most forearm fractures are treated with cast
immobilization. Surgical treatment (fixation with plates and screws) is used primarily
for fractures that are comminuted or significantly displaced. In 1996, approximately
31% of Medicare patients with forearm fractures underwent surgical intervention. In
1996, rates of proximal forearm and shaft fracture varied by a factor of six, from 0.3
per 1,000 Medicare enrollees to 1.8.
Among the hospital referral regions where rates of proximal forearm and shaft fracture were substantially higher than the United States average of 0.9 per 1,000
Medicare enrollees were Binghamton, New York (1.8); Takoma Park, Maryland (1.4);
Detroit (1.4); Philadelphia (1.3); Providence, Rhode Island (1.2) and Cleveland (1.2).
Forearm Fractures per 1,000 Medicare Enrollees
Among the hospital referral regions where rates of proximal forearm and shaft fracture
were lower than average were Honolulu (0.3); Seattle (0.4); Portland, Oregon (0.4);
Little Rock, Arkansas (0.5); Knoxville, Tennessee (0.5) and Spokane, Washington (0.6).
Figure 4.14. Forearm Fractures (1996)
Rates of forearm (proximal forearm or shaft) fracture varied by a factor of six,
from 0.3 per 1,000 Medicare enrollees to 1.8, after adjusting for differences in
population age, sex and race. Each point represents one of the 306 hospital referral
FRACTURES
Map 4.11. Forearm Fractures (1996)
Twenty-eight regions had rates at least 30% higher than the national
average. Seventy regions had rates more than 25% below the national
average.
San Francisco
Chicago
New York
Detroit
121
122
Wrist Fractures
Wrist fractures, also known as Colles’, Smith’s, or Barton’s fractures, are the second
most common fracture in the elderly, after hip fracture. Wrist fracture most commonly occurs as a result of a fall onto an outstretched arm. Patients with
osteoporosis are at particularly high risk for this type of fracture. In 1996, approximately 96,000 Medicare patients sustained wrist fractures, of whom 85% were
women. In 1996, wrist fracture rates varied by a factor of almost four, from 1.5 per
1,000 Medicare enrollees to 5.7.
Among the hospital referral regions with high rates of wrist fracture were Huntsville,
Alabama (5.7); Tuscaloosa, Alabama (5.6); Birmingham, Alabama (5.4); Philadelphia
(5.0); Winston-Salem, North Carolina (4.9) and Ann Arbor, Michigan (4.7).
Wrist Fractures per 1,000 Medicare Enrollees
Among the hospital referral regions where rates of wrist fracture were lower than average
were Everett, Washington (1.5); San Francisco (1.6); Stockton, California (1.7); San
Jose, California (1.8); Portland, Oregon (1.9) and Sacramento, California (2.0).
Figure 4.15. Wrist Fractures (1996)
Rates of wrist fracture varied by a factor of almost four, from 1.5 per 1,000
Medicare enrollees to 5.7, after adjusting for differences in population age, sex
United States.
FRACTURES
Map 4.12. Wrist Fractures (1996)
Twenty-nine regions had rates at least 30% higher than the national
average. Sixty-one regions had rates more than 25% below the national
average.
San Francisco
Chicago
New York
Detroit
123
124
Surgical Treatment of Wrist Fractures
Most wrist fractures are treated without surgery (usually with closed reduction and
cast immobilization). Surgery is most commonly recommended for complex fractures and when bone alignment cannot be restored otherwise. Several different
procedures are used, including external fixation (pins through the skin), internal
fixation (internal plates and/or screws), and bone grafting. The general goal of all
these procedures is to achieve acceptable alignment and reduce otherwise high risks
of permanent loss of wrist function or range of motion. The proportion of wrist
fractures treated surgically varied by a factor of almost ten, from 5.1% of all wrist
fractures to 50.7%.
Among the hospital referral regions with rates of surgical repair of wrist fracture
higher than the United States average of 16.5% were Olympia, Washington
(50.7%); Casper, Wyoming (48.3%); Joplin, Missouri (41.3%); Little Rock, Arkansas (38.2%); Anchorage, Alaska (36.7%) and Seattle (36.0%).
Proportion of Wrist Fractures Treated with Surgery
Among the hospital referral regions where the proportion of wrist fractures treated
surgically was lower than average were Greenville, North Carolina (5.1%); White
Plains, New York (6.4%); Detroit (7.7%); Hackensack, New Jersey (7.9%);
Morristown, New Jersey (8.6%) and Royal Oak, Michigan (9.1%).
Figure 4.16. Proportion of Wrist Fractures Treated with Surgery (1996)
Proportions of wrist fractures treated surgically varied by a factor of nearly ten, from
5.1% to 50.7%. Each point represents one of the 306 hospital referral regions in the
United States.
FRACTURES
Map 4.13. Wrist Fractures Treated with Surgery (1996)
In seven hospital referral regions, at least 40% of wrist fractures were treated
surgically. In 30 regions, the proportion was less than 10%.
San Francisco
Chicago
New York
Detroit
125
126
Explaining Variation in Fracture Incidence and Treatment
Why do the incidence rates of different fractures vary among geographic regions?
Chance alone is an unlikely explanation — the observed variations are too large.
Given the consistent patterns in large regions of the country, it is also unlikely that
regional differences in fracture incidence can be explained by random errors in how
fractures are coded at different hospitals. Finally, it is unlikely that variation in fracture rates can be attributed to regional differences in known risk factors for fractures.
Regional fracture rates described in this chapter are adjusted for age, sex and race, the
three variables known to be most associated with risks of both osteoporosis and fracture.
Distinct geographic patterns suggest that other risk factors must be at work. With most
of the eight fractures described in this chapter, fracture rates were consistently high in
parts of the Midwest, Mid-Atlantic and South Atlantic states, particularly Ohio, Pennsylvania, Kentucky, Alabama and South Carolina. Reasons for higher fracture rates in
these areas are unknown — there is no current evidence to suggest higher prevalence of
osteoporosis or other risk factors for fractures (e.g., differences in water supply fluoridation) in these regions. Further epidemiologic study is needed to identify possible
environmental, occupational, and health status factors underlying our observations.
There is also wide geographic variation in how fractures are treated in different
regions. Although treatment of some fractures is relatively uniform (e.g., almost all
patients with hip fractures undergo surgery), the proportion of patients undergoing
surgery for other types of fractures varied substantially. It is useful to consider potential
explanations for this phenomenon:
■ Fracture severity. A major determinant of whether surgery is recommended is fracture severity. Fractures that are open, complex, or severely displaced are much more
likely to be treated surgically. Unfortunately, there is no evidence to suggest that some
geographic regions consistently treat more severe fractures than others.
■ Diagnostic intensity. In some cases, the use of surgery is strongly related to diagnostic intensity — how hard physicians look for surgically treatable disease. For
FRACTURES
example, regional rates of carotid endarterectomy are highly correlated with regional
rates of carotid ultrasound (necessary to identify patients with carotid stenosis but no
symptoms). It is unlikely, however, that this phenomenon explains variations in rates
of surgery for fractures, which are diagnosed after discrete injuries and are almost always symptomatic.
■ Workforce supply. Regional rates of some surgical procedures are influenced by
the regional supply of surgical specialists. For example, hospital referral regions with
relatively high numbers of orthopaedic surgeons have, on average, higher rates of both
spine surgery and joint replacement. The same phenomenon is not apparent with fracture care, however. Even for relatively “discretionary” fractures, the proportion of
Medicare patients (1996-97) undergoing surgery in different regions was not correlated with orthopaedic workforce supply (e.g., R2 = 0.04 for ankle fractures, R2 = 0.02
for wrist fractures).
The best explanation for regional variation in the use of surgery for different types of
fractures is variation in physicians’ practice styles. Although orthopaedists might all
agree on non-operative treatment for the most minor of fractures and on surgery for
the most major fractures, there is a substantial gray zone requiring clinical discretion
between those poles. Just how complex or displaced does a fracture need to be to exceed the threshold for surgical intervention? Regional variation in surgical rates
suggests a need for practice guidelines and greater efforts to build professional consensus about optimal management of many common fractures.
127
128
Chapter Four
Table Notes
Fracture rates are expressed as rates per 1,000 Medicare enrollees and are adjusted for age,
sex and race. Rates of surgical treatment of fractures are expressed as crude proportions.
Rates were determined from Medicare Part B (physician) claims, and exclude Medicare
enrollees who were members of risk-bearing health maintenance organizations.
adjustment methods, and methods used to calculate proportions.
FRACTURES
129
CHAPTER FOUR TABLE
Rates of Fracture and Proportion of Fractures Treated Surgically by Hospital Referral Regions (1996 and
1996-97)
Ho
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t
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e
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at
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ctu )
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ac 6re
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ac 6
re
c
ra 9 6
re
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re
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tu
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re
tu
er Fra 199 Fra
im urg
t F (19
ctu
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ctu
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le y (1
ac
c
a
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s
tu
a
r
i
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(
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a
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a
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o
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ra T
a
rm
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rF
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H u r us l l y
An all
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Ti all
tF
im e
Fr
me ure
of gic
of te
of e ica orea
kle
of gic
of gic
mu
ox tm
r is
p
bia
% Trea
Hu r act
% Hum urg
W
% Sur
Ti
% Sur
Pr Trea
F
Hi
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Fe
An
F
S
*
nt
n
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re 7)
a
9
c
di 96Me (19
ula
tio
n
e
at
d
e
at
t*
d
e
ur
Alabama
Birmingham
512,494
8.8
0.9
0.9
27.5
2.5
32.2
2.3
11.9
0.8
36.5
1.0
5.4
91,156
9.5
0.9
0.8
35.1
1.6
35.2
2.0
7.7
0.6
40.0
0.6
4.6
25.0
Huntsville
110,114
8.8
0.7
0.8
31.3
2.2
32.1
2.2
9.0
0.6
40.0
1.2
5.7
15.1
Mobile
13.3
Dothan
16.0
156,629
8.1
0.9
0.8
26.3
1.7
32.7
2.0
7.7
0.8
30.2
1.1
3.4
Montgomery
96,196
8.8
0.9
1.1
21.6
2.6
27.2
2.1
14.0
0.9
42.7
1.2
4.8
17.4
Tuscaloosa
56,676
9.3
1.2
0.9
38.3
2.6
33.1
2.4
19.7
1.1
27.3
(0.9)
5.6
19.4
55,021
6.0
0.6
0.5
1.5
67.1
0.9
(0.5)
2.5
36.7
Alaska
Anchorage
0.4
Arizona
Mesa
100,972
7.8
0.7
0.3
46.4
0.9
58.9
0.5
38.0
0.4
64.3
0.6
2.5
28.5
Phoenix
348,192
8.2
0.6
0.4
53.3
0.9
67.9
0.8
29.8
0.5
63.0
0.6
2.2
26.6
0.9
59.3
0.8
0.4
68.4
0.5
3.1
58.3
0.8
64.2
0.7
33.3
0.4
49.0
0.6
2.2
0.8
Sun City
90,946
7.3
0.4
0.2
Tucson
134,697
8.0
0.7
0.4
34.2
Arkansas
Fort Smith
86,809
8.2
0.6
0.4
38.9
0.9
68.8
0.5
45.8
0.4
59.4
Jonesboro
60,650
8.2
0.6
0.4
57.7
0.9
71.9
0.6
53.8
0.5
51.6
2.0
20.9
2.8
32.2
Little Rock
372,224
8.6
0.7
0.4
47.0
1.0
68.1
0.8
34.8
0.5
63.6
0.5
3.0
38.2
Springdale
93,352
8.6
0.6
0.3
53.1
1.0
68.5
0.7
38.1
0.3
53.3
0.5
2.3
23.4
Texarkana
68,023
9.3
0.7
0.5
36.1
1.2
56.6
1.4
23.6
0.5
52.9
0.6
3.4
25.0
Orange County
266,228
6.8
0.6
0.3
54.5
0.9
59.8
0.8
26.9
0.4
58.9
0.6
2.3
23.3
Bakersfield
112,711
8.1
0.7
0.4
57.5
1.1
53.7
0.6
28.4
0.5
31.6
0.4
2.2
17.2
California
Chico
69,073
8.0
0.7
0.4
50.0
1.0
77.1
0.6
48.7
0.5
60.6
0.5
2.0
27.8
Contra Costa County
100,388
6.0
0.5
0.3
53.1
0.8
53.8
1.0
20.0
0.4
60.5
0.4
1.9
28.6
Fresno
144,032
6.5
0.6
0.3
53.2
1.1
65.8
0.7
32.0
0.4
53.1
0.5
2.2
33.8
Los Angeles
972,263
6.3
0.6
0.4
45.3
0.8
53.5
0.8
25.3
0.4
55.3
0.6
2.4
18.9
Modesto
105,228
7.4
0.6
0.3
62.5
1.0
60.0
0.5
24.1
0.3
0.6
1.8
21.2
64,570
6.8
0.6
0.3
1.0
67.2
0.9
20.6
0.5
48.4
0.6
1.9
21.9
169,920
6.0
0.4
0.4
31.6
0.9
48.3
0.8
24.8
0.5
52.4
0.7
2.3
15.7
Palm Spa/Rancho Mir
57,440
7.1
0.7
0.5
53.6
1.1
58.5
1.6
12.0
0.4
(0.9)
2.5
Redding
84,868
7.3
0.6
0.4
54.5
1.3
48.6
1.0
22.7
0.5
50.0
0.6
2.4
23.8
292,139
7.5
0.5
0.3
51.5
0.8
62.9
0.8
28.0
0.5
50.0
0.6
2.0
26.6
14.9
Napa
Alameda County
Sacramento
Salinas
62,442
6.5
0.6
1.0
53.8
0.8
28.6
0.3
0.5
2.4
San Bernardino
165,215
7.3
0.7
0.4
41.3
1.0
55.0
0.8
28.8
0.6
52.0
0.7
2.2
22.5
San Diego
315,936
6.6
0.6
0.4
52.1
1.0
58.7
0.8
27.5
0.5
47.2
0.7
2.3
26.9
San Francisco
203,727
5.6
0.4
0.2
68.6
0.7
66.0
0.7
28.3
0.3
70.1
0.6
1.6
24.7
San Jose
163,058
6.0
0.5
0.3
58.2
0.7
51.4
0.6
30.0
0.5
47.4
0.4
1.8
10.1
1.0
61.0
0.9
(0.6)
2.0
36.4
0.6
60.7
0.7
24.6
0.4
55.6
0.5
2.3
13.3
1.4
49.4
1.2
25.3
0.6
47.4
0.8
3.3
15.5
San Luis Obispo
40,051
7.1
0.7
(0.5)
San Mateo County
97,217
5.9
0.4
0.3
Santa Barbara
59,737
6.5
0.4
0.4
* per 1,000 Medicare Enrollees (1996-97)
** per 1,000 Medicare Enrollees (1996)
130
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H u r us l l y
An all
W all
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tF
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Fr
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of gic
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of e ica orea
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of gic
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p
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% Hum urg
% Trea
Hu r act
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Santa Cruz
40,158
7.6
0.4
(0.6)
Santa Rosa
65,118
6.8
0.4
0.5
Stockton
68,724
7.9
0.8
0.3
Ventura
81,849
7.1
0.5
0.4
e
at
d
e
at
t*
d
e
ur
1.4
33.3
1.6
0.7
(0.9)
3.3
0.8
76.0
0.6
55.8
0.3
0.5
1.6
21.7
0.9
66.1
1.0
26.9
0.5
43.8
0.6
1.7
23.0
62.9
1.2
51.0
0.9
23.4
0.6
59.3
0.9
2.4
11.5
41.7
Colorado
Boulder
26,915
8.7
(0.8)
(1.1)
1.3
48.6
1.9
(1.0)
3.3
Colorado Springs
108,377
8.7
0.8
0.6
21.7
1.5
44.0
1.2
18.7
0.5
28.6
0.6
3.0
Denver
260,204
8.3
0.8
0.8
25.7
1.6
36.2
1.5
14.9
0.7
36.4
1.0
3.1
13.3
49,429
7.8
0.8
0.6
1.6
50.0
1.2
25.4
0.7
51.4
(0.7)
2.6
27.3
(0.7)
2.2
18.2
21.7
0.7
(0.9)
3.4
23.1
(1.0)
3.2
20.0
Fort Collins
Grand Junction
61,036
5.0
0.4
0.5
37.5
0.9
35.7
0.6
Greeley
58,804
7.8
0.7
0.6
39.5
1.1
50.0
1.1
Pueblo
32,543
7.7
(0.8)
(1.2)
46.2
1.9
29.7
1.6
(0.9)
0.4
34.1
(0.9)
25.9
Connecticut
Bridgeport
157,925
7.4
0.7
0.7
24.0
1.6
28.2
1.5
9.8
0.6
29.8
1.0
3.0
Hartford
360,958
7.2
0.8
0.8
28.1
2.0
27.3
1.9
13.5
0.8
22.9
1.0
3.6
10.9
New Haven
331,229
7.4
0.8
0.8
24.1
1.9
29.7
1.7
10.7
0.5
33.9
0.8
3.4
10.3
140,097
8.4
0.9
1.0
14.4
2.3
24.4
2.6
8.7
0.7
33.3
0.9
4.3
11.5
399,140
7.5
0.8
0.7
17.4
2.1
27.3
2.2
9.0
0.7
27.7
1.1
3.9
9.4
Bradenton
92,103
7.5
0.6
0.4
35.1
1.2
35.2
1.6
17.9
0.9
32.6
1.0
3.6
8.2
Clearwater
159,414
8.1
0.7
0.7
35.5
1.6
35.5
2.0
18.2
0.8
42.2
1.0
3.7
12.2
Fort Lauderdale
583,034
7.8
0.7
0.6
21.7
1.6
29.1
2.2
11.5
0.8
41.1
1.0
3.9
11.2
Fort Myers
326,788
7.0
0.7
0.8
26.3
1.8
31.1
2.4
10.7
0.8
44.4
1.0
4.0
19.0
Gainesville
96,428
8.2
0.8
0.7
29.7
1.2
45.0
1.4
15.8
0.8
22.1
0.6
3.4
21.9
Delaware
Wilmington
Washington
Florida
Hudson
146,135
7.4
0.7
0.5
30.8
1.5
32.4
2.0
8.0
0.7
38.3
0.8
4.0
14.8
Jacksonville
211,676
8.4
0.9
0.6
25.8
1.5
33.2
1.7
15.6
0.7
44.5
1.0
3.8
17.2
84,575
8.0
0.5
0.4
1.2
32.7
1.7
17.3
0.7
42.6
0.6
3.4
19.4
Miami
393,275
7.6
0.7
0.6
27.5
1.2
31.7
2.2
11.9
0.8
43.1
0.9
3.5
11.3
Ocala
167,287
7.0
0.9
0.6
34.5
1.2
38.1
1.4
13.4
0.6
61.2
0.7
3.5
12.9
Orlando
689,170
7.8
0.8
0.6
28.8
1.3
35.3
1.7
14.2
0.6
42.4
0.9
3.3
21.2
Ormond Beach
89,977
7.1
1.0
0.7
49.2
1.4
39.3
1.7
17.2
0.8
57.4
Panama City
45,885
8.8
0.7
0.5
1.3
41.7
1.7
18.8
0.6
Pensacola
155,577
8.6
0.7
0.6
23.2
1.5
37.1
1.7
19.2
0.6
Sarasota
185,449
7.5
0.7
0.7
26.8
1.5
33.1
2.2
13.3
0.7
St. Petersburg
117,248
8.3
0.9
0.9
31.6
1.6
31.7
2.5
10.7
Tallahassee
143,281
8.5
0.7
0.6
30.0
1.7
33.3
1.9
Tampa
166,552
8.2
0.8
0.6
29.8
1.0
33.1
1.6
Lakeland
1.0
3.3
19.3
(0.8)
3.0
17.5
29.4
0.7
3.5
14.0
39.5
0.9
4.0
18.7
0.7
43.2
1.2
4.5
11.6
13.3
0.7
29.3
0.6
4.1
17.0
20.9
0.7
40.7
0.7
3.4
21.2
Georgia
Albany
43,737
9.0
0.9
0.9
43.2
2.2
40.0
2.3
20.3
(0.7)
Atlanta
709,776
8.9
1.0
0.9
31.8
1.9
38.3
1.8
14.2
0.7
Augusta
124,998
7.9
0.7
0.6
28.0
1.7
37.1
1.5
10.1
0.8
67,186
8.9
0.8
0.9
29.1
2.1
30.3
1.8
18.6
0.6
Macon
143,726
8.9
0.8
0.6
35.4
1.4
43.4
1.4
18.0
0.7
Rome
61,084
10.7
0.8
1.0
35.6
2.2
42.2
1.8
19.6
0.7
Columbus
(1.0)
4.7
17.9
0.9
4.0
13.1
24.1
0.8
3.8
11.1
41.7
(0.9)
4.8
14.1
37.6
0.9
3.4
20.3
44.4
0.9
4.4
21.5
40.1
FRACTURES
Ho
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Savannah
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H u r us l l y
An all
W all
Ti all
tF
im e
Fr
me ure
of gic
of te
of e ica orea
kle
of gic
of gic
mu
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p
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% Hum urg
% Trea
Hu r act
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9
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tio
n
131
e
at
d
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at
t*
d
e
ur
144,069
8.3
0.8
0.6
34.9
1.4
36.8
1.5
15.7
0.9
32.1
1.1
3.6
17.5
172,137
4.9
0.3
0.2
54.8
0.5
53.9
0.4
42.6
0.3
67.4
0.3
1.5
15.6
138,789
6.8
0.5
0.5
57.8
1.0
69.2
0.7
24.0
0.5
66.2
0.5
2.2
23.3
33,686
6.4
(0.8)
(0.7)
1.3
58.1
1.3
(0.8)
1.8
40.0
Hawaii
Honolulu
Idaho
Boise
Idaho Falls
(0.5)
Illinois
Aurora
33,076
6.2
(1.1)
(0.5)
1.3
34.9
1.4
(1.2)
2.7
Blue Island
177,705
6.9
0.7
0.6
37.5
1.8
38.9
1.6
15.0
0.7
46.3
1.2
3.4
16.9
Chicago
418,751
6.9
0.7
0.7
28.9
1.5
34.3
2.0
9.9
0.8
44.2
0.9
4.0
17.5
12.6
Elgin
(0.5)
82,955
7.3
0.7
0.8
19.0
1.7
34.3
1.4
0.5
33.3
1.0
3.5
Evanston
211,299
7.0
0.8
0.7
23.3
1.5
34.5
1.7
10.1
0.7
27.0
1.2
3.5
9.6
Hinsdale
62,193
7.9
0.9
0.8
30.8
2.2
26.4
1.7
15.0
1.4
19.1
1.2
3.8
17.2
Joliet
Melrose Park
98,864
7.4
1.0
0.6
28.6
1.6
31.6
1.6
11.5
0.8
37.2
0.9
3.6
14.6
247,020
7.2
0.7
0.7
29.8
1.6
35.1
1.7
14.4
0.8
35.2
1.0
3.7
12.9
23.4
Peoria
187,488
8.1
0.7
0.6
44.9
1.4
45.6
1.0
16.4
0.6
50.9
0.7
2.9
Rockford
169,843
7.5
0.8
0.5
38.5
1.6
36.3
1.3
15.6
0.7
39.2
0.8
3.6
8.2
Springfield
251,275
8.7
0.9
0.6
28.1
1.4
50.5
1.2
22.4
0.6
44.4
0.8
3.0
15.5
Urbana
108,324
7.4
0.7
0.7
28.0
11.8
0.6
31.0
38,114
6.6
0.6
(0.6)
Bloomington
1.8
31.8
1.5
1.4
35.7
1.0
(0.7)
0.7
3.5
14.1
(0.9)
2.7
40.7
Indiana
Evansville
193,076
8.1
0.7
0.7
31.8
1.4
43.3
1.1
16.1
0.5
45.0
0.7
3.3
17.2
Fort Wayne
194,325
8.1
0.8
0.7
26.1
1.7
38.7
1.6
12.9
0.6
36.7
1.0
3.9
19.5
Gary
112,108
6.8
0.8
0.8
26.7
2.0
34.5
2.0
15.3
0.9
33.7
0.8
3.9
24.0
Indianapolis
562,927
8.5
0.9
0.9
27.9
2.0
33.0
1.9
13.8
0.7
38.4
1.0
3.7
15.3
Lafayette
44,725
6.1
0.6
0.6
37.9
1.6
24.0
1.3
Muncie
44,983
9.1
0.9
0.6
1.8
30.5
2.1
Munster
18.2
0.6
(0.7)
3.1
0.8
(0.8)
4.5
78,111
6.7
0.8
0.6
1.5
32.5
1.7
11.4
0.8
43.1
0.8
4.1
10.7
South Bend
163,476
7.6
0.7
0.7
40.5
2.0
34.1
1.9
16.4
0.7
38.6
0.7
4.0
18.0
Terre Haute
52,008
9.6
0.7
0.8
41.5
1.8
40.6
2.0
23.2
0.7
62.2
(1.0)
3.7
22.9
Iowa
Cedar Rapids
70,095
8.2
0.8
0.6
28.3
1.8
42.6
1.5
1.0
33.3
0.9
4.0
7.4
Davenport
137,016
7.9
0.7
0.7
26.3
1.9
37.2
1.9
11.1
0.8
31.0
1.0
4.2
25.1
Des Moines
273,014
7.6
0.8
0.6
25.0
1.9
36.5
1.2
12.7
0.8
30.7
13.3
43,202
5.9
0.4
0.4
1.3
44.8
1.2
Dubuque
Iowa City
82,274
7.8
0.7
0.5
Mason City
52,362
6.8
0.7
0.3
31.0
0.4
1.1
3.0
(0.8)
2.8
1.9
31.0
1.2
11.4
0.5
26.7
1.0
3.6
1.6
41.9
1.0
18.3
0.8
33.3
(0.7)
3.4
19.8
Sioux City
78,286
7.5
0.8
0.5
2.1
40.0
1.4
13.2
0.8
35.9
1.1
3.7
18.2
Waterloo
62,652
6.9
0.8
0.7
1.8
43.1
1.4
16.8
0.5
33.3
0.9
2.8
21.7
Kansas
Topeka
108,659
8.8
0.7
0.6
39.7
1.7
47.0
1.3
24.2
0.6
46.4
0.7
3.3
23.1
Wichita
345,977
8.4
0.9
0.6
36.3
1.7
41.0
1.2
19.6
0.8
32.0
0.8
3.0
18.8
Kentucky
Covington
68,244
9.8
0.7
1.0
2.5
32.2
3.4
7.5
1.0
32.4
1.2
4.9
Lexington
305,383
7.5
0.6
0.6
28.2
1.6
36.9
1.3
11.4
0.5
41.9
0.7
3.3
14.8
Louisville
363,643
8.5
0.7
0.6
35.0
1.8
36.1
1.4
12.7
0.6
49.8
0.7
4.2
18.5
132
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An all
W all
Ti all
tF
im e
Fr
me ure
of gic
of te
of e ica orea
kle
of gic
of gic
mu
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p
bia
% Hum urg
% Trea
Hu r act
W
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36,224
8.9
(0.7)
(0.5)
112,083
8.9
0.6
0.7
e
at
45.8
d
e
at
t*
1.3
38.0
1.0
1.7
42.9
1.8
d
e
ur
(0.5)
18.1
0.5
54.0
(0.9)
3.7
15.7
0.6
3.4
23.7
Louisiana
Alexandria
Baton Rouge
Houma
67,615
8.6
0.8
0.5
41.7
1.3
52.3
1.5
18.3
0.4
57.7
0.4
2.5
19.5
110,899
8.7
0.7
0.7
19.7
1.2
34.6
1.2
18.6
0.7
34.8
0.9
2.6
16.2
1.8
33.3
11.2
0.5
52.0
0.5
2.1
17.4
0.8
38.5
(0.8)
2.9
17.1
0.6
57.1
0.8
1.9
28.0
44,008
7.3
0.7
0.5
1.0
40.9
0.8
119,614
6.9
0.8
0.4
31.4
1.1
47.2
1.1
Lake Charles
52,954
7.7
0.8
0.5
42.3
1.6
28.6
2.0
Metairie
77,485
8.6
0.5
0.5
1.0
46.8
1.1
Lafayette
Monroe
0.5
16.0
67,383
8.7
0.6
0.5
34.3
1.2
55.3
1.1
19.0
0.7
40.9
0.7
2.7
New Orleans
139,937
8.0
0.7
0.5
44.9
0.8
61.5
0.9
27.7
0.7
53.9
0.8
2.0
Shreveport
165,575
8.7
0.6
0.5
42.2
1.1
56.8
1.0
27.5
0.4
35.9
0.9
2.6
24.1
30,442
8.4
(0.8)
(0.6)
1.5
54.3
1.5
(0.9)
54.2
(1.0)
2.7
34.2
Slidell
27.0
Maine
Bangor
109,864
7.3
0.6
0.7
33.3
1.8
33.7
1.5
15.1
0.6
50.7
0.6
4.0
21.9
Portland
256,587
7.7
0.7
0.7
34.5
2.0
36.1
2.0
18.0
0.6
36.7
0.6
3.6
16.0
Baltimore
494,344
7.2
0.8
0.8
26.8
2.1
29.4
2.3
11.4
0.8
39.4
1.1
3.8
13.2
Salisbury
100,285
7.4
0.8
0.7
24.6
2.1
30.7
2.2
12.6
0.7
36.1
1.2
3.9
13.6
Takoma Park
121,878
7.1
0.8
0.7
14.8
2.2
26.8
2.4
6.4
1.2
26.1
1.4
4.2
6.3
Boston
958,947
7.7
0.7
0.7
25.0
1.7
31.2
1.6
13.3
0.7
32.1
1.0
3.2
10.6
Springfield
177,284
7.7
0.7
0.6
29.4
1.5
37.9
1.4
16.5
0.7
28.3
0.8
3.3
11.5
Worcester
119,864
7.8
0.6
0.8
24.0
2.0
28.2
2.3
10.2
0.8
34.9
0.7
2.7
11.1
Ann Arbor
261,026
7.3
0.8
0.8
25.1
2.5
26.7
2.0
9.2
0.7
27.5
1.0
4.7
13.0
Dearborn
141,149
6.0
0.8
0.8
33.7
1.8
38.0
1.7
8.2
0.6
44.0
1.1
4.3
14.8
Detroit
441,063
6.5
0.8
0.7
23.3
2.1
24.7
2.2
8.5
0.6
38.0
1.4
4.5
7.7
Flint
117,281
7.4
0.7
0.6
17.9
2.1
30.0
1.7
9.4
0.5
43.9
1.1
3.6
10.4
Grand Rapids
225,955
7.0
0.7
0.5
29.0
1.8
41.7
1.4
17.1
0.6
44.2
0.9
3.2
15.0
Kalamazoo
153,631
6.8
0.7
0.7
25.7
2.1
37.7
1.7
9.8
0.6
33.0
1.0
3.3
14.1
Lansing
125,596
6.9
0.8
0.7
30.3
2.3
31.4
2.0
9.4
0.6
32.1
1.2
4.0
18.4
Maryland
Massachusetts
Michigan
Marquette
64,706
6.8
0.7
0.6
29.3
1.8
45.3
1.0
21.5
0.5
56.3
0.6
3.0
Muskegon
68,935
6.7
0.6
0.6
45.2
1.8
29.3
2.0
8.8
0.6
41.5
0.9
3.3
Petoskey
50,319
6.4
0.9
0.6
2.2
35.5
1.6
0.8
32.4
(1.6)
2.6
Pontiac
14.9
72,683
7.2
0.6
0.8
20.3
2.2
33.3
1.8
0.6
43.6
1.0
3.7
Royal Oak
160,221
6.5
0.7
0.7
24.5
1.8
33.7
1.8
11.0
0.6
41.5
1.0
3.7
9.1
Saginaw
190,701
6.4
0.6
0.6
32.0
2.1
30.4
2.0
11.4
0.6
44.0
0.9
4.3
9.1
St. Joseph
39,098
7.0
0.7
(0.8)
2.1
47.6
1.6
(0.7)
50.0
(0.7)
4.3
Traverse City
64,324
6.9
0.5
0.5
2.0
38.9
1.3
0.9
27.8
1.0
3.2
12.0
12.1
Minnesota
Duluth
106,639
5.9
0.5
0.3
69.4
1.3
56.9
0.6
28.6
0.4
46.7
0.5
2.2
21.0
Minneapolis
554,141
6.3
0.6
0.5
38.3
1.6
44.7
0.9
22.9
0.5
43.3
0.7
3.0
17.3
Rochester
109,813
7.1
0.6
0.5
36.7
1.6
37.3
0.9
30.1
0.4
45.7
27.3
St. Cloud
50,683
7.3
0.7
0.4
2.1
34.9
1.2
St. Paul
146,928
7.4
0.7
0.5
1.5
42.7
0.8
32.9
0.4
24.8
0.5
33.8
0.8
2.6
(0.6)
3.8
0.7
2.9
18.3
FRACTURES
Ho
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H u r us l l y
An all
W all
Ti all
tF
im e
Fr
me ure
of gic
of te
of e ica orea
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of gic
of gic
mu
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r is
p
bia
% Hum urg
% Trea
Hu r act
W
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133
e
at
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ur
Mississippi
Gulfport
37,942
8.4
(0.8)
(0.9)
1.4
34.6
2.1
(0.9)
3.4
Hattiesburg
63,256
9.2
0.9
0.8
28.6
1.5
44.6
2.1
14.4
0.8
31.1
(1.0)
3.4
23.7
Jackson
232,871
8.2
0.7
0.6
33.6
1.4
44.3
1.1
13.3
0.5
36.8
0.8
2.7
16.9
Meridian
52,837
8.4
0.7
0.7
44.4
2.0
42.0
1.2
0.7
45.5
(0.9)
2.9
17.9
Oxford
33,823
10.5
(0.8)
(0.6)
2.5
38.0
1.9
(0.8)
(1.0)
4.0
39.3
Tupelo
89,351
9.3
1.0
0.7
1.6
54.0
1.5
0.8
4.0
19.5
47.5
(0.7)
16.7
0.7
31.7
27.1
Missouri
Cape Girardeau
75,339
8.9
0.4
0.4
37.9
0.9
66.7
0.6
26.1
0.5
61.5
0.5
2.8
21.6
Columbia
174,601
8.4
0.8
0.8
39.3
1.6
55.0
1.3
21.9
0.7
52.7
0.8
3.1
20.3
Joplin
102,279
8.6
0.8
0.6
39.7
1.4
46.4
0.9
24.5
0.6
45.5
0.8
3.1
41.3
Kansas City
448,003
8.8
0.9
0.7
31.6
1.9
41.9
1.5
11.4
0.8
39.5
1.1
3.7
13.8
Springfield
216,274
8.8
0.8
0.6
36.4
1.2
50.4
1.0
12.5
0.5
47.1
0.6
3.1
24.3
St. Louis
739,963
8.8
0.8
0.8
27.5
2.0
37.1
2.0
15.0
0.7
42.8
1.0
3.8
18.5
56.7
1.3
54.7
0.7
29.4
0.5
41.8
27.9
1.2
59.2
0.5
Montana
Billings
123,836
7.1
0.7
0.5
Great Falls
39,696
7.3
0.8
(0.5)
0.6
3.0
(0.6)
2.8
Missoula
84,932
8.2
0.6
0.6
41.7
1.6
60.2
1.0
26.4
0.6
33.9
40.8
0.8
3.1
18.3
Lincoln
153,839
7.7
0.7
0.6
34.7
1.6
44.4
1.2
26.1
Omaha
291,816
7.7
0.8
0.6
38.7
1.8
48.8
1.1
23.9
0.6
31.5
0.9
2.9
22.3
0.7
39.6
0.9
3.0
22.0
(0.4)
Nebraska
Nevada
Las Vegas
155,466
7.5
0.6
0.6
46.2
1.1
46.0
1.3
25.7
0.6
56.4
0.5
2.6
17.2
Reno
111,501
7.7
0.6
0.6
56.1
1.3
58.0
1.1
18.5
0.5
59.2
0.6
2.6
22.4
New Hampshire
Lebanon
106,147
6.9
0.4
0.5
49.0
1.3
56.3
0.8
27.5
0.3
58.3
0.6
2.2
24.6
Manchester
163,580
7.9
0.7
0.6
45.7
1.7
39.3
1.2
18.3
0.5
30.7
0.8
3.1
15.4
New Jersey
Camden
632,500
7.2
0.8
0.7
23.6
1.9
29.8
2.0
10.6
0.7
33.1
1.0
3.8
13.9
Hackensack
293,936
6.8
0.7
0.6
29.7
1.5
31.6
1.7
16.7
0.9
39.1
1.0
3.4
7.9
Morristown
201,609
6.8
0.6
0.6
23.6
1.9
32.2
1.4
16.6
0.7
34.1
0.8
3.8
8.6
New Brunswick
181,188
7.0
0.5
0.4
36.3
1.4
33.1
1.1
10.8
0.6
36.6
0.7
3.0
8.8
Newark
321,937
6.0
0.7
0.6
29.8
1.4
34.6
1.5
15.1
0.7
38.6
0.8
3.4
10.6
Paterson
77,333
7.9
0.8
0.8
29.5
1.6
32.3
1.7
8.8
0.7
18.6
0.4
3.5
Ridgewood
82,131
7.3
0.7
0.7
18.6
1.8
27.2
1.7
11.0
0.7
33.9
1.1
4.0
8.7
216,388
8.2
0.8
0.5
41.0
1.1
49.2
0.8
25.4
0.5
38.2
0.6
2.4
14.3
New Mexico
Albuquerque
New York
Albany
456,704
7.2
0.7
0.7
21.3
2.1
25.6
2.0
9.2
0.8
34.0
0.8
3.6
9.4
Binghamton
106,086
7.4
0.7
0.8
19.0
2.2
25.8
2.0
10.4
0.7
34.1
1.8
2.8
6.9
Bronx
187,753
6.6
0.7
0.6
14.4
1.0
24.5
1.5
10.6
0.5
43.4
0.8
2.8
9.2
Buffalo
374,703
7.3
0.8
0.8
13.7
2.1
21.9
2.4
8.8
0.7
25.6
1.0
4.3
7.5
Elmira
106,968
7.3
0.6
0.8
18.0
2.4
26.3
2.2
0.6
28.2
0.9
3.8
8.7
East Long Island
880,064
6.8
0.6
0.6
19.7
1.5
26.9
2.1
8.9
0.7
28.7
1.0
3.7
9.2
Manhattan
859,852
6.3
0.6
0.5
28.2
1.2
31.3
1.8
12.6
0.7
30.2
0.9
3.3
11.1
Rochester
276,081
6.7
0.7
0.7
14.9
1.8
26.4
1.9
10.9
0.6
29.7
0.8
3.6
9.9
134
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Fr
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% Hum urg
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Syracuse
262,651
7.5
0.6
0.7
16.2
2.1
25.6
2.0
6.6
0.6
29.1
1.0
3.5
7.9
White Plains
226,460
7.2
0.7
0.8
15.5
2.1
23.4
2.2
8.9
0.7
31.5
1.1
4.2
6.4
Asheville
184,420
9.1
0.7
0.6
30.3
1.6
53.0
0.9
20.2
0.5
39.8
0.6
3.2
17.6
Charlotte
386,048
9.5
0.8
0.8
39.2
1.8
39.3
1.5
18.4
0.6
35.0
0.8
4.3
15.3
Durham
288,207
8.9
0.7
0.6
35.9
1.4
50.3
1.1
22.9
0.5
43.4
0.6
3.6
22.5
North Carolina
Greensboro
126,165
8.9
0.9
0.9
31.8
2.1
43.5
2.0
12.7
0.8
28.7
1.0
5.1
12.3
Greenville
168,005
8.7
0.9
0.7
36.0
1.6
34.3
1.7
12.6
0.5
31.4
0.8
4.5
5.1
Hickory
62,917
10.3
0.7
0.8
28.0
2.3
39.7
1.8
14.3
0.6
1.3
4.6
7.7
Raleigh
269,625
8.2
0.7
0.6
32.0
1.4
51.9
1.1
14.6
0.7
0.7
4.1
12.9
Wilmington
Winston-Salem
33.3
84,924
8.8
0.8
0.8
31.7
1.8
30.8
1.7
16.7
0.8
37.5
1.2
4.0
15.8
242,406
9.5
0.8
0.9
33.2
2.2
38.5
1.9
10.0
0.6
30.8
1.0
4.9
11.9
1.0
4.1
34.4
17.7
0.8
34.7
0.7
4.0
14.3
North Dakota
Bismarck
Fargo Moorhead
62,096
7.7
0.9
0.6
31.4
1.8
41.8
1.2
141,367
7.2
0.6
0.5
38.4
1.7
38.2
1.1
44.0
Grand Forks
47,069
7.0
0.6
0.5
Minot
38,653
7.9
0.7
(0.6)
0.6
1.8
35.2
1.4
0.7
(0.8)
2.8
1.8
46.5
1.0
(0.5)
(0.9)
4.2
40.2
20.7
Ohio
Akron
166,925
7.9
0.7
0.6
29.1
1.7
34.6
1.9
12.5
0.6
30.6
0.9
3.2
Canton
168,884
7.5
0.9
0.7
22.0
2.2
32.4
1.9
15.3
0.6
34.9
0.8
3.7
9.8
Cincinnati
342,847
9.1
1.0
1.1
25.4
2.4
30.8
2.4
11.7
0.7
33.9
1.2
4.6
12.3
Cleveland
514,477
7.5
0.9
0.9
23.6
2.3
29.7
2.1
9.3
0.8
33.3
1.2
4.5
15.2
Columbus
589,133
8.0
0.8
0.7
28.4
2.1
36.4
1.9
11.5
0.7
32.5
1.0
3.6
18.2
Dayton
270,065
8.2
0.8
0.9
26.6
13.5
Elyria
57,518
7.9
1.0
0.8
Kettering
91,048
8.6
0.7
0.7
23.1
2.1
30.0
2.1
8.1
0.8
39.4
1.0
4.1
2.4
25.2
2.4
12.5
0.8
31.9
(1.7)
4.7
15.4
2.2
26.0
2.6
7.5
0.9
32.5
1.0
4.1
16.1
Toledo
236,591
7.8
1.0
1.0
22.8
2.3
30.1
2.1
9.6
0.8
36.8
1.3
4.0
17.9
Youngstown
216,042
7.0
0.8
0.8
23.7
2.4
27.0
2.2
13.1
0.8
35.6
0.9
4.2
14.7
Oklahoma
Lawton
47,560
8.5
0.8
0.5
44.0
1.5
44.6
1.2
0.7
34.3
(0.7)
3.3
27.5
Oklahoma City
389,841
8.9
0.7
0.6
33.3
1.2
46.1
0.9
17.3
0.5
45.2
0.8
2.6
27.7
Tulsa
271,618
8.7
0.8
0.5
43.7
1.2
49.1
1.0
16.2
0.6
37.6
0.9
3.1
13.3
Oregon
Bend
41,180
6.3
(0.4)
(0.3)
0.9
57.9
0.6
Eugene
153,816
5.9
0.4
0.3
57.9
0.9
69.7
0.6
25.0
0.3
60.0
0.3
1.6
Medford
113,111
6.6
0.6
0.3
41.9
1.3
58.8
0.8
20.5
0.4
57.5
0.5
1.9
27.8
Portland
282,862
7.3
0.5
0.3
56.6
1.0
66.8
0.6
27.0
0.3
44.9
0.4
1.9
24.1
50,048
5.8
0.3
0.6
43.8
0.7
68.4
0.9
Salem
1.7
0.3
27.2
1.6
Pennsylvania
Allentown
279,344
7.8
0.8
1.0
19.5
2.7
24.8
2.8
8.9
0.8
31.3
1.0
4.5
Altoona
85,570
7.4
0.7
0.8
19.4
2.9
20.8
2.6
12.1
0.6
45.5
0.8
3.8
16.1
15.2
Danville
122,884
7.1
0.9
0.9
13.8
3.1
22.8
2.4
12.3
0.8
24.3
0.9
4.3
22.4
Erie
213,144
8.0
0.9
0.8
15.6
2.3
30.9
2.4
10.6
0.8
28.5
1.1
4.1
17.3
Harrisburg
234,150
7.7
0.8
0.8
22.8
2.5
28.6
2.5
10.7
0.7
29.9
1.0
4.3
19.1
Johnstown
80,983
5.6
0.7
0.7
28.3
2.6
23.5
2.3
9.2
0.5
40.9
0.5
3.2
20.9
Lancaster
130,521
7.2
0.7
0.7
32.2
2.1
33.6
2.0
12.3
0.8
31.4
1.2
3.8
15.6
FRACTURES
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Fr
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of gic
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Philadelphia
761,844
8.0
0.9
1.0
20.1
2.5
24.6
2.8
11.2
0.9
36.7
1.3
5.0
25.5
Pittsburgh
856,834
7.2
0.8
0.9
21.1
2.4
28.3
2.2
11.2
0.7
37.7
1.0
4.0
17.1
Reading
147,068
7.6
0.8
0.9
28.1
2.8
32.2
2.7
11.9
0.7
45.1
0.9
4.3
32.3
53,139
7.0
0.9
0.5
2.4
22.5
2.0
0.7
30.8
(0.6)
3.9
17.3
10.2
Sayre
Scranton
100,067
7.6
0.9
0.9
18.7
2.6
23.7
2.6
8.2
0.6
31.1
1.0
4.4
Wilkes-Barre
80,181
6.6
0.9
0.9
20.3
2.5
22.6
3.0
15.9
0.8
32.8
0.8
4.5
12.6
York
91,191
7.3
0.9
0.7
33.3
2.3
29.6
2.1
10.0
0.6
38.3
0.9
4.5
12.5
272,795
6.7
0.7
0.8
20.6
2.0
27.5
2.2
11.8
0.8
35.5
1.2
3.6
12.5
Rhode Island
Providence
South Carolina
Charleston
167,673
8.0
1.0
0.8
19.0
2.0
23.1
2.5
9.1
0.9
28.2
1.2
5.1
8.8
Columbia
223,053
8.6
0.9
0.8
22.6
2.0
26.8
2.5
10.4
0.8
33.1
1.0
4.5
16.7
12.2
Florence
79,967
8.1
0.7
1.0
16.4
2.2
24.4
2.6
1.0
27.9
1.1
4.4
Greenville
176,797
9.1
0.9
1.0
21.7
2.2
32.0
2.4
9.9
0.7
35.3
0.9
4.7
9.3
84,989
9.5
0.8
1.1
24.2
2.6
31.2
2.9
11.4
0.7
36.4
0.8
5.0
12.4
Rapid City
44,470
7.7
0.8
0.5
1.7
46.2
0.8
0.8
35.1
(0.7)
3.5
Sioux Falls
229,172
7.0
0.8
0.6
32.8
1.5
46.5
1.0
20.3
0.7
39.4
0.8
2.9
26.4
21.0
Spartanburg
South Dakota
Tennessee
Chattanooga
150,251
9.2
0.7
0.5
38.3
1.6
55.2
1.3
24.9
0.5
50.7
0.8
3.6
Jackson
90,862
9.0
0.5
0.5
59.1
1.1
64.9
0.3
43.3
0.4
63.6
0.6
2.5
25.7
Johnson City
61,042
9.3
0.8
0.7
40.5
1.5
59.6
0.9
31.0
0.4
0.6
3.0
47.4
23.6
Kingsport
134,376
8.6
0.6
0.7
34.4
1.9
42.9
1.4
17.4
0.5
31.4
0.7
3.7
Knoxville
306,297
8.7
0.6
0.6
40.8
1.5
54.6
1.0
21.5
0.5
53.2
0.5
3.2
21.6
Memphis
355,268
8.7
0.7
0.6
34.6
1.5
40.6
1.1
17.1
0.6
30.6
0.9
3.4
23.8
Nashville
479,948
9.5
0.7
0.6
42.8
1.5
51.1
1.1
18.7
0.5
45.8
0.6
3.1
19.3
Texas
Abilene
86,747
8.4
0.8
0.4
34.2
1.1
53.0
0.8
20.8
0.5
37.2
0.9
2.9
20.6
Amarillo
102,436
9.1
1.1
0.4
54.1
1.2
55.5
0.8
28.1
0.6
46.0
0.8
2.7
19.3
Austin
155,017
8.8
0.7
0.5
39.5
1.3
39.6
1.4
15.9
0.6
52.2
0.9
3.4
18.4
Beaumont
110,830
8.4
0.7
0.6
28.6
1.3
51.5
1.3
14.3
0.8
44.3
0.7
2.6
24.8
37,121
8.2
0.8
(0.5)
1.0
65.7
1.0
Bryan
Corpus Christi
(0.4)
2.5
95,484
7.2
1.1
0.8
29.7
1.6
29.3
1.6
15.2
1.1
46.5
0.8
3.9
12.9
Dallas
541,927
8.8
0.8
0.6
35.3
1.2
43.7
1.4
15.8
0.6
37.5
0.8
3.0
21.8
El Paso
166,767
7.0
0.8
0.4
46.3
1.1
43.7
1.2
16.1
0.6
59.8
0.8
2.4
29.5
Fort Worth
228,615
8.9
0.8
0.5
24.6
1.4
48.0
1.3
15.8
0.7
43.6
0.9
2.7
21.1
Harlingen
84,691
5.6
0.8
0.6
31.9
1.1
41.9
0.8
20.3
0.7
43.4
1.1
3.5
21.1
Houston
615,858
8.0
0.7
0.5
41.7
1.2
47.5
1.1
24.9
0.6
47.6
0.7
2.5
22.2
Longview
47,038
7.4
0.5
0.6
1.3
57.6
1.5
16.7
0.8
34.3
(1.0)
2.9
Lubbock
152,404
10.0
1.1
0.6
47.7
1.2
51.1
0.8
24.0
0.6
64.3
0.8
2.8
Mcallen
71,206
5.2
0.7
0.5
46.9
0.9
40.6
0.8
20.0
0.5
33.3
0.5
2.5
43.8
21.3
0.6
57.1
Odessa
65,407
8.6
0.8
0.5
San Angelo
42,104
8.8
0.7
0.5
San Antonio
322,124
7.6
0.8
0.6
Temple
Tyler
61,627
7.2
0.6
0.3
137,277
8.5
0.8
0.6
1.0
41.2
1.3
1.3
49.1
0.9
31.0
1.5
44.2
1.3
0.7
48.9
34.1
1.4
52.3
0.3
18.8
0.7
0.9
19.0
0.3
1.1
23.0
0.7
37.9
44.4
28.3
1.0
3.1
(0.5)
2.8
27.9
0.8
3.0
30.7
0.5
2.1
16.9
0.9
2.8
19.8
136
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t
ea
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nt
Victoria
38,792
7.5
0.8
(0.5)
0.9
58.3
1.1
Waco
81,736
9.2
0.8
0.3
0.8
50.8
1.0
Wichita Falls
56,709
8.9
0.5
0.5
41.4
1.2
57.1
1.0
Ogden
54,866
6.8
0.7
0.8
32.6
2.2
30.1
Provo
51,575
7.2
0.6
0.4
1.4
58.7
259,354
6.8
0.7
0.6
43.4
1.8
139,146
7.4
0.6
0.5
31.1
ed
e
ur
56.8
(0.4)
(0.7)
2.3
0.5
43.2
0.7
2.6
28.1
0.6
41.7
(0.4)
2.6
33.3
1.8
14.0
0.8
35.6
(1.0)
3.5
25.5
1.2
17.7
0.7
31.4
(0.7)
3.1
17.9
42.2
1.3
20.9
0.7
31.6
0.9
3.3
16.9
1.5
44.7
1.0
17.9
0.5
35.3
0.4
2.6
11.5
13.0
Utah
Salt Lake City
Vermont
Burlington
Virginia
Arlington
209,501
7.9
0.9
0.8
22.3
2.0
34.2
2.2
10.9
0.8
32.7
0.9
3.7
Charlottesville
118,182
8.7
0.8
0.7
28.9
2.1
36.1
1.9
15.9
0.7
33.3
1.3
3.5
6.5
62,710
10.5
0.8
0.8
36.7
2.2
39.0
1.8
0.7
54.8
(0.6)
4.5
15.0
Lynchburg
Newport News
99,649
8.2
0.6
0.7
29.4
2.2
28.8
2.1
14.3
0.7
46.9
1.3
4.4
9.6
Norfolk
217,774
8.2
0.7
0.9
29.8
2.0
28.7
1.9
13.4
0.9
32.2
0.9
4.5
11.4
Richmond
306,150
8.7
0.8
0.7
22.6
1.9
36.9
1.7
12.0
0.6
45.5
0.8
4.3
14.0
Roanoke
189,533
8.9
0.7
0.8
30.3
2.1
33.3
1.8
12.8
0.6
35.9
0.8
3.7
12.0
79,370
8.2
0.5
0.8
38.1
2.3
31.7
1.9
12.7
0.6
41.3
1.0
3.3
Winchester
Washington
Everett
72,618
7.7
0.6
0.4
43.3
1.2
71.4
0.8
32.2
0.3
0.4
1.5
36.2
Olympia
60,845
7.5
0.6
0.5
66.7
1.1
71.4
0.7
27.9
0.4
47.8
0.3
2.1
50.7
Seattle
378,233
7.2
0.6
0.3
58.3
1.2
63.3
0.7
32.7
0.3
64.1
0.4
2.1
36.0
Spokane
269,710
7.8
0.7
0.3
55.7
1.1
70.9
0.6
33.3
0.4
57.7
0.6
2.2
21.2
Tacoma
102,183
7.4
0.5
0.4
65.0
1.1
71.4
0.5
60.0
0.3
68.6
0.6
2.0
26.6
Yakima
55,663
7.9
0.6
0.4
44.0
1.5
50.6
0.8
(0.7)
3.6
16.5
0.3
West Virginia
Charleston
249,276
7.3
0.6
0.7
40.5
2.1
34.1
1.8
16.1
0.5
44.8
0.9
3.7
24.8
Huntington
98,963
8.7
0.7
0.8
25.6
2.4
31.3
2.4
11.9
0.7
31.4
1.0
4.9
32.0
Morgantown
109,926
7.6
0.7
0.7
27.5
2.1
35.7
1.7
18.9
0.7
36.5
0.8
3.5
13.3
Wisconsin
Appleton
76,638
6.6
0.7
0.5
36.6
2.0
35.9
1.0
0.7
28.6
0.9
4.1
17.3
Green Bay
130,120
6.3
0.7
0.5
45.5
1.6
42.2
0.8
19.8
0.6
48.9
0.6
3.5
21.8
La Crosse
93,929
7.0
0.6
0.6
37.9
1.8
43.8
1.1
13.0
0.6
30.4
0.7
3.3
9.6
Madison
221,656
6.4
0.6
0.5
37.1
1.4
45.6
0.8
13.0
0.6
30.5
0.8
2.7
11.9
Marshfield
107,048
7.2
0.6
0.8
23.8
1.8
36.4
1.2
15.2
0.8
30.2
0.9
4.1
15.8
Milwaukee
551,015
6.8
0.7
0.7
24.3
1.8
36.0
1.7
9.8
0.8
27.3
1.0
4.2
11.9
Neenah
59,869
7.0
0.6
0.6
43.2
1.6
42.0
1.2
0.8
31.4
0.8
3.5
24.3
Wausau
53,502
7.4
0.7
0.9
28.6
1.8
42.9
1.2
0.7
46.2
(0.7)
3.6
44,079
7.9
0.7
0.6
50.0
1.1
76.0
1.0
26.8
0.5
55,013,603
7.7
0.7
0.6
30.3
1.7
37.2
1.6
14.3
0.6
Wyoming
Casper
2.7
48.3
3.4
16.5
United States
38.7
0.9
CHAPTER FIVE
Conclusions
138
Conclusions
The Dartmouth Atlas of Musculoskeletal Health Care describes how health care for
musculoskeletal injuries and disease is currently being delivered in the United States.
Several themes arise: the variability in the distribution of the workforce; the geographic variation in the incidence of fractures; the increasing use of musculoskeletal
procedures; and the marked geographic variation in the likelihood that patients with
musculoskeletal injuries and disease will undergo surgical treatment.
The Orthopaedic Workforce
In an ideal world, the orthopaedic workforce would be rationally deployed and engaged in delivering care that has been demonstrated to be effective and that patients
are known to want. This would be achieved by measuring population injury and
disease and by ascertaining individuals’ preferences about valid treatment options.
If these conditions prevailed, it would be possible to recruit workforces appropriate
to meet the demand for effective care.
There is little evidence that orthopaedic surgeons and neurosurgeons practice where
they do in response to demand for their services, as measured by population health
factors. This raises an important question: Do populations living in areas with
fewer orthopaedic surgeons have inadequate access to an important resource? Or
is there an oversupply in areas with more orthopaedic surgeons? What is the “right”
number of surgeons needed to perform musculoskeletal procedures in a given area?
How many more (or how many fewer) physicians should be trained and deployed
to meet, but not exceed, the need for their services?
Because there is no direct way to measure the demand for musculoskeletal surgery,
one way to begin answering these questions about the orthopaedic workforce capacity and distribution is “benchmarking.” Benchmarking uses a given area or practice
as a standard against which to measure levels of staffing. This method compares
workforce capacity in local markets to those in other regions, looking as well at local
utilization patterns. Using this information, local planners can address the question
of how many orthopaedic surgeons are needed, at least in relative terms.
CONCLUSIONS
Workforce planning in musculoskeletal health care must also account for the distribution of the different types of specialists performing musculoskeletal procedures.
Most musculoskeletal procedures are performed by orthopaedic surgeons, although
neurosurgeons perform nearly 70% of all spine operations. The types of specialists
performing major spine surgery vary markedly among geographic areas. Much of this
regional variation cannot be easily explained; it probably reflects the patterns in which
physicians develop their practices and recruit new surgeons of the same specialty.
Which surgeons should be performing major orthopaedic operations? In areas like
major joint arthroplasty and spine surgery, should subspecialty certification be required? Should there be minimal procedure volume standards? To answer these
questions, more information is needed about the importance of additional specialty training and procedural volume in determining patient outcomes with
different procedures.
Increasing Use of Musculoskeletal Procedures
Rates of surgery for many common conditions are increasing rapidly. In the Medicare population, for example, rates of total joint replacement surgery more than
doubled between 1988-1997. Over the same time period, rates of spine surgery in
Medicare patients increased by 57%.
Is increasing use of surgery good for patients? It probably is for some musculoskeletal conditions. There is accumulating evidence that total joint replacement is the
most effective therapy for patients with severe hip or knee osteoarthritis and that
these procedures significantly improve patients’ quality of life. Recent studies suggest that total joint replacement might even be underutilized in some patient
groups, including women and minorities (a possibility also suggested by data presented in Chapter Three of this Atlas).
For people with other conditions, however, more surgery might not provide a benefit. For example, the value of spine surgery for different subgroups of patients with
139
140
herniated discs and spinal stenosis is debatable. Spine fusion and spinal instrumentation — which have nearly doubled over the past decade — are even more
controversial.
Variation in the Use of Musculoskeletal Procedures
While geographic variation in the use of surgery has long been recognized, not all
surgical procedures are equally variable. As described in earlier editions of the Atlas,
rates of colon resection, like rates of hospitalization for hip fracture, vary only
slightly between regions. Other procedures, such as back surgery for herniated disc,
are highly variable, depending on region.
What distinguishes low variation from high variation surgery? In general, low variation procedures are non-discretionary; they are used to treat clinical conditions for
which physicians agree on the most appropriate treatment strategy. In addition,
patient and doctor preferences are aligned — both parties have the same goals.
Conversely, high variation procedures involve physician discretion; the variability
reflects underlying problems in medical decision making that occur because of inadequate science and failure to take patient preferences into account.
■ Sometimes, medical science is inadequate to provide definitive information on
which treatment is likely to provide the best outcome for a given patient. In these
cases, procedure rates vary because physicians disagree about the effectiveness of
surgery.
■ Sometimes, the scientific evidence regarding outcomes is adequate, but the available treatments have different risks and benefits, which only the patient can assess.
The fact that patient preferences are unevenly incorporated into treatment decisions
results in high variation in procedure rates.
Treatment rates for musculoskeletal disease among Medicare patients vary much
more than would be expected by chance alone or by regional differences in the
CONCLUSIONS
141
prevalence of injury or disease. Rates of elective total joint arthroplasty were least
variable, with rates varying four-fold among regions — about twice the variability
of hip fracture repair. Other musculoskeletal procedures, however, were among the
most variable of all the surgical procedures examined by the Atlas project to date.
Among the common procedures varying more than ten-fold were some types of
arthroscopy (shoulder), surgery for common fractures (wrist fractures and others),
and many common spine operations.
Figure 5.1. Profiles of Variation in the Use of Surgical Treatment (1996-97)
The use of surgery for hip fracture was least variable; nearly all hip fractures were treated surgically. The range of variation in surgical treatment of
other kinds of musculoskeletal problems was far greater; lumbar discectomy was the most variable. Each point represents the rate of surgery in each
of the 306 hospital referral regions.
142
Hip
Fracture
(1996-97)
Femur
Fracture
(1996-97)
Total Joint
Arthroplasty
(1996-97)
Knee
Arthroscopy
(1996-97)
Ankle
Fracture
(1996-97)
Spine
Surgery
(1996-97)
Carpal Tunnel
Release
(1997)
Lumbar
Discectomy
(1996-97)
12.8
19.4
22.3
29.4
30.3
30.7
31.1
42.4
1.0
1.5
1.7
2.3
2.4
2.4
2.4
3.3
2.2
4.2
4.4
7.9
5.8
5.9
5.5
15.4
1.2
1.2
1.3
1.4
1.6
1.5
1.5
1.7
6
31
26
50
79
49
60
64
4
13
45
40
46
59
67
75
Index of Variation
Ratio to CV of treatment of hip fracture
Range of Variation
Table 5.1. Use of Surgical Treatment (1996-97)
There is little variation in how hip fractures are treated; virtually all fractures are treated surgically (coefficient of variation = 12.8). By contrast, there
is substantial variation in rates of surgery for ankle fracture (coefficient of variation = 30.3), and even more variation in rates of lumbar discectomy
(coefficient of variation = 42.4). The extremal ratio — the proportion of fractures treated surgically in the region with the highest proportion, compared
to the region with the lowest proportion — varies from 2.2 for hip fracture to 15.4 for lumbar discectomy. Only four hospital referral regions had rates
of surgical repair of hip fractures at least 30% higher than the national average, and only six had rates more than 25% below the average, but 75
regions had rates of lumbar discectomy at least 30% higher than the national average, and 64 regions had rates more than 25% below the average.
Which Rate is Right?
Such large regional variations in practice style reflect a lack of consensus among
physicians about how best to treat musculoskeletal injury and disease. This represents both a challenge and an opportunity for physicians caring for patients with
musculoskeletal injury and disease. Standardizing the treatment of musculoskeletal
injury and disease in the United States will require more controlled clinical trials
and outcomes analysis, as well as practice guidelines and consensus statements from
the professional societies of the relevant specialties. Equally important, reducing
regional variation in musculoskeletal health care will require better and more explicit
approaches to incorporating patient preferences into treatment decisions.
Appendix on Methods
144
Appendix on Methods
1. The Geography of Health Care in the United States
1.1 Files used in the Atlas
The Atlas depends on the integrated use of databases provided by the American
Hospital Association (AHA), the American Medical Association, the American
Osteopathic Association, and several federal agencies, including the Agency for
Health Care Policy and Research, the Bureau of the Census, the Health Care Financing Administration, the National Center for Health Statistics, and the
Department of Veterans’ Affairs. Table 1 lists these files, along with a short description of each. Not all of these files were used in the Musculoskeletal Atlas.
Table 1. Files Used in the Atlas
Medicare Files
File
Year Used
(Sample)
Source
Description and Use in Analysis
Denominator File
1996/1997
(100%)
HCFA
Contains one record for each Medicare beneficiary, and includes demographic information (age,
sex, race), residence (ZIP Code), program eligibility and mortality. Used to determine
denominators for utilization rates and to determine mortality.
MEDPAR File
1996/1997
(100%)
HCFA
One record for each hospital stay by Medicare beneficiaries. Includes data on dates of
admission / discharge, diagnoses, procedures and Medicare reimbursements to the hospital.
Used for (1) allocation of acute care resources and physicians and (2) numerators for utilization
rates.
Medicare Provider
of Services File
1997
HCFA
Includes a record for each hospital eligible to provid e inpatient care through Medicare. Includes
location and resource data. Used in measuring acute care resource investments.
HCFA
Includes physician/supplier claims for services paid by the Part B program in 1995b and 96. A
majority of services are provided in office, inpatient, outpatient, home, and nursing home
settings. Used to measure physician visit rates, and rates of certain diagnostic procedures and
preventive services.
1993 – 1997
Part B Standard
Analytical Variable (5%)
1996/1997
Length File
(100%)
APPENDIX ON METHODS
Resource Files
File
Year Used
Source
American Hospital
Association
Annual Survey of
Hospitals
1996
American
Hospital
Association
Includes a record for each hospital registered with the AHA. Used in measuring acute care
resources (beds, personnel).
Physician File
1996
American Medical Includes one record for each allopathic physician with practice ZIP Code, self -designated
Association
specialty, major professional activities, and federal / non-federal status. Used to determine
specialty-specific counts of physicians in each health care market.
UPIN File
1996
HCFA
Provides unique physician identifier, their primary and secondary specialties and zip code
locations of practice, credentials, age, and licensing state. Used in the analysis of physician visit
rates.
Year Used
Source
National Ambulatory 1989-1994
Medical Care Survey
(NAMCS)
NTIS
Ambulatory services from samples of patient records selected from a national sample of office based physicians. Allows estimation of age-sex specific use rates by specialty. Used for agesex adjustment of physician workforce.
Population files
1998
Claritas, Inc.,
Arlington, VA
1990 STF3 data from the U.S. Bureau of the Census was adapted by Claritas, Inc. to 1997 ZIP
Code geography; includes 1998 age-sex specific estimated counts of residents in the ZIP Code.
Used (1) for age-sex adjustment, (2) as denominator for rates of allocated and adjusted
resources.
ZIP Code boundary
files
1997
Geographic Data
Technology,
Lebanon, NH
Includes records for each ZIP Code with the coordinates of the boundary precisely specified.
Used as basis for mapping HSAs and HRRs and for assigning ZIP Codes appropriately.
Other Files
File
145
146
1.2 Defining Hospital Service Areas
Hospital Service Areas (HSAs) represent local health care markets for communitybased inpatient care. The definitions of HSAs used in the 1996 through 1999
editions of the Dartmouth Atlas were retained in the Musculoskeletal Atlas. HSAs
were originally defined in three steps using 1993 provider files and 1992-93 utilization data. First, all acute care hospitals in the 50 states and the District of
Columbia were identified from the American Hospital Association Annual Survey
of Hospitals and the Medicare Provider of Services files and assigned to a location
within a town or city. The list of towns or cities with at least one acute care hospital
(N=3,953) defined the maximum number of possible HSAs. Second, all 1992 and
1993 acute care hospitalizations of the Medicare population were analyzed according to ZIP Code to determine the proportion of residents’ hospital stays that
occurred in each of the 3,953 candidate HSAs. ZIP Codes were initially assigned to
the HSA where the greatest proportion (plurality) of residents were hospitalized.
Approximately 500 of the candidate HSAs did not qualify as independent HSAs because the plurality of patients resident in those HSAs were hospitalized in other HSAs.
The third step required visual examination of the ZIP Codes used to define each
HSA. Maps of ZIP Code boundaries were made using files obtained from Geographic Data Technologies (GDT) and each HSA’s component ZIP Codes were
examined. In order to achieve contiguity of the component ZIP Codes for each
HSA, “island” ZIP Codes were reassigned to the enclosing HSA, and/or HSAs were
grouped into larger HSAs (See the Appendix on the Geography of Health Care in
The United States for an illustration). Certain ZIP Codes used in the Medicare files
were restricted in their use to specific institutions (e.g., a nursing home) or a post
office. These “point ZIPs” were assigned to their enclosing ZIP Code based on the
ZIP Code boundary map.
This process resulted in the identification of 3,436 HSAs, ranging in total 1998 population from 604 (Turtle Lake, North Dakota) to 3,067,356 (Houston) in the 1999
edition of the Atlas. Thus, the HSA boundaries remained the same but the HSA
populations may have changed between the different editions of the Atlas. In most
APPENDIX ON METHODS
HSAs, the majority of Medicare hospitalizations occurred in a hospital or hospitals
located within the HSA. See the Appendix on the Geography of Health Care in the
United States in the 1999 edition of the Dartmouth Atlas for further details.
1.3 Defining Hospital Referral Regions
Hospital referral regions (HRRs) represent health care markets for tertiary medical
care. As defined previously in the 1996 Atlas, each HRR contained at least one HSA
that had a hospital or hospitals that performed major cardiovascular procedures and
neurosurgery in 1992-93. Three steps were taken to define HRRs.
First, the candidate hospitals and HRRs were identified. A total of 862 hospitals
performed at least 10 major cardiovascular procedures (DRGs 103-107) on Medicare enrollees in both years. These hospitals were located within 458 HSAs, thereby
defining the maximum number of possible HRRs. Further checks verified that all
458 HSAs included at least one hospital performing the specified major neurosurgical procedures (DRGs 1-3 and 484).
Second, we calculated in each of the 3,436 HSAs in the United States the proportion of major cardiovascular procedures performed in each of the 458 candidate
HRRs in 1992-93. Each HSA was then assigned provisionally to the candidate
HRR where most patients went for these services.
Third, HSAs were reassigned or further grouped to achieve (a) geographic contiguity, unless major travel routes (e.g., interstate highways) justified separation (this
occurred in only two cases, the New Haven, Connecticut, and Elmira, New York,
HRRs); (b) a minimum population size of 120,000; and (c) a high localization
index. Because of the large number of hospitals providing cardiovascular services in
California, several candidate California HRRs met the above criteria but were found
to perform small numbers of cardiovascular procedures. These HRRs were further
aggregated according to county boundaries to achieve stability of cardiovascular
surgery rates within the areas.
147
148
The process resulted in the definition of 306 hospital referral regions which ranged in
total 1998 population from 126,329 (Minot, North Dakota) to 9,288,694 (Los Angeles) in the Musculoskeletal Atlas. See the Appendix on the Geography of Health Care
in the United States in the 1999 edition of the Dartmouth Atlas for further details.
1.4 Populations of HSAs and HRRs
Total population counts were estimated for residents of all ages in each HSA using
1995 ZIP Code level files obtained from GDT and Claritas, Inc. The Claritas file
is based on the latest U.S. Census STF3B ZIP Code file, updated to account for
changes in ZIP Code definitions. Population counts for HRRs are the sum of the
counts of the constituent HSAs. These serve as denominators for estimating rates
for catheterization laboratory resource and cardiovascular physician workforce
allocations.
For rates that apply to the Medicare population for the years 1996 or 1996-97,
enrollee counts were obtained from the Medicare Denominator file. The 1996 and
1997 Medicare enrollee population included those alive and age 65 to age 99 on
June 30, 1996 and 1997, respectively, and were summed to give person-years. For
all rates, the numerator and the denominator counts exclude those who were enrolled in risk bearing HMOs on June 30.
2. Orthopaedic Surgery Workforce Rates
The geographic analysis of the orthopaedic workforce supply was performed by two
separate approaches: 1) based on physicians’ self-designation of themselves as clinically active orthopaedists, primarily from the AMA file; and 2) based on numbers
of physicians performing specific orthopaedic procedures on Medicare patients. The
first approach was felt to estimate an “upper bound” estimate of the supply of
orthopaedists, because some physicians may incorrectly classify themselves as
orthopaedists and because some orthopaedists may incorrectly classify themselves as
clinically active. The second approach was felt to represent the “lower bound” estimate: it excludes some clinically active orthopaedists whose practice does not involve
APPENDIX ON METHODS
Medicare patients (e.g., exclusively pediatric or sports medicine orthopaedists) and
orthopaedists whose only exposure to Medicare patients occurs in the context of riskbearing Medicare managed care.
2.1 Estimates of Orthopaedist Supply Based on Self-Designation
We identified all physicians self-designating themselves as orthopaedic surgeons (regardless of any subspecialty designations), as listed in the AMA or AOA masterfiles. We
also included self designated orthopaedists from the Medicare UPIN file, as long as
these physicians did not have another primary specialty assignment listed in the
AMAor AOA files. We included only clinically active orthopaedists — those physicians listed in the masterfiles as spending at least half of their professional time
providing direct patient care. Clinically active orthopaedists were then allocated to
hospital referral regions based on the zip code of their primary practice location.
2.2 Estimates of Orthopaedist Supply Based on Clinical Activity in Medicare Patients
This approach required the same three basic steps: 1) identifying the potential pool
of practicing physicians involved in musculoskeletal health care; 2) determining
whether they were “clinically active;” and 3) allocating them to the geographic
regions they serve and calculating adjusted per capita rates.
2.3 Identifying the Potential Pool of Practicing Orthopaedists
Based on unique provider identifiers (UPIN numbers), we used a 100% national
sample of the 1996 part B claims database to identify all physicians listed as the
primary operator for at least two procedures believed to be performed (almost) exclusively by orthopaedic surgeons, including total joint arthroplasty and operations
for humerus, hip, femur, or tibia fractures. (These procedures and codes used to
identify them are described later in the Appendix).
We also considered physicians with at least two procedures from the following procedure categories: spine (any), wrist or hand (operative fracture care or carpel tunnel
release), foot (open procedures for bunions, hammertoes, Morton’s neuromas, or
fractures), and arthoscopy. Because many of these procedures are also performed by
149
150
other types of surgical specialists, we counted as potential orthopaedists those with
claims indicating the primary UPIN designation of orthopaedics.
2.4 Identifying Clinically Active Orthopaedists. To determine which of these
orthopaedists were clinically active, we identified all procedure claims submitted for
Medicare patients in 1996 across the entire range of CPT codes for musculoskeletal
procedures. Surgeons were then ranked according to clinical activity, defined by the
total work-based relative value units (RVUs) associated with these procedures. Surgeons falling in the lowest 2.5% of clinical activity were considered insufficiently
active to be considered clinically active (97.5% of orthopaedists are clinically active
in the AMA’s Physician Characteristics and Distribution in the U.S., 1997–98).
Table 2. Procedure-based Workload of Orthopaedic Surgeons in Medicare (1996).
Surgeon
Percentile
Classification of
Clinical Activity
Number of
Procedures
Min
Inactive
2
6.3
1%
Inactive
2
36.2
2.5%
Inactive
2
57.2
5%
Active
4
90.4
10%
Active
7
162.9
25%
Active
18
389.2
50%
Active
39
797.8
75%
Active
68
1351.4
90%
Active
104
1999.9
95%
Active
131
2525.6
99%
Active
211
4093.6
Max
Active
805
19335.8
Total RVUs
2.5 Allocation of Clinically Active Physicians. Clinically active physicians were
then allocated to the HSAs in which they provided services in 1996. Based on workbased RVUs, the proportion of work performed by each physician on patients
residing in different HSAs was calculated. Each FTE physician was allocated to the
APPENDIX ON METHODS
HSAs in which their patients resided. For example, if Dr. Smith performed 80% of
her total RVUs for patients residing in the Portland, ME HSA, 10% in the
Biddeford, ME HSA, and 10% in the Bridgeton, ME HSA, her workforce allocation would be 0.8 FTE to Portland and 0.1 FTE each to Biddeford and Bridgeton.
The total number of specialists in each HSA was then the sum of the total number
of FTEs allocated to that area (often a non-integer). The total number of physicians
in each HRR is the sum of these specialists allocated to the HSAs comprising the
HRR. The allocated supply of orthopaedic physicians in each HRR was adjusted for
age and sex using the indirect method, as described in Section 4, using the 1995
U.S. population as the standard.
2.6 Specialist “Marketshare”
Chapters Two through Four describe for different procedures the proportion of
cases done by different types of specialists. In contrast to the analyses of workforce
supply (above), specialist marketshare was determined by identifying the Unique
Physician Identification Number (UPIN) accompanying each procedure claim.
Specialty designation was then determined from information contained in the
UPIN file, which contains background information and other characteristics of
physicians who submit bills to Medicare. In this file, primary clinical specialty is
self-designated by physicians.
We used the following codes to identify different specialist groups:
Table 3. Codes Used to Identify Specialist Groups
Specialist Group in the Atlas
UPIN Codes and Specialty Label
Orthopaedic Surgeons
20 Orthopaedic surgery
Neurosurgeons
14 Neurosurgery
Plastic Surgeons
24 Plastic surgery
Podiatrists
48 Podiatry
General Surgeons
02 General surgery
28 Colorectal surgery
33 Thoracic surgery
77 Vascular surgery
91 Surgical oncology
151
152
Although the large majority of physicians cite a single specialty, the UPIN file contains two fields. In creating mutually exclusive physician specialty categories, we
used a hierarchical algorithm, in the order indicated by the table. For each procedure, a small percentage of physicians had none of the above codes. Their specialty
was classified as “Other.” Thus, an individual physician with codes for both orthopaedic surgery and podiatry would be counted as an orthopaedist. The UPIN file
also contains a code for hand surgery. In the Atlas, we did not consider this a discrete surgical subspecialty and used accompanying codes to classify these physicians.
3. Surgical Procedure Rates
Surgical rates represent counts of the number of procedures that occurred in a defined time period (the numerator) for a specific population (the denominator). The
counts of procedures were based on Medicare Part B data for 1996 and 1997. The
denominator is the 1996-97 Medicare enrollee population defined in Section 1.5
that was enrolled in Medicare part Part B on June 30, 1996 or 1997. To ensure that
the population included in the numerator corresponded to the denominator population, we excluded records likely to be invalid or duplicative (processing indicator
variable = I or M; allowable charges = 0) and records for patients enrolled in riskbearing health maintenance organizations.
3.1 Procedures Examined in the Atlas
The specific procedures, or “numerator events,” and the codes used to identify the
events in the file are given in Table 4, and were identified from Part B claims using
CPT codes. Selection of procedure codes was based on review of the literature and/
or consultation with clinical experts.
APPENDIX ON METHODS
Table 4. Codes Used to Identify Procedures
1. Femoral Neck/Intertroch - Operative
CPT code
Description
1996 count #
1997 count #
27235
PERCUTANEOUS SKELETAL FIXATION, FEMORAL FX, PROXIMAL, NECK
16,191
16,164
27236
OPEN TREATMENT, FEMORAL FX, PROXIMAL, NECK, INT FIXATION/PROSTHETIC
89,382
87,752
27244
OPEN TREATMENT, INTER/PER/SUBTROCHANTERIC FEMORAL FX; W/
PLATE/SCREW TYPE IMPLANT
106,664
104,312
27245
OPEN TREATMENT, INTER/PER/SUBTROCHANTERIC FEMORAL FX; W/
INTRAMEDULLARY IMPLANT
4,529
4,701
216,766
212,929
1996 count #
1997 count #
3,244
3,001
462
456
2,139
1,945
573
541
6,418
5,943
1996 count #
1997 count #
TOTAL
2. Femoral Neck/Intertroch - Nonoperative
CPT code
Description
27230
CLOSED TREATMENT, FEMORAL FX, PROXIMAL END, NECK; W/O MANIPULATION
27232
CLOSED TREATMENT, FEMORAL FX, PROXIMAL END, NECK; W/ MANIPULATION
27238
CLOSED TREATMENT, INTER/PER/SUBTROCHANTERIC FEMORAL FX; W/O
MANIPULATION
27240
CLOSED TREATMENT, INTER/PER/SUBTROCHANTERIC FEMORAL FX; W/
MANIPULATION
TOTAL
3. Femur Below Intertroch - Operative
CPT code
Description
27506
OPEN TREATMENT, FEMORAL SHAFT FX, W/ INSERTION, INTRAMEDULLARY
IMPLANT
6,153
5,901
27507
OPEN TREATMENT, FEMORAL SHAFT FX W/ PLATE/SCREWS, W/WO CERCLAGE
3,040
3,009
27509
PERCUTANEOUS SKELETAL FIXATION, FEMORAL FX, DISTAL END
313
318
27511
OPEN TREATMENT, FEMORAL SUPRACONDYLAR/TRANSCONDYLAR FX W/O
INTERCONDYLAR EXTENSION
2,995
3,106
27513
OPEN TREATMENT, FEMORAL SUPRACONDYLAR/TRANSCONDYLAR FX W/
2,003
2,144
27514
OPEN TREATMENT, FEMORAL FX, DISTAL END, MEDIAL/LATERAL CONDYLE,
W/WO INT/EXT FIXATION
1,237
1,200
15,741
15,678
1996 count #
1997 count #
TOTAL
4. Femur Below Intertroch - Nonoperative
CPT code
Description
27500
CLOSED TREATMENT, FEMORAL SHAFT FX, W/O MANIPULATION
1,178
1,117
27501
CLOSED TREATMENT, SUPRACONDYLAR/TRANSCONDYLAR FEMORAL FX, W/O
MANIPULATION
1,374
1,458
27502
CLOSED TREATMENT, FEMORAL SHAFT FX, W/ MANIPULATION, W/WO
SKIN/SKELETAL TRACTION
768
692
153
154
27503
CLOSED TREATMENT, SUPRACONDYLAR/TRANSCONDYLAR FEMORAL FX, W/
MANIPULATION
928
931
27508
CLOSED TREATMENT, FEMORAL FX, DISTAL END, MEDIAL/LATERAL CONDYLE,
W/O MANIPULATION
1,523
1,429
27510
CLOSED TREATMENT, FEMORAL FX, DISTAL END, MEDIAL/LATERAL CONDYLE,
W/ MANIPULATION
525
470
6,296
6,097
1996 count #
1997 count #
1,768
1,758
930
856
87
91
276
273
TOTAL
5. Proximal Tibia and Tibial Shaft - Operative
CPT code
Description
27535
OPEN TREATMENT, TIBIAL FX, PROXIMAL; UNICONDYLAR, W/WO INT/EXT
FIXATION
27536
OPEN TREATMENT, TIBIAL FX, PROXIMAL; BICONDYLAR, W/WO INT/EXT
FIXATION
27540
OPEN TREATMENT, INTERCONDYLAR SPINE/TUBEROSITY FX, KNEE, W/WO
INT/EXT FIXATION
27756
PERCUTANEOUS SKELETAL FIXATION, TIBIAL SHAFT FX
27758
OPEN TREATMENT, TIBIAL SHAFT FX, W/ PLATE/SCREWS, W/WO CERCLAGE
27759
OPEN TREATMENT, TIBIAL SHAFT FX, INTRAMEDULLARY IMPLANT, W/WO
SCREWS/CERCLAGE
999
893
1,415
1,482
29850
ARTHROSCOPICALLY AIDED TREATMENT, FX, KNEE W/WO MANIPULATION; W/O
INT/EXT FIXATION
47
44
29851
ARTHROSCOPICALLY AIDED TREATMENT, FX, KNEE W/WO MANIPULATION; W/
INT/EXT FIXATION
16
11
29855
ARTHROSCOPICALLY AIDED TREATMENT, TIBIAL FX, PROXIMAL; UNICONDYLAR,
167
219
29856
ARTHROSCOPICALLY AIDED TREATMENT, TIBIAL FX, PROXIMAL; BICONDYLAR,
13
20
5,718
5,647
1996 count #
1997 count #
5,757
5,754
TOTAL
6. Proximal Tibia and Tibial Shaft - Nonoperative
CPT code
Description
27530
CLOSED TREATMENT, TIBIAL FX, PROXIMAL; W/O MANIPULATION
27532
CLOSED TREATMENT, TIBIAL FX, PROXIMAL; W/ SKELETAL TRACTION
690
580
27538
CLOSED TREATMENT, INTERCONDYLAR SPINE/TUBEROSITY FX, KNEE, W/WO
MANIPULATION
171
164
27750
CLOSED TREATMENT, TIBIAL SHAFT FX; W/O MANIPULATION
3,702
3,755
27752
CLOSED TREATMENT, TIBIAL SHAFT FX; W/ MANIPULATION, W/WO SKELETAL
TRACTION
3,414
3,206
13,734
13,459
TOTAL
APPENDIX ON METHODS
7. Ankle - Operative
CPT code
Description
27766
OPEN TREATMENT, MEDIAL MALLEOLUS FX, W/WO INT/EXT FIXATION
27792
OPEN TREATMENT, DISTAL FIBULAR FX, W/WO INT/EXT FIX
2,547
2,629
27814
OPEN TREATMENT, BIMALLEOLAR ANKLE FX, W/WO INT/EXT FIXATION
7,789
7,703
27822
OPEN TREATMENT, TRIMALLEOLAR ANKLE FX, MEDIAL/LATERAL MALLEOLUS;
W/O FIXATION
4,750
4,526
27823
OPEN TREATMENT, TRIMALLEOLAR ANKLE FX, MEDIAL/LATERAL MALLEOLUS; W/
FIXATION
971
885
27826
OPEN TREATMENT, FX, WT BEARING ARTICULAR SURFACE, DISTAL TIBIA, W/
FIXATION; FIBULA
69
83
27827
OPEN TREATMENT, FX, WT BEARING ARTICULAR SURFACE/PORTION, DISTAL
TIBIA, W/ FIXATION; TIBIA
201
229
27828
OPEN TREATMENT, FX, WT BEARING ARTICULAR SURFACE, DISTAL TIBIA, W/
FIXATION; FIBULA &TIBIA
416
464
27829
OPEN TREATMENT, DISTAL TIBIOFIBULAR JOINT DISRUPTION, W/WO INT/EXT
FIXATION
343
373
17,942
17,804
1996 count #
1997 count #
2,710
2,588
375
316
19,933
19,070
2,050
1,741
TOTAL
1996 count #
1997 count #
856
912
8. Ankle - Nonoperative
CPT code
Description
27760
CLOSED TREATMENT, MEDIAL MALLEOLUS FX; W/O MANIPULATION
27762
CLOSED TREATMENT, MEDIAL MALLEOLUS FX; W/ MANIPULATION, W/WO
SKIN/SKELETAL TRACTION
27786
CLOSED TREATMENT, DISTAL FIBULAR FX (LATERAL MALLEOLUS); W/O
MANIPULATION
27788
CLOSED TREATMENT, DISTAL FIBULAR FX (LATERAL MALLEOLUS); W/
MANIPULATION
27808
CLOSED TREATMENT, BIMALLEOLAR ANKLE FX, (W/ POTTS); W/O MANIPULATION
3,776
3,618
27810
CLOSED TREATMENT, BIMALLEOLAR ANKLE FX, (W/ POTTS); W/ MANIPULATION
2,027
1,903
27816
CLOSED TREATMENT, TRIMALLEOLAR ANKLE FX; W/O MANIPULATION
784
762
27818
CLOSED TREATMENT, TRIMALLEOLAR ANKLE FX; W/ MANIPULATION
1,235
1,122
27824
CLOSED TREATMENT, FX, WT BEARING ARTICULAR PORTION, DISTAL TIBIA; W/O
MANIPULATION
387
394
27825
CLOSED TREATMENT, FX, WT BEARING ARTICULAR PORTION, DISTAL TIBIA; W/
SKELETAL TRACTION
184
193
33,461
31,707
TOTAL
155
156
9. Metatarsal/Other Toe - Operative
CPT code
Description
1996 count #
1997 count #
28476
PERCUTANEOUS SKELETAL FIXATION, METATARSAL FX, W/ MANIPULATION,
EACH
167
152
28485
OPEN TREATMENT, METATARSAL FX, W/WO INT/EXT FIXATION, EACH
789
744
28496
PERCUTANEOUS SKELETAL FIXATION, FX GREAT TOE, PHALANX/PHALANGES,
W/ MANIPULATION
41
50
28505
OPEN TREATMENT, FX GREAT TOE, PHALANX/PHALANGES W/WO INT/EXT
FIXATION
302
292
28525
OPEN TREATMENT, FX, PHALANX/PHALANGES, NOT GREAT TOE, EACH
230
192
1,529
1,430
1996 count #
1997 count #
21,770
21,957
TOTAL
10. Metatarsal/Other Toe - Nonoperative
CPT code
Description
28470
CLOSED TREATMENT, METATARSAL FX; W/O MANIPULATION, EACH
28475
CLOSED TREATMENT, METATARSAL FX; W/ MANIPULATION, EACH
1,717
1,636
28490
CLOSED TREATMENT, FX GREAT TOE, PHALANX/PHALANGES; W/O
MANIPULATION
2,936
2,926
28495
CLOSED TREATMENT, FX GREAT TOE, PHALANX/PHALANGES; W/ MANIPULATION
437
425
28510
CLOSED TREATMENT, FX, PHALANX/PHALANGES, NOT GREAT TOE; W/O
MANIPULATION, EACH
7,433
7,259
28515
CLOSED TREATMENT, FX, PHALANX/PHALANGES, NOT GREAT TOE; W/
MANIPULATION, EACH
1,660
1,549
35,953
35,752
1996 count #
1997 count #
3,803
3,804
418
424
TOTAL
11. Proximal Humerus - Operative
CPT code
Description
23615
OPEN TREATMENT, PROXIMAL HUMERAL FX, W/WO INT/EXT
FIXATION/TUBEROSITY REPAIR;
23630
OPEN TREATMENT, GREATER HUMERAL TUBEROSITY FX W/WO INT/EXT
FIXATION
23670
OPEN TREATMENT, SHOULDER DISLOCATION, W/ FX, GREATER TUBEROSITY,
W/WO EXTERNAL FIXATION
41
36
23680
OPEN TREATMENT, SHOULDER DISLOCATION, W/ SURGICAL/ANATOMICAL NECK
FX
49
38
4,311
4,302
TOTAL
APPENDIX ON METHODS
12. Proximal Humerus - Nonoperative
CPT code
Description
23600
CLOSED TREATMENT, PROXIMAL HUMERAL FX; W/O MANIPULATION
1996 count #
1997 count #
33,127
32,414
23605
CLOSED TREATMENT, PROXIMAL HUMERAL FX; W/ MANIPULATION
5,043
4,485
23620
CLOSED TREATMENT, GREATER HUMERAL TUBEROSITY FX; W/O
MANIPULATION
2,579
2,596
23625
CLOSED TREATMENT, GREATER HUMERAL TUBEROSITY FX; W/ MANIPULATION
328
293
23665
CLOSED TREATMENT, SHOULDER DISLOCATION, W/ FX, GREATER TUBEROSITY,
W/ MANIPULATION
68
53
23675
CLOSED TREATMENT, SHOULDER DISLOCATION, W/ HUMORAL NECK FX, W/
MANIPULATION
58
33
41,203
39,874
1996 count #
1997 count #
TOTAL
13. Humeral Shaft, Distal Humerus - Operative
CPT code
Description
24515
OPEN TREATMENT, HUMERAL SHAFT FX W/ PLATE/SCREWS, W/WO CERCLAGE
1,204
1,191
24516
OPEN TREATMENT, HUMERAL SHAFT FX, W/ INSERTION, INTRAMEDULLARY
IMPLANT
2,837
2,743
24538
PERCUTANEOUS SKELETAL FIXATION, SUPRACONDYLAR/TRANSCONDYLAR
HUMERAL FX
385
417
24545
OPEN TREATMENT, HUMERAL SUPRACONDYLAR/TRANSCONDYLAR FX; W/O
1,004
916
24546
OPEN TREATMENT, HUMERAL SUPRACONDYLAR/TRANSCONDYLAR FX; W/
739
767
24566
PERCUTANEOUS SKELETAL FIXATION, HUMERAL EPICONDYLAR FX,
MEDIAL/LATERAL, W/ MANIPULATION
19
15
24575
OPEN TREATMENT, HUMERAL EPICONDYLAR FX, MEDIAL/LATERAL
134
150
24579
OPEN TREATMENT, HUMERAL CONDYLAR FX, MEDIAL/LATERAL
529
498
24582
OPEN TREATMENT, HUMERAL CONDYLAR FX, MEDIAL/LATERAL,
W/MANIPULATION
54
42
24586
OPEN TREATMENT, PERIARTICULAR FX/DISLOCATION, ELBOW;
414
382
24587
OPEN TREATMENT, PERIARTICULAR FX/DISLOCATION, ELBOW; W/ IMPLANT
20
25
7,339
7,146
1996 count #
1997 count #
TOTAL
14. Humeral Shaft, Distal Humerus - Nonoperative
CPT code
Description
24500
CLOSED TREATMENT, HUMERAL SHAFT FX; W/O MANIPULATION
6,323
6,213
24505
CLOSED TREATMENT, HUMERAL SHAFT FX; W/ MANIPULATION; W/WO SKELETAL
TRACTION
2,492
2,356
24530
CLOSED TREATMENT, SUPRACONDYLAR/TRANSCONDYLAR HUMERAL FX; W/O
MANIPULATION
1,734
1,826
157
158
24535
CLOSED TREATMENT, SUPRACONDYLAR/TRANSCONDYLAR HUMERAL FX; W/
MANIPULATION
571
563
24560
CLOSED TREATMENT, HUMERAL EPICONDYLAR FX, MEDIAL/LATERAL; W/O
MANIPULATION
548
497
24565
CLOSED TREATMENT, HUMERAL EPICONDYLAR FX, MEDIAL/LATERAL; W/
MANIPULATION
65
59
24576
CLOSED TREATMENT, HUMERAL CONDYLAR FX, MEDIAL/LATERAL; W/O
MANIPULATION
530
472
24577
CLOSED TREATMENT, HUMERAL CONDYLAR FX, MEDIAL/LATERAL; W/
MANIPULATION
113
111
12,376
12,097
1996 count #
1997 count #
TOTAL
15. Proximal Forearm, Shaft - Operative *
CPT code
Description
24635
OPEN TREATMENT, MONTEGGIA TYPE, FX DISLOCATION, ELBOW
448
24665
OPEN TREATMENT, RADIAL HEAD/NECK FX W/WO INT FIXATION/RADIAL HEAD
EXCISION;
564
24666
OPEN TREATMENT, RADIAL HEAD/NECK FX W/WO INT FIXATION/RADIAL HEAD
EXCISION; W/ PROTHESIS
98
24685
OPEN TREATMENT, ULNAR FX PROXIMAL W/WO INT/EXT FIXATION
25515
OPEN TREATMENT, RADIAL SHAFT FX, W/WO INT/EXT FIXATION
406
25525
OPEN TREATMENT, RADIAL SHAFT FX, W/ FIXATION/CLOSED TREATMENT,
DISLOCATION, DISTAL RADIOULNAR JOINT
135
25526
OPEN TX, RADIAL SHAFT FX, W/ FIXATION/OPEN TX, DISTAL RADIOULNAR JOINT,
W/ REPAIR TRIANGULAR CARTILAGE
25545
OPEN TREATMENT, ULNAR SHAFT FX, W/WO INT/EXT FIXATION
4,632
63
506
25574
OPEN TREATMENT, RADIAL & ULNAR SHAFT FXS, W/ FIXATION; RADIUS/ULNA
255
25575
OPEN TREATMENT, RADIAL & ULNAR SHAFT FXS, W/ FIXATION; BOTH RADIUS &
ULNA
875
TOTAL
7,982
16. Proximal Forearm, Shaft - Nonoperative *
CPT code
Description
1996 count #
24620
CLOSED TREATMENT, MONTEGGIA TYPE, FX DISLOCATION, ELBOW, W/
MANIPULATION
24650
CLOSED TREATMENT, RADIAL HEAD/NECK FX; W/O MANIPULATION
24655
CLOSED TREATMENT, RADIAL HEAD/NECK FX; W/ MANIPULATION
24670
CLOSED TREATMENT, ULNAR FX, PROXIMAL END (OLECRANON PROCESS); W/O
MANIPULATION
1,726
25500
CLOSED TREATMENT, RADIAL SHAFT FX; W/O MANIPULATION
2,155
25505
CLOSED TREATMENT, RADIAL SHAFT FX; W/ MANIPULATION
25520
CLOSED TREATMENT, RADIAL SHAFT FX, W/ DISLOCATION, DISTAL RADIOULNAR
JOINT
184
6,315
530
875
52
1997 count #
APPENDIX ON METHODS
25530
CLOSED TREATMENT, ULNAR SHAFT FX; W/O MANIPULATION
2,158
25535
CLOSED TREATMENT, ULNAR SHAFT FX; W/ MANIPULATION
25560
CLOSED TREATMENT, RADIAL & ULNAR SHAFT FXS; W/O MANIPULATION
1,907
25565
CLOSED TREATMENT, RADIAL & ULNAR SHAFT FXS; W/ MANIPULATION
1,852
TOTAL
354
18,108
17. Wrist - Operative *
CPT code
Description
25611
PERCUTANEOUS SKELETAL FIXATION, DISTAL RADIAL FX/EPIPHYSEAL
SEPARATION, W/ MANIPULATION
25620
OPEN TREATMENT, DISTAL RADIAL FX/EPIPHYSEAL SEPARATION
TOTAL
1996 count #
1997 count #
12,339
4,034
16,373
18. Wrist - Nonoperative *
CPT code
Description
25600
CLOSED TREATMENT, DISTAL RADIAL FX; W/O MANIPULATION
44,055
25605
CLOSED TREATMENT, DISTAL RADIAL FX; W/ MANIPULATION
47,315
TOTAL
1996 count #
1997 count #
91,370
19. Spine - Operative
CPT code
Description
1996 count #
1997 count #
22325
OPEN TREATMENT, VERTEBRAL FX/DISLOCATION, POSTERIOR APPROACH, 1
FRACTURED VERTEBRAE, LUMBAR
82
145
22326
FRACTURED VERTEBRAE CERVICAL
214
253
22327
FRACTURED VERTEBRAE THORACIC
47
93
22328
OPEN TREATMENT, VERTEBRAL FX/DISLOCATION, POSTERIOR APPROACH;
ADDITIONAL FRACTURED VERTEBRAE OR DISLOCATED SEGMENT
56
72
399
563
1996 count #
1997 count #
1,346
997
1996 count #
1997 count #
TOTAL
20. Spine - Nonoperative
CPT code
Description
22505
MANIPULATION, SPINE REQUIRING ANESTHESIA, ANY REGION
31. Cervical Disc and Stenosis
CPT code
Description
22210
OSTEOTOMY, SPINE, POSTERIOR/POSTEROLATERAL APPROACH, 1 VERTEBRAL
SEGMENT; CERVICAL
41
52
22220
OSTEOTOMY, SPINE, W/ DISKECTOMY, ANTERIOR APPROACH, SINGLE;
CERVICAL
32
31
159
160
63001
LAMINECTOMY, W/O FACETECTOMY/FORAMINOTOMY/DISKECTOMY, 1/2
SEGMENTS; CERVICAL
472
442
63015
LAMINECTOMY W/O FACETECTOMY/FORAMINOTOMY/DISKECTOMY, > 2
SEGMENTS; CERVICAL
1,164
1,204
63020
LAMINOTOMY W/ PARTIAL FACETECTOMY/FORAMINOTOMY/HERNIATED
DISKECTOMY; 1 INTERSPACE; CERVICAL
1,198
1,200
63045
LAMINECTOMY, FACETECTOMY & FORAMINOTOMY, 1 SEGMENT; CERVICAL
2,897
2,983
63075
DISKECTOMY, ANTERIOR; CERVICAL, 1 INTERSPACE
6,213
6,519
63076
DISKECTOMY, ANTERIOR; CERVICAL, ADD'L INTERSPACE
3,214
3,470
63081
VERTEBRAL CORPECTOMY, ANTERIOR; CERVICAL, 1 SEGMENT
1,893
2,126
63082
VERTEBRAL CORPECTOMY, ANTERIOR; CERVICAL, ADD'L SEGMENT
1,322
1,405
18,446
19,432
1996 count #
1997 count #
20,721
20,816
541
691
21,262
21,507
1996 count #
1997 count #
TOTAL
32. Lumbar Disc
CPT code
Description
63030
DISKECTOMY; 1 INTERSPACE, LUMBAR
63056
TRANSPEDICULAR APPROACH, 1 SEGMENT; LUMBAR (TRANSFACET/LATERAL
EXTRAFORAMINAL)
TOTAL
33. Lumbar Stenosis
CPT code
Description
63005
LAMINECTOMY W/O FACETECTOMY/FORAMINOTOMY/DISKECTOMY, 1/2
SEGMENTS; LUMBAR
2,615
2,370
63017
SEGMENTS; LUMBAR
2,704
2,357
63047
LAMINECTOMY. FACETECTOMY & FORAMINOTOMY, 1 SEGMENT; LUMBAR
39,553
43,076
44,872
47,803
1996 count #
1997 count #
TOTAL
34. Spine Fusion
CPT code
Description
20930
ALLOGRAFT, SPINE SURGERY ONLY; MORSELIZED
6
1
20931
ALLOGRAFT, SPINE SURGERY ONLY; STRUCTURAL
1,929
2,801
20936
AUTOGRAFT, SPINE SURGERY; LOCAL, SAME INCISION
19
†
20937
AUTOGRAFT, SPINE SURGERY; MORSELIZED, SEPARATE INCISION
7,378
9,465
20938
AUTOGRAFT, SPINE SURGERY; STRUCTURAL, BICORTICAL/TRICORTICAL,
SEPARATE INCISION
5,408
5,742
22548
ARTHRODESIS, ANTERIOR TRANSORAL/EXTRAORAL
22554
ARTHRODESIS, ANTERIOR INTERBODY, W/ DISKECTOMY; CERVICAL BELOW C2
22556
ARTHRODESIS, ANTERIOR INTERBODY, W/ DISKECTOMY; THORACIC
101
112
7,465
8,124
437
388
APPENDIX ON METHODS
22558
ARTHRODESIS, ANTERIOR INTERBODY, W/ DISKECTOMY; LUMBAR
1,027
1,385
22585
ARTHRODESIS, ANTERIOR INTERBODY; ADD'L INTERSPACE
5,054
5,721
22590
ARTHRODESIS, POSTERIOR TECHNIQUE, CRANIOCERVICAL
333
317
22595
ARTHRODESIS, POSTERIOR TECHNIQUE, ATLAS-AXIS
744
781
22600
ARTHRODESIS, POSTERIOR/POSTEROLATERAL TECHNIQUE, SINGLE LEVEL;
CERVICAL BELOW C2
1,237
1,292
22610
ARTHRODESIS, POSTERIOR/POSTEROLATERAL, SINGLE LEVEL; THORACIC
654
627
22612
ARTHRODESIS, POSTERIOR/POSTEROLATERAL, SINGLE LEVEL; LUMBAR
12,353
13,778
22614
ARTHRODESIS, POSTERIOR/POSTEROLATERAL, SINGLE LEVEL; ADD'L SEGMENT
11,756
12,944
22625
LUMBAR SPINE FUSION
310
†
22630
ARTHRODESIS, POSTERIOR INTERBODY W/ LAMINECTOMY/DISKECTOMY,
SINGLE INTERSPACE; LUMBAR
817
2,115
22632
ARTHRODESIS, POSTERIOR INTERBODY, SINGLE INTERSPACE; ADD'L
INTERSPACE
425
722
22650
LUMBAR SPINE FUSION, EXT SEG.
452
†
22800
ARTHRODESIS, POSTERIOR, SPINAL DEFORMITY, W/WO CAST; UP TO 6
VERTEBRAL SEGMENTS
311
343
22802
ARTHRODESIS, POSTERIOR, SPINAL DEFORMITY, W/WO CAST; 7 TO 12
VERTEBRAL SEGMENTS
218
205
22804
ARTHRODESIS, POSTERIOR, SPINAL DEFORMITY; 13+ VERTEBRAL SEGMENTS
44
37
22808
ARTHRODESIS, ANTERIOR, SPINAL DEFORMITY, W/WO CAST; 2 TO 3 VERTEBRAL
SEGMENTS
114
123
22810
SEGMENTS
76
105
22812
ARTHRODESIS, ANTERIOR, SPINAL DEFORMITY, W/WO CAST; 8+ SEGMENTS
14
19
22820
BONE GRAFT
694
†
22830
EXPLORATION, SPINAL FUSION
763
813
60,139
67,960
1996 count #
1997 count #
1,227
1,473
TOTAL
35. Fusion Plus Hardware
CPT code
Description
22840
POSERIOR NON-SEGMNTL INSTRUMENTATION, PEDICLE(1 INTRSPCE),
ATLANTOAXIAL/FACET SCREW FIXATN, SUBLAMINAR WIRING AT C1
22841
INT SPINAL FIXATION, WIRING, SPINOUS PROCESSES
22842
POSTERIOR SEGMENTAL INSTRUMENTATION; 3-6 VERTEBRAL SEGMENTS
4
1
8,580
8,788
22843
473
551
22844
POSTERIOR SEGMENTAL INSTRUMENTATION; 13+ VERTEBRAL SEGMENTS
63
62
22845
ANTERIOR INSTRUMENTATION; 2 TO 3 VERTEBRAL SEGMENTS
3,690
4,434
22846
454
588
22847
ANTERIOR INSTRUMENTATION; 8+ VERTEBRAL SEGMENTS
3
3
22849
REINSERTION, SPINAL FIXATION DEVICE
235
217
161
162
22851
APPLICATION, INTERVERTEBRAL BIOMECHANICAL DEVICE(S) TO VERTEBRAL
DEFECT/INTERSPACE
TOTAL
721
3,153
15,450
19,270
1996 count #
1997 count #
36. Other Spine Surgery
CPT code
Description
20250
BX, VERTEBRAL BODY, OPEN; THORACIC
400
303
20251
BX, VERTEBRAL BODY, OPEN; LUMBAR/CERVICAL
340
342
22100
PARTIAL EXCISION, POSTERIOR VERTEBRAL COMPONENT, SINGLE; CERVICAL
39
42
22101
PARTIAL EXCISION, POSTERIOR VERTEBRAL COMPONENT, SINGLE; THORACIC
36
47
22102
PARTIAL EXCISION, POSTERIOR VERTEBRAL COMPONENT, SINGLE; LUMBAR
112
75
22110
PARTIAL EXCISION, VERTEBRAL BODY, W/O SPINAL CORD/NERVE ROOT
DECOMPRESSION, SINGLE, CERVICAL
49
69
22112
DECOMPRESSION, SINGLE, THORACIC
22
28
22114
DECOMPRESSION, SINGLE, LUMBAR
69
61
22212
SEGMENT; THORACIC
15
22
22214
SEGMENT; LUMBAR
122
99
22222
THORACIC
50
57
22224
OSTEOTOMY, SPINE, W/ DISKECTOMY, ANTERIOR APPROACH, SINGLE; LUMBAR
22850
REMOVAL, POSTERIOR NONSEGMENTAL INSTRUMENTATION
46
66
189
211
22852
REMOVAL, POSTERIOR SEGMENTAL INSTRUMENTATION
1,292
1,315
22855
REMOVAL, ANTERIOR INSTRUMENTATION
185
251
22899
UNLISTED PROC, SPINE
178
248
63003
SEGMENTS; THORACIC
206
169
63012
LAMINECTOMY W/ REMOVAL, ABNORMAL FACETS; LUMBAR
694
716
63016
SEGMENTS; THORACIC
155
154
63035
LAMINOTOMY W/PARTIAL FACETECTOMY/FORAMINOTOMY/HERNIATED
DISKECTMY; ADD'L INTERSPACE, CERVICAL/LUMBAR
8,700
8,637
63040
DISKECTOMY; RE-EXPLORATION, CERVICAL
153
148
63042
DISKECTOMY; RE-EXPLORATION, LUMBAR
5,138
5,657
63046
LAMINECTOMY, FACETECTOMY & FORAMINOTOMY, 1 SEGMENT; THORACIC
546
584
63048
LAMINECTOMY, FACETECTOMY & FORAMINOTOMY; ADD'L SEGMENT,
CERVICAL/THORACIC/LUMBAR
50,007
53,754
63055
TRANSPEDICULAR APPROACH, 1 SEGMENT; THORACIC
254
243
APPENDIX ON METHODS
63064
COSTOVERTEBRAL APPROACH, ADD'L SEGMENT; THORACIC
63077
DISKECTOMY, ANTERIOR; THORACIC, 1 INTERSPACE
95
91
133
223
63085
VERTEBRAL CORPECTOMY, TRANSTHORACIC; THORACIC, 1 SEGMENT
422
429
63087
VERTEBRAL CORPECTOMY, THORACOLUMBAR, LOWER THORACIC/LUMBAR; 1
SEGMENT
324
387
63090
VERTEBRAL CORPECTOMY, TRANSPERITONEAL/RETROPERITONEAL, LOWER
THORACIC/LUMBAR/SACRAL
356
459
63170
LAMINECTOMY W/ MYELOTOMY, CERVICAL, THORACIC/THORACOLUMBAR
22
26
63180
LAMINECTOMY/SECTION, DENTATE LIGAMENTS, CERVICAL; 1/2 SEGMENTS
6
2
63185
LAMINECTOMY W/ RHIZOTOMY; 1/2 SEGMENTS
75
55
63191
LAMINECTOMY W/ SECTION, SPINAL ACCESSORY NERVE
2
1
63194
LAMINECTOMY W/ CORDOTOMY, W/ SECTION, ONE SPINOTHALAMIC TRACT, 1
STAGE; CERVICAL
6
1
63195
STAGE; THORACIC
15
7
63196
LAMINECTOMY W/ CORDOTOMY, W/ SECTION, BOTH SPINOTHALAMIC TRACTS, 1
STAGE; CERVICAL
1
0
63197
STAGE; THORACIC
9
3
63199
STAGES WITHIN 14 DAYS, THORACIC
2
1
63200
LAMINECTOMY, W/ RELEASE, TETHERED SPINAL CORD, LUMBAR
40
38
63250
LAMINECTOMY, EXCISION/OCCLUSION, AVM, SPINAL CORD; CERVICAL
18
16
63251
LAMINECTOMY, EXCISION/OCCLUSION, AVM, SPINAL CORD; THORACIC
36
31
63252
LAMINECTOMY, EXCISION/OCCLUSION, AVM, SPINAL CORD; THORACOLUMBAR
13
15
63265
LAMINECTOMY, EXCISION, NON-NEOPLASTIC LESION, EXTRADURAL; CERVICAL
128
143
63266
LAMINECTOMY, EXCISION, NON-NEOPLASTIC LESION, EXTRADURAL; THORACIC
216
275
63267
LAMINECTOMY, EXCISION, NON-NEOPLASTIC LESION, EXTRADURAL; LUMBAR
695
757
63270
LAMINECTOMY, EXCISION, INTRASPINAL LESION OTHER THAN NEOPLASM,
INTRADURAL; CERVICAL
33
35
63271
INTRADURAL; THORACIC
97
82
63272
INTRADURAL; LUMBAR
199
195
63275
LAMINECTOMY, BX/EXCISION, INTRASPINAL NEOPLASM; EXTRADURAL,
CERVICAL
142
147
63276
THORACIC
679
703
63277
LAMINECTOMY, BX/EXCISION, INTRASPINAL NEOPLASM; EXTRADURAL, LUMBAR
458
393
63280
LAMINECTOMY, BX/EXCISION, INTRASPINAL NEOPLASM; INTRADURAL,
EXTRAMEDULLARY, CERVICAL
128
140
63281
EXTRAMEDULLARY, THORACIC
413
377
163
164
63282
EXTRAMEDULLARY, LUMBAR
253
228
63285
INTRAMEDULLARY, CERVICAL
47
64
63286
INTRAMEDULLARY, THORACIC
109
87
63287
49
48
63290
LAMINECTOMY, BX/EXCISION, INTRASPINAL NEOPLASM; EXTRADURALINTRADURAL LESION, ANY LEVEL
48
53
63300
VERTEBRAL CORPECTOMY, 1 SEGMENT; EXTRADURAL, CERVICAL
90
71
63301
VERTEBRAL CORPECTOMY, 1 SEGMENT; EXTRADURAL, THORACIC,
TRANSTHORACIC APPROACH
80
69
63302
THORACOLUMBAR APPROACH
16
18
63303
VERTEBRAL CORPECTOMY, 1 SEGMENT; EXTRADURAL, LUMBAR/SACRAL,
TRANS/RETROPERITONEAL APPROACH
41
49
63304
VERTEBRAL CORPECTOMY, 1 SEGMENT; INTRADURAL, CERVICAL
7
11
63305
VERTEBRAL CORPECTOMY, 1 SEGMENT; INTRADURAL, THORACIC,
18
11
63306
5
4
63307
VERTEBRAL CORPECTOMY, 1 SEGMENT; INTRADURAL, LUMBAR/SACRAL,
12
7
74,535
79,050
1996 count #
1997 count #
TOTAL
41. THA Hip Arthroplasty
CPT code
Description
27125
HEMIARTHROPLASTY, HIP, PARTIAL
14,956
13,820
27130
ARTHROPLASTY, ACETABULAR/PROXIMAL FEMORAL PROSTHETIC
REPLACEMENT, W/WO AUTOGRAFT
71,067
70,165
27132
CONVERSION, PREVIOUS HIP SURGERY TO TOTAL HIP REPLACEMENT, W/WO
AUTOGRAFT/ALLOGRAFT
4,091
4,135
27134
REVISION, TOTAL HIP ARTHROPLASTY; BOTH COMPONENTS, W/WO
AUTOGRAFT/ALLOGRAFT
8,644
8,629
27137
REVISION, TOTAL HIP ARTHROPLASTY; ACETABULAR COMPONENT, W/WO
AUTOGRAFT/ALLOGRAFT
3,950
4,173
27138
REVISION, TOTAL HIP ARTHROPLASTY; FEMORAL COMPONENT ONLY, W/WO
ALLOGRAFT
2,480
2,314
105,188
103,236
TOTAL
APPENDIX ON METHODS
42. TKR Knee Arthroplasty
CPT code
Description
1996 count #
1997 count #
27440
ARTHROPLASTY, KNEE, TIBIAL PLATEAU;
17
20
27441
ARTHROPLASTY, KNEE, TIBIAL PLATEAU; W/ DEBRIDEMENT & PARTIAL
SYNOVECTOMY
45
31
27442
ARTHROPLASTY, FEMORAL CONDYLES/TIBIAL PLATEAU(S), KNEE;
68
66
27443
ARTHROPLASTY, FEMORAL CONDYLES/TIBIAL PLATEAUS; W/
DEBRIDEMENT/SYNOVECTOMY
114
89
27445
ARTHROPLASTY, KNEE, HINGE PROSTHESIS
216
230
27446
ARTHROPLASTY, KNEE, CONDYLE & PLATEAU; MEDIAL/LATERAL
COMPARTMENT
1,355
1,044
27447
ARTHROPLASTY, KNEE/CONDYLE/PLATEAU; MEDIAL & LATERAL
COMPARTMENTS
148,922
149,352
27486
REVISION, TOTAL KNEE ARTHROPLASTY, W/WO ALLOGRAFT; 1 COMPONENT
4,189
4,456
27487
REVISION, TOTAL KNEE ARTHROPLASTY; FEMORAL/TIBIA COMPONENTS, W/WO
ALLOGRAFT
8,379
8,630
163,305
163,918
1996 count #
1997 count #
TOTAL
43. TSA Shoulder Arthroplasty/reconstruction
CPT code
Description
23120
CLAVICULECTOMY; PARTIAL
11,224
12,710
23420
RECONSTRUCTION, COMPLETE SHOULDER (ROTATOR) CUFF AVULSION,
CHRONIC (INCLUDES ACROMIOPLASTY)
23,467
24,877
23470
ARTHROPLASTY, GLENOHUMERAL JOINT; HEMIARTHROPLASTY
4,932
5,076
23472
ARTHROPLASTY, GLENOHUMERAL JOINT; TOTAL SHOULDER
3,369
3,453
23616
OPEN TREATMENT, PROXIMAL HUMERAL FX, W/WO INT/EXT
FIXATION/TUBEROSITY, REPAIR; W/PROSTHESIS
1,968
2,196
44,960
48,312
1996 count #
1997 count #
TOTAL
44. Knee Arthroscopy
CPT code
Description
29870
ARTHROSCOPY, KNEE, DX, W/WO SYNOVIAL BX (SEP PROC)
1,440
1,280
29871
ARTHROSCOPY, KNEE, SURGICAL; INFECTION, LAVAGE & DRAINAGE
1,618
1,528
29874
ARTHROSCOPY, KNEE, SURGICAL; REMOVAL, LOOSE/FB
1,160
1,221
29875
ARTHROSCOPY, KNEE, SURGICAL; SYNOVECTOMY, LIMITED (SEP PROC)
1,493
1,541
29876
ARTHROSCOPY, KNEE, SURGICAL; SYNOVECTOMY, MAJOR, 2 +
COMPARTMENTS
5,067
5,949
29877
ARTHROSCOPY, KNEE, SURGICAL; DEBRIDEMENT/SHAVING, ARTICULAR
CARTILAGE (CHONDROPLASTY)
31,438
31,270
165
166
29879
ARTHROSCOPY, KNEE, SURGICAL; ABRASION ARTHROPLASTY (W/
CHONDROPLASTY/MULTPLE DRILLING/MICROFX)
29880
ARTHROSCOPY, KNEE, SURGICAL; W/ MENISCECTOMY, MEDIAL & LATERAL
21,394
23,837
29881
ARTHROSCOPY, KNEE, SURGICAL; W/ MENISCECTOMY, MEDIAL/LATERAL
44,477
45,817
29882
ARTHROSCOPY, KNEE, SURGICAL; W/ MENISCUS REPAIR, MEDIAL/LATERAL
610
596
29883
ARTHROSCOPY, KNEE, SURGICAL; W/ MENISCUS REPAIR, MEDIAL & LATERAL
474
510
113,946
118,877
1996 count #
1997 count #
1,992
1,563
TOTAL
4,775
5,328
45. Shoulder Arthroscopy
CPT code
Description
29815
ARTHROSCOPY, SHOULDER, DX, W/WO SYNOVIAL BX (SEP PROC)
29819
ARTHROSCOPY, SHOULDER, SURGICAL; W/ REMOVAL, LOOSE BODY/FB
117
151
29820
ARTHROSCOPY, SHOULDER, SURGICAL; SYNOVECTOMY, PARTIAL
182
242
29821
ARTHROSCOPY, SHOULDER, SURGICAL; SYNOVECTOMY, COMPLETE
159
199
29822
ARTHROSCOPY, SHOULDER, SURGICAL; DEBRIDEMENT, LIMITED
1,443
2,134
29823
ARTHROSCOPY, SHOULDER, SURGICAL; DEBRIDEMENT, EXTENSIVE
2,500
3,637
29825
ARTHROSCOPY, SHOULDER, SURGICAL; W/ LYSIS, ADHESIONS, W/WO
MANIPULATION
117
135
29826
ARTHROSCOPY, SHOULDER, SURGICAL; DECOMPRESSION, SUBACROMIAL
SPACE W/ PARTIAL ACROMIOPLASTY
7,865
9,637
14,375
17,698
1996 count #
1997 count #
TOTAL
61. Carpal Tunnel Release - Open **
CPT code
Description
64721
NEUROPLASTY &/OR TRANSPOSITION; MEDIAN NERVE AT CARPAL TUNNEL
62,170
62. Carpal Tunnel Release - Endoscopic **
CPT code
Description
29848
ENDOSCOPY, WRIST, SURGICAL, W/ RELEASE, TRANSVERSE CARPAL LIGAMENT
1996 count #
1997 count #
6,057
63. Bunion Surgery
CPT code
Description
28290
1996 count #
1997 count #
HALLUX VALGUS CORRECTION; W/WO SESAMOIDECTOMY; SIMPLE
EXOSTECTOMY
3,156
2,903
28292
HALLUX VALGUS CORRECTION; W/WO SESAMOIDECTOMY; KELLER,
MCBRIDE/MAYO TYPE
15,246
14,490
28293
HALLUX VALGUS CORRECTION; W/WO SESAMOIDECTOMY; RESECTION, JOINT
W/ IMPLANT
2,407
2,219
28294
HALLUX VALGUS CORRECTION; W/WO SESAMOIDECTOMY; W/ TENDON
TRANSPLANTS
379
303
APPENDIX ON METHODS
28296
HALLUX VALGUS CORRECTION; W/WO SESAMOIDECTOMY; W/ METATARSAL
OSTEOTOMY
11,929
11,664
28297
HALLUX VALGUS CORRECTION; (BUNION), W/WO SESAMOIDECTOMY; LAPIDUS
TYPE PROC
186
198
28298
HALLUX VALGUS CORRECTION; (BUNION), W/WO SESAMOIDECTOMY; PHALANX
OSTEOTOMY
1,680
1,664
28299
HALLUX VALGUS CORRECTION; OTHER
1,181
1,250
36,164
34,691
1996 count #
1997 count #
TOTAL
64. Major Amputations
CPT code
Description
27590
AMPUTATION, THIGH, THROUGH FEMUR, ANY LEVEL;
24,831
24,095
27591
AMPUTATION, THIGH, THROUGH FEMUR, ANY LEVEL; IMMEDIATE FITTING
TECHNIQUE W/ 1ST CAST
149
125
27592
AMPUTATION, THIGH, THROUGH FEMUR, ANY LEVEL; OPEN, CIRCULAR
(GUILLOTINE)
349
340
27598
DISARTICULATION AT KNEE
753
849
27880
AMPUTATION, LEG, THROUGH TIBIA & FIBULA;
18,857
17,799
27881
AMPUTATION, LEG, THROUGH TIBIA & FIBULA; W/ IMMEDIATE FITTING W/ 1ST
CAST
917
890
27882
AMPUTATION, LEG, THROUGH TIBIA & FIBULA; OPEN, CIRCULAR (GUILLOTINE)
646
665
46,502
44,763
1996 count #
1997 count #
837
768
5,483
5,404
TOTAL
65. Minor Amputations (Foot/toe)
CPT code
Description
28800
AMPUTATION, FOOT; MIDTARSAL
28805
AMPUTATION, FOOT; TRANSMETATARSAL
28810
AMPUTATION, METATARSAL, W/ TOE, SINGLE
14,094
14,182
28820
AMPUTATION, TOE; METATARSOPHALANGEAL JOINT
15,297
15,322
28825
AMPUTATION, TOE; IP JOINT
5,564
5,640
41,275
41,316
1996 count #
1997 count #
TOTAL
GLOBAL SPINE Surgery CPTs
CPT code
Description
20250
BX, VERTEBRAL BODY, OPEN; THORACIC
400
303
20251
BX, VERTEBRAL BODY, OPEN; LUMBAR/CERVICAL
340
342
22100
PARTIAL EXCISION, POSTERIOR VERTEBRAL COMPONENT, SINGLE; CERVICAL
39
42
22101
PARTIAL EXCISION, POSTERIOR VERTEBRAL COMPONENT, SINGLE; THORACIC
22102
PARTIAL EXCISION, POSTERIOR VERTEBRAL COMPONENT, SINGLE; LUMBAR
36
47
112
75
167
168
22110
DECOMPRESSION, SINGLE, CERVICAL
22112
DECOMPRESSION, SINGLE, THORACIC
22
28
22114
DECOMPRESSION, SINGLE, LUMBAR
69
61
22210
SEGMENT; CERVICAL
41
52
22212
SEGMENT; THORACIC
15
22
22214
SEGMENT; LUMBAR
122
99
22220
CERVICAL
32
31
22222
THORACIC
50
57
22224
OSTEOTOMY, SPINE, W/ DISKECTOMY, ANTERIOR APPROACH, SINGLE; LUMBAR
46
66
22325
FRACTURED VERTEBRAE, LUMBAR
82
145
22326
FRACTURED VERTEBRAE, CERVICAL
214
253
22327
FRACTURED VERTEBRAE, THORACIC
47
93
7,465
8,124
437
388
1,027
1,385
333
317
22554
ARTHRODESIS, ANTERIOR INTERBODY, W/ DISKECTOMY; CERVICAL BELOW C2
22556
ARTHRODESIS, ANTERIOR INTERBODY, W/ DISKECTOMY; THORACIC
22558
ARTHRODESIS, ANTERIOR INTERBODY, W/ DISKECTOMY; LUMBAR
22590
ARTHRODESIS, POSTERIOR TECHNIQUE, CRANIOCERVICAL
22595
ARTHRODESIS, POSTERIOR TECHNIQUE, ATLAS-AXIS
22600
ARTHRODESIS, POSTERIOR/POSTEROLATERAL TECHNIQUE, SINGLE LEVEL;
CERVICAL BELOW C2
22610
ARTHRODESIS, POSTERIOR/POSTEROLATERAL, SINGLE LEVEL; THORACIC
22612
ARTHRODESIS, POSTERIOR/POSTEROLATERAL, SINGLE LEVEL; LUMBAR
22630
69
744
781
1,237
1,292
654
627
12,353
13,778
ARTHRODESIS, POSTERIOR INTERBODY W/ LAMINECTOMY/DISKECTOMY,
SINGLE INTERSPACE; LUMBAR
817
2,115
22800
ARTHRODESIS, POSTERIOR, SPINAL DEFORMITY, W/WO CAST; UP TO 6
VERTEBRAL SEGMENTS
311
343
22802
ARTHRODESIS, POSTERIOR, SPINAL DEFORMITY, W/WO CAST; 7 TO 12
VERTEBRAL SEGMENTS
218
205
22804
ARTHRODESIS, POSTERIOR, SPINAL DEFORMITY; 13+ VERTEBRAL SEGMENTS
44
37
22808
SEGMENTS
114
123
22810
SEGMENTS
76
105
APPENDIX ON METHODS
22812
ARTHRODESIS, ANTERIOR, SPINAL DEFORMITY, W/WO CAST; 8+ SEGMENTS
22830
EXPLORATION, SPINAL FUSION
22840
POST NON-SEGMENTAL INSTRUMENTATION, PEDICLE(1 INTERSPACE),
ATLANTOAXIAL/FACET SCREW FIXATN
22841
INT SPINAL FIXATION, WIRING, SPINOUS PROCESSES
14
19
763
813
1,227
1,473
4
1
22842
8,580
8,788
22843
473
551
22844
POSTERIOR SEGMENTAL INSTRUMENTATION; 13+ VERTEBRAL SEGMENTS
63
62
22845
3,690
4,434
22846
454
588
22847
ANTERIOR INSTRUMENTATION; 8+ VERTEBRAL SEGMENTS
3
3
22849
REINSERTION, SPINAL FIXATION DEVICE
235
217
22850
REMOVAL, POSTERIOR NONSEGMENTAL INSTRUMENTATION
189
211
22851
APPLICATION, INTERVERTEBRAL BIOMECHANICAL DEVICE(S) TO VERTEBRAL
DEFECT/INTERSPACE
721
3,153
22852
REMOVAL, POSTERIOR SEGMENTAL INSTRUMENTATION
1,292
1,315
22855
REMOVAL, ANTERIOR INSTRUMENTATION
185
251
22899
UNLISTED PROC, SPINE
178
248
63001
SEGMENTS; CERVICAL
472
442
63003
SEGMENTS; THORACIC
206
169
63005
LAMINECTOMY W/O FACETECTOMY/FORAMINOTOMY/DISKECTOMY, 1/2
SEGMENTS; LUMBAR
2,615
2,370
63012
LAMINECTOMY W/ REMOVAL, ABNORMAL FACETS; LUMBAR
694
716
63015
SEGMENTS; CERVICAL
1,164
1,204
63016
SEGMENTS; THORACIC
155
154
63017
SEGMENTS; LUMBAR
2,704
2,357
63020
DISKECTOMY; 1 INTERSPACE, CERVICAL
1,198
1,200
63030
DISKECTOMY; 1 INTERSPACE, LUMBAR
20,721
20,816
63035
LAMINOTOMY W/PARTL FACETECTOMY/FORAMINOTOMY/HERNIATED
DISKECTOMY; ADD'L INTRSPACE, CERVICAL/LUMBAR
8,700
8,637
63040
DISKECTOMY; RE-EXPLORATN, CERVICAL
153
148
63042
DISKECTOMY; RE-EXPLORATN, LUMBAR
5,138
5,657
63045
LAMINECTOMY, FACETECTOMY & FORAMINOTOMY, 1 SEGMENT; CERVICAL
2,897
2,983
63046
LAMINECTOMY, FACETECTOMY & FORAMINOTOMY, 1 SEGMENT; THORACIC
546
584
169
170
63047
LAMINECTOMY. FACETECTOMY & FORAMINOTOMY, 1 SEGMENT; LUMBAR
39,553
43,076
63048
LAMINECTOMY, FACETECTOMY & FORAMINOTOMY; ADD'L SEGMENT,
CERVICAL/THORACIC/LUMBAR
50,007
53,754
63055
TRANSPEDICULAR APPROACH, 1 SEGMENT; THORACIC
254
243
63056
TRANSPEDICULAR APPROACH, 1 SEGMENT; LUMBAR (TRANSFACET/LATERAL
EXTRAFORAMINAL)
541
691
63064
COSTOVERTEBRAL APPROACH, ADD'L SEGMENT; THORACIC
95
91
63075
DISKECTOMY, ANTERIOR; CERVICAL, 1 INTERSPACE
6,213
6,519
63077
DISKECTOMY, ANTERIOR; THORACIC, 1 INTERSPACE
133
223
63081
VERTEBRAL CORPECTOMY, ANTERIOR; CERVICAL, 1 SEGMENT
1,893
2,126
63085
VERTEBRAL CORPECTOMY, TRANSTHORACIC; THORACIC, 1 SEGMENT
422
429
63087
VERTEBRAL CORPECTOMY, THORACOLUMBAR, LOWER THORACIC/LUMBAR; 1
SEGMENT
324
387
63090
VERTEBRAL CORPECTOMY, TRANSPERITONEAL/RETROPERITONEAL, LOWER
THORACIC/LUMBAR/SACRAL
356
459
63170
LAMINECTOMY W/ MYELOTOMY, CERVICAL, THORACIC/THORACOLUMBAR
22
26
63180
LAMINECTOMY/SECTION, DENTATE LIGAMENTS, CERVICAL; 1/2 SEGMENTS
6
2
63185
LAMINECTOMY W/ RHIZOTOMY; 1/2 SEGMENTS
75
55
63191
LAMINECTOMY W/ SECTION, SPINAL ACCESSORY NERVE
2
1
63194
STAGE; CERVICAL
6
1
63195
STAGE; THORACIC
15
7
63196
STAGE; CERVICAL
1
0
63197
STAGE; THORACIC
9
3
63199
STAGES W/IN 14 DAYS, THORACIC
2
1
63200
LAMINECTOMY, W/ RELEASE, TETHERED SPINAL CORD, LUMBAR
40
38
63250
LAMINECTOMY, EXCISION/OCCLUSION, AVM, SPINAL CORD; CERVICAL
18
16
63251
LAMINECTOMY, EXCISION/OCCLUSION, AVM, SPINAL CORD; THORACIC
36
31
63252
LAMINECTOMY, EXCISION/OCCLUSION, AVM, SPINAL CORD; THORACOLUMBAR
13
15
63265
LAMINECTOMY, EXCISION, NON-NEOPLASTIC LESION, EXTRADURAL; CERVICAL
128
143
63266
LAMINECTOMY, EXCISION, NON-NEOPLASTIC LESION, EXTRADURAL; THORACIC
216
275
63267
LAMINECTOMY, EXCISION, NON-NEOPLASTIC LESION, EXTRADURAL; LUMBAR
695
757
63270
INTRADURAL; CERVICAL
33
35
63271
INTRADURAL; THORACIC
97
82
63272
INTRADURAL; LUMBAR
199
195
APPENDIX ON METHODS
63275
CERVICAL
142
147
63276
THORACIC
679
703
63277
LAMINECTOMY, BX/EXCISION, INTRASPINAL NEOPLASM; EXTRADURAL, LUMBAR
458
393
63280
EXTRAMEDULLARY, CERVICAL
128
140
63281
EXTRAMEDULLARY, THORACIC
413
377
63282
EXTRAMEDULLARY, LUMBAR
253
228
63285
INTRAMEDULLARY, CERVICAL
64
47
63286
109
87
63287
INTRAMEDULLARY, THORACOLUMBAR
49
48
63290
LAMINECTOMY, BX/EXCISION, INTRASPINAL NEOPLASM; EXTRADURALINTRADURAL LESION, ANY LEVEL
48
53
63300
VERTEBRAL CORPECTOMY, 1 SEGMENT; EXTRADURAL, CERVICAL
90
71
63301
80
69
63302
16
18
63303
VERTEBRAL CORPECTOMY, 1 SEGMENT; EXTRADURAL, LUMBAR/SACRAL,
41
49
63304
VERTEBRAL CORPECTOMY, 1 SEGMENT; INTRADURAL, CERVICAL
7
11
63305
18
11
63306
5
4
63307
VERTEBRAL CORPECTOMY, 1 SEGMENT; INTRADURAL, LUMBAR/SACRAL,
12
7
196,996
213,113
Table Notes:
# Total number of procedures performed in 1996 and 1997 on Medicare (Part B) enrollees aged 65-99.
* Only 1996 counts were used for these procedures.
** Only 1997 counts were used for these procedures.
† CPT codes no longer valid in 1997.
171
172
In calculating procedure rates for surgical procedures, we allowed only claims submitted by the primary operator; procedures identified by claims submitted by
assistants only were not included. As many as 45% of claims for major surgical procedures were submitted by both primary operators and assistants, increasing the risk
of double counting events due to a simple date mismatch.
Patients were counted as having only one event per category per day. Thus a patient
with multiple CPT codes for operative management of a femur fracture occurring
on the same day was counted as having a single procedure. However, patients with
codes in two or more procedure groups were counted as having multiple events.
Thus, a patient with codes for surgical repair of both femur and tibia fractures on
a single day would be included in the rates for each procedure.
For any given procedure, we placed no restrictions on the total number of numerator events one patient could have in a single year. However, to avoid potential
double counting due to billing errors, we used a one-day “window” in calculating
rates of surgical procedures. In other words, codes within a single procedure group
occurring on consecutive days were counted as a single procedure; codes on days 1
and 3 counted as two procedures.
For procedures involving an extremity, Medicare claims are not reliable for classifying laterality (e.g., right vs. left lower extremity). For similar reasons, we did not
attempt to differentiate between unilateral and bilateral procedures (e.g., total knee
replacement).
3.2 Adjusted Surgical Procedure Rates and their Precision
Rates were adjusted using the indirect method for age, sex and race, using the corresponding 1996 or the 1996-97 national Medicare population as the standard, as
described in Section 4.
APPENDIX ON METHODS
Although standard errors of the rates were not reported, these estimates are, for the
most part, precisely determined. The minimum Medicare population in an HRR is
14,497 residents in Boulder, Colorado. The following precisions were obtained in
the smallest HRR (the “worst case scenario”) for an event rate of 5 per 1,000:
■ For procedures related to the entire HRR, the precision would be ±12%.
■ For procedures in a median-sized HRR (N=64,000) the precision would be ±6%.
In general, if we denote the event rate as p and the population size as N, the standard error is (p/N)^0.5 and the precision, expressed as a percent of the true rate, is
(s.e.(p)/p)*100%.
3.3 Calculation of Proportions
In several situations, we focus on proportions instead of rates, e.g., the proportion
of lumbar laminectomy patients undergoing fusion, the proportion of wrist fracture
patients undergoing surgical treatment. The usual confidentiality suppression rules
were applied separately to the events in the numerator and denominator: rates were
suppressed according to current HCFA confidentiality guidelines. For reasons of statistical precision, the rate was also suppressed when there were fewer than 26
denominator events. Therefore, all such proportions that differ from the national
proportion by more than 0.2 on an absolute scale are statistically significant.
4. Calculation of Age, Sex and Race Adjusted Rates
Medicare procedure and diagnostic test rates were adjusted using the indirect
method for the following strata: sex, race (black, non-black) and age (65-69, 70-74,
75-79, 80-84, 85-99). The standard population for procedure and diagnostic test
rates was the 1996 or the 1996-97 Medicare population corresponding to the numerator (see Section 1.5). The expected counts within HSAs were computed as
weighted averages of the stratum-specific crude rates in the standard population and
were obtained using weighted least squares regression, weighting by the stratumspecific population. Observed and expected counts at the HSA level were summed
to the HRR levels. For procedures and diagnostic tests, these were obtained sepa-
173
174
rately for each year and summed across years before summing to the HRR level.
Indirectly standardized rates for HRRs were then computed from observed and
expected counts (Breslow and Day, 1987).
This procedure was slightly modified for calculating workforce rates. The allocated
orthopaedist rates were adjusted for age and sex using the indirect method using the
1995 U.S. population as a standard. Because the national age-sex specific physician
workforce rates are not known, these were estimated using age and sex specific
ambulatory visit rates for cardiology services from the 1989-1994 National Ambulatory Care Survey (NAMCS). These estimates were used to calculate the expected
orthopaedist supply in each HSA, by specialty. The expected counts were summed
to the HRR levels and used to calculate indirectly standardized rates.
5. The Orthopaedic Workforce Prediction Model
The workforce projection model was created using Stella® software. Starting with
the current supply of orthopaedic surgeons, this simulation model accounts for the
number of physicians entering and leaving the orthopaedic workforce over time. It
also accounts for projected changes in the population, as well as projected trends in
physician workload based on changes in the age and sex distribution of orthopaedic
surgeons.
The current supply of orthopaedic surgeons, stratified by age and sex, was obtained
using methods described earlier. Assumptions about the number of new orthopaedic surgeons entering the workforce were based on the current number of
graduates from accredited orthopaedic surgery training programs, obtained from the
1997 AMA Annual Survey of GME programs (JAMA 1998; 290:844). The proportion of international medical graduates (IMGs) that was assumed to remain in the
U.S. workforce was based on the overall proportion of IMG resident physicians
who were Native U.S. citizens, Naturalized U.S. citizens, or permanent U.S. residents (about 50%). The number expected to be clinically active was based on the
overall proportion of total physicians within that specialty listed as clinically active
APPENDIX ON METHODS
in the AMA Master File. Assumptions about the numbers of orthopaedic surgeons
leaving the workforce were based on age and sex-specific death and retirement rates
provided by the Bureau of Health Professions.
Per capita rates for a given year were obtained by dividing the total number of clinically active physicians projected for that year by the projected population (based on
the U.S. Bureau of Census Middle Series projection). We adjusted the projected
physician rates for the age and sex of the future population using the same indirect
method as was used to adjust the current physician rates across geographic areas
(Section 6). Adjustment for physician characteristics was performed using age and
sex specific average weekly hours worked from the Bureau of Health Professions.
Orthopaedic surgeons were adjusted using the rates of hours worked per week for
surgical subspecialists.
6. Benchmarking
The variations in per capita resource and physician workforce allocations among
HRRs provide the basis for asking “What if?” questions. Benchmarking methods
used in Chapter Two provide estimates of the total excess or deficit numbers of physicians that would be expected if observed staffing patterns for a given benchmark
region (HRR) pertained nationally. The number of physicians in the nation in excess or deficit of the chosen benchmark is obtained by evaluating:
(U.S. rate – Benchmark rate) x (U.S. Population/100,000)
Using analogous methods, benchmarking can also be used to compare adjusted
physician supply rates between different HRRs or health plans.
175
176
7. Measures of Variation and Association
Hospital Employees per 1,000 Residents in HRRs
7.1 The Distribution Graph
The distribution graphs used in the Atlas provide a simple way to show the dispersion
in particular rates of health care resources and utilization among the 306 hospital referral
regions. For example, Figure 2.2 from the 1998 edition of the Dartmouth Atlas shows
the distribution of hospital employees per 1,000 residents in each of the 306 hospital
referral regions. The vertical axis shows the rate of hospital employees per 1,000 residents. The Bronx, which had 27.6 employees per thousand residents, is represented by
the highest point on the graph. Chicago, which had 21.8, and Manhattan, which had
21.6 employees per 1,000 residents, are represented by the two next lowest points on the
graph. Thus, some areas which do not have exactly the same number of hospital employees per thousand residents are arrayed on a single line because their rates fall into a “bin”
between two values.
This chart summarizes two features of the data. The first is a measure of dispersion; if
the number of employees per 1,000 (or whatever measure is on the vertical axis) in the
highest hospital referral region is two or three times higher than the number of employees per 1,000 residents in the lowest hospital
referral region, it suggests substantial variation in
health care resources. Second, the distribution
graph shows whether the variation is caused by just
a few outliers — hospital referral regions that for
various reasons are very different from the rest of
the country — or whether the variation is pervasive and widespread across the country. In the
example above, there is widespread dispersion
across the country, but one area, the Bronx, does
stand apart from all other areas.
Figure 2.2. Hospital Employees Allocated to
Hospital Referral Regions (1995)
APPENDIX ON METHODS
177
Age Sex Race and Illness Adjusted
Medical Discharges per 1,000 Medicare Enrollees
7.2 Measures of Association (R2 and
Regression Lines)
In the Atlas, we often suggest that some
factors may be related in a systematic way
to other factors. For example, in the 1998
edition of the Dartmouth Atlas we hypothesized that regions with high rates of
beds per 1,000 residents also have high
rates of hospitalization for medical conditions. To capture the degree and extent of
the association between hospital beds and
Acute Care Beds per 1,000 Residents (1996)
medical hospitalizations in Figure 3.5, we
Figure 3.5. The Association Between Hospital Beds per 1,000 Residents and Age, Sex,
put hospital beds per 1,000 residents on
Race and Illness Adjusted Hospitalization Rates for Medical Conditions per 1,000
the horizontal axis and hospitalization
rates per 1,000 Medicare enrollees on the
vertical axis, and placed a point on the graph for each of the 306 hospital referral
regions. If hospital beds and hospitalization rates were negatively correlated, so
that regions with higher beds per 1,000 residents had lower per capita discharges,
then we might expect to see the cloud of points tilted downward, running from
northwest to southeast. Conversely, if they were positively correlated — as they in
fact are — the cloud of points would run from southwest to northeast on the graph,
as seen in Figure 3.5.
It is sometimes difficult to discern from this cloud of points the relationship between two variables. A linear regression line provides the best fit of the data and
summarizes the relationships between them. A measure of the “goodness of fit” or
the extent to which hospital beds per 1,000 residents predicts hospitalizations per
1000 residents is the R2, which is defined as the proportion of total variation in
the vertical axis (hospitalizations) that is explained by variation in the horizontal
axis (beds). It ranges from 0 to 1, where 1 is perfect correlation and 0 means that
178
the two variables are completely unrelated. In Figure 3.5, the R2 of the relationship between medical hospitalizations and hospital beds is 0.56, which means that
the two are closely related — that 56% of the variation in medical hospitalizations per 1,000 residents is related to the bed supply.
The regression lines and R2 statistics given in the text are not weighted for the size
of the population. Weighted and unweighted R2 statistics were similar.
Appendix on the
Geography of Health Care
in the United States
APPENDIX ON THE GEOGRAPHY OF HEALTH CARE IN THE UNITED STATES
Appendix on the Geography of Health Care in the United States*
The use of health care resources in the United States is highly localized. Most
Americans use the services of physicians whose practices are nearby. Physicians, in
turn, are usually affiliated with hospitals that are near their practices. As a result,
when patients are admitted to hospitals, the admission generally takes place within
a relatively short distance of where the patient lives. This is true across the United
States. Although the distances from homes to hospitals vary with geography –
people who live in rural areas travel farther than those who live in cities – in general
most patients are admitted to a hospital close to where they live which provides an
appropriate level of care.
The Medicare program maintains exhaustive records of hospitalizations, which
makes it possible to define the patterns of use of hospital care. When Medicare
enrollees are admitted to hospitals, the program’s records identify both the
patients’ places of residence (by ZIP Code) and the hospitals where the
admissions took place (by unique numerical identifiers). These files provide a
reliable basis for determining the geographic pattern of health care use, because
research shows that the migration patterns of patients in the Medicare program
are similar to those for younger patients.
Medicare records of hospitalizations were used to define 3,436 geographically distinct hospital service areas in the United States. In each hospital service area, most
of the care received by Medicare patients is provided in hospitals within the area.
Based on the patterns of care for major cardiovascular surgery and neurosurgery,
hospital service areas were aggregated into 306 hospital referral regions; this Atlas
reports on patterns of care in these hospital referral regions.
How Hospital Service Areas Were Defined
Hospital service areas were defined through a three-step process. First, all acute care
hospitals in the 50 states and the District of Columbia were identified from the
American Hospital Association and Medicare provider files and assigned to the
town or city in which they were located. The name of the town or city was used
*Abstracted from the 1996 edition of the Dartmouth Atlas of Health Care
181
182
as the name of the hospital service area, even though the area might have extended
well beyond the political boundary of the town. For example, the Mt. Ascutney
Hospital is in Windsor, Vermont. The area is called the Windsor hospital service
area, even though the area serves several other communities.
In the second step, all 1992 and 1993 Medicare hospitalization records for each
hospital were analyzed to ascertain the ZIP Code of each of its patients. When a
town or city had more than one hospital, the counts were added together. Using a
plurality rule, each ZIP Code was assigned on a provisional basis to the town containing the hospitals most often used by local residents.
The analysis of the patterns of use of care by Medicare patients led to the provisional assignment of five post office ZIP Codes to the Windsor hospital service area.
ZIP Code
05037
05048
05053
05062
05089
Community
Name
Brownsville
Hartland
Pomfret
Reading
Windsor
1990
Population
415
1,730
245
614
5,406
% of Medicare Discharges
to Mt. Ascutney Hospital
52.8
46.8
52.6
36.8
63.2
The third step involved the visual examination of the ZIP Codes using a computergenerated map to make sure that the ZIP Codes included in the hospital service
areas were contiguous. In the case of the Windsor area, inspection of the map led
to the reassignment of Pomfret to the Lebanon hospital service area. In the final determination, the Windsor hospital service area contained four communities and a total
population of 8,165. (See Map A2)
Details about the method of constructing hospital service areas are given in the
Appendix on Methods.
183
NH-Lebanon HSA 30013
NH-Plymouth HSA 30021
NH-New London HSA 30017
NH-Claremont HSA 30002
VT-Windsor HSA 47014
VT-Springfield HSA 47011
VT-Rutland HSA 47010
VT-Randolph HSA 47009
ZIP Code Boundary
HSA Boundary
State Boundary
Interstate Highway
Referral Hospital
Community Hospital
Map A. ZIP Codes Assigned to the Windsor, Vermont Hospital Service Area
The analysis of the pattern of use of hospitals revealed that Medicare enrollees living
in the five ZIP Code areas in light blue most often used the Mt. Ascutney Hospital
in Windsor, Vermont. To maintain geographic continuity of hospital service areas,
the Pomfret ZIP Code 05053 was reassigned to the Lebanon hospital service area.
The Windsor hospital service area contained four communities, with a 1990 census
of 8,165. During 1992-93, there were 679 hospitalizations among the Medicare
population; 394 (58%) were to Mt. Ascutney Hospital, 131 to the Mary Hitchcock
Memorial Hospital, and 154 to other hospitals.
184
Hospital Service Areas in the United States
The documentation of the patterns of use of hospitals according to Medicare
enrollee ZIP Codes during 1992-93 led to the aggregation of approximately 42,000
ZIP Codes into 3,436 hospital service areas. In each area, more Medicare patients
were hospitalized locally than in any other single hospital service area. The propensity of patients to use local hospitals is measured by the localization index, which is
the percentage of all residents’ hospitalizations that occur in local hospitals (the
number of local hospitalizations of residents divided by all hospitalizations of residents). This index varied from a low of 17.9% to over 94%. More than 85% of
Americans lived in hospital service areas where the majority of Medicare hospitalizations occurred locally. More than 51% lived in areas where the localization index
exceeded 70%.
Figure A3. Cumulative Percentage of Population of the
United States According to the Hospital Service Area
Localization Index (1992-93)
The localization index is the proportion of all hospitalizations for
area residents that occur in a hospital or hospitals within the area.
The figure shows the localization index for Medicare patients in
3,436 hospital service areas, according to the cumulative proportion of the population living in the region. Most of the population
lived in regions where more than 50% of hospitalizations occurred
locally.
In 1993, most Americans lived in hospital service areas with three or fewer local hospitals.
Eighty-two percent, or 2,830, of all hospital
service areas, which comprised 39% of the
population in 1990, had only one hospital.
Four hundred twenty-eight hospital service
areas, which comprised 23% of the United
States population, had either two or three hospitals. One hundred seventy-eight, or less than
6% of hospital service areas, had four or more
local hospitals and comprised about 37% of the
population of the United States.
Map B. Hospital Service Areas According to the Number of
Acute Care Hospitals
Thirty-nine percent of the population of the United States lived in areas with
one hospital (buff); 15% lived in areas with two hospitals (light orange); 8.4%
lived in areas with three hospitals ( bright orange); and 37% of the population
lived in areas with four or more hospitals within the hospital service area (red).
185
Count of Acute Care Hospitals
by Hospital Service Area (1993)
4 or more (178 HSAs)
3
(106)
2
(322)
1
(2,830)
Not Populated
186
How Hospital Referral Regions Were Defined
Hospital service areas make clear the patterns of use of local hospitals. A significant
proportion of care, however, is provided by referral hospitals that serve a larger
region. Hospital referral regions were defined in this Atlas by documenting where
patients were referred for major cardiovascular surgical procedures and for neurosurgery. Each hospital service area was examined to determine where most of its
residents went for these services. The result was the aggregation of the 3,436 hospital service areas into 306 hospital referral regions. Each hospital referral region had
at least one city where both major cardiovascular surgical procedures and neurosurgery were performed. Maps were used to make sure that the small number of
“orphan” hospital service areas – those surrounded by hospital service areas allocated
to a different hospital referral region – were reassigned, in almost all cases, to ensure
geographic contiguity. Hospital referral regions were pooled with neighbors if their
populations were less than 120,000 or if less than 65% of their residents’ hospitalizations occurred within the region.
Hospital referral regions were named for the hospital service area containing the
referral hospital or hospitals most often used by residents of the region. The regions
sometimes cross state boundaries. The Evansville, Indiana, hospital referral region
(Map C) provides an example of a region that is located in three states: Illinois, Indiana, and Kentucky. In this region, three hospitals provided cardiovascular surgery
services. Two were in Evansville; a third hospital, in Vincennes, Indiana, also provided cardiovascular surgery, but in the years of this study residents of the Vincennes
area used cardiovascular and neurosurgery procedures provided in Evansville more
frequently than those in Vincennes, resulting in the assignment of the Vincennes
hospital service area to the Evansville hospital referral region.
Map C also provides an example of a region with a population too small to meet the
minimum criterion for designation as a hospital referral region. The Madisonville, Kentucky, hospital service area met the criterion as a hospital referral region on the basis of
the plurality rule, but its population was less than 57,000. The area was assigned to the
Paducah, Kentucky, hospital referral region because hospitals in Paducah were the second
most commonly used place of care for cardiovascular and neurosurgical procedures.
187
Acute Care Hospital Beds
Fewer than 50
50 to 99
100 to 249
250 to 499
500 or more
Symbols for hospitals performing
major cardiovascular surgery are
in red.
HSA Boundary
State Boundary
Interstate Highway
Expressway
Map C. Hospital Service Areas Assigned to the Evansville, Indiana,
Hospital Referral Region
Hospital referral regions are named for the hospital service area containing the
referral hospital or hospitals most often used by residents of the region. Hospital
referral regions overlap state boundaries in every state except Alaska and Hawaii.
The Evansvillle, Indiana, hospital referral region is in parts of three states: Illinois,
Indiana, and Kentucky.
188
Maps of Hospital Referral Regions in the United States
The maps on the following pages outline the boundaries of the hospital referral
regions. Although in some regions more than one city provided referral care, each
hospital referral region was named for the city where most patients receiving major
cardiovascular surgical procedures and neurosurgery were referred for care.
Map D. New England Hospital Referral Regions
189
190
Map E. Northeast Hospital Referral Regions
Map F. South Atlantic Hospital Referral Regions
191
192
Map G. Southeast Hospital Referral Regions
Map H. South Central Hospital Referral Regions
193
194
Map I. Southwest Hospital Referral Regions
Map J. Great Lakes Hospital Referral Regions
195
196
Map K. Upper Midwest Hospital Referral Regions
Map L. Rocky Mountains Hospital Referral Regions
197
198
Map M. Pacific Northwest Hospital Referral Regions
Map N. Pacific Coast Hospital Referral Regions
199
ENDNOTE
201
Endnote
Concerning issues of quality improvement, see:
Chassin MR; Galvin RW; and the National Roundtable on Health Care Quality: “The Urgent Need to Improve Health Care Quality.” JAMA
1998 — Vol. 280, No. 11; 1000-1005.
Weinstein, JN, Brown, PW, Hanscom, B, Walsh, T, Nelson, EC., Designing an Ambulatory Clinical Practice for Outcomes Improvement —
From Vision to Reality: The Spine Center at Dartmouth-Hitchcock, Year One. Quality Management in Health Care, 8(2):1-20, 2000.
For a further description of the systematic coefficient of variation see:
McPherson K, Wennberg JE, Hovine OB, Clifford P. Small-area variations in the use of common surgical procedures: an international comparison of New England, England and Norway. N Eng J Med. 1982;307;1310-1314.
Other publications in the Dartmouth Atlas series:
Wennberg, JE, Cooper MM, editors, The Dartmouth Atlas of Health Care. American Hospital Publishing, Inc. Chicago, IL 1997.
Wennberg JE, Cooper MM, editors, The Dartmouth Atlas of Health Care 1999: The Quality of Medical Care in the United States. American Hospital Publishing, Inc. Chicago, IL 1999.
Wennberg DW, Birkmeyer JD, editors, The Dartmouth Atlas of Cardiovascular Health Care. American Hospital Publishing, Inc. Chicago, IL 2000.
Cronenwett JL, Birkmeyer JD, editors, The Dartmouth Atlas of Vascular Health Care. American Hospital Publishing, Inc. Chicago, IL 2000.
For a general discussion of whether more medical care results in better outcomes, see:
Fisher, ES, Wennberg, JE, Stukel TA, Skinner JS, Sharp SM, Freeman, JL, Gittelsohn, AM: “Associations Between Hospital Capacity, Utilization and Medicare Mortality in the United States: Might More Be Worse?” Center for the Evaluative Clinical Sciences Working Paper, 1993.
Fisher, ES, Welch HG, “Avoiding the Unintended Consequences of Growth in Medical Care: How Might More Be Worse?” JAMA 1999, Vol.
281, No. 5: 446-453.
For more on shared decision making, see:
Barry MJ, Fowler FJ, Mulley AG, Henderson JV, Wennberg JE. Patient reactions to a program designed to facilitate patient participation in
treatment decisions for benign prostatic hyperplasia. Med Care. 1995;33;771-782.
Wagner EH, Barrett P, Barry MJ, Barlow W, Fowler FJ. The effect of a shared decisionmaking program on rates of surgery for benign prostatic hyperplasia: pilot results. Med Care. 1995;33;765-770.
Barry MJ, Cherkin DC, Chang YC, Fowler FJ, Skates S. A randomized trial of a multimedia shared decision-making program for men facing a treatment decision for benign prostatic hyperplasia. Disease Management and Clinical Outcomes. 1997;1:5-114.
Phelan EA, Deyo RA, Cherkin DC, Weinstein JN, Howe, JF, Ciol MA, and Mulley AG. Helping Patients Decide About Back Surgery: A
Randomized Trial of an Interactive Video Program. Spine, In Press, 2000.
Weinstein JN “The Missing Piece: Embracing Shared Decision-Making to Reform Healthcare” Spine 24(26):1999.
202
Publications of interest concerning orthopaedic surgery:
Hawker, Gilliann A.; Wright, James G.; Coyte, Peter C.; Williams J. Ivan; Harvey, Bart; Blazier, Richard; Badley, Elizabeth M. Differences
between men and women in the rate of use of hip and knee arthroplasty. NEJM 342(14):1016-1022, 2000.
Cherkin, D., R. Deyo, et al. (1994). “Physician variation in diagnostic testing for low back pain: who you see is what you get.” Arthritis &
Rheumatism 37(1): 15-22.
Cherkin, D. C., R. A. Deyo, et al. (1994). “An International Comparison of Back Surgery Rates.” Spine 19(11): 1201-1206.
Birkmeyer, NJO, Weinstein JN. Surgical Treatment of Low Back Pain, Effective Clinical Practice, 2:218-227, 1999.
Kuntz KM, Snider RK, Weinstein JN, Pope MH, Katz, JN. Cost-Effectiveness of Fusion With and Without Instrumentation for Patients with
Degenerative Spondylolisthesis and Spinal Stenosis. Spine 25(9):1132-1139, 2000.
Weinstein JN. The Hippocratic Enigma. Spine 21(8):905-909, April 15, 1996.
Weinstein JN “The Missing Piece: Embracing Shared Decision-Making to Reform Healthcare” Spine 24(26):, 1999.
Phelan EA, Deyo RA, Cherkin DC, Weinstein JN, Howe, JF, Ciol MA, and Mulley AG. Helping Patients Decide About Back Surgery: A
Randomized Trial of an Interactive Video Program. Spine, In Press, 2000.
Deyo RA, Cherkin DC, Weinstein JN, Howe JF, Ciol MA, Mulley AD. Involving Patients in Clinical Decisions: Impact of an Interactive
Video Program on Use of Back Surgery. Medical Care (In Press).
Weinstein JN, Goodman D, Wennberg, JE. Commentary: The Orthopaedic Workforce: Which Rate is Right? J Bone & Joint Surgery 80A(3):327-330, March, 1998.
Lee PP, Jackson CA, Relles DA. Demand-based assessment of workforce requirements for orthopaedic services. J Bone & Joint Surgery 80A(3):313-26, March 1998.
Lurie JD, Weinstein JN. Geographic Variation and Shared Decision-Making: Implications for the Orthopaedic Workforce. Clinical Orthopedics and Related Research, 2000. In Press.
Karagas MR, Lu-Yao GL, Barrett JA, Beach ML, Baron JA. Heterogeneity of hip fracture: age, race, sex, and geographic patterns of femoral
neck and trochanteric fractures among the US elderly. Am J Epid 143(7):677-82, 1996, Apr 1.
Barret JA, Baron JA, Karagas, MR, Beach ML. Fracture risk in the U.S. Medicare population. J Clin Epid 52(3):243-9, 1999 Mar.
Weinstein JN Editorial: “The Tortoise and the Hare: Is there a place in spine surgery for randomized trials?” Spine 25(1): 2000.
Fanuele, Jason C., M.S.; Nancy J. O. Birkmeyer, Ph.D.; William A. Abdu, M.D.;James N. Weinstein, D.O., M.S. The Impact of Spinal Problems On the Health Status Of Patients: Have We Underestimated the Effect? Spine June 15, 2000.
ENDNOTE
203
Appendix on Methods:
The National Committee for Quality Assurance (NCQA) internet site can be accessed at: www.ncqa.org
The Health Plan Employer Data and Information Set (HEDIS) can be accessed at the NCQA internet site: www.ncqa.org
The Berenson-Eggers Type of Service File (BETOS) can be accessed ata the internet site of the Health Care Financing Administration:
www.hcfa.gov
See also:
Wennberg JE, Freeman JL, Culp WJ. Are hospital services rationed in New Haven or over-utilized in Boston? Lancet. 1987;1(8543):11851188.
Breslow NE, Day NE. Statistical Methods in Cancer Research. Volume II - The Design and Analaysis of Cohort Studies. Lyon: IARC, 1987.
On the Geographic Practice Cost Index (GPCI) developed by Zuckerman, Welch, and Pope. See:
Pope GC, Welch WP, Zuckerman S, Henderson MG, “Cost of practice and geographic variation in Medicare fees. Health Affairs.
1989;8(3):117-28.
The Dartmouth Atlas of Musculoskeletal Health Care is based, in part, on data supplied by
The American Hospital Association
The American Medical Association
The American Osteopathic Association
The Health Care Financing Administration
The National Center for Health Statistics
Technology Marketing Group, Inc.
The United States Census
The United States Department of Defense
Claritas, Incorporated
Data analyses were performed using
Software developed by the Center for the Evaluative Clinical Sciences
using SAS® on HP® equipment running the UNIX® system software
Maps and map databases were generated using
MapInfo® software
Highway map coordinates from MapInfo®
ZIP Code map coordinates from GDT®
Claritas 3H. Custom Dataset for US ZIP Codes from Claritas®

The Dartmouth Atlas of Musculoskeletal Health Care

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