Implementation of HIV/AIDS Care and Treatment Services in Tanzania

Transcription

THE UNITED REPUBLIC OF TANZANIA
MINISTRY OF HEALTH AND SOCIAL WELFARE
NATIONAL AIDS
CONTROL PROGRAMME
Implementation of HIV/AIDS Care and
Treatment Services in Tanzania
Report Number 3
May 2013
THE UNITED REPUBLIC OF TANZANIA
MINISTRY OF HEALTH AND SOCIAL WELFARE
NATIONAL AIDS
CONTROL PROGRAMME
Report Number 3
May 2013
C May 2013
Ministry of Health and Social Welfare
National AIDS Control Programme (NACP)
P.O. Box 11857
Dar es Salaam - Tanzania
Tel: +255 22 2131213, Fax: +255 22 2138282
Email: [email protected]
Website: www.nacp.go.tz
ISBN No. 978 - 9987 - 650 - 81 - 1
Contents
Contents..............................................................................................................................................i
Acknowledgement.............................................................................................................................ii
Executive Summary............................................................................................................................v
1. Introduction......................................................................................................................................1
1.1
HIV/AIDS Situation in Tanzania............................................................................................1
1.2
National Response...................................................................................................................1
1.3
National HIV/AIDS Care and Treatment Plan (NCTP)..........................................................2
1.4
Recommendations from the CTC 2 report.................................................................................2
2. Methods...........................................................................................................................................4
2.1 Overview of care and treatment’s Patient Monitoring System....................................................4
2.2 Formats for data reporting...............................................................................................................4
2.3 National Database.........................................................................................................................4
2.4 Analysis of Adults in Pre-ART Care.............................................................................................6
2.5 Analysis of Adults on ART...........................................................................................................6
2.6 Pediatric HIV and AIDS infection...................................................................................................9
2.7 Tracing unreported transfers, lost to follow up and clients picking drugs from multiple facilities....9
3. Results from the National Database..............................................................................................13
3.1 Care and treatment facilities reporting to national database............................................................13
3.2 National Estimates of people Infected with HIV and in need of ART.........................................15
3.3 Estimates of number of people with advanced HIV disease (in need of......................................18
3.4 Estimates of number of facilities per 1000 people infected with HIV.........................................19
4. Results fromAnalysis ofAdults in Pre-ART Care........................................................................21
4.1 Baseline Characteristics..............................................................................................................21
5. Results from analysis of Adults on ART....................................................................................27
5.1 Baseline characteristics...............................................................................................................27
5.2 Mortality in adults who started on ART......................................................................................31
5.3 Hazard rates and ratios..................................................................................................................34
5.4 Analysis of adults No Longer On Treatment (NLOT).................................................................37
5.5 Improvements in CD4 counts........................................................................................................39
5.6 Improvements in BMI................................................................................................................40
5.7 Switches to second-line therapy....................................................................................................42
5.8 Discussion.......................................................................................................................................47
5.9 Limitations.....................................................................................................................................50
5.10Recommendations................................................................................................................................51
6. Results from Analysis of Children on ART...............................................................................53
6.1 Baseline characteristics..............................................................................................................53
6.2.Outcomes of children starting ART...........................................................................................58
6.3. Mortality in children who started on ART..................................................................................59
6.4 Discussion.......................................................................................................................................63
6.5Recommendations.................................................................................................................................64
7. Tracing lost to follow up of HIV clients from multiple facilities in Mwanza region......................65
7.1 Introduction.....................................................................................................................................65
7.2 Results.........................................................................................................................................65
7.3 Discussion.................................................................................................................................................69
7.4 Recommendations....................................................................................................................................70
Annex A.........................................................................................................................................................73
Competing risks analysis................................................................................................................................73
Annex B.........................................................................................................................................................74
PARTICIPANTS TO PREPARATORY AND ANALYSIS WORKSHOPS...............................................74
i
Acknowledgement
This report narrates the delivery of HIV Care and Treatment in Tanzania, using the routine data
collected at clinics throughout the country. This report builds on the first Care and Treatment report
for Tanzania in 2008, and the second Care and Treatment report in 2010. The report comes from a
series of workshops and training sessions in Dar-es-Salaam and Mwanza, involving many people and
organisations in Tanzania and in other countries. Moreover, the organisation for the 2013 report has
been developed by these dedicated groups working on set objectives, and this has been a learning
exercise for all. The success of this report demonstrates the hard work and dedication of these groups.
The National AIDS Control Programme (NACP), under the jurisdiction of the Ministry of Health and
Social Welfare (MoHSW), has provided the direction and leadership for this work, and without their
inputs this report would never have been completed. We are indebted to the health workers in all Care
and Treatment Centres in Tanzania for providing Care and Treatment to HIV infected people, and for
collecting the data, which has enable the monitoring and evaluation of the work they are doing.
We thank participants to the preparatory and analysis workshops that has produced this report. We are
grateful for the Technical assistance for the analysis of the data which was provided by London
School of Hygiene and Tropical Medicine (LSHTM), the National Institution for Medical Research
(NIMR Mwanza), Kilimanjaro Christian Medical University College (KCMU College), University
Computing Centre (UCC), and AIDS Relief. We are also grateful for the financial support for the two
workshops came from WHO Tanzania Country office. Special thanks to Jim Todd for spearheading
both write ups (report 2 and 3) and empowering those involved in advanced statistical analysis skills.
We also wish to thank the following individuals for their work in preparing and finalising this report:
Aifello
Sichalwe
KCMC
MSc
Epidemiology and Biostatistics student
Anath Rwebembera – NACP HIV Pediatric
Programme officer
Angela Ramadhani – NACP Program
Manager
Apaililia Kibona – Clinician Kilwa Road
Police Hospital
Bonita Kilama- NACP Acting Head,
Epidemiology unit
Denna Michael- NIMR Mwanza Researcher
Elaine Baker- UCC Systems Analyst
Emanuel
MwendoKCMC
MSc
Epidemiology and Biostatistics student
Filemon Tenu- KCMC MSc Epidemiology
and Biostatistics student
Fiona Vanobberghen - LSHTM & MITUNIMR MWANZA Medical Statistics Fellow
Gretchen Antelman- ICAP Research And
Evaluation Director
ii
Jim Todd – LSHTM and NIMR (Reader in
Applied Biostatistics)
Joseph Nondi- KCMC MSc Epidemiology
and Biostatistics student
Robert Josiah –NACP Deputy Program
Manager
Munda Elias- Applied Epidemiology Field
Resident
Naimi Mbogo – CSSC M&E Officer
Neema Makyao- NACP KP Coordinator
Paul Mahunga- TUNAJALI M&E Officer
Ramadhani Mwiru- MDH Nutrition Support
Coordinator
Raphael Isingo – NIMR Mwanza Statistician
Sesil Latemba – Pharmaccess M&E officer
Tuhuma Tulli – Futures Group SI Advisor
Gretchen Antelman- ICAP Research And
Evaluation Director
Hilda Mmari- Tanzania Prison Headquarters
HIV Services Head
James Juma – IeDEA Coordinator
Japhet Kamala- UCC PEPFAR Support
Jennifer Ward- CDC Research Fellow
Tuhuma Tulli – Futures Group SI Advisor
Veryeh Sambu- Data Manager- NACP
Juma Mwinula – Medical Director Armed
Forces –JKT
Mariam Ngaeje- AGPHAI M&E Manager
Moses Kehengu- Futures Group ICT
Specialist
The Ministry is committed to use this report as evidence base to strengthen delivery of HIV
Care and Treatment services in Tanzania.
Dr. Donan W. Mmbando
Chief Medical Officer
Ministry of Health and Social Welfare
iii
Abbreviations
AIDS Acquired Immune Deficiency Syndrome
ANC Ante-natal Care
ART Antiretroviral Therapy
BMI
Body Mass Index
C&T Care and treatment
CHMT Council Health Management Team
CTC Care and treatment clinics
DACC District AIDS Control Coordinator
DMO District Medical Officer
EWI
Early Warning Indicators for HIV Drug Resistance
GFATM The Global Fund To Fight AIDS, Tuberculosis and Malaria
HBC Home-based care
HIV Human Immunodeficiency Virus
HSHSP-III
Health Sector HIV/AIDS Strategic Plan Number III
HTC
HIV Testing and Counseling
ICAP
At Columbia University, (formerly International Centre forAIDS Care and Treatment Programs)
MMAM”Mpango wa maendeleo ya afya ya msingi” National Plan for Development of
Primary Health Care
M&E Monitoring and evaluation
MOHSW Ministry of Health and Social Welfare
NACP National AIDS Control Programme
NBS
National Bureau of Statistics
NLOT
No Longer On Treatment (patients who have not attended CTC for atleast 3
consecutive months)
NTCP
National Care and Treatment Plan
PEPFAR President’s Emergency Plan for AIDS Relief
PITC
Provider Initiated Testing and Counselling
PLHIV People living with HIV/AIDS
PMS
Patient Monitoring System
PMTCT Prevention of Mother-to-Child Transmission
RACC Regional AIDS Control Coordinator
RHMT Regional Health Management Team
RMO Regional Medical Officer
TACAIDS Tanzanian Commission for AIDS
TB Tuberculosis
THIS
Tanzania HIV/AIDS Indicator Survey
THMIS
Tanzania HIV/AIDS and Malaria Indicator Survey
UA
Universal Access (WHO recommended Indicators for Universal Access to ART)
UNAIDS
Joint United Nations Programme on HIV/AIDS
UNGASS
United Nations General Assembly Special Session on AIDS
VCT Voluntary counselling and testing (now often referred to as CITC, client initiated
testing and counselling)
WHO World Health Organisation
iv
Executive Summary
The National HIV/AIDS Care and Treatment Plan for Tanzania mainland was launched in 2004,
with the aim of providing quality care and treatment for all people living with HIV/AIDS (PLHIV)
in Tanzania. In 2010, the second report on Care and Treatment Clinic (CTC) services showed the
impact of anti-retroviral therapy (ART) on the lives of PLHIV. This report (the third CTC report)
builds on the second report and provides an update on the impact of ART on PLHIV, and tackles
the further analysis of important data collected by the CTC program. These reports will become a
regular part of the monitoring of the CTC program, and help policy makers and funders evaluate
the program.
Although there are many health facilities approved to deliver care and treatment in Tanzania, data
are available on the numbers in care and treatment from 848 health facilities in Tanzania. Using
the 2012 Tanzania HIV and Malaria Indicator survey (THMIS) and other resource materials, the
projections done estimate 1.2 million PLHIV in the country among which 123,000 are children. This
reports estimates that 65% of adults and 35% of children who need ART in Tanzania are receiving
ART, which indicates that further efforts are needed to reach all those in need of HIV treatment.
However, the need for services is uneven, with 5 regions in Tanzania having 50% of PLHIV
(626,000 in total), and further efforts are needed to deliver CTC in regions of greatest need.
Since the second CTC report, the data collection and reporting systems for the CTC program have
been improved. Harmonisation of the reporting system has also contributed to better records being
sent to NACP for this report, and for regular monitoring of the program. For this report, individuallevel patient records have been extracted from the national database and used for the analysis. The
analysis has used a database with over 600,000 adult patients, and 8 million visits over 8 years of
follow-up, which are spread across 19 regions in Tanzania. Therefore we believe that the results
NACP, and utilised the personnel who have been trained in statistical analysis within NACP and
partner organisations. These data and the results that can be generated from them are invaluable in
in Tanzania, and are an inspiration to many in other countries.
In line with the second report on Implementing HIV/AIDS Care and Treatment Services in Tanzania,
of 2010, we found that two thirds of adults enrolling in CTC and starting ART are female. In children,
the numbers of male and female children presenting for care and starting ART are similar. This
analysis of data to the end of 2011 shows that the numbers newly enrolling in CTC and starting ART
have remained consistent since 2009, and are no longer increasing as they did for the years 2004
through 2008. With new guidelines for ART eligibility, the median CD4 count for those starting
ART has increased over the past 3 years, from 214 cells/µl in 2007 to 245 cells/µl in 2010, however
CD4 documentation is poor in both pre and post ART initiation.
The survival of PLHIV has improved since the second CTC report in 2010. This report shows that
9.7% in 2008, to 7.3% for those who started ART in 2010. There may be several reasons for the
better survival, including the fact that PLHIV are now accessing ART with higher CD4 counts. After
per annum.
v
This report shows that many of those who start ART continue to drop out of treatment and are
classified as No Longer on Treatment (NLOT). The analysis shows that as much as one quarter of
those who start ART are no longer receiving treatment from the clinic where they initiated ART.
This is of serious concern, and this report devotes a chapter to the analysis of those who are lost to
follow up, using the IQTools software developed by AIDS Relief, and applied to data from Mwanza
region. Initial analysis shows that only 7% could be traced using the tool on the CTC3 data. Further
research on the best way to use IQTools to trace those lost to follow up is being undertaken and
hopefully subsequent results can show what has happened to these people.
For the first time, we have analyzed data on switching to second line treatment, using competing
risks analysis which allows for the different (competing) outcome of death, within a time-to-event
model. The results show that less than 1% of those who initiated first line treatment were observed
to subsequently switch to second line treatment. The rate of switching was constant and consistent
across time, with about 0.35% switching in any year, giving a cumulative hazard rate of 2.4% after
6 years on ART. In this analysis, we only looked at those who actually switched to second line
drugs, and we did not assess the need for second line drugs. This will be an aim for future work,
but it will require good data on CD4 counts and treatment failures to determine what proportion of
PLHIV require a change from first line to second line drugs and thus determine whether this need
is being met within the current CTC program.
This report has also analyzed patient level data for children and it has revealed children to have
as impressive survival than adults. Using data from around 26,500 children who started ART, the
mortality in the first year was 9.1% for those who started in 2005, and 8.3% for those who started
in 2010. Data on children show smaller effects of sex, and better treatment outcomes. Risk of death
was seen to be higher among children without baseline CD4 count, WHO Stage 4 and 3.
vi
1. Introduction
1.1
HIV/AIDS Situation in Tanzania
HIV and AIDS continue to be a cause of high morbidity and mortality in the world. More impact
is seen in developing countries. WHO reported about 2.5 million children less than 15 years to be
living with HIV infection and about 330,000 children were newly infected by HIV in 2011 (UNAIDS
Report 2012). More than 90% of the children 0-14 years who acquired HIV infection in 2011 live in
Sub-Saharan Africa. Between 2009 and 2011 many Sub-Saharan countries have registered decline
in acquisition of new HIV infection in children ranging between 1-59%. In Tanzania the decline in
acquiring new HIV infection among children is between 1-19% (UNAIDS Report 2012).
Tanzania is facing similar trend in terms of HIV/AIDS burden. The National HIV prevalence is
estimated to be 5.1% (THMIS 2012) with highest prevalence in Njombe region (14.8%). Prevalence
of HIV among children 0-14 years is calculated from adult prevalence. With this projection, 123,000
children aged 0-14 years are estimated to be living with HIV and AIDS in Tanzania by end of 2011.
Provision of pediatric HIV care and treatment in Tanzania is faced with many challenges. The
biggest challenge is low enrolment of children into HIV care and ART services. Programmatic data
from NACP shows current rate stands at 8% but the national target is 20%. Other challenges include
poor retention of children under care and treatment and improper documentation of services offered
to those already enrolled into care 1 UNAIDS 2012, 2PMS Review. Furthermore, data analysis,
data use and dissemination of results for planning purposes is also a challenge especially at level
of health facilities3. Due to this, MOHSW Tanzania has introduced data analysis exercise to ensure
that pediatric HIV statistics are available and utilized for policy and decision making in Tanzania.
of the analysis results in decision making.
1.2
National Response
Tanzania has a generalised HIV epidemic as in most sub-Saharan African countries where the
prevalence of HIV among pregnant women is 6.9% 4 The national response is coordinated by the
Tanzania Commission for AIDS (TACAIDS), which was established through the enactment of a law,
Act No. 22 of 2001 by Parliament. TACAIDS is legally mandated to provide strategic leadership, and
In the health sector, the NACP has the responsibility of providing prevention, care, treatment and
support services across the entire health care system. The NACP is also responsible for monitoring
and evaluating the health sector response to HIV and AIDS.
1
UNAIDS (2012)Global Report: UNAIDS Report on the Global AIDS Epidemic 2012
UNAIDS, Geneva.
2
HIV Care Patient Monitoring System Review (2010)
3
HIV/AIDS/STI Surveillance Report number 22
4
Surveillance of HIV and Syphilis infections among Antenatal Clinic Attendees 2008
1
1.3 National HIV/AIDS Care and Treatment Plan (NCTP)
The government of the United Republic of Tanzania launched the HIV/AIDS Care and Treatment Plan
in October 2003, and since 2004, a rapid roll-out of antiretroviral treatment (ART) has been ongoing.
A number of partners have been working with the Government of Tanzania in this undertaking,
including the governments of Sweden, Canada, Norway, the Netherlands and Japan, along with the
US government through the President’s Emergency Plan for AIDS Relief (PEPFAR). UN Agencies
such as UNDP, UNAIDS and WHO have also collaborated with international funding through
the Global Fund for AIDS, TB and Malaria (GFATM). The implementation of care and treatment
services at the health facility level has been regionalised, with each partner - AIDS Relief, Elizabeth
Glaser Pediatric AIDS Foundation (EGPAF), TUNAJALI (Deloitte/Family Health International),
International Centre for AIDS Care and Treatment Program (ICAP), Muhimbili Dar City Harvard
(MDH) and Walter Reed - supporting one or multiple regions, and PharmAccess International (PAI)
supporting the military and the uniformed forces. To contribute further to Tanzania’s development
goals in health, the USG has now set transition mechanism to strengthen health systems and create
ownership for sustainability of health programs particularly HIV care and treatment programs.
Most of the implementing partners have collaborated with local NGO’s or establish one to facilitate
the transitions.
The University Computing Centre (UCC) supports database systems, including both the CTC2
database used at a large number of HIV care and treatment clinics and the CTC3 Macrodatabase
database at NACP. By the end of 2011, Tanzania had more than 1100 health facilities approved to
provide care and treatment services, estimated to enable more than 1,000,000 patients to access
HIV Care services.
Guidance on the implementation of care and treatment services is obtained from the Health Sector
HIV/AIDS Strategic Plan (HSHSP-II) that runs from 2008 to 2012 including thematic areas
covering care and treatment, and health systems strengthening. The interventions in the thematic
areas include provision of services in the health facilities and community, laboratory strengthening,
surveillance, operational research and public health evaluations. To facilitate entry to care and
treatment services, new HIV testing approaches such as provider-initiated testing and counselling
(PITC) and home-based counselling and testing have been introduced in several regions.
Tanzania has benefitted from the investment in data collection at the CTCs. Through the generous
support of various donors (PharmAccess International, PEPFAR, Global Fund for AIDS, TB and
Malaria) a rigorous system of routine data collection was initiated to record the patients attending
CTC. Through the years, the systems has been revised to accommodate new or changed recording and
reporting obligations from implementing partners, donors and international reporting commitments
such as Global AIDS Response Progress Reporting formerly UNGASS and the Universal Access
to HIV services .
1.4
Recommendations from the CTC 2 report
The second CTC report in 2010 made 9 recommendations about the provision of care and treatment
for HIV positive people in Tanzania, and the reporting and analysis of data from the CTC programme
(www.nacp.go.tz ).
2
This report will update the descriptive and analytical tables and results using de-identified patientlevel CTC data, and provide evidence for future recommendations.
Following the recommendations of the second report, the data collection tools used in CTC clinics
were revised and improved (Recommendation 1). The new reporting procedures are simplified using
only one aggregated report (the monthly report) and supplemented by collection of the data extracted
from the CTC2 database in clinics collecting electronic data (Recommendation 4). Since 2010 there
has been greater recognition of the difficulties in tracking and tracing patients who transfer from
one clinic to another, or who are lost to follow up (Recommendations 2 & 3). There is still an urgent
need for further improvement in retention of patients in the CTC, but this report includes an extra
chapter highlighting efforts to address this problem. The recommendation that regular analyses and
reporting of the CTC data is undertaken (Recommendation 6), is taken up by this analysis 3 years
later, but we recognise that it would be useful to have update reports annually. It would also be
useful to disseminate findings more widely, and to bring the analysis programs to the regional and
district level (Recommendation 5), but this will take resources and training, and may take longer to
achieve (although it remains the long term vision). From the second CTC report, one peer-reviewed
paper has been published 5, and one is in preparation ready for submission (Recommendation 8).
This report shows that the distribution of HIV services in Tanzania is not uniform, and appropriate
ways to address these inequalities are needed (Recommendation 7). Although much has been done,
fully achieving improved access and HIV service coverage can only be achieved through greater
local participation, continued investment in the various WHO Building Blocks for Health System
Strengthening as well as Community System Strengthening avenues (Recommendation 9).
5
Somi, G., Keogh, S. C., Todd, J., Kilama, B., Wringe, A., van den Hombergh, J., Malima, K., Josiah, R., Urassa, M.,
Swai, R. and Zaba, B. (2012), Low mortality risk but high loss to follow-up among patients in the Tanzanian national
HIV care and treatment programme. Tropical Medicine & International Health, 17: 497–506. doi: 10.1111/j.13653156.2011.02952.x
3
2. Methods
2.1 Overview of care and treatment’s Patient Monitoring System
The patient monitoring system for National AIDS Control Program (NACP) runs through sets of
recording and reporting tools. The system is distinctively centralized, with a national database which
recognizes each patient by their unique CTC ID number from all over the country. All individuals
enrolled in care are supplied with client held CTC-1 cards. Elementary records of patient encounters
are captured within facility-held CTC-2 cards, which are the foundation of both the paper and
electronic system in use. Health facilities with paper HIV Care Patient monitoring system provide
aggregated reports (Quarterly and Cohort) while facilities with electronic system (CTC2 database)
has additional reports it can provide including exportation of patient level data. The quarterly report
provide cross sectional information of clients receiving Care and Treatment services whereas the
Cohort report provides longitudinal details and key treatment outcomes of client on ART. Reports
from facilities are aggregated by District AIDS Control Coordinators (DACCs) office then forwarded
to Regional AIDS Control Coordinators (RACCs) office for regional compilation. After regional
compilation, reports are forwarded to NACP for national compilation and verification.
2.2 Formats for data reporting
The de-identified export files (without patient names) from facility-level electronic databases (CTC2 micros) are received at NACP’s central database (CTC2 Macro) via email. The regionalized
program’s implementing partners together with regions’ Regional AIDS Control Coordinators
facilitate this process. The national database contains applications which enable it to generate
aggregate reports from individual level data and to combine individual level data with other
aggregate data received at each quarter. The national care and treatment database contains two main
sections – a “patient-level” section and an “aggregate-level” section. The “patient-level” section
primarily contains data exported electronically from clinics using an electronic database, mostly the
CTC-2 micros databases. The “aggregate-level” section contains data received on paper summary
reports from clinics and entered into the national database at the NACP.
2.3 National Database
Data definitions and Formats
The data were abstracted as per regions and facilities reporting on aggregate and individual level
and presented independently such that no single facility producing both reports was counted twice.
The counts were abstracted for persons currently in care and patients on ART for both adults and
children as of 2011 in each region. Total of 348 clinics had reported comprehensive patient level
data for 2011, and for these clinics patient level data was used. For a further 379 clinics, data on
current numbers of patients for 2011 was available from quarterly aggregate reports only.
Patient-level data: Current in care and on ART and New enrolments and on ART overtime
For each clinic in the set of 348, patient level data for 2011 was retrieved. For each quarter of
2011, a count was done of unique adult or child (<15 yrs on 1 July 2011) patients visiting the clinic
during that quarter to obtain current in care counts. Similarly, counts were done of adult and child
patients visiting the clinic in each quarter, who had an ARV status of 2-Start, 3-Continue, 4-Change
or 6-Restart at the last visit observed during that quarter to obtain current on ART counts. For each
4
clinic the 4-quarter average was then calculated for adults and for children or those in care and those
on ART.
The earliest visit date for each patient was established as the enrolment date, and the clinic at which
that first visit took place was set as the clinic where the patient enrolled. All patients enrolled were
then counted by year of enrolment, adult/child status and sex. Visits on ART were compiled as a
list of all visits with an ARV status of 2-Start, 3-Continue, 4-Change or 6-Restart, and the earliest
visit from this list for each patient was provisionally set as the ART start date, with the clinic where
the visit took place set as the clinic where ART was started. However, where reported transfer-in
records showed an earlier ART start date than the earliest visit on ART for a patient, that patient was
excluded from the patient level analysis (as it can be assumed that the patient started ART at a nonelectronic-reporting clinic). Age at ART start was calculated as the year ART was started minus the
year of birth. These ART start dates were then counted by year of ART initiation, adult/child status,
and sex.
Selection of reporting facilities for patient-level analyses
The reporting by facilities varies depending on whether they are either paper only or both paper and
electronic sites. Whereas the latter provides comprehensive extracts that are suitable for analyses, the
former are only capable of producing quarterly, indicators-driven aggregate (containing summary
data for estimation of cumulative and current numbers) and patients’ cohort data. At any given time
of the year, there are facilities reporting completely, partially, in duplicate, or not at all. Prior to
analyses, the final dataset was de-duplicated (by site and quarter) – with the most comprehensive
report being chosen for inclusion where there was more than one available.
Aggregate Data: Current in Care and on ART and New enrolments and on ART over time
For each clinic not already counted among the 348 electronic (patient level) reporting sites, aggregate
level data were examined from quarterly reports in both old and new formats. Care was taken not
to double-count figures reported as subsets (for example pregnant women in the new report are
reported as subsets of the total as well as being included in the relevant female age groups). Age
groupings are different in the old and new report formats, but both allowed a breakdown by adult
and child less than 15. Total current in care and total current on ART (a subset of those in care) for
adults and for children was calculated for each clinic and quarter. Figures were averaged across all
2011 quarters for which data was available. Totals were then calculated across clinics, by region.
Data on new patients from both old monthly reports and new quarterly reports were used after deduplication. For 66 clinics, more than 12 months’ data in a calendar year were reported (mostly
during 2011). For sites where monthly and quarterly reports covered the same period, a manual
review was undertaken of all reports in order to exclude duplication and select the most complete
report where there were differences observed in total counts. Age and sex breakdowns different
between the old and new format reports, but both allowed breakdown by sex and adult/child. Totals
of new in care and new on ART were calculated for each clinic for, by adult/child status and sex.
Data Compilations
In some cases the above analyses found a number calculated from patient-level data and a number
calculated from aggregate-level data for the same clinic for the same year for the same category (care
or ART). This occurred when clinics reported both patient-level data and aggregate-level reports
5
for the same time period. In these cases, the higher number was taken for that clinic for that year,
resulting in only one number for each clinic for each year for each category for each adult/child
and sex combination. These figures were then summed up across clinics. Table 2.4 represents the
de-duplicated totals for all 860 clinics nationwide with exception of ART sites in Rukwa, Ruvuma
and Mbeya.
2.4 Analysis of Adults in Pre-ART Care
Adults ages 15 years and older from 2005 to 2011 were included in the analysis. Patient and Visits
Tables, containing demographic and clinical data, respectively, from the CTC3 macrodatabase
were merged, matching on the unique CTC Number. Exclusion criteria included ever having prior
exposure to ART (except for PMTCT prophylaxis), and age less than 15 years at time of enrolment.
Enrolment date into pre-ART care was defined as the earliest CTC visit date. Enrolment year was
then extracted from the enrolment visit date. The dataset was transposed into a ‘flat’ file where all
patient’s characteristics were in a single record.
Variables specific to ART initiation, including adherence to ART and reason for ART initiation, were
dropped, as they were not pertinent to the pre-ART population of interest. Body Mass Index (BMI)
at enrolment date was calculated for each client by dividing weight at enrolment by the square of
average height while in pre-ART care. As height is not expected to change much in adults (though
slightly more among adolescents 15-20), average height was used to take into account actual growth
as well as measurement error. BMI was then classified in accordance with current WHO category
definitions for underweight (<18.5 kg), normal weight (18.5–25 kg), and overweight (>25 kg).
Weight at each visit was also categorized as under 45 kg, 45–54 kg, and 55 kg or over. A binary
variable identifying those clients with confirmed TB was created, grouping all those classified as
‘confirmed TB’ from codes such as ‘yes’, ‘confirm TB’, ‘TB RX’, ‘SS+’, ‘CXR+’, ‘START TB’,
‘CTN TB’, ‘RESTART TB’) All other TB categories including suspected TB and negative screening
results were grouped into the new variable as ‘unconfirmed TB.’ The binary variable TB screening
was also created, grouping all variables related to screening, including chest x-ray results, sputum
sample results, and suspected TB into the new variable yes, screened for TB. Lastly, the source of
client referrals included the sub-categories PMTCT, VCT, and TB to reflect referrals from programs
offering HIV services.
Baseline descriptive statistics were carried out for this population at enrolment date for the variables
age, sex, marital status, facility level, referral source, BMI, weight, WHO clinical stage, CD4 cell
count, pregnancy status, TB status, and functional status.
To determine baseline CD4 count trends over time, a new variable for first CD4 count while in preART care was created to also capture those pre-ART clients with missing CD4 counts at enrolment
date. All analyses were carried out using Stata version 12 statistical software (StataCorp. 2011.
Stata Statistical Software: Release 12. College Station, TX: StataCorp LP).
2.5 Analysis of Adults on ART
Data compilation
Patient-level data were taken from the 348 clinics identified from the national CTC3 database.
The 348 clinics used in this analysis do not represent every region in Tanzania (Rukwa, Mbeya
and Ruvuma did not send the extract files to the national database in time for the analysis). Data
6
included all adult clinic visits from the first visit in 2004 through to 2012. The data were cleaned
using Stata version 12, and two data files were generated: one with a single record for each person,
and one with a record for each clinic visit (multiple records per person). All data used the CTC
patient number as the unique identifier for the individual, which allowed the tracing of patients
across clinics, with some patients attending up to 5 different clinics.
A preliminary workshop was organised by NACP/MOHSW in June 2012, and identified the
objectives and analyses for adults on ART. It was held at NIMR, Ocean Road and facilitated by
staff from LSHTM based at NIMR Mwanza and NACP staff. Participants included staff members
from implementing partner organisations as well as TACAIDS, MOHSW, and other organisations.
Participants were requested to adapt the analyses which were performed for the second report in
2010, but additional longitudinal cohort methodology for this third report was introduced. The
preliminary analysis using this new methodology was based on data from Zamzam hospital in
Kagera, Meru Hospital in Arusha, Vikindu Dispensary and Mwananyamala Hospital, Sekou Toure
hospital in Mwanza and Mirembe Hospital in Dodoma. The data from these sites were provided
for the workshop for learning purposes. A follow-up workshop was held from 28th January to 8th
February 2013 were all analyses in this report are a result of that workshop.
Data definitions
Patients were included in the analysis if they had started ART at one of the 348 clinics and were 15
years or over at the time that they initiated treatment. All data were censored at 31 December 2011.
The outcomes for ART patients included:
i)
ii)
iii)
Current on ART
Died
Lost to follow up (LTFU)
Patients were defined as LTFU if they had not attended any clinic (either the original clinic, or one
they had transferred to of the 348 clinics) in the 6 months preceding 31 December 2011 (i.e. had
last attended before 1 July 2011), and were not known to have died. Patients were considered to be
currently on ART if they were not LTFU and had not died. Patient follow-up was censored at the
earliest of: date of death, 6 months after the last clinic visit, or 31 December 2011. Patients were
defined as No Longer On Treatment (NLOT) if they had died or were LTFU.
If patients had transferred between clinics and records were available from more than one clinic, then
the records were cross-checked to ensure that the ART start date used for the analyses corresponded
to the earliest reported date of ART start.
Data analysis
The data analyses were conducted using Stata version 12. Patient characteristics at the time of
ART initiation were described by the year of ART initiation, using frequencies and percentages
for categorical variables, and the median and interquartile range (IQR) for continuous variables.
Categories were combined for variables where some categories had very few participants. We
investigated the distributions of some characteristics of interest by sex, by plotting bar graphs.
Kaplan-Meier estimation methods were used to calculate the probabilities of patients surviving
7
up to 7 years after starting ART, stratified by baseline characteristics of interest. Those who were
LTFU were censored. Cox proportional hazards models were used to investigate the associations of
baseline characteristics with mortality. Since estimating mortality using CTC data is complicated
due to the unknown survival status of patients who were LTFU, as a sensitivity analysis we analysed
deaths and LTFU combined (NLOT).
To define values at ART initiation for time-varying measurements, such as CD4 count and weight,
we used the most recent value up to 3 months prior to ART initiation, or if no such value existed
then the earliest CD4 within 2 weeks after ART initiation. To define values at switch to secondline therapy, we applied the same rule for CD4 selection (3 months before switch up to 2 weeks
after switch). When incorporating time-dependent data into models, we carried forward the last
observation for up to 3 months.
In order to investigate CD4 changes over time after ART initiation, we plotted the median and
IQR of CD4 count in six-monthly intervals, by baseline characteristics of interest. For each sixmonth interval, we allowed a window of -3 months to +2 weeks. For example, CD4 counts from 3
months up to just before 9 months were permitted to be included as the CD4 measurement “at” 6
months. However, we ensured each patient contributed at most one CD4 measurement during each
interval; if there was more than one such measurement available, then the value measured closest
to the nominal six-month interval was chosen (with preference for earlier). The six-month intervals
were chosen to achieve a balance between the ideal interval between CD4 count measurements for
patients on ART according to national guidelines (6months) and ensuring sufficient numbers of
patients in each interval. A similar strategy was adopted to show BMI changes after ART initiation.
Our final aim was to investigate the rate of, and factors associated with, switch to second-line
therapy. While outcome for this analysis was switch to second-line therapy, death is a “competing
risk” in this analysis because clients who died on first-line therapy could not reach the outcome of
switching to second-line and it is highly likely that switch to second-line is associated with mortality
(those who did not switch when they needed to are more likely to die). The usual approaches for
time-to-event analyses, such as Kaplan-Meier estimation and Cox proportional hazards models (as
we used for mortality and NLOT), will be biased in the presence of competing risks. Therefore we
used appropriate methods, such as “competing risks regression”, to account for the competing risk
of death. LTFU was assumed to be uninformative (not associated with the outcome). However,
analogously to the mortality analysis, it is likely that those LTFU are more likely to have died,
therefore future analyses should also consider combining LTFU and death (“NLOT”) as a competing
risk. We firstly used unadjusted models and then a fully adjusted model. We combined the CD4
categories of 350-499 and ≥500 cells/mm3 into a single category (≥350 cells/mm3) due to few
patients with such high CD4 counts. We considered only a subset of the variables which we thought
would be most relevant to switching, namely facility level and ownership, year of ART initiation,
sex, age, weight, WHO stage, CD4 count and ARV regimen.
Individuals with intermittently missing data did not contribute follow-up during that time, but could
re-enter the risk set when measurements were available. Intermittent regimens of duration ≤14 days
were ignored. Individuals who changed to an unknown ART regimen were censored at that time.
Individuals with missing ART information from the date when they were last known to be on firstline therapy until the date they switched to second-line therapy were assumed to have switched to
second-line therapy at the mid-point between these dates.
8
2.6 Pediatric HIV and AIDS infection
Data for pediatric analysis was obtained from the CTC2 database and includes all clients on care and
on ART below 15 years. The data used has been extracted from 348 health Facilities who reported
to NACP by end of 2012
Data analysis
The data analysis in this report was conducted using Stata version 12.0. Children characteristics at
the time of ART initiation were described using cross-tabulations, frequencies and percentages for
categorical variables, and using means, standard deviations and medians for continuous variables.
Baseline characteristics of interest included sex, weight and age at ART initiation and CD4 at
baseline. Clinical characteristics at baseline included WHO stage, CD4 % or count, body weight
and age. The closest measurements of CD4% or CD4 count were recorded from the time of ART
initiation which was taken to be 3 months prior to, or up to 2 weeks after initiating ART. At baseline
and subsequent clinic visits, the number of pediatric clients screened for TB were recorded and
calculated.
The number of Person-years lived following ART initiation were computed, taking into account the
date when ART was started, the last date the patient was seen, and the date of the data exported from
each CTC 2 database. Kaplan-Meier technique was used to calculate the probabilities of patients
surviving up to 5 years after starting ART and stratifying by baseline characteristics of interest.
Smoothed hazard rates were also obtained to find the location of the peak period of mortality
following treatment initiation and age- specific mortality rates were calculated to assess the effect
of current age on mortality.
In order to investigate CD4 changes over time after ART initiation, the lowest CD4 (%) or count
per patient in each six month period following ART start was identified (where available). The six
month periods corresponded to the recommended frequency for CD4 counts for ART patients (ref).
For each six-month period from ART start, the median value of CD4 for all patients with CD4
data available was calculated. The median values for each follow up period were then displayed
graphically by baseline characteristics of interest. Weight for age was used to identify change in
weight gain after ART initiation.
2.7 Tracing unreported transfers, lost to follow up and clients picking drugs from multiple facilities
Patient level data from Mwanza 50 CTCs were used to assess unreported transfers, Lost to Follow
up and clients picking drugs from multiple facilities. The IQ tool developed by Futures Group was
used to assess clients who were LTFU or had unreported transfer. Data of clients who had been on
ART and with unknown or non documented treatment outcomes were subjected to IQ tool to see if
there are any duplicates using unique CTC Id number. The duplicates generated were then analysed
using SPSS version 17 by assessing the socio-demographic characteristics from CTC2 database
export files.
Data Extraction
Patient-level data were retrieved from the CTC2 databases. All data were included from the time the
facility began providing services up to September 2012. All patient data were exported to Access
9
using the ‘export for analysis’ feature from the CTC2 database. This feature excluded personal
identifying data such as names, but did include the CTC unique patient identifying number and
contact data (address where a client lives, including division, ward, village or street), which were
used for patient tracing.
Software: IQ Tools
IQ Tools is software that deals with data validation, query building and reporting to allow facilities
to develop their own analyses and reports. It has been built in the .net framework with an SQL
back-end database in order to extract data. IQ Tools can connect to Microsoft SQL server, Microsoft
Access, MySQL or any other backend for data analysis. The query merge within IQ Tool has been
created purposely for tracing LTFU.
In this analysis the IQ Tools was used to trace patients based on the information available in the
CTC 2 database. This includes the CTC Number (the ID of the patient), the ward, village, and subvillage of the patient’s home.
Inclusion of facilities
Of the total 97 facilities registered and active in Mwanza, 29 are fully-computerised, 47 are purely
paper-based, and 21 are paper-based but have data clerks visiting the facilities to support data
entry. All of the 29 fully-computerised facilities were included in this analysis, except Bugando
Paediatrics Department, which is a subset of the Bugando Referral Hospital. Nineteen of the paperbased clinics were included in this analysis; two clinics were excluded because one had problems
with the patient identifiers, and the other’s database was corrupted. It was not feasible to include
the purely paper-based facilities in this analysis. Therefore, a total of 47 (48% of 97) facilities were
included in the analysis.
Data Definitions
For this analysis, we used the following definitions:
HIV Clients “in care” include all those who have received care at the facility. These include both;
those who have not started ART and those who have started ART.
HIV Clients “on ART” include all those who have started treatment including transfers in while
on treatment.
LTFU (Lost to follow up): A patient was defined as LTFU if he/she was more than 3 months late
for the last recorded appointment with no reports of death or transfer-out. We determined this in
two ways: first, on the CTC2 database there is a variable indicating status, which includes report
of LTFU from the clinics; and second, if a patient had no visit for over 3 consecutive months, we
declared them as LTFU from the time of their last scheduled appointment. Patients meeting either
of these definitions were considered LFTU. Of note, patients could be LTFU from one health
facility, traced to another health facility, and then LTFU again. Therefore, rather than consider
LTFU at the patient level, we refer to “LTFU- episodes”. Each patient can experience more than
one LTFU episode.
Traced patients: These are patients who were considered LTFU and were subsequently found to be
accessing care and treatment in another facility. We aimed to trace all of the LTFU episodes. This
process is described in detail below.
10
Figure 2.1: Tracing LTFU
All LTFU episodes
Patient ID matched in another facility (with inter
date of presentation)
Patient ID not matched in any other facility
TRACED
Contact address (with included division, ward,
village or street) matched in another facility
with later date of presentation)
Date of birth and sex matched
TRACED
Any one of: date of birth and sex not matched
NOT TRACED
Tracing LTFU within the same district
The following analyses were done for each district in Mwanza region:
At the start of the analysis, we compiled one dataset for each facility within the district. This dataset
contained information of all clients who were enrolled in care at that facility during the time period
of the analysis (up to 30th September 2012). For each patient, information was extracted on the date
of enrolment, the date of the last appointment in the facility, and whether he/she was on ART or not.
In order to trace LTFU in a particular facility within the district, each facility in turn was considered
was used to trace these patients in all other facilities within the same district. This was done using the
including patient ID, along with the name of the health facility where they were considered as LTFU
was traced. The ID of the traced client was then compared in all health facilities within the same
clients were then counted only once in the health facility even if he/she was found active in more
than one other facility. If the client was found to have presented at the traced facility prior to the
date of presentation at the master facility, this original facility became the patient’s master facility.
as “traced”. For example, if a patient attended (master) clinic X, then clinic Y (all within a given
district), then he/she was counted as LTFU and traced (once) in relation to the master clinic X.
This process allowed us to trace patients after the last date of attendance in the master clinic.
However it does not record circular movement by patients whereby a patient may move from one
11
the most recent record in clinic X, this would be identified as a patient LTFU from clinic Y and
traced in clinic X, rather than the opposite.
This process was repeated for each facility acting as the master facility. Of note, if a person was
LTFU and/or traced in relation to multiple master facilities, then this person was counted multiple
times. For example if a person attended clinic X, then clinic Y and then clinic Z, then he/she would
be counted as LTFU from clinic X and traced to clinic Y, and then LTFU from clinic Y and traced to
clinic Z. Therefore our denominator for this analysis was the number of “LTFU-episodes”, where
one person could contribute to the denominator more than once. Our outcome of interest was
the number of “traced-episodes”, where one person could contribute to this more than once. The
ideal scenario is that 100% of LTFU episodes are traced. These analyses were repeated separately
for each of the 8 districts in Mwanza region: Nyamagana, Ilemela, Magu, Kwimba, Missungwi,
Sengerema, Geita and Ukerewe.
Tracing lost to follow up across districts within Mwanza region
From each facility, a dataset containing a list of those remaining LTFU (who had not been traced to
facilities within their district) was generated. These datasets were merged to make a district-level
dataset that was then used to trace LTFU from other districts.
Each district was set as a master, in order to trace LTFU in the particular district within Mwanza.
The same algorithm and methods used to identify those traced between multiple facilities was used
to trace LTFU between districts. However, since this part of the analysis was performed across
districts, it did not yield information about the facilities from which patients were LTFU or traced.
Data analysis:
Data analysis was done using SPSS version 17. Clients who were LTFU were matched to the export
files of the CTC2 database to get more information about their characteristics including their marital
status. Cross tabulations, frequencies and percentages for categorical variables were used to display
the results. Results are presented for all patients in care (“in care”) and those who were on ART just
before being lost to follow up from their master facility (“on ART”; a subset of those in care).
12
3. Results from the National Database
3.1 Care and treatment facilities reporting to national database
In 2011, the NACP started phasing out a previous cross-sectional reporting format consisting of
separate monthly reports (with data on new and cumulative patients) and quarterly reports (with
data on current patients) and instead introduced a new quarterly report with combined data on new
cumulative and current patients. As a result of this transition of systems and reporting formats in
2011, the national dataset for this past year is composed of a combination of patient-level data,
aggregate-level data in old report formats, and aggregate-level data in the new report format. Table
3.1 shows that while 860 clinics reported in any format during 2011, only 727 reported in a way
which included data on current numbers of patients. The table below shows the reports received in
various formats by region.
Table 3.1 Number of clinics submitting CTC reports in 2011 by type of report
Report Formats
Number of clinics
reporting in 2011
Comprehensive patient-level data electronically
348
No comprehensive electronic data, but reported new quarterly
report (including data on current patients)
341
No comprehensive electronic data, no new quarterly report,
but reported old quarterly report (including data on current
patients)
38
No comprehensive electronic data, no quarterly report,
but reported opted out monthly reports (with data on new
patients but not current patients)
133
Total facilities reporting in any format in 2011
860
The regional distribution of reporting facilities in all formats, ranging from incomplete to complete
below highlights the results.
13
Figure 3.1: Regional distributions of counts of facilities reporting in various formats
The national database contained patient-level data from 441 clinics (both electronically imported
data and pre-2007 patient level data entered through CTC3 forms). Of these, 363 of these clinics
reported recent data, signified by having at least some data (more than 10 visits) during 2011.
Of these, 14 clinics did not have any data signifying that they only very recently began services,
and were excluded, leaving 349 clinics. One of these clinics was found to have non-continuous
data (with two years data missing from a stream of several years), and was excluded, leaving 348
clinics. These 348 clinics have data going back various lengths of time. Figure 3.2 below shows the
distribution of the 348 selected clinics across various program times, since 2004.
Fig 3.2: Clinics providing patient level data in 2011, by year start of care and treatment records.
14
Only 348 facilities which electronic data covering the year 2011 in its entirety with a continuous
stream of data from other years were included in individual level data analyses. We note that recent
electronic data was not submitted in time from Mbeya, Rukwa and Ruvuma regions to be included
in this patient-level analysis. Numbers of new clients enrolled is observed to decrease after 2009.
Figure 3.3: Number of new patients enrolled in care, by year
Figure 3.4: Number of new patients on ART by year

3.2 National Estimates of people Infected with HIV and in need of ART
Regional Population Estimates
The regional estimates of selected populations groups were obtained from age-specific 2002 census
projections (National Bureau of Statistics (NBS) Ref6). The populations were broken into three
age groups, 0-14 years, 15-49 years of age, and those over 50 years of age. The NBS-projected
population growth of 2.8% was used, and the same growth was applied to all 21 regions of mainland
Tanzania and to all age groups.
Estimates of Prevalence of HIV/AIDS, and numbers Infected with HIV.
The regional HIV prevalence estimates among adults (age 15-49) from the Tanzania HIV/AIDS and
Malaria Indicator Survey (THMIS) in 2012 were used for this report. The prevalence of adults aged
6
United Republic of Tanzania: Ministry of Planning, Economy and Empowerment, National Bureau of Statistics. Regional and
Districts Projections Vol XII, Dec 2006, Dar-es-Salaam, Tanzania
15
50+ was estimated as 63% of the prevalence of adults aged 15-49 years, based on the unpublished
estimated by dividing the adults’ (age 15-49) regional HIV prevalence by 8, which is close to the
estimate from 1987 which showed the ratio of the HIV prevalence in children to adults was 1:7.
However recent evidence, from a community study in Uganda where ART has been made available,
suggests that the ratio of HIV prevalence between children and adults may be may be closer to 1:15,8
which
HIV.
The counts of persons infected with HIV were generated by multiplying the regional HIV prevalence
estimates for each age group by the respective groups’ regional population estimates. This is totalled
over the 21 regions to produce a national total of adults and children living with HIV. The population
counts, the prevalence of HIV and the number of people infected with HIV by region and age group
are then applied to the number of facilities in each region, enabling the calculation of the number of
clinics per 1000 persons infected with HIV.
7
Jain V1, Liegler T, Kabami J, Chamie G , Clark TD, Black D, Geng EH, Kwarisiima D, Wong JK, Abdel-Mohsen M, Sonawane
N, Aweeka FT, Thirumurthy H, Petersen ML, Charlebois ED, Kamya MR, Havlir DV; SEARCH Collaboration. Assessment of
population-based
Clin Infect Dis.
2013 Feb;56(4):598-605. doi: 10.1093/cid/cis881. Epub 2012 Dec 12.
16
17
43,169,325
Total
19,167,877
729,497
1,182,221
953,519
684,533
1,306,312
969,781
598,584
356,404
680,141
928,000
1,260,005
868,306
530,650
1,680,154
442,600
761,655
588,518
2,013,719
610,135
1,218,102
805,041
0-14 years
20,106,422
840,823
1,742,551
1,004,777
906,355
1,152,255
780,688
853,591
463,639
645,035
815,914
1,246,606
1,068,973
644,917
1,680,535
517,848
683,409
688,503
1,698,058
651,248
1,028,479
992,218
15-49 years
0.4
0.9
0.4
1.1
0.6
0.4
0.5
0.4
0.2
0.6
1.1
0.5
0.5
0.5
0.7
0.8
0.9
0.9
0.4
0.7
0.3
0.6
3,895,026
0-14 yrs +
Col 6/15.6
141,304
270,131
205,521
173,397
203,104
140,724
216,999
121,841
116,595
147,993
236,473
224,918
173,668
308,691
126,210
113,136
135,063
286,887
142,682
196,468
213,221
50+ years
5.1
3.2
6.9
2.9
9.1
4.8
3.4
3.8
2.9
1.5
4.5
9.0
3.8
4.1
4.2
5.9
6.2
7.0
7.4
3.3
5.5
2.4
15-49 yrs
3.2
2.0
4.3
1.8
5.7
3.0
2.1
2.4
1.8
0.9
2.8
5.6
2.4
2.6
2.6
3.7
3.9
4.4
4.6
2.1
3.4
1.5
123,037
2,918
10,197
3,457
7,787
7,838
4,122
2,843
1,292
1,275
5,220
14,175
4,124
2,720
8,821
3,264
5,903
5,150
18,627
2,517
8,374
2,415
1,031,371
26,906
120,236
29,139
82,478
55,308
26,543
32,436
13,446
9,676
36,716
112,195
40,621
26,442
70,582
30,553
42,371
48,195
125,656
21,491
56,566
23,813
15-49 yrs
Col3*Col6
122,071
2,826
11,649
3,725
9,862
6,093
2,990
5,154
2,208
1,093
4,162
13,302
5,342
4,450
8,103
4,654
4,384
5,909
13,269
2,943
6,754
3,198
50+yrs
Col4*Col7
8
9
10
Estimated number HIV positive
50+yrs 0-14 yrs
Col6/1.6 Col2*col5
National population estimates
5
6
7
HIV prevalence from 2012 AIS **
-Estimated population from 2002 census, with projected annual increase of 2.8% per annum (Tanzania Population Projections for 2011)
-HIV prevalence in adults aged 15-49 years taken from 2012 THMIS
-HIV prevalence in children aged 0-14 years taken as the HIV prevalence in adults aged 15-49 divide by 8
-HIV prevalence in adults of age 50 years or more taken as the HIV prevalence in adults aged 15-49 divide by 1.6 (TAZAMA project, Mwanza: unpublished data).
1,711,624
3,194,903
2,163,817
1,764,285
2,661,671
1,891,193
1,669,174
941,884
1,441,771
1,891,907
2,743,084
2,162,197
1,349,235
3,669,380
1,086,658
1,558,200
1,412,084
3,998,664
1,404,065
2,443,049
2,010,480
Total
Arusha
Dar es Salaam
Dodoma
Iringa
Kagera
Kigoma
Kilimanjaro
Lindi
Manyara
Mara
Mbeya
Morogoro
Mtwara
Mwanza
Pwani
Rukwa
Ruvuma
Shinyanga
Singida
Tabora
Tanga
Region
2
1
3
4
Estimated Mainland Tanzania Population in 2011*
Table 3.2: Population by age and region: Total population, estimated HIV prevalence, and number infected with HIV
1,276,479
32,650
142,082
36,320
100,127
69,239
33,655
40,433
16,946
12,044
46,098
139,671
50,087
33,611
87,506
38,471
52,658
59,254
157,552
26,951
71,694
29,427
Total
Col8+Col9+Col10
11
3.3 Estimates of number of people with advanced HIV disease (in need of ART)
The new WHO guidelines recommend that ART eligibility begins when CD4 counts reach 350 cells/
mm3 or below. The Tanzania NACP has begun implementing the proposed eligibility threshold
2011. We have obtained this by adding the following:
-
All those (both adults and children) who were on ART in 2010 multiplied by 0.9, which
is an estimate of the 12 month survival rate for those ON ART
All HIV-infected children under 2 years of age. From the 2010 data, 22% of children
aged 0-14 years were under the age of 2 years. We have applied this to the HIV positive
children in each region.
Of the remaining HIV positive persons (both adults and children) in 2010, we estimated
that 20% would become eligible for ART during the following 12 months.
By adding these three components, we obtained the counts of persons with advanced HIV disease,
who should be receiving ART in 2011 by region, for adults and children separately. It should be
noted that these estimates cover only people who are already in care.
18
Table 3.3 Population of HIV positive adults and children, the number with advanced HIV disease
and the number and proportion on ART in 2011, by region
HIV Positive
Region
Col A
HIV positive
Col B
Adults
(15+ yrs)
Col C
Children
(0-14 yrs)
Patients in need of ART
Numbers with advanced
HIV disease
Col D
Col H
Adults
Children
(15+ yrs)
(0-14 yrs)
Patients on ART
Proportion of those with
Patients on ART
advanced disease on
Col L
Col I
Col J
Col K
Adults
Children
Adults
Children
(15+ yrs)
(0-14 yrs)
(15+ yrs)
(0-14 yrs)
Arusha
29,732
2,918
10,022
1482
3720
486
37%
33%
Dar es Salaam
10,197
Tanga
131,885
32,864
92,340
61,401
29,534
37,590
15,654
10,769
40,878
125,496
45,963
30,892
78,686
35,207
46,755
54,104
138,925
24,434
63,320
27,012
2,415
53,485
10,447
31,842
17,676
7,005
12,791
5,233
3,386
12,514
59,399
14,703
7,951
25,658
9,783
11,895
15,287
34,070
6,691
17,824
12,259
5521
1576
3856
3261
1633
1615
630
572
2128
8605
1874
1129
3832
1409
2323
2183
7377
1064
3469
1478
43758
7574
32423
4298
2027
7089
3925
1516
6474
47847
9468
4431
20825
6176
10615
11486
7155
3350
12653
1897
3353
788
2709
368
194
968
360
124
343
3228
724
377
1527
906
408
811
472
264
1037
250
82%
72%
102%
24%
29%
55%
75%
45%
52%
81%
64%
56%
81%
63%
89%
75%
21%
50%
71%
15%
61%
50%
70%
11%
12%
60%
57%
22%
16%
38%
39%
33%
40%
64%
18%
37%
6%
25%
30%
17%
Total
1,153,441
123,037
379,921
57,015
248,707
19697
65%
35%
Dodoma
Iringa
Kagera
Kigoma
Kilimanjaro
Lindi
Manyara
Mara
Mbeya
Morogoro
Mtwara
Mwanza
Pwani
Rukwa
Ruvuma
Shinyanga
Singida
Tabora
3,457
7,787
7,838
4,122
2,843
1,292
1,275
5,220
14,175
4,124
2,720
8,821
3,264
5,903
5,150
18,627
2,517
8,374
3.4 Estimates of number of facilities per 1000 people infected with HIV
This indicator measures both, the burden of care and service provision and program coverage in
the currently reporting clinics and in each region respectively. It should be noted that, although the
information are extracted for reporting facilities, the indicator is statistically comparable against
total facilities ever authorized to provide care and treatment services. Table 3.5 below summarizes
the counts of clinics available in each region per 1000 individuals infected with HIV.
19
Table 3.4 Number of persons infected with HIV, care and treatment clinics, and
numbers of clinics per 1000 Persons infected with HIV by region in 2011.
Arusha (a)
Dar es Salam
26,349
184,806
Health facilities approved for CTC
CTC clinics reporting
Clinics with Clinics with Total
Clinics with
Patient level Aggregate
current
figures
data**
data
only**
28
1
29
52
13
65
Dodoma
39,414
28
12
40
1.01
Iringa
166,202
37
18
55
0.33
Kagera
46,340
23
9
32
0.69
Kigoma
16,755
7
13
20
1.19
Kilimanjaro
19,524
17
9
26
1.33
Lindi
21,380
8
52
60
2.81
Manyara
11,423
12
9
21
1.84
Mara
74,528
10
3
13
0.17
Mbeya
135,716
0
49
49
0.36
Morogoro
64,526
26
14
40
0.62
Mtwara
28,349
1
50
51
1.80
Mwanza
110,945
27
8
35
0.32
Pwani
41,882
14
19
33
0.79
Rukwa
39,344
0
21
21
0.53
Ruvuma
47,828
0
41
41
0.86
Shinyanga
148,477
15
6
21
0.14
Singida
21,047
8
0
8
0.38
Tabora
78,679
8
27
35
0.44
Tanga
56,500
27
5
32
0.57
Total
1,380,013
348
379
727
0.53
Region
Number
of people
infected with
HIV *
* Taken from Table 2.2
20
Number of facilities
per 1000 infected
with HIV
1.10
0.35
4. Results from Analysis of Adults in Pre-ART Care
4.1 Baseline Characteristics
A total of 440,880 adult pre-ART clients ages 15 and above were enrolled in CTCs from 2005
– 2011. Their characteristics are shown in Tables 4.1 and 4.2. A total of 144,401 (32.7%) males
and 296,471 (67.3%) females and 8 adults with unknown sex were enrolled in pre-ART care by
December 31, 2011. The female to male ratio (2:1) was similar across all calendar years.
The overall age structure of those who never started ART was similar for all years. About 40%
of both male and female clients were between 30-39 years old at pre-ART enrolment. Overall,
however, the age pattern differed by sex, with older males (45% age 40 and over) and younger
females (35% under age 29).
The percent of clients screened for TB increased over time from 87% from 2005-2007 to 94% in
2010, with a slight decrease in 2011 to 93%. Confirmed cases of TB decreased over time, from 3%
in 2007 or before to approximately 0% in 2011.
The number of pre-ART HIV positive clients enrolled into CTCs increased from 2003 to 2009, and
decreased from 2009 to 2011. From 2005 or before to 2011, mean first CD4 cell count increased
from 243 to 323 cells/µL.
21
Table 4.1 Baseline demographic characteristics of 440,880 pre-ART clients at CTC enrollment
2007 or before
2008
Category
n
%
n
%
n
%
n
%
Total
Sex
Male
Female
Age (years)
121,671
27.6
80,036
18.2
86,831
19.7
83,672
19.0
68,670
15.6
39,886
81,781
32.8
67.2
26,273
53,759
32.8
67.2
28,184
58,647
32.5
67.5
27,269
56,403
32.6
67.4
22,789
45,881
33.2
66.8
32,414
26.6
23,241
29.4
26,056
30.0
24,653
29.5
19,692
28.7
49,584
27,198
12,468
7
40.8
22.4
10.3
0.0
31,856
16,756
8,182
1
39.8
20.9
10.2
0.0
34,145
17,742
8,886
2
39.3
20.4
10.2
0.0
32,665
17,312
9,036
6
39.0
20.7
10.8
0.0
26,911
14,393
7,658
16
39.2
21.0
11.2
0.0
15–29
30–39
40–49
≥50
Missing Median
(IQR)
Marital status
Single
Married
Cohabiting
Divorced
Widowed
Missing
Facility level
Hosp
Health C.
Dispensary
Facility type
Private
Government
Referred from
PMTCT
VCT
TB
Missing
2009
2010
2011
n
%
35.3 (29.6-42.5)
34.9 (29.1-42.2)
34.8 (28.9-41.9)
35.0 (28.9-42.2)
35.2 (29.0-42.5)
23,204
51,114
1,039
8,144
10,019
28,151
24.8
54.7
1.1
8.7
10.7
23.1
17,252
42,887
1,153
8,040
8,026
2,678
21.6
53.6
1.4
10.1
10.0
3.4
18,508
47,684
1,300
8,793
7,702
2,844
21.3
54.9
1.5
10.1
8.9
3.3
18,502
46,154
1,405
8,480
6,977
2,154
22.1
55.2
1.7
10.1
8.3
2.6
15,543
36,114
1,319
7,021
5,503
3,170
22.6
52.6
1.9
10.2
8.0
4.6
105,461
4,515
8,441
86.8
3.7
7.0
62,320
9,151
6,783
78.2
11.5
8.5
55,596
17,629
12,065
64.5
20.5
14.0
48,184
19,136
14,879
58.1
23.1
17.9
38,095
16,507
13,042
55.9
24.2
19.1
8,418
10.3
5,507
10.1
6,391
10.3
6,170
10.3
4,601
9.4
73,192
89.7
49,457
89.9
55,846
89.7
54,046
89.7
44,165
90.6
13
50
87
0.0
0.0
0.1
1
53
40
0.0
0.1
0.1
8
36
31
0.0
0.0
0.0
1
109
42
0.0
0.1
0.1
2
48
8
0.0
0.1
0.0
121,521
99.9
79,935
99.9
86,742
99.9
83,502
99.8
68,606
99.9
*The high number in Missing category may be due to importation of data and version differences
of CTC 2 cards used
22
Table 4.2 Baseline clinical characteristics of 440,880 pre-ART clients at CTC Enrolment
2007 or before
2008
2009
2010
2011
Category
n
%
n
%
n
%
N
%
n
%
Total
Weight
<45kg
45–54kg
55+kg
Missing
BMI
Underweight
Normal
Overweight
Missing
WHO stage
1
121,671
27.6
80,036
18.2
86,831
19.7
83,672
19.0
68,670
15.6
23,912
43,666
47,473
6,620
19.7
35.9
39.0
5.4
14,773
29,438
31,704
4,121
18.5
36.8
39.6
5.2
15,263
31,603
35,135
4,830
17.6
36.4
40.5
5.6
14,596
30,114
34,641
4,321
17.4
36.0
41.4
5.2
12,092
25,114
29,033
2,431
17.6
36.6
42.3
3.5
5,892
13,053
4,118
98,608
4.8
10.7
3.4
81.0
5,516
11,804
3,470
59,246
6.9
14.8
4.3
74.0
5,564
12,463
3,502
65,302
6.4
14.4
4.0
75.2
5,073
11,837
3,595
63,167
6.1
14.2
4.3
75.5
6,641
14,005
4,246
43,778
9.7
20.4
6.2
63.8
20,398
16.8
18,960
23.7
23,445
27.0
24,557
29.4
22,068
32.1
2
22,838
18.8
19,875
24.8
23,221
26.7
24,493
29.3
19,733
28.7
3
32,500
26.7
25,855
32.3
25,102
28.9
22,568
27.0
18,410
26.8
13,496
11.1
9,541
11.9
8,554
9.9
7,965
9.5
6,845
10.0
32,439
26.7
5,805
7.3
6,509
7.5
4,089
4.9
1,614
2.4
5,077
4.2
3,333
4.2
3,518
4.1
3,258
3.9
3,021
4.4
50-199
10,230
8.4
6,615
8.3
6,591
7.6
5,369
6.4
5,423
7.9
≥200
Missing
17,114
14.1
12,943
16.2
13,900
16.0
11,461
13.7
9,961
14.5
89,250
73.4
57,145
71.4
62,822
72.4
63,584
76.0
50,265
73.2
4
Missing
CD4 cell count
<50
Median (IQR)
Functional status
Working
214 (88-395)
240 (97-438)
247 (101-438)
245 (95-439)
225 (89-418)
80,223
65.9
65,805
82.2
76,468
88.1
76,191
91.1
62,616
91.2
Ambulatory
14,958
12.3
11,600
14.5
8,469
9.8
6,008
7.2
4,916
7.2
Bed-ridden
1,955
1.6
2,032
2.5
1,711
2.0
1,300
1.6
922
1.3
Missing
Pregnancy
status (among
responding
women only)
Yes
24,535
20.2
599
0.8
183
0.2
173
0.2
216
0.3
4,045
5.1
5,959
11.2
7,196
12.4
8,088
14.5
7,106
15.7
No
TB status
74,811
94.9
47,290
88.8
51,032
87.6
47,776
85.5
38,132
84.3
3,341
2.8
2,390
3.0
1,743
2.0
380
0.5
1
0.0
118,330
97.3
77,646
97.0
85,088
98.0
83,292
99.6
68,669
100.0
Screened
106,274
87.4
74,957
93.7
81,487
93.9
78,974
94.4
63,742
92.8
Not screened
15,397
12.7
5,079
6.4
5,344
6.2
4,698
5.6
4,928
7.2
Unconfirmed TB
TB screening
23
Figure 4.1 Age structure of pre-ART clients at Enrolment, by sex
Figure 4.2 Enrolment into pre-ART care from 2003 – 2011
24
Figure 4.3 Mean first CD4 count from 2003 – 2011
Discussion
Potential explanations for higher enrolment of females into pre-ART care may be that women have
more opportunities/access to health care delivery services or that females have more proactive
health-seeking behaviors. The age structure by sex is reflective of the epidemic in Tanzania whereby
women with HIV are younger than men with HIV. Women of reproductive age have access to
PMTCT services, resulting in earlier diagnosis and subsequently earlier entry into pre-ART care.
National guidelines state that 100% of clients should be screened for TB upon enrolment into CTC.
We see that screening is generally above 90%; however this is not completely compliant with the
guidelines. The decrease in confirmed TB cases over time may be due to changes in reporting
indicators in the CTC2 form, decrease in overall risk of acquiring TB as more PLHIVs enrol early
for Care and Treatment services.
The low number of new clients enrolled into care in 2003 and 2004 may be due to the early stages
of the program, lack of awareness and availability of CTC services, stigma, discrimination, or poor
linkages between counselling and testing intervention with care and treatment services. The increase
in enrolment of clients from until 2009 is likely attributed to national scale-up of care and treatment
services in Tanzania. The decrease in enrolment from 2009 to 2011 may be due to stabilization of
the epidemic and the positive effect of ART program as for treatment for prevention.
The trend of increasing first CD4 cell count over time indicates that clients are enrolling in care
earlier. This could be due to increased access to services and increased knowledge regarding seeking
care. We see that mean CD4 count is above the eligibility criteria for initiating ART (CD4 of 250
cells/µL), showing that the majority of clients are receiving pre-ART care before reaching eligible
status.
25
Recommendations
• Implement strategies that promote male involvement in health-seeking and enhancing men’s
access to health care services. There is need to increase the proportion of men who test for
HIV and improve linkage to HIV care for those who test HIV-positive.
• Promote strategies that will increase male engagement in reproductive child health , testing
and counselling services.
• Support more intervention that increase education and dialogue about intergenerational sex.
• Improve TB screening documentation by having targeted supportive supervision and
mentoring to health providers in Care and Treatment sites as a component in HIV and TB
collaborative activities.
• Consider developing strategies to enrol more people in care and treatment services using
innovative testing approaches.
26
5. Results from analysis of Adults on ART
5.1 Baseline characteristics
A total of 255,143 patients aged ≥15 years were observed to initiate ART in the 348 electronic clinics
by 31st December 2011. Their characteristics are shown in Table 5.1 by year of ART initiation.
Over time, care of adults initiating ART has devolved from hospitals to health centres and dispensaries,
with 91.1% of patients initiating ART in 2005 or before doing so in hospitals, compared to 58.7%
in 2011. The ownership of facilities in which people initiated ART remained fairly stable over time,
with 68.3% being government, 24.4% faith-based/voluntary and 7.3% private in 2011.
Overall, 88,530 (35%) of the included patients were male and 166,607 (65%) were female. The
male to female ratio was similar across all calendar years of ART initiation. The age distribution was
similar across all years, with 20% aged 15-29 years, 41% aged 30-39 years, 26% aged 40-49 years
and 13% aged 50 years or older. The age pattern differed between the two sexes with males tending
to be older than females at the time of starting ART (Figure 5.1). For example, only 12% of males
were under the age of 30 years at ART initiation, compared to 25% of females, and 18% of males
were aged 50 years or older, compared to 10% of females.
Over 50% of patients initiating ART were married or cohabiting, and around 25% single, with
the distribution of marital status remaining relatively stable for the past 4 years. Functional status
improved over time, with more people working, and fewer bed-ridden at ART initiation. Similarly,
more people initiated ART at WHO stages 1 and 2 in later years, compared to more initiating at
stages 3 and 4 in previous years. Further, fewer people initiated at CD4 <200 cells/mm3 in recent
years compared to previously. Of note, 70.1% of all patient-visits were missing BMI, mainly due to
height not ever being recorded for that patient.
The most common regime used initially was d4T, 3TC and NVP but, since the elimination of d4T
since 2010, the use of ZDV, 3TC and EFV has increased substantially, from 3.9% in 2007 to 41.0%
in 2011.
Figure 5.2 shows that, at the time of starting ART, 16,389 (27%) males had CD4 count <50 cells/mm3
compared to 24,243 (21%) females. Further, 27% of males were WHO stage 4 at ART initiation,
compared to 22% of females. This indicates that men were initiating ART at later stages of disease
compared to women (Fig 5.3)
27
28
Category
BMI, kg/m2
Weight, kg
Divorced/
separated
533
Widowed
951
Working
6109
Ambulatory 883
Bed-ridden
120
<45
2301
45-<55
3824
55+
4802
<18.5
715
1060
1093
20,544
680
7.0
12.5
86.0
12.4
1.7
21.1
35.0
44.0
25.8
2007
(%)
1205 7.5
1757 11.0
13,951 84.4
2238 13.5
351
2.1
5553 24.0
9041 39.1
8533 36.9
1592 29.7
n
2008
(%)
n
3100
3574
23,753
7023
871
8039
12,939
12,444
2611
8795
20,122
2395
11,598
22,101
6629
14,330
8649
4084
37
6835
10.6
12.2
75.1
22.2
2.8
24.1
38.7
37.2
29.3
4714
5376
35,632
7161
1030
9750
17,440
16,495
4083
11.2
12.7
81.3
16.3
2.4
22.3
39.9
37.8
29.5
28.1 10,218 25.5
64.3 26,669 66.6
7.7
3188 8.0
34.4 15,594 35.3
65.6 2 8,565 64.7
19.7 9015 20.4
42.5 18,347 41.6
25.7 11,246 25.5
12.1 5553 12.6
31-44 36
31-44
23.4 9611 22.8
5191
5174
41,574
5111
924
10,320
18,377
18,353
4198
10,244
28,744
3816
16,682
30,958
9883
19,592
12,030
6136
36
10,170
(%)
11.3
11.3
87.3
10.7
1.9
21.9
39.1
39.0
29.1
23.9
67.2
8.9
35.0
65.0
20.7
41.1
25.3
12.9
30-44
22.1
13.2
17.2
68.1
1.5
18.7
2009
6.8 3212 7.3 6220
6.8 4415 10.1 8120
84.1 35,389 80.6 32,187
2.3
915
2.1 714
33,699 13.2 44,161 17.3 47,642
n
4.5 2282
4.7 2287
87.9 28,234
2.9
771
23,418 9.2
30.7
5881 26.5
64.6
14,997 67.5
4.8
1336 6.0
34.0
8164 34.9
66.0
15,253 65.1
17.8
4513 19.3
42.5
9890 42.3
28.2
6248 26.7
11.6
2749 11.8
31-44 37
31-44
25.4
3953 24.7
4.1
1.5
91.1
3.3
11,277 4.4
2005 or before
2006
N
(%)
n
(%)
Dispensary
460
Health centre 174
Hospital
10,274
Other
369
Facility ownership Faith-based/
voluntary
3376
Government 7101
Private
524
Sex
Male
3837
Female
7439
Age, years
15-29
2004
30-39
4783
40-49
3171
≥50
1304
Median (IQR) 37
Marital status Single
1928
Married/
Facility level
Total (row %)
Variable
2010
(%)
5254
4871
44,311
3621
792
10,169
18,836
19,228
3988
10,653
30,510
3003
16,954
31,796
10,020
19,890
12,218
6620
37
10,758
7704
10,300
29,729
576
11.1
10.3
90.9
7.4
1.6
21.1
39.1
39.9
27.5
24.1
69.1
6.8
34.8
65.2
20.6
40.8
25.1
13.6
30-44
22.7
16.0
21.3
61.5
1.2
48,750 19.1
n
2011
4940
4302
42,345
3231
587
9350
17,585
18,710
4851
10,258
28,684
3062
15,701
30,495
9462
18,792
11,539
6394
37 31-44
10,073
8009
10,448
26,874
451
46,196
n
11.2
9.7
91.7
7.0
1.3
20.5
38.5
41.0
29.2
56.3
22.8
24.4
68.3
7.3
33.9
66.0
20.5
40.7
25.0
13.9
17.5
22.8
58.7
1.0
18.1
(%)
Table 5.1. Baseline characteristics of the 255,143 patients aged ≥15 years who were observed to initiate ART in one of the 348 clinics
by 31 December 2011.
29
18.5-<25
1568
25+
493
1
712
2
1153
3
3100
4
1883
<50
2113
50-199
4119
200-349
1197
350-499
158
500+
73
Median (IQR) 105
d4T, 3TC, NVP 9044
ZDV, 3TC, NVP 861
ZDV, 3TC,EFV 421
Other first line 333
Second Line 86
56.5
17.8
10.4
16.8
45.3
27.5
27.6
53.8
15.6
2.1
1.0
43-177
84.2
8.0
3.9
3.1
1.0
3028 56.6 5056 56.8 7984 57.7
733
13.7 1238 13.9 1769 12.8
1116 7.1
1755 5.9 3235 7.7
2957 18.8 4801 16.1 8037 19.1
7171 45.5 14,013 47.0 20,309 48.4
4525 28.7 9264 31.1 10402 24.8
4400 27.0 6066 23.9 7872 22.9
8730 53.5 13,699 54.0 18,322 53.4
2645 16.2 4675 18.4 6819 19.9
361
2.2
600
2.4
817 2.4
197
1.2
333
1.3
482 1.4
105 45-182 119 52-191 123 55-195
18,937 83.3 26,291 79.3 34,112 78.0
991
4.4
1748 5.3 3339 7.6
1181 5.2
3118 9.4 4131 9.4
1194 5.3 1751 5.3
2059 4.7
443
2.0
265 1.0
100
<1
8402 58.2
1826 12.7
4142 9.1
10,318 22.7
21,107 46.3
9978 22.0
7849 22.4
18,267 52.1
7569 21.6
930
2.7
483
1.4
130
57-201
21,599 45.9
6885 14.6
15,030 32.0
1749 3. 7
1785 3.8
8385
2106
5239
12,173
20,733
9896
6760
14,939
8058
988
533
140
9862
11,424
21,544
1663
3864
57.9
14.6
10.9
25.3
43.2
20.6
21.6
47.8
25.8
3.2
1.7
60-221
20.4
23.6
44.6
3.4
8.0
9237
2528
5686
11,446
19,475
9152
5572
12,593
7596
1073
535
148
6907
13,876
18,831
4795
1498
55.6
15.2
12.4
25.0
42.6
20.0
20.4
46.0
27.8
3.9
2.0
65-234
5.1
30.2
41.0
10.5
3.3
IQR = interquartile range; BMI=body mass index; d4T=stavudine; 3TC=lamivudine; NVP=nevirapine; ZDV=zidovudine; EFV=efavirenz.
Notes:
1. Percentages are column percentages, except for the first row where the percentages are row percentages.
2. The totals by characteristic may not sum to the total number of people due to missing data as follows: 1652 (1%) facility level; 21,567 (8%) facility ownership; 6 (<1%)
sex; 52 (<1%) age; 22,590 (8.9%) marital status; 13,525 (5.3%) functional status; 3054 (1.2%) weight; 178,752 (70.1%) BMI; 21,366 (8.4%) WHO stage; 77,720 (30.5%)
CD4 count; and 3426 (1.3%) ARV regime
WHO stage
CD4 cell count
ARV regimen
Figure 5.1. Age at ART initiation, by sex.
Figure 5.2. CD4 count at ART initiation, by sex.
30
Figure 5.3. WHO stage at ART initiation, by sex.
5.2 Mortality in adults who started on ART
Among the 255,143 patients who were observed to initiate ART by 31 December 2011, 255,141 had available
follow-up data (after permitting 6 months until considered LTFU) and were included in the survival analyses;
the remaining two initiated treatment on 31 December 2011 and therefore did not contribute any follow-up
time. Table 5.2 shows the numbers of patients who started ART in each year, and the mortality within each
calendar year cohort. Deaths and mortality rates are shown by the year in which the patient started ART, and
by time since ART initiation.
Table 5.2. Number of deaths and cumulative mortality risk, by year of ART initiation.
2005 or
before
11,277
2006
2007
2008
2009
2010
2011
23,418
33,699
44,161
47,642
48,750
46,196
Within 6 months
In 6 months-<1
year
In 1-<2 years
1166
2420
2609
3438
3327
2993
2165
272
502
651
726
631
503
105
307
607
761
819
672
298
-
In 2-<5 years
447
819
795
551
198
-
-
In 5-<7 years
123
76
-
-
-
-
-
2315
4424
4816
5534
4828
3794
2270
45,871
81,112
99,804
107,970
88,485
59,502
20,922
Individuals on ART
Number of deaths
Total
Person-years
Cumulative risk of death per 100 persons initiating treatment
Within 6 months
10.3
10.3
7.7
7.8
7.0
6.1
5.5
By 1 year
13.0
12.8
9.9
9.7
8.5
7.3
-
By 2 years
16.2
15.9
12.8
12.1
10.4
-
-
By 5 years
21.5
20.9
-
-
-
-
-
By 7 years
24.2
-
-
-
-
-
-
31
A total of 27,981 deaths were reported to the CTC clinics and included in this analysis, which
represents 11.0% of all the patients observed to initiate ART. The risk of dying within 6 months of
in 2005 or before, to 5.5% (5.3 to 5.7) for those who started in 2011. The risk of dying before
completing 1 year on ART fell from 13.0% (12.4 to 13.6) in those who started in 2005 or before, to
7.3% (7.1 to 7.6) for those who started in 2010 (could not be assessed for those who started in 2011,
as they had less than 1 year of follow up at the time of analysis).
Figure 5.4. Cumulative probability of death after ART initiation.
Figure 5.4 shows cumulative mortality risk over time following initiation on ART. The table in the
Figure 5.5. Cumulative probability of death after ART initiation, by sex.
32
Figure 5.5 shows that the cumulative probability of death over time was higher among males than
females. For example, the probability of death among men was 9.3% by 6 months and 24.0% by
7 years, compared to 6.2% and 16.0%, respectively, among women. This may be due to severity
of disease at the time of ART initiation, as 27% of males initiated with CD4 count <50 cells/mm3,
compared to only 21% of females, and 27% of men were WHO stage 4 at ART initiation, compared
to 22% of women (see Figures 5.2 and 5.3).
Figure 5.6a. Cumulative probability of death after ART initiation for males, by weight at initiation.
kg
<45 (%)
45-54 (%)
55 + (%)
0.50
6 months
18.4
10.6
5.7
1 year
22.3
13.3
7.4
2 years
26.9
16.6
9.8
5 years
35.1
23.2
15.1
7 years
38.4
27.7
18.3
0.40
0.30
0.20
0.10
0.00
0
1
2
3
4
5
Time since ART initiation, years
Weight at ART initiation, kg
<45
45-<55
6
7
55+
Figures 5.6a and 5.6b show that the cumulative probability of death over time for both males and
females was higher among those with weight less than 45kg at ART initiation, compared to those
with higher body weight. For example, the probability of death among males with weight <45kg
at ART initiation by 6 months was 18.4% compared to 5.7% among those with weight ≥55kg. For
Figure 5.6b. Cumulative probability of death after ART initiation for females, by weight at initiation.
0.50
Kg
<45 (%)
45-54 (%)
55 + (%)
0.40
6 months
11.7
5.0
3.0
1 year
14.0
6.3
4.0
2 years
17.1
8.3
5.4
5 years
21.4
11.8
8.6
2
3
4
5
6
7 years
23.3
14.5
11.1
0.30
0.20
0.10
0.00
0
1
Weight at ART initiation, kg
<45
45-<55
7
55+
33
Figure 5.7. Cumulative probability of death after ART initiation, by CD4 count at initiation.
Cells/mm3
<50 (%)
50-199 (%)
200-349 (%)
350-499 (%)
>=500 (%)
Missing
0.50
0.40
6 months
12.8
5.6
3.8
5.8
6.0
8.3
1 year
15.2
7.2
5.1
7.7
7.7
10.3
2 years
18.2
9.2
6.8
10.0
10.4
13.2
5 years
22.6
13.1
10.7
13.3
15.0
18.8
7 years
25.5
17.2
14.0
15.4
16.0
21.0
0.30
0.20
0.10
0.00
0
1
2
3
4
5
CD4 at ART initiation, cells/mm
<50
350-499
50-199
500+
6
7
3
200-349
Missing
Figure 5.7 shows that the cumulative probability of death over time was higher among those with
CD4 counts of less than 50 cells/mm3 at ART initiation. For example, the probability of death by
6 months was 12.8% for those who initiated with CD4 <50 cells/mm3, compared to 5.8% among
those with CD4 350-499 cells/mm3. We can also see that those with no CD4 count recorded at ART
initiation were at somewhat higher risk of death, with probability of death by 6 months of 8.3%.
5.3 Hazard rates and ratios
Mortality rates in the survival analysis can best be described as hazard rates, which estimate the
probability of death at any instant in time (see 2010 report www.nacp.go.tz). Hazard rates, estimated
in Cox regression models, were calculated from the time patients started ART and used in statistical
models to formally compare the hazards experienced by sub-groups within the cohort, represented
by hazard ratios.
Table 5.3 compares the hazard rates between groups with different baseline characteristics. The
baseline group for each factor was chosen as the most frequently reported characteristic. Of note,
Cox regression model.
Encouragingly, our results indicate a lower risk of death in later years, which may be related to
presentation earlier in infection, improved disease management, and, most likely, increased
The results show a higher risk of death in patients enrolled in dispensaries and health centres
compared to hospitals (adjusted hazard ratio = 1.12, 95% CI 1.05-1.20; and 1.14, 95% CI 1.09-1.21,
respectively), in faith-based compared to government facilities (adjusted hazard ratio = 1.10, 95%
34
CI 1.05-1.14) and those who were single compared to married/cohabiting patients (adjusted hazard
ratio= 1.22, 95% CI 1.17-1.27).
For the different age groups, there is a significant increased risk of death for older age groups, but
this is likely to be due to higher underlying mortality rates in older people.
The results show clearly a substantially lower risk of death in females compared to males, which
persists after adjustment for other baseline characteristics (adjusted hazard ratio = 0.63, 95% CI
0.60-0.65).
Regarding functional status, patients who were bedridden or ambulatory at the time of ART initiation
had higher risk of death compared to patients who were working (adjusted hazard ratio = 2.94, 95%
CI 2.71-3.20; and 1.72, 95% CI 1.64-1.79, respectively).
The results show an increased risk of mortality with lower weight and higher (worse) WHO stage
at ART initiation, as anticipated. Similarly, lower CD4 count at ART initiation was associated with
higher mortality risk, although there was also an increased risk of death for those who initiated
with CD4 counts above 500 cells/μl, compared to 200-349 cells/μl, even after adjustment for other
covariates. Initiating at such high CD4 counts is unusual, and it may be that these patients were
particularly sick, which may not be completely captured by the variables measured.
35
Table 5.3. Mortality hazard ratios for baseline characteristics of adults who initiated ART
Characteristic
Facility level
Facility ownership
Year of starting ART
Sex
Age, years
Marital status
Functional status
Weight, kg
BMI, kg/m2
WHO stage
CD4 count, cells/mm3
ARV regimen
Category
Dispensary
Health centre
Hospital
Other 0.59
HR
0.82
0.92
1
Crude
95% CI
0.78-0.85
0.89-0.95
(reference)
0.53-0.66
p-value
<0.001
<0.001
HR
Adjusted*
95% CI
1.12
1.14
1
0.79
1.05-1.20
1.09-1.21
(reference)
0.68-0.92
1.10
1
0.97
1.05-1.14
(reference)
0.90-1.06
1.36
1.15
0.96
1
0.97
0.92
0.88
1.25-1.48
1.07-1.23
0.91-1.01
(reference)
0.92-1.02
0.87-0.98
0.81-0.95
1
0.63
(reference)
0.60-0.65
0.95
1
1.07
1.35
0.90-1.00
(reference)
1.03-1.12
1.28-1.42
1.22
1.17-1.27
Faith-based/
voluntary
Government
Private
1.09
1
0.84
1.06-1.12
(reference)
0.79-0.88
2005 or before
2006
2007
2008
2009
2010
2011
1.42
1.38
1.08
1
0.87
0.75
0.70
1.35-1.49
1.33-1.44
1.04-1.12
(reference)
0.84-0.91
0.72-0.78
0.67-0.74
Male
Female
1
0.64
(reference)
0.63-0.66
15-29
30-39
40-49
≥50
1.00
1
1.06
1.33
0.97-1.03
(reference)
1.03-1.10
1.28-1.37
Single
Married/
cohabiting
Divorced/
separated
Widowed
1.18
1.14-1.21
1
(reference)
1
(reference)
1.09
0.91
1.05-1.14
0.87-0.95
1.05
0.96
0.99-1.11
0.09-1.01
Working
Amburatory
Bedridden
1
2.36
4.60
(reference)
2.29-2.43
4.37-4.85
1
1.72
2.94
(reference)
1.64-1.79
2.71-3.20
<45
45-54
55+
2.71
1.52
1
2.63-2.80
1.48-1.57
(reference)
2.24
1.37
1
2.13-2.34
1.32-1.43
(reference)
Underweight
Normal
Overweight
2.22
1
0.69
2.10-2.36
(reference)
0.61-0.76
1
2
3
4
0.42
0.66
1
2.03
0.39-0.45
0.63-0.68
(reference)
1.97-2.08
<50
50-199
200-349
350-499
500+
2.79
1.35
1
1.44
1.51
2.67-2.92
1.29-1.41
(reference)
1.30-1.59
1.32-1.72
d4T, 3TC, NVP 1
ZDV, 3TC, NVP 0.60
ZDV, 3TC, EFV 0.72
1.03
0.87
(reference)
0.58-0.63
0.70-0.74
0.98-1.08
0.81-0.93
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.58
0.78
1
1.46
0.53-0.63
0.74-0.82
(reference)
1.40-1.52
2.10
1.29
1
1.12
1.19
1.99-2.22
1.22-1.36
(reference)
0.99-1.27
1.02-1.39
1
0.87
0.84
0.96
1.09
(reference)
0.81-0.92
0.80-0.88
0.89-1.02
0.98-1.21
p-value
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
* Adjusted for all variables indicated, namely facility level, facility ownership, year of starting ART, sex, age, marital status, functional status, weight, WHO stage, CD4
count and ARV regimen. BMI was omitted from this multivariable model due to many missing values.
Note: p-values are from Wald tests.
36
5.4 Analysis of adults No Longer On Treatment (NLOT)
The results from the analysis of time to NLOT (death and LTFU combined) are shown in Table 5.4. Numbers
NLOT and cumulative risks are shown by the year in which the patient initiated ART, and by time since ART
initiation.
Table 5.4. Number NLOT and cumulative risk of NLOT, by year of ART initiation.
2005 or
before
11,277
2006
2007
2008
2009
2010
2011
23,418
33,699
44,161
47,642
48,750
46,196
Within 6 months
In 6 months-<1
year
1166
2420
2609
3438
3327
2993
2165
1422
3727
5703
7917
9059
9894
3476
In 1-<2 years
1078
2024
3472
4369
5174
3644
-
In 2-<5 years
1705
3814
5314
4825
2434
-
-
In 5-<7 years
673
605
-
-
-
-
-
7+ years
1
-
-
-
-
-
-
Total
6045
12,590
17,098
20,549
19,994
16,531
5641
Person-years
45,870
81,112
99,803
107,970
88,485
59,502
20,922
Individuals on ART
Number NLOT
Cumulative risk of NLOT per 100 persons initiating treatment
Within 6 months
13.6
14.2
12.1
11.8
11.6
11.1
10.7
By 1 year
23.0
26.3
24.7
25.7
26.0
26.4
-
By 2 years
32.5
34.9
35.0
35.6
36.9
-
-
By 5 years
47.6
51.2
-
-
-
-
-
By 7 years
56.2
-
-
-
-
-
-
A total of 98,448 adults were recorded as being no longer on treatment (NLOT), representing 38.6%
from 13.6% for those who started ART in 2005 or before to 10.7% among those who started in 2011,
the NLOT rates by 1 year increased from 23.0% for those who started ART in 2005 or before to
26.4% for those who started in 2010.
as LTFU if more than 6 months has passed since ART initiation, which explains the sudden rise in
the cumulative probability of NLOT at 6 months of follow-up.
initiation patients were only included within the NLOT group if they were reported as having died.
The cumulative risk of NLOT rose rapidly to 11.8% by 6 months, 25.7% by 1 year, and up to 61.9%
by 7 years.
37
Figure 5.8. Cumulative probability of NLOT after starting ART.
Figure 5.9. Cumulative probability of NLOT after starting ART, by sex.
Figure 5.9 shows that males were consistently around 5-9% more likely to be NLOT than females
over time. For example, the risk of NLOT by 6 months was 14.4% among men, compared to 10.3%
among women, and by 7 years was 67.8% among men compared to 58.8% among women.
38
5.5 Improvements in CD4 counts
Improvement in CD4 counts after starting ART is an important indicator of the restoration of the
just less than 100 cells/mm3. The overall picture is of sustained improvement up to 6 years on ART,
.
Figure 5.10. Improvements in CD4 count after initiating ART.
Figure 5.11. `Improvements in CD4 counts after initiating ART, by sex.
The median CD4 count in females initiating ART was slightly higher than for males (Figure 5.11),
39
and over time the rate of improvement in CD4 counts appeared to be better for females compared to
males. Of note, the graph is only plotted to 5 years, due to less data when we divide into sub-groups.
800
600
3
CD4 count cells/mm (Median)
Figure 5.12. Improvements in CD4 counts after initiating ART, by CD4 at ART initiation.
400
200
0
0
1
2
3
CD4 count at ART initiation, cells/mm
<50
350-499
50-199
500+
4
5
3
200-349
As expected, patients who started ART with a lower CD4 count showed better improvements in the
(Figure 5.12). However, even after 5 years there remained some differences in the median CD4
counts between groups, with those who initiated at lower CD4 counts having lower CD4 counts up
to 5 years later. Of note, the “bumpy” line for those who initiated with CD4 counts ≥500 cells/mm3
is due to few patients included in that group and therefore greater uncertainty and more variable
estimates.
One limitation to this analysis is that CD4 counts can only be measured in those who survive
and come to the CTC clinic regularly. Thus if people with lower CD4 counts were more likely to
measurement of survivors. This means that in reality, the improvements in CD4 after ART initiation
may not be as good as indicated here.
5.6 Improvements in BMI
A similar analysis was done for BMI and showed that the median BMI increased after ART initiation,
and then plateaued (Figure 5.13). At ART initiation, the median BMI was around 20 kg/m2, and
after 2 years this had increased to around 22.5 kg/m2 and remained stable out to 5 years.
40
Figure 5.13. Increase in BMI following ART initiation.
BMI, kg/m
2
25
20
53094
15
0
27873
1
8599
572
2
3
4
5
Median
6
7
Upper and lower quartiles
Values shown are the number contributing a measurement at each time-point
Figure 5.14. Increase in BMI following ART initiation, by sex.
22
2
BMI, kg/m (median)
23
21
20
0
1
2
3
Male
4
5
Female
Figure 5.14 shows that, at the time of ART initiation, women had higher BMI than men. Following
ART initiation, both sexes had similar improvements in BMI, although over time women remained
with higher average BMI compared to men.
41
These BMI analyses suffer from the same limitation as the analysis of improvements in CD4 counts,
in that BMI can only be measured in those who survive and come to the CTC clinic regularly.
Further, BMI could not be calculate for the majority of patients due to height often not being
recorded.
5.7 Switches to second-line therapy
A total of 243,676 adults were observed to initiate first-line ART and are included in this analysis.
Table 5.5 shows the patient characteristics at first-line ART initiation, by whether they later
switched, died, or were censored (using the first event observed; for example, if a patient switched
to second-line and then later died, they are counted as switched only and not died). The distributions
of characteristics at ART initiation were similar to those for the whole dataset (see Table 5.1). The
proportions of patients observed to switch to second-line therapy were low across all subgroups of
patients, at around 1%.
42
line therapy, death or censored.
Outcome (row %)
Category Total (column %)
(N=243,676) Switched (N=16668)
Dispensary
26,552 (11%)
152 (0.6%)
Facility level
Health centre
35,870 (15%)
92 (0.3%)
Hospital
175,582 (73%)
1283 (0.7%)
Other
4177 (2%)
141 (3%)
Facility ownership
Faith-based/
voluntary
56,258 (25%)
535 (1%)
Government
150,834 (68%)
967 (0.6%)
Private
16,235 (7%)
112 (0.7%)
2005 or before
10,659 (4%)
315 (3%)
Year started ART
2006
22,303 (9%)
515 (2%)
2007
32,908 (14%)
372 (1%)
2008
43,641 (18%)
234 (0.5%)
2009
45,263 (19%)
121 (0.3%)
2010
44,493 (18%)
67 (0.2%)
2011
44,409 (18%)
44 (0.1%)
Sex
Male
84,622 (35%)
603 (0.7%)
Female
159,050 (65%)
1065 (0.7%)
Age, years
15-29
49,126 (20%)
301 (0.6%)
30-39
100,910 (41%)
724 (0.7%)
40-49
62,232 (26%)
445 (0.7%)
≥50
31,361 (13%)
197 (0.6%)
50,868 (23%)
362 (0.7%)
Marital status
Single
Married/cohabiting
122,531 (55%)
766 (0.6%)
Divorced/separated
23,920 (11%)
123 (0.5%)
Widowed
25,119 (11%)
186 (0.7%)
Working
198,448 (86%)
1267 (0.6%)
Functional status
Ambulatory
28,395 (12%)
177 (0.6%)
Bed-ridden
4484 (2%)
13 (0.3%)
Weight, kg
<45
53,032 (22%)
303 (0.6%)
45-<55
94,074 (39%)
576 (0.6%)
55+
93,801 (39%)
767 (0.8%)
BMI, kg/m2
<18.5
19,826 (29%)
249 (1%)
18.5-<25
39,715 (58%)
461 (1%)
25+
9485 (14%)
131 (1%)
WHO stage
1
20,538 (9%)
157 (0.8%)
2
48,846 (22%)
306 (0.6%)
3
101,993 (45%)
624 (0.6%)
53,000 (24%)
275 (0.5%)
<50
38,851 (23%)
431 (1%)
CD4 cell count
50-199
87,056 (51%)
657 (0.8%)
200-349
36,952 (22%)
195 (0.5%)
350-499
4617 (32%)
27 (0.6%)
500+
2379 (1%)
9 (0.4%)
d4T, 3TC,NVP
126,752 (52%)
1151 (1%)
ARV regimen
ZDV, 3TC, NVP
39,124 (16%)
171 (0.4%)
64,256 (26%)
268 (0.4%)
ZDV, 3TC, EFV
13,544 (6%)
Other first line
78 (0.6%)
Variable
Died (N=25,892)
2203 (8%)
3303 (9%)
19,893 (11%)
296 (7%)
6400 (11%)
15,614 (10%)
1488 (9%)
2084 (20%)
4095 (18%)
4605 (14%)
5367 (12%)
4428 (10%)
3243 (7%)
2070 (5%)
11,402 (13%)
14,490 (9%)
4879 (10%)
10,236 (10%)
6714 (11%)
4056 (13%)
5778 (11%)
11,874 (10%)
2471 (10%)
2298 (9%)
16,857 (8%)
5564 (20%)
1405 (31%)
9112 (17%)
9729 (10%)
6482 (7%)
2126 (10%)
2010 (5%)
320 (3%)
809 (4%)
3070 (6%)
9757 (10%)
9590 (18%)
6548 (17%)
7730 (9%)
2339 (6%)
412 (6%)
230 (10%)
16,994 (13%)
2501 (6%)
4882 (8%)
1515 (11%)
Censored (N=216,116)
24,197 (91%)
32,475 (91%)
154,406 (88%)
3740 (90%)
49,323 (88%)
134,253 (89%)
14,635 (90%)
8260 (78%)
17,693 (79%)
27,931 (85%)
38,040 (87%)
40,714 (90%)
41,183 (93%)
42,295 (95%)
72,6170 (86%)
143,495 (90%)
43,946 (89%)
89,50 (89%)
55,073 (89%)
27,108 (86%)
44,728 (88%)
109,891 (90%)
21,326 (89%)
22,635 (90%)
180,324 (91%)
22,654 (80%)
3066 (68%)
43,617 (82%)
83,769 (89%)
86,552 (92%)
18, 655 (89%)
39,363 (94%)
9535 (95%)
19,572 (95%)
45,470 (93%)
91,612 (90%)
43,135 (81%)
31,872 (82%)
78,669 (90%)
34,418 (93%)
4178 (90%)
2140 (90%)
108,607 (86%)
36,452 (93%)
59,106 (92%)
11,951 (88%)
BMI=body mass index; d4T=stavudine; 3TC=lamivudine; NVP=nevirapine; ZDV=zidovudine; EFV=efavirenz.
Note: totals by characteristic may not add up to the overall totals shown due to missing data, as follows: 1495 (0.6%) facility level;
20,349 (8%) facility ownership; 4 (<0.1%) sex; 47 (<0.1%) age; 21,238 (9%) marital status; 12,349 (5%) functional status; 2769
(1%) weight; 174,650 (72%) BMI; 19,299 (9%) WHO stage; and 73,821 (30%) CD4 count.
43
The second-line regimens to which patients switched are shown in Table 5.6, with the most common
regimen being ABC, ddI and LPV/r (82%). The reasons for switch were not reported for 857 (51%)
of individuals; reasons given are shown in Table 5.7. At switch to second line ART, CD4 results
were available for 975 (58% of 1668) participants, with 234 (24%), 494 (51%), 156 (16%), 53 (5%)
and 38 (4%) <50, 50-199, 200-349, 350-499 and ≥500 cells/mm3, respectively.
Table 5.6. Second-line regimens used when switching from first-line therapy.
N
(total=1668)
(%)
ABC, ddI, LPV/r
1336
(82%)
ABC, ddI, SQV/r
44
(3%)
ABC, ddI, NFV
52
(3%)
ABC, ddI, ATV/r
4
(0.2%)
TDF, 3TC, LPV/r
20
(1%)
Second-line regimen
TDF, FTC, LPV/r
145
(9%)
ABC, 3TC, LPV/r
17
(1%)
Other, not specified
20
(1%)
ABC=abacavir, ddI=didanosine, LPV=lopinavir, SQV=saquinavir, NFV=nelfinavir, ATV=atazanivir, TDF=tenofovir,
3TC=lamivudine, FTC=emtricitabine.
Note: “/r” indicates the protease inhibitor was boosted with low-dose ritonavir.
Table 5.7. Reasons for switch to second-line therapy.
N
(total=1683)
6
(0.4%)
Peripheral neuropathy
15
(0.9%)
Other adverse event
75
(5%)
Clinical treatment failure
106
(6%)
Immunological treatment failure
464
(28%)
Poor adherence
5
(0.3%)
Patient decision
2
(0.1%)
Pregnancy
5
(0.3%)
Stock out
35
(2%)
Other, not specified
98
(6%)
Missing
857
(51%)
Reason
Start tuberculosis treatment
(%)
Two patients had no follow-up after ART initiation (both initiated treatment on 31 December 2011)
and are excluded from further analyses. 25,892 (11%) died while on first-line therapy. Figure 5.15
shows the cumulative probabilities of switch to second-line therapy and death over time, analysed
as competing risks. For example, the probability of death was 9.6% by 1 year after first-line ART
initiation and 17.4% by 6 years, while the probability of switching was 0.21% by 1 year and 2.61%
by 6 years. Of note, the death probabilities in this analysis are different from those presented in the
earlier mortality analysis, due to the incorporation of switching as a competing risk (in this analysis,
we are looking at the first event of death or switching only, and we do not count deaths which occur
after switching to second-line therapy). Also, the graph is only plotted to 6 years compared to 7
years in the mortality analysis, since we have less follow-up here (only to the first event of death or
switching). Figure 5.15 shows that the probability of switching is very low compared to the risk of
death, with the risk of death approximately 7 times the probability of switching by 6 years.
44
From the unadjusted models presented in Table 5.8, we see that there were differences in switching
rates by facility level and ownership, with those who initiated ART in health centres less likely to
switch, those in other facilities more likely to switch, and no difference for those in dispensaries,
compared to hospitals. Those who initiated in faith-based/voluntary facilities were more likely to
switch compared to those in government facilities. There was an overall trend towards less frequent
switching with later year of ART initiation. Females were less likely to switch than men. There
was a difference in the switching rates by weight at ART initiation, with those weighing <55kg less
likely to switch than those ≥55kg. We found that those with lower CD4 counts at ART initiation,
and lower (better) WHO stage, were more likely to switch. For example, those with CD4 count <50
or 50-199 cells/mm3 at ART initiation were 93% and 24%, respectively, times more likely to switch
than those who initiated ART with CD4 counts of 200-349 cells/mm3, but with no difference for
those who initiated with CD4 ≥350 cells/mm3. For current (time-updated) CD4 count, there was a
clear trend with greater probability of switching at lower CD4 counts, as we would expect. There
weight (p=0.35).
The results of the full multivariable model were similar to the univariable results, except after
adjustment for other variables there was no longer evidence of a difference in switching rates by sex
(p=0.74) and there was evidence of more frequent switching among those who initiated on non-d4Tsub-HR 1.60, 95% CI 1.32-1.93). In addition, the direction of effect for CD4 at ART initiation
reversed, with those with lowest CD4 at ART initiation less likely to switch; this is likely to be due
to the adjustment of current (time-updated) CD4, where the association between lower current CD4
count and switch became even stronger.
Subsequent work will investigate the need for treatment based on immunological failure, and switch
to second line treatment following immunological failure.
45
Table 5.8. Factors associated with switch to second-line therapy.
Variable
Univariable models
Sub-HR
(95% CI)
Characteristics at ART initiation
Facility level
Dispensary
Health centre
Hospital
Other
Facility ownership
Faith-based/ voluntary
Government
Private
Year of ART start
2005 or before
2006
2007
2008
2009
2010
2011
Sex
Male
Female
Age, years
15-29
30-39
40-49
≥50
Weight, kg
<45
45-<55
55+
WHO stage
I
II
III
IV
CD4, cells/mm3
<50
50-199
200-349
≥350
First regimen
d4T-based
1.11
0.53
1
3.66
(0.94,1.31)
(0.43,0.66)
(reference)
(3.07,4.35)
1.36
1
0.98
(1.22,1.51)
(reference)
(0.81,1.19)
3.32
3.06
1.76
1
0.69
0.66
0.97
(2.79,3.96)
(2.61,3.58)
(1.49,2.07)
(reference)
(0.56,0.86)
(0.50,0.87)
(0.70,1.35)
1
0.90
(reference)
(0.81,0.99)
0.94
1
0.97
0.92
(0.82,1.08)
(reference)
(0.86,1.09)
(0.79,1.08)
0.77
0.78
1
(0.67,0.88)
(0.70,0.87)
(reference)
1.42
1.14
1
0.89
(1.19,1.70)
(1.00,1.31)
(reference)
(0.77,1.03)
1.93
1.24
1
1.02
(1.63,2.29)
(1.05,1.45)
(reference)
(0.72,1.46)
1
1.02
Time-dependent characteristics
Current weight, kg
<45
1.10
45-<55
0.98
55+
1
Current CD4, cells/mm3
<50
4.62
50-199
3.65
200-349
1
≥350
0.35
(reference)
(0.91,1.14)
(0.95,1.28)
(0.88,1.10)
(reference)
(3.72,5.75)
(3.03,4.39)
(reference)
(0.27,0.45)
P
Final multivariable model
Sub-HR
(95% CI)
p
<0.001
<0.001
<0.001
<0.001
0.03
0.68
<0.001
<0.001
<0.001
0.73
0.35
<0.001
0.94
0.55
1
2.83
(0.66,1.33)
(0.39,0.78)
(reference)
(2.20,3.64)
1.95
1
1.03
(1.65,2.30)
(reference)
(0.65,1.63)
2.47
3.55
1.96
1
0.69
0.59
1.17
(1.83,3.33)
(2.76,4.59)
(1.51,2.55)
(reference)
(0.47,1.00)
(0.37,0.93)
(0.75,1.83)
1
0.97
(reference)
(0.82,1.15)
1.01
0.88
1
1.03
(0.81,1.25)
(reference)
(0.72,1.06)
(0.81,1.32)
0.72
0.74
1
(0.49,1.04)
(0.59,0.94)
(reference)
1.37
1.31
1
1.10
(1.07,1.74)
(1.08,1.58)
(reference)
(0.88,1.36)
0.50
0.48
1
1.24
(0.37,0.66)
(0.39,0.62)
(reference)
(0.76,1.74)
1
1.60
(reference)
(1.32,1.93)
1.15
1.22
1
(0.76,1.74)
(0.96,1.56)
(reference)
6.17
4.04
1
0.26
(4.15,8.43)
(3.24,5.04)
(reference)
(0.19,0.36)
Note: p-values are from Wald tests.
46
<0.001
<0.001
<0.001
<0.001
0.74
0.50
0.04
0.01
<0.001
<0.001
0.26
<0.001
5.8 Discussion
Baseline characteristics of adults starting ART
The baseline characteristics of adults starting ART show that males consistently represent only
around a third of people initiating ART. Females were initiating ART at a younger age compared to
males, and were on average in a less-advanced stage of disease compared to males. This is likely to
be related to women being enrolled into care at earlier stages of disease, as discussed in Chapter 4
Throughout the years, the greatest proportion of patients initiating ART were married or cohabiting.
We observed a trend towards higher CD4 count, lower (better) WHO stage, and better functional
status among those initiating ART in recent, compared to earlier, years. However, in 2011, over
60% of adults initiating ART were in WHO clinical stages 3 or 4 and similarly over 60% had CD4
counts of less than 200 cells/mm3. This indicates that even further improvements in identifying HIVpositive individuals for enrolment into care and initiation of ART are needed, particularly for males.
The use of ARV regimens has changed over the years, with zidovudine-based combinations
becoming more common compared to stavudine-based combinations. The ARV regimen of d4T,
3TC and NVP was prescribed in 15% of patients starting ART in 2011.
We found that BMI was missing for around 70% of patients, primarily due to height often not being
recorded. BMI is an important factor in survival, and improved quality of life through CD4 count
restoration. BMI can only be assessed if height and weight are measured and recorded in the CTC2
card, and included in the submission to the national CTC3 database. Further, around 30% of patients
had missing CD4 counts at the time of ART start. Incomplete information on CD4 count hinders the
assessment of the improvement in health, and subsequent clinical treatment.
While the proportion of patients with missing BMI dropped from 70% among those who initiated
in each of 2009 and 2010 to 64% in 2011, the proportion with missing CD4 count increased from
26% in 2009 to 36% in 2010 and 41% in 2011. Actions need to be taken to improve reporting of all
these clinical factors.
Outcomes of adults starting ART
Mortality analysis
Results showed that, overall, 27,981 patients (11.0%) died out of the 255,143 patients initiating
ART by 31 December 2011, representing a rise from the mortality rate of 9.5% reported in the
previous report 10. Survival analysis showed that the probability of death was highest in the first
6 months on ART. However, the risk of dying within the first 6 months of ART initiation fell from
10.3% for those who started ART in 2005 or before, to 5.5% for those who started ART in 2011.
The improvement in survival compared to previous report could be due to patients starting ART
at a better clinical and immunological status compared to earlier years, as shown in our baseline
results. The improvement in survival may also be due to better management of patients, or greater
availability of ARVs in more primary health care facilities.
Similar to the previous report, mortality in females was lower than in males, which could be due to
the greater severity of disease in males at ART initiation. The results also showed that mortality was
highest among patients weighing less than 45 kg, and with CD4 counts less than 50 cells/mm3, at
ART initiation. This reinforces our recommendations that greater efforts need to be made to enrol
10
Implementation of HIV Care and Treatment Services in Tanzania (2011)
47
patients, and particularly men, into care and initiate ART before substantial damage to the immune
system occurs in order to optimise their prognosis.
Mortality over time (from 1+ year after ART initiation) was higher for men compared to women,
and the same picture was observed for increases in CD4 counts over time by sex. This suggests that
men may have poorer adherence to ART compared to women as well as being more likely to initiate
ART at more immune-compromised states compared to women.
We found that patients initiating ART at lower level facilities had poorer survival compared to those
initiating in hospitals. Lastly, patients who initiated on non-stavudine containing ART regimen had
better survival.
Patients No Longer On Treatment
While the cumulative number of patients no longer on treatment (NLOT) has gradually increased
over the past 7 years from 2005, the drop-out rate within 6 months after ART initiation has been
decreasing gradually since 2005. For example, the drop-out rate by 6 months was 13.6 per 100
person-years for those who started ART in 2005 or before compared to 10.7 per 100 person-years
for those who started in 2011. However, the drop-out rate by one year was 23.0 per 100 person-years
for those started ART in 2005 or before and increased to 26.4 per 100 person-years for those who
started in 2010. These data show that the retention of patients on ART is a big challenge especially
over long-term follow-up. Primarily, facilities need to ensure that counselling is effective to ensure
retention and drug compliance. In addition, methods to ensure adequate tracking mechanisms are
needed for patients who do not keep appointments.
Information available on patients NLOT show that of these, male patients are more likely to be
NLOT compared to females. This higher discontinuation among males could be due to the higher
mobility patterns that men typically exhibit compared to women. Alternatively, it could perhaps be
at ART initiation, with men typically at later stages of disease. It may be that there are differences
in health seeking behaviour between men and women with respect to HIV care, or there may be
other behavioural reasons. This report highlights the need to explore the problems affecting those
who become NLOT.
The reason for conducting this analysis of NLOT was to obtain a “worst-case” scenario for mortality,
since there are concerns that often the reason for patients becoming LTFU is due to death. We
have shown that the NLOT rates are around 40% higher than the mortality rates by 7 years after
ART initiation. This means either that we are underestimating mortality rates of patients initiating
on ART, or clinics are performing poorly in retaining patients on ART in care. It is likely that a
combination of these factors contributes. In order to better understand this issue, there is a need
to strengthen death reports at clinic level, while at the same time encouraging clinics to improve
retention rates.
It may be that many of those who have dropped out of care or treatment may have registered at
another clinic, without the proper transfer forms. This would imply that the cumulative numbers
than once. Such patients would also be counted as LTFU in our analysis, yet may be under care in
48
another clinic under a different identifier. Further, some of those which we have considered LTFU
may have transferred care to a clinic not captured in the 348 clinics which are included in this
analysis.
Special analysis done using IQ tools on available electronic data (narrated in chapter 7) from
Mwanza shows there is need to improve recording of transfers out of the clinic, and matching these
to records of patients transferring in to another clinic. Since publication of the last report, the NACP
has developed a tracking mechanism in the health facilities to improve retention and linkages with
community based services. The tracking mechanism is foreseen to improve documentation of those
not attending CTC for proper patient and programme monitoring. However, challenges still exist the
tracking mechanism developed is used by personnel in the community who is yet to be recognised
as an established cadre in the health system. The community health providers work as volunteers
in many settings. There is need to assess their role and how they can be better used in community
health services including retention and linkages of PLHIV clients. Further analysis using IQ tool or
other means for remaining regions is crucial to understand regional differences in retention rates.
CD4 counts and BMI improvement after ART initiation
While we found substantial improvements in CD4 counts following ART initiation, the median
increased by less than 100 cells/mm3 over the first year11. As expected, we observed larger increases
in CD4 count among those who initiated with lower CD4 counts, although those who had initiated
with the lowest CD4 counts remained with lower values up to 5 years after ART initiation. We
observed a similar pattern comparing men and women, in that on average men initiated with lower
CD4 counts and remained with lower CD4 counts up to 5 years later, compared to women. There
appeared to be some divergence in the CD4 curves, perhaps related to poorer adherence among men.
Following ART initiation, improvements in BMI were observed over the first year, and then
stabilised. Comparing men and women, similar patterns were observed as for CD4 counts, in that
men initiated ART at lower BMI and remained at lower BMI up to 5 years later.
ARV use and switching to second-line treatment
Overall, the numbers of patients switching to second-line therapy were low. By 6 years after ART
initiation, the risk of death was approximately 7 times the probability of switching (approximately
17% and 3%, respectively).
We found that reasons for changes to ARV regimens were in general poorly recorded, with reasons
missing from the time of switch for 51% of the patients who were observed to switch. Further, CD4
count at the time of switch was missing for 42% of patients.
We found differences in switching rates by health facility type and ownership, perhaps indicating
that standardisation of care with respect to switching to second-line treatment is needed nationally.
We found that switching to second-line ART was less common in later years of ART initiation,
11
Geng EH, Bwana MB, Muyindike W, Glidden DV, Bangsberg DR, Neilands TB, Bernheimer I, Musinguzi N,
Yiannoutsos CT, Martin JN. Failure to initiate antiretroviral therapy, loss to follow-up and mortality among HIVinfected patients during the pre-ART period in Uganda. J Acquir Immune Defic Syndr. 2013 Jun 1;63(2):e64-71. doi:
10.1097/QAI.0b013e31828af5a6.
49
probably in part reflecting improved ARV regimes and management. As expected, and reflecting
national guidelines for management of patients with immunological failure,those with lower current
CD4 count were much more likely to switch. After adjustment for confounders, those weighing
more, with lower (better) WHO stage at ART initiation, with higher CD4 at ART initiation and who
initiated on non-d4T-based regimens were more likely to switch.
This analysis does not assess the need for switch to second line, nor whether this need was
met. It would be interesting and informative to use these data to explore when patients should
be switching to second-line therapy in order to optimise their future prognosis, but this analysis
would be hampered by the large missingness of longitudinal data, particularly CD4. Therefore, we
recommend strengthening the recording of key data such as reasons for changes of ARV regimens
and CD4 counts over time in order to facilitate such analyses in the future.
5.9 Limitations
Despite the large amount of data available for this report, there are several limitations to our findings.
There are limitations in the data that are reported to CTC by the patients, and that can be collected
in the CTC database. It is unlikely that all deaths are reported to the clinic for several reasons,
including lack of disclosure, and subsequent household dissolution. This indicates that the mortality
on ART treatment in this report may be a lower estimate of the actual mortality experienced after
ART initiation. The data are also unable to explain the high numbers who are no longer in treatment
(NLOT) shown in this report. This seems to be consistent across the country, and deserves more
research to find out whether such patients have died or moved to another clinic.
The analysis of these data did not take into account the socio-economic status or other demographic
characteristics of the patients enrolled in HIV care and treatment. Nothing at all is known about
clinics that did not return data extract forms, so their role in ART outcomes could not be assessed.
The attrition rate of patients from care and treatment programmes in Tanzania is high, highlighting
the need for concerted efforts to improve tracing mechanisms to document the true outcomes for
these patients, and to encourage them to return to care in the case of default. Specific studies are also
necessary to shed light on the likely proportion of patients among those lost to follow up who have
died as opposed to those who moved clinic without adequate documentation or who defaulted from
care. The barriers to accessing CTC services are largely not known, although research has shown
that transport, financial, and logistical barriers, including unfamiliarity with large hospital clinics,
play a role 12,13,14. Whilst the roll-out of treatment is helping to overcome many of these obstacles,
the provision of ART within smaller dispensaries and health centres brings its own challenges in
terms of data collection and patient care, and needs to be closely monitored.
Survival analysis, using hazard rates to calculate lifetable and to show Kaplan-Meier graphs of
cumulative probability of death, give robust results and are far more flexible than traditional cohort
12
Roura M, Busza J, Wringe A, Mbata D, Urassa M, Zaba B. Barriers to sustaining antiretroviral treatment in Kisesa, Tanzania: a
socio-ecological approach to understanding attrition from the ART program. AIDS Patient Care and STDs. PMID:19203295, Feb
2009.
13
Wringe A, Roura M, Urassa M, Busza J, Athanas V, Zaba B. Doubts, denial and divine intervention: Understanding delayed
attendance and poor retention rates at a HIV treatment programme in rural Tanzania. AIDS Care, 2009; 21(5):632-7
14
Mshana G, Wamoyi J, Busza J, Zaba B, Urassa M. Barriers to accessing antiretroviral therapy in Kisesa, Tanzania: a qualitative
study of early rural referrals to the national programme. AIDS Patient Care STDS. 2006 Sep;20(9):649-57
50
analysis. The analysis in this report only included baseline characteristics of patients at the time
of ART initiation, as explanatory factors. This analysis did not consider the evolving effect on
welfare of variables that change over time, such as CD4 counts or weight gain. The analysis of
improvements in CD4 counts, and body mass index (BMI) in this analysis use the simple calculation
of medians at each six monthly interval, which suffers from bias due to the drop out of those who
died or dropped out of treatment. More advanced multi level statistical models will provide better
estimates for these outcomes, but the analysis programs need to be developed.
The wealth of data collected and collated in the CTC3 database deserves further analysis. Extra
support to the epidemiology section of NACP, along with some resources, and personnel, dedicated
to analysis would assist in the preparation of the data and performing more complex analyses. The
further analysis would enhance our understanding of the outcomes of patients who enrol in care,
and initiate treatment in Tanzania. This would also inform many other countries, in similar phase
of the HIV epidemic, about the successes and problems experienced in the delivery of care and
treatment in Tanzania. Further analysis of the data would also provide insights that could inform
data collection, and reporting, in the future, ensuring that more relevant and useful data can be
analysed in the future.
This analysis of the clinic visit data is based on all clinic visits, which did not distinguish between
regular scheduled visits, and extra visits for clinical treatment. Further analysis is needed to look at
clinic visits in more detail and to find out what proportion of clinic visits were unscheduled. Some
patients were given scheduled appointments for longer periods than 1 month, and this needs to be
included in any analysis on clinic visits.
5.10 Recommendations
• In order to improve quality of data, we recommend that Implementation partners should
focus to improve quality of data in their respective regions through continued support and
training to healthcare workers and data managers. It is also recommended that , Regional ,
District and implementing partners should ensure conducting regular data quality assessment
preferably in every quarter
• Currently some data are not adequately recorded eg height, CD4 results. We recommend
that facilities/healthcare workers should be educated on the importance of recording all
necessary information. This will minimise the number of missing values in some variables
which are very important in the analysis.
• There is a need to strengthen linkage between care and treatment clinics and home – based
care services in order to minimise LTF , increase retention and improve documentation of
vital status for NLOTs patients
• A further development, improvement and adoption of the system for the recording of patients
who transfer from one clinic to another ( IQ tool) is needed. This will enable more accurate
analysis, and also better ways of monitoring the transfers, and outcome of patients.
• More effort should be made to enable district and regional authorities to analyse and use
these data to improve their services. The role of the implementing partners in working
with the district and regional authorities, and helping to develop capacity to analyse and
understand these data needs to be made clear. The levels of support for regional and district
authorities should be agreed, so that analysis programs and best practice can be shared across
the country.
51
• Implement strategies that promote male involvement in health-seeking and enhancing men’s
access to health care services. There is need to increase the proportion of men who test for
HIV and improve linkage to HIV care for those who test HIV-positive. This will assist men
to have better ART treatment outcomes.
• Consider developing strategies to enrol more people in care and treatment services using
innovative testing approaches.
• Conduct a second review on the HIV Care Patient Monitoring System to document gaps and
functionalities. The data collection tools need to be revised and updated, and this process
needs to be continually evaluated to ensure the data collected are necessary and useful.
• Links with other health services should be developed. In addition sharing of data between
different services should be facilitated.
52
6. Results from Analysis of Children on ART
6.1 Baseline characteristics
A total of 26,527 patients aged less than 15 years were observed to initiate ART in the 348 electronic clinics
by 31st December 2011. Their characteristics are shown in Table 6.1 by year of ART initiation.
Overall, 13,071 (49.3%) of the included patients were male and 13,456 (50.7%) were female. The male to
female ratio was similar across all calendar years of ART initiation. The age distribution was similar across
all years, with 9.2% aged <1 year, 18.6% aged 1-2 years, 21.6% aged 3-5 years and 50.8% aged 6 -14 years.
About half (47.8%) of children initiating ART had weight between 10 and 20 kilograms and 7.5 % of the
children’s weight was unrecorded. More than half (57.6%) of children initiated ART at WHO stages 2 and 3.
With CD4, more than 50% of children had unrecorded CD4 baseline measure.
53
54
No baseline
CD4
Count
200+
50-199
Cell
1,395
Not recorded
23.0
53.1
1,345
14.6
9.3
55.0
5.2
21.6
10.8
7.3
2.2
12.8
51.5
23.3
10.3
62.7
22.4
10.5
4.4
50.4
49.6
9.6
(%)
582
371
236
131
4
<50
548
3
CD4
274
2
Stage
186
1
56
323
31+
Not recorded
WHO
1,304
591
10-20
21-30
4-10
260
1,589
6-14
Median (IQR)
Weight
< 10
567
265
3-5
1-2
(years)
113
1,278
Female
<1
1,256
Male
Sex
Age
2,534
n
Total
Category
2005 or before
1,857
801
514
332
1,528
272
950
504
250
76
281
1,778
797
3-10
572
2,032
760
508
204
1,783
1,721
3,504
n
53.0
22.9
14.7
9.5
43.6
7.8
27.1
14.4
7.1
2.2
8.0
50.7
22.8
16.3
57.9
21.7
14.6
5.8
50.9
49.1
13.2
(%)
2006
2,077
1,165
618
305
672
580
1,569
901
444
71
270
2,147
886
3-10
791
2264
959
714
228
2,109
2,056
4,165
n
49.9
24.0
14.8
7.3
16.1
13.9
37.7
21.6
10.6
1.7
6.5
51.6
21.3
19.0
54.3
23.0
17.2
5.5
50.6
49.4
15.7
(%)
2007
2,284
1,275
601
347
318
593
1,820
1,171
605
74
334
2,244
852
2-9
1,003
2,301
1,034
840
332
2,264
50.7
28.3
13.3
7.7
7.1
13.2
40.4
26.0
13.4
1.6
7.4
49.8
18.9
22.3
51.1
22.9
18.6
7.4
50.2
(%)
4,507 17.0
2,243 49.8
n
2008
2,417
1,198
582
440
373
576
1,665
1,218
805
85
322
2,201
840
2-9
1,189
2,169
1038
927
503
2,373
2,264
4,637
n
52.1
25.8
12.6
9.5
8.0
12.4
35.9
26.3
17.4
1.8
6.9
47.5
18.1
25.6
46.7
22.4
20.0
10.9
51.2
48.8
17.5
(%)
2009
Table 6.1 Baseline characteristics of 26,527 children started on ART by December 2011
2,339
888
383
343
166
544
1,461
1,091
691
86
243
1,747
644
2-9
1233
1,701
826
891
535
2,034
1,919
3,953
n
59.2
22.5
9.7
8.7
4.2
13.8
37.0
27.6
17.5
2.2
6.2
44.2
16.3
31.2
43.0
20.9
22.5
13.5
51.4
48.6
14.9
(%)
2010
2,037
592
320
278
103
503
1,298
808
515
60
211
1,249
570
2-9
1,137
1,418
532
749
528
1,615
1,612
3,227
n
63.1
18.4
9.9
8.6
3.2
15.6
40.2
25.0
16.0
1.9
6.5
38.7
17.7
35.2
43.9
16.5
23.2
16.4
50.1
49.9.
12.2
(%)
2011
14356
6501
3389
2281
4555
3199
9311
5967
3495
508
1984
1267
5180
6185
13474
5716
4894
2443
13456
13071
26,527
Total
54.1
24.5
12.8
8.6
17.2
12.1
35.1
22.5
13.2
1.9
7.5
47.8
19.5
23.3
50.8
21.6
18.6
9.2
50.7
49.3
100
(%)
A total of 13,071 (49.3%) males and 13,456 (50.7%) females were observed to initiate ART by the
end of December 2011. The male to female ratio (1:1) was similar across all calendar years over
time there’s a trend towards younger patients being enrolled. E.g. 4.4% were less than 1 year old in
2005 or before, compared to 16.4% in 2011. The age pattern between the two sexes was similar at
time of starting ART (Figure 6.1).
Figure 6.1 Age of children starting ART by sex
The weight structure between the two sexes was similar at the time of starting ART. However,
majority of children starting ART weighed between 10-20 Kg (52.5% males and 50.8% females) as
shown in Figure 6.2.
Figure 6.2 Weight of children starting ART by sex
55
The weight structure by age show majority of under 2 year olds start with less than 10 kgs. For those
aged between 3 and 14 years majority of children started ART weighing between 10-20 as shown
in Figure 6.3.
Figure 6.3 Weight of children starting ART by age.
The patten of CD4 counts at time of initiation of ART was similar for both sexes, although about
54% of the children initiated ART without a reported baseline CD4 count. About 21% of those who
were initated ART had CD4 count of less than 200 cells/μl. However, CD4 count is not a good
measure of immunity in children; CD4 percentage for children younger than 6 years would be a
better indicator of level of immunity, yet most clinics did not take CD4% for children below 6 years
and for the few clinics who documented CD4% this was reported as no baseline CD4 count.
Figure 6.4. CD4 counts of children starting ART by sex.
56
Figure 6.5. CD4 counts of children starting ART by age.
Majority of children (69.3% males and 68.9% females- Fig 6.6a) were started on ART with WHO
stage 3 or 4. For children below one year 60.3% started ART with WHO stage 3 and 4 and children
aged 1-2 years 73.2% started ART with stage 3 and 4. This is late stage disease; the ART protocol
urges all children below 2 years to start ART regardless of CD4 count or WHO stage. (Figure 6.6)
Figure 6.6. Baseline WHO stage of children starting ART by age at initiation
57
Figure 6.6a. Baseline WHO stage of children starting ART by sex
6.2. Outcomes of children starting ART
Overall, about 60% of children were observed to be still on follow up after ART initiation. About
30% were lost to follow up and remaining 10% (1% transferred out and 9% had died). About 40%
of children initiated on ART were no longer on treatment.(Fig 6.7)
Figure 6.7. The outcome of children who were observed to initiate ART at last visit by sex
58
6.3. Mortality in children who started on ART
Among the 26,527 patients who were observed to initiate ART by 31 December 2011, 25,890 had
available follow-up data (after permitting 6 months until considered LTFU) and were included in
the survival analyses. Table 6.2 shows the numbers of patients who started ART in each year, and
the mortality within each calendar year cohort. Deaths and mortality rates are shown by the year
in which the patient started ART, and by time since ART initiation. A total of 2,204 deaths were
reported to the CTC clinic in children who had started ART from 2005 or before to December 2011.
This represents 8.5% of the 25,890 children who started ART and had follow up data.
Table 6.2. Mortality rates of children by year of ART initiation from 2005 or before to 2011.
2005 or
before
2006
2007
2008
2009
2010
2011
2,847
3,482
4,102
4,771
4,615
4,402
235
263
306
285
303
194
58
31
20
344
55
43
26
387
61
36
12
415
52
19
356
27
-
-
Total deaths
29
25
18
178
330
194
Person-years
7,288
10,494
10,857
10,357
8,911
5,574
2,111
By 1 year
By 2 years
By 3 years
6.9
9.1
11.0
8.7
11.3
12.7
8.0
10.0
11.7
8.1
9.9
11.2
6.6
7.8
9.3
7.2
6.1
8.3
-
By 4 years
12.5
13.7
12.8
11.7
-
-
-
Individuals
1,671
started on ART
Deaths
Within 1 year
106
In 1-2 years
In 2-3 years
Mortality rate
Figure 6.8 shows how cumulated mortality risk changes over time following initiation on ART. The
of death of 0.05 at 6 years.
59
Figure 6.8. Cumulative probablity of death of children after ART initiation.
Figure 6.9 shows that the cumulative probability of death over time is slightly higher among males
than females. Log rank test result show no statistical significance (p- value 0.9288 χ2 =0.01) between
female and male children.
Figure 6.9. Cumulative probablity of death of children after ART initiation by sex.
Figure 6.10 shows that lower CD4 counts at ART initiation were associated with higher mortality.
Of note, those without a baseline CD4 count had similar mortality over the first 2 years after ART
initiation as those who initiated with CD4 counts <50 cells/μl. Children without baseline CD4 test
results may have been of same characteristics with those with CD4 counts <50 cells/μl.
60
Figure 6.10. Cumulative probablity of death of children after ART initiation by CD4 count.
Figure 6.11 shows that children with WHO stage 3 or 4 disease at ART initiation had higher mortality
compared to those who initiated ART with stage 1 or 2 disease. This scenario is also seen in adult
ART patients.
Figure 6.11. Cumulative probablity of death of children after ART initiation by WHO stage.
61
Hazard rates and ratios
Table 6.3 compares the hazard rates between groups with different baseline characteristics. The
baseline group for each factor was chosen as the most frequent reported characteristic. The results
ratio = 0.84, 95% CI 0.77-0.92). For the different age groups after adjustment there was apparent
less than 1 year against 1-2 years. The hazard ratios show the increased hazard in those initiating
ART with lower weight, higher (worse) WHO stage, and with lower CD4 count. After adjusting
associated with mortality and survival. Important to note the higher risk of death among those with
no baseline CD4 count even after adjusting for other covariates.
Table 6.3. Hazard ratios for baseline characteristics of paediatric patients who initiated ART
from 2005 or before to 2011.
Crude
Characteristic
Category
Hazard ratio
95% CI
Hazard ratio
95% CI
Sex
Male
0.82
0.76-0.89
0.84
0.77-0.92
Female
1
<1
3.25
2.82-3.75
1.05
0.85-1.30
1-2
2.07
1.82-2.36
0.88
0.74-1.06
3-5
1
6-14
1.09
0.97-1.23
1.27
1.10-1.47
<10
3.28
2.90-3.72
3.88
3.09-4.86
10-20
1.15
1.01-1.30
1.43
1.24-1.66
21-30
1
Age (years)
Weight (kg)
31+
WHO stage
CD4 cell count
62
Adjusted
1.24
1
1
1
0.89-1.73
1.24
0.86-1.79
1
1
1
2
1.12
0.89-1.42
1.42
1.12-1.80
3
1.71
1.39-2.11
2.08
1.68-2.57
4
3.98
3.21-4.92
3.87
3.12-4.81
<50
2.70
2.32-3.14
2.55
2.16-3.01
50-199
1.34
1.14-1.57
1.54
1.30-1.84
≥200+
No baseline CD4
1
2.41
2.14-2.71
1
1.75
1.54-1.98
Fig 6.12.Smoothed hazard rates for children patients following initiation on ART
Figure 6.12 shows the hazard rates over time from ART initiation. This confirms the high mortality
(hazard) experienced in the first few months on ART, which is around 6% per person-year, or
0.6% per person-month. This high hazard comes down over the first 12 months, and at 12 months
the hazard is around 3% per person-year. The hazard reduces even further to an underlying rate of
2% per person-year
6.4 Discussion
Baseline characteristics of children starting ART
The baseline characteristics of children starting ART show both male and female children of all
ages have equal opportunities in starting ART. Throughout the years, there is no gender and age
differences in children across years as what is observed in adults in HIV Care Provision. Looking at
weight children initiate ART, we observed about half of children initiating ART had weight between
10 and 20 kilograms and only 7.5 % of the children’s weight was unrecorded. We also observe, more
than half ( 57.6%) of children initiating ART at WHO stages 2 and 3 and more than 50% of children
had unrecorded CD4 baseline measure. This indicates a need of intervention to have children tested
for CD4 for eligibility assessment and initiated on ART promptly.
Outcomes of children starting ART
Mortality Analysis
Among the 26,527 patients who were observed to initiate ART by 31 December 2011, 25,890 had
available follow-up data (after permitting 6 months until considered LTFU). Survival analysis
showed that the probability of death was highest in the first 6 months on ART. A total of 2,204
deaths were reported to the CTC clinic in children who had started ART from 2005 or before to
December 2011. This represents 8.5% of the 25,890 children who started ART and had follow up
data. This is the first analysis for children on ART using routinely collected data. Through the years
the mortality rate in year one ranged from 6.1 to 8.7 deaths per 1000 person years and for year 4
it varied from 11.7 to 13.7 deaths per 1000 person years. More needs to be done to have better
management of children patients and have availability of ARVs for children.
63
Looking at the hazard ratios, key findings are seen for children on ART that need attention. There is
significant difference in risk of death between male and female children as well as by age groups
higher in children aged 6-14 years, those initiating with lower weight, higher WHO stage ( 3 and
4) and with lower CD4 count. Even on adjusting for baseline factors, the mentioned factors still
remained to be significantly associated with mortality and survival. Children with no baseline CD4
counts had higher risk of death even after adjusting for other variables. This suggest importance of
having an initiative to have every child before ART initiation gets a CD4 test done.
6.5 Recommendations
• There is need to improve quality of data, we recommend that Implementing entities ( Partners, RHMT and CHMT ) empower health care providers in data quality principles
through training, supervision and mentoring. Regular data verification at health facilities
with HIV Care services should use or establish quality improvement teams towards having
quality data.
• Key data for monitoring progress of children in ART is not adequately recorded e.g height,
CD4 results, HIV adherence session. We recommend that facilities/healthcare workers
should be educated on the importance of recording all necessary information. This will
minimise the number of missing values in some variables which are very important in
patient level analysis.
• There is a need to strengthen linkage between care and treatment clinics, Reproductive Child
Health services, and home – based care services in order to minimise LTF , increase retention
and improve documentation of vital status for NLOTs patients as analysis reveal children
still on ART was only 60% and 40% were no longer in ART services. We recommend
special back to HIV Care services initiatives specifically focused on paediatrics.
• Carry out further analysis for existing paediatric data for better programming and evidence
decision making. E.g. Analyse paediatric patient level using anthropometric measures, ART
progress by implementing partner and region, adherence to national guidelines, pre ART
characteristics of HIV children.
64
7. Tracing lost to follow up of HIV clients from multiple facilities in Mwanza region
7.1 Introduction
Lost to follow up (LTFU) is commonly reported in care and treatment health facilities in Tanzania.
It is estimated that 18% and 13% of adults and children, respectively, who started ART in 2007
were LTFU one year later (NACP HIV Care Report, 2010). The LTFU reported is due to unreported
deaths, self-transfer to other care and treatment clinics without documentation, and disengagement
from care.
Patient level data from Mwanza 50 CTCs were used to assess unreported transfers, Lost to Follow
up and clients picking drugs from multiple facilities. The IQ tool developed by Futures Group was
used to assess clients who were LTFU or had unreported transfer. Data of clients who had been on
ART and with unknown or non documented treatment outcomes were subjected to IQ tool to see if
there are any duplicates using unique CTC Id number. The duplicates generated were then analysed
using SPSS version 17 by assessing the socio-demographic characteristics from CTC2 database
export files. The main objective of this detailed analysis is to describe magnitude and pattern of
traced and non traced LTFU in multiple health facilities in Mwanza region, Tanzania. Specifically
tracing the LTFU if receiving services in other facilities in the region, compare characteristics of
traced and non traced LTFU and document movement pattern of traced clients
7.2 Results
Information from Table 1 reveal there were 24,790 episodes of LTFU “in care” from the 47 health
facilities in Mwanza region, of which 1601 (6.5%) were traced. Of the total 7,900 episodes of
LTFU of those “on ART”, 620 (8%) were traced.
Within districts, Kwimba had the highest proportion of LTFU episodes who were traced, with 10%
and 15% of LTFU traced within that district among those “in care” and “on ART” respectively.
Ilemela district had the lowest proportion of traced LTFU episodes within that district: 1% and
0.2% who were “in care” and “on ART”, respectively.
Among those traced outside the districts, Ilemela district had the highest proportion of LTFU
episodes traced: 12% and 14% among those “in care’ and “on ART” respectively.
65
Table 7.1: Total Lost to follow up and patients traced in care and treatment in Mwanza region
as of 30th September 2012.
District name
Total Lost to Total
LTFU
follow-up (LTFU) traced within
the district
Care ART
2698
408
1697
1078
396
182
732
709
Total LTFU to be Total
LTFU
traced
outside Traced outside
the district
the district
Total LTFU traced
Care
% ART
Traced
%
Care
Traced
ART
Care
% ART
Traced
% Care
Traced
%
ART
%
Traced
137
13
144
175
87
110
38
72
2%
1%
3%
4%
10%
6%
3%
2%
1%
0%
4%
9%
15%
1%
3%
1%
2663
407
1625
984
335
180
713
703
190
192
127
91
39
71
31
84
3%
12%
3%
2%
5%
4%
2%
2%
3%
14%
4%
5%
7%
6%
3%
3%
5%
13%
6%
6%
14%
10%
5%
4%
105
60
144
148
83
13
41
26
4%
15%
8%
14%
21%
7%
6%
4%
NYAMAGANA
ILEMELA
GEITA
MAGU
KWIMBA
MISUNGWI
UKEREWE
SENGEREMA
6004
1636
4925
4159
914
1894
1441
3817
35
1
72
94
61
2
19
6
5867
1623
4781
3984
827
1784
1403
3745
TOTAL
24790 7900 776 3% 290 4% 24014 7610
70
59
72
54
22
11
22
20
327
205
271
266
126
181
69
156
825 3% 330 4% 1601 6% 620 8%
From a total of 24,790 patients who were lost to follow up while in care (including those on ART)
1601 (6%) were traced while out of 7,900 patients who were lost to follow up while on treatment
620 (8%) were traced and were active within Mwanza region. Kwimba has highest 10% and 15%
patients on care and on ART respectively for patients who were traced within Kwimba district while
Ilemela districts have 12% and 14% on care and on ART respectively patients who were traced
outside Ilemela Districts.
Figure 7.1: Age distribution of traced patients who were considered lost to follow up
Out of 1601 patients traced 1483(92.6%) were adult while 118 (7.4%) were children.
66
Table 7.2: Distribution of marital status by sex among patients traced
M A R I TA L
STATUS
SEX
TOTAL
Female
%
Male
%
8
53%
7
47%
15
1%
146
464
313
70%
61%
72%
64
298
123
30%
39%
28%
210
762
436
103
78%
29
22%
132
13%
48%
27%
8%
30
1064
65%
66%
16
537
35%
34%
46
3%
Percentage
COHABITING
DIVORCED
MARRIED
SINGLE
WIDOW
MISSING
DATA
TOTAL
1601
Person’s Chi Square 26.15 and p-value is <0.01
Out of Total lost to follow up traced, married were more traced as compared to unmarried people
while female who were married were traced more than married male.
Table 7.3: Distribution of lost to follow up traced by facility type
Movement by Level of Facility
Health Center to Hospital
Hospital to Hospital
Hospital to Health Centre
Hospital to Dispensary
Dispensary to Health Centre
Dispensary to Hospital
Dispensary to Dispensary
Health Centre To Health Centre
Number
Patients
74
235
276
45
21
11
5
72
Health Centre to Dispensary
37
of
Percent
10%
30%
36%
6%
3%
1%
1%
9%
5%
Of the 741 patients whose movement were traced, majority of patients moved from Hospital to
Health centre 276(36%) and from Hospital to Hospital (30%). More analysis was done to clients
who were found lost to follow up at hospital and were traced in other hospitals. The results shows
majority 61% were found lost to follow up at district level hospital and moved to other district
hospital mostly Faith based organization Hospital (DDH)
67
Figure 7.2: Movement of Patients from Hospital to Hospital Level
DH = District Hospital ,
RH = Regional Hospital
Thirty seven percent of patients lost to follow up at district hospital were traced at Bugando referral
hospital.
Figure 7.3: Missed duration by number of patients after being traced in HIV patients who
were considered LTFU while on ART
The median duration is 680.5 and IQR is 417-680. Majority of patients traced have been lost to
follow up for more than one year.
68
7.3 Discussion
This is the first analysis of its kind in Tanzania to trace clients who are considered LTFU.
Understanding LTFU patterns may help to develop strategies to improve retention or tracing
mechanisms of HIV clients. The 6.5% of LTFU who were traced after analyzing only 48% of
facilities indicates an even higher proportion would be traced if all health facilities in this region
were analyzed, as well as tracing LTFU outside the region. In the future, we hope to expand the
analysis to include all facilities and all regions in Tanzania to get the true picture of LTFU.
The IQTools used to trace LTFU HIV clients compared only unique ID and some contact information
such as village, ward, and division, and excluded other important criteria of comparison of patient
identification such as name and treatment supporter. These are the data available to the CTC 2
database ‘export for analysis’ feature, and they represent the minimum tracing that can be done
with anonymous CTC2 data. This is likely to have reduced the list of LTFU traced as there may be
some clients using the same name but different unique IDs, or they register as new clients in the
other clinics. Using more patient information could have increased the number of LTFU traced.
However, we would not expect to be able to trace all of those LTFU as some might have died
without being reported, moved out of the region which is not covered here, or moved to a clinic not
captured in this analysis. There might also be some patients who presented to a clinic with different
contact information and date of birth (DOB) and obtained a new patient ID for which the IQTools
cannot capture. Different methods should be identified to capture those patients who are not using
unique identification numbers provided by the Ministry of Health. The tracing can include the use
of additional identifiers within available CTC 2 data such as names and telephone numbers.
Few LTFU episodes were traced at Ilemela region within the district. Instead, the majority of LTFU
episodes from Ilemela district were traced outside of the district. Ilemela is the most urban district
and home to Sekou Toure Regional Hospital, Bugando Referral Hospital and other private hospitals
that are better equipped than other districts’ hospitals. These might have resulted in the new
registration or enrolment of clients at Ilemela health facilities from other districts when they were
sick in order to receive better HIV care and management, who then returned to their home district
after their status improved without notifying their mother facilities located at Ilemela district.
The movement pattern among traced LTFU showed multiple trends. A high proportion of LTFU
were traced in health centers. The movement from hospitals to health centers could be due to new
health facilities opening within the district and patients sought to access services nearer their homes.
For patients who moved from dispensary to health centers or from health centers to hospital, this
may be due to patients’ preference for better services that may not be found in the lower health
facilities where they were accessing services before.
Among the LTFU episodes traced, those who were on ART were traced more often than those who
were not on ART; this may be due to health seeking behavior for those who were on ART as they
had already experienced health deterioration before. An HIV client on ART might have decided to
seek more medical attention in other facilities if he/she finds the need for more care; it may also be
due to service availability within the same facility. An HIV client may decide to stay in a facility
where s/he gets all services like CD4 testing and other investigations. A linkage analysis between
CTCs that provide all services and those not providing all services can be done to confirm the
69
LTFU patients’ movement across CTC facilities.
During data analysis, some LTFU episodes were seen in multiple facilities within the same period.
Although the number was very small, there is a need to look at patients attending and picking
ARVs in multiple clinics.
7.4 Recommendations
• Most of patients found to be missing in most Health facilities and hospitals were found
in referral hospital or other facilities which seem to provide more services like CD4 test
and advance clinical management services. There is a need therefore to upgrade Health
Facilities to have more advanced services.
• It was noted that some clients move within district level facilities. Patients Health Education
on appropriate transfer should be done at facility level and Council Health management
teams (CHMT) should lead the exercise of tracing Lost to follow up within the district.
• More married people were LTFU and traced compared with unmarried; a need for a special
study about disclosure among married is important to know the reason for most married
patients to be lost to follow up. The Family information in the CTC2 forms was very
important variables which used to show the HIV status at family level. The variable needs
to be re-included to capture this important information.
• Some patients were observed to attend multiple facilities and were found lost again within
the facility they moved into. The social and behaviour characteristics of these people need
to be done.
• A self transfer without proper documentation and referrals should be discouraged at any
facility and patients should be encouraged to request for transfer out note.
• A need for proper feedback across facilities is crucial as it will reduce patients who have
moved from one facility and are accessing treatment in the other facilities.
• The analysis showed 8% of people who were lost to follow up were traced within Mwanza
region. We recommend more analysis done in all regions to get the pattern of patients lost
and overall patients lost at National level.
70
8. REPORTING ON NATIONAL AND INTERNATIONAL INDICATORS
to report on the following national and international indicators. The indicators reported here are
Universal Access (UA), United Nations General Assembly Session on AIDS (UNGASS) and HIV
Drug Resistance Early Warning Indicators (EWI) as well as national care and treatment indicators.
Table 9.1 Indicators for Care and Treatment in Tanzania.
INDICATOR
Percentage of health facilities that offer
ART (UA)
EXPLANATION
2
Percentage of adults and children with
advanced HIV infection receiving
antiretroviral therapy(UNGASS)
Estimates for adults
children in 2011
3
HIV known to be on treatment 12
months after initiation antiretroviral
therapy(UNGASS)
Estimated for 3 yearly cohorts
(2008, 2009 and 2010).
2008-74%
2009-74%
2010- 74%
2008-81%
2009-80%
2010-78%
4
HIV known to be on treatment 24
months after initiation antiretroviral
therapy (UA)
Estimated for three yearly
cohorts (2007, 2008 and 2009)
2007-65%
2008-65%
2009- 63%
2007-72%
2008-72%
2009-70%
5
Percentage of adults and
HIV known to be on
months after initiation
therapy(UA)
HIV known to be on
therapy(UA)
HIV known to be on
therapy (UA)
children with
treatment 36
antiretroviral
cohorts (2006, 2007, and
2008)
2006-59%
2007-58%
2008-57%
2006-66%
2007-65%
2008-65%
children with
treatment 48
antiretroviral
cohorts (2005, 2006, and
2007)
2005-57%
2006-54%
2007-52%
2005-68%
2006-62%
2007-61%
children with
treatment 60
antiretroviral
Estimated for two yearly
cohorts (2005, and 2006)
2005-52%
2006-49%
N/A
8
Percentage of individuals starting ART
who are prescribed a standard regimen
(EWI)
Estimated for 4 cohorts in
2008, 2009, 2010 and 2011
2008-99.7%
2009-96%
2010-92%
2011-97%
2008-99.7%
2009-98%
2010-96%
2011-97%
9
Percentage lost to follow-up during the
12 months after starting ART (EWI)
(2008, 2009 & 2010).
2008-16.0%
2009-17.5%
2010-19.1%
2008-11.1%
2009-13.8%
2010-14.8%
(2008, 2009 & 2010).
2008-99%
2009-94%
2010-93%
2011-98%
2008-99%
2009-96%
2010-97%
2011-98%
1
6
7
10
months later(EWI)
From National database
and
RESULTS
UPDATE: 1156 out of 6216 =
18.6%
Adults
Children
54.5%
31.3%
71
These indicators use the traditional cohort analyses, based on a life-table with the assessment of
survival and loss to follow up at 12, 24 and 36 months. This is similar to the cohort analyses
performed on monthly cohorts at clinic level in Tanzania, and does not allow for censoring during
the period analysed. For indicators 3,4,5,9 and 10, the numbers in the analysis are the same as those
used in main report Tables 5.4 and 6.2, but the analyses in Chapter 5 are based on the cumulated
hazard function, which allows for censoring over the period of analysis and the analysis in chapter
6 does not show lost to follow up in children. This explains the slight differences between the
estimates in the 2 tables.
For the Early Warning Indicator (EWI) on the percentage of patients starting a standard ART regime
line treatments at 12 months were used, with around 11% of adults, coded as not being on first line
drug regimes after 12 months, although some of these may be on new regimes that had not been
given codes on the CTC cards. For all EWI the denominators for the analysis are all patients starting
ART in the calendar year.
72
Annex A
Competing risks analysis
This report has used survival analysis to show the risk of death among those who initiate ART.
Survival analysis is the analysis of the time to an event. With this type of analysis, usually the event
is called a failure (such as death), and lack of the event is survival (or continued existence). Thus
survival and death in these analyses are mutually exclusive events, and if one happens it is at the
expense of the other (if you die, then you do not survive, and if you survive, then you do not die).
The other important aspect to this analysis is that everyone starts at time zero, and we need to define
what time zero is. In the analyses in this report time zero is the date when the patient started ART
– before they start ART the time is not used in the analysis, after starting ART, each day counts as
time at risk for the survival analysis (see Annex to the previous Care and Treatment report for more
details of survival analysis).
In survival analyses people be lost-to-follow-up, if they do not die, and yet they stop coming to clinic.
The technical term for this is censoring, and this was also explained in the Annex to the previous
Care and Treatment report. With the traditional survival analysis, it is assumed that becoming lostto-follow-up is not associated with the event. In other words those who are censored are at the same
risk of death as those who are still being followed up. This may not be true, but we do not have any
further information to estimate mortality if the person does not come to clinic, and so this is the only
assumption we can make.
For some analyses in this report then survival and death are not mutually exclusive. Another event
may happen that takes the person out of the risk set (or cohort) in the analysis. This happens for
HIV positive people in care, who are not yet eligible for ART. In these analyses, we would like to
know how long they wait before getting onto ART. The choice is between getting onto ART, and not
getting onto ART. However there is a third event that could happen which upsets the simple survival
analysis, and this is death. While waiting to get onto ART, some of those will die, and hence can
never initiate ART. Unlike those who are lost-to-follow-up, who could at a later date initiate ART,
those who die have no chance of initiating ART, and hence we would be wrong to treat them in the
same way as those who are lost-to-follow-up. To compound this problem it is usually the case that
those who die are much more likely to be in need of ART. Hence if we censor them at their date of
death, treating them exactly the same way as those who were lost-to-follow-up, then we would be
biasing the estimate of the time people wait to initiate ART.
The solution we have used in this report is to treat death as a competing risk, and to simultaneously
calculate the cumulative probability of both accessing ART (the event of interest) and death (the
competing risk). We used Stata 12 for our analyses, and Stata has a command stcrreg to enable the
sub-hazard functions of these two events to be calculated simultaneously. Stata used the method of
Fine and Gray 1999 15 to calculate these sub-hazards.
15 Fine, J. P., and R. J. Gray. 1999. A proportional hazards model for the subdistribution of a competing risk. Journal
of the American Statistical Association 94: 496-509.
73
Annex B
PARTICIPANTS TO PREPARATORY AND ANALYSIS WORKSHOPS
NAME
INSTITUTION
TITLE
Aifello Sichalwe (Dr)
KCMC & MoHSW
MSc Epidemiology and Applied
Biostatistics student
Anath Rwebembera
MOHSW- NACP
Pediatric services program officer
Annette Almeida (Dr)
ICAP
M&E Director
Apaililia Kibona
POLICE
Medical Officer
Aska Hasegawa
NACP/JICA
Project Coordinator
Bernard Rabiel
MOHSW-NACP
M&E officer
Bhavin Jani (Dr)
CSSC
M&E officer
Bonita Kilama (Dr)
MOHSW-NACP
Acting Head, Epidemiology Unit
Cyprian Makwaya (Dr)
MUHAS
Senior Lecturer and Biostatistician
Deborah Kajoka (Dr)
MOHSW - PMTCT
National Coordinator
Denna Michael (Dr)
NIMR Mwanza
Elaine Baker- Guni
UCC
Software Developer
Elia Mbaga (Dr)
MUHAS
Epidemiologist and lecturer
KCMC & NIMR, Mwanza
Emanuel Martin (Dr)
Essau Amenye
AIDSRELIEF
Strategic Information officer
Ester Mungure
MDH
Data Manager ( MDH )
Filemoni Tenu
KCMC & NIMR Tanga
LSHTM & MITU/NIMR
Research Fellow in Medical Statistics
Fiona Ewings (Dr)
MWANZA
Florence Ndaturu
MOHSW-NACP
Program officer
George Laizer
MOHSW NACP
ICT officer
Germana Leyna (Dr)
MUHAS
Epidemiologist and lecturer
Godwin Mnuo (Dr )
CDC
TB/HIV officer
Gretchen Antelman
ICAP
Research Director
Helena Haule
DELOITTE
M&E officer
Heriana Wilbert
PASADA
M&E officer
Herilinda Temba (Dr)
MUHAS
Hilda Mmari (Dr)
Prisons Head Quarters
Medical Officer
Humphrey Mkali
MDH
Data Manager ( MDH)
Humphrey Shao (Dr )
ICAP
Research Officer
James Juma
MOHSW-NACP
IeDEA Coordinator
Japhet Kamala
UCC
PEPFAR Support
Jennifer Ward
CDC
Fellow
Jenny Tiberio
UCSF
Program analyst
Jerry Gibson
CDC
Senior Clinical Advisor
Jim Todd
LSHTM& NIMR- TAZAMA
Reader in Applied Biostatistics
MOHSW-NACP & KCMC
Joseph Nondi
74
Julius Mboya
Juma Mwinula ( Dr )
Karugira Rweyemamu (Dr)
Levina Lema
Mariam Ngaeje (Dr)
Mohamed Mfaume (Dr)
Moses Kehengu
Mpoki Kajigili
Munda Elias (Dr)
Naimi Mbogo
Neema Makyao
Nobuhiro Kadoi
Paul Mahunga
Ramadhani Mwiru (Dr)
Raphael Isingo
Rita Mkama
Sesil Latemba
Stephanie Kovacs
Theopista Komba
Touma Ngwanakilala
Tuhuma Tulli (Dr)
Tumaini Francis
Veryeh Sambu
Victor Katemana
Werner Maokola (Dr)
Yasin Shangari
Zebedayo Sekirasa
DOD
TPDF
FELTP resident
MOHSW - PMTCT
AGPHAI
CDC
AIDSRELIEF
PHARMACCESS
MUHAS
M&E CSSC
MOHSW-NACP
NACP/JICA
DELOITTE
MDH
NIMR Mwanza
DELOITTE
PAI
CDC
EGPAF
EGPAF
AIDSRELIEF
ICAP M&E
MOHSW-NACP
AIDSRELIEF
MOHSW-NACP
MOHSW NACP
MOHSW-NACP
M&E officer
Medical Services Director
MUHAS
Strategic Information officer
M&E officer
Care and Treatment officer
IT programmer
M&E officer
FELTP resident
M&E officer
KP coordinator
Advisor
M&E officer
Nutrition Support Coordinator
Statistician
M&E officer
M&E officer
Fellow
M&E officer
M&E officer
Strategic Information Advisor
officer
Data Manager
ICT officer
TB/HIV officer
ICT officer
Program officer – Care and Treatment
75