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. Author manuscript; available in PMC: 2016 May 1.
Published in final edited form as: J Acquir Immune Defic Syndr. 2015 May 1;69(1):98–108. doi: 10.1097/QAI.0000000000000553

Retention of Adult Patients on Antiretroviral Therapy in Low- and Middle-Income Countries: Systematic Review and Meta-analysis 2008–2013

Matthew P Fox 1,2,3,*, Sydney Rosen MPA 1,3
PMCID: PMC4422218  NIHMSID: NIHMS657470  PMID: 25942461

Abstract

Background

We previously published systematic reviews of retention in care after antiretroviral therapy initiation among general adult populations in sub-Saharan Africa. We estimated 36-month retention at 73% for publications from 2007–2010. This report extends the review to cover 2008–2013 and expands it to all low- and middle-income countries.

Methods

We searched PubMed, Embase, Cochrane Register, and ISI Web of Science from January 1, 2008 to December 31, 2013 and abstracts from AIDS and IAS from 2008–2013. We estimated retention across cohorts using simple averages and interpolated missing times through the last time reported. We estimated all-cause attrition (death, loss to follow-up) for patients receiving first-line ART in routine settings in low- and middle-income countries.

Results

We found 123 papers and abstracts reporting retention for 154 patient cohorts and 1,554,773 patients in 42 countries. Overall, 43% of all patients not retained were known to have died. Unweighted averages of reported retention was 78%, 71% and 69% at 12, 24, and 36 months after treatment initiation, respectively. We estimated 36-month retention at 65% in Africa, 80% in Asia, and 64% in Latin America and the Caribbean. From lifetable analysis, we estimated retention at 12, 24, 36, 48 and 60 months at 83%, 74%, 68%, 64% and 60%, respectively.

Conclusions

Retention at 36 months on treatment averages 65–70%. There are several important gaps in the evidence-base, which could be filled by further research, especially in terms of geographic coverage and duration of follow-up.

Keywords: retention, attrition, loss to follow-up, HIV, antiretroviral therapy, meta-analysis, systematic review, low and middle income countries

INTRODUCTION

The success of national antiretroviral therapy (ART) programs in expanding access to treatment for HIV/AIDS in low- and middle-income countries is undisputed. As of the end of 2013, some 11.7 million adults and children were estimated to be on ART1, representing almost two thirds of those eligible for ART under current guidelines2. Recent studies have observed large reductions in mortality and corresponding increases in life expectancy in some of the hardest hit countries and populations3,4.

A large and growing body of research, conducted largely since 2008, has identified poor retention in HIV care, both before and after ART initiation, as one of the most important factors in determining the overall impact of treatment. Systematic reviews of retention after ART initiation in sub-Saharan Africa, conducted by the authors in 20075 and 20106, estimated 24 month retention to average 62% in the years leading up to 2007 and 76% between 2007 and 2009. The remaining one quarter to one third of all patients initiated on treatment were either known to have died or were lost to follow-up with unknown outcomes. Of these, some unknown proportion likely “self-transferred” to another facility and remain alive and in care, a proportion estimated in a recent pooled analysis to average 18.6% of those lost to follow-up7. Still, the loss of up to a third of patients over two years—and of more in each year after that—is regarded as a threat to the sustainability of HIV treatment programs and an important target for intervention2.

Although average retention in sub-Saharan Africa appeared to improve between the two earlier reviews, there were also substantial differences in the volume and methods of the papers included. It is thus difficult to determine whether the observed difference reflect a real improvement or is merely an artifact of research. These previous reviews were limited, moreover, to general adult populations in sub-Saharan Africa through mid-2009. Current retention rates reported by the World Health Organization (WHO) vary widely between countries and regions [1], and there have been important changes in both WHO guidelines and national ART programs since 2008. In order to assist policy makers, program managers, and funding agencies in understanding and targeting their efforts, we updated and expanded the current review to estimate retention on ART among general adult populations from all low- and middle-income regions from 2008 through 2013.

METHODS

Our goal was to estimate all-cause attrition from and retention in care for adult patients receiving first-line ART in routine service delivery settings in World Bank-defined low- and middle-income countries. All-cause attrition was defined as death or loss to follow-up. When such data were reported, we excluded patients who transferred to other sites, as their outcomes are unknown. Patients who were reported as stopping treatment but remaining in care were counted as retained.

We included observational studies describing retention in HIV treatment programs published or presented in 2008 or later. We included cohorts receiving standard first-line ART at any type or level of facility that followed prevailing national treatment guidelines. We excluded clinical trials, intervention evaluations (including home-based care), and studies providing care that patients wouldn’t receive under usual practice, as indicated by each study’s authors. We included standard of care arms from studies evaluating interventions in non-randomized trials. Cohorts where ≥50% of patients were reported to be from high-risk or “key” populations—men who have sex with men, injection drug users, prisoners, female sex workers—and cohorts that limited enrollment to pregnant women, and subsets of the general population with low CD4 counts or tuberculosis were excluded; key populations will be reported on in a separate publication. Cohorts combining adults and pediatric or adolescent patients that did not stratify results by age groups were included only if over 50% were ≥18. We have reported on pediatric patients separately8.

Where multiple reports described a single cohort, we chose the one with the most complete data and/or longest follow-up. If a report described multiple cohorts, we included it only if the data could be stratified by country and there was no other report of any of the cohorts individually. If the data were disaggregated by cohort, only cohorts that were also reported in other sources were excluded. We required that studies follow patients from ART initiation to a mean or median of at least six months of follow-up. Studies had to report or provide enough information to estimate all-cause attrition (death and loss) for at least one of the following time points: 6, 12, or 18 months, or a later twelve-month interval after treatment initiation. We placed no restrictions on how cohorts assessed mortality among patients lost, but excluded studies that reported mortality but not loss to follow-up.

To identify studies, we searched PubMed, Embase, the Cochrane Register, and ISI Web of Science from January 1, 2008 to July 28, 2013 for English language publications. Within each index, we combined “antiretroviral” and any of “Africa”/“Asia”/“Central America”/“Mexico”/“South America”/“Middle East”/"Eastern Europe"/“Caribbean Region” with any of the following: “retention”/“attrition”/“adherence”/ “mortality”/“loss to follow-up”/“efficacy”/ or “evaluation.” We searched conference abstracts from AIDS and IAS conferences from 2008 to 2013 using “attrition,” “retention,” or “loss to follow-up.” (We did not search CROI as its website archives were unavailable throughout the review process.)

We then conducted three secondary searches. First, to capture journals that are not MeSH indexed, we searched again in PubMed, substituting region names with individual country names for all low an middle income countries (LMIC) for which we did not initially include at least two cohorts. Second, we searched PubMed to determine if any conference abstracts identified in the primary search had been published as full-text articles. Finally, we repeated each search in PubMed for the period from August 1, 2013 to January 9, 2014, when the database for the search was closed.

MF supervised the primary search and SR conducted the secondary searches. After excluding those whose titles were not relevant, abstracts were read to determine eligibility. Full text articles were reviewed by both authors to confirm eligibility. Uncertainties were resolved through consensus of both authors.

Statistical Analysis

For cohorts reporting retention to a particular time point (as a proportion or Kaplan-Meier estimate), retention at each point was defined as that reported by the cohort. For cohorts that didn’t report retention at specific time points but provided data on attrition (numbers of subjects lost or died), retention was defined as the proportion alive and in care and assigned to the time closest to the median follow-up.

For analysis, countries were grouped into four regions: Africa (including North Africa); Asia (including Pacific island states); Europe and Central Asia (ECA); and Latin America and the Caribbean (LAC) (including South and Central America and Caribbean island states).

Analysis of reported retention proportions

We estimated mean retention across cohorts using simple averages unweighted by sample size. As each cohort reported to different time periods, we also interpolated any missing time period possible. For example, if a cohort reported 12- and 24-month retention, we interpolated 6- and 18-month retention, assuming a linear decline between two points.

Meta-analysis of retention rates

We synthesized the data in a meta-analysis stratified by last time period reported to. We plotted each retention estimate and its 95% confidence interval (CI) using forest plots and combined estimates using a random effects regression with a Freeman and Tukey arcsine transformation9. We created a patient-level dataset for each study with all attrition occurring at the time period when it was reported. We summarized retention using Kaplan-Meier curves and estimated retention over time using lifetable analysis. We report no confidence intervals for these estimates as the sample size creates misleadingly narrow intervals.

Sensitivity analysis

We plotted mean retention by last time period reported to assess whether cohorts reporting to longer time periods were more likely to report higher retention at earlier time periods than cohorts reporting to shorter time periods. If they were, it would suggest publication bias in later years of follow-up, in that cohorts with worse retention stopped reporting after shorter durations of follow-up than those with better retention. To create upper and lower bounds on true retention, given the varying time periods reported to, we conducted a sensitivity analysis to consider the best-case, worst-case and midpoint scenarios for retention. The best-case scenario assumed no additional attrition from the last period reported to through 60 months. The worst-case scenario assumed retention continued along the same linear trend as was observed between baseline and the last time period reported. The midpoint scenario is the average of the two.

RESULTS

Our primary search identified 3517 unique articles and 6846 abstract citations; an additional 1236 articles were identified by our secondary searches. Of these, 123 met the inclusion criteria (97 articles, 26 abstracts, as depicted in Appendix 1). These studies reported on 154 patient cohorts, described in Appendix 2, and 1,554,773 patients.

A total of 42 countries were represented: 24 in Africa (114 cohorts), 10 in Asia (28 cohorts), and 8 in LAC (12 cohorts). Nearly 75% of all cohorts were from Africa. Within Africa, 24% of cohorts, about 18% of all included cohorts, were from South Africa. In Asia, nearly half of the cohorts came from India, but large cohorts from Thailand and China accounted for 68% and 22% of all patients from Asia, respectively. One third of the LAC cohorts came from Brazil and nearly half the LAC patients came from Haiti. We found no studies from the Middle East, Eastern Europe, or Central Asia reporting on general population adult cohorts. The Europe and Central Asia region (ECA) is therefore not included in the results below.

Most patients initiated ART in their early- to mid-30s, with CD4 counts well below 200 cell/mm3 (Appendix 2). Just under two thirds of patients in Africa were female, while in all other regions >50% were male. While not perfectly monotonic, there is some trend toward higher starting CD4 counts over time, with average or median CD4 counts at 113 cell/mm3 among patients initiating in 2001/2 and 154 cell/mm3 for those starting in 2009/10 (mean difference 41.2; 95%CI: 9.4 to 72.3). Most (74%) cohorts had relatively short follow-up of one or two years, while the rest reported to three or four years (20%) or longer (6%).

Attrition from each cohort by the end of that cohort’s follow-up, stratified by reason for attrition, is reported in Table 1. For cohorts that distinguished between deaths and losses (n=113), an unweighted average of 43% of patients not retained were known to have died, while the remaining 57% were lost. Definitions of loss to follow-up ranged from 1 to 12 months late for the next scheduled clinic visit and 1 to 16 months since the last clinic visit. The most common definition was to categorize patients as lost if they were ≥3 months late for a scheduled visit or did not return for >6 months after the last completed visit (definitions in Appendix 3).

Table 1.

Median follow-up and rates of patient attrition, as reported, from antiretroviral treatment programs

Study Code N Median or mean follow-up (months) Died (A) Lost to follow-up
(B)		 Total attrition from ART
(C) (C=A+B)		 Total retained
(D) (D=1-C)		 Transferred care
(E)		 Total retained at original site
(F) (F=D-E)
Africa
Botswana 1 633 41.9 23.4% 19.9% 43.4% 56.6% 19.1% 37.5%
Botswana 2 102,713 35.0 10.0% 14.9% 24.8% 75.2% 75.2%
Burkina Faso 1 4,255 22.6 11.4% 8.2% 19.6% 80.4% 3.6% 76.7%
Burkina Faso 2 5,608 23.2 12.8% 7.4% 20.2% 79.8% 3.7% 76.1%
Burkina Faso 3 867 11.2 5.7% 8.5% 14.2% 85.8% 85.8%
Cameroon 1 600 12.0 2.8% 50.0% 52.8% 47.2% 10.7% 36.5%
Cameroon 2a 330 12.0 4.5% 30.0% 34.5% 65.5% 65.5%
Cameroon 2b 295 12.0 2.4% 13.9% 16.3% 83.7% 83.7%
Cameroon 3 1,187 58.0 35.0% 6.1% 41.1% 58.9% 18.5% 40.4%
Cameroon 4 2,920 6.2 5.6% 39.5% 45.1% 54.9% 0.3% 54.5%
Cameroon 5 141 12.0 9.6% 34.6% 44.1% 55.9% 3.5% 52.3%
Cote d’Ivoire 1 1,573 6.0 9.2% 13.1% 22.2% 77.8% 1.4% 76.4%
Cote d’Ivoire 2 10,211 7.7 11.5% 14.0% 25.5% 74.5% 3.0% 71.5%
Cote d’Ivoire 3 3,682 36.0 12.1% 6.9% 19.0% 81.0% 81.0%
Cote d’Ivoire 4 1,008
Cote d’Ivoire 5 247 17.3 4.7% 12.7% 17.4% 82.6% 82.6%
DRC 1 68 19.0 6.2% 18.3% 24.5% 75.5% 1.4% 74.1%
DRC 2 1,450 16.4% 14.9% 31.3% 68.7% 1.5% 67.2%
Ethiopia 1 1,540 24.0 6.4% 15.6% 22.0% 78.0% 12.5% 65.6%
Ethiopia 2 1,709 24.0 11.1% 22.6% 33.7% 66.3% 7.9% 58.4%
Ethiopia 3 1,537 24.0 6.4% 15.6% 22.0% 78.0% 12.5% 65.5%
Ethiopia 4 37,466 24.0
Ethiopia 5 321 18.6% 8.2% 26.8% 73.2% 1.2% 71.9%
Ethiopia 6 1,428 17.7 12.8% 15.2% 28.0% 72.0% 12.0% 60.0%
Gambia 1 308 12.1 19.6% 3.8% 23.4% 76.6% 7.1% 69.4%
Ghana 1 3,054 30.0 7.7% 20.4% 28.1% 71.9% 71.9%
Ghana 2 290 18.0 2.4% 14.1% 16.6% 83.4% 83.4%
Ghana 3 91 36.0 21.7% 8.4% 30.1% 69.9% 8.8% 61.1%
Guinea Bissau 1 2,351 20.5 10.3% 44.1% 54.4% 45.6% 3.5% 42.1%
Kenya 1 1,307 9.0 4.2% 14.8% 19.1% 80.9% 80.9%
Kenya 2a 120 12.0 5.8% 12.5% 18.3% 81.7% 81.7%
Kenya 2b 120 12.0 0.8% 19.2% 20.0% 80.0% 80.0%
Kenya 2c 120 12.0 5.8% 10.0% 15.8% 84.2% 84.2%
Kenya 3 830 18.0 29.4% 29.4% 70.6% 70.6%
Kenya 4 301 12.0 5.2% 7.6% 12.7% 87.3% 3.3% 84.0%
Kenya 5 1,676 3.2% 42.5% 45.7% 54.3% 6.4% 47.9%
Lesotho 1 3,394 13.0 3.0% 7.0% 10.0% 90.0% 90.0%
Lesotho 2 4,064 12.0 9.3% 2.5% 11.8% 88.2% 88.2%
Lesotho 3 3,747 17.4 11.2% 15.0% 26.2% 73.8% 73.8%
Malawi 1 12,004 12.0
Malawi 2a 397 6.0
Malawi 2b 1,868 6.0
Malawi 2c 2,142 6.0
Malawi 2d 1,893 6.0
Malawi 2e 3,164 6.0
Malawi 2f 1,264 6.0
Malawi 2g 6,994 6.0
Malawi 3 253,154
Morocco 1 412 3.6% 11.4% 15.0% 85.0% 85.0%
Mozambique 1 142 22.2
Mozambique 2 11,793 7.4 14.9% 17.4% 32.3% 67.7% 6.1% 61.6%
Mozambique 3 471 6.0 16.4% 16.4% 83.6% 2.8% 80.9%
Mozambique 4 2,005 24.0
Mozambique 5 7,636
Mozambique 6 2,596
Mozambique 7 1,417 120.9
Mozambique 8 9,692 13.1
Nigeria 1 4,785 28.1 3.0% 21.7% 24.7% 75.3% 5.1% 70.2%
Nigeria 2 1,034 13.8 3.6% 21.2% 24.8% 75.2% 0.1% 75.1%
Nigeria 3 5,760 7.1 25.9% 25.9% 74.1% 74.1%
Nigeria 4 12,764 6.0
Rwanda 1 306 12.0 7.4% 3.3% 10.7% 89.3% 2.3% 87.0%
Senegal 1 403 98.0 30.5% 9.4% 40.0% 60.0% 60.0%
South Africa 01 3,162 28.8 11.8% 20.9% 32.7% 67.3% 10.3% 57.0%
South Africa 02 47,285 14.8 6.3% 9.5% 15.8% 84.2% 84.2%
South Africa 03 1,154 17.4 6.6% 20.2% 26.8% 73.2% 16.3% 56.9%
South Africa 04 226 12.8% 87.2% 87.2%
South Africa 05 267 6.0 7.6% 8.0% 15.5% 84.5% 1.1% 83.3%
South Africa 06 9,102 12.0 12.9% 14.2% 27.1% 72.9% 2.1% 70.8%
South Africa 07 735 12.0 12.1% 14.0% 26.1% 73.9% 7.9% 66.0%
South Africa 08 2,102 1.9% 15.4% 17.3% 82.7% 0.3% 82.4%
South Africa 09 15,060 21.6 18.2% 27.6% 45.7% 54.3% 15.2% 39.1%
South Africa 10 49,383
South Africa 11 40,176 20.5 14.2% 22.9% 37.1% 62.9% 6.0% 56.9%
South Africa 12 6,411 18.4 8.4% 10.3% 18.7% 81.3% 4.8% 76.6%
South Africa 13 1,380 12.0 2.1% 14.1% 16.2% 83.8% 83.8%
South Africa 14 1,353 24.0 9.6% 2.7% 12.3% 87.7% 4.7% 83.0%
South Africa 15 609 12.0 18.6% 14.6% 33.2% 66.8% 66.8%
South Africa 16a 1,794 76.8 18.1% 28.2% 46.3% 53.7% 9.9% 43.8%
South Africa 16b 2,154 44.3 18.5% 32.0% 50.5% 49.5% 10.4% 39.1%
South Africa 16c 2,617 38.0 15.9% 31.3% 47.2% 52.8% 10.5% 42.3%
South Africa 16d 1,996 31.0 15.3% 28.5% 43.8% 56.2% 9.4% 46.8%
South Africa 16e 2,185 25.0 14.4% 21.3% 35.7% 64.3% 7.7% 56.6%
South Africa 16f 2,481 17.3 10.2% 20.3% 30.6% 69.4% 6.0% 63.5%
South Africa 17 684 36.0 18.7% 5.5% 24.2% 75.8% 4.1% 71.7%
South Africa 18 309 8.4 15.9% 7.4% 23.3% 76.7% 76.7%
South Africa 19 2,835 22.0
South Africa 20 2,817 24.0 2.0% 11.2% 13.2% 86.8% 86.8%
South Africa 21 4,674 33.2 17.4% 10.6% 28.0% 72.0% 5.6% 66.5%
South Africa 22 11,397 13.1 14.8% 20.6% 35.4% 64.6% 16.7% 47.9%
Swaziland 1 769 12.0 - 17.2% 17.2% 82.8% 82.8%
Swaziland 2 2,510
Tanzania 1 1,463 12.0 8.8% 12.9% 21.7% 78.3% 78.3%
Tanzania 2 255,143 11.0% 11.0% 89.0% 89.0%
Tanzania 3 320 10.9 33.3% 10.9% 44.2% 55.8% 10.9% 44.9%
Tanzania 4 12,842 8.8 13.1% 22.7% 35.8% 64.2% 64.2%
Tanzania 5 1,458
Togo 1 16,617 6.0 1.7% 1.7% 98.3% 98.3%
Uganda 1 399 12.0 4.3% 17.5% 21.8% 78.2% 78.2%
Uganda 2 8,835 37.0 3.8% 3.7% 7.6% 92.4% 92.4%
Uganda 3 3,628 22.9% 22.9% 77.1% 77.1%
Uganda 4 22,315 31.0 6.7% 6.4% 13.1% 86.9% 86.9%
Uganda 5 5,633 22.5 8.4% 11.3% 19.8% 80.2% 1.2% 79.0%
Uganda 6 289 72.0 3.5% 4.8% 8.3% 91.7% 0.0% 91.7%
Uganda 7 1,763 48.0 15.6% 21.4% 37.0% 63.0% 8.2% 54.8%
Uganda 8 27,425
Uganda 9 1,472
Zambia 1 3,902 12.0
Zambia 2 89,339 10.0 9.5% 13.7% 23.2% 76.8% 76.8%
Zambia 3 1,084 11.9% 6.4% 18.3% 81.7% 6.0% 75.8%
Zambia 4 1,457
Zimbabwe 1 592 15.2 9.5% 12.3% 21.8% 78.2% 78.2%
Zimbabwe 2 3,919 16.3
Zimbabwe 3 3,030 120.9
Asia
Cambodia 1 2,840 48.0 13.9% 6.4% 20.3% 79.7% 3.0% 76.7%
Cambodia 2 1,010 30.0 7.2% 8.0% 15.2% 84.8% 2.0% 82.9%
Cambodia 3 549 28.8 10.4% 12.3% 22.7% 77.3% 77.3%
Cambodia 4 467 13.2 7.1% 4.2% 11.4% 88.6% 5.5% 83.2%
China 1 67,732 20.0 10.9% 14.7% 25.6% 74.4% 74.4%
China 2 1,014 9.0% 2.7% 11.6% 88.4% 88.4%
India 1 230 12.0 10.8% 5.2% 16.0% 84.0% 7.8% 76.1%
India 2a 150 8.1% 14.1% 22.2% 77.8% 10.0% 67.8%
India 2b 148 9.5% 21.2% 30.7% 69.3% 7.4% 61.9%
India 3 631 21.0 13.8% 24.8% 38.6% 61.4% 11.3% 50.2%
India 4 972 24.0 12.8%
India 5 717 3.8% 32.4% 36.1% 63.9% 63.9%
India 6 239 41.4 10.0% 43.5% 53.6% 46.4% 46.4%
India 7 142 44.0 12.0% 12.0% 88.0% 88.0%
India 8a 43 6.0 20.9% 18.6% 39.5% 60.5% 60.5%
India 8b 44 6.0 6.8% 15.9% 22.7% 77.3% 77.3%
India 8c 43 6.0 7.0% 46.5% 53.5% 46.5% 46.5%
India 9 3,159 26.0 15.1% 15.5% 30.6% 69.4% 69.4%
Indonesia 96 8.2 19.8% 14.3% 34.1% 65.9% 5.2% 60.7%
Laos 1 913 21.7 13.1% 4.9% 18.0% 82.0% 10.7% 71.2%
Myanmar 1 5,963 36.0 14.3% 6.8% 21.0% 79.0% 3.5% 75.4%
Nepal 1 1,049 19.1 14.1% 4.6% 18.7% 81.3% 18.9% 62.4%
Papua New Guinea 1 993 24.0
Thailand 1 36 0.0% 0.0% 0.0% 100.0% 100.0%
Thailand 2 213,753 42.0 9.7% 10.6% 20.3% 79.7% 79.7%
Vietnam 1 466 16.5 8.6% 2.1% 10.7% 89.3% 89.3%
Vietnam 2 11,432
Vietnam 3 1,604
LAC
Brazil 1 541 0.4% 5.2% 5.5% 94.5% 94.5%
Brazil 2 516 0.2% 5.4% 5.6% 94.4% 94.4%
Brazil 3 522 12.0 3.6% 5.2% 8.8% 91.2% 91.2%
Brazil 4 702 22.0 1.4% 6.1% 7.5% 92.5% 92.5%
Dominican Republic 1 1,207 20.0 15.0% 12.8% 27.8% 72.2% 72.2%
Guyana 1 25 72.0 16.7% 16.7% 33.3% 66.7% 4.0% 62.7%
Haiti 1 4,717 27.0 12.7% 12.8% 25.4% 74.6% 74.6%
Honduras 1 328 12.0 10.1% 0.6% 10.7% 89.3% 89.3%
Jamaica 1 476 40.0 8.0% 16.2% 24.2% 75.8% 75.8%
Nicaragua 1 166 14.4 21.6% 2.0% 23.5% 76.5% 8.5% 68.0%
Peru 1 873 12.0 8.8% 3.1% 11.9% 88.1% 88.1%
Peru 2 55 18.2% 10.9% 29.1% 70.9% 70.9%
All (averages) 10,096 22.7 10.6% 15.0% 24.7% 75.3% 6.8% 71.7%
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Retention on ART as reported

Table 2 shows retention at each time period reported to, by country. Simple average retention for select time points is plotted in Figure 1. Details are presented in Figure 2, which illustrates retention rates and 95% CIs at 12, 24, 36 and 48 months using forest plots. Simple average retention with no interpolation of missing values averaged 78% at 12 months, 71% at 24 months, and 69% at 36 months across all regions.

Table 2.

Summary of retention at specified time points after ART initiation, by country

Country Retained at months on ART
6 12 18 24 36 48 60 72 84 96
Africa
Botswana 74% 70% 51%
Burkina Faso 75% 80%
Cameroon 66% 65% 47% 35% 47%
Cote d’Ivoire 78% 81% 67% 74% 71% 48%
DRC 81% 75% 65% 57% 63%
Ethiopia 76% 74% 72% 73%
Gambia 82% 75% 73%
Ghana 83% 71%
Guinea Bissau 46%
Kenya 80% 80% 64% 58% 55% 45% 39% 36%
Lesotho 89% 80% 67%
Malawi 83% 80% 77% 72% 68% 64% 54%
Morocco 85%
Mozambique 83% 72% 65% 56% 51% 60%
Nigeria 77% 75% 75%
Rwanda 89%
Senegal 60%
South Africa 85% 77% 71% 75% 67% 50% 74% 63%
Swaziland 84% 82% 77% 74% 69% 66%
Tanzania 82% 68% 64% 61% 56% 49% 38%
Togo 98%
Uganda 88% 83% 86% 76% 79% 69% 57% 92%
Zambia 81% 79% 72% 68% 59% 54%
Zimbabwe 91% 80% 79% 72% 64%
Regional average 82% 76% 71% 69% 67% 57% 61% 64% 37% 60%
Asia
Cambodia 89% 77% 85% 80%
China 94% 91% 87% 86% 76%
India 66% 81% 78% 67% 75% 74%
Indonesia 66%
Laos 88%
Myanmar 92% 89% 82% 72%
Nepal 81%
Papua New Guinea 80% 73% 68% 63%
Thailand 100% 80%
Vietnam 87% 81% 89% 74% 67% 63%
Regional average 77% 84% 86% 79% 72% 71% 74%
LAC
Brazil 94% 91% 92%
Dominican Republic 72%
Guyana 67%
Haiti 75%
Honduras 89%
Jamaica 76%
Nicaragua 76%
Peru 79%
Regional average 94% 85% 74% 84% 76% 67%
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Figure 1.

Figure 1

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Average retention at specified time points, by region*

* Note: Y axis starts at 40%

Figure 2.

Figure 2

Figure 2

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– Forest plots of retention by time period reported to at 12, 24, 36 and 48 months on ART*

* Figure of 48 month retention includes 48 month retention (sometimes interpolated) for all cohorts reporting beyond 48 months

To determine if average retention changed over calendar time, we compared attrition at 12 months in the 66 cohorts completing enrollment before 2008 to 12-month attrition in the 19 starting enrollment on or after 2008. Retention was slightly lower in the later (post-2008) cohorts, averaging 74.3% retention vs. 78.4% in earlier cohorts.

We looked for publication bias by plotting weighted average attrition by last time point reported to. Studies with shorter follow-up periods reported higher attrition at any given time point than did studies with longer follow-up (Appendix 4). Studies reporting only to 12 months, for example, retained an average of 84% of patients at 12 months, while those reporting to 36 months retained an average of 91% of patients at 12 months. This suggests some publication bias; had the studies that reported retention at 12 months continued to follow their cohorts, they would likely have had poorer 36 month retention than those that did report to 36 months.

Meta-Analysis of ART Retention

We plotted Kaplan-Meier survival curves by region (Figure 3a-d) and estimated retention by lifetable analysis. These may be regarded as the most accurate of our aggregate estimates of retention, as they take into account the full set of data available. From this analysis, we estimate 12-, 24-, 36-, 48- and 60-month retention at 83%, 74%, 68%, 64% and 60%, respectively. Asia fared better than Africa or LAC in these estimates, with 36-month retention of 80% in Asia, 65% in Africa, and 64% in LAC.

Sensitivity analysis

Both publication bias and the possibility that cohorts with resources to publish also have more resources for retaining patients suggest simple averages may overestimate true retention. On the other hand, reported loss to follow-up may overestimate true loss to care, as patients who self-transfer to other facilities are often reported as lost. We undertook a sensitivity analysis in which we modeled expected attrition under best-case, worst-case, and midpoint scenarios (Appendix 5). As we previously found for adults in sub-Saharan Africa, there is little variation among the three scenarios up to 24 months on ART. By 36 months, the difference widens, and continues to expand through 60 months. The midpoint estimate of retention at 36 months is 67%. The worst- and best-case estimates at the same time point are 62% and 72%, respectively.

DISCUSSION

This review of 154 general adult patient cohorts comprising 1,554,773 patients from 42 low- and middle-income countries published or presented from 2008 to 2013 allowed us to estimate ART retention with excellent precision. We found that adult 36 month retention averaged 65% in Africa, 80% in Asia, and 64% in Latin America and the Caribbean. Although average starting CD4 counts appear to be rising, attrition also shows some evidence of increasing over time. In considering change over time, however, it should be noted that most cohorts in this review enrolled patients under earlier, more restrictive treatment eligibility guidelines (i.e. CD4 count threshold of 200 cells/mm3 rather than the 350 threshold that is common now).

Since our first two reviews, several reviews have considered other aspects of retention and other regions or populations. These include syntheses of reasons for stopping treatment10,11 and pooled analyses of data from multiple cohorts in a region12. Quantitative results have generally been similar to ours, though several authors have noted that the definition of loss can influence estimated rates13,14. Of importance in interpreting this review is work on the ultimate outcomes of patients categorized as lost15. Studies that actively track lost patients suggest that while many have died or are untraceable, a large minority have re-initiated ART at another site (self-transferred). The term “lost to follow-up” should therefore be regarded as a catchall that includes informal self-transfers and undocumented deaths. It may over-estimate national treatment program ART attrition, while also underestimating the proportion of deaths.

Unlike our previous reviews, for which long-term data were scarce, our current review provides a robust estimate of retention beyond two years. Our lifetable results estimated overall adult retention at 83%, 74%, 68%, 64% and 60% after 12, 24, 36, 48, and 60 months on ART, respectively. We saw a steady reduction in annual attrition after 24 months, suggesting annual attrition slows but is not eliminated in later years on ART. A 2013 WHO report on low- and middle-income countries found similar 12-month retention (86%) but estimated 60-month retention at 72%2. These estimates come from 23 cohorts of ≥2000 patients, and therefore may not be representative of typical cohorts in resource-limited settings. Alternatively as the countries included do not perfectly overlap our analysis and mainly report on recent retention, they could indicate retention is highly variable over long-term follow-up. Longer follow-up in nationally representative cohorts is needed to discern the reasons for these differences.

In this review we included counties outside of sub-Saharan Africa, which allowed us to investigate regional differences in retention. We found some variation, with lifetable estimates of 36-month retention estimated at 65% in Africa, 80% in Asia, and 64% in LAC. Our review was not able to explain these differences, though they may have to do with differences in patient care-seeking behavior, socioeconomic status, experiences with the health care system, distances to the clinics or baseline disease status. We also note that although we excluded studies where >50% of patients were explicitly reported to be drawn from key populations, it is likely that cohorts in Asia and Latin America and the Caribbean, where most countries have concentrated HIV epidemics, included much larger proportions of MSM and IDU, in particular, than those from Africa. Future work is needed to define more accurately the specific populations from which study cohorts are drawn, confirm the variation between regions and populations, and to explain its implications for retention.

As noted above, we found a modest chronological trend toward increasing attrition in later years. Average 12-month retention for cohorts enrolling all patients before 2008, 78%, was higher than for cohorts that started enrolling in 2008 or later, 74%. Because the included cohorts vary widely, by location, population, and other factors, it is impossible to know whether this difference reflects a real trend toward poorer retention or is an artifact of the review. These results suggest that at a minimum, there is no broad trend toward improvement in retention over time. While our finding of increased attrition over time was not robust, it is consistent with findings from the South Africa national treatment program16 and other African treatment programs17. There are many possible explanations for this. It is possible, for example, that as programs scale up they are less able to focus on retention. It is also possible that earlier treatment initiation, as reflected in many countries’ treatment guidelines starting in 2008, is associated with less mortality but more loss to follow-up17. We note that reviews like this one cannot readily address questions of the impact of guideline changes, largely because estimates of retention are rarely reported by either calendar year or patients’ year of ART initiation. This precludes ascribing any cohort’s retention estimate to a specific time period in relation to prevailing guidelines. We encourage future cohort studies to report outcomes by year of treatment inititiation.

Our review identified some important gaps in the retention literature. Roughly 70% of all included studies were African cohorts. While our inclusion criteria covered all low- and middle-income countries, we found relatively few cohorts reporting in English outside Asia and Africa. It is understandable that a majority of research is done in Africa, which has the majority of HIV-infected individuals. We identified no studies meeting our inclusion criteria reporting on general adult populations from Europe and Central Asia. A parallel review of retention in high-risk, rather than general, populations found only one eligible study from the ECA region18. While we stratified our analysis by geographic region, both Asia and LAC had limited country variation, modest numbers of studies, and smaller cohorts. Within Africa, North Africa provided only one cohort, and most studies came from Southern or Eastern Africa, with minimal representation from Central and West Africa. Within Asia, the Middle East is missing entirely, and several very large countries (Malaysia, Pakistan, Bangladesh, Indonesia) had only one or no studies available (these countries were also absent from or poorly represented in the parallel high-risk populations review). Finally few cohorts reported retention beyond 36 months. While 12 or 24 months follow-up captures the high attrition immediately after starting treatment, it doesn’t shed light on the long-term effects of resistance, toxicities, treatment fatigue, and treatment failure, which may only develop after five years or more.

This review has several limitations. First, as noted, we identified publication bias that would be expected to overestimating retention as cohorts with worse attrition were systematically underrepresented. Second, for cohorts reporting overall retention along with median follow-up duration, we ascribed retention to the period closest to the median. This can have an unpredictable effect on estimates; in some cases it will overestimate and in others underestimate retention. Third, large cohorts (e.g. Malawi, China, Thailand) may have had overly strong influence on the results. Fourth, in cases where we calculated cohort retention, we excluded transfer patients. Cohorts that reported Kaplan-Meier analyses often censored patients at transfer, which could bias retention estimates. In addition many patients who transfer care informally are likely reported as lost. Fifth, we accepted each report’s own definition of loss to follow-up as we did not have access to primary data that would have allowed us to apply a common definition. The definition of loss to follow up certainly matters, as has been made clear by other authors13,14. It is unclear, however, how the lack of a standard definition affected our aggregate estimates, as whatever standard definition was applied would have led to some studies overestimating and some underestimating attrition. Sixth, our results, particularly retention at 6 and 12 months, could be biased by the fact that we were forced to interpolate data between time points not reported and chose linear interpolation as the best approach. While attrition is often linear after the first year on treatment, it tends not to be during the first year. For the cohorts where 6 and/or 12 month retention was interpolated, this would likely cause an overestimate of early retention. Seventh, we excluded non-English language publications, which may explain the limited data outside Asia and Africa. Eighth, some cohorts used patient tracing which could have influenced retention rates. Finally, the keywords and MeSH terms used to index publications about ART retention are not consistent across publications. As a result, it is difficult to construct searches in databases like PubMed that are both inclusive and precise.

In conclusion, we found that among 1,554,773 general population patients from low- and middle-income countries, overall retention at 12, 24 and 36 months was estimated to be 83%, 74% and 68% respectively. There appear to be substantial regional differences, with 36-month retention estimated at 65% in Africa, 80% in Asia, and 64% in LAC. As most of the reviewed cohorts came from sub-Saharan Africa, more retention data from low- and middle-income countries outside sub-Saharan Africa are needed to create a robust picture of retention throughout resource-limited settings.

Supplementary Material

Supplemental Digital Content

Figure 3.

Figure 3

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a–d Kaplan-Meir curves of time to attrition for all adults and stratified by region*

* Kaplan Meir data use interpolated estimates

Acknowledgments

This works was supported by the World Health Organization, USAID and NIH. The funders had no role in the study design, the collection, analysis, and interpretation of data, in the writing of the report, approval of the manuscript or in the decision to submit the paper for publication. The authors declare no competing interests. Matthew Fox designed the study, oversaw data collection, drafted parts of the manuscript and approved the final version. Sydney Rosen also designed the study, drafted parts of the manuscript and approved the final version. Matthew Fox had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Footnotes

Conflict of interest and sources of funding: We declare no conflicts of interest.

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