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      Cohort Profile: The PharmAccess African (PASER-M) and the TREAT Asia (TASER-M) Monitoring Studies to Evaluate Resistance—HIV drug resistance in sub-Saharan Africa and the Asia-Pacific

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          Abstract

          How did the study come about? According to World Health Organization (WHO) estimates, 33.4 million people were infected with the human immunodeficiency virus (HIV) type 1 globally at the end of 2008. 1 Sub-Saharan Africa and Asia are the two regions that have the highest HIV prevalence, with 22.4 and 4.7 million people infected, respectively. 1 During the 5 years prior, access to combination antiretroviral therapy (ART) in low- and middle-income countries increased 10-fold to reach 4 million people, providing coverage to 28% of those in need. 2 Several studies have reported significant reductions in HIV-related morbidity and mortality for individuals with access to treatment in these regions. 3–5 In resource-limited settings, to facilitate the rapid expansion of access to ART, WHO recommends a standardized, public-health approach. 6 This is in contrast to the individualized patient-management strategies in developed countries, based on routinely available diagnostic monitoring. 7 Standardized first-line ART regimens consist of a non-nucleoside reverse transcriptase inhibitor (NNRTI) and a dual nucleoside/nucleotide reverse transcriptase inhibitor (NRTI) backbone, available in some countries as generic fixed-dose combinations. 6 Recommended second-line regimens combine a ritonavir-boosted protease inhibitor (PI) with two previously unused and/or recycled NRTIs. 6 Routine HIV viral-load monitoring is not generally available in resource-limited countries and treatment failure is frequently identified based on immunological definitions and/or the occurrence of clinical events. 6 Virological breakthrough may be detected late while the failing regimen is continued, thus facilitating the acquisition and accumulation of drug resistance-associated mutations. 8 Drug-resistant HIV variants may compromise the effectiveness of subsequent lines of treatment and their transmission to newly infected individuals has severe public health consequences. 9 , 10 To date, ART programmes have been implemented without accompanying HIV drug resistance (HIVDR) monitoring. Monitoring studies are hampered by the lack of a molecular laboratory infrastructure required for genotypic resistance testing, logistical challenges related to maintaining specimen integrity in remote settings and the high costs of testing. 11 Challenges to scaling up ART in resource-limited countries, such as absence of routine virological monitoring and limited choices of drug regimen, advocate for the development of a global public-health framework to monitor and prevent the emergence of HIVDR and thus maximize long-term ART effectiveness. 12 HIV-1 subtype B is the predominant viral subtype in North America, Western Europe and Australia, and antiretroviral (ARV) drugs have been developed on this subtype. However, in sub-Saharan Africa and Asia, the genetic diversity in HIV subtypes and circulating recombinant forms (CRFs), resulting from recombination between subtypes within a dually infected person, is extensive. 13 Although current evidence is limited, some reports have suggested that the propensity to develop HIVDR and the spectrum of mutations that emerge during drug selective pressure, may differ across subtypes and CRFs. 14–17 Viral heterogeneity may, therefore, have implications for rates of disease progression and patient response to ART, warranting further study of inter-subtype differences in mutational pathways to resistance. To help assess the extent of HIVDR in sub-Saharan Africa and Asia, a collaborative bi-regional programme was established, called LAASER [Linking African and Asian Societies for an Enhanced Response (LAASER) to HIV/AIDS; http://www.laaser-hivaids.org] with the primary aim of increasing regional capacities for the monitoring of HIVDR. PharmAccess Foundation, a non-profit organization dedicated to the strengthening of health systems and improving access to quality basic health care in sub-Saharan Africa, has developed the PharmAccess African Studies to Evaluate Resistance (PASER). TREAT Asia (Therapeutics, Research, Education and AIDS Training in Asia) is a network of clinics, hospitals and research institutions working to ensure safe and effective delivery of HIV/AIDS treatment throughout the Asia-Pacific and has developed the TREAT Asia Studies to Evaluate Resistance (TASER). Both PASER and TASER programmes incorporate a monitoring and evaluation (M) and a surveillance (S) protocol. Laboratories providing genotyping results for PASER and TASER are required to participate in the TREAT Asia Quality Assurance Scheme (TAQAS), which is an ongoing assessment programme to build genotyping laboratory capacity, described elsewhere. 18 The focus of this cohort profile is the monitoring and evaluation protocols, PASER-M and TASER-M. How are PASER and TASER set up and how are they funded? Through the LAASER programme, PASER and TASER receive financial support from the Dutch Ministry of Foreign Affairs through a partnership with Stichting Aids Fonds, PharmAccess Foundation, TREAT Asia (a programme of amfAR, The Foundation for AIDS research) and International Civil Society Support. PASER-M is coordinated by PharmAccess Foundation, in collaboration with the Amsterdam Institute for Global Health and Development (AIGHD) and the Virology Department at the University Medical Center Utrecht, The Netherlands. TASER-M is coordinated by TREAT Asia and its statistical and data management centre is the National Centre in HIV Epidemiology and Clinical Research (NCHECR), The University of New South Wales in Sydney, Australia. PASER constitutes a newly established collaboration between HIV treatment clinics, laboratories with the capacity to perform genotypic sequencing and research centres. Thirteen clinical sites and two reference laboratories in six African countries (Kenya, Nigeria, South Africa, Uganda, Zambia and Zimbabwe) are collaborating on PASER-M (Figure 1a). Details of the PASER-M collaborating clinical sites are summarized in Supplementary Table 1 available as Supplementary data at IJE online. Ethics approvals were obtained from the Academic Medical Center Institutional Review Board (IRB) and local IRBs. Sites are government, non-government, faith-based or private clinics and hospitals, situated in major cities or urban areas. ART was introduced at the sites at various time points between 1992 and 2006 (median: 2004). Of the 13 sites, 11 provided drugs, consultations and routine laboratory testing free of charge. HIV viral load testing is available at 8 of the 13 sites. TASER collaborating sites are selected from within the existing TREAT Asia network, based on their laboratory capacity to perform genotypic sequencing, as described elsewhere. 18 , 19 Sites that do not have internal laboratory genotyping capacity can participate through collaboration with a TAQAS-certified laboratory. Eleven clinical and laboratory sites in six Asian countries (China, Indonesia, Malaysia, Philippines, South Korea and Thailand) are collaborating on TASER-M (Figure 1b). Ethics approvals were obtained from local IRBs having Federal wide Assurances (FWAs) in place from the United States Office for Human Research Protections. FWAs are required for TASER sites as they participate in the International Epidemiologic Databases to Evaluate AIDS (IeDEA) initiative, described elsewhere. 20 Sites are generally government- or university-based clinics and hospitals or private clinics, situated in major cities and other urban areas. Those with ethics approvals prior to June 2010 are shown in Figure 1b. ART has been available in Asia for more than 10 years, even in less-resourced countries in the region, and all TASER-M clinical sites have on-site viral load testing. Figure 1 Geographical location of (a) PASER-M collaborating sites and (b) TASER-M collaborating sites Clinical sites follow their national guidelines to assess eligibility for ART initiation in accordance with the WHO recommendations. 6 Genotypic resistance testing on PASER and TASER clinical specimens are performed in TAQAS-certified genotyping laboratories. 18 Laboratories are encouraged to become accredited members of the WHO/HIVResNet HIV Drug Resistance Laboratory network. 21 Population-based nucleotide sequencing of the HIV protease (PR) and partial reverse transcriptase (RT) gene regions is performed on plasma specimens, which have HIV RNA of more than 1000 copies/ml. Plasma is obtained from blood collected in EDTA tubes which is locally stored at −80°C and, if required, batch-shipped on dry ice to a genotyping laboratory. PASER-M genotypic testing is concentrated in two central reference laboratories and thus depends on a robust cold-chain and web-based specimen tracking system for managing specimen shipments. Approximately half of TASER-M clinical sites have an on-site or local genotyping laboratory. Most genotyping laboratories amplify viral sequences using in-house methods, based on assembled commercially available assay components and laboratory-specific sequencing and amplification primers. One TASER laboratory uses the commercial kit TruGene (Bayer HealthCare, Tarrytown, NY, USA). The online Stanford interpretation system is used by most laboratories to identify drug resistance-associated mutations. 22 Resistance genotyping is generally performed retrospectively (i.e. not real-time) for all participants. Details of the genotyping laboratories are summarized in Supplementary Table 2 available as Supplementary data at IJE online. PASER and TASER sequences are submitted to the ViroScore database (Advanced Biological Laboratories SA, France) for data storage. What do PASER-M and TASER-M cover and who is included in the sample? The monitoring studies are multi-centre prospective cohort designs with sequential patient enrolment. Patient eligibility criteria are listed in Table 1. The main study objectives are to assess prevalence and incidence of HIVDR, mutational patterns and factors associated with HIVDR in persons initiating first-line ART or switching to a second-line regimen due to treatment failure under routine circumstances. Participants are required to sign informed consent prior to study enrolment and must initiate or switch ART within 30 days (PASER-M) or 181 days (TASER-M) following baseline specimen collection. Regimen switch due to treatment failure may be determined clinically, as assessed by disease progression, immunologically, by CD4 cell count or virologically, by HIV viral load. A single drug substitution, due to toxicity or intolerance, is not considered a regimen switch. Each site aims to recruit a total of 240 participants. Second-line participants are recruited among first-line participants failing therapy and the clinical site patient population. The recommended maximum site-specific enrolment period is 18 months. Table 1 Patient eligibility criteria for PASER-M and TASER-M Inclusion criteria     Confirmed HIV-1 infection     ≥18 years of age     Eligible a for initiation of a first-line ART regimen or switch from a first-line ART regimen (containing at least three antiretroviral drugs and taken for ≥6 months) to a second-line ART regimen due to virological, immunological and/or clinical failure     Signed informed consent for study participation prior to enrolment Exclusion criteria     Currently taking ART (minimum of three-drug regimen), if initiating a first-line ART regimen b     Pregnancy at enrolment c     HIV-1/2 dual infection (in endemic countries only)c aEligibility for ART initiation defined in accordance with national ART guidelines (i.e. advanced immunodeficiency as defined by CD4 cell count less than 200 or less than 350 cells/µl, or advanced clinical disease according to WHO clinical stage/CDC classification). bSpecified PASER-M definition: re-initiation of a first-line ART regimen <30 days after stopping previous first-line ART (previous use of antiretroviral prophylaxis or mono/dual therapy is not an exclusion criterion). cExclusion criteria applicable to PASER-M only. How often are participants followed-up? What data are being collected? Participants are followed-up as per local standard of care guidelines. The frequency of follow-up visits for patients varies by site (range: every 1–6 months). The studies make use of clinical data collected during routine visits and recorded in medical records. HIV viral load measurement and, if the HIV RNA value is more than 1000 copies/ml, genotypic resistance testing is performed on plasma specimens taken at baseline, prior to regimen switch due to treatment failure and at annual follow-up. For patients failing a first-line regimen, the treatment failure data collection becomes the new baseline for the second-line regimen. Annual follow-up is then calculated from this point. For patients failing a second-line ART, the treatment failure data collection is the final assessment prior to the patient going off study. PASER-M clinical data are recorded on standardized hard-copy data forms, which are completed at 3-monthly intervals and entered in a web-based clinical data system, called the HAART Monitoring System. PharmAccess performs quality assurance measures, which include (i) source data verification during 3- to 6-monthly site audits, (ii) checks to identify data entry inconsistencies or suspect data values and (iii) specimen tracking. TASER-M site personnel extract clinical data from site databases and medical records collected as part of usual care. From March 2008 to March 2009, TASER-M data were submitted electronically to NCHECR on a quarterly basis, as part of study start up, then at 6-monthly intervals. At each transfer, NCHECR performs quality assurance measures, which include (i) checks to identify data entry inconsistencies or suspect data values, (ii) specimen tracking and (iii) ARV history completeness. Annually, a random 10% of TASER-M patients are selected for internal site audit where submitted data are compared with patient medical records. The studies capture standardized virological and genotypic data at protocol determined intervals. Genotyping data consist of HIV subtype and HIVDR mutations, including insertions and deletions. TASER-M also records discordant subtypes, i.e. when the PR and RT region subtypes differ. Laboratory specimen tracking information is recorded during specimen processing, allowing assessment of pre-analytical and assay validity. Genotyping laboratories complete an annual laboratory survey that includes the dynamic range of the virological assay used, the regions of PR and RT genome routinely sequenced and the interpretation algorithm used. Observational patient data includes demographic parameters, physical measures, Centres for Disease Control and Prevention (CDC) class (TASER-M) or WHO clinical stage (PASER-M), serology of hepatitis and syphilis (TASER-M), opportunistic infections, current ART regimen, ARV history, concomitant medications, routine laboratory parameters (including CD4 counts) and assessment of drug adherence. Main analyses will include age, sex, ethnicity, HIV exposure category, WHO clinical stage (PASER-M) or CDC class (TASER-M), viral hepatitis co-infection status, CD4 count, HIV viral load, HIV subtype, drug adherence, ARV history and ART regimen as covariates. Predictors of drug resistance will be assessed using logistic regression models. Incidence of drug resistance will be summarized using person-years methods and Kaplan–Meier plots. Cox proportional hazards models will be used to assess risk factors associated with developing drug resistance. What is the anticipated attrition? The actual attrition in PASER-M and TASER-M cannot currently be accurately estimated because the duration of follow-up in the databases is still limited. In sub-Saharan Africa patient retention in routine ART programmes has been estimated at 61.623 to 66.8% 2 at 24 months on ART, attrition being mainly due to loss of follow-up and early death. 23 Therefore, in PASER-M, the original site-specific sample size was calculated accounting for 20% loss to follow-up and 25% mortality after 24 months. Attrition is expected to vary between sites as a result of differences in patient populations, care provided and provisions for tracing lost to follow-up. TASER-M sites are generally sourced from the ongoing TREAT Asia HIV Observational Database (TAHOD). 19 Loss to follow-up for TAHOD was 6.9/100 person-years for the 12-month period from September 2007 to September 2008. Since TASER-M monitors specified outcomes, we speculate that TASER-M follow-up will be similar to TAHOD or better. What has been found? PASER-M Patient recruitment commenced in March 2007 and was completed in September 2009. Of the 13 sites, 12 reached the site-specific target of 240 participants, enrolling a total of 3005 participants. Excluding patients with protocol violations (n = 16) and key data missing (n = 4), 2985 patients were included in the analysis. Of these, 2736 (91.6%) were eligible for a first-line ART regimen and 249 (8.3%) were eligible for second-line ART due to treatment failure. Patient characteristics are summarized in Table 2. For first-line patients, the median age was 36.8 years [inter-quartile range (IQR) 31.3–42.6] and 58% were women. HIV exposure was predominantly reported as heterosexual contact. More than 60% had advanced disease (classified as WHO stages III or IV) and 37% had pre-therapy CD4 counts of less than 100 cells/µl. Across all 13 sites, median baseline CD4 counts of first-line patients were less than 200 cells/µl (site median 135 cells/μl, range 93–191). Median baseline HIV viral load was 4.9 log10 copies/ml (IQR 4.2–5.5). The most frequently prescribed first-line regimens were based on NNRTIs (99.7%), i.e. efavirenz (EFV) and nevirapine (NVP) at 60 and 40%, respectively. First-line dual NRTI backbones were predominantly lamivudine (3TC)/zidovudine (AZT) (37%), emtricitabine (FTC)/tenofovir (TDF) (34%) and 3TC/stavudine (d4T) (26%). Overall, 67% of patients started a 3TC-containing first-line regimen. Among patients initiating first-line ART, 95% (n = 2 598) reported to be ARV-naive and 5% (n = 138) had previous ARV experience, which included ART (n = 60), mono/dual therapy (n = 6), single-dose NVP for prevention of mother-to-child transmission of HIV (PMTCT) (n = 35), combination therapy for PMTCT (n = 19), and unspecified (n = 22). Compared with ARV-naive first-line patients, ARV-experienced first-line patients had higher median CD4 counts (177 vs 133 cells/μl, P < 0.0001), were younger (median 34.7 vs 37.0 years, P < 0.0001) and were more likely to be female (76.1 vs 57.5%; P < 0.001). Other baseline characteristics did not differ between ARV-naive and ARV-experienced patients. Table 2 Baseline patient characteristics, by region and line of ART PASER-M (Africa) TASER-M (Asia) Initiation of first-line ART a Switch to second-line ART b Initiation of first-line ART a Switch to second-line ART b Total ARV-naive ARV-experienced c ARV-naive ARV-experienced c Patients, n (%) 3713 2598 (87.0) 138 (4.6) 249 (8.3) 693 (95.2) 10 (1.4) 25 (3.4)     Sex         Female, n (%) 1988 (53.5) 1494 (57.5) 105 (76.1) 124 (49.8) 239 (34.5) 10 (100.0) 16 (64.0)         Male, n (%) 1725 (46.5) 1104 (42.5) 33 (23.9) 125 (50.2) 454 (65.5) 0 (0.0) 9 (36.0)     Age (years), median (IQR) 36.9 (31.7–43.3) 37.0 (31.2–42.8) 34.7 (29–2–40.2) 38.6 (32.9–44.2) 36.5 (31.1–43.2) 33.1 (27.4–38.4) 36.5 (32.4–41.9)         18–29 707 (19.0) 490 (18.9) 39 (28.3) 28 (11.2) 143 (20.6) 3 (30.0) 4 (16.0)         30–39 1668 (44.9) 1176 (45.3) 65 (47.1) 112 (45.0) 298 (43.0) 5 (50.0) 12 (48.0)         ≥40 1338 (36.0) 932 (35.9) 34 (24.6) 109 (43.8) 252 (36.4) 2 (20.0) 9 (36.0)     HIV exposure, n (%)         Heterosexual contact 2583 (69.6) 1731 (66.6) 108 (78.3) 191 (76.7) 520 (75.0) 10 (100.0) 23 (92.0)         Homosexual contact 134 (3.6) 4 (0.2) 0 (0.0) 0 (0.0) 128 (18.5) 0 (0.0) 2 (8.0)         Other d 996 (26.8) 863 (33.2) 30 (21.7) 58 (23.3) 45 (6.5) 0 (0.0) 0 (0.0)     WHO clinical stages, n (%)         I 493 (16.5) 393 (15.1) 25 (18.1) 75 (30.1) na na na         II 711 (23.8) 623 (24.0) 33 (23.9) 55 (22.1) na na na         III 1281 (42.9) 1145 (44.1) 59 (42.8) 77 (30.9) na na na         IV 500 (16.7) 437 (16.8) 21 (15.2) 42 (16.9) na na na     CDC classification, n (%)     A 302 (41.5) na na na 296 (42.7) 0 (0.0) 6 (24.0)     B     166 (22.8) na na na 152 (21.9) 10 (100.0) 4 (16.0)     C 260 (35.7) na na na 245 (35.4) 0 (0.0) 15 (60.0)     Ever pulmonary tuberculosis, n (%) 741 (20.0) 569 (21.9) 25 (18.1) 74 (29.7) 69 (10.0) na 4 (16.0)     Hepatitis B e , n (%) 36 (4.9) na na na 35 (5.1) 0 (0.0) 1 (4.0)     Hepatitis C f , n (%) 56 (7.7) na na na 55 (7.9) 0 (0.0) 1 (4.0) History of ARV drug use, n (%) 422 (11.4) na 138 (100.0) 249 (100.0) na 10 (100.0) 25 (100.0)     ART 334 (9.0) na 60 (43.5) 249 (100.0) na 0 (0.0) 25 (100.0)     Mono or dual therapy 10 (0.3) na 6 (4.3) 4 (1.6) na 0 (0.0) 0 (0.0)     Single-dose NVP for PMTCT 36 (1.0) na 35 (25.4) 1 (0.4) na 0 (0.0) 0 (0.0)     Combination therapy for PMTCT 31 (0.8) na 19 (13.8) 2 (0.8) na 10 (100.0) 0 (0.0)     Unspecified 22 (0.6) na 22 (15.9) 0 (0.0) na 0 (0.0) 0 (0.0) CD4 cell count (cells/μl), median (IQR) 129 (56–205) 133 (62–204) 177 (92–262) 125 (46–-196) 99 (33.5–201) 169 (151–222) 197 (109–299)     <100 1456 (39.2) 975 (37.5) 38 (27.5) 102 (41.0) 337 (48.6) 1 (10.0) 3 (12.0)     100–199 1215 (32.7) 914 (35.2) 42 (30.4) 81 (32.5) 164 (23.7) 6 (60.0) 8 (32.0)     ≥200 1007 (27.1) 702 (27.0) 57 (41.3) 64 (25.7) 171 (24.7) 3 (30.0) 10 (40.0)     Unknown 25 (0.7) 7 (0.3) 1 (0.7) 2 (0.8) 21 (3.0) 0 (0.0) 4 (16.0) HIV-1 RNA (log10 copies/ml), median (IQR) 4.9 (4.3–5.5) 4.9 (4.3–5.6) 4.8 (4.2–5.5) 4.1 (3.2–5.0) 5.0 (5.4–6.8) 4.8 (4.5–5.0) 4.0 (3.6–4.5) ART regimen     NNRTI-based triple regimen 3330 (89.7) 2590 (99.7) 135 (97.8) 2 (0.8) 593 (85.6) 10 (100.0) 0 (0.0)         AZT-containing 1302 (35.1) 964 (37.2) 51 (37.8) 1 (0.4) 170 (28.7) 10 (100.0) 0 (0.0)         TDF-containing 1088 (29.3) 868 (33.5) 49 (36.3) 1 (0.4) 110 (18.5) 0 (0.0) 0 (0.0)         d4T-containing 833 (22.4) 690 (26.6) 33 (24.4) 0 (0.0) 276 (46.5) 0 (0.0) 0 (0.0)         ABC-containing 106 (2.9) 68 (2.6) 2 (1.5) 0 (0.0) 37 (6.2) 0 (0.0) 0 (0.0)         3TC-containing 2387 (71.7) 1746 (67.4) 89 (65.9) 0 (0.0) 542 (91.4) 10 (100.0) 0 (0.0)         FTC-containing 942 (28.3) 843 (32.5) 48 (35.6) 0 (0.0) 49 (8.3) 0 (0.0) 0 (0.0)     PI-based triple regimen 351 (9.5) 6 (0.2) 0 (0.0) 247 (99.2) 73 (10.5) 0 (0.0) 25 (100.0)     Triple NRTI regimen 29 (0.8) 2 (0.1) 3 (2.2) 0 (0.0) 24 (3.5) 0 (0.0) 0 (0.0)     NNRTI+PI-based triple regimen 3 (0.1) 0 (0.0) 0 (0.0 0 (0.0) 3 (0.4) 0 (0.0) 0 (0.0) Data are n (%) of patients, unless otherwise indicated. na, not available; ART, combination antiretroviral therapy; ARV, antiretroviral; CDC, US Center for Disease Control and Prevention; WHO, World Health Organization; PMTCT, prevention of mother-to-child transmission of HIV-1; TB, tuberculosis; IQR, interquartile range; NVP, nevirapine; d4T, stavudine; AZT, zidovudine; TDF, tenofovir; ABC, abacavir; 3TC, lamivudine; FTC, emtricitabine; NRTI, nucleoside reverse transcriptase inhibitor; NNRTI, non-nucleoside reverse transcriptase inhibitor; PI, protease inhibitor. aEligibility for ART initiation in accordance with national ART guidelines (i.e. advanced immunodeficiency as defined by CD4 cell count less than 200 or less than 350 cells/µl, or advanced clinical disease according to WHO clinical stages or CDC classification). bRegimen switch due to treatment failure, defined by local standard of care guidelines as determined clinically, immunologically or virologically. cARV-experienced is defined as any previous use of ARVs, i.e. (first-line) ART, mono/dual therapy and/or PMTCT. dIncludes recipients of blood products, injecting drug users, perinatal transmission and unknown exposures. eHepatitis B positive status was defined as being HBsAg positive. fHepatitis C positive status was defined as being HCV antibody positive. For the 249 (8.3%) patients switching to second-line ART, the median age was 38.6 years (IQR 32.9–44.2) and sex was equally distributed. HIV exposure was predominantly heterosexual contact and 48% of patients had advanced disease (classified as WHO stages III or IV). Median CD4 count was 125 cells/μl (IQR 46–196). Median pre-switch HIV viral load was 4.1 log10 copies/ml (IQR 3.2–5.0). Ritonavir-boosted lopinavir (LPV) was the PI used almost exclusively (98%). As shown in Table 3, analysis of the first available 1795 viral sequences demonstrated that the most common HIV subtypes in the cohort were C (1216, 68.7%), A (338, 18.7%) and D (179, 10.0%). The first PASER report published in 2008 reviewed the available data on HIVDR in sub-Saharan Africa. 11 Baseline HIVDR data from Lusaka, Zambia, has recently been published. 24 International presentations have summarized preliminary baseline HIVDR mutations and subtype distributions. 25 , 26 Table 3 HIV subtypes and circulating recombinant forms a by region and country PASER-M (Africa) TASER-M (Asia) b Total South Africa Zambia Uganda Kenya Nigeria Zimbabwe Thailand Hong Kong Malaysia n (%) 2523 (100.0) 624 (24.7) 583 (23.1) 410 (16.3) 140 (5.5) 21 (0.8) 17 (0.7) 542 (21.5) 111 (4.4) 75 (3.0) A 340 (13.6) 2 (0.3) 4 (0.7) 235 (57.3) 97 (69.1) 1 (4.8) 1 (0.2) B 114 (4.5) 3 (0.5) 37 (7.0) 55 (49.5) 19 (25.3) C 1236 (49.2) 617 (98.9) 571 (97.9) 9 (2.2) 19 (13.6) 17 (100.0) 2 (1.8) 1 (1.3) D 179 (7.1) 1 (0.2) 1 (0.2) 160 (39.0) 17 (12.1) G 11 (0.4) 2 (0.3) 1 (0.2) 2 (1.4) 6 (28.6) CRF01_AE 585 (23.2) 485 (89.5) 52 (46.8) 48 (64.0) CRF02_AG 20 (0.8) 1 (0.2) 3 (0.5) 1 (0.2) 14 (66.7) 1 (0.2) Other recombinants or discordant subtypes 32 (1.3) 2 (0.3) 1 (0.2) 2 (1.4) 18 (3.3) 2 (1.8) 7 (9.3) Unclassified 6 (0.2) 3 (0.7) 3 (2.1) Data are n (%) of subtypes/CRFs, unless otherwise indicated. CRF, circulating recombinant form. aHIV subtypes were determined from the pol sequences, using the REGA HIV-1 subtyping algorithm version 2.0 (http://dbpartners.stanford.edu/RegaSubtyping) and/or Stanford HIV drug resistance database (http://hivdb.stanford.edu). bThe collaborating sites in Philippines, South Korea and Indonesia had not yet provided sequence data at time of the current analysis. TASER-M Patient recruitment commenced in March 2007 and for the March 2009 transfer, seven sites from Thailand, Hong Kong and Malaysia provided data. Of 773 patients, 755 (97.7%) commenced ART within 181 days of baseline specimen collection and 728 (96.4%) participants had genotypic data available. Of these, 693 (95.2%) ARV-naive patients and 10 (1.4%) ARV-experienced patients, following prior PMTCT, were eligible for first-line regimens. A further 25 (3.5%) patients were eligible for second-line ART following first-line treatment failure. Patient characteristics are summarized in Table 2. For ARV-naive first-line patients, the median age was 36.5 years. Almost two-thirds of patients were male and HIV exposure was predominantly heterosexual contact. More than one-third of patients were classified as CDC class C and almost half of the patients had pre-therapy CD4 counts less than 100 cells/µl. Median baseline HIV viral load was 5.0 log10 copies/ml (IQR 5.4–6.8) and the most common first-line regimens were based on NNRTIs (85.6%). Excluding 14 (2.4%) patients on a randomized clinical trial with a blinded NNRTI component, NVP was more commonly prescribed than EFV at 56 and 42%, respectively. First-line dual NRTI backbones were predominantly 3TC/d4T (47%), 3TC/AZT (29%) and 3TC/TDF (10%). For first-line PI regimens, the favoured NRTI backbone was FTC/TDF (33%) compared with 3TC/d4T (30%) or 3TC/AZT (18%). For the ritonavir-boosted PI component, atazanavir (ATZ) was only slightly favoured over LPV at 43 vs 41%, respectively. Overall, 542 (91.4%) of ARV-naive patients started a 3TC-containing first-line regimen. The 10 PMTCT patients received perinatal prophylaxis of AZT/3TC/NVP (n = 7), AZT (n = 2) or AZT/NVP (n = 1) for between 14 and 102 days and all were prescribed AZT/3TC/NVP as first-line regimens. For the 25 (3.4%) second-line patients, 22 (88%) were of Thai ethnicity and the median age was 36.5 years (IQR 32.4–41.9). Females were in the majority (64%), HIV exposure was predominantly heterosexual (92%) and 60% of patients had experienced at least one CDC class C event. Of 21 patients with CD4 counts available within 6 months of starting a second-line therapy, the median CD4 count was 197 (IQR 109–299). Median pre-switch HIV viral load was 4.0 log10 copies per/ml (IQR 3.6–4.5). All patients were on PI-based regimens, following failure on first-line NNRTI-based regimens (median duration 30.3 months). The most commonly prescribed PI component was ritonavir-boosted LPV (88%). As shown in Table 3, from analysis of the 728 available viral sequences, the most common subtypes were CRF01_AE (584, 80.2%) and subtype B (111, 15.2%). Non-CRF01_AE recombinants were identified in eight (1.1%) patient specimens. For 21 (2.9%) specimens, the subtype differed between PR and RT regions, suggesting dual infection or recombination. International presentations have summarized 2009 baseline HIVDR mutations and subtype distributions. 27 , 28 Complete baseline and prospective outcome data for PASER-M and TASER-M are anticipated to become available in 2010–13. What are the main strengths and weaknesses? Programmes that monitor national and regional levels of primary and secondary HIVDR contribute to evidence-based recommendations to inform treatment guidelines and provide feedback on the success of HIV treatment and prevention programmes. PASER and TASER, with TAQAS, are developing capacity in sub-Saharan Africa and the Asia-Pacific for coordinated HIVDR monitoring and genotypic laboratory testing. The study protocols are harmonized with the WHO HIV Drug Resistance Prevention Survey protocol. 29 An important strength is the large number of patients and sites participating, representing a diverse spectrum of patient populations, clinic types, ART regimens and HIV subtypes. Opportunities exist to investigate the impact of drug resistance on HIV natural history, rates of disease progression and response to treatment in non-B subtypes. Data from genotypic resistance testing will also provide insight into the population genetics and dynamics of transmitted HIVDR in the region. PASER-M and TASER-M have several limitations. First, patient samples at each site are not necessarily representative of the site, country or region. Second, data quality depends on the completeness of clinical information captured through routine patient care. In PASER-M, data may have been collected under varying conditions, since some sites had no or limited research experience at study initiation. Third, at some sites, study initiation was delayed by several months due to the time required for contract negotiation, IRB study approval and, in TASER-M, procurement of FWAs. After study initiation, recruiting the required number of patients within the recommended 18-month period proved difficult for some sites, due to asymptomatic patients not seeking care or treatment, cost of medication or low-prevalence in their setting. Fourth, HIVDR monitoring activities in resource-limited countries in sub-Saharan Africa are limited by high costs of laboratory testing. To address this challenge, PASER has initiated a public–private consortium, called Affordable Resistance Test for Africa (ART-A), which aims to develop affordable test algorithms for the detection and interpretation of HIVDR for use in laboratories and clinics (http://www.arta-africa.org). How can I collaborate? Where can I find out more? Ownership of individual site data remains with the contributing site. Sites are represented by their principal investigators on the respective PASER and TASER Steering Committees. Research is to be the subject of peer-reviewed publications and analysis priorities are driven by a concept sheet process. Both studies accept concept proposals from external researchers for review, if submitted in collaboration with one or more of the site principal investigators. The PASER and TASER protocols contribute data under the LAASER partnership (http://www.laaserhivaids.org) and TASER also contributes data to IeDEA. 20 Collaborating sites are also encouraged to make an appropriate subset of their data available to Ministry of Health in their respective countries in order to contribute to local efforts in monitoring HIVDR. Questions regarding participation, research concepts or requests for data should be sent to Tobias Rinke de Wit, email: t.rinkedewit@pharmaccess.org (PASER), or Thida Singtoroj, email: thida.singtoroj@treatasia.org (TASER). Supplementary Data Supplementary Data are available at IJE online. Funding The PharmAccess African Studies to Evaluate Resistance is an initiative of PharmAccess Foundation, supported by the Ministry of Foreign Affairs of The Netherlands through a partnership with Stichting Aids Fonds (grant no 12454). The TREAT Asia Studies to Evaluate Resistance is an initiative of TREAT Asia, a programme of amfAR, The Foundation for AIDS Research, with major support provided by the Ministry of Foreign Affairs of The Netherlands through a partnership with Stichting Aids Fonds (grant no 12454), and with additional support from amfAR and the National Institute of Allergy and Infectious Diseases (NIAID) of the U.S. National Institutes of Health (NIH) and the National Cancer Institute (NCI) as part of the International Epidemiologic Databases to Evaluate AIDS (IeDEA) (grant no U01AI069907). Queen Elizabeth Hospital and the Integrated Treatment Centre are supported by the Hong Kong Council for AIDS Trust Fund. The National Centre in HIV Epidemiology and Clinical Research is funded by the Australian Government Department of Health and Ageing and is affiliated with the Faculty of Medicine, The University of New South Wales. The funders had no role in the study design, data collection, data analysis, data interpretation, decision to publish or writing of the report. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of any of the institutions mentioned above. Supplementary Material Supplementary Data

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          Patient Retention in Antiretroviral Therapy Programs in Sub-Saharan Africa: A Systematic Review

          Introduction In the half decade since the first large-scale antiretroviral treatment (ART) programs for HIV/AIDS were launched in sub-Saharan Africa, much attention has focused on patients' day-to-day adherence to antiretroviral (ARV) medications [1–3]. Long-term retention of patients in treatment programs, a prerequisite for achieving any adherence at all, has received far less attention. Perhaps because most large scale treatment providers have few resources available to track missing patients, most studies treat patient attrition as a side issue and focus solely on describing those patients who are retained. Moreover, adherence can be assessed over very short periods, whereas long-term retention requires, by definition, long-standing programs. Attrition from antiretroviral treatment programs is generally divided into four categories. The two most common are (1) the death of the patient—several studies have reported high rates of early mortality—and (2) “loss to follow-up,” a catch-all category for patients who miss scheduled clinic visits or medication pickups for a specified period of time. Some patients remain in care but stop taking ARV medications (3). Others transfer to other facilities and continue on ART (4). Treatment discontinuation raises some of the same concerns about drug resistance that incomplete adherence does and, even worse, negates much of the benefit sought by those implementing treatment programs. Patients with clinical AIDS who discontinue ART will likely die within a relatively short time [4]. High rates of attrition from treatment programs thus pose a serious challenge to program implementers and constitute an inefficient use of scarce treatment resources. In this study, we analyzed reported treatment program retention and attrition in sub-Saharan Africa in order to document the magnitude of the problem and help policy makers and program managers address the challenge of patient retention. Methods Definitions For this review, “retention” refers to patients known to be alive and receiving highly active ART at the end of a follow-up period. “Attrition” is defined as discontinuation of ART for any reason, including death, loss to follow-up, and stopping ARV medications while remaining in care. Transfer to another ART facility, where reported, is not regarded as attrition—patients who transfer are assumed to be retained. We accepted the varying definitions of loss to follow-up used by the respective studies. Many studies considered patients lost if they were more than 3 mo late for a scheduled consultation or medication pickup, but some studies used more or less stringent definitions ranging from 1 to 6 mo late for a scheduled consultation or medication pick-up. Inclusion and Exclusion Criteria Studies were included in the review if they reported the proportion of adult HIV-1 patients retained in highly active ART programs implemented in service delivery (nonresearch) settings in sub-Saharan Africa. All patients who initiated ART had to be included in the report, not just those still in care at the time of censoring (i.e., only intention-to-treat analyses were included). Clinical trials, including Phase 3 trials, were excluded, although some subjects of reviewed studies transferred into the treatment program from a clinical trial. A median follow-up period of at least six full months (26 weeks) was also required. Studies that reported mortality but not other categories of attrition and studies that reported only on-treatment analyses, or where we were unable to determine whether the study was intention-to-treat or not, were also excluded. A few of the reviewed studies did not differentiate between adult and pediatric patients; those that considered only pediatric patients were excluded. Search Strategy To identify eligible studies, we conducted a systematic search of the English-language published literature, gray literature (project reports available online), and conference abstracts between 2000 and 2007. The search included Ovid Medline (1996 to July 2007), EMBASE (inception to July 2007), ISI Web of Science (August 2002 to July 2007), the Cumulative Index to Nursing & Allied Health Literature (2002 to July 2007), and the Cochrane Database of Systematic Reviews (inception to second quarter 2007). We also searched the abstracts of the conferences of the International AIDS Society (inception to 2006), the Conference on Retroviruses and Opportunistic Infections (inception to 2007), the HIV Implementers' Meetings (2006–2007), and the South African AIDS Conference (2005–2007). The bibliographies of five recently published reviews of treatment outcomes, mortality, or ARV adherence in resource-constrained settings were also searched [1–3,5,6]. Our search strategy combined the terms “antiretroviral” and “Africa” or “developing countries” with each of retention/attrition/loss to follow-up/mortality/evaluation/efficacy. When more than one source reported on the same cohort of patients, the source containing the most detailed data about retention and attrition or the longest follow-up period was selected for the review. Although non-English databases were not searched, English-language abstracts of non-English papers identified in our search were included. Eligible studies were identified by the first author (SR) and eligibility confirmed by the other authors (MF and CG). It should be noted that the Antiretroviral Therapy in Low Income Countries (ART-LINC) collaboration has recently reported aggregate 1 y mortality and loss to follow-up rates for 13 cohorts in sub-Saharan Africa [5]. Some of the patients in these cohorts are included in the studies reviewed here. To avoid duplication, findings from the ART-LINC cohorts were not included in this analysis but are noted in the discussion. Data Analysis Most studies reported patient attrition at months 6, 12, and/or 24 after treatment initiation. We therefore used these same intervals in this analysis. For papers that reported on intervals other than 6, 12, or 24 mo, we classified the reported attrition rate using the nearest time point. If the report did not list attrition rates by time, but did list a median duration of observation, we estimated attrition at the 6, 12, or 24 mo interval closest to the reported observation period. In some cases, follow-up periods and/or retention rates were calculated by the authors using data provided in the article or extracted from figures (e.g. Kaplan-Meier survival curves). Where appropriate, we calculated weighted averages for demographic features of the cohort participants or other factors related to the studies. For proportions, averages were weighted by the inverse of their variances [1 ÷ (p × [1 − p] ÷ n), where p is the proportion and n is the sample size]. Because we did not have the individual patient data for continuous variables nor their standard deviations, we were unable to calculate variances for these variables. In these situations, we weighted by cohort size. In some instances, studies reported follow-up to 12 or 24 mo but did not report on intermediate retention rates. In plotting attrition for such studies over time we used extrapolated values, taking the midpoint between the known adjacent values. For example, if a study reported to 12 mo but did not report the 6 mo value, we defined the 6 mo value as the midpoint between 0 and 12 mo, with 100% at baseline representing all of those who initially started therapy. We calculated weighted average attrition rates at each interval (6, 12, and 24 mo) for the reported numbers of participants remaining when using reported values and for the estimated numbers of participants remaining when using extrapolated values. Selected demographic variables relating cohort or program characteristics to attrition rates were analyzed using linear regression. Because only a few studies reported beyond 24 mo, we were unable to calculate any meaningful summary statistics beyond the 24 mo mark. To estimate aggregate average attrition rates at 6, 12, and 24 mo we used several approaches. Attrition for each program was plotted separately and attrition rates calculated as the percentage of patients lost per month. We also plotted Kaplan-Meier survival curves using the 6, 12, and 24 mo intervals as the step-down points. Fewer studies presented 12 mo data than 6 mo data, however, and fewer still contained 24 mo data. Many of the studies with the highest attrition contributed data only for the shorter time intervals. Given the concern that shorter durations of reporting could be associated with lower rates of patient retention, we also conducted sensitivity analyses to model possible future retention. For the best-case scenario, we optimistically assumed that no further attrition would occur beyond the last reported observation and extrapolated the last reported retention value forward to 24 mo. In the worst-case scenario, we extrapolated the slope of attrition forward in time, assuming that each cohort's attrition would continue along the same slope from the last reported observation to month 24. We assigned a lower limit of 0% in those situations where the estimated future retention rate fell below 0%. Our midpoint scenario was the mean of the best- and worst-case scenarios. Analyses were conducted using Excel, SAS version 8.2, and SPSS version 11.0. Results We included 32 publications reporting on 33 patient cohorts totaling 74,289 patients in 13 countries in our analysis. These studies were selected from a total of 871 potentially relevant, unique citations identified in our search (Figure 1). Figure 1 Study Flow Chart Table 1 summarizes key features of the studies, including the sites at which they were conducted. Not all of the publications reported all the details we sought about program and patient characteristics and retention, but all provided at least one indicator of patient retention after a median follow-up period of at least 6 mo. The studies report on patients who initiated ART as early as 1996, though most enrolled their cohorts between 2001 and 2004. The studies were published or presented between 2002 and 2007, with the majority appearing as peer reviewed articles in 2006 or 2007. Most of the programs were implemented by the public sector (17 of 33, 52%). Of 33 cohorts, 15 (45%) fully subsidized the cost of ART; six (18%) were partially subsidized; and six (18%) required patients to pay fully for their care; the rest did not report their payment structure. Roughly half were single-site programs (15 of 33, 45%); multi-site programs contributed data from between two and 69 sites. Table 1 Characteristics of Antiretroviral Treatment Programs and Patient Cohorts Included in This Analysis (extended on next page) Table 1 Extended. Table 1 also provides the population characteristics of the cohorts studied. The weighted mean age of the cohort participants was 35.5 y, and 53.7% of all patients were female (range 6%–70%). All but one cohort had median starting CD4+ T cell counts at or well below 200 × 103 cells/mm3, with a weighted mean starting CD4+ T cell count of 132 × 103 cells/mm3 (range 43–204). Table 2 presents the proportion of patients from each cohort who remained alive and under treatment with antiretroviral medications, transferred to another treatment facility, died, were lost to follow-up, or discontinued treatment with ARVs but remained in care at the end of the median follow-up period. Bearing in mind that we excluded studies with less than 6 mo median follow-up, the weighted average follow-up was 9.9 mo, after which time overall retention of patients alive, in care, and on ART was 77.5%. Table 2 Median Follow-Up and Rates of Patient Attrition, as Reported, from Antiretroviral Treatment Programs Across all the cohorts, the largest contributor to attrition was loss to follow-up (56% of attrition), followed by death (40% of attrition). The widely varying definitions of loss to follow-up used by the studies are indicated in Table 2. A small fraction (4% of attrition) discontinued ART but remained under care at the same site. Table 3 reports overall retention at 6, 12, and 24 mo. SA 1 had the highest retention at 12 mo. While this program did not report for 6 mo, at 12 mo its retention of 90% was still higher than the highest reported value among the programs that reported their 6 mo outcomes. The programs with the lowest retention at each time point were Malawi 4 (55%) at 6 mo; Uganda 2 (49%) at 12 mo; and Uganda 1 (46%) at 24 mo. Malawi 4 did not report beyond 6 mo and Uganda 2 did not report beyond 12 mo, but were both on a trajectory toward even lower retention rates at the later time points. Table 3 Retention of Patients at 6, 12, and 24 Months after Initiation of ART Using linear regression, we found no association between 6 mo attrition rates and cohort size (p = 0.32), attrition and baseline CD4+ cell counts (p = 0.72), proportion of women (p = 0.23), or year of program initiation (p = 0.40). Programs that required no payment had higher retention rates at 6 mo compared to those requiring partial or full payment (86.5% versus 76.7%, p = 0.01). Figures 2A–2C plot attrition rates for each cohort separately. The studies are clustered on the basis of duration of reporting. By 6 mo, 9 of 33 cohorts (27%) had 20% or greater attrition rates; by 12 mo this proportion had risen to 16 of 25 reporting cohorts (64%). Figure 2 Attrition Rates by Reporting Duration (A) Studies reporting to 6 mo median follow-up. (B) Studies reporting to 12 mo median follow-up. (C) Studies reporting to 24 mo median follow-up. (D) Weighted mean attrition rates by duration cohort. SA, South Africa. The weighted mean retention rates as reported in the studies were 79.8% at 6 mo, 75.1% at 12 mo, and 61.6% at 24 mo. As an alternative approach, we also plotted Kaplan-Meier survival curves at months 6, 12, and 24 for all the studies combined. The largest fall-off occurred between 6 and 12 mo; overall retention was approximately 89% by 6 mo, 70% by 1 year, and just under 60% by 2 years. Four of the eight studies in Figure 2A with attrition of at least 20% at 6 mo included data only to 6 mo. Similarly, of the cohorts with data at 12 mo and attrition of 25% or more, six of 11 did not extend beyond 12 mo. We therefore calculated the slopes for attrition rates for each group of cohorts in Figure 2A–2C separately, to determine if the average monthly attrition rates differed as the duration of reporting increased. As shown in Figure 2D, the weighted mean attrition rates were 3.3%/month, 1.9%/month, and 1.6%/month for studies reporting to 6 mo, 12 mo, and 24 mo, respectively, raising the possibility that shorter durations of reporting were associated with lower retention rates. Given this apparent reporting bias, we were concerned that reporting average retention rates using the simple aggregate weighted averages reported above would overestimate actual retention. We therefore conducted sensitivity analyses to model attrition rates under three different scenarios. As shown in Figure 3, all three scenarios are the same at 6 mo, with approximately 80% retention. Under the best-case scenario, further attrition would be negligible, with more than 76% still retained by the end of 2 y. Under the worst-case scenario, 76% of patients would be lost by 2 y. The midpoint scenario predicted patient retention of 50% by 2 y. Figure 3 Sensitivity Analysis for Attrition Discussion The analysis presented here suggests that ART programs in Africa are retaining, on average, roughly 80% of their patients after 6 mo on ART and between one-fourth and three-fourths of their patients by the end of 2 y, depending on the estimating method used. Prior to the availability of ART in Africa, the median interval from HIV infection to AIDS-related death was under 10 y; once a patient was diagnosed with AIDS, median survival was less than 1 y [7]. Since most patients in Africa initiate ART only after an AIDS diagnosis, most ART patients would have died within a year had antiretroviral therapy not been available. Each patient who is retained in care and on ART can thus be regarded as a life saved and a source of tremendous benefit to patients' families and communities. For those who have struggled to launch and expand treatment programs in resource-constrained settings, reaching a 60% patient retention—and thus survival—rate after two years of treatment, as estimated by the Kaplan-Meier survival analysis, in just a few years' time is an extraordinary accomplishment. It is also noteworthy in the global context: in developed countries, adherence to medication for chronic diseases in general averages only 50% [8]. Similarly, treatment completion rates for tuberculosis, which requires a temporary rather than permanent commitment to adherence and a less demanding dosing schedule, average 74% in the African region, with a range among countries from 22% to 94% [9]. Taken in the context of medication adherence in general, the record of African ART programs lies within the bounds of previous experience. At the same time, however, losing up to half of those who initiate ART within two years is cause for concern. From the data as reported, attrition averaged roughly 22% at 10 mo of follow-up. This average comprised mainly deaths (40% of attrition) and losses to follow-up (56%). In comparison, the ART-LINC Collaboration, which analyzed data from 18 cohorts across the developing world, reported loss to follow-up rates among the 13 sub-Saharan African cohorts averaging 15% (range 0%–44%) in the first year after initiation; mortality averaged 4.2% across all 18 cohorts (African regional rate not provided) [5]. On the basis of our survival and sensitivity analyses, we believe that actual attrition is higher than the 22% average we report, mainly because the programs with the highest attrition were least likely to provide data beyond the first 6 mo of ART. There are several plausible explanations for the higher attrition seen among programs with shorter durations of reporting. One possibility is that limited availability of resources to a given program could affect both its ability to retain patients and to conduct long-term surveillance of its outcomes. Another, less pessimistic explanation is that shorter durations of reporting reflect newer programs that are still in the process of developing optimal strategies for patient retention: had they reported at a later point in their implementation, retention rates might have been higher. The magnitude of the under-reporting bias is also uncertain, although our sensitivity analysis gives a plausible range between two implausible extremes (the best case being implausible because it assumes zero further attrition beyond the point of last reporting, and the worst case because it assumes that there will be constant attrition over time, rather than reaching a plateau or at least slowing substantially). The midpoint scenario suggests that approximately half of all patients started on ART were no longer on treatment at the end of two years. One of the principal challenges to this analysis is interpreting the large proportion of attrition from “loss to follow-up.” Some of these patients undoubtedly represent unrecorded deaths, but others may be patients who identified alternative sources for ART or had taken an extended “break” from therapy, to which they will return when their condition worsens again or they obtain the financial resources needed for transport or clinic fees. One study in Malawi discovered, for example, that 24% of patients originally recorded as lost to follow-up re-enrolled at the same site two years later when ART became free of charge [10]. For some of the studies included in this analysis, on the other hand, the unrecorded death explanation is more persuasive. For example, the Zambia 1 cohort of more than 16,000 patients reported 21% loss to follow-up after approximately 6 mo [11]. The scope and scale of this program means that it is the primary source of ART in Lusaka, making it unlikely that most of the estimated 3,300 lost patients could have found alternative sources of care. A recent attempt to trace lost-to-follow-up patients in Malawi determined that 50% had died, 27% could not be found, and most of the rest had stopped ART [12]. Because those reporting on these cohorts do not know what ultimately happened to patients categorized as lost to follow-up, high loss to follow-up rates can have varied interpretations. A good deal of research on barriers to adherence and reasons for treatment discontinuation has been published [13]. Important barriers to adherence include cost of drugs and/or transport, fear of disclosure or stigma, and side effects [14,15]. Some of these barriers can be addressed relatively easily, for example by providing transport vouchers to ensure that patients can attend the clinic; others, such as stigma, require more profound changes. In any case, high reported rates of loss to follow-up are a strong call to improve patient tracing procedures, to minimize the number of patients who fall into the difficult-to-address category of “lost, reason unknown.” Given that the long-term prognosis of ART patients is inversely related to starting CD4+ T cell counts [16,17], an additional issue to consider is the low median starting CD4+ cell counts reported by every one of the studies in this analysis. This problem has been identified previously, particularly in South Africa [18–20]. The analysis here makes clear that the problem is nearly universal in Africa and cuts across all types of treatment programs. It is evident in the high death rates reported by some studies after only a few months of follow-up, such as Malawi 3. There is a high degree of heterogeneity in retention rates between the different cohorts in our analysis and among categories of attrition. Some programs appear to have been highly successful in retaining patients, while others clearly struggled to do so. Some programs have suffered high mortality rates but low loss to follow-up, others the opposite. Early mortality, which may be largely due to the late stage at which many patients present for treatment, requires interventions different from those needed to address later loss to follow-up, about which very little is known. Interventions to address the various types of attrition must thus be tailored to local circumstances. The success of some programs with very high retention may provide examples that others can follow. The findings here can thus be seen as a part of an ongoing process to identify and solve problems within existing treatment programs, even as we expand their scope and launch new ones. Our analysis has a number of limitations, chiefly that incomplete reporting forced us to extrapolate some values. Extrapolating backward assumes that attrition rates are distributed linearly over time, which is unlikely to be the case. Evidence from this and other studies suggest that the highest attrition occurs during the first 6 mo. However, this limitation only pertains to the shape of the attrition curves, not to their final end points. Extrapolating forward, which we used only in the sensitivity analysis to establish the hypothetical “worst case” scenario, also suffers from this limitation, compounded by the fact that our confidence in the forward boundary is limited. In addition, our analysis is necessarily limited to publicly available reports and thus potentially subject to publication bias. Researchers may be less inclined to publish long-term outcomes from cohorts that have experienced very high early attrition. It is also likely that programs with better access to resources, both financial and human, are also better able to monitor, analyze, and publish their results. Our aggregate findings may thus represent the better-resourced programs in Africa. In conclusion, African ART programs are retaining about 60% of their patients in the first two years. This average masks a great deal of heterogeneity, however. At one end of the spectrum represented by the reviewed studies, two-year retention neared 90%; at the other end, attrition reached 50%. Better information on those who are lost to follow-up is urgently needed. Since losses to follow-up account for the majority of all attrition in more than half of the studies reviewed, the problem of attrition cannot be addressed effectively without better means to track patients. Only then can we address the pressing question of why patients drop out and what conditions, assistance, or incentives will be needed to retain them. Supporting Information Protocol S1 Search Protocol (85 KB DOC) Click here for additional data file.
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            Global and regional distribution of HIV-1 genetic subtypes and recombinants in 2004.

            To estimate the global and regional distribution of HIV-1 subtypes and recombinants in 2004. A study was conducted in which molecular epidemiological data on HIV-1 subtype distribution in individual countries were combined with country-specific estimates of the number of people living with HIV. HIV-1 subtype data were collected for 23 874 HIV-1 samples from 70 countries, which together accounted for 89% of all people living with HIV worldwide in 2004. The proportions of HIV-1 infections due to various subtypes detected in each country were combined with the number of HIV infected people in the respective countries to generate regional and global HIV-1 subtype distribution estimates. Subtype C accounted for 50% of all infections worldwide in 2004. Subtypes A, B, D and G accounted for 12%, 10%, 3% and 6%, respectively. The subtypes F, H, J and K together accounted for 0.94% of infections. The circulating recombinant forms CRF01_AE and CRF02_AG each were responsible for 5% of cases, and CRF03_AB for 0.1%. Other recombinants accounted for the remaining 8% of infections. All recombinant forms taken together were responsible for 18% of infections worldwide. Combining data on HIV-1 subtype distribution in individual countries with country-specific estimates of the number of people living with HIV provided a good method to generate estimates of the global and regional HIV-1 genetic diversity in 2004. The results could serve as an important resource for HIV scientists, public health officials and HIV vaccine developers.
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              Population-level effect of HIV on adult mortality and early evidence of reversal after introduction of antiretroviral therapy in Malawi

              Summary Background Malawi, which has about 80 000 deaths from AIDS every year, made free antiretroviral therapy available to more than 80 000 patients between 2004 and 2006. We aimed to investigate mortality in a population before and after the introduction of free antiretroviral therapy, and therefore to assess the effects of such programmes on survival at the population level. Methods We used a demographic surveillance system to measure mortality in a population of 32 000 in northern Malawi, from August, 2002, when free antiretroviral therapy was not available in the study district, until February, 2006, 8 months after a clinic opened. Causes of death were established through verbal autopsies (retrospective interviews). Patients who registered for antiretroviral therapy at the clinic were identified and linked to the population under surveillance. Trends in mortality were analysed by age, sex, cause of death, and zone of residence. Findings Before antiretroviral therapy became available in June, 2005, mortality in adults (aged 15–59 years) was 9·8 deaths for 1000 person-years of observation (95% CI 8·9–10·9). The probability of dying between the ages of 15 and 60 years was 43% (39–49) for men and 43% (38–47) for women; 229 of 352 deaths (65·1%) were attributed to AIDS. 8 months after the clinic that provided antiretroviral therapy opened, 107 adults from the study population had accessed treatment, out of an estimated 334 in need of treatment. Overall mortality in adults had decreased by 10% from 10·2 to 8·7 deaths for 1000 person-years of observation (adjusted rate ratio 0·90, 95% CI 0·70–1·14). Mortality was reduced by 35% (adjusted rate ratio 0·65, 0·46–0·92) in adults near the main road, where mortality before antiretroviral therapy was highest (from 13·2 to 8·5 deaths per 1000 person-years of observation before and after antiretroviral therapy). Mortality in adults aged 60 years or older did not change. Interpretation Our findings of a reduction in mortality in adults aged between 15 and 59 years, with no change in those older than 60 years, suggests that deaths from AIDS were averted by the rapid scale-up of free antiretroviral therapy in rural Malawi, which led to a decline in adult mortality that was detectable at the population level. Funding Wellcome Trust and British Leprosy Relief Association.
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                Author and article information

                Journal
                Int J Epidemiol
                Int J Epidemiol
                ije
                intjepid
                International Journal of Epidemiology
                Oxford University Press
                0300-5771
                1464-3685
                February 2012
                10 November 2010
                10 November 2010
                : 41
                : 1
                : 43-54
                Affiliations
                1PharmAccess Foundation, Amsterdam Institute for Global Health and Development, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands, 2National Centre in HIV Epidemiology and Clinical Research, UNSW, Sydney, Australia, 3School of Public Health and Community Medicine, UNSW, Sydney, Australia, 4Joint Clinical Research Center, Kampala, Uganda, 5HIV-NAT/Thai Red Cross AIDS Research Centre, Bangkok, Thailand, 6Lusaka Trust Hospital, Lusaka, Zambia, 7Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand, 8Themba Lethu Clinic, Clinical HIV Research Unit, University of the Witwatersrand, Johannesburg, South Africa, 9YRG Centre for AIDS Research and Education, Chennai, India, 10Muelmed Hospital, Pretoria, South Africa, 11Research Institute for Health Sciences, Chiang Mai University, Chiang Mai, Thailand, 12International Center for Reproductive Health Kenya, Mombasa, Kenya, 13Department of Medicine, Queen Elizabeth Hospital, Hong Kong, China, 14Newlands Clinic, Harare, Zimbabwe, 15Chiang Rai Regional Hospital, Chiang Rai, Thailand, 16Lagos University Teaching Hospital, Lagos, Nigeria, 17Hospital Sungai Buloh, Kuala Lumpur, Malaysia, 18Department of Molecular Medicine and Haematology, University of the Witwatersrand, and National Health Laboratory Services, Johannesburg, South Africa, 19University of Malaya, Kuala Lumpur, Malaysia, 20Department of Virology, University Medical Center, Utrecht, The Netherlands and 21AIDS Prevention and Research Centre, National Yang-Ming University, Taipei, Taiwan
                Author notes
                *Corresponding author. Trinity Building C, Pietersbergweg 17, 1105 BM Amsterdam, The Netherlands. E-mail: r.hamers@ 123456pharmaccess.org

                These authors contributed equally to this work and share joint first authorship

                See Appendix 1 for institutional affiliations

                Article
                dyq192
                10.1093/ije/dyq192
                3304520
                21071386
                52045242-d2f4-40c2-a22a-95c2449477c4
                Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2010; all rights reserved.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 16 September 2010
                Page count
                Pages: 12
                Categories
                Cohort Profiles

                Public health
                Public health

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