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Mortality trends in the era of antiretroviral therapy: evidence from the Network for Analysing Longitudinal Population based HIV/AIDS data on Africa (ALPHA)
research-article
Author(s):
Georges Reniers
a ,
Emma Slaymaker
a ,
Jessica Nakiyingi-Miiro
b ,
Constance Nyamukapa
c ,
Amelia Catharine Crampin
a
,
d ,
Kobus Herbst
e ,
Mark Urassa
f ,
Fred Otieno
g ,
Simon Gregson
c
,
h ,
Maquins Sewe
g ,
Denna Michael
f ,
Tom Lutalo
i ,
Victoria Hosegood
e
,
j ,
Ivan Kasamba
b ,
Alison Price
a
,
d ,
Dorean Nabukalu
i ,
Estelle Mclean
a ,
Basia Zaba
a
Publication date (Electronic):
20 November 2014
Journal:
AIDS (London, England)
Publisher:
Lippincott Williams & Wilkins
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Abstract
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Abstract
Background:
The rollout of antiretroviral therapy (ART) is one of the largest public health interventions in Eastern and Southern Africa of recent years. Its impact is well described in clinical cohort studies, but population-based evidence is rare.
Methods:
We use data from seven demographic surveillance sites that also conduct community-based HIV testing and collect information on the uptake of HIV services. We present crude death rates of adults (aged 15–64) for the period 2000–2011 by sex, HIV status, and treatment status. Parametric survival models are used to estimate age-adjusted trends in the mortality rates of people living with HIV (PLHIV) before and after the introduction of ART.
Results:
The pooled ALPHA Network dataset contains 2.4 million person-years of follow-up time, and 39114 deaths (6893 to PLHIV). The mortality rates of PLHIV have been relatively static before the availability of ART. Mortality declined rapidly thereafter, with typical declines between 10 and 20% per annum. Compared with the pre-ART era, the total decline in mortality rates of PLHIV exceeds 58% in all study sites with available data, and amounts to 84% for women in Masaka (Uganda). Mortality declines have been larger for women than for men; a result that is statistically significant in five sites. Apart from the early phase of treatment scale up, when the mortality of PLHIV on ART was often very high, mortality declines have been observed in PLHIV both on and off ART.
Conclusion:
The expansion of treatment has had a large and pervasive effect on adult mortality. Mortality declines have been more pronounced for women, a factor that is often attributed to women's greater engagement with HIV services. Improvements in the timing of ART initiation have contributed to mortality reductions in PLHIV on ART, but also among those who have not (yet) started treatment because they are increasingly selected for early stage disease.
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Most cited references18
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Patient Retention in Antiretroviral Therapy Programs in Sub-Saharan Africa: A Systematic Review
Sydney Rosen, Matthew Fox, Christopher J Gill (2007)
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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Life Expectancies of South African Adults Starting Antiretroviral Treatment: Collaborative Analysis of Cohort Studies
Leigh F Johnson, Joël Mossong, Rob Dorrington … (2013)
Introduction Estimates of life expectancies of HIV-infected individuals are important in providing information to patients about their long-term prognosis, in projecting the future costs of HIV-related care, and in forecasting the likely future demographic and socioeconomic impact of HIV/AIDS [1]–[3]. Several estimates of the life expectancy of HIV-positive adults in high-income countries have been published [4]–[13], and many of these studies have shown dramatic improvements in life expectancy following the introduction of highly active antiretroviral treatment (ART). However, only one previous study has directly estimated the life expectancy of patients receiving ART in a low-income country [14]. The dearth of estimates from low- and middle-income countries is a reflection of both the later introduction of ART (with less time to accumulate long-term survival data) and the problems associated with obtaining accurate mortality estimates in these countries. High rates of loss to follow-up [15], together with high mortality in those lost to follow-up (LTFU) [16], mean that mortality is often substantially underestimated. In the absence of reliable empirical estimates, modellers have made conservative assumptions about the life expectancy of adults starting ART in low- and middle-income countries, typically around 10 y [17]–[19]. Uncertainty regarding long-term HIV mortality is frequently manifested in insurance companies' refusal of life insurance applications by HIV-positive individuals, or acceptance on very restrictive terms. In South Africa there exists a unique opportunity to obtain accurate estimates of ART mortality in a middle-income country experiencing a generalised HIV epidemic. With around 90% of adult deaths recorded through the country's vital registration system [20]–[23], South Africa is perhaps the only African country with levels of vital registration high enough to permit independent estimation of mortality rates in patients [24]. HIV/AIDS has had a profound demographic impact in South Africa [20],[25],[26], but access to ART has expanded rapidly since 2004, with ART reaching almost 1.8 million South Africans by mid-2011 [27]. The combination of high mortality ascertainment and large patient numbers allows for greater precision in the estimation of ART mortality than is possible in most low- and middle-income countries. The objective of the present study is to estimate the life expectancy of patients starting ART in South Africa, using data from a large collaboration of ART programmes. Methods Cohort Description and Selection of Patients The International Epidemiologic Databases to Evaluate AIDS Southern Africa (IeDEA-SA) is a collaboration of ART programmes in southern Africa [28]. All IeDEA-SA programmes obtained ethical approval from relevant local institutions before contributing anonymised patient data to this collaborative analysis. In addition, the collaboration obtained approval from the University of Cape Town Human Research Ethics Committee to receive and analyse these collaborative data. As all analyses were performed with de-identified data, most patients did not provide individual consent to participate in the study [28]. This analysis is limited to six programmes providing ART to adults in South Africa [29],[30]: the Aurum workplace treatment programme, the Aurum community treatment programme, the Hlabisa HIV treatment and care programme, the Khayelitsha HIV treatment programme, McCord Hospital, and Themba Lethu Clinic. The treatment centres are situated across three of South Africa's nine provinces (Western Cape, Gauteng, and KwaZulu-Natal), mostly in urban areas, and most of the programmes operate in the public health sector. Over the period in which patients initiated ART (March 2001 to February 2010), South African treatment guidelines in the public health sector recommended that adult ART initiation be deferred until the patient's CD4 count was below 200 cells/µl or the patient had progressed to World Health Organization clinical stage IV disease [31]. We included patients who were aged 15 y or older at the time of starting ART, were treatment-naïve, and started ART in 2001 or later. Patients were excluded from the main analysis if they had missing baseline CD4 values (no CD4 measurement between 182 d before and 14 d after starting ART) or if their baseline viral load was below 400 copies/ml (as these patients were unlikely to be ART-naïve). Estimation of Mortality We calculated observation time as starting at the time of ART initiation and ending at the date of death or the date of analysis closure, whichever occurred first. Patients were considered to be LTFU if there was no record of their attendance at the clinic for at least 182 d prior to database closure and their patient records did not indicate that they had died. The date of database closure differed for each of the participating programmes, and was calculated as the last recorded visit date in each programme. The date of analysis closure was defined for each programme to be the date 182 d prior to the date of database closure, in order to allow sufficient time to determine the LTFU status of each patient. In the case of patients who were LTFU, the approach to calculating person-years of observation (PYO) differed depending on whether the programme had recorded the patient's civil identity document (ID) number and used this information to check the vital status of the patient against the national population register. In the case of LTFU patients with ID information, we censored observation at the date of analysis closure or the date of death recorded on the population register. Inverse probability weighting was used to ensure that LTFU patients with IDs were weighted up to represent the LTFU patients without IDs; this meant assigning zero weight to the LTFU patients who had no ID (so that the censoring dates for these patients were irrelevant) and assigning weights equal to the inverse of the probability of having ID information, in those patients who had IDs. This approach has been used previously in estimating mortality rates in South African patients receiving ART [21],[32], and is analogous to the weighting methods that are commonly used to correct for non-informative censoring through double sampling [33]. Inverse probability weights were calculated by applying separate logistic regression models to the LTFU patients in each cohort, to predict the probability of having recorded ID information. In one of the cohorts, problems with the recording of date of last clinic attendance prevented us from applying the LTFU definition and using inverse probability weighting. In this cohort, analysis was restricted to those patients with IDs, and observation time was censored at the date of death or date of analysis closure (the latter being defined as the date 30 d prior to when the national population register was last checked). In a sensitivity analysis we considered the effect of applying the same method to all of the other cohorts. Statistical Methods We used a relative survival approach to model the excess mortality attributable to HIV, relative to non-HIV mortality rates in South Africa, over different durations from ART initiation [34],. The relative survival model was applied separately to male and female mortality data, allowing for four covariates: age, cohort, number of years since ART initiation, and CD4 category at ART initiation (Figure S1). HIV-associated mortality was assumed to be an exponential function of age, based on an examination of standardised mortality rates in each 5-y age interval (Figure S2). We grouped individuals into one of four baseline CD4 categories: <50, 50–99, 100–199, and ≥200 cells/µl. Mortality rates were assumed to be constant over each integer age and integer duration. We defined four duration categories: the first 12 mo after starting ART, months 13–24 after starting ART, months 25–36 after starting ART, and more than 36 mo after starting ART. The model was fitted separately for the first 12 mo and durations longer than 12 mo, to allow for differences in the effect of age, CD4 count, and cohort at different durations. A more detailed mathematical description of the model is provided in section 1 of Text S1. We obtained estimates of non-HIV mortality, by age and sex, from an independent demographic model of the South African population [36] and used these mortality rates to calculate the life expectancy of HIV-negative individuals, for comparison purposes. The demographic model derives non-HIV mortality rates based on a modification of the Brass logit life table system, which takes into account estimates of South African mortality prior to the AIDS epidemic, changes over time in recorded numbers of deaths, and modelled estimates of trends in AIDS mortality (a more detailed explanation of the method is provided in section 2 of Text S1). We fitted the relative survival model to the data using a maximum likelihood approach in STATA 11.0 (StataCorp), assuming that the number of deaths in each age, sex, cohort, CD4, and duration category was Poisson-distributed. Parametric bootstrapping was used to generate 1,000 alternative parameter estimates [37]. We developed a C++ programme to calculate the life table and life expectancy for various combinations of age, sex, cohort, and baseline CD4 categories. The programme was also used to estimate the fraction of patients starting ART who were expected to die from causes unrelated to HIV. We ran this programme for each of the 1,000 bootstrap-sampled parameter estimates to generate distributions of life expectancy estimates, and calculated means and 95% confidence intervals from these. Results obtained for individual cohorts were averaged for the purpose of presenting overall results. Mathematical details regarding the model fitting procedure and life expectancy calculations are included in sections 3–5 of Text S1. Sensitivity Analyses To assess the sensitivity of the results to the high early mortality after ART initiation, we estimated life expectancy for patients who had survived 24 mo since ART initiation. We also limited the analysis to patients with IDs, to assess the effect of longer follow-up time (with later administrative censoring), and also to assess the effect of not applying inverse probability weighting in LTFU patients. To assess sensitivity to non-HIV mortality rates, the relative survival model was refitted after increasing the assumed non-HIV mortality rates by 50%. In addition, we refitted the model using a negative binomial model in place of the Poisson model, to assess possible bias due to over-dispersion [38]. To assess the effect of including patients with missing baseline CD4 values, we repeated the analysis after assigning CD4 values to these patients using multiple imputation [39]. Because the relative survival approach differs substantially from the more widely used abridged life table method, we also estimated life expectancy using the abridged life table approach [40],[41]. Mortality rates were calculated in each 5-y age band, stratified by sex and baseline CD4 category but not by duration. Because of the small number of patients at older ages, all observations at age 55 y and above were grouped together in a single upper age interval, consistent with the approach adopted in other abridged life table studies [9],[11],[14]. Confidence intervals were calculated using bootstrapping. Results Analysis was based on 37,740 adults who started ART between March 2001 and February 2010. Table 1 shows the patient characteristics at the time of ART initiation. Relatively few patients were aged 55 y or older (3.7%), and relatively few patients had CD4 counts ≥200 cells/µl (13.2%). Following ART initiation, 2,066 deaths were recorded in patient record systems, and 16,250 patients were LTFU or were considered to have unreliable information regarding their last visit date. Of the 16,250, 13,968 had a recorded ID, and in these patients with ID, 2,947 deaths were identified in the population register. After including deaths recorded in the population register and applying the inverse probability weighting to the LTFU patients, there were 5,782 deaths during 69,514 person-years, for a mean follow-up of 1.84 y (median 1.69 y). The mortality rate was 83.2 per 1,000 PYO, and was substantially higher in males (99.8 per 1,000 PYO) than in females (72.6 per 1,000 PYO). Although mortality rates were high during the first 12 mo after starting ART, mortality rates reduced to low levels at longer durations (Figure S1 and S2). The estimated parameters of the model of HIV mortality are included in section 6 of Text S1. 10.1371/journal.pmed.1001418.t001 Table 1 Patient characteristics at start of ART. Characteristic n Percent Sex Male 14,528 39.5% Female 23,212 61.5% Age 15–24 y 2,697 7.1% 25–34 y 15,584 41.3% 35–44 y 12,699 33.6% 45–54 y 5,357 14.2% 55+ y 1,403 3.7% CD4 count <50 cells/µl 10,411 27.6% 50–99 cells/µl 7,642 20.2% 100–199 cells/µl 14,689 38.9% ≥200 cells/µl 4,998 13.2% Year of ART initiation 2001–2003 913 2.4% 2004 3,079 8.2% 2005 6,122 16.2% 2006 9,683 25.7% 2007 8,630 22.9% 2008 5,956 15.8% 2009–2010 3,357 8.9% Table 2 summarises the model estimates of life expectancies at ART initiation. The most significant factor determining life expectancy of treated patients was age at ART initiation; the average life expectancy of men starting ART varied between 27.6 y (95% CI: 25.2–30.2) at age 20 y and 10.1 y (95% CI: 9.3–10.8) at age 60 y, while corresponding estimates in women were 36.8 (95% CI: 34.0–39.7) and 14.4 (95% CI: 13.3–15.3), respectively. Life expectancies were also significantly influenced by baseline CD4 counts; life expectancies in patients with baseline CD4 counts ≥200 cells/µl were between 70% (95% CI: 62%–77%) and 86% (95% CI: 81%–90%) of those in HIV-negative adults of the same age and sex, while patients starting ART with CD4 counts of <50 cells/µl had life expectancies that were between 48% (95% CI: 43%–55%) and 61% (95% CI: 54%–67%) of those in HIV-negative adults (Figure 1). 10.1371/journal.pmed.1001418.g001 Figure 1 Life expectancies of patients starting ART, as proportions of life expectancies of HIV-negative adults. Proportions are plotted by age at ART initiation and baseline CD4 count, for men (A) and women (B). Bars represent means, and error bars represent 95% confidence intervals. 10.1371/journal.pmed.1001418.t002 Table 2 Life expectancies (additional years of life) at ART initiation by age, sex, and baseline CD4 count. Age at ART Initiation (in Years) Men Women Baseline CD4 Count Baseline CD4 Count <50 50–99 100–199 200+ Alla Uninfected <50 50–99 100–199 200+ Alla Uninfected 20 21.7 (19.2–24.5) 27.3 (24.6–30.1) 30.6 (27.8–33.5) 31.2 (27.7–34.6) 27.6 (25.2–30.2) 44.8 29.5 (26.2–33.0) 36.5 (33.1–39.9) 40.0 (36.9–43.0) 43.1 (40.1–46.0) 36.8 (34.0–39.7) 52.9 25 19.8 (17.7–22.4) 25.0 (22.6–27.5) 28.1 (25.5–30.6) 28.7 (25.6–31.6) 25.3 (23.3–27.5) 40.7 27.2 (24.1–30.4) 33.7 (30.6–36.7) 36.9 (34.1–39.5) 39.6 (37.0–42.2) 33.9 (31.5–36.5) 48.3 30 18.1 (16.2–20.3) 22.7 (20.7–25.1) 25.6 (23.4–27.8) 26.2 (23.5–28.7) 23.0 (21.3–25.0) 36.7 24.9 (22.2–27.7) 30.8 (28.1–33.5) 33.7 (31.3–36.0) 36.1 (33.7–38.4) 31.0 (28.8–33.3) 43.8 35 16.3 (14.6–18.3) 20.5 (18.7–22.6) 23.1 (21.2–25.0) 23.6 (21.2–25.9) 20.7 (19.3–22.4) 32.8 22.6 (20.1–25.1) 27.9 (25.5–30.3) 30.6 (28.3–32.6) 32.6 (30.5–34.6) 28.1 (26.1–30.1) 39.4 40 14.5 (13.1–16.2) 18.2 (16.6–19.9) 20.5 (18.9–22.1) 21.0 (18.9–22.9) 18.4 (17.1–20.0) 28.8 20.3 (18.1–22.5) 25.0 (23.0–27.1) 27.4 (25.5–29.1) 29.1 (27.3–30.8) 25.2 (23.3–27.0) 34.9 45 12.7 (11.4–14.2) 16.0 (14.5–17.4) 18.0 (16.6–19.3) 18.4 (16.7–20.0) 16.2 (15.0–17.5) 24.9 18.0 (16.1–20.0) 22.2 (20.3–24.0) 24.3 (22.6–25.8) 25.7 (24.2–27.1) 22.3 (20.7–23.9) 30.7 50 11.0 (9.9–12.3) 13.8 (12.6–15.0) 15.5 (14.4–16.7) 16.0 (14.5–17.3) 14.0 (13.0–15.1) 21.2 15.8 (14.1–17.5) 19.4 (17.8–20.9) 21.3 (19.8–22.5) 22.5 (21.2–23.6) 19.5 (18.1–20.9) 26.6 55 9.5 (8.5–10.5) 11.8 (10.8–12.8) 13.3 (12.3–14.2) 13.7 (12.4–14.8) 12.0 (11.1–12.9) 17.9 13.7 (12.2–15.1) 16.8 (15.4–18.1) 18.4 (17.1–19.4) 19.3 (18.3–20.3) 16.9 (15.6–18.0) 22.7 60 8.0 (7.1–8.9) 10.0 (9.1–10.8) 11.2 (10.3–11.9) 11.5 (10.5–12.4) 10.1 (9.3–10.8) 14.8 11.7 (10.4–12.8) 14.3 (13.1–15.3) 15.6 (14.6–16.5) 16.4 (15.5–17.2) 14.4 (13.3–15.3) 19.1 95% confidence intervals are shown in brackets. a Standardised to the baseline CD4 distribution in Table 1. Figure 2 shows the estimated fraction of patients starting ART who are expected to die from causes unrelated to HIV, if non-HIV mortality rates are the same in ART patients as they are in the HIV-negative population. This fraction was higher at older ages (up to 68% [95% CI: 58%–77%] in men and 80% [95% CI: 72%–87%] in women), as the estimated mortality rates in the HIV-negative population increased more steeply with respect to age than the excess HIV mortality rates in ART patients. The fraction was also higher in women than in men, because HIV-related mortality increased more steeply with respect to age in men than in women (Table 1 of Text S1). 10.1371/journal.pmed.1001418.g002 Figure 2 Proportion of individuals starting ART who are expected to die from causes unrelated to HIV. Proportions are plotted by age at ART initiation and baseline CD4 count, for men (A) and women (B). Bars represent means, and error bars represent 95% confidence intervals. Life expectancies differed substantially between cohorts. For men starting ART at age 35 y, life expectancy varied between 16.6 y (95% CI: 14.4–19.2) and 28.6 y (95% CI: 26.2–30.3). Life expectancy in women starting ART at age 35 y varied between 23.6 y (95% CI: 21.4–25.9) and 35.5 y (95% CI: 33.3–36.9). The average life expectancies in Table 2 were closest to those of the public sector programmes operating in urban areas. In the sensitivity analysis that included only patients with recorded ID (n = 30,287), total PYO increased (83,199 PYO) and average follow-up time increased (2.75 y) because of the later analysis closure date. In these patients, 1,451 deaths were recorded through patient record systems, and a further 3,760 were identified through the national population register, yielding a crude mortality rate of 62.6 per 1,000 PYO. Modelled mortality rates were lower in patients starting ART after 2006 than in patients starting ART in 2006 or earlier, and life expectancies were therefore calculated separately for the two enrolment periods. Women with recorded ID who started ART in 2006 or earlier had similar life expectancies to those in the main analysis, but men with recorded ID who started ART in 2006 or earlier had life expectancies about 12% higher than those in the main analysis (Table 3). Life expectancies in patients with IDs who started ART after 2006 were in turn higher than those in patients with IDs who started ART before or in 2006 (Table 3; Figure 3). In the subset of these patients starting ART after 2006 with baseline CD4 counts ≥200 cells/µl, life expectancies were between 82% (95% CI: 77%–87%) and 88% (95% CI: 84%–91%) of those in HIV-negative individuals of the same age and sex. 10.1371/journal.pmed.1001418.g003 Figure 3 Life expectancies of patients with recorded ID, starting ART after 2006. Life expectancies are plotted by age at ART initiation and baseline CD4 count, for men (A) and women (B). Bars represent means, and error bars represent 95% confidence intervals. 10.1371/journal.pmed.1001418.t003 Table 3 Sensitivity analysis of life expectancies. Patients with Recorded ID Sex and Age Main Analysis Started ART in 2006 or Earlier Started ART after 2006 Patients Who Have Survived 24 mo after ART Start Abridged Life Table Method Multiple Imputation of Missing CD4 Countsa Negative Binomial Model Non-HIV Mortality Rates Increased by 50% Men Age 25 y 25.3 (23.3–27.5) 28.4 (26.7–30.0) 30.6 (28.8–32.3) 30.2 (27.8–32.8) 12.0 (11.2–12.8) 25.6 (23.7–27.5) 25.2 (23.0–27.6) 24.4 (22.6–26.3) Age 35 y 20.7 (19.3–22.4) 23.3 (22.2–24.5) 25.0 (23.7–26.1) 24.9 (23.0–26.9) 11.7 (11.1–12.4) 20.9 (19.5–22.5) 20.7 (19.1–22.5) 19.8 (18.6–21.2) Age 45 y 16.2 (15.0–17.5) 18.1 (17.3–19.0) 19.2 (18.4–20.0) 19.5 (18.0–21.0) 10.2 (9.4–11.3) 16.3 (15.2–17.4) 16.2 (14.8–17.5) 15.1 (14.2–16.0) Age 55 y 12.0 (11.1–12.9) 13.3 (12.7–13.9) 14.0 (13.4–14.5) 14.5 (13.4–15.5) 9.6 (8.3–11.6) 12.0 (11.2–12.8) 12.0 (11.0–12.9) 10.8 (10.2–11.3) Women Age 25 y 33.9 (31.5–36.5) 34.3 (32.3–36.2) 35.9 (33.8–37.8) 39.1 (36.7–41.4) 15.6 (14.9–16.9) 34.0 (31.9–36.0) 33.7 (30.7–36.5) 32.3 (30.3–34.3) Age 35 y 28.1 (26.1–30.1) 28.6 (26.8–30.0) 29.7 (28.1–31.2) 32.4 (30.6–34.3) 14.5 (13.5–16.3) 28.1 (26.5–29.6) 27.8 (25.4–30.0) 26.4 (24.9–27.8) Age 45 y 22.3 (20.7–23.9) 22.7 (21.4–23.9) 23.6 (22.3–24.7) 25.9 (24.5–27.2) 12.8 (11.3–15.5) 22.4 (21.1–23.5) 22.1 (20.2–23.7) 20.4 (19.3–21.4) Age 55 y 16.9 (15.6–18.0) 17.2 (16.1–18.0) 17.7 (16.7–18.5) 19.6 (18.6–20.5) 11.7 (9.0–16.3) 16.9 (16.0–17.7) 16.6 (15.2–17.7) 14.9 (14.2–15.6) 95% confidence intervals are shown in brackets. Main analysis includes all patients with CD4 measurements at time of ART initiation. Life expectancies in the main analysis are calculated from the time of ART initiation, using the relative survival method. All calculations are standardised to the baseline CD4 distribution in Table 1. a CD4 values were imputed for 6,156 (14.0%) eligible adults starting ART. Further sensitivity analyses are presented in Table 3. Estimates of life expectancy in patients who had survived 24 mo after starting ART were 15%–20% higher than those in patients of the same age who had just started ART. The use of the abridged life table method led to substantially lower estimates of life expectancy in patients starting ART at young ages, compared with the relative survival approach; the differences between the abridged life table and relative survival estimates were less substantial in patients starting ART at older ages. The use of multiple imputation to assign baseline CD4 values to individuals with missing information led to almost no change in life expectancy. The use of negative binomial models in place of Poisson models also produced almost identical model results. After refitting the model with 50% higher non-HIV mortality rates, life expectancy estimates were reduced by 1–2 y. Further comparison of different model estimates by CD4 strata is included in sections 7–12 of Text S1. Discussion This analysis shows that South African patients starting ART have life expectancies around 80% of normal life expectancy, provided that they start treatment before their CD4 count drops below 200 cells/µl. Life expectancies are also 15%–20% higher in patients who have survived to 24 mo after starting ART than in patients of the same age who have just started therapy. Although these results are encouraging, programmes in resource-limited settings experience major challenges with late diagnosis, low uptake of CD4 testing, loss from pre-ART care, and delayed ART initiation [42]. Individuals who start ART also frequently interrupt treatment in the South African setting [43], and these interruptions are often associated with poorer immunological recovery and the development of drug resistance [44]. Health services need to overcome these challenges if near-normal life expectancies are to be achieved for the majority of HIV-positive South Africans. South African treatment guidelines have recently changed, and it is now recommended that all HIV-infected adults should start ART when their CD4 counts fall below 350 cells/µl. Recent campaigns to increase the uptake of HIV testing [45] combined with dramatic growth in rates of ART enrolment in South Africa [27] should lead to a substantially increased proportion of patients starting ART at CD4 counts above 200 cells/µl. However, over the period to which this analysis relates, most of the patients starting ART at CD4 counts above 200 cells/µl did so because they qualified for treatment on clinical grounds. Such patients are likely to experience higher mortality than asymptomatic patients with CD4 counts above 200 cells/µl [46],[47]. These estimates of life expectancy for adults who have initiated ART with CD4 counts above 200 cells/µl may therefore be underestimates of the life expectancies in future, when a greater fraction of such individuals are likely to be asymptomatic. A key strength of this analysis is that it incorporates data from the South African population register to obtain more accurate estimates of mortality than are usually possible in African countries. Because patients with ID information are not censored at the date of loss to follow-up, these estimates of life expectancy are inclusive of patients who are not retained in care. However, there is likely to be a small proportion of deaths (probably around 5%) that are recorded neither in the patient record system nor in the vital registration system. Although this figure is lower than the proportion of deaths missing from the vital registration system because of the added information from patient record systems, it may nevertheless lead to life expectancies of ART patients being slightly exaggerated. Predicting future mortality for HIV-positive patients on ART is challenging. In addition to the historical bias towards enrolment of symptomatic patients, mentioned previously, our mortality estimates do not take into account potential future reductions in mortality that may occur as new drugs and salvage regimens [48] and innovations in patient management are introduced [49]. In the sensitivity analysis that was limited to patients with recorded IDs, we found higher life expectancies in patients who started ART after 2006 than in patients starting ART in or before 2006. This result may be due to a change in the disease severity of patients starting ART over time (a factor that we have not fully controlled for) or possibly improvements in regimen options and patient management. A limitation of this analysis is the short average duration of follow-up (1.84 y), which we have addressed by controlling for differences in mortality by duration. Our method of controlling for differences by duration is based on the assumption that mortality rates are piecewise constant over 1-y duration intervals. To the extent that there is residual variation in mortality by duration, for which we have not fully controlled, our method will be biased towards overestimating mortality when the average follow-up time is short, because the observation time in each duration interval will be weighted toward the lower end of the interval, where mortality rates are likely to be highest. This bias is evident when comparing the results from the main analysis with the results from the analysis that is limited to patients with IDs; the longer average follow-up time (2.75 y) leads to slightly lower mortality rates over each duration interval (results not shown) and hence increased life expectancies. Due to the small number of patients on ART at long durations, mortality data have been aggregated for all durations greater than 36 mo. This approach may be reasonable, as average CD4 counts tend to stabilise after the first 36 mo of treatment [50],[51]. However, if HIV-related mortality continues to decline with increasing duration, the lack of data at long durations could result in some underestimation of life expectancy. On the other hand, the accumulation of drug resistance mutations at longer durations could cause long-term increases in HIV-related mortality, and mathematical modelling suggests that life expectancy may be sensitive to the number of treatment options available [52]. The principle advantage of the relative survival model, compared with the abridged life table method that is more commonly used in the estimation of life expectancy, is that it adjusts for differences in mortality by duration, which are particularly marked in resource-limited settings [53]. Although both of the measures presented here are period life expectancies rather than cohort life expectancies [54], controlling for differences in mortality by duration yields average survival times closer to those that might be expected in actual cohorts of patients initiating ART. Not controlling for duration means that the life expectancies represent the expected survival if age-specific mortality rates in the future were to remain unchanged at the high average levels measured between 2001 and 2010, which means that the abridged life table method gives too much weight to the high mortality rates soon after ART initiation. Another advantage of the relative survival approach is that it incorporates information on non-HIV mortality in the general population, which is likely to be relatively more significant at older ages. This analysis suggests that a substantial proportion of deaths in ART patients are likely to be unrelated to their HIV infection. It is therefore important that comparisons of life expectancies of HIV patients in different regions take into account differences in non-HIV mortality between settings, and the relative survival model provides a framework within which this comparison can be achieved. Studies of life expectancies of ART patients in high-income countries that have excluded high-risk groups (people who inject drugs and patients starting ART in advanced disease) have generally estimated life expectancies that are 73%–99% of those in the general population [4],[7],[12],[55]. However, studies that have not excluded high-risk groups have estimated life expectancies that are 51%–66% of those in the general population [5],[12],[56]. These results are roughly consistent with our ratios: 62%–75% when all CD4 categories are combined, and 70%–86% when analysis is restricted to patients with baseline CD4 counts ≥200 cells/µl (Figure 1). Our ratios become even higher (87%–96%) when considering patients who started ART with CD4 counts ≥200 cells/µl and who survived their first 2 y after ART initiation. Only one other study in Africa has estimated the life expectancy of patients starting ART [14]. This study, conducted in Uganda, estimated lower life expectancy in patients starting ART at young ages, compared with our South African estimates, but generated higher estimates than those obtained in South Africa for patients starting ART at older ages. These differences may be partly attributable to differences in methodology, as Table 3 shows that the abridged life table method (used in the Ugandan study) is likely to generate lower estimates of life expectancy at younger ages. Some of the difference may also be attributable to differences in baseline CD4 counts, which were generally higher in Uganda (the proportion with baseline CD4 count ≥100 cells/µl was 65%, compared to 52% in South Africa). Some of the difference may also be explained by differences in the approach to determining the mortality of patients LTFU, which was assumed to be 30% in the Ugandan analysis. The generalisability of our findings—even within South Africa—is open to question. The South African cohorts participating in the IeDEA-SA Collaboration are relatively well-resourced programmes with substantial research support, mostly in urban centres. Life expectancies differed between cohorts because of differences in patients' socioeconomic characteristics as well as differences in models of ART delivery. The average results that we have presented are likely to be typical of public sector programmes in urban areas, but mortality rates may differ in rural treatment programmes and in private sector programmes. The assumption that the average non-HIV mortality rates in South Africa apply in all cohorts could also be problematic, although estimates of life expectancy did not change substantially when the model was refitted with 50% higher non-HIV mortality rates. These findings might not be typical of programmes in most other African countries, as South Africa is an upper middle-income country with rates of non-HIV mortality lower than in most other African countries [57]. HIV-related mortality in South African ART patients may also be lower than in other African countries due to virological monitoring of ART patients, which is routine in South Africa but not in most other African countries [58]. These results have important implications for the pricing models used by life insurance companies, as well as the demographic and epidemiological models that are used to forecast the impact and cost of ART programmes in low- and middle-income countries. These models have typically assumed that life expectancy after ART initiation is around 10 y [17]–[19]. Assumptions of longer life expectancy would significantly reduce the forecasts of AIDS mortality, but would also significantly increase long-term projections of numbers of patients receiving ART. With the anticipated increase in the fraction of patients starting ART at higher CD4 counts in future, long-term survival can be expected to increase even further. It is therefore critical that appropriate funding systems and innovative ways to reduce costs are put in place, to ensure the long-term sustainability of ART delivery in low- and middle-income countries. Supporting Information Figure S1 Cumulative survival after ART initiation, compared to age-standardised survival rates in the HIV-negative population. HIV-positive survival curves are calculated by grouping all ages and cohorts together and applying Kaplan-Meier methods to calculate proportions surviving, after including data from the national population register and applying inverse probability weighting. HIV-negative survival curves are calculated for a hypothetical cohort with the same initial age distribution as the HIV-positive cohort. (PDF) Click here for additional data file. Figure S2 Annual mortality rates stratified by age, sex, and time since ART initiation. Dots represent observed mortality rates, standardised to the CD4 distribution in Table 1 and calculated over 5-y age groups. Vertical lines represent 95% confidence intervals. (PDF) Click here for additional data file. Text S1 Technical appendix. (PDF) Click here for additional data file.
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Profile: the KEMRI/CDC Health and Demographic Surveillance System--Western Kenya.
David Obor, Kevin deCock, Bernard Nahlen … (2012)
The KEMRI/Centers for Disease Control and Prevention (CDC) Health and Demographic Surveillance System (HDSS) is located in Rarieda, Siaya and Gem Districts (Siaya County), lying northeast of Lake Victoria in Nyanza Province, western Kenya. The KEMRI/CDC HDSS, with approximately 220 000 inhabitants, has been the foundation for a variety of studies, including evaluations of insecticide-treated bed nets, burden of diarrhoeal disease and tuberculosis, malaria parasitaemia and anaemia, treatment strategies and immunological correlates of malaria infection, and numerous HIV, tuberculosis, malaria and diarrhoeal disease treatment and vaccine efficacy and effectiveness trials for more than a decade. Current studies include operations research to measure the uptake and effectiveness of the programmatic implementation of integrated malaria control strategies, HIV services, newly introduced vaccines and clinical trials. The HDSS provides general demographic and health information (such as population age structure and density, fertility rates, birth and death rates, in- and out-migrations, patterns of health care access and utilization and the local economics of health care) as well as disease- or intervention-specific information. The HDSS also collects verbal autopsy information on all deaths. Studies take advantage of the sampling frame inherent in the HDSS, whether at individual, household/compound or neighbourhood level.
Author and article information
Contributors
Basia Zaba:
On behalf of
: on behalf of the ALPHA Network
Journal
Journal ID (nlm-ta): AIDS
Journal ID (iso-abbrev): AIDS
Journal ID (publisher-id): AIDS
Title:
AIDS (London, England)
Publisher:
Lippincott Williams & Wilkins
ISSN
(Print):
0269-9370
ISSN
(Electronic):
1473-5571
Publication date
(Print):
November
2014
Publication date
(Electronic):
20
November
2014
Volume: 28
Issue: 4
Pages: S533-S542
Affiliations
[b
]Research Unit on AIDS, Medical Research Council/Uganda Virus Research Institute, Entebbe,
Uganda
[c
]Manicaland HIV/STD prevention project, Bio-medical Research and Training Institute,
Harare, Zimbabwe
Author notes
Correspondence to Georges Reniers. E-mail:
georges.reniers@
123456lshtm.ac.uk
Article
Accession ID: 00015DOI: 10.1097/QAD.0000000000000496
PMC ID: 4251911
PubMed ID: 25406756
SO-VID: 8afca049-46a8-41cf-a73a-15a551f63069
Copyright © © 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins
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This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License, where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially. http://creativecommons.org/licenses/by-nc-nd/4.0
History
Date
received
: 22
September
2014
Date
revision received
: 22
September
2014
Date
accepted
: 22
September
2014
Categories
Subject:
The 2013/14 UNAIDS Estimates Methods: Extending The Scope and Granularity of HIV Estimates
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