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Comparative effectiveness and cost-effectiveness of antiretroviral therapy and pre-exposure prophylaxis for HIV prevention in South Africa
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Abstract
Background
Antiretroviral therapy (ART) and oral pre-exposure prophylaxis (PrEP) are effective in reducing HIV transmission in heterosexual adults. The epidemiologic impact and cost-effectiveness of combined prevention approaches in resource-limited settings remain unclear.
Methods
We develop a dynamic mathematical model of the HIV epidemic in South Africa’s adult population. We assume ART reduces HIV transmission by 95% and PrEP by 60%. We model two ART strategies: scaling up access for those with CD4 counts ≤ 350 cells/μL (Guidelines) and for all identified HIV-infected individuals (Universal). PrEP strategies include use in the general population (General) and in high-risk individuals (Focused). We consider strategies where ART, PrEP, or both are scaled up to 100% of remaining eligible individuals yearly. We measure infections averted, quality-adjusted life-years (QALYs) gained and incremental cost-effectiveness ratios over 20 years.
Results
Scaling up ART to 50% of eligible individuals averts 1,513,000 infections over 20 years (Guidelines) and 3,591,000 infections (Universal). Universal ART is the most cost-effective strategy at any scale ($160-$220/QALY versus comparable scale Guidelines ART expansion). General PrEP is costly and provides limited benefits beyond ART scale-up ($7,680/QALY to add 100% PrEP to 50% Universal ART). Cost-effectiveness of General PrEP becomes less favorable when ART is widely given ($12,640/QALY gained when added to 100% Universal ART). If feasible, Focused PrEP is cost saving or highly cost effective versus status quo and when added to ART strategies.
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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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The Cost and Impact of Scaling Up Pre-exposure Prophylaxis for HIV Prevention: A Systematic Review of Cost-Effectiveness Modelling Studies
Gabriela B Gomez, Annick B. Borquez, Kelsey Case … (2013)
Introduction Since the announcement of the results of HIV pre-exposure prophylaxis (PrEP) trials and the HPTN052 early treatment for prevention trial, there have been crucial policy discussions about the use of antiretroviral (ARV) drugs to prevent HIV acquisition or transmission. With regards to PrEP, encouraging results were first reported for men and transgender women who have sex with men in the iPrEX trial [1], which showed a 44% (95% CI 15–63) reduction in HIV acquisition with a daily dose of tenofovir/emtricitabine (TDF/FTC). In two large trials, the Partners PrEP [2] and TDF2 [3] studies, PrEP was found to be effective in reducing the risk of heterosexual HIV transmission using either TDF or TDF/FTC daily (Partners PrEP) and TDF/FTC daily (TDF2). However, FEM-PrEP [4], a trial recruiting heterosexual women in South Africa, Tanzania, and Kenya for daily TDF/FTC was closed prematurely in 2011 for futility as was the oral TDF arm of the VOICE trial [5] in women in South Africa, Uganda, and Zimbabwe. Two topical PrEP trials have tested the efficacy of 1% TDF gel and a third, FACTS001 [6], is currently recruiting women in South Africa. The CAPRISA 004 trial [7] in Kwa Zulu-Natal found that pre- and post-coital vaginal TDF gel reduced women's acquisition risk by 39% (95% CI 6–60) but the VOICE trial stopped its gel arm when it became evident that daily gel use was safe but not effective [8]. Clinical guidance on oral PrEP has already been offered by the US Centers for Disease Control and Prevention, the Southern African HIV Clinicians Society, World Health Organization (WHO), and the British Association for Sexual Health and HIV [9]–[13]. An advisory panel to the US Food and Drug Administration recently recommended oral TDF/FTC for preventive use among people at higher risk of HIV exposure [14]. As PrEP emerges as an option for inclusion in the HIV prevention toolbox, it is important for national policy and decision makers to identify where PrEP may fit best within already established HIV prevention programming (and budgets) and the potential implications of introducing such policy changes. In particular, decision makers need information translating the trial results into potential population-level impact and cost-effectiveness to ensure that any additional investment will have the maximum possible effect on the epidemic. Economic and mathematical models provide a framework to integrate information on efficacy, effectiveness, costs, and patient outcomes to support decision making and resource allocation [15]. However, due to their complexity, dependence on assumptions made, and inherent uncertainties, generalising results from these models can be difficult. In this review, we aim to assess published cost-effectiveness models that have evaluated the expected health gains and costs of PrEP interventions. Specifically, our objectives are: (1) to describe modelling approaches of cost-effectiveness analyses of PrEP; (2) to compare the effects of epidemiological and cost assumptions on cost-effectiveness results; and (3) to explore the potential impact on cost-effectiveness estimates of five issues raised by policy makers [16]–[18] when considering PrEP implementation: prioritisation, adherence, behaviour change, toxicity, and resistance. Methods We performed a systematic review of the published literature following the protocol available in Text S2 and adhering to the PRISMA guidelines for reporting of systematic reviews (Text S1: PRISMA checklist) [19] and guidelines for appraisal of economic evaluations [20]. Search Strategy, Inclusion Criteria, and Study Selection A broad strategy using both MeSH headings and free text, with no language limitations, was used to search PubMed/Medline, ISI Web of Knowledge (including Web of Science, Current Contents Connect, Derwent Innovations Index, CABI: CAB Abstracts, and Journal Citation Reports), Centre for Reviews and Dissemination databases (including DARE - Database of Abstracts of Reviews of Effects, NHS EED - NHS Economic Evaluation Database, and HTA database - health technology assessments), EconLIT, and region-specific databases (African Index Medicus, Eastern Mediterranean Literature (WHO), Index Medicus for South-East Asia Region, LILACS for Latin America). Our searches covered all published research up to the last search performed 14 January 2013 with no limitations on publication date. The following keywords were used: “cost” AND “tenofovir OR pre-exposure prophylaxis OR chemoprophylaxis OR PrEP” AND “HIV.” Citations and bibliographies of full text reports retrieved were reviewed for additional relevant articles. Abstracts from international conferences identified in the searches were also reviewed, as was the website of the International AIDS Economic Network. Experts were consulted for additional studies. We included all modelling studies reporting both cost and impact of a potential roll-out of a PrEP programme. We excluded those studies where costs were not assessed. No restrictions were made on the type of model, geography, mode of transmission, or impact (effectiveness) metric chosen. We included studies looking at both topical and systemic PrEP products. Full published papers were eligible, as well as abstracts from conferences providing sufficient information. Two authors (GBG and AB) screened titles and abstracts to identify potentially relevant articles. Full text reports of these articles were assessed independently for inclusion. Data Extraction and Analysis Data were extracted from selected studies by one reviewer (GBG) into prepared data sheets and independently cross-checked by a second assessor (AB). For conference abstracts selected for inclusion, we contacted the first author listed for further information. Extracted information on the study design included the type of study, viewpoint of analysis, timeframe, setting and population, background HIV prevalence or incidence, mode of HIV transmission, and a detailed description of alternative programmes compared in the studies (baseline scenario and PrEP scenario). We also tabulated data on the impact including risk heterogeneity, efficacy or effectiveness of PrEP, adherence (to programme or individual), behavioural change expected after introduction of PrEP, resistance, toxicity due to PrEP use, and disability-adjusted life year (DALY)/quality-adjusted life year (QALY) assumptions. A description of economic assumptions includes expected drug cost, other service costs, costs above service level, downstream antiretroviral treatment (ART) costs averted, discount rates, and, finally, cost-effectiveness results by metric and the conclusions presented in each publication. Prioritised scenarios were defined as those scenarios where PrEP was offered to specific sub-populations within the population modelled. While providing a critical assessment and narrative review of the studies included, we did not attempt to perform a meta-analysis due to the variability across the studies in reporting outcomes. Therefore, we adjusted estimates of cost-effectiveness for inflation to US$2012 to be able to compare studies from different years [21]. For those studies reporting cost/DALY averted, cost/QALY averted, or cost/life-year saved (LYS), we compared the estimates to a benchmark for cost-effectiveness [22] of one times the gross domestic product per capita (GDP/capita) per DALY averted, per QALY gained, or per LYS, depending on the unit of outcome used by each study. While DALYs, QALYs, and LYS are not equivalent, and decision rules vary by setting, this gives a broad indication of potential cost-effectiveness. The values for current GDP/capita were sourced from the World Bank databank for each country [23]. There is much controversy around decision rules [24], and while the comparison against GDP is the conventional approach, it should be noted that this may not represent the true opportunity cost in countries where less cost-effective health interventions are not being implemented at scale. Results We screened 961 titles and abstracts retrieved from 14 electronic databases. After performing web searches and consulting experts in the field, 36 full text articles were evaluated. We also reviewed the reference lists and citations of these articles. Of these 36, 13 studies were included in the review [25]–[37]: 11 peer-reviewed publications and two peer-reviewed conference abstracts (Figure 1). Articles excluded are listed in Table S1 and a summary of conclusions of the articles included are presented in Table S2. 10.1371/journal.pmed.1001401.g001 Figure 1 Flow diagram of study selection. Region-specific databases can be accessed as follows: African Index Medicus, http://indexmedicus.afro.who.int/; Eastern Mediterranean Literature, http://www.emro.who.int/; Index Medicus for South-East Asia Region, http://www.hellis.org/; LILACS, Latin America, http://www.bireme.br/iah2/homepagei.htm. We present in Tables 1 to 4 the data extracted from the studies reviewed by study design, description of alternative programmes compared, impact, and cost assumptions. All studies were published between 2007 and 2013 and modelled the impact and cost, from a health care provider perspective, of PrEP scale-up in diverse settings. These settings included: heterosexual transmission in generalised epidemics in sub-Saharan Africa—the Southern Africa region [25], South Africa [28],[30],[31],[32],[36],[37]), and other modes of transmission in concentrated epidemics—among people who inject drugs (PWID) in Ukraine [33]; and men who have sex with men (MSM) in the USA [26],[29],[34],[35] and in Peru [27]. Timeframes varied from 5 to 20 y. All studies focused the models on high prevalence/incidence populations (Table 1). 10.1371/journal.pmed.1001401.t001 Table 1 Study design. Reference Study Type Setting/MoT Population Timeframe HIV Incidence/Prevalence Generalised epidemics in southern Africa Abbas [25] Deterministic simulation; Risk heterogeneity by age, sex, sexual behaviour, and HIV drug resistance Southern Africa/Heterosexual 15–49 y; General population 10 y Prevalence: 20%a Pretorius [30] Deterministic simulation; Risk heterogeneity by age and sex South Africa/Heterosexual 15–49 y; General population 10 y (programme scale-up: 5 y) Prevalence: ±20% in 2008b Hallett [28] Microsimulation; Risk heterogeneity by age, sex, sexual behaviour, and conception intentions or pregnancyc South Africa/Heterosexual Serodiscordant couples Each person is tracked until his/her 50th y n/a Williams [32] Deterministic simulation; Risk heterogeneity not included South Africa/Heterosexual 15–49 y; General population From 2012 to 2020 (scale-up by 2015) Prevalence: approximately 16% in 2012b Walensky [31] Monte Carlo state simulation; Risk heterogeneity by age South Africa/Heterosexual Women at higher risk Each person is tracked until death Incidence: 25 y, 1.0% Alistar [37] Compartmental dynamic simulation Risk heterogeneity by sexual behaviour behaviour (number of partners and condom use) South Africa/Heterosexual 15–49 y; General population 20 y Initial prevalence in adults: 17.9% and initial incidence: 1.4% Cremin [36] Deterministic simulation;Risk heterogeneity by age, sex, male circumcision status, behavioural; risk (partner change rate, condom use) South Africa/Heterosexual 15–54 y; General population 10 y (programme scale-up: 5 y) Age- and sex-specific prevalence peaking at 30–44 y (women: >40% and 35–44 y men: >30%). Concentrated epidemics among MSM in high-income countries Desai [26] Stochastic simulation; Risk heterogeneity by age, sexual risk behaviourd USA (NYC)/MSM 13–40 y; High risk MSM 5 y Prevalence: 14.6% in 2008 Paltiel [34] Monte Carlo state simulation; Risk heterogeneity by age (assumed higher incidence by age group) USA/MSM Average 34 y; High risk MSM Each person is tracked until death Incidence: 1.6% annual Koppenhaver [29] Compartmental dynamic simulation Risk heterogeneity not included USA (urban)/MSM 13–40 y; All MSM 20 y Prevalence: 17.5% Juusola [35] Deterministic simulation; Risk heterogeneity by sexual behavioure USA/MSM 13–64 y; 20 y Prevalence: 12.3%; Incidence: 0.8% annual Concentrated epidemics among MSM in low- and middle-income countries Gomez [27] Deterministic simulation; Risk heterogeneity by sexual behaviour Peru (Lima)/MSM All MSM 10 y (programme scale-up: 5 y) Incidence: MMSM, 1%; MMSW, 2.5%; SW, 3.1%; Trans, 7.3% Concentrated epidemics among PWID in low- and middle-income countries Alistar [33] Compartmental dynamic simulation Risk heterogeneity by IDU behaviour Ukraine/IDU and heterosexual 15–49 y 20 y Initial prevalence: 41.2% PWID, 1% general population Study type refers to the type of model and the inclusion of risk heterogeneity in the population modelled. Setting/MoT refers to the geographical setting and the mode of transmission modelled. a Female∶male ratio 1.66, based on data from urban antenatal care attendees in Zambia. b Model initiated at a high prevalence then fitted to Department of Health data. c Two types of couples were defined: (1) lower risk couples based on reported data from the Partners in Prevention HSV/HIV Transmission Study [49], and (2) couples at a higher risk reflecting a higher incidence. “Partners in Prevention” assumptions: incidence low (1.8/100 person-years at risk, high condom use); “more typical couples” assumptions: 50% of serodiscordant couples involved HIV-1 infected men. Compared to the partners in prevention cohort: condom use within the stable partnership was reduced by 25%, 50% more of the HIV-1 uninfected partners in couples had external partners, and frequency of unprotected sex with external partners was doubled. d Very high risk was defined as a participant reporting unprotected sex in the last 6 mo or in exchange for money or drugs, anonymous sex, ≥5 sexual or needle sharing partners, and/or an STI diagnosis in the last 6 mo. e The authors run the model separately for low risk and high risk populations. Therefore PrEP use in one group does not have an impact on the other (the mixing is considered totally assortative). IDU, injection drug use; MMSM, men who mostly have sex with men; MMSW, men who mostly have sex with women; n/a, not applicable; SW, sex worker; Trans, transgender or trans-sexual; USA (NYC), United States of America (New York City). 10.1371/journal.pmed.1001401.t002 Table 2 Alternative programmes compared. Reference Base Comparison Scenario PrEP Intervention PrEP Regimen Prioritisation Coverage Generalised epidemics in southern Africa Abbas [25] No PrEP. ART was not modelled. Once daily oral dosing No prioritisation: general population. By sexual activity: two highest sexual activity groups prioritised. By age: 15–20 y group prioritised. Percent of the population using PrEP: Optimistic scenario, 75%; Neutral scenario, 50%; Pessimistic scenario, 25% Pretorius [30] No PrEP. ART coverage expands at its current rate. ART efficacy: 90% reduction in transmission probability. Once daily oral dosing No prioritisation: 15–35 y; By age: 15–25 y, or 25–35 y Percent of women using PrEP: 20%, dropout rate:1.5% Hallett [28] No PrEP. ART initiation for the infected partner when CD4 cell count fell below 200 cells/ml. In a separate scenario, expansion of eligibility criteria for ART initiation was included (below 350 CD4 cells/ml). Once daily oral dosing No prioritisation: Always use PrEP after diagnosis partner. By timing: Up to partner's ART init; up to partner's ART init+1 y; during conception/pregnancya Percent of the population using PrEP: see prioritisation Williams [32] No PrEP. The scale-up of ARV therapy was not modelled. Vaginal gel, two doses pericoitally PrEP used only by women Percent of sex acts protected: High: 90%, Medium: 50%, Low: 25% Walensky [31] No PrEP. Patients identified as HIV infected received ART as per guidelines. Vaginal gel, two doses pericoitally PrEP used only by women. By age: ≤25 y (high inc. group) Cohort-wide PrEP use continues until HIV infection or death. Alistar [37] No PrEP. 40% HIV infected patients received ART as per guidelines. ART efficacy: 95% reduction in transmission probability. Once daily oral dosing No prioritisation: general use; By sexual activity: groups of high number of partners and low condom use Rate of recruitment into the program: 25%, 50%, 75%, 100%. Included a rate of dropout from PrEP. Cremin [36] ART efficacy: 96% reduction in transmission probability. Baseline scenarios varied: from status quo with current scale-up of ART to counterfactual including MC and ART scale-up. All scenarios included a 7/100 PY dropout rate while on ART. Once daily oral dosing No prioritisation: 15–54 y; By age: 15–24 y Percent of the population group using PrEP: 40%, 80% Concentrated epidemics among MSM in high-income countries Desai [26] No PrEP. The scale-up of ARV therapy was not modelled. Once daily oral dosing No prioritisation—results not shown. Results for scenarios targeting high risk MSM only. 25% high riskb; (5.2% of all MSM)c; Discontinuation rate: 40% per year Paltiel [34] No PrEP. Patients identified as HIV infected received ART as per guidelines. Once daily oral dosing No prioritisation: all MSM. By age: US$15,000 per person-year in the USA) (Table 4). We present all cost-effectiveness estimates in Table 5 by epidemiological context and scenario modelled. 10.1371/journal.pmed.1001401.t005 Table 5 Cost-effectiveness estimates by scenario. Reference Scenario Description: Prioritisation Estimate Measure US$ in Publication 2012US$ Generalised epidemics in southern Africa Abbas [25] Pessimistic: high sexual activity group Cost/infection averted 2,949–9,923 3,450–11,609 Pessimistic: 15–20 y Cost/infection averted 20,202–67,970 23,636–79,525 Pessimistic: no prioritisation Cost/infection averted 20,164–67,842 23,591–79,375 Neutral: high sexual activity group Cost/infection averted 1,160–3,904 1,357–4,567 Neutral: 15–20 y Cost/infection averted 8,968–30,173 10,492–35,302 Neutral: no prioritisation Cost/infection averted 9,629–32,398 11,265–37,905 Optimistic: high sexual activity group Cost/infection averted 638–2,147 746–2,512 Optimistic: 15–20 y Cost/infection averted 5,723–19,254 6,695–22,527 Optimistic: no prioritisation Cost/infection averted 6,812–22,918 7,970–26,814 Pretorius [30] Optimistic: women 15–25 y, no behaviour change Cost/infection averted >25,000 >26,625 Optimistic: women 15–35 y, no behaviour change Cost/infection averted >22,500 >23,963 Optimistic: women 25–35 y, no behaviour change Cost/infection averted >20,000 >21,300 Medium efficacy: women 25–35 y, behaviour change Cost/infection averted >30,000 >31,950 Hallett [28] Efficacy range, high risk: conception or pregnancy use Cost/infection averted −6,000 to 8,000 −6,192 to 8,256 Efficacy range, low risk: conception or pregnancy use Cost/infection averted −2,000 to 12,000 −2,064 to 12,384 Efficacy range, high risk: up to ART initiation Cost/infection averted −2,200 to 21,000 −2,270.4 to 21,672 Efficacy range, high risk: always use PrEP Cost/infection averted 0–26,000 0–26,832 Efficacy range, low risk: always use PrEP Cost/infection averted 6,000–66,000 6,192–68,112 Optimistic, low risk, high ART cost: up to ART initiation Cost/infection averted 3,000 3,096 Optimistic, low risk, high ART cost: up to ART initiation +1 y Cost/infection averted 3,000 3,096 Optimistic, high risk: up to ART initiation Cost/QALY gained −200 to 500 −206 a to 516 a Optimistic, low risk: up to ART initiation Cost/QALY gained 260–1,600 268 a –1,651 a Pessimistic, high risk: up to ART initiation Cost/QALY gained 700–1,900 722 a –1,960 a Pessimistic, low risk: up to ART initiation Cost/QALY gained 2,500–4,900 2,580 a –5,056 a Williams [32] CAPRISA efficacy: high coverage Cost/infection averted 420–2,982 447–3,175 CAPRISA efficacy: low coverage Cost/infection averted 562–4,222 598–4,496 CAPRISA efficacy: high coverage Cost/DALY averted 18–130 19 a –138 a CAPRISA efficacy: low coverage Cost/DALY averted 27–181 28 a –193 a Walensky [31] CAPRISA efficacy, test freq 3 mo: high incidence women Cost/life year saved 1,600 1,704 a CAPRISA efficacy, test freq 1 mo: high incidence women Cost/life year saved 2,700 2,876 a Alistar [37] b PrEP: no prioritisation recruitment rate 25% to 100%, no ART expansion Cost/QALY gained 1,200 1,200 a PrEP: high risk group recruitment rate 50% to 100%, no ART expansion Cost/QALY gained CS CS a PrEP: no prioritisation recruitment rate 25% to 100%, ART +25% as per guidelines Cost/QALY gained 980–1,050 980 a –1,050 a PrEP: high risk group recruitment rate 100%, ART +25% as per guidelines Cost/QALY gained 50 50 a PrEP: no prioritisation recruitment rate 25% to 100%, ART +50% as per guidelines Cost/QALY gained 900–1,000 900 a –1,000 a PrEP: high risk group recruitment rate 100%, ART +50% as per guidelines Cost/QALY gained 160 160 a PrEP: no prioritisation recruitment rate 25% to 100%, ART +75% as per guidelines Cost/QALY gained 860–970 860 a –970 a PrEP: high risk group recruitment rate 100%, ART +75% as per guidelines Cost/QALY gained 210 210 a PrEP: no prioritisation recruitment rate 25% to 100%, ART +100% as per guidelines Cost/QALY gained 840–950 840 a –950 a PrEP: high risk group recruitment rate 100%, ART +100% as per guidelines Cost/QALY gained 230 230 a PrEP: no prioritisation recruitment rate 25% to 100%, universal ART +25% Cost/QALY gained 810–940 810 a –940 a PrEP: high risk group recruitment rate 100%, universal ART +25% Cost/QALY gained 220 220 a PrEP: no prioritisation recruitment rate 25% to 100%, universal ART +50% Cost/QALY gained 760–900 760 a –900 a PrEP: high risk group recruitment rate 100%, universal ART +50% Cost/QALY gained 280 280 a PrEP: no prioritisation recruitment rate 25% to 100%, universal ART +75% Cost/QALY gained 740–890 740 a –890 a PrEP: high risk group recruitment rate 100%, universal ART +75% Cost/QALY gained 290 290 a PrEP: no prioritisation recruitment rate 25% to 100%, universal ART +100% Cost/QALY gained 740–880 740 a –880 a PrEP: high risk group recruitment rate 100%, universal ART +100% Cost/QALY gained 300 300 a Cremin [36] c PrEP: no prioritisation, cov 4.4% of 15–54 y (baseline: status quo, current ART scale-up) Cost/infection averted 9,390 9,390 PrEP: prioritisation, cov 7.3% of 15–24 y (baseline: status quo, current ART scale-up) Cost/infection averted 10,540 10,540 No PrEP, 80% universal ART (baseline: 80% ART200 and 80% MC) Cost/infection averted 10,530 10,530 PrEP: 15–24 y cov 40%, 80% universal ART (baseline: 80% ART200, 80% MC, 80% ART350) Cost/infection averted 39,900 39,900 PrEP: 15–54 y cov 80%, 80% universal ART (baseline: 80% ART200, 80% MC) Cost/infection averted 20,500 20,500 Concentrated epidemics among MSM in high-income countries Desai [26] d Exposure, pessimistic: high adherence Cost/QALY gained 6,661–36,268 7,793 e –42,433 e Exposure, pessimistic: medium adherence Cost/QALY gained 55,167–84,774 64,545 f –99,185 f Exposure, pessimistic: low adherence Cost/QALY gained 113,601–143,208 132,913 f–167,553 Adherence, pessimistic: high adherence Cost/QALY gained CS–8,158 CS e –9,545 e Adherence, pessimistic: medium adherence Cost/QALY gained CS–10,327 CS e –12,082 e Adherence, pessimistic: low adherence Cost/QALY gained CS–13,499 CS e –15,793 e Basic, pessimistic: high adherence Cost/QALY gained CS–15,099 CS e –17,665 e Basic, pessimistic: medium adherence Cost/QALY gained 17,168–46,775 20,086 e –54,726 f Basic, pessimistic: low adherence Cost/QALY gained 66,896–96,502 78,268 f –112,907 Exposure, optimistic: high adherence Cost/QALY gained CS–9,925 CS e –11,612 e Exposure, optimistic: medium adherence Cost/QALY gained 13,307–42,914 15,569 e –50,209 f Exposure, optimistic: low adherence Cost/QALY gained 46,502–76,109 54,407 f –89,047 f Adherence, optimistic: high adherence Cost/QALY gained CS CS e Adherence, optimistic: medium adherence Cost/QALY gained CS CS e Adherence, optimistic: low adherence Cost/QALY gained CS CS e Basic, optimistic: high adherence Cost/QALY gained CS–1,009 CS e –1,180 e Basic, optimistic: low adherence Cost/QALY gained 37,947–67,553 44,398 e –79,037 f Basic, optimistic: medium adherence Cost/QALY gained CS–28,393 CS e –33,220 e Paltiel [34] Medium efficacy: no prioritisation Cost/QALY gained 298,000 359,984 High efficacy: no prioritisation Cost/QALY gained 107,000 129,256 f Medium efficacy, low cost Cost/QALY gained 114,000 137,712 f Medium efficacy: young Cost/QALY gained 189,000 228,312 Koppenhaver [29] High adherence: no prioritisation Cost/QALY gained 353,739 376,732 iPrEX adherence: no prioritisation Cost/QALY gained 570,273 607,341 Juusola [35] Cov 100%, PrEP cost US$26/d, no resistance: high risk MSM Cost/QALY gained 52,443 55,852 f Cov100%, PrEP cost US$26/d, no resistance: no prioritisation Cost/QALY gained 216,480 230,551 Cov 100%, high eff, PrEP cost US$26/d, no resistance: high risk MSM Cost/QALY gained 35,080 37,360 e Cov 100%, high eff, PrEP cost US$26/d, no resistance: no prioritisation Cost/QALY gained 146,228 155,733 Cov 100%, PrEP cost US$15/d, no resistance: no prioritisation Cost/QALY gained 131,277 139,810 f Cov 100%, PrEP cost US$50/d, no resistance: high risk MSM Cost/QALY gained 104,516 111,310 f Cov 100%, PrEP cost (50% ARV), no resistance: high risk MSM Cost/QALY gained 25,165 26,801 e Cov 100%, PrEP cost (75% ARV), no resistance: high risk MSM Cost/QALY gained 38,804 41,326 e Cov 100%, no resistance, 8% reduction QoL: high risk MSM. Cost/QALY gained 95,006 101,181 f Cov 100%, PrEP cost US$26/d, resistance: high risk MSM Cost/QALY gained 57,861 61,622 f Cov 100%, PrEP cost US$26/d, resistance: no prioritisation Cost/QALY gained 233,040 248,188 Cov 50%, PrEP cost US$26/d, no resistance: high risk MSM Cost/QALY gained 44,556 47,452 e Cov50%, PrEP cost US$26/d, no resistance: no prioritisation Cost/QALY gained 188,421 200,668 Cov 50%, high eff, PrEP cost US$26/d, no resistance: high risk MSM Cost/QALY gained 26,766 28,506 e Cov 50%, high eff, PrEP cost US$26/d, no resistance: no prioritisation Cost/QALY gained 120,080 127,885 f Cov 50%, PrEP cost US$15/d, no resistance: no prioritisation Cost/QALY gained 113,935 121,341 f Cov 50%, PrEP cost US$50/d, no resistance: high risk MSM Cost/QALY gained 89,658 95,486 f Cov 50%, PrEP cost (50% ARV), no resistance: high risk MSM Cost/QALY gained 20,930 22,290 e Cov 50%, PrEP cost (75% ARV), no resistance: high risk MSM Cost/QALY gained 32,743 34,871 e Cov 50%, no resistance, 8% reduction QoL: high risk MSM. Cost/QALY gained 72,762 77,492 f Cov 50%, PrEP cost US$26/d, resistance: high risk MSM Cost/QALY gained 56,492 60,164 f Cov 50%, PrEP cost US$26/d, resistance: no prioritisation Cost/QALY gained 226,325 241,036 Cov 20%, PrEP cost US$26/d, no resistance: high risk MSM Cost/QALY gained 40,279 42,897 e Cov20%, PrEP cost US$26/d, no resistance: no prioritisation Cost/QALY gained 172,091 183,277 Cov 20%, high eff, PrEP cost US$26/d, no resistance: high risk MSM Cost/QALY gained 22,374 23,828 e Cov 20%, high eff, PrEP cost US$26/d, no resistance: no prioritisation Cost/QALY gained 105,066 111,895 f Cov 20%, PrEP cost US$15/day, no resistance: no prioritisation Cost/QALY gained 103,841 110,591 f Cov 20%, PrEP cost US$50/d, no resistance: high risk MSM Cost/QALY gained 81,593 86,897 f Cov 20%, PrEP cost (50% ARV), no resistance: high risk MSM Cost/QALY gained 18,637 19,848 e Cov 20%, PrEP cost (75% ARV), no resistance: high risk MSM Cost/QALY gained 29,458 31,373 e Cov 20%, no resistance, 8% reduction QoL: high risk MSM Cost/QALY gained 62,431 66,489 f Cov 20%, PrEP cost US$26/d, resistance: high risk MSM Cost/QALY gained 78,884 84,011 f Cov 20%, PrEP cost US$26/d, resistance: no prioritisation Cost/QALY gained 303,091 322,792 Concentrated epidemics among MSM in low- and middle-income countries Gomez [27] Low coverage: high prioritisation Cost/DALY averted 403–637 415 g –657 g Low coverage: some prioritisation Cost/DALY averted 447–707 461 g –729 g Low coverage: no prioritisation Cost/DALY averted 1,076–1,702 1,110 g –1,756 g High coverage: high prioritisation Cost/DALY averted 665–1,052 686 g –1,085 g High coverage: some prioritisation Cost/DALY averted 886–1,400 914 g –1,445 g High coverage: no prioritisation Cost/DALY averted 1,125–1,779 1,161 g –1,835 g Concentrated epidemics among PWID in low- and middle-income countries Alistar [33] MMT 25%, no PrEP Cost/QALY gained 530 546 h MMT 25%, ART 80% (for IDU and general population), no PrEP Cost/QALY gained 870 896 h MMT 25%, ART 80% (for IDU and general population), PrEP 25% to 50% Cost/QALY gained 3,080–3,910 3,172 h –4,027 i PrEP 25% to 50% Cost/QALY gained 14,590–14,680 15,028–15,120 MMT 25%, PrEP 25% to 50% Cost/QALY gained 4,800–6,100 4,944 i –6,283 i ART 80% (for IDU and general population), PrEP 25% to 50% Cost/QALY gained 3,290–4,210 3,389 h –4,336 i Thresholds used to determine cost-effectiveness, based on World Bank database [23]. Bold-black signifies an estimate is cost-effective or very cost-effective with regards to the country-specific threshold. a For South Africa, an intervention is considered very cost-effective at a threshold of less than 1× GDP per capita, US$8,070. b In Alistar et al., several scenarios were considered for ART recruitment rates of 25%, 50%, 75%, and 100% in addition to the 40% status quo coverage as per guidelines and following universal access. c In Cremin et al., several scenarios were considered for ART coverage. ART200: coverage of ART in HIV-infected people starting at CD4 count of <200 cells/ml; ART350: coverage of ART in HIV-infected people starting at CD4 count of <350 cells/ml; universal ART: coverage of ART in HIV-infected people starting at any CD4 count level. d In Desai et al., the authors considered three effectiveness mechanisms: basic, adherence-dependent, and exposure-dependent. e For USA, an intervention is considered very cost-effective at a threshold of less than 1× GDP per capita, US$48,442. f For USA, an intervention is considered cost-effective between 1× GDP per capita, US$48,442 and 3× GDP per capita, US$145,326. g For Peru, an intervention is considered very cost-effective at a threshold of less than 1× GDP per capita, US$ US$6,009. h For Ukraine, an intervention is considered very cost-effective at a threshold of less than 1× GDP per capita, US$3,615. i For Ukraine, an intervention is considered cost-effective between 1× GDP per capita, US$3,615 and 3× GDP per capita, US$10,845. cov., coverage; CS, cost saving; freq, frequency; MC, male circumcision; MMT, methadone maintenance treatment; QoL, quality of life; resist., resistance. Generalised Epidemics in Southern Africa (n = 7) Studies on topical PrEP and two studies on oral PrEP suggest the intervention to be cost-effective (topical PrEP: <200 US$/DALY [32], <3,000 US$/LYS [31]; oral PrEP: <5,000 US$/QALY [28], <2,800 US$/QALY [37]) using benchmarks for cost-effectiveness specific to South Africa [22]. Three studies reported cost/infection averted only, estimates ranging from US$1,000 to 39,900 [25],[30],[36]. For topical PrEP, the two studies presented different estimates of cost-effectiveness: less cost-effective in Walensky et al. [31] (
- Record: found
- Abstract: found
- Article: found
Expanding ART for Treatment and Prevention of HIV in South Africa: Estimated Cost and Cost-Effectiveness 2011-2050
Reuben Granich, James G. Kahn, Rod Bennett … (2012)
Background Antiretroviral Treatment (ART) significantly reduces HIV transmission. We conducted a cost-effectiveness analysis of the impact of expanded ART in South Africa. Methods We model a best case scenario of 90% annual HIV testing coverage in adults 15–49 years old and four ART eligibility scenarios: CD4 count <200 cells/mm3 (current practice), CD4 count <350, CD4 count <500, all CD4 levels. 2011–2050 outcomes include deaths, disability adjusted life years (DALYs), HIV infections, cost, and cost per DALY averted. Service and ART costs reflect South African data and international generic prices. ART reduces transmission by 92%. We conducted sensitivity analyses. Results Expanding ART to CD4 count <350 cells/mm3 prevents an estimated 265,000 (17%) and 1.3 million (15%) new HIV infections over 5 and 40 years, respectively. Cumulative deaths decline 15%, from 12.5 to 10.6 million; DALYs by 14% from 109 to 93 million over 40 years. Costs drop $504 million over 5 years and $3.9 billion over 40 years with breakeven by 2013. Compared with the current scenario, expanding to <500 prevents an additional 585,000 and 3 million new HIV infections over 5 and 40 years, respectively. Expanding to all CD4 levels decreases HIV infections by 3.3 million (45%) and costs by $10 billion over 40 years, with breakeven by 2023. By 2050, using higher ART and monitoring costs, all CD4 levels saves $0.6 billion versus current; other ART scenarios cost $9–194 per DALY averted. If ART reduces transmission by 99%, savings from all CD4 levels reach $17.5 billion. Sensitivity analyses suggest that poor retention and predominant acute phase transmission reduce DALYs averted by 26% and savings by 7%. Conclusion Increasing the provision of ART to <350 cells/mm3 may significantly reduce costs while reducing the HIV burden. Feasibility including HIV testing and ART uptake, retention, and adherence should be evaluated.
Author and article information
Contributors
Sabina S Alistar
Philip M Grant
Eran Bendavid
Journal
Journal ID (nlm-ta): BMC Med
Journal ID (iso-abbrev): BMC Med
Title:
BMC Medicine
Publisher:
BioMed Central
ISSN
(Electronic):
1741-7015
Publication date Collection: 2014
Publication date
(Electronic):
17
March
2014
Volume: 12
Page: 46
Affiliations
Article
Publisher ID: 1741-7015-12-46DOI: 10.1186/1741-7015-12-46
PMC ID: 4003813
PubMed ID: 24629217
SO-VID: ffae3491-1a7c-4624-823f-e0add776fc07
Copyright © Copyright © 2014 Alistar et al.; licensee BioMed Central Ltd.
License:
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.
History
Date
received
: 24
August
2013
Date
accepted
: 12
February
2014
Categories
Subject:
Research Article
ScienceOpen disciplines: Medicine
Data availability:
ScienceOpen disciplines: Medicine