Major donor initiatives to combat HIV along with increasing in-country commitments to curb the epidemic have facilitated rapid HIV treatment scale-up in low- and middle-income countries.1 However, although a minority of countries have achieved universal antiretroviral therapy (ART) access or coverage targets of 80% or higher, these targets remain challenging in many settings.1 The global economic crisis and waning international political commitment to HIV prevention and treatment efforts globally have contributed to declines in donor disbursements for ART provision and decreasing country-level budgets.2,3 Diminishing resources for HIV treatment occur against a backdrop of increased need for ART because of efforts to improve case identification and retention in care4; demonstrated survival benefit among those receiving ART5,6; revised World Health Organization (WHO) guidelines calling for earlier ART initiation7,8; and new clinical evidence that ART decreases the risk of transmitting HIV.9,10
Limited funding, health system, and social service capacity to address the increasing demand for ART will require country-specific policy decisions at the national level. Haiti, a poor Caribbean country with a per capita income of approximately US $650 annually,11 has mounted a successful response to the HIV/AIDS epidemic with assistance from international donors, similar to other resource-limited settings. For example, evidence suggests that HIV prevalence in Haiti has declined over the past decade,12 with the percentage of Haitian adults, aged 15–49 years, living with HIV currently at about 1.9%.13 WHO estimates that of those medically eligible for treatment according to current WHO guidelines,7 the number of HIV-infected individuals receiving ART in Haiti has increased significantly, from 5% in 2004 to more than 40% in 2009.14 Despite these gains, however, funding in Haiti and other low-income settings remains limited. For example, US President's Emergency Plan for AIDS Relief funds allocated to Haiti decreased from US $164.1 million in fiscal year 2010 to US $158.5 million in fiscal year 2011.15 Global Fund disbursements to Haiti remained relatively flat in 2011 and 2012 at approximately US $15.5 million.16 Future availability of resources for expanded treatment according to current WHO guidelines (ie, ART initiation at WHO stages III–IV or CD4 count <350 cells per microliter) versus previous guidelines (ie, ART initiation at WHO stage IV or CD4 count <200 cells per microliter) is uncertain. In this context, our objective was to forecast the potential lives saved resulting from further ART expansion in Haiti, potential lives lost as a result of not continuing to expand ART availability, and fraction of those eligible to receive ART with and without further expansion.
We developed a multicohort mathematical model of untreated and treated HIV disease in Haiti. The model assesses 2 policy-relevant eras of HIV/AIDS treatment: (1) antiretroviral scale-up between 2004 and 2009 and (2) 10-year policy projections beginning in 2010, reflecting a period of funding uncertainty for HIV treatment. Model inputs were estimated from 30 years of well-characterized natural history cohort data, ART cohort data, and a randomized trial of early versus delayed ART, all collected from the Haitian Study Group for Kaposi's Sarcoma and Opportunistic Infections (GHESKIO) clinic in Port-au-Prince, Haiti.8,17–22 Additional model inputs came from a model verification process informed by country-level reports from Haiti on ART scale-up between 2004 and 2009.13 We evaluated the performance of alternative ART expansion scenarios at the population level, including the total number of deaths and HIV-infected individuals alive, the number receiving ART, and ART coverage, defined as the fraction of those eligible to receive ART who receive it. We conducted sensitivity analyses to assess the impact of uncertain model input parameters and policy-related variables on our results. The model is implemented in Microsoft Excel 2010 (Microsoft Corporation, Redmond, Washington) (Additional methodological details are available in the Supplemental Digital Content, http://links.lww.com/QAI/A412).
We evaluated 5 ART initiation scenarios: (1) No New ART initiation (worst case), (2) Restricted ART capacity to current treatment levels (Fixed Capacity), (3) Current Rates of ART initiation for patients with CD4 counts <200 cells per microliter and CD4 counts 200–350 cells per microliter (status quo), (4) Limited Expansion, increasing rates of ART initiation for patients with CD4 counts 200–350 cells per microliter to implement the change from previous to current WHO guidelines,13,27 and (5) Full Expansion, increasing rates of ART initiation for all patients with CD4 counts <350 cells per microliter (best case) (Table 1). Scenarios with limited treatment capacity, as occurs with the No New ART and Fixed Capacity scenarios, are designed to reflect treatment capacity constraints that may face Haiti's health sector and other similar health sectors in the current funding climate. In each of the scenarios, individuals eligible to initiate ART face competing risks for other events—including loss from care and AIDS- and non–AIDS-related death—that can precede ART initiation despite eligibility. To evaluate the number of deaths averted during the scale-up period (ie, 2004–2009), we also evaluate a No ART scenario, in which we assumed that no ART was available for HIV-infected individuals in Haiti.
The scenarios were defined to reflect the current situation in Haiti and other resource-poor countries with constrained budgets in several ways. First, in accordance with the international guidelines and clinical practice in Haiti, individuals are treated with up to 2 sequential ART regimens, receive semiannual CD4 tests, and have quarterly clinic visits unless otherwise clinically indicated.7 Second, disease progression in individuals receiving ART varies based on when, in the course of disease, ART is initiated, with ART initiation earlier in the course of disease (eg, at CD4 count <350 cells per microliter) associated with less rapid disease progression and improved health outcomes.8 Third, detection of ART failure and switching to second-line therapy occurs based on clinical and immunologic monitoring of treatment response in HIV-infected patients.7 In addition, individuals who are in care but not receiving ART have a decreased risk of disease progression and mortality compared with those who are not in care, including those who are lost.23,24 Finally, once an HIV-infected individual is lost from treatment and/or care, the individual is not eligible to return and is assumed to follow natural history disease progression until death. Although this assumption may be a simplification, the literature suggests a high risk of mortality among patients lost from ART programs in resource-limited settings.25
We developed a state-transition (Markov) model for multiple cohorts of treated and untreated HIV-infected individuals.8,13,17–21 The model is defined by a set of 12 mutually exclusive and collectively exhaustive health states. Movement between health states occurs probabilistically through a series of possible events within an annual model cycle.26,27 For each cohort, the model simulates strategy-specific clinical disease progression and mortality that varies depending on engagement with clinical care over time. Cohorts of hypothetical prevalent and newly HIV-infected patients progress through mutually exclusive stages of untreated disease (Fig. 1): CD4 count >350 cells per microliter (ie, asymptomatic HIV disease), CD4 count 200–350 cells per microliter (ie, intermediate HIV disease with and without symptoms), and CD4 count <200 cells per microliter (ie, AIDS). Within each stage, the model is structured such that individuals may remain out of care, enter into care, initiate ART, become lost from treatment or care, or die, representing 12 main health states; death was modeled as an absorbing state. The model applies transition probabilities to govern the fraction of each cohort moving among the health states. Those individuals eligible for ART can be in care and receive either first- or second-line ART. Individuals may die of AIDS- or non–AIDS-related causes; mortality risk varies based on both disease stage and clinical engagement.
The model begins in 2004, the beginning of antiretroviral scale-up in Haiti, with the prevalent cohort distributed across health states and clinical care events based on population-level data. Successive cohorts of newly HIV-infected individuals enter the model annually in each of the following 15 years. The model predicts treated and untreated disease progression, mortality, and engagement with clinical care for each cohort between 2004 and 2009 (the ART scale-up period in Haiti), with inputs adjusted such that outcomes approximate the historical data available from Haiti during this time frame.28 The model then forecasts scenario-specific outcomes for the policy projection period, 2010–2020. Annual outcomes are summed across the cohorts in terms of mortality, number of individuals on ART, and ART coverage for each of the HIV treatment expansion scenarios.
Model Input Parameter Estimation
We used patient-level data from 3 Haitian observational cohorts and a randomized, controlled trial conducted in Haiti to derive model inputs—in the form of transition probabilities—for treated and untreated disease progression of individual cohorts (Table 2).8,17–22 Patient-level natural history data came from a prospective, longitudinal cohort of 436 patients, including 40 seroconverters, recruited by the GHESKIO clinic in Port-au-Prince, Haiti, between September 1985 and September 1998.17,18,20 To derive model inputs for HIV-infected individuals initiating ART with CD4 200–350 cells per microliter, we used prospective, longitudinal cohort data on 910 HIV-infected adults collected by GHESKIO between March 2003 and May 2009.19,21,22 To derive model inputs for individuals initiating ART with AIDS, patient-level data came from 408 patients in the early treatment group of the CIPRA HT-001 randomized, controlled trial of early versus delayed ART conducted at GHESKIO.8
To derive the model inputs, we performed incidence density analysis of the patient-level data and estimated event rates. Event rates were calculated by summing the total number of events (eg, the number lost from ART conditional on ART initiation at CD4 count <200 cells per microliter, the number of untreated individuals with CD4 count 200–350 cells per microliter progressing to AIDS), relative to the total person-time at risk for the event. In deriving the event rates from the patient-level data, subjects who were event free were right censored at death, the end of the study, or the last documented clinic visit if lost from care or transferred to another care facility.29 Following typical practice, censoring was considered independent and non-informative, such that individuals who were fully followed (and therefore not censored) during the study period were similar to those who were not fully followed (or censored). The patient-level data were analyzed using STATA software, release 11 (StataCorp, College Station, TX). We calculated annual event rates to correspond with the model's annual cycle length. We assumed that the number of events from the patient-level data occurred in a Poisson process (ie, continuously, independently, and at a constant rate) with time between events having an exponential distribution, allowing conversion of the event rates to probabilities for use as inputs in the model.30
Additional model inputs were derived from a model verification process involving calibration of the model to population-level data from Haiti.28 We first identified uncertain model inputs, including the number of newly HIV-infected individuals annually and their engagement with clinical care (ie, probabilities of linkage to care, pre-ART retention in care, and ART enrollment). Next, multiple uncertain model input parameters were systematically and simultaneously varied. We then identified those input values resulting in model estimates of the number on ART annually that minimized the percent deviation between model predictions and historical data on the number receiving ART annually in Haiti. At the time this analysis was conducted, national data on the number receiving ART annually were available for 2004–2009.13 Finally, the means of the input values represented in the best-fitting parameter sets were used as model inputs for policy projections beginning in 2010. The model allowed the number newly infected annually to vary between 2005 and 2009; however, in the base case, this parameter was held constant at 2009 levels of 8600 per year over the 10-year policy projection period.
One-way sensitivity analyses were conducted to evaluate the impact on results of uncertainty in model input parameters and in policy-related variables, including natural history disease progression, antiretroviral effectiveness, and HIV incidence (Table 2). Ranges were defined by the 95% confidence intervals derived from the patient-level data, estimated bounds for both population-level inputs and parameters derived during the model verification process, or by ±25% for the adjusted disease progression parameters.8,14,17–22,28
We also conducted several targeted sensitivity analyses to reflect clinically and policy-relevant concerns that may affect health outcomes and be important to decision makers. First, we varied the time of detection of first-line antiretroviral failure and switching to second-line ART and, in turn, mortality risk on second-line ART, reflecting potential earlier detection by using new HIV RNA monitoring technologies (HIV RNA monitoring for earlier ART failure detection, Table 2).31 Second, we considered the effects of ART on HIV transmission9,10 by linearly decreasing the number of newly HIV-infected annually between 1% and 20%. Finally, we assessed the impact of simultaneous policies that increased HIV testing and linkage to care and retention in treatment and care (optimal policy improvement, Table 2).
Verification of Model Performance: ART Scale-up in Haiti, 2004–2009
Model estimates of the number on ART in Haiti between 2004 and 2009 are within 7% for each year of the reported data and within 1% on average for the entire period.14,28 Model-based antiretroviral coverage estimates between 2004 and 2009 fall within the confidence intervals of other published estimates,14 with the model predicting 36.4% of those eligible for ART at a CD4 count <350 cells per microliter receiving it in 2009.
By 2010, the model estimates 11,500 deaths averted since the beginning of ART scale-up in 2004 and of the estimated 103,500 individuals living with HIV 27,300 are receiving ART. The model predicts that ART coverage increases to 57.4% and 40.3% for medical ART eligibility thresholds defined by CD4 count <200 and <350 cells per microliter, respectively. Approximately 15,600 individuals are estimated to be in care but off ART, with approximately three-quarters of those individuals eligible to receive treatment according to current WHO guidelines.
Base Case Policy Projections, 2010–2020
If ART initiation continues at current (ie, 2010) rates, the number on ART will increase to 43,300 (+58.6% over 10 years) by 2020, with 89,700 deaths estimated between 2010 and 2020 (Table 3, panel A and Fig. 2). Compared with ART initiation at current rates, limited ART expansion (ie, an increased rate of ART initiation for patients with CD4 counts 200–350 cells per microliter to implement the change from the previous to current WHO guidelines7,32) will increase the number on ART by 5400 (+12.9%) and avert 3000 deaths (–3.3%) by 2020. The Full Expansion scenario will increase the number on ART by 7400 (+17.1%) and avert 4300 deaths (−4.8%) by 2020. Restricting ART initiation to achieve constant ART capacity will reduce the number on ART by 15,700 (−36.3%) and result in 10,200 (+11.4%) additional cumulative deaths, whereas No New ART initiation will reduce the number on ART by 25,600 (−59.1%) and increase cumulative deaths by 15,200 (+16.9%).
By 2020, ART coverage according to current guidelines will reach 62.1% with Full Expansion, compared with 56.0% with expansion at current rates and 28.7% in the No New ART scenario. The number of HIV-infected individuals in care and eligible for, but not receiving, ART will fall to 6500 with Full Expansion compared with 8600 at current rates and 16,200 in the No New ART scenario. With predicted cumulative deaths ranging from 85,400 (best case) to 104,900 (worst case), the model estimates as many as 19,500 lives could be saved through ART expansion over the next decade. This is equivalent to nearly 20% of the number of individuals estimated to be living with HIV in Haiti in 2010.
When univariate sensitivity analyses were conducted to assess the impact of uncertainty in the model parameters, results are most sensitive to HIV incidence, untreated HIV disease progression, and the probability of pre-ART loss from care (see Tables S2-S4 and Figure S2, Supplemental Digital Content, http://links.lww.com/QAI/A412). Variation in these parameters had a greater impact on treatment-related outcomes (eg, ART coverage) than survival. For example, given the range of estimates for the number newly infected annually, ART coverage estimates vary from −12.8% to +4.4% (ie, ART coverage 25%–65%) for the No New ART (worst case) and Full Expansion (best case) scenarios, respectively. In contrast, estimated deaths vary from −5.4% to +6.4% (ie, 4600–6700 cumulative deaths) for the worst and best scenarios. Results are less sensitive to disease progression on ART and HIV testing and linkage to care.
We also conducted targeted sensitivity analyses to reflect specific concerns important to decision makers. We found that results are not sensitive to assumptions regarding earlier detection of antiretroviral failure and switching to second-line ART and lower mortality on second-line ART, resulting from HIV RNA treatment monitoring for patient management. For example, cumulative deaths over 10 years decreased by 1500 to 103,400 (−1.4%) for the No New ART scenario and by 1900 to 85,400 (−2.2%) for the Full Expansion scenario. Decreasing HIV incidence over time to reflect the prevention benefit of ART reduces cumulative deaths across all strategies over the analytic time horizon. The mortality difference between the Full Expansion and No New ART scenarios declines from 19,500 (base case) to 19,400 (1% decrease in the number newly infected annually) to 17,600 (20% annually) cumulative deaths averted over 10 years. ART coverage for all scenarios increases as the annual percent decrease in HIV incidence increases.
An optimal policy improvement in which HIV testing and linkage to care and retention in treatment and care are simultaneously improved results in 52,300 on ART by 2020 and 86,400 cumulative deaths over 10 years, if ART initiation continues at current rates (Table 3, panel B). Further treatment expansion will increase the number on ART by 11,700 (+22.4%) and avert 6800 deaths (−7.9%) by 2020. ART coverage by 2020 ranges from 30.0% (No New ART scenario) to 72.2% (Full Expansion scenario).
Our results show that by 2020, treatment capacity at current ART enrollment rates in Haiti must increase by approximately 16,000 slots compared with treatment capacity in 2010, representing a 58.6% capacity increase over 10 years. Near-universal ART coverage, as defined by WHO's 80% coverage targets,1 can begin to be realized in Haiti but requires taking additional, simultaneous steps to improve engagement with clinical care, including case identification and linkage to care, pre-ART retention in care, and retention on treatment, in addition to efforts to increase treatment expansion.
Findings from this analysis can inform country-level, HIV-related planning efforts in resource-limited settings. Enrolling new patients on ART at rates that reflect scale-up under the previous WHO HIV treatment guidelines32 (ie, according to the Current Rates scenario) will require nearly 60% more treatment slots in 10 years. Implementing the current WHO HIV treatment guidelines7 at a level consistent with previous scale-up (ie, the Limited Expansion scenario) will require over 75% more treatment slots in 10 years, whereas enrollment of all treatment-eligible individuals on ART will require approximately 85% more treatment slots at the end of this decade. Efficiency improvements in care delivery (eg, integration of care across disease domains, task delegation), continued donor funding along with country-level financial sustainability plans, and human resource capacity building will need to be addressed simultaneously if treatment capacity increases are to continue.33
In addition to continued treatment expansion, improvements in case identification and linkage to care, retention in pre-ART care, and retention on ART will all be required to increase ART coverage. This analysis suggests that these complementary efforts should focus in particular on retaining HIV-infected patients in care once identified and linked to care but before initiating ART. In the current analysis, along with a policy of full treatment expansion and efforts to improve case identification and treatment retention, increases in pre-ART retention of more than 70% were required to begin to achieve universal ART coverage. Similar opportunities for improvement regarding pre-ART retention in care exist in sub-Saharan African settings,34 even though challenges remain to effectively retain newly diagnosed individuals in care. There may be a similar potential benefit from improved retention on treatment in settings other than Haiti.35
We interpret our findings in the context of recent reports of the number on ART in Haiti. As of May 2012, 37,841 individuals were actively on ART in Haiti (written personal communication with JW Pape, MD, dated July 19, 2012), which is consistent with the results from the increasing capacity and policy improvement scenarios considered in the current analysis. Treatment expansion and other policy improvements not only will increase the number on ART and ART coverage but may also reduce tuberculosis-associated mortality and prevent new cases of tuberculosis, particularly when ART is initiated earlier in disease progression.36 As data on these benefits continue to emerge, the model can be updated to provide additional insight into the health impact of treatment expansion.
Our analysis has several limitations. First, we assume that regimen-specific treatment effectiveness is fixed over the 10-year policy projection period. However, given the relatively short analytic time horizon, it is unlikely that improvements in ART effectiveness would have major impact on our policy conclusions that are driven by mortality among those who lack access to care. Second, the analysis does not consider changes in adherence to ART over time. Although changes in adherence could affect mortality in this population,37,38 these effects would not be seen at the community level over the analysis period. Third, our projections reflected the adult HIV-infected population in Haiti and did not explicitly include HIV-infected children. Finally, the current analysis does not explicitly account for population-level disease dynamics. Despite recent evidence indicating ART may decrease the risk of HIV transmission at the patient level,9,10 there was a relatively small impact on our results across strategies when we reduced the number of new HIV infections for the duration of our forecast.
This analysis suggests that expanding access to ART will save lives and that near-universal access may be achievable. This will require a better understanding by HIV care providers and funders of how treatment-related resources can be more effectively and efficiently targeted to achieve these goals, including providing an adequate workforce, sufficient health-care facilities, and necessary logistics support. Efforts to sustain international financing for ART must be coupled with strategies to improve health system efficiency to prevent avoidable deaths and increase access to ART.
The authors are indebted to Adias Marcelin, Heejung Bang, PhD, Ashley Eggman, MS, and Jared Leff, MS, for their assistance.
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HIV/AIDS; antiretroviral therapy; mortality; resource-limited settings; simulation
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