Person:

Freedberg, Kenneth

Objective: To estimate the prevalence of drug-resistant tuberculosis (TB) and describe the resistance patterns in patients commencing antiretroviral therapy (ART) in an HIV clinic in Durban, South Africa. Design Cross-sectional cohort study. Methods Consecutive HIV-infected adults (≥18y/o) initiating HIV care were enrolled from May 2007–May 2008, regardless of signs or symptoms of active TB. Prior TB history and current TB treatment status were self-reported. Subjects expectorated sputum for culture (MGIT liquid and 7H11 solid medium). Positive cultures were tested for susceptibility to first- and second-line anti-tuberculous drugs. The prevalence of drug-resistant TB, stratified by prior TB history and current TB treatment status, was assessed. Results: 1,035 subjects had complete culture results. Median CD4 count was 92/µl (IQR 42–150/µl). 267 subjects (26%) reported a prior history of TB and 210 (20%) were receiving TB treatment at enrollment; 191 (18%) subjects had positive sputum cultures, among whom the estimated prevalence of resistance to any antituberculous drug was 7.4% (95% CI 4.0–12.4). Among those with prior TB, the prevalence of resistance was 15.4% (95% CI 5.9–30.5) compared to 5.2% (95% CI 2.1–8.9) among those with no prior TB. 5.1% (95% CI 2.4–9.5) had rifampin or rifampin plus INH resistance. Conclusions: The prevalence of TB resistance to at least one drug was 7.4% among adults with positive TB cultures initiating ART in Durban, South Africa, with 5.1% having rifampin or rifampin plus INH resistance. Improved tools for diagnosing TB and drug resistance are urgently needed in areas of high HIV/TB prevalence.

Background: In resource-limited settings, HIV budgets are flattening or decreasing. A policy of discontinuing antiretroviral therapy (ART) after HIV treatment failure was modeled to highlight trade-offs among competing policy goals of optimizing individual and population health outcomes. Methods: In settings with two available ART regimens, we assessed two strategies: (1) continue ART after second-line failure (Status Quo) and (2) discontinue ART after second-line failure (Alternative). A computer model simulated outcomes for a single cohort of newly detected, HIV-infected individuals. Projections were fed into a population-level model allowing multiple cohorts to compete for ART with constraints on treatment capacity. In the Alternative strategy, discontinuation of second-line ART occurred upon detection of antiretroviral failure, specified by WHO guidelines. Those discontinuing failed ART experienced an increased risk of AIDS-related mortality compared to those continuing ART. Results: At the population level, the Alternative strategy increased the mean number initiating ART annually by 1,100 individuals (+18.7%) to 6,980 compared to the Status Quo. More individuals initiating ART under the Alternative strategy increased total life-years by 15,000 (+2.8%) to 555,000, compared to the Status Quo. Although more individuals received treatment under the Alternative strategy, life expectancy for those treated decreased by 0.7 years (−8.0%) to 8.1 years compared to the Status Quo. In a cohort of treated patients only, 600 more individuals (+27.1%) died by 5 years under the Alternative strategy compared to the Status Quo. Results were sensitive to the timing of detection of ART failure, number of ART regimens, and treatment capacity. Although we believe the results robust in the short-term, this analysis reflects settings where HIV case detection occurs late in the disease course and treatment capacity and the incidence of newly detected patients are stable. Conclusions: In settings with inadequate HIV treatment availability, trade-offs emerge between maximizing outcomes for individual patients already on treatment and ensuring access to treatment for all people who may benefit. While individuals may derive some benefit from ART even after virologic failure, the aggregate public health benefit is maximized by providing effective therapy to the greatest number of people. These trade-offs should be explicit and transparent in antiretroviral policy decisions.

Background: Point-of-care CD4 tests at HIV diagnosis could improve linkage to care in resource-limited settings. Our objective is to evaluate the clinical and economic impact of point-of-care CD4 tests compared to laboratory-based tests in Mozambique. Methods and Findings: We use a validated model of HIV testing, linkage, and treatment (CEPAC-International) to examine two strategies of immunological staging in Mozambique: (1) laboratory-based CD4 testing (LAB-CD4) and (2) point-of-care CD4 testing (POC-CD4). Model outcomes include 5-y survival, life expectancy, lifetime costs, and incremental cost-effectiveness ratios (ICERs). Input parameters include linkage to care (LAB-CD4, 34%; POC-CD4, 61%), probability of correctly detecting antiretroviral therapy (ART) eligibility (sensitivity: LAB-CD4, 100%; POC-CD4, 90%) or ART ineligibility (specificity: LAB-CD4, 100%; POC-CD4, 85%), and test cost (LAB-CD4, US$10; POC-CD4, US$24). In sensitivity analyses, we vary POC-CD4-specific parameters, as well as cohort and setting parameters to reflect a range of scenarios in sub-Saharan Africa. We consider ICERs less than three times the per capita gross domestic product in Mozambique (US$570) to be cost-effective, and ICERs less than one times the per capita gross domestic product in Mozambique to be very cost-effective. Projected 5-y survival in HIV-infected persons with LAB-CD4 is 60.9% (95% CI, 60.9%–61.0%), increasing to 65.0% (95% CI, 64.9%–65.1%) with POC-CD4. Discounted life expectancy and per person lifetime costs with LAB-CD4 are 9.6 y (95% CI, 9.6–9.6 y) and US$2,440 (95% CI, US$2,440–US$2,450) and increase with POC-CD4 to 10.3 y (95% CI, 10.3–10.3 y) and US$2,800 (95% CI, US$2,790–US$2,800); the ICER of POC-CD4 compared to LAB-CD4 is US$500/year of life saved (YLS) (95% CI, US$480–US$520/YLS). POC-CD4 improves clinical outcomes and remains near the very cost-effective threshold in sensitivity analyses, even if point-of-care CD4 tests have lower sensitivity/specificity and higher cost than published values. In other resource-limited settings with fewer opportunities to access care, POC-CD4 has a greater impact on clinical outcomes and remains cost-effective compared to LAB-CD4. Limitations of the analysis include the uncertainty around input parameters, which is examined in sensitivity analyses. The potential added benefits due to decreased transmission are excluded; their inclusion would likely further increase the value of POC-CD4 compared to LAB-CD4. Conclusions: POC-CD4 at the time of HIV diagnosis could improve survival and be cost-effective compared to LAB-CD4 in Mozambique, if it improves linkage to care. POC-CD4 could have the greatest impact on mortality in settings where resources for HIV testing and linkage are most limited. Please see later in the article for the Editors' Summary

Understanding HIV transmission dynamics is critical to estimating the potential population-wide impact of HIV prevention and treatment interventions. We developed an individual-based simulation model of the heterosexual HIV epidemic in South Africa and linked it to the previously published Cost-Effectiveness of Preventing AIDS Complications (CEPAC) International Model, which simulates the natural history and treatment of HIV. In this new model, the CEPAC Dynamic Model (CDM), the probability of HIV transmission per sexual encounter between short-term, long-term and commercial sex worker partners depends upon the HIV RNA and disease stage of the infected partner, condom use, and the circumcision status of the uninfected male partner. We included behavioral, demographic and biological values in the CDM and calibrated to HIV prevalence in South Africa pre-antiretroviral therapy. Using a multi-step fitting procedure based on Bayesian melding methodology, we performed 264,225 simulations of the HIV epidemic in South Africa and identified 3,750 parameter sets that created an epidemic and had behavioral characteristics representative of a South African population pre-ART. Of these parameter sets, 564 contributed 90% of the likelihood weight to the fit, and closely reproduced the UNAIDS HIV prevalence curve in South Africa from 1990–2002. The calibration was sensitive to changes in the rate of formation of short-duration partnerships and to the partnership acquisition rate among high-risk individuals, both of which impacted concurrency. Runs that closely fit to historical HIV prevalence reflect diverse ranges for individual parameter values and predict a wide range of possible steady-state prevalence in the absence of interventions, illustrating the value of the calibration procedure and utility of the model for evaluating interventions. This model, which includes detailed behavioral patterns and HIV natural history, closely fits HIV prevalence estimates.

Background: Mobile HIV screening may facilitate early HIV diagnosis. Our objective was to examine the cost-effectiveness of adding a mobile screening unit to current medical facility-based HIV testing in Cape Town, South Africa. Methods and Findings: We used the Cost Effectiveness of Preventing AIDS Complications International (CEPAC-I) computer simulation model to evaluate two HIV screening strategies in Cape Town: 1) medical facility-based testing (the current standard of care) and 2) addition of a mobile HIV-testing unit intervention in the same community. Baseline input parameters were derived from a Cape Town-based mobile unit that tested 18,870 individuals over 2 years: prevalence of previously undiagnosed HIV (6.6%), mean CD4 count at diagnosis (males 423/µL, females 516/µL), CD4 count-dependent linkage to care rates (males 31%–58%, females 49%–58%), mobile unit intervention cost (includes acquisition, operation and HIV test costs, $29.30 per negative result and $31.30 per positive result). We conducted extensive sensitivity analyses to evaluate input uncertainty. Model outcomes included site of HIV diagnosis, life expectancy, medical costs, and the incremental cost-effectiveness ratio (ICER) of the intervention compared to medical facility-based testing. We considered the intervention to be “very cost-effective” when the ICER was less than South Africa's annual per capita Gross Domestic Product (GDP) ($8,200 in 2012). We projected that, with medical facility-based testing, the discounted (undiscounted) HIV-infected population life expectancy was 132.2 (197.7) months; this increased to 140.7 (211.7) months with the addition of the mobile unit. The ICER for the mobile unit was $2,400/year of life saved (YLS). Results were most sensitive to the previously undiagnosed HIV prevalence, linkage to care rates, and frequency of HIV testing at medical facilities. Conclusion: The addition of mobile HIV screening to current testing programs can improve survival and be very cost-effective in South Africa and other resource-limited settings, and should be a priority.

Background: Despite expanding access to antiretroviral therapy (ART), most of the estimated 2.3 to 2.5 million HIV-infected individuals in India remain undiagnosed. The questions of whom to test for HIV and at what frequency remain unclear. Methods: We used a simulation model of HIV testing and treatment to examine alternative HIV screening strategies: 1) current practice, 2) one-time, 3) every five years, and 4) annually; and we applied these strategies to three population scenarios: 1) the general Indian population (“national population”), i.e. base case (HIV prevalence 0.29%; incidence 0.032/100 person-years [PY]); 2) high-prevalence districts (HIV prevalence 0.8%; incidence 0.088/100 PY), and 3) high-risk groups (HIV prevalence 5.0%; incidence 0.552/100 PY). Cohort characteristics reflected Indians reporting for HIV testing, with a median age of 35 years, 66% men, and a mean CD4 count of 305 cells/µl. The cost of a rapid HIV test was $3.33. Outcomes included life expectancy, HIV-related direct medical costs, incremental cost-effectiveness ratios (ICERs), and secondary transmission benefits. The threshold for “cost-effective” was defined as 3x the annual per capita GDP of India ($3,900/year of life saved [YLS]), or for “very cost-effective” was <1x the annual per capita GDP ($1,300/YLS). Results: Compared to current practice, one-time screening was very cost-effective in the national population (ICER: $1,100/YLS), high-prevalence districts (ICER: $800/YLS), and high-risk groups (ICER: $800/YLS). Screening every five years in the national population (ICER: $1,900/YLS) and annual screening in high-prevalence districts (ICER: $1,900/YLS) and high-risk groups (ICER: $1,800/YLS) were also cost-effective. Results were most sensitive to costs of care and linkage-to-care. Conclusions: In India, voluntary HIV screening of the national population every five years offers substantial clinical benefit and is cost-effective. Annual screening is cost-effective among high-risk groups and in high-prevalence districts nationally. Routine HIV screening in India should be implemented.