Impact of opportunistic diseases on chronic mortality in HIV-infected adults in Côte d'Ivoire

Objective: To estimate incidence rates of opportunistic diseases (ODs) and mortality for patients with and without a history of OD among HIV-infected patients in Côte d'Ivoire. Methods: Using incidence density analysis, we estimated rates of ODs and chronic mortality by CD4 count in patients in a cotrimoxazole prophylaxis trial in Abidjan before the highly active antiretroviral therapy (HAART) era. Chronic mortality was defined as death without a history of OD or death more than 30 days after an OD diagnosis. We used Poisson's regression to examine the effect of OD history on chronic mortality after adjusting for age, gender, and current CD4 count.Results: Two hundred and seventy patients (40% male, mean age 33 years, median baseline CD4 count 261 cells/µl) were followed up for a median of 9.5 months. Bacterial infections and tuberculosis were the most common severe ODs. Of 47 patients who died, 9 (19%) died within 30 days of an OD, 26 (55%) died more than 30 days after an OD, and 12 (26%) died with no OD history. The chronic mortality rate was 31.0/100 person-years for those with an OD history, and 11.1/100 person-years for those with no OD history (rate ratio (RR) 2.81, 95% confidence interval (CI): 1.43 - 5.54). Multivariate analysis revealed that OD history remained an independent predictor of mortality (RR 2.15, 95% CI: 1.07 - 4.33) after adjusting for CD4 count, age and gender.Conclusions: Before the HAART era, a history of OD was associated with increased chronic HIV mortality in Côte d'Ivoire, even after adjusting for CD4 count. These results provide further evidence supporting OD prophylaxis in HIVinfected patients.South African Medical Journal Vol. 96(6) 2006: 526-52

Mobile HIV Screening in Cape Town, South Africa: Clinical Impact, Cost and Cost-Effectiveness

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

Cost-Effectiveness of Preventing Loss to Follow-up in HIV Treatment Programs: A Côte d'Ivoire Appraisal

Based on data from West Africa, Elena Losina and colleagues predict that interventions to reduce dropout rates from HIV treatment programs (such as eliminating copayments) will be cost-effective

Cost-Effectiveness of HIV Testing Referral Strategies among Tuberculosis Patients in India

Background: Indian guidelines recommend routine referral for HIV testing of all tuberculosis (TB) patients in the nine states with the highest HIV prevalence, and selective referral for testing elsewhere. We assessed the clinical impact and cost-effectiveness of alternative HIV testing referral strategies among TB patients in India. Methods and Findings: We utilized a computer model of HIV and TB disease to project outcomes for patients with active TB in India. We compared life expectancy, cost, and cost-effectiveness for three HIV testing referral strategies: 1) selective referral for HIV testing of those with increased HIV risk, 2) routine referral of patients in the nine highest HIV prevalence states with selective referral elsewhere (current standard), and 3) routine referral of all patients for HIV testing. TB-related data were from the World Health Organization. HIV prevalence among TB patients was 9.0% in the highest prevalence states, 2.9% in the other states, and 4.9% overall. The selective referral strategy, beginning from age 33.50 years, had a projected discounted life expectancy of 16.88 years and a mean lifetime HIV/TB treatment cost of US$100. The current standard increased mean life expectancy to 16.90 years with additional per-person cost of US$10; the incremental cost-effectiveness ratio was US$650/year of life saved (YLS) compared to selective referral. Routine referral of all patients for HIV testing increased life expectancy to 16.91 years, with an incremental cost-effectiveness ratio of US$730/YLS compared to the current standard. For HIV-infected patients cured of TB, receiving antiretroviral therapy increased survival from 4.71 to 13.87 years. Results were most sensitive to the HIV prevalence and the cost of second-line antiretroviral therapy. Conclusions: Referral of all patients with active TB in India for HIV testing will be both effective and cost-effective. While effective implementation of this strategy would require investment, routine, voluntary HIV testing of TB patients in India should be recommended

Scaling Up the 2010 World Health Organization HIV Treatment Guidelines in Resource-Limited Settings: A Model-Based Analysis

Rochelle Walensky and colleagues use a model-based analysis to examine which of the 2010 WHO antiretroviral therapy guidelines should be implemented first in resource-limited settings by ranking them according to survival, cost-effectiveness, and equity

Resource Utilization and Cost-Effectiveness of Counselor- vs. Provider-Based Rapid Point-of-Care HIV Screening in the Emergency Department

Routine HIV screening in emergency department (ED) settings may require dedicated personnel. We evaluated the outcomes, costs and cost-effectiveness of HIV screening when offered by either a member of the ED staff or by an HIV counselor.We employed a mathematical model to extend data obtained from a randomized clinical trial of provider- vs. counselor-based HIV screening in the ED. We compared the downstream survival, costs, and cost-effectiveness of three HIV screening modalities: 1) no screening program; 2) an ED provider-based program; and 3) an HIV counselor-based program. Trial arm-specific data were used for test offer and acceptance rates (provider offer 36%, acceptance 75%; counselor offer 80%, acceptance 71%). Undiagnosed HIV prevalence (0.4%) and linkage to care rates (80%) were assumed to be equal between the screening modalities. Personnel costs were derived from trial-based resource utilization data. We examined the generalizability of results by conducting sensitivity analyses on offer and acceptance rates, undetected HIV prevalence, and costs.Estimated HIV screening costs in the provider and counselor arms averaged $8.10 and$31.00 per result received. The Provider strategy (compared to no screening) had an incremental cost-effectiveness ratio of $58,700/quality-adjusted life year (QALY) and the Counselor strategy (compared to the Provider strategy) had an incremental cost-effectiveness ratio of$64,500/QALY. Results were sensitive to the relative offer and acceptance rates by strategy and the capacity of providers to target-screen, but were robust to changes in undiagnosed HIV prevalence and programmatic costs.The cost-effectiveness of provider-based HIV screening in an emergency department setting compares favorably to other US screening programs. Despite its additional cost, counselor-based screening delivers just as much return on investment as provider based-screening. Investment in dedicated HIV screening personnel is justified in situations where ED staff resources may be insufficient to provide comprehensive, sustainable screening services

Assessing the Performance of a Computer-Based Policy Model of HIV and AIDS

BACKGROUND. Model-based analyses, conducted within a decision analytic framework, provide a systematic way to combine information about the natural history of disease and effectiveness of clinical management strategies with demographic and epidemiological characteristics of the population. Among the challenges with disease-specific modeling include the need to identify influential assumptions and to assess the face validity and internal consistency of the model. METHODS AND FINDINGS. We describe a series of exercises involved in adapting a computer-based simulation model of HIV disease to the Women's Interagency HIV Study (WIHS) cohort and assess model performance as we re-parameterized the model to address policy questions in the U.S. relevant to HIV-infected women using data from the WIHS. Empiric calibration targets included 24-month survival curves stratified by treatment status and CD4 cell count. The most influential assumptions in untreated women included chronic HIV-associated mortality following an opportunistic infection, and in treated women, the 'clinical effectiveness' of HAART and the ability of HAART to prevent HIV complications independent of virologic suppression. Good-fitting parameter sets required reductions in the clinical effectiveness of 1st and 2nd line HAART and improvements in 3rd and 4th line regimens. Projected rates of treatment regimen switching using the calibrated cohort-specific model closely approximated independent analyses published using data from the WIHS. CONCLUSIONS. The model demonstrated good internal consistency and face validity, and supported cohort heterogeneities that have been reported in the literature. Iterative assessment of model performance can provide information about the relative influence of uncertain assumptions and provide insight into heterogeneities within and between cohorts. Description of calibration exercises can enhance the transparency of disease-specific models.National Institute of Allergy and Infectious Diseases (R37 AI042006, K24 AI062476

Violent Deaths of Iraqi Civilians, 2003–2008: Analysis by Perpetrator, Weapon, Time, and Location

Madelyn Hsiao-Rei Hicks and colleagues provide a detailed analysis of Iraqi civilian violent deaths during 2003-2008 of the Iraq war and show that of 92,614 deaths, unknown perpetrators caused 74% of deaths, Coalition forces 12%, and Anti-Coalition forces 11%

Don't Fall Off the Adaptation Cliff: When Asymmetrical Fitness Selects for Suboptimal Traits

The cliff-edge hypothesis introduces the counterintuitive idea that the trait value associated with the maximum of an asymmetrical fitness function is not necessarily the value that is selected for if the trait shows variability in its phenotypic expression. We develop a model of population dynamics to show that, in such a system, the evolutionary stable strategy depends on both the shape of the fitness function around its maximum and the amount of phenotypic variance. The model provides quantitative predictions of the expected trait value distribution and provides an alternative quantity that should be maximized (“genotype fitness”) instead of the classical fitness function (“phenotype fitness”). We test the model's predictions on three examples: (1) litter size in guinea pigs, (2) sexual selection in damselflies, and (3) the geometry of the human lung. In all three cases, the model's predictions give a closer match to empirical data than traditional optimization theory models. Our model can be extended to most ecological situations, and the evolutionary conditions for its application are expected to be common in nature