Background Avahan, the India AIDS initiative of the Bill & Melinda Gates Foundation, is the largest targeted HIV preventive intervention in the world. We examine evidence for its overall impact, and estimate HIV infections averted across Avahan districts.
Methods A mathematical model of HIV transmission among high-risk groups and the general population was developed. It was parameterised using data from serial cross-sectional surveys (IBBAs) within a Bayesian framework, to reproduce HIV prevalence trends amongst female sex workers (FSWs), their clients, and men who have sex with men (MSM) in 24 South Indian districts. We test whether these prevalence trends are more consistent with self-reported increases in consistent condom use (CCU) following Avahan, or a counterfactual assuming CCU increased at slower pre- Avahan rates. To assess this we used a Bayes factor, which also measures strength of evidence for the impact estimates. Using regression analysis, the prevention impact in the IBBA districts is extrapolated to all Avahan districts.
Results In 13/24 districts, modelling suggests medium to strong evidence for the large self-reported increase in CCU since Avahan implementation. Elsewhere evidence is weaker, with CCU generally already high pre- Avahan. Approximately 32,700 HIV infections (95% credibility interval 17,900–61,600) were averted over four years in IBBA districts with moderate/strong evidence. Adding districts with weaker evidence increases this to 62,800 (32,000–118,000), and extrapolation suggests that 202,000 (98,300–407,000) infections were averted across all 69 Avahan districts in South India, increasing to 606,000 (290,000–1,193,000) over ten years. Over four (ten) years, 42% (57%) of HIV infections were averted.
Conclusion This is the first evaluation of Avahan to account for the causal pathway of the intervention, changing risk behaviour in FSWs and MSM to avert HIV infections in these groups and the general population. The findings suggest considerable impact can be achieved from targeted behavioural HIV prevention initiatives.
- Intervention evaluation
- Mathematical modeling