Background Considerable efforts have been invested to evaluate the impact of the Avahan project, the India AIDS initiative, targeted to high risk groups. One measure of impact is the number of HIV infections prevented (PF) due to increases in condom use between FSWs and clients following the start of Avahan. PF estimates hinge on knowledge of the frequency of condom use over time. As there is no data on condom use prior to the first round of data collection post-Avahan, these trends must be estimated indirectly. We aim, using a Bayesian framework and Monte Carlo Markov Chains (MCMC) methodology, to determine if the trends in HIV prevalence among FSWs and clients can be used to infer changes in condom use between FSW and clients before and after the start of Avahan.
Methods We used serial rounds of cross-sectional behavioural and biological survey (IBBA) data and a deterministic compartmental model of HIV transmission among FSW/clients in Mysore and Belgaum districts coupled with Bayesian inference procedures. Condom use was modelled as the fraction of FSW commercial sex acts protected by condoms. IBBA data was used to specify the model prior parameter distributions and estimate HIV prevalence at three time points among FWS, at one time point among their clients. The Particle MCMC algorithm was used to explore the posterior density of our complex parameter distribution, and to derive estimates of the evolution of condom use over time using either 2 or 3 rounds of HIV prevalence data among FSW.
Results For both districts, the results reveal a clear increase in condom use around the start of Avahan in 2004 (Abstract P1-S6.06 figure 1). Abstract P1-S6.06 figure 1 suggests condom use before the intervention (jan 1994) was lower in Mysore (15.7%, 95% CI 0 to 48%) than in Belgaum (19%, 95% CI 0 to 58%). In both districts, post intervention condom use stabilised at values above 80% (95% CI Mysore 65 to 100% and Belgaum 50 to 100%). Lastly, Abstract P1-S6.06 figure 1 A),C) vs B),D) show the information gained by using 3 rounds of data instead of 2.
Conclusions This is the first application of Particle MCMC in an intervention monitoring context. Our results consolidate previous back projections suggesting that condom use significantly increased since the start of Avahan, which sheds additional light on the potential intervention impact. This study illustrates the use of flexible Bayesian inference methodology to estimate time-varying parameters. It also informs the design of prevalence surveys for intervention monitoring.
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