A Bayesian approach to estimate changes in condom use from limited HIV prevalence data

TL;DR

This paper introduces a Bayesian inference method using particle Markov Chain Monte Carlo to estimate changes in condom use over time from limited HIV prevalence data, aiding evaluation of intervention impacts.

Contribution

It is the first application of particle MCMC in this context, incorporating dynamic HIV models to infer behavioral changes from scarce data.

Findings

Effective detection of condom use increase during interventions

Good sensitivity and specificity in identifying behavior change

Method applicable to real-world HIV intervention evaluation

Abstract

Evaluation of HIV large scale interventions programme is becoming increasingly important, but impact estimates frequently hinge on knowledge of changes in behaviour such as the frequency of condom use (CU) over time, or other self-reported behaviour changes, for which we generally have limited or potentially biased data. We employ a Bayesian inference methodology that incorporates a dynamic HIV transmission dynamics model to estimate CU time trends from HIV prevalence data. Estimation is implemented via particle Markov Chain Monte Carlo methods, applied for the first time in this context. The preliminary choice of the formulation for the time varying parameter reflecting the proportion of CU is critical in the context studied, due to the very limited amount of CU and HIV data available We consider various novel formulations to explore the trajectory of CU in time, based on diffusion-driven trajectories and smooth sigmoid curves. Extensive series of numerical simulations indicate that informative results can be obtained regarding the amplitude of the increase in CU during an intervention, with good levels of sensitivity and specificity performance in effectively detecting changes. The application of this method to a real life problem illustrates how it can help evaluate HIV intervention from few observational studies and suggests that these methods can potentially be applied in many different contexts.