A Bayesian approach to estimate changes in condom use from limited human immunodeficiency virus prevalence data

Abstract

Evaluation of large-scale intervention programmes against human immunodeficiency virus (HIV) is becoming increasingly important, but impact estimates frequently hinge on knowledge of changes in behaviour such as the frequency of condom use 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 an HIV transmission dynamics model to estimate condom use 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 condom use is critical in the context studied, because of the very limited amount of condom use and HIV data available. We consider various novel formulations to explore the trajectory of condom use over time, based on diffusion-driven trajectories and smooth sigmoid curves. Numerical simulations indicate that informative results can be obtained regarding the amplitude of the increase in condom use 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 demonstrates how it can help in evaluating HIV interventions based on a small number of prevalence estimates, and it opens the way to similar applications in different contexts.

Keywords: Bayesian inference; Condom use; Epidemic modelling; Human immunodeficiency virus infections; Particle Markov chain Monte Carlo methods; Time varying parameter.

Figure 1

Flow diagram of the model

Figure 2

Bias of each model as a function of the true amplitude of the shift in use of condoms, estimated from 1000 simulations: , DPR model; , deterministic sigmoid model; , DBM model

Figure 3

Receiver operating characteristics curve when testing for ΔU>0.2 ( ), ΔU>0.3 (– – –) and ΔU>0.4 (), under a Brownian motion trajectory prior: the curves were estimated from 1000 simulations; very similar shapes are obtained for the alternative trajectory priors

Figure 4

Estimates obtained for the Mysore district: (a) reconstructed prevalence trajectory among FSWs when condom use is modelled with Brownian motion; (b) reconstructed prevalence trajectory among clients when condom use is modelled with Brownian motion; (c) reconstructed condom use trajectory when modelled with Brownian motion; (d) reconstructed condom use trajectory when modelled with the deterministic sigmoid model