Smoothing age/time structure of HIV prevalence, for optimal use in synthetic cohort based incidence estimation

Abstract

Population-based surveys which ascertain HIV status are conducted in heavily affected countries, with the estimation of incidence being a primary goal. Numerous methods exist under the umbrella of ‘synthetic cohort analysis’, by which we mean estimating incidence from the age/time structure of prevalence (given knowledge on mortality). However, not enough attention has been given to how serostatus data is ‘smoothed’ into a time/age-dependent prevalence, so as to optimise the estimation of incidence.To support this and other related investigations, we developed a comprehensive simulation environment in which we simulate age/time structured SI type epidemics and surveys. Scenarios are flexibly defined by demographic rates (fertility, incidence and mortality – dependent, as appropriate, on age, time, and time-since-infection) without any reference to underlying causative processes/parameters. Primarily using 1) a simulated epidemiological scenario inspired by what is seen in the hyper-endemic HIV affected regions, and 2) pairs of cross-sectional surveys, we explored A) options for extracting the age/time structure of prevalence so as to optimise the use of the formal incidence estimation framework of Mahiane et al, and B) aspects of survey design such as the interaction of epidemic details, sample-size/sampling-density and inter-survey interval.Much as in our companion piece which crucially investigated the use of ‘recent infection’ (whereas the present analysis hinges fundamentally on the estimation of the prevalence gradient) we propose a ‘one size fits most’ process for conducting ‘synthetic cohort’ analyses of large population survey data sets, for HIV incidence estimation: fitting a generalised linear model for prevalence, separately for each age/time point where an incidence estimate is desired, using a ‘moving window’ data inclusion rule. Overall, even in very high incidence settings, sampling density requirements are onerous.The general default approach we propose for fitting HIV prevalence to data as a function of age and time appears to be broadly stable over various epidemiological stages. Particular scenarios of interest, and the applicable options for survey design and analysis, can readily be more closely investigated using our approach. We note that it is often unrealistic to expect even large household based surveys to provide meaningful incidence estimates outside of priority groups like young women, where incidence is often particularly high.

The added value of recent-infection testing in population-based HIV surveys

“…In general, reliable informative incidence estimation requires very large sample sizes (i.e., very high sampling densities across some age range) and works best when incidence is very high. This, coupled with the epidemiological/sociological importance of incidence among the young, suggests, as we have previously noted (6), that one consider focusing on this group as an informative and important sentinel population, rather than attempting to obtain incidence estimates for all ageswhich may simply not be feasible. For these younger ages, recency ascertainment does not really improve single time point estimates.…”

“…In two companion articles (5,6), we have systematically explored the optimal extraction of this age/time structure from population survey data. To recap:…”