Estimation of HIV-1 Incidence Using a Testing History-Based Method; Analysis From the Population-Based HIV Impact Assessment Survey Data in 12 African Countries.

BACKGROUND: Estimating HIV incidence is essential to monitoring progress in sub-Saharan African nations toward global epidemic control. One method for incidence estimation is to test nationally representative samples using laboratory-based incidence assays. An alternative method based on reported HIV testing history and the proportion of undiagnosed infections has recently been described. METHODS: We applied an HIV incidence estimation method which uses history of testing to nationally representative cross-sectional survey data from 12 sub-Saharan African nations with varying country-specific HIV prevalence. We compared these estimates with those derived from laboratory-based incidence assays. Participants were tested for HIV using the national rapid test algorithm and asked about prior HIV testing, date and result of their most recent test, and date of antiretroviral therapy initiation. RESULTS: The testing history-based method consistently produced results that are comparable and strongly correlated with estimates produced using a laboratory-based HIV incidence assay (ρ = 0.85). The testing history-based method produced incidence estimates that were more precise compared with the biomarker-based method. The testing history-based method identified sex-, age-, and geographic location-specific differences in incidence that were not detected using the biomarker-based method. CONCLUSIONS: The testing history-based method estimates are more precise and can produce age-specific and sex-specific incidence estimates that are informative for programmatic decisions. The method also allows for comparisons of the HIV transmission rate and other components of HIV incidence among and within countries. The testing history-based method is a useful tool for estimating and validating HIV incidence from cross-sectional survey data.

Respondent-driven sampling and the homophily configuration graph.

Respondent-Driven Sampling (RDS) is a popular method for surveying hard-to-reach populations, especially in the public health domain. Adjusting for the complex sampling mechanism of the RDS procedure is challenging. We propose a new model for the RDS mechanism motivated by a graph model, which we call the Homophily Configuration Graph. Under this model, we develop a new estimator for population proportions that is robust to seed bias, differential activity, differential recruitment and short recruitment chains. We also connect it to existing RDS theory by showing that, if the sample fraction is small, our estimator limits to the popular Salganik-Heckathorn estimator. We perform simulation studies on both empirically observed networks and networks with known statistical properties, suggesting that this new estimator has less bias than currently recommended estimators.