Accounting for nonsampling error in estimates of HIV epidemic trends from antenatal clinic sentinel surveillance

OBJECTIVES The aim of the study was to propose and demonstrate an approach to allow additional nonsampling uncertainty about HIV prevalence measured at antenatal clinic sentinel surveillance (ANC-SS) in model-based inferences about trends in HIV incidence and prevalence. DESIGN Mathematical model fitted to surveillance data with Bayesian inference. METHODS We introduce a variance inflation parameter (Equation is included in full-text article.)that accounts for the uncertainty of nonsampling errors in ANC-SS prevalence. It is additive to the sampling error variance. Three approaches are tested for estimating (Equation is included in full-text article.)using ANC-SS and household survey data from 40 subnational regions in nine countries in sub-Saharan, as defined in UNAIDS 2016 estimates. Methods were compared using in-sample fit and out-of-sample prediction of ANC-SS data, fit to household survey prevalence data, and the computational implications. RESULTS Introducing the additional variance parameter (Equation is included in full-text article.)increased the error variance around ANC-SS prevalence observations by a median of 2.7 times (interquartile range 1.9-3.8). Using only sampling error in ANC-SS prevalence (Equation is included in full-text article.), coverage of 95% prediction intervals was 69% in out-of-sample prediction tests. This increased to 90% after introducing the additional variance parameter (Equation is included in full-text article.). The revised probabilistic model improved model fit to household survey prevalence and increased epidemic uncertainty intervals most during the early epidemic period before 2005. Estimating (Equation is included in full-text article.)did not increase the computational cost of model fitting. CONCLUSIONS We recommend estimating nonsampling error in ANC-SS as an additional parameter in Bayesian inference using the Estimation and Projection Package model. This approach may prove useful for incorporating other data sources such as routine prevalence from Prevention of mother-to-child transmission testing into future epidemic estimates.