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

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Abstract

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

Methods:

JOURNAL/aids/04.02/00002030-201704001-00008/math_8MM1/v/2017-07-25T101142Z/r/image-tiff

Methods:

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

Methods:

JOURNAL/aids/04.02/00002030-201704001-00008/math_8MM2/v/2017-07-25T101142Z/r/image-tiff

Methods:

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

Results:

JOURNAL/aids/04.02/00002030-201704001-00008/math_8MM3/v/2017-07-25T101142Z/r/image-tiff

Results:

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

Results:

JOURNAL/aids/04.02/00002030-201704001-00008/math_8MM4/v/2017-07-25T101142Z/r/image-tiff

Results:

, coverage of 95% prediction intervals was 69% in out-of-sample prediction tests. This increased to 90% after introducing the additional variance parameter

Results:

JOURNAL/aids/04.02/00002030-201704001-00008/math_8MM5/v/2017-07-25T101142Z/r/image-tiff

Results:

. 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

Results:

JOURNAL/aids/04.02/00002030-201704001-00008/math_8MM6/v/2017-07-25T101142Z/r/image-tiff

Results:

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.

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