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

File Description SizeFormat 
00002030-201704001-00008.pdfPublished version458.73 kBAdobe PDFView/Open
Title: Accounting for nonsampling error in estimates of HIV epidemic trends from antenatal clinic sentinel surveillance
Authors: Eaton, JW
Bao, L
Item Type: Journal Article
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 s2 in fl 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 s2 in fl 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 s2 in fl 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 (s2 in fl ¼ 0), coverage of 95% prediction intervals was 69% in out-of-sample prediction tests. This increased to 90% after introducing the additional variance parameter s2 in fl. 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 s2 in fl 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.
Issue Date: 1-Apr-2017
Date of Acceptance: 16-Jan-2017
ISSN: 0269-9370
Start Page: S61
End Page: S68
Journal / Book Title: AIDS
Volume: 31
Issue: Supplement 1
Copyright Statement: © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Sponsor/Funder: National Institutes of Health
Funder's Grant Number: 1R03AI125001-01A1
Keywords: Science & Technology
Life Sciences & Biomedicine
Infectious Diseases
ANC sentinel surveillance
EPP model
HIV epidemic trends
mathematical model
statistical uncertainty
06 Biological Sciences
11 Medical And Health Sciences
17 Psychology And Cognitive Sciences
Publication Status: Published
Open Access location:
Appears in Collections:Epidemiology, Public Health and Primary Care

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commonsx