ArticleAIDS (London, England)2017
Accounting for nonsampling error in estimates of HIV epidemic trends from antenatal clinic sentinel surveillance.
Article in AIDS (London, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 11 citations in OpenAlex.
- Responsive population-based cohorts as platforms for characterising pathogen- and population-level infection dynamics for epidemic prevention, preparedness and response.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2025Article
- Dynamic models augmented by hierarchical data: an application of estimating HIV epidemics at sub-national level.Biostatistics (Oxford, England) · 2024Article
- Triangulation of Routine Antenatal HIV Prevalence Data and Adjusted HIV Estimates in Mozambique.Journal of acquired immune deficiency syndromes (1999) · 2024Article
- Subnational HIV incidence trends in Malawi: large, heterogeneous declines across space.medRxiv : the preprint server for health sciences · 2023Article
- Article
- HIV surveillance based on routine testing data from antenatal clinics in Malawi (2011-2018): measuring and adjusting for bias from imperfect testing coverage.AIDS (London, England) · 2019Article
- The Estimation and Projection Package Age-Sex Model and the r-hybrid model: new tools for estimating HIV incidence trends in sub-Saharan Africa.AIDS (London, England) · 2019Article
- Incidence estimation from sentinel surveillance data; a simulation study and application to data from the Belgian laboratory sentinel surveillance.BMC public health · 2019Article
- Demographic change and HIV epidemic projections to 2050 for adolescents and young people aged 15-24.Global health action · 2019Article
- Statistical models for incorporating data from routine HIV testing of pregnant women at antenatal clinics into HIV/AIDS epidemic estimates.AIDS (London, England) · 2017Article
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
objectivesThe 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.
designMathematical model fitted to surveillance data with Bayesian inference.
methodsWe 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.
resultsIntroducing 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.
conclusionsWe 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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