ArticleAIDS (London, England)2017
Incorporation of hierarchical structure into estimation and projection package fitting with examples of estimating subnational HIV/AIDS dynamics.
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 6 papers.
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Who cites it
6 citing papers in PubMed.
- Dynamic models augmented by hierarchical data: an application of estimating HIV epidemics at sub-national level.Biostatistics (Oxford, England) · 2024Article
- Subnational mapping of HIV incidence and mortality among individuals aged 15-49 years in sub-Saharan Africa, 2000-18: a modelling study.The lancet. HIV · 2021Article
- 90-90-90 cascade analysis on reported CLHIV infected by mother-to-child transmission in Guangxi, China: a modeling study.Scientific reports · 2020Article
- 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
- Demographic change and HIV epidemic projections to 2050 for adolescents and young people aged 15-24.Global health action · 2019Article
- Accounting for nonsampling error in estimates of HIV epidemic trends from antenatal clinic sentinel surveillance.AIDS (London, England) · 2017Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectivesThe article aims to give Spectrum/estimation and projection package (EPP) users and the scientific community a basic understanding of the underlying statistical model used to incorporate hierarchical structure in HIV subnational estimation, and to show how it has been implemented in the Spectrum/EPP interface for improving subepidemic estimation. The article also provides recommended default settings for this new model.
methodsWe apply a generalized linear mixed-effects model on antenatal clinics prevalence data to get area-specific prevalence and uncertainty estimates, and transform those estimates to auxiliary data. We then fit the EPP model to both the observed data and auxiliary data.
resultsWe apply the proposed methods to four countries with different levels of data availability. We compare the out-of-sample prediction accuracy of the proposed method with varying auxiliary sample sizes and EPP without auxiliary data.
conclusionWe find that borrowing information from data-rich areas to data-sparse areas using our proposed method improves EPP fit in data-sparse areas. We recommend using the sample size estimated from generalized linear mixed-effects model as the default auxiliary sample size.
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