ArticleAIDS (London, England)2014
Incorporating incidence information within the UNAIDS Estimation and Projection Package framework: a study based on simulated incidence assay data.
Article in AIDS (London, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Use of HIV Recency Assays for HIV Incidence Estimation and Other Surveillance Use Cases: Systematic Review.JMIR public health and surveillance · 2022Pooled it
- Using hepatitis C prevalence to estimate HIV epidemic potential among people who inject drugs in the Middle East and North Africa.AIDS (London, England) · 2015Pooled it
- Probabilistic HIV recency classification-a logistic regression without labeled individual level training data.The annals of applied statistics · 2023Article
- 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
- Sequential immunizations with a panel of HIV-1 Env virus-like particles coach immune system to make broadly neutralizing antibodies.Scientific reports · 2018Article
- Strengthening HIV surveillance: measurements to track the epidemic in real time.African journal of AIDS research : AJAR · 2016Article
- Development and future directions for the Joint United Nations Programme on HIV/AIDS estimates.AIDS (London, England) · 2014Review
- Embracing different approaches to estimating HIV incidence, prevalence and mortality.AIDS (London, England) · 2014Review
- Improvements in prevalence trend fitting and incidence estimation in EPP 2013.AIDS (London, England) · 2014Article
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3 authors.
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Abstract
objectivesThe objective of this study is to extend the UNAIDS incidence estimation model, the UNAIDS Estimation and Projection Package (EPP), so that it can incorporate data from incidence assays.
methodsWe propose combining the likelihood of the incidence assay data with the likelihood of other data, in a manner that is consistent with the biomarker-based incidence estimator using incidence assay data. Two calibrating parameters specify the performance of the incidence assays: the false recent rate and the mean duration of recent infection. We then use synthetic data, based on prevalence data obtained from antenatal clinic surveillances, and in some cases household surveys, from 24 countries, to examine the impact of including incidence assay data, under circumstances wherein the incidence assay data imply the same or a different incidence rate as that inferred from the prevalence data alone, and wherein incorrect calibrating parameters for the incidence assay data are used.
resultsUsing incidence assay data, in addition to prevalence data, can improve estimate by narrowing uncertainty intervals in derived HIV incidence estimates, and by providing information on levels or trends in incidence that were not apparent in the prevalence data alone. However, the effect is relatively modest if the sample size of the incidence assay survey is small and results can be biased if the calibrating parameters for the incidence assay data are not known accurately.
conclusionIncorporating information from incidence assays in the manner proposed has the potential to improve estimates. Further work will examine in more detail the circumstances under which the contribution of incidence assay data would be most valuable.
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