ArticlePloS one2020
A new method for estimating HIV incidence from a single cross-sectional survey.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
6 citing papers in PubMed.
- Field validation and application of the luminex triplex HIV assay to estimate HIV prevalence and HIV-1 incidence in Nigeria.PLOS global public health · 2025Article
- A likelihood approach to incorporating self-report data in HIV recency classification.Biometrics · 2024Article
- Probabilistic HIV recency classification-a logistic regression without labeled individual level training data.The annals of applied statistics · 2023Article
- HIV Incidence, Recent HIV Infection, and Associated Factors, Kenya, 2007-2018.AIDS research and human retroviruses · 2023Article
- Estimating HIV incidence over a decade in Zimbabwe: A comparison of the catalytic and Farrington models.PLOS global public health · 2023Article
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Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Estimating incidence from cross-sectional data sources is both important to the understanding of the HIV epidemic and challenging from a methodological standpoint. We develop a new incidence estimator that measures the size of the undiagnosed population and the amount of time spent undiagnosed in order to infer incidence and transmission rates. The estimator is calculated using commonly collected information on testing history and HIV status and, thus, can be deployed in many HIV surveys without additional cost. If ART biomarker status and/or viral load information is available, the estimator can be adjusted for biases in self-reported testing history. The performance of the estimator is explored in two large surveys in Kenya, where we find our point estimates to be consistent with assay-derived estimates, with much smaller standard errors.
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Registered trials
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