2017
DOI: 10.1089/aid.2016.0123
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Estimating HIV Incidence Using a Cross-Sectional Survey: Comparison of Three Approaches in a Hyperendemic Setting, Ndhiwa Subcounty, Kenya, 2012

Abstract: Comparison of different methods showed variations in incidence estimates, but they were in agreement to identify most-at-risk groups. The use and comparison of several distinct approaches for estimating incidence are important to provide the best-supported estimate of HIV incidence in the population.

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Cited by 5 publications
(8 citation statements)
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“…Most estimates were drawn from prospective studies (45 [88%] of 51 studies), with cohort data from observational research or intervention trials, while six (12%) studies estimated recent HIV infection among HIVpositive samples from cross-sectional surveys. 7,21,22,33,40,42 The cohort studies estimated incidence from repeated observations, most often imputing the timing of seroconversion from the midpoint or a random-point between the latest-negative and earliest-positive antibody test dates. Details of statistical methods for estimation were not always available or consistently reported across studies.…”
Section: Resultsmentioning
confidence: 99%
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“…Most estimates were drawn from prospective studies (45 [88%] of 51 studies), with cohort data from observational research or intervention trials, while six (12%) studies estimated recent HIV infection among HIVpositive samples from cross-sectional surveys. 7,21,22,33,40,42 The cohort studies estimated incidence from repeated observations, most often imputing the timing of seroconversion from the midpoint or a random-point between the latest-negative and earliest-positive antibody test dates. Details of statistical methods for estimation were not always available or consistently reported across studies.…”
Section: Resultsmentioning
confidence: 99%
“…Ruzagira et al (2011) 14 Okiria et al (2014) 35 Borgdorff et al (2018) 20 , Birdthistle et al (2018) 19 Biraro et al (2013) 36 Santelli et al (2015) 39 Santelli et al (2013) 38 Grabowski et al (2017) 37 Grabowski et al (2017) 37 Santelli et al (2015) 38 Blaizot et al (2017) 21 Blaizot et al (2017) 21 Geis et al (2011) 34…”
Section: Female Subgroupmentioning
confidence: 99%
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“…In a previous community‐randomized study, the primary HIV incidence endpoint was determined by analysing samples collected in a cross‐sectional survey of > 46,000 individuals . In that study, the testing algorithm used for cross‐sectional HIV incidence estimation included viral load as a biomarker for non‐recent infection ; low viral load is also used as a biomarker for non‐recent infection in an algorithm that is widely used for cross‐sectional HIV incidence estimation in surveillance studies . Further studies are needed to assess the performance of these algorithms in settings where ART is initiated early in HIV infection since individuals with recent infection who are virally suppressed from ART would be misclassified as having non‐recent infection.…”
Section: Discussionmentioning
confidence: 99%
“…The EQAPOL LAg EQA program, which is jointly managed by Duke University and Vitalant Research Institute and is supported by the NIH/NIAID, has successfully launched an international EQA/proficiency testing program for the HIV-1 LAg assay. The assays utilized in this program are CDC-approved and widely employed globally to classify HIV infections as recently acquired or longstanding for the purposes of cross-sectionally estimating incidence in population-level studies [2225]. Increasingly, expanded use-cases for individual-level infection staging and improved estimation of individual time of infection (in combination of diagnostic test results) are being contemplated and piloted.…”
Section: Discussionmentioning
confidence: 99%