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Assessing the use of surveillance data to estimate the impact of prevention interventions on HIV incidence in cluster-randomized controlled trials

BACKGROUND: In cluster-randomized controlled trials (C-RCTs) of HIV prevention strategies, HIV incidence is expensive to measure directly. Surveillance data on HIV diagnoses or viral suppression could provide cheaper incidence estimates. We used mathematical modelling to evaluate whether these measu...

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Autores principales: Mitchell, Kate M., Dimitrov, Dobromir, Hughes, James P., Moore, Mia, Vittinghoff, Eric, Liu, Albert, Cohen, Myron S., Beyrer, Chris, Donnell, Deborah, Boily, Marie-Claude
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7938213/
https://www.ncbi.nlm.nih.gov/pubmed/33285419
http://dx.doi.org/10.1016/j.epidem.2020.100423
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author Mitchell, Kate M.
Dimitrov, Dobromir
Hughes, James P.
Moore, Mia
Vittinghoff, Eric
Liu, Albert
Cohen, Myron S.
Beyrer, Chris
Donnell, Deborah
Boily, Marie-Claude
author_facet Mitchell, Kate M.
Dimitrov, Dobromir
Hughes, James P.
Moore, Mia
Vittinghoff, Eric
Liu, Albert
Cohen, Myron S.
Beyrer, Chris
Donnell, Deborah
Boily, Marie-Claude
author_sort Mitchell, Kate M.
collection PubMed
description BACKGROUND: In cluster-randomized controlled trials (C-RCTs) of HIV prevention strategies, HIV incidence is expensive to measure directly. Surveillance data on HIV diagnoses or viral suppression could provide cheaper incidence estimates. We used mathematical modelling to evaluate whether these measures can replace HIV incidence measurement in C-RCTs. METHODS: We used a US HIV transmission model to simulate C-RCTs of expanded antiretroviral therapy(ART), pre-exposure prophylaxis(PrEP) and HIV testing, together or alone. We tested whether modelled reductions in total new HIV diagnoses, diagnoses with acute infection, diagnoses with early infection(CD4 > 500 cells/μl), diagnoses adjusted for testing volume, or the proportion virally non-suppressed, reflected HIV incidence reductions. RESULTS: Over a two-year trial expanding PrEP alone, modelled reductions in total diagnoses underestimated incidence reductions by a median six percentage points(pp), with acceptable variability(95 % credible interval −14,−2pp). For trials expanding HIV testing alone or alongside ART + PrEP, greater, highly variable bias was seen [−20pp(−128,−1) and −30pp(−134,−16), respectively]. Acceptable levels of bias were only seen over longer trial durations when levels of awareness of HIV-positive status were already high. Expanding ART alone, only acute and early diagnoses reductions reflected incidence reduction well, with some bias[−3pp(−6,−1) and −8pp(−16,−3), respectively]. Early and adjusted diagnoses also reliably reflected incidence when scaling up PrEP alone[bias −5pp(−11,1) and 10pp(3,18), respectively]. For trials expanding testing (alone or with ART + PrEP), bias for all measures explored was too variable for them to replace direct incidence measures, unless using diagnoses when HIV status awareness was already high. CONCLUSIONS: Surveillance measures based on HIV diagnoses may sometimes be adequate surrogates for HIV incidence reduction in C-RCTs expanding ART or PrEP only, if adjusted for bias. However, all surveillance measures explored failed to approximate HIV incidence reductions for C-RCTs expanding HIV testing, unless levels of awareness of HIV-positive status were already high.
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spelling pubmed-79382132021-03-08 Assessing the use of surveillance data to estimate the impact of prevention interventions on HIV incidence in cluster-randomized controlled trials Mitchell, Kate M. Dimitrov, Dobromir Hughes, James P. Moore, Mia Vittinghoff, Eric Liu, Albert Cohen, Myron S. Beyrer, Chris Donnell, Deborah Boily, Marie-Claude Epidemics Article BACKGROUND: In cluster-randomized controlled trials (C-RCTs) of HIV prevention strategies, HIV incidence is expensive to measure directly. Surveillance data on HIV diagnoses or viral suppression could provide cheaper incidence estimates. We used mathematical modelling to evaluate whether these measures can replace HIV incidence measurement in C-RCTs. METHODS: We used a US HIV transmission model to simulate C-RCTs of expanded antiretroviral therapy(ART), pre-exposure prophylaxis(PrEP) and HIV testing, together or alone. We tested whether modelled reductions in total new HIV diagnoses, diagnoses with acute infection, diagnoses with early infection(CD4 > 500 cells/μl), diagnoses adjusted for testing volume, or the proportion virally non-suppressed, reflected HIV incidence reductions. RESULTS: Over a two-year trial expanding PrEP alone, modelled reductions in total diagnoses underestimated incidence reductions by a median six percentage points(pp), with acceptable variability(95 % credible interval −14,−2pp). For trials expanding HIV testing alone or alongside ART + PrEP, greater, highly variable bias was seen [−20pp(−128,−1) and −30pp(−134,−16), respectively]. Acceptable levels of bias were only seen over longer trial durations when levels of awareness of HIV-positive status were already high. Expanding ART alone, only acute and early diagnoses reductions reflected incidence reduction well, with some bias[−3pp(−6,−1) and −8pp(−16,−3), respectively]. Early and adjusted diagnoses also reliably reflected incidence when scaling up PrEP alone[bias −5pp(−11,1) and 10pp(3,18), respectively]. For trials expanding testing (alone or with ART + PrEP), bias for all measures explored was too variable for them to replace direct incidence measures, unless using diagnoses when HIV status awareness was already high. CONCLUSIONS: Surveillance measures based on HIV diagnoses may sometimes be adequate surrogates for HIV incidence reduction in C-RCTs expanding ART or PrEP only, if adjusted for bias. However, all surveillance measures explored failed to approximate HIV incidence reductions for C-RCTs expanding HIV testing, unless levels of awareness of HIV-positive status were already high. 2020-11-20 2020-12 /pmc/articles/PMC7938213/ /pubmed/33285419 http://dx.doi.org/10.1016/j.epidem.2020.100423 Text en The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mitchell, Kate M.
Dimitrov, Dobromir
Hughes, James P.
Moore, Mia
Vittinghoff, Eric
Liu, Albert
Cohen, Myron S.
Beyrer, Chris
Donnell, Deborah
Boily, Marie-Claude
Assessing the use of surveillance data to estimate the impact of prevention interventions on HIV incidence in cluster-randomized controlled trials
title Assessing the use of surveillance data to estimate the impact of prevention interventions on HIV incidence in cluster-randomized controlled trials
title_full Assessing the use of surveillance data to estimate the impact of prevention interventions on HIV incidence in cluster-randomized controlled trials
title_fullStr Assessing the use of surveillance data to estimate the impact of prevention interventions on HIV incidence in cluster-randomized controlled trials
title_full_unstemmed Assessing the use of surveillance data to estimate the impact of prevention interventions on HIV incidence in cluster-randomized controlled trials
title_short Assessing the use of surveillance data to estimate the impact of prevention interventions on HIV incidence in cluster-randomized controlled trials
title_sort assessing the use of surveillance data to estimate the impact of prevention interventions on hiv incidence in cluster-randomized controlled trials
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7938213/
https://www.ncbi.nlm.nih.gov/pubmed/33285419
http://dx.doi.org/10.1016/j.epidem.2020.100423
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