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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.
Mitchell, Kate M; Dimitrov, Dobromir; Hughes, James P; Moore, Mia; Vittinghoff, Eric; Liu, Albert; Cohen, Myron S; Beyrer, Chris; Donnell, Deborah; Boily, Marie-Claude.
Afiliação
  • Mitchell KM; Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom; HIV Prevention Trials Network Modelling Centre, Imperial College London, London, United Kingdom. Electronic address: kate.mitchell@i
  • Dimitrov D; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, USA.
  • Hughes JP; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, USA; Department of Biostatistics, University of Washington, Seattle, USA.
  • Moore M; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, USA.
  • Vittinghoff E; Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, USA.
  • Liu A; Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, USA; Bridge HIV, Population Health Division, San Francisco Department of Public Health, San Francisco, USA.
  • Cohen MS; Institute for Global Health and Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
  • Beyrer C; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA.
  • Donnell D; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, USA.
  • Boily MC; Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom; HIV Prevention Trials Network Modelling Centre, Imperial College London, London, United Kingdom.
Epidemics ; 33: 100423, 2020 12.
Article em En | MEDLINE | ID: mdl-33285419
ABSTRACT

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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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Infecções por HIV / Ensaios Clínicos Controlados Aleatórios como Assunto Tipo de estudo: Clinical_trials / Incidence_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Limite: Adult / Humans / Male Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Infecções por HIV / Ensaios Clínicos Controlados Aleatórios como Assunto Tipo de estudo: Clinical_trials / Incidence_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Limite: Adult / Humans / Male Idioma: En Ano de publicação: 2020 Tipo de documento: Article