Evidence map›Paper›PMID 25781323›Full record

ArticlePLoS medicine2015

Reassessment of HIV-1 acute phase infectivity: accounting for heterogeneity and study design with simulated cohorts.

Steve E Bellan, Jonathan Dushoff, Alison P Galvani, Lauren Ancel Meyers

Abstract read
In one paragraph

Article in PLoS medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
52citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

52 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  6. Review
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  11. Observational
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  14. Article
  15. Large age shifts in HIV-1 incidence patterns in KwaZulu-Natal, South Africa.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  16. Article
  17. Neurologic Complications of Acute HIV Infection.Current treatment options in infectious diseases · 2020
    Article
  18. Article
  19. Article
  20. Implementation of Syringe Services Programs to Prevent Rapid Human Immunodeficiency Virus Transmission in Rural Counties in the United States: A Modeling Study.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2020
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Steve E BellanCenter for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, Texas, United States of America.
Jonathan DushoffDepartment of Biology, McMaster University, Hamilton, Ontario, Canada.
Alison P GalvaniYale School of Public Health, Yale University, New Haven, Connecticut, United States of America; Department of Ecology and Evolution, Yale University, New Haven, Connecticut, United States of America.
Lauren Ancel MeyersDepartment of Integrative Biology, The University of Texas at Austin, Austin, Texas, United States of America; The Santa Fe Institute, Santa Fe, New Mexico, United States of America.

Funding

Impacts of Individual and Social Behavior on Influenza Dynamics and ControlU01GM087719 · NIGMS · YALE UNIVERSITY · PI GALVANI, ALISON P, MEYERS, LAUREN ANCEL · 2009 to 2018
$6.3M
International Clinics on Infectious Disease Dynamics and Data (ICI3D Program)R25GM102149 · NIGMS · UNIVERSITY OF FLORIDA · PI PULLIAM, JULIET RC, WELTE, ALEX · 2012 to 2015
$696k
NIGMS NIH HHS R25 GM102149NIGMS NIH HHS R25GM102149NIGMS NIH HHS U01 GM087719NIGMS NIH HHS U01GM087719
6 · The paper itself

Abstract

backgroundThe infectivity of the HIV-1 acute phase has been directly measured only once, from a retrospectively identified cohort of serodiscordant heterosexual couples in Rakai, Uganda. Analyses of this cohort underlie the widespread view that the acute phase is highly infectious, even more so than would be predicted from its elevated viral load, and that transmission occurring shortly after infection may therefore compromise interventions that rely on diagnosis and treatment, such as antiretroviral treatment as prevention (TasP). Here, we re-estimate the duration and relative infectivity of the acute phase, while accounting for several possible sources of bias in published estimates, including the retrospective cohort exclusion criteria and unmeasured heterogeneity in risk. METHODS AND

findingsWe estimated acute phase infectivity using two approaches. First, we combined viral load trajectories and viral load-infectivity relationships to estimate infectivity trajectories over the course of infection, under the assumption that elevated acute phase infectivity is caused by elevated viral load alone. Second, we estimated the relative hazard of transmission during the acute phase versus the chronic phase (RHacute) and the acute phase duration (dacute) by fitting a couples transmission model to the Rakai retrospective cohort using approximate Bayesian computation. Our model fit the data well and accounted for characteristics overlooked by previous analyses, including individual heterogeneity in infectiousness and susceptibility and the retrospective cohort's exclusion of couples that were recorded as serodiscordant only once before being censored by loss to follow-up, couple dissolution, or study termination. Finally, we replicated two highly cited analyses of the Rakai data on simulated data to identify biases underlying the discrepancies between previous estimates and our own. From the Rakai data, we estimated RHacute = 5.3 (95% credibility interval [95% CrI]: 0.79-57) and dacute = 1.7 mo (95% CrI: 0.55-6.8). The wide credibility intervals reflect an inability to distinguish a long, mildly infectious acute phase from a short, highly infectious acute phase, given the 10-mo Rakai observation intervals. The total additional risk, measured as excess hazard-months attributable to the acute phase (EHMacute) can be estimated more precisely: EHMacute = (RHacute - 1) × dacute, and should be interpreted with respect to the 120 hazard-months generated by a constant untreated chronic phase infectivity over 10 y of infection. From the Rakai data, we estimated that EHMacute = 8.4 (95% CrI: -0.27 to 64). This estimate is considerably lower than previously published estimates, and consistent with our independent estimate from viral load trajectories, 5.6 (95% confidence interval: 3.3-9.1). We found that previous overestimates likely stemmed from failure to account for risk heterogeneity and bias resulting from the retrospective cohort study design. Our results reflect the interaction between the retrospective cohort exclusion criteria and high (47%) rates of censorship amongst incident serodiscordant couples in the Rakai study due to loss to follow-up, couple dissolution, or study termination. We estimated excess physiological infectivity during the acute phase from couples data, but not the proportion of transmission attributable to the acute phase, which would require data on the broader population's sexual network structure.

conclusionsPrevious EHMacute estimates relying on the Rakai retrospective cohort data range from 31 to 141. Our results indicate that these are substantial overestimates of HIV-1 acute phase infectivity, biased by unmodeled heterogeneity in transmission rates between couples and by inconsistent censoring. Elevated acute phase infectivity is therefore less likely to undermine TasP interventions than previously thought. Heterogeneity in infectiousness and susceptibility may still play an important role in intervention success and deserves attention in future analyses.

Indexed as

Acute-Phase ReactionBiasHIV-1Research DesignSexual PartnersViral LoadAcute DiseaseBayes TheoremCohort StudiesComputer SimulationFamily CharacteristicsHeterosexualityHIV InfectionsHIV SeropositivityHumansPatient Selection

Identifiers

PMID25781323
PMCPMC4363602

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.