Acute-Stage Transmission of HIV

Romero-Severson, Ethan; Alam, Shah Jamal; Volz, Erik; Koopman, James S.

doi:10.1097/ede.0b013e318294802e

Cited by 13 publications

(11 citation statements)

References 16 publications

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“…Finally, some forms of risk heterogeneity will affect AF early at the population level but cannot be observed in stable couples cohorts, because they do not affect transmission to a stable partner. Examples include partner acquisition rates and tendency to maintain concurrent relationships (i.e., episodic risk behavior [ 11 , 12 ]).…”

Section: Discussionmentioning

confidence: 99%

“…This will occur, for example, when there is a high prevalence of concurrent partnerships [ 10 ], when there is a generally fast partner switching rate, or if individuals exhibit episodic risk behavior (risk “volatility”) [ 11 ]. Nonlinear interactions between acute phase infectivity and patterns of sexual contact may increase AF early far more than the sum of their separate effects [ 10 , 12 ]. Thus, it is necessary to understand acute phase infectivity in the context of sexual contact patterns to assess their joint contributions to transmission dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Reassessment of HIV-1 Acute Phase Infectivity: Accounting for Heterogeneity and Study Design with Simulated Cohorts

et al. 2015

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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 (d acute) 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 d acute = 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) × d acute, 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 crit...

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reassessment of HIV-1 Acute Phase Infectivity: Accounting for Heterogeneity and Study Design with Simulated Cohorts

et al. 2015

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“…We have illustrated here how episodic risk is likely to considerably amplify those effects. Consequently, collecting more data on risk heterogeneity and episodic risk (or its continuous generalization, risk volatility17) is highly important in order to make the best possible decisions about how to allocate finite HIV control resources.…”

Section: Discussionmentioning

confidence: 99%

“…For example, dichotomized episodic risk can be replaced by continuous risk volatility1720. Another is incorporating important aspects of sexual behavior that are not included in the present model, such as long-term monogamous or semi-monogamous partnerships, temporal changes in insertive or receptive behavior21 or temporal changes in condom use with different types of partners.…”

Section: Discussionmentioning

confidence: 99%

Strong influence of behavioral dynamics on the ability of testing and treating HIV to stop transmission

Henry

Koopman

2015

Sci Rep

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Choosing between strategies to control HIV transmission with antivirals requires understanding both the dynamics affecting those strategies' effectiveness and what causes those dynamics. Alternating episodes of high and low contact rates (episodic risk) interact with increased transmission probabilities during early infection to strongly influence HIV transmission dynamics. To elucidate the mechanics of this interaction and how these alter the effectiveness of universal test and treat (UT8T) strategies, we formulated a model of UT8T effects. Analysis of this model shows how and why changing the dynamics of episodic risk changes the fraction of early transmissions (FET) and the basic reproduction number (R0) and consequently causes UT8T to vary from easily eliminating transmission to having little effect. As the length of risk episodes varies from days to lifetimes, FET first increases, then falls. Endemic prevalence varies similarly. R0, in contrast, increases monotonically and is the major determinant of UT8T effects. At some levels of episodic risk, FET can be high, but eradication is easy because R0 is low. At others FET is lower, but a high R0 makes eradication impossible and control ineffective. Thus changes in individual risk over time must be measured and analyzed to plan effective control strategies with antivirals.

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“…Therefore we focused our transmission risk model on CIAI episodes by the transmitting HIV-positive partners, and assumed a 1.38% (95% CI 1.02%–1.86%) risk per act of transmitting the disease, although this may be an underestimation for those with AHI, where transmission risk is greatest during the initial weeks and months [27, 28]. …”

Section: Cost Per Infection (Ie Transmission) Avertedmentioning

confidence: 99%

Screening for acute HIV infection in community-based settings: Cost-effectiveness and impact on transmissions

Hoenigl

Chaillon

Mehta

et al. 2016

Journal of Infection

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Objectives To determine cost-effectiveness of three community-based acute HIV infection (AHI) testing algorithms compared to HIV antibody testing alone by focusing on the potential of averting new infections occurring within a one-year time horizon among men who have sex with men (MSM). Methods Data sources for model parameters included actual cost and prevalence data derived from a community-based AHI screening program in San Diego, and published studies. Main outcome measure was costs per infection averted (IA). The lower end of the cost range of discounted lifetime costs of an HIV infection (i.e. $236,948) was used for defining cost-effectiveness. Results The most sensitive algorithm for AHI detection, which was based on HIV nucleic acid amplification testing, was estimated to prevent between 5 and 45 transmissions, with simulated costs per infection averted between $965 and $141,256 when compared to HIV antibody testing alone. Conclusion AHI testing was cost-effective in preventing new HIV infections among at risk MSM in San Diego, and also among other MSM populations with similar HIV prevalence but lower proportions of AHI diagnoses. These results indicate that community based AHI testing among MSM in the United States can pay for itself over the long run.

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Acute-Stage Transmission of HIV

Cited by 13 publications

References 16 publications

Reassessment of HIV-1 Acute Phase Infectivity: Accounting for Heterogeneity and Study Design with Simulated Cohorts

Reassessment of HIV-1 Acute Phase Infectivity: Accounting for Heterogeneity and Study Design with Simulated Cohorts

Strong influence of behavioral dynamics on the ability of testing and treating HIV to stop transmission

Screening for acute HIV infection in community-based settings: Cost-effectiveness and impact on transmissions

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