Assessing evidence for behaviour change affecting the course of HIV epidemics: A new mathematical modelling approach and application to data from Zimbabwe

Hallett, Timothy B.; Gregson, Simon; Mugurungi, Owen; Gonese, Elizabeth; Garnett, Geoff P.

doi:10.1016/j.epidem.2009.03.001

Cited by 82 publications

(94 citation statements)

References 31 publications

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“…The model calibration allows uncertainty about factors that determine the amount of early transmission, including the relative infectiousness during early infection, heterogeneity in propensity for sexual risk behavior, assortativity in sexual partner selection, reduction in risk propensity over the life course, and populationwide reductions in risk behavior in response to the epidemic (32,33). This results in multiple combinations of parameter values that are consistent with the observed epidemic and variation in the amount of early transmission.…”

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confidence: 50%

“…The model is calibrated to longitudinal HIV prevalence data from South Africa using a Bayesian framework. Thus, the model accounts for not only the early epidemic growth rate highlighted in previous research (5, 9, 18), but also the heterogeneity and sexual behavior change to explain the peak and decline in HIV incidence observed in sub-Saharan African HIV epidemics (32,33).The model calibration allows uncertainty about factors that determine the amount of early transmission, including the relative infectiousness during early infection, heterogeneity in propensity for sexual risk behavior, assortativity in sexual partner selection, reduction in risk propensity over the life course, and populationwide reductions in risk behavior in response to the epidemic (32,33). This results in multiple combinations of parameter values that are consistent with the observed epidemic and variation in the amount of early transmission.…”

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Why the proportion of transmission during early-stage HIV infection does not predict the long-term impact of treatment on HIV incidence

Eaton

Hallett

2014

Proc. Natl. Acad. Sci. U.S.A.

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Antiretroviral therapy (ART) reduces the infectiousness of HIVinfected persons, but only after testing, linkage to care, and successful viral suppression. Thus, a large proportion of HIV transmission during a period of high infectiousness in the first few months after infection ("early transmission") is perceived as a threat to the impact of HIV "treatment-as-prevention" strategies. We created a mathematical model of a heterosexual HIV epidemic to investigate how the proportion of early transmission affects the impact of ART on reducing HIV incidence. The model includes stages of HIV infection, flexible sexual mixing, and changes in risk behavior over the epidemic. The model was calibrated to HIV prevalence data from South Africa using a Bayesian framework. Immediately after ART was introduced, more early transmission was associated with a smaller reduction in HIV incidence rate-consistent with the concern that a large amount of early transmission reduces the impact of treatment on incidence. However, the proportion of early transmission was not strongly related to the long-term reduction in incidence. This was because more early transmission resulted in a shorter generation time, in which case lower values for the basic reproductive number (R 0 ) are consistent with observed epidemic growth, and R 0 was negatively correlated with long-term intervention impact. The fraction of early transmission depends on biological factors, behavioral patterns, and epidemic stage and alone does not predict long-term intervention impacts. However, early transmission may be an important determinant in the outcome of short-term trials and evaluation of programs.HIV incidence | antiretroviral therapy | early infection | HIV prevention intervention | mathematical model R ecent studies have confirmed that effective antiretroviral therapy (ART) reduces the transmission of HIV among stable heterosexual couples (1-3). This finding has generated interest in understanding the population-level impact of HIV treatment on reducing the rate of new HIV infections in generalized epidemic settings (4). Research, including mathematical modeling (5-10), implementation research (11), and major randomized controlled trials (12)(13)(14), are focused on how ART provision might be expanded strategically to maximize its public health benefits (15, 16).One concern is that if a large fraction of HIV transmission occurs shortly after a person becomes infected, before the person can be diagnosed and initiated on ART, this will limit the potential impact of HIV treatment on reducing HIV incidence (9,17,18). Data suggest that persons are more infectious during a short period of "early infection" after becoming infected with HIV (19-22), although there is debate about the extent, duration, and determinants of elevated infectiousness (18, 23). The amount of transmission that occurs also will depend on patterns of sexual behavior and sexual networks (17,(24)(25)(26)(27). There have been estimates for the contribution of early infection to transmission from math...

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Why the proportion of transmission during early-stage HIV infection does not predict the long-term impact of treatment on HIV incidence

Eaton

Hallett

2014

Proc. Natl. Acad. Sci. U.S.A.

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“…We employ a mathematical model that builds upon our previous work [17][18][19] to describe the dynamic spread of HIV. Equations are available in the Supplemental Appendix of the paper by Anderson et al 17 Here we describe key details of the model structure (appendix p. 1).…”

Section: Model Designmentioning

confidence: 99%

Optimum resource allocation to reduce HIV incidence across sub-Saharan Africa: a mathematical modelling study

et al. 2016

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“…Model based on Hallett and colleagues. 5,6 Vaccine doses available in country Vaccine doses distributed Children vaccinated Measles incidence Measles mortality Models can also be used to assess how an intervention should be rolled out. In the case of the randomised trials of HPV vaccine, because of the effi cacy of the vaccine, quite simple models show that vaccination is cost eff ective.…”

Section: The Role Of Models In Programme Evaluationsmentioning

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Mathematical models in the evaluation of health programmes

Garnett

Hallett

Steketee³

et al. 2011

The Lancet

216

184

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Modelling is valuable in the planning and evaluation of interventions, especially when a controlled trial is ethically or logistically impossible. Models are often used to calculate the expected course of events in the absence of more formal assessments. They are also used to derive estimates of rare or future events from recorded intermediate points. When developing models, decisions are needed about the appropriate level of complexity to be represented and about model structure and assumptions. The degree of rigor in model development and assessment can vary greatly, and there is a danger that existing beliefs inappropriately infl uence judgments about model assumptions and results.

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Assessing evidence for behaviour change affecting the course of HIV epidemics: A new mathematical modelling approach and application to data from Zimbabwe

Abstract: The model and associated analysis framework provide a robust way to evaluate the evidence for changes in risk behaviour affecting the course of HIV epidemics, avoiding confounding by the natural evolution of HIV epidemics.

Cited by 82 publications

References 31 publications

Why the proportion of transmission during early-stage HIV infection does not predict the long-term impact of treatment on HIV incidence

Why the proportion of transmission during early-stage HIV infection does not predict the long-term impact of treatment on HIV incidence

Optimum resource allocation to reduce HIV incidence across sub-Saharan Africa: a mathematical modelling study

Mathematical models in the evaluation of health programmes

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