Contribution of high risk groups’ unmet needs may be underestimated in epidemic models without risk turnover: A mechanistic modelling analysis
Background: Epidemic models of sexually transmitted infections (STIs) are often used to characterize the contribution of risk groups to overall transmission by projecting the transmission population attributable fraction (tPAF) of unmet prevention and treatment needs within risk groups. However, evi...
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doaj-a8ede09a9c9a47c8bb168048edbf1e3e2021-04-02T19:57:44ZengKeAi Communications Co., Ltd.Infectious Disease Modelling2468-04272020-01-015549562Contribution of high risk groups’ unmet needs may be underestimated in epidemic models without risk turnover: A mechanistic modelling analysisJesse Knight0Stefan D. Baral1Sheree Schwartz2Linwei Wang3Huiting Ma4Katherine Young5Harry Hausler6Sharmistha Mishra7MAP Centre for Urban Health Solutions, Unity Health Toronto, CanadaDeptartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, USADeptartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, USAMAP Centre for Urban Health Solutions, Unity Health Toronto, CanadaMAP Centre for Urban Health Solutions, Unity Health Toronto, CanadaTB HIV Care, South AfricaTB HIV Care, South AfricaMAP Centre for Urban Health Solutions, Unity Health Toronto, Canada; Division of Infectious Disease, Department of Medicine, University of Toronto, Canada; Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Canada; Institute of Medical Sciences, University of Toronto, Canada; Corresponding author. Division of Infectious Disease, Department of Medicine, University of Toronto, Canada.Background: Epidemic models of sexually transmitted infections (STIs) are often used to characterize the contribution of risk groups to overall transmission by projecting the transmission population attributable fraction (tPAF) of unmet prevention and treatment needs within risk groups. However, evidence suggests that STI risk is dynamic over an individual’s sexual life course, which manifests as turnover between risk groups. We sought to examine the mechanisms by which turnover influences modelled projections of the tPAF of high risk groups. Methods: We developed a unifying, data-guided framework to simulate risk group turnover in deterministic, compartmental transmission models. We applied the framework to an illustrative model of an STI and examined the mechanisms by which risk group turnover influenced equilibrium prevalence across risk groups. We then fit a model with and without turnover to the same risk-stratified STI prevalence targets and compared the inferred level of risk heterogeneity and tPAF of the highest risk group projected by the two models. Results: The influence of turnover on group-specific prevalence was mediated by three main phenomena: movement of previously high risk individuals with the infection into lower risk groups; changes to herd effect in the highest risk group; and changes in the number of partnerships where transmission can occur. Faster turnover led to a smaller ratio of STI prevalence between the highest and lowest risk groups. Compared to the fitted model without turnover, the fitted model with turnover inferred greater risk heterogeneity and consistently projected a larger tPAF of the highest risk group over time. Implications: If turnover is not captured in epidemic models, the projected contribution of high risk groups, and thus, the potential impact of prioritizing interventions to address their needs, could be underestimated. To aid the next generation of tPAF models, data collection efforts to parameterize risk group turnover should be prioritized.http://www.sciencedirect.com/science/article/pii/S2468042720300282Mathematical modellingTransmissionRisk heterogeneityTurnoverSexually transmitted infectionPopulation attributable fraction |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jesse Knight Stefan D. Baral Sheree Schwartz Linwei Wang Huiting Ma Katherine Young Harry Hausler Sharmistha Mishra |
spellingShingle |
Jesse Knight Stefan D. Baral Sheree Schwartz Linwei Wang Huiting Ma Katherine Young Harry Hausler Sharmistha Mishra Contribution of high risk groups’ unmet needs may be underestimated in epidemic models without risk turnover: A mechanistic modelling analysis Infectious Disease Modelling Mathematical modelling Transmission Risk heterogeneity Turnover Sexually transmitted infection Population attributable fraction |
author_facet |
Jesse Knight Stefan D. Baral Sheree Schwartz Linwei Wang Huiting Ma Katherine Young Harry Hausler Sharmistha Mishra |
author_sort |
Jesse Knight |
title |
Contribution of high risk groups’ unmet needs may be underestimated in epidemic models without risk turnover: A mechanistic modelling analysis |
title_short |
Contribution of high risk groups’ unmet needs may be underestimated in epidemic models without risk turnover: A mechanistic modelling analysis |
title_full |
Contribution of high risk groups’ unmet needs may be underestimated in epidemic models without risk turnover: A mechanistic modelling analysis |
title_fullStr |
Contribution of high risk groups’ unmet needs may be underestimated in epidemic models without risk turnover: A mechanistic modelling analysis |
title_full_unstemmed |
Contribution of high risk groups’ unmet needs may be underestimated in epidemic models without risk turnover: A mechanistic modelling analysis |
title_sort |
contribution of high risk groups’ unmet needs may be underestimated in epidemic models without risk turnover: a mechanistic modelling analysis |
publisher |
KeAi Communications Co., Ltd. |
series |
Infectious Disease Modelling |
issn |
2468-0427 |
publishDate |
2020-01-01 |
description |
Background: Epidemic models of sexually transmitted infections (STIs) are often used to characterize the contribution of risk groups to overall transmission by projecting the transmission population attributable fraction (tPAF) of unmet prevention and treatment needs within risk groups. However, evidence suggests that STI risk is dynamic over an individual’s sexual life course, which manifests as turnover between risk groups. We sought to examine the mechanisms by which turnover influences modelled projections of the tPAF of high risk groups. Methods: We developed a unifying, data-guided framework to simulate risk group turnover in deterministic, compartmental transmission models. We applied the framework to an illustrative model of an STI and examined the mechanisms by which risk group turnover influenced equilibrium prevalence across risk groups. We then fit a model with and without turnover to the same risk-stratified STI prevalence targets and compared the inferred level of risk heterogeneity and tPAF of the highest risk group projected by the two models. Results: The influence of turnover on group-specific prevalence was mediated by three main phenomena: movement of previously high risk individuals with the infection into lower risk groups; changes to herd effect in the highest risk group; and changes in the number of partnerships where transmission can occur. Faster turnover led to a smaller ratio of STI prevalence between the highest and lowest risk groups. Compared to the fitted model without turnover, the fitted model with turnover inferred greater risk heterogeneity and consistently projected a larger tPAF of the highest risk group over time. Implications: If turnover is not captured in epidemic models, the projected contribution of high risk groups, and thus, the potential impact of prioritizing interventions to address their needs, could be underestimated. To aid the next generation of tPAF models, data collection efforts to parameterize risk group turnover should be prioritized. |
topic |
Mathematical modelling Transmission Risk heterogeneity Turnover Sexually transmitted infection Population attributable fraction |
url |
http://www.sciencedirect.com/science/article/pii/S2468042720300282 |
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