Contribution of high risk groups' unmet needs may be underestimated in epidemic models without risk turnover: a mechanistic modelling analysis
| Autor: | Jesse Knight, Linwei Wang, Sharmistha Mishra, Harry Hausler, Sheree Schwartz, Stefan Baral, Huiting Ma, Katherine Young |
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| Rok vydání: | 2020 |
| Předmět: |
030231 tropical medicine
Population Psychological intervention Dynamical Systems (math.DS) Lower risk lcsh:Infectious and parasitic diseases Herd immunity law.invention Risk heterogeneity 03 medical and health sciences 0302 clinical medicine Risk groups Population attributable fraction law FOS: Mathematics Medicine Transmission lcsh:RC109-216 030212 general & internal medicine Original Research Article Mathematics - Dynamical Systems Quantitative Biology - Populations and Evolution education STI sexually transmitted infection education.field_of_study Sexually transmitted infection Data collection Mathematical modelling business.industry Applied Mathematics Health Policy Populations and Evolution (q-bio.PE) Turnover 3. Good health HIV human immunodeficiency virus Infectious Diseases Transmission (mechanics) FOS: Biological sciences Attributable risk tPAF transmission population attributable fraction business Demography |
| Zdroj: | Infectious Disease Modelling Infectious Disease Modelling, Vol 5, Iss, Pp 549-562 (2020) |
| DOI: | 10.48550/arxiv.2001.02744 |
| Popis: | 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. Graphical abstract Image 1 Highlights • A new framework for parameterizing turnover in risk groups is developed. • Mechanisms by which turnover influences sexually transmitted infection (STI), prevalence in risk groups are examined. • Turnover reduces the ratio of equilibrium STI prevalence in high vs low risk groups. • Inferred risk heterogeneity is higher when fitting transmission models with turnover. • Ignoring turnover in risk could underestimate the transmission population attributable fraction (tPAF), of high risk groups to the overall epidemic. |
| Databáze: | OpenAIRE |
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