AIDS (London, England) · 2017
Sexual network drivers of HIV and herpes simplex virus type 2 transmission.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Omori Ryosuke, Abu-Raddad Laith J
Аннотация
OBJECTIVES: HIV and herpes simplex virus type 2 (HSV-2) infections are sexually transmitted and propagate in sexual networks. Using mathematical modeling, we aimed to quantify effects of key network statistics on infection transmission, and extent to which HSV-2 prevalence can be a proxy of HIV prevalence.
DESIGN/METHODS: An individual-based simulation model was constructed to describe sex partnering and infection transmission, and was parameterized with representative natural history, transmission, and sexual behavior data. Correlations were assessed on model outcomes (HIV/HSV-2 prevalences) and multiple linear regressions were conducted to estimate adjusted associations and effect sizes.
RESULTS: HIV prevalence was one-third or less of HSV-2 prevalence. HIV and HSV-2 prevalences were associated with a Spearman's rank correlation coefficient of 0.64 (95% confidence interval: 0.58-0.69). Collinearities among network statistics were detected, most notably between concurrency versus mean and variance of number of partners. Controlling for confounding, unmarried mean/variance of number of partners (or alternatively concurrency) were the strongest predictors of HIV prevalence. Meanwhile, unmarried/married mean/variance of number of partners (or alternatively concurrency), and clustering coefficient were the strongest predictors of HSV-2 prevalence. HSV-2 prevalence was a strong predictor of HIV prevalence by proxying effects of network statistics.
CONCLUSION: Network statistics produced similar and differential effects on HIV/HSV-2 transmission, and explained most of the variation in HIV and HSV-2 prevalences. HIV prevalence reflected primarily mean and variance of number of partners, but HSV-2 prevalence was affected by a range of network statistics. HSV-2 prevalence (as a proxy) can forecast a population's HIV epidemic potential, thereby informing interventions.
DESIGN/METHODS: An individual-based simulation model was constructed to describe sex partnering and infection transmission, and was parameterized with representative natural history, transmission, and sexual behavior data. Correlations were assessed on model outcomes (HIV/HSV-2 prevalences) and multiple linear regressions were conducted to estimate adjusted associations and effect sizes.
RESULTS: HIV prevalence was one-third or less of HSV-2 prevalence. HIV and HSV-2 prevalences were associated with a Spearman's rank correlation coefficient of 0.64 (95% confidence interval: 0.58-0.69). Collinearities among network statistics were detected, most notably between concurrency versus mean and variance of number of partners. Controlling for confounding, unmarried mean/variance of number of partners (or alternatively concurrency) were the strongest predictors of HIV prevalence. Meanwhile, unmarried/married mean/variance of number of partners (or alternatively concurrency), and clustering coefficient were the strongest predictors of HSV-2 prevalence. HSV-2 prevalence was a strong predictor of HIV prevalence by proxying effects of network statistics.
CONCLUSION: Network statistics produced similar and differential effects on HIV/HSV-2 transmission, and explained most of the variation in HIV and HSV-2 prevalences. HIV prevalence reflected primarily mean and variance of number of partners, but HSV-2 prevalence was affected by a range of network statistics. HSV-2 prevalence (as a proxy) can forecast a population's HIV epidemic potential, thereby informing interventions.