Macalester College, Saint Paul, MN, United States.
Division of Health Policy and Management, University of Minnesota School of Public Health, Minneapolis, MN, United States.
Vaccine. 2018 Sep 5;36(37):5572-5579. doi: 10.1016/j.vaccine.2018.07.058. Epub 2018 Aug 6.
Sexual mixing between heterogeneous population subgroups is an integral component of mathematical models of sexually transmitted infections (STIs). This study compares the fit of different mixing representations to survey data and the impact of different mixing assumptions on the predicted benefits of hypothetical human papillomavirus (HPV) vaccine strategies.
We compared novel empirical (data-driven) age mixing structures with the more commonly-used assortative-proportionate (A-P) mixing structure. The A-P mixing structure assumes that a proportion of sexual contacts - known as the assortativity constant, typically estimated from survey data or calibrated - occur exclusively within one's own age group and the remainder mixes proportionately among all age groups. The empirical age mixing structure was estimated from the National Survey on Sexual Attitudes and Lifestyles 3 (Natsal-3) using regression methods, and the assortativity constant was estimated from Natsal-3 as well. Using a simplified HPV transmission model under each mixing assumption, we calibrated the model to British HPV16 prevalence data, then estimated the reduction in steady-state prevalence and the number of infections averted due to expanding HPV vaccination from 12- through 26-year-old females alone to 12-year-old males or 27- to 39-year-old females.
Empirical mixing provided a better fit to the Natsal-3 data than the best-fitting A-P structure. Using the model with empirical mixing as a reference, the model using the A-P structure often under- or over-estimated the benefits of vaccination, in one case overestimating by 2-fold the number of infections prevented due to extended female catch-up in a high vaccine uptake setting.
An empirical mixing structure more accurately represents sexual mixing survey data, and using the less accurate, yet commonly-used A-P structure has a notable effect on estimates of HPV vaccination benefits. This underscores the need for mixing structures that are less dependent on unverified assumptions and are directly informed by sexual behavior data.
在性传播感染（STI）的数学模型中，不同亚人群之间的性混合是一个不可或缺的组成部分。本研究比较了不同混合表示对调查数据的拟合程度，以及不同混合假设对假设人乳头瘤病毒（HPV）疫苗策略预测效益的影响。
我们比较了新颖的经验（数据驱动）年龄混合结构与更常用的 assortative-proportionate（A-P）混合结构。A-P 混合结构假设一部分性接触 - 称为 assortativity 常数，通常从调查数据或校准中估计 - 仅发生在自己的年龄组内，其余部分按比例混合在所有年龄组之间。经验年龄混合结构是从国家性态度和生活方式调查 3（Natsal-3）中使用回归方法估计的，assortativity 常数也是从 Natsal-3 中估计的。在每种混合假设下使用简化的 HPV 传播模型，我们对模型进行了校准，以适应英国 HPV16 流行率数据，然后估计了由于扩大 HPV 疫苗接种从 12 岁至 26 岁的女性扩展到 12 岁的男性或 27 岁至 39 岁的女性，在稳定状态流行率和感染人数减少方面的效益。
经验混合比最佳拟合的 A-P 结构更好地拟合了 Natsal-3 数据。使用经验混合模型作为参考，使用 A-P 结构的模型经常低估或高估疫苗接种的效益，在一种情况下，在高疫苗接种率的情况下，由于女性追赶扩大，估计的感染预防数量高估了 2 倍。
经验混合结构更准确地代表了性混合调查数据，而使用不太准确但常用的 A-P 结构对 HPV 疫苗效益的估计有显著影响。这突显出需要依赖未经证实的假设并且直接从性行为数据中获得信息的混合结构。
