MRC Centre for Global Infectious Disease Analysis and National Institute for Health Research (NIHR) Health Protection Research Unit in Modelling and Health Economics, Imperial College London School of Public Health, London, UK.
Centre for Applied Statistics Courses, UCL Great Ormond Street Institute of Child Health, University College London, London, UK.
Int J Epidemiol. 2021 May 17;50(2):510-517. doi: 10.1093/ije/dyaa202.
Chlamydia is the most commonly diagnosed sexually transmitted infection worldwide. Mathematical models used to plan and assess control measures rely on accurate estimates of chlamydia's natural history, including the probability of transmission within a partnership. Several methods for estimating transmission probability have been proposed, but all have limitations.
We have developed a new model for estimating per-partnership chlamydia transmission probabilities from infected to uninfected individuals, using data from population-based surveys. We used data on sexual behaviour and prevalent chlamydia infection from the second UK National Study of Sexual Attitudes and Lifestyles (Natsal-2) and the US National Health and Nutrition Examination Surveys 2009-2014 (NHANES) for Bayesian inference of average transmission probabilities, across all new heterosexual partnerships reported. Posterior distributions were estimated by Markov chain Monte Carlo sampling using the Stan software.
Posterior median male-to-female transmission probabilities per partnership were 32.1% [95% credible interval (CrI) 18.4-55.9%] (Natsal-2) and 34.9% (95%CrI 22.6-54.9%) (NHANES). Female-to-male transmission probabilities were 21.4% (95%CrI 5.1-67.0%) (Natsal-2) and 4.6% (95%CrI 1.0-13.1%) (NHANES). Posterior predictive checks indicated a well-specified model, although there was some discrepancy between reported and predicted numbers of partners, especially in women.
The model provides statistically rigorous estimates of per-partnership transmission probability, with associated uncertainty, which is crucial for modelling and understanding chlamydia epidemiology and control. Our estimates incorporate data from several sources, including population-based surveys, and use information contained in the correlation between number of partners and the probability of chlamydia infection. The evidence synthesis approach means that it is easy to include further data as it becomes available.
衣原体是全球最常见的性传播感染。用于规划和评估控制措施的数学模型依赖于对衣原体自然史的准确估计，包括在伴侣关系中传播的概率。已经提出了几种估计传播概率的方法，但都有其局限性。
我们使用基于人群的调查数据，开发了一种新的模型，用于估计从感染个体到未感染个体的每对伴侣的衣原体传播概率。我们使用来自英国第二次全国性态度和生活方式调查（Natsal-2）和美国 2009-2014 年全国健康和营养检查调查（NHANES）的性行为和流行衣原体感染数据，通过贝叶斯推理，对报告的所有新异性恋伴侣中的平均传播概率进行了推断。使用 Stan 软件通过马尔可夫链蒙特卡罗抽样对后验分布进行了估计。
每对伴侣的男性到女性的后验中位数传输概率分别为 32.1%（95%可信区间[CrI] 18.4-55.9%）（Natsal-2）和 34.9%（95%CrI 22.6-54.9%）（NHANES）。女性到男性的传输概率分别为 21.4%（95%CrI 5.1-67.0%）（Natsal-2）和 4.6%（95%CrI 1.0-13.1%）（NHANES）。后验预测检查表明该模型具有良好的规定性，尽管报告的伴侣数量与预测的数量之间存在一些差异，尤其是在女性中。
该模型提供了具有相关不确定性的每对伴侣传播概率的严格统计学估计，这对于建模和理解衣原体流行病学和控制至关重要。我们的估计结合了来自多个来源的数据，包括基于人群的调查，并利用了伴侣数量与衣原体感染概率之间的相关性所包含的信息。证据综合方法意味着很容易随着新数据的出现而包含更多数据。
