Abstract
BackgroundChlamydia 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.MethodsWe 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.ResultsPosterior 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.ConclusionsThe 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.
Highlights
Chlamydia is the most commonly-diagnosed sexually transmitted infection worldwide
Numerous models have been developed for these purposes[7] but a comparison of three individual-based models found they produced very different results.[8]. A key parameter in any transmission-dynamic model is the transmission probability per infectious contact, where a “contact” may be defined either as a partnership or as a sex act
We have described a new statistical model for inferring the per-partnership transmission probability of a sexually transmitted infection, and have applied it to population-level data on chlamydia from the UK and the US
Summary
In there were 1,382 and 3,694 chlamydia diagnoses per 100,000 15-24-year-old US men and women, respectively,(1) and 1,342 and 2,637 in England.[2] There is marked geographic variation in chlamydia burden,(3) and the effectiveness of widespread testing and/or screening in chlamydia control remains uncertain,(4,5) but the need for cost-effective control measures becomes ever-clearer as evidence for the link to pelvic inflammatory disease (PID) is strengthened[6] yet resources for sexual health services are reduced. Mathematical models are important tools for assessing and predicting the effectiveness and cost-effectiveness of chlamydia control policies. 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
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