Per-partnership transmission probabilities for Chlamydia trachomatis infection: evidence synthesis of population-based survey data

Background: 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.

Methods: 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 behavior 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.

Results: 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.

Conclusions: 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.