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 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.
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.
Bibliographical noteFunding Information: J.L. and P.J.W. were supported by the National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU) in Modelling Methodology at Imperial College London in partnership with Public Health England (PHE) (grant number HPRU-2012-10080). P.J.W. was also supported by the NIHR HPRU in Modelling and Health Economics, a partnership between PHE, Imperial College London and LSHTM, for funding (grant number NIHR200908). Additionally, P.J.W. was supported by the MRC Centre for Global Infectious Disease Analysis (grant number MR/R015600/1); this award is jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth & Development Office (FCDO) under the MRC/FCDO Concordat agreement and is also part of the EDCTP2 programme supported by the European Union (EU). M.J.P. was supported by the NIHR Birmingham Biomedical Research Centre at the University Hospitals Birmingham NHS Foundation Trust and the University of
Birmingham. This paper presents independent research, and the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The views expressed are those of the authors and not necessarily those of the Department of Health and Social Care, EU, FCDO, MRC, National Health Service, NIHR, or PHE
Open Access: This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher Copyright: © The Author(s) 2020. Published by Oxford University Press on behalf of the International Epidemiological Association.
Citation: Joanna Lewis, Peter J White, Malcolm J Price, Per-partnership transmission probabilities for Chlamydia trachomatis infection: evidence synthesis of population-based survey data, International Journal of Epidemiology, Volume 50, Issue 2, April 2021, Pages 510–517.
- Bayesian statistics
- evidence synthesis
- mathematical model
- population-based survey