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Original research
Using infection prevalence, seroprevalence and case report data to estimate chlamydial infection incidence
  1. Patrick A Clay1,
  2. Emily D Pollock1,
  3. Casey E Copen1,
  4. E Gloria Anyalechi2,
  5. Damien C Danavall1,
  6. Jaeyoung Hong1,
  7. Christine M Khosropour3,
  8. Eboni Galloway1,
  9. Ian H Spicknall1
  1. 1 Division of STD Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
  2. 2 Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
  3. 3 Department of Epidemiology, University of Washington, Seattle, Washington, USA
  1. Correspondence to Dr Patrick A Clay, Division of STD Prevention, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA; ruq9{at}cdc.gov

Abstract

Objectives To measure the effectiveness of chlamydia control strategies, we must estimate infection incidence over time. Available data, including survey-based infection prevalence and case reports, have limitations as proxies for infection incidence. We therefore developed a novel method for estimating chlamydial incidence.

Methods We linked a susceptible infectious mathematical model to serodynamics data from the National Health and Nutritional Examination Survey, as well as to annual case reports. We created four iterations of this model, varying assumptions about how the method of infection clearance (via treatment seeking, routine screening or natural clearance) relates to long-term seropositivity. Using these models, we estimated annual infection incidence for women aged 18–24 and 25–37 years in 2014. To assess model plausibility, we also estimated natural clearance for the same groups.

Results Of the four models we analysed, the model that best explained the empirical data was the one in which longer-lasting infections, natural clearance and symptomatic infections all increased the probability of long-term seroconversion. Using this model, we estimated 5910 (quartile (Q)1, 5330; Q3, 6500) incident infections per 100 000 women aged 18–24 years and 2790 (Q1, 2500; Q3, 3090) incident infections per 100 000 women aged 25–37 years in 2014. Furthermore, we estimated that natural clearance rates increased with age.

Conclusions Our method can be used to estimate the number of chlamydia infections each year, and thus whether infection incidence increases or decreases over time and after policy changes. Furthermore, our results suggest that clearance via medical intervention may lead to short-term or no seroconversion, and the duration of untreated chlamydial infection may vary with age, underlining the complexity of chlamydial infection dynamics.

  • chlamydia infections
  • models, theoretical
  • epidemiology

Data availability statement

No data are available. All data used in this publication are publicly available from the National Health and Nutrition Examination Survey, the National Survey of Family Growth, CDC STI surveillance reports and US census data.28–31

https://doi.org/10.1136/sextrans-2023-055808

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    Data availability statement

    No data are available. All data used in this publication are publicly available from the National Health and Nutrition Examination Survey, the National Survey of Family Growth, CDC STI surveillance reports and US census data.28–31

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    Footnotes

    • Handling editor Lenka Vodstrcil

    • Correction notice This article has been corrected since it was first published online. The author E Gloria Anyalechi was incorrectly listed as Gloria E Anyalechi.

    • Contributors PAC primarily built model, conducted analysis and wrote manuscript. IHS conceptualised project and gave input on model structure along with EDP. GEA, CMK, JH and DCD gave subject matter advice on chlamydia serology, lab testing methods and sampling statistics. CEC and GEA coordinated project. All authors contributed to editing the manuscript. PAC accepts full responsibility for the work and/or the conduct of the study, had access to the data, and controlled the decision to publish.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Disclaimer The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.