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Using infection prevalence, seroprevalence and case report data to estimate chlamydial infection incidence


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

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