The Impact of Screening and Partner Notification on Chlamydia Prevalence and Numbers of Infections Averted in the United States, 2000-2015: Evaluation of Epidemiologic Trends Using a Pair-Formation Transmission Model

Abstract

Population-level effects of control strategies on the dynamics of Chlamydia trachomatis transmission are difficult to quantify. In this study, we calibrated a novel sex- and age-stratified pair-formation transmission model of chlamydial infection to epidemiologic data in the United States for 2000-2015. We used sex- and age-specific prevalence estimates from the National Health and Nutrition Examination Surveys, case report data from national chlamydia surveillance, and survey data from the Youth Risk Behavior Survey on the proportion of the sexually active population aged 15-18 years. We were able to reconcile national prevalence estimates and case report data by allowing for changes over time in screening coverage and reporting completeness. In retrospective analysis, chlamydia prevalence was estimated to be almost twice the current levels in the absence of screening and partner notification. Although chlamydia screening and partner notification were both found to reduce chlamydia burden, the relative magnitude of their estimated impacts varied in our sensitivity analyses. The variation in the model predictions highlights the need for further data collection and research to improve our understanding of the natural history of chlamydia and the pathways through which prevention strategies affect transmission dynamics.

Keywords: chlamydia; mathematical modeling; reproductive health; sexually transmitted infections; surveillance.

Figure 1.

Schematic of the simulated model population and the pair formation process used to simulate chlamydia transmission, with arrows reflecting the aging of the population. A) Unpaired women; B) pairs of men and women, which represent long-term partnerships; C) unpaired men.

Figure 2.

Natural history of chlamydia transmission, with arrows showing the transitions between health states.

Figure 3.

Model-estimated prevalence of chlamydia infection (mean values (circles) and 95% credible intervals (bars)) in the United States in 2015 in a calibrated model (current level) and in 4 counterfactual scenarios: 1) keeping screening at the year 2000 level, 2) no partner notification (PN), 3) no screening, and 4) no screening or PN. Results are presented for women aged 15–24 years (A), women aged 25–54 years (B), men aged 15–24 years (C), and men aged 25–54 years (D). Calibration scenario 1: more constrained priors on reporting and screening; calibration scenario 2: less constrained priors on reporting and more constrained priors on screening; calibration scenario 3: more constrained priors on reporting and less constrained priors on screening; calibration scenario 4: less constrained priors on reporting and screening.

Figure 4.

Model-estimated cumulative numbers of chlamydia cases averted (mean values (circles) and 95% credible intervals (bars)) in the United States during 2000–2015 when comparing 4 counterfactual scenarios with a calibrated model (current level). Results are presented for women aged 15–24 years (A), women aged 25–54 years (B), men aged 15–24 years (C), men aged 25–54 years (D), women aged 15–54 years (E), and men aged 15–54 years (F). Calibration scenario 1: more constrained priors on reporting and screening; calibration scenario 2: less constrained priors on reporting and more constrained priors on screening; calibration scenario 3: more constrained priors on reporting and less constrained priors on screening; calibration scenario 4: less constrained priors on reporting and screening. PN, partner notification.