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Reproductive Tract Infections Research / Cervical Cancer and HPV Research · Journal article
Open Forum Infectious Diseases · September 2, 2026
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This is a Bayesian hierarchical modeling study that estimates state-level chlamydia and gonorrhea prevalence and incidence among U.S. women by calibrating to surveillance laboratory test positivity and diagnosis rates across 2019–2023. The model generates novel state-level burden estimates but is an indirect inference framework not validated against independent prevalence surveys, limiting confidence in point estimates and their use for immediate policy change.
Bayesian hierarchical mathematical modeling analysis. Women in the United States, aged ≥15 years, stratified by age band and state; national-level calibration to chlamydia prevalence and test coverage.. Intervention: Bayesian hierarchical mathematical model. United States, disaggregated to state level.
National chlamydia prevalence in women aged 15–24 years: 4.6% (95% UI: 4.2–4.9%) National gonorrhea prevalence in women aged 15–24 years: 0.4% (0.4–0.5%) National chlamydia testing coverage: 33.4% (33.2–33.7%)
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State and local public health officials may use these modeled estimates to identify priority jurisdictions for chlamydia and gonorrhea screening and prevention efforts. However, estimates are derived from indirect calibration and should be treated as planning benchmarks rather than definitive prevalence measurements; direct validation through population-based surveys would strengthen confidence in state-level action.
Mathematical modeling study using indirect calibration to surveillance data; produces state-level estimates but lacks direct validation and relies on assumptions about undiagnosed infections.
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State and local public health officials may use these modeled estimates to identify priority jurisdictions for chlamydia and gonorrhea screening and prevention efforts. However, estimates are derived from indirect calibration and should be treated as planning benchmarks rather than definitive prevalence measurements; direct validation through population-based surveys would strengthen confidence in state-level action.
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Abstract Background Estimates of chlamydia and gonorrhea prevalence and incidence have typically focused on national-level epidemiology. Disaggregated estimates are useful for local public health decision making. Methods We developed a Bayesian hierarchical modeling framework to estimate the incidence and prevalence of chlamydia and gonorrhea for women in the United States. The model was calibrated to indicators from different data sources, including state and age-specific laboratory test positivity (data pooled over 2019–2023), and diagnosis rates (average of 2019-2023). At the national level, we calibrated to chlamydia prevalence and test coverage. We used the model to estimate state-level and national-level prevalence, incidence, and screening rates among women by state and age (15-24, 25-34, 35-44, 45-54, ≥55 years). Results For women aged 15–24 years, national-level chlamydia prevalence was estimated at 4.6% (95% uncertainty interval: 4.2-4.9%), gonorrhea prevalence was 0.4% (0.4–0.5%), and testing coverage was 33.4% (33.2–33.7.0%). Across states, corresponding chlamydia incidence estimates were 1.9-3.1 times as high as observed diagnosis rates; gonorrhea incidence estimates were 3.3-4.7 times as high as diagnosis rates. Chlamydia prevalence in women aged 15–24 years ranged 2.3%- 7.8% across states, and chlamydia incidence ranged 2.8%-12.4%. Gonorrhea prevalence ranged 0.09%-1%, and gonorrhea incidence ranged 0.5%-6.0%. Conclusions This study presents a new analytic framework for estimating incidence and prevalence at the state level. By integrating laboratory, diagnosis, and survey data, we provide more granular, actionable estimates of STI burden.
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