Excess Tuberculosis Incidence in the United States During COVID-19: A State, Age, and Race/Ethnicity Analysis and Structural Drivers of Variation (2020–2023)

Background: The COVID-19 pandemic disrupted healthcare systems and disease surveillance worldwide, potentially affecting tuberculosis (TB) detection and control. While global analyses have documented major TB setbacks, the extent to which pandemic-related disruptions altered TB incidence patterns across U.S. demographic and geographic groups remains unclear. This study aimed to quantify excess TB incidence (newly reported TB cases) across U.S. jurisdictions, age groups, and racial/ethnic populations during 2020--2023, and to assess structural factors associated with geographic disparities in excess TB burden. Methods: We used a sub-epidemic ensemble modeling framework applied to annual U.S. TB incidence data, defined as newly reported TB cases. Models were calibrated to pre-pandemic trends (2010--2019) and used to generate counterfactual forecasts for 2020--2023. Because publicly available TB surveillance data are one-way stratified, we calibrated separate models for each jurisdiction, age group, and racial/ethnic category. Excess TB cases were defined as the difference between observed and expected counts, with 95% prediction intervals estimated via bootstrap simulation. Analyses were classified by jurisdiction (50 states, along with D.C. and Puerto Rico), age (11 groups from younger than 1 to greater than 85 years), and race/ethnicity (8 groups). A Poisson error structure was applied consistently across all models. To investigate predictors of state-level excess TB burden, we performed backward stepwise ordinary least squares (OLS) regression using seven candidate predictors: population density, percentage foreign-born, poverty rate, HIV prevalence, incarceration rate, homelessness rate, and percentage American Indian/Alaska Native (AI/AN) population. Results: Excess TB burden varied widely across jurisdictions. Texas (410 cases [95%PI: 59--930]), New York (380 [200--680]), Florida (260 [61--600]), and California (200 [62--500]) had the highest excess case counts. Population-adjusted analyses revealed a markedly different pattern, with Alaska showing the largest excess rate (13 per 100,000 [0–35]), emphasizing disproportionate impacts in smaller but structurally vulnerable jurisdictions. Working-age adults carried the greatest excess burden, particularly those aged 35--44 (650 cases [300--1200]) and 25--34 (630 [330--1100]). Large racial and ethnic disparities were observed: the Hispanic population experienced the highest excess burden (1,700 cases [1,100--2,500]), with notable excess also among American Indian/Alaska Native populations (140 cases [61--210]) despite their small population share, while the Asian population showed no excess case counts. Several jurisdictions and the 55--64 age group had uncertainty intervals including zero, suggesting patterns consistent with pre-pandemic trends. Stepwise regression identified four predictors of state-level excess TB cases: percentage foreign-born (positive association), incarceration rate (positive association), homelessness rate (positive association), and population density (negative association), with an adjusted \((R^2)\) of 0.36. Conclusions: The COVID-19 pandemic had uneven effects on TB incidence across the United States. Estimated excess TB incidence likely reflects a combination of delayed diagnosis, disruptions to routine surveillance and care, and post-pandemic rebound in case detection, rather than increased transmission alone. Working-age Hispanic adults and residents of jurisdictions with high proportions of foreign-born individuals, elevated incarceration rates, and substantial homelessness experienced the greatest excess burden.