Abstract

Background: This study aims to thoroughly examine the relationship between weather conditions and reported pulmonary tuberculosis (PTB) cases in Haikou City. The goal is to provide a scientific basis for developing more targeted and timely prevention measures, thereby enhancing local efforts to control PTB. Methods: In this retrospective time-series study from 2010 to 2023, a total of 22,083 PTB cases (15,723 males and 6360 females) were analyzed. Using monthly PTB case counts (overall and stratified by sex and age) and meteorological variables, descriptive analyses along with distributed lag non-linear models (DLNMs) and generalized additive distributed lag models (GADLMs) combined with quasi-Poisson regression model regression were applied. Results: Taking the median of each meteorological factor as the reference, temperature exhibited significant delayed effects. For the overall population, temperatures between 14.30 °C and 24.24 °C posed significant lagged risks, with the effect decreasing as temperature rose. At the minimum monthly average temperature (14.30 °C), the relative risk (RR) peaked at lag 7 months (RR = 1.37, 95% CI: 1.23–1.53). Sex-stratified analysis showed similar patterns for males (14.30–23.94 °C) and females (14.30–23.84 °C), with peak RRs of 1.40 (1.25–1.58) and 1.37 (1.21–1.56), respectively, at 14.30 °C. In the adults group, temperatures between 14.30 °C and 25.45 °C produced significant lagged effects (maximum lag 14 months), with the highest RR of 1.42 (1.27–1.60) at lag 8 under 14.30 °C. The older age group showed a linear association. Average relative humidity exhibited a weak lag-4 correlation with PTB counts in some subgroups, but model testing indicated no statistically significant lagged risk effect in any group. Conclusions: A significant nonlinear lagged exposure–response relationship exists between temperature and PTB incidence in Haikou, with population-specific variations, while relative humidity shows no significant lagged effect. These findings underscore the necessity of incorporating temporal lag effects and population differences into regional PTB early warning and prevention strategies.

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