Combined effects of air pollution and meteorological factors on the risk of newly tuberculosis cases: a time-series study in Tibet, China

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Abstract The Tibet Autonomous Region, the world’s highest region, faces high tuberculosis (TB) rates, exacerbated by its unique environment and climate. However, the combined impact of air pollution and meteorological factors exposure on the prevalence of TB remains understudied. Daily data on TB cases, air pollutants (PM 10 , PM 2.5 , SO 2 , NO 2 , O 3 , and CO), and meteorological factors (temperature, precipitation, and wind speed) between 2019 and 2023 were collected. Weighted Quantile Sum (WQS) regression and Bayesian Kernel Machine Regression (BKMR) models were employed to assess the combined and individual effects of these environmental factors on TB prevalence. Moreover, gender, occupation, and year-specific distinction were explored using subgroup analysis. A total of 18,347 new TB cases were reported during the study period, with a positive association between environmental factors and the prevalence of TB. The WQS model showed a positive combined effect of environmental factors on daily TB cases (OR:1.58, 95%CI:1.46–1.71), and the weight of precipitation and PM 10 were 0.55 and 0.59, respectively. BKMR analysis further confirmed a positive association between overall environmental factors and TB, highlighting precipitation as the most significant independent factor and potential interactions among environmental variables. Subgroup analyses confirmed a consistent positive association between environmental factors and TB cases, with PM 10 and precipitation being most influential, precipitation more significant for females, and PM 10 dominant among farmers and students. Exposure to air pollution and meteorological factors has a significant impact on the cases of TB. Notably, PM 10 and precipitation are identified as primary determinants, with distinct variations observed among females, farmers, and students. Accurate estimates are essential for informing public health interventions, optimizing resource allocation, and developing effective clinical strategies in high-altitude regions.

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