Short-term effects of air pollution on the infectious disease spectrum in Shanghai, China: a time-series analysis from 2013 to 2019

2013年至2019年上海空气污染对传染病谱的短期影响:时间序列分析

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Abstract

BACKGROUND: Epidemiological evidence on the effects of air pollution on infectious diseases remains inconsistent, highlighting the need for further research and analysis. We aimed to investigate the relationship between exposure to fine particulate matter (PM(2.5)) and ozone (O(3)) and the risk of national notifiable infectious diseases in Shanghai, a megacity in China. METHODS: A double-pollutant model was used for each air pollutant, utilizing time-series analysis to separately apply single and distributed lag models (DLMs) to assess the exposure-lag-response relationship for 43 national notifiable infectious diseases (NNIDs) from 2013 to 2019. The model was adjusted for seasonality, long-term trends, mean temperature, relative humidity, and other air pollutants. Analysis was further conducted for seven NNID categories (vaccine-preventable; bacterial; gastrointestinal and enterovirus; sexually transmitted and bloodborne; vector-borne; zoonotic; and quarantinable diseases) as well as specific diseases. RESULTS: The study included 661,267 NNID cases and found that PM(2.5) and O(3) exposures were associated with increased NNID risks, although not within the same categories. A 10 μg/m(3) increase in O(3) was associated with a higher risk of total NNIDs (relative risk [RR] at lag 1 month: 1.29, 95% confidence interval [CI]: 1.02-1.65), vaccine-preventable diseases (RR at lag 1 month: 1.75, 95% CI: 1.02-3.01), and sexually transmitted and bloodborne diseases (RR at lag 2 month: 1.12, 95% CI: 1.00-1.26). However, the association with PM(2.5) remained inconclusive. CONCLUSION: These findings suggest a potential link between ambient air pollution exposure and the risk of infectious diseases, highlighting the urgent need for a comprehensive understanding of the relationship between air pollution and notifiable infectious diseases, as well as an in-depth evaluation of disparities across the disease spectrum.

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