4.7 Article

Ambient air pollution and daily hospital admissions: A nationwide study in 218 Chinese cities

Journal

ENVIRONMENTAL POLLUTION
Volume 242, Issue -, Pages 1042-1049

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.envpol.2018.07.116

Keywords

Air pollution; Hospital admission; Time-series; China

Funding

  1. Key Project of Natural Science Funds of China [81230066]
  2. National Natural Science Fund Projects of China [81473043]
  3. National Natural Science Foundation of China [71402003]

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There have been few large multicity studies to evaluate the acute health effects of ambient air pollution on morbidity risk in developing counties. In this study, we examined the short-term associations of air pollution with daily hospital admissions in China. We conducted a nationwide time-series study in 218 Chinese cities between 2014 and 2016. Data on daily hospital admissions counts were obtained from the National Health Insurance Database for Urban Employees covering 0.28 billion enrollees. We used generalized additive model with Poisson regression to estimate the associations in each city, and we performed random-effects meta-analysis to pool the city-specific estimates. More than 60 million hospital admissions were analyzed in this study. At the national-average level, each 10 mu g/m(3) increase in PM10, SO2 , and NO2, and 1 mg/m(3) increase in CO at lag 0 day was associated with a 0.29% (95% CI, 0.23% -036%), 1.16% (95% CI, 0.92%-1.40%), 1.68% (95% CI, 1.40%-1.95%), and 2.59% (95% CI, 1.69%-3.50%) higher daily hospital admissions, respectively. The associations of air pollution with hospital admissions remained statistically significant at levels below the current Chinese Ambient Air Quality Standards. The effect estimates were larger in cities with lower air pollutants levels or higher air temperatures and relative humidity, as well as in the elderly. In conclusion, our findings provide robust evidence of increased hospital admissions in association with short-term exposure to ambient air pollution in China. (C) 2018 Elsevier Ltd. All rights reserved.

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