The impact of ambient air pollution on hospital admissions

被引:0
|
作者
Massimo Filippini
Giuliano Masiero
Sandro Steinbach
机构
[1] Swiss Federal Institute of Technology in Zurich (ETH Zurich),Department of Management, Technology and Economics (D
[2] Università della Svizzera Italiana (USI),MTEC)
[3] University of Bergamo,Institute of Economics (IdEP)
[4] University of Connecticut,Department of Management, Information and Production Engineering (DIGIP)
关键词
Ambient air pollution; Dispersion model; Hospital admissions; Count panel data; I10; Q51; Q53;
D O I
暂无
中图分类号
学科分类号
摘要
Ambient air pollution is the environmental factor with the most significant impact on human health. Several epidemiological studies provide evidence for an association between ambient air pollution and human health. However, the recent economic literature has challenged the identification strategy used in these studies. This paper contributes to the ongoing discussion by investigating the association between ambient air pollution and morbidity using hospital admission data from Switzerland. Our identification strategy rests on the construction of geographically explicit pollution measures derived from a dispersion model that replicates atmospheric conditions and accounts for several emission sources. The reduced form estimates account for location and time fixed effects and show that ambient air pollution has a substantial impact on hospital admissions. In particular, we show that SO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{SO}}_{2}$$\end{document} and NO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{NO}}_{2}$$\end{document} are positively associated with admission rates for coronary artery and cerebrovascular diseases while we find no similar correlation for PM10 and O3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{O}}_{3}$$\end{document}. Our robustness checks support these findings and suggest that dispersion models can help in reducing the measurement error inherent to pollution exposure measures based on station-level pollution data. Therefore, our results may contribute to a more accurate evaluation of future environmental policies aiming at a reduction of ambient air pollution exposure.
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页码:919 / 931
页数:12
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