The impact of ambient air pollution on hospital admissions.
Air Pollutants
/ adverse effects
Air Pollution
/ adverse effects
Animals
Cerebrovascular Disorders
/ epidemiology
Coronary Artery Disease
/ epidemiology
Environmental Monitoring
Hospitalization
/ statistics & numerical data
Humans
Nitrogen Dioxide
/ adverse effects
Ozone
/ adverse effects
Particulate Matter
/ adverse effects
Sulfur Dioxide
/ adverse effects
Switzerland
/ epidemiology
Ambient air pollution
Count panel data
Dispersion model
Hospital admissions
Journal
The European journal of health economics : HEPAC : health economics in prevention and care
ISSN: 1618-7601
Titre abrégé: Eur J Health Econ
Pays: Germany
ID NLM: 101134867
Informations de publication
Date de publication:
Aug 2019
Aug 2019
Historique:
received:
18
02
2018
accepted:
01
04
2019
pubmed:
24
4
2019
medline:
18
2
2020
entrez:
24
4
2019
Statut:
ppublish
Résumé
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 [Formula: see text] and [Formula: see text] are positively associated with admission rates for coronary artery and cerebrovascular diseases while we find no similar correlation for PM10 and [Formula: see text]. 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.
Identifiants
pubmed: 31011845
doi: 10.1007/s10198-019-01049-y
pii: 10.1007/s10198-019-01049-y
doi:
Substances chimiques
Air Pollutants
0
Particulate Matter
0
Sulfur Dioxide
0UZA3422Q4
Ozone
66H7ZZK23N
Nitrogen Dioxide
S7G510RUBH
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
919-931Références
J Health Econ. 2009 May;28(3):688-703
pubmed: 19328569
Rev Saude Publica. 2013 Dec;47(6):1209-12
pubmed: 24626559
J Health Econ. 2014 Sep;37:219-31
pubmed: 25105867
J Health Econ. 2015 May;41:30-45
pubmed: 25655338
Environ Int. 2017 Feb;99:275-281
pubmed: 27939045
Environ Health. 2018 Apr 18;17(1):41
pubmed: 29669550