Department of Management, Technology and Economics (D-MTEC), Swiss Federal Institute of Technology in Zurich (ETH Zurich), Zurich, Switzerland.
Institute of Economics (IdEP), Università della Svizzera Italiana (USI), Lugano, Switzerland.
Eur J Health Econ. 2019 Aug;20(6):919-931. doi: 10.1007/s10198-019-01049-y. Epub 2019 Apr 22.
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.
大气污染是对人类健康影响最大的环境因素。一些流行病学研究为大气污染与人类健康之间的关系提供了证据。然而，最近的经济文献对这些研究中使用的识别策略提出了挑战。本文通过利用瑞士的医院入院数据，研究大气污染与发病率之间的关系，为正在进行的讨论做出了贡献。我们的识别策略基于从复制大气条件并考虑多个排放源的扩散模型中得出的具有明确地理位置的污染措施的构建。简化形式的估计考虑了位置和时间固定效应，表明大气污染对医院入院率有重大影响。具体来说，我们表明[公式：见正文]和[公式：见正文]与冠状动脉和脑血管疾病的入院率呈正相关，而我们没有发现 PM10 和[公式：见正文]之间存在类似的相关性。我们的稳健性检验支持这些发现，并表明扩散模型有助于减少基于站点水平污染数据的污染暴露测量中固有的测量误差。因此，我们的结果可能有助于更准确地评估旨在减少大气污染暴露的未来环境政策。
