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Acute effect of daily fine particulate matter pollution on cerebrovascular mortality in Shanghai, China: a population-based time series study
Stockholm University, Faculty of Social Sciences, Department of Public Health Sciences. Örebro University, Sweden.ORCID iD: 0000-0002-2088-0530
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Number of Authors: 52019 (English)In: Environmental science and pollution research international, ISSN 0944-1344, E-ISSN 1614-7499, Vol. 26, no 25, p. 25491-25499Article in journal (Refereed) Published
Abstract [en]

Numerous studies have investigated the impacts of ambient fine particulate matter (PM2.5) on human health. In this study, we examined the association of daily PM2.5 concentrations with the number of deaths for the cerebrovascular disease on the same day, using the generalized additive model (GAM) controlling for temporal trend and meteorological variables. We used the data between 2012 and 2014 from Shanghai, China, where the adverse health effects of PM2.5 have been of particular concern. Three different approaches (principal component analysis, shrinkage smoothers, and the least absolute shrinkage and selection operator regularization) were used in GAM to handle multicollinear meteorological variables. Our results indicate that the average daily concentration of PM2.5 in Shanghai was high, 55 mu g/m(3), with an average daily death for cerebrovascular disease (CVD) of 62. There was 1.7% raised cerebrovascular disease deaths per 10 mu g/m(3) increase in PM2.5 concentration in the unadjusted model. However, PM2.5 concentration was no longer associated with CVD deaths after controlling for meteorological variables. The results were consistent in the three modelling techniques that we used. As a large number of people are exposed to air pollution, further investigation with longer time period including individual-level information is needed to examine the association.

Place, publisher, year, edition, pages
2019. Vol. 26, no 25, p. 25491-25499
Keywords [en]
Fine particulate matter, Cerebrovascular mortality, Generalized additive model, Multicollinearity, Principal component analysis, Shrinkage smoother, Least absolute shrinkage and selection operator
National Category
Earth and Related Environmental Sciences Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
URN: urn:nbn:se:su:diva-174970DOI: 10.1007/s11356-019-05689-8ISI: 000483698500009PubMedID: 31264151OAI: oai:DiVA.org:su-174970DiVA, id: diva2:1364242
Available from: 2019-10-22 Created: 2019-10-22 Last updated: 2019-12-04Bibliographically approved

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