Open AccessAcute Effects of Nitrogen Dioxide on Cardiovascular Mortality in Beijing: An Exploration of Spatial Heterogeneity and the District-specific Predictors
Author(s) -
Kai Luo,
Runkui Li,
Wenjing Li,
Zongshuang Wang,
Xin−Ming Ma,
Ruiming Zhang,
Xin Fang,
Zhenglai Wu,
Yang Cao,
Qun Xu
Publication year - 2016
Publication title -
scientific reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 213
ISSN - 2045-2322
DOI - 10.1038/srep38328
Subject(s) - medicine , confidence interval , hazard ratio , beijing , logistic regression , proportional hazards model , environmental health , spatial heterogeneity , demography , china , geography , biology , ecology , archaeology , sociology
The exploration of spatial variation and predictors of the effects of nitrogen dioxide (NO 2 ) on fatal health outcomes is still sparse. In a multilevel case-crossover study in Beijing, China, we used mixed Cox proportional hazard model to examine the citywide effects and conditional logistic regression to evaluate the district-specific effects of NO 2 on cardiovascular mortality. District-specific predictors that could be related to the spatial pattern of NO 2 effects were examined by robust regression models. We found that a 10 μg/m 3 increase in daily mean NO 2 concentration was associated with a 1.89% [95% confidence interval (CI): 1.33–2.45%], 2.07% (95% CI: 1.23–2.91%) and 1.95% (95% CI: 1.16–2.72%) increase in daily total cardiovascular (lag03), cerebrovascular (lag03) and ischemic heart disease (lag02) mortality, respectively. For spatial variation of NO 2 effects across 16 districts, significant effects were only observed in 5, 4 and 2 districts for the above three outcomes, respectively. Generally, NO 2 was likely having greater adverse effects on districts with larger population, higher consumption of coal and more civilian vehicles. Our results suggested independent and spatially varied effects of NO 2 on total and subcategory cardiovascular mortalities. The identification of districts with higher risk can provide important insights for reducing NO 2 related health hazards.