Impact of urbanization factors on mortality due to unintentional injuries using panel data regression model and spatial-temporal analysis

Environmental Science and Pollution Research(2019)

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摘要
Unintentional injuries pose a great risk for human health in China. Few studies have focused on unintentional injuries at national level from urbanization perspective. The panel data of mortality rate of transportation accidents (TA), fall and drowning and sinking (DS) is investigated, and urbanization development index is collected. Global Moran’s I and linear regression of panel data are applied to determine the spatial distribution and spatial influencing factors of unintentional injuries. The results are the following: (1) The unintentional injury such as TA, fall, and DS shows clear non-uniformity of spatial distribution and relative immobility through time. (2) A 10,000 tons increase in SO 2 emission amount (SO 2 EA) and emission of smoke and dust (ESD) can result in 15.7 and 12.5 increases in TA death in eastern region, respectively. Meanwhile, A 10,000 tons increase in NO x emission amount can cause 15.1 increase in TA death in western region. For every 100 billion yuan increase in GDP, the fall death can reduce by 8.4 in central region. One bed increase in number of hospital beds per 10,000 population (NHBP) is favorable for decreasing in fall death by 16.7 in eastern region. However, increase in number of workers enjoying industrial injury (NEWII) does not reduce the fall death in eastern region. (3) For every 1 ten thousand people increase in number of students in ordinary high schools (NSOHS) is conductive to reducing DS death by 7.8 in the western region. Our findings show that there exist spatial differences for urbanization influencing TA, fall, and DS death in eastern, western, and central regions. This study is expected to provide a reference for unintentional injuries control in those three regions.
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关键词
Unintentional injuries,Spatio-temporal distribution,Urbanization,Global Moran’s I,Linear regression of panel data
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