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Abstract
Studies have demonstrated that ambient temperature was associated with transport accidents; however, little is known about the temporal change and spatial heterogeneity of this association. This study investigated the temporal change and spatial variation in the association between temperature and transport accident mortality in Japan using daily time-series data from 1972 to 2019. First, we used time-stratified case-crossover analyses with a distributed lag nonlinear model to estimate the risks of transport accident mortality with temperature in 47 prefectures in Japan. We then pooled the estimates to obtain the risk at the country level through multivariate meta-analysis. In addition, we divided the whole time period into five sub-periods to explore temporal changes in the association and fitted the mixed-effects meta-regression to identify climatic, demographic, and socioeconomic factors that may explain the spatial heterogeneity. We found that temperature was positively associated with transport accident mortality, with a percent change (PC) of 1.47% (95% confidence interval [CI]: 1.10%, 1.85%) increase in transport accident mortality per 1 °C increase in daily mean temperature. The risk of transport accident mortality associated with temperature has decreased from 1972 [PC = 2.14% (95% CI: 1.51%, 2.77%)] to 2000 [PC = 0.89% (95% CI: 0.21%, 1.57%)] but increased slightly from 2001 [PC = 1.13% (95% CI: 0.47%, 1.48%)] to 2019 [PC = 1.60% (95% CI: 0.73%, 2.48%)]. The association between temperature and transport accident mortality was larger in relatively less developed prefectures, as explained by demographic and socioeconomic factors (e.g., total population, proportion of older people and females, and number of general hospitals). Our findings may help to better understand the association between high temperature and transport accident mortality and underlying potential mechanisms, which can provide implications for public health policies to reduce the mortality burden from transport accidents in the future.
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Details
; Chung, Yeonseung 2
; Sim, Kisung 3 ; Hashizume, Masahiro 4 ; Honda, Yasushi 5
; Lee, Whanhee 6
; Kim, Yoonhee 7
1 Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo , Tokyo, Japan
2 Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Republic of Korea
3 AI Architecture Group, Architecture Team, Development Center, Samsung SDS, Republic of Korea
4 Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo , Tokyo, Japan; School of Tropical Medicine and Global Health, Nagasaki University , Japan
5 School of Tropical Medicine and Global Health, Nagasaki University , Japan; Faculty of Health and Sport Sciences, University of Tsukuba , Japan; Center for Climate Change Adaptation, National Institute for Environmental Studies , Japan
6 School of Biomedical Convergence Engineering, College of Information and Biomedical Engineering, Pusan National University , Yangsan, Republic of Korea
7 Department of Global Environmental Health, Graduate School of Medicine, The University of Tokyo , Tokyo, Japan




