2018
DOI: 10.1007/s11356-018-3653-z
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Socio-geographic disparity in cardiorespiratory mortality burden attributable to ambient temperature in the United States

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Cited by 42 publications
(27 citation statements)
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“…We further found that cold-related YLL rates were higher in LDRs compared with HDRs, which was consistent with previous findings that socioeconomic factors have a modifying effect on the temperature-mortality count relationship [10,31]. However, the heat-related YLL rates were similar between HDRs and LDRs, which implies that development level does not play an important role in heat-related YLL rates.…”
Section: Discussionsupporting
confidence: 91%
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“…We further found that cold-related YLL rates were higher in LDRs compared with HDRs, which was consistent with previous findings that socioeconomic factors have a modifying effect on the temperature-mortality count relationship [10,31]. However, the heat-related YLL rates were similar between HDRs and LDRs, which implies that development level does not play an important role in heat-related YLL rates.…”
Section: Discussionsupporting
confidence: 91%
“…Hajat et al found that lower economic status [e.g., gross domestic product (GDP)] associated with higher heat risk [ 14 , 15 ]. Low average education also have been found worsen cold- and heat-related mortality burden [ 10 ]. Possible reasons for this finding were the potential differences in social environment and living conditions among different development regions such as population structure and density, lifestyle and availability to adequate health care services or home air conditioners.…”
Section: Discussionmentioning
confidence: 99%
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“…We used two-stage time series analysis to predict the prefecture-specific non-linear lag impact of temperature on OHCA, as described previously (Gasparrini et al, 2016;Onozuka and Hagihara, 2017c;Zhang et al, 2019;Zhang et al, 2017). Briefly, first, we investigated the association between temperature and OHCA in individual prefectures using a time-series quasi-Poisson regression model combined with a distributed lag non-linear model, adjusting for season, long-term trends, and day of the week.…”
Section: Estimation Of Exposure-response Relationshipsmentioning
confidence: 99%