2020
DOI: 10.1038/s41598-020-65344-y
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Effect of temperature on accidental human mortality: A time-series analysis in Shenzhen, Guangdong Province in China

Abstract: Health-risk assessments of temperature are central to determine total non-accidental human mortality; however, few studies have investigated the effect of temperature on accidental human mortality. We performed a time-series study combined with a distributed lag non-linear model (DLNM) to quantify the non-linear and delayed effects of daily mean temperature on accidental human mortality between 2013 and 2017 in Shenzhen, China. The threshold for effects of temperature on accidental human mortality occurred bet… Show more

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Cited by 12 publications
(10 citation statements)
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“…Relatively cold temperatures, have been shown to present a risk factor for mortality [ 43 ] and specifically for OHCA [ 1 , 37 , 44 ]. This is consistent with our findings, pointing at the few hours prior to the event as the most vulnerable with respect to extremely cold temperatures as compared to the local median of 21 °C.…”
Section: Discussionmentioning
confidence: 99%
“…Relatively cold temperatures, have been shown to present a risk factor for mortality [ 43 ] and specifically for OHCA [ 1 , 37 , 44 ]. This is consistent with our findings, pointing at the few hours prior to the event as the most vulnerable with respect to extremely cold temperatures as compared to the local median of 21 °C.…”
Section: Discussionmentioning
confidence: 99%
“…Therefore, a statistical model for the expected daily deaths from heat/cold was developed and calibrated by adjusting a polynomial fit to the daily mortality data versus Tmean averaged during the previous days and considering the period spanning 2009–2019. Following the methodologies applied by several authors (Gasparrini et al 2015 ; Lian et al 2020 ; Péres et al 2020 ), we performed sensitivity tests for the individual lag times which maximize observed daily deaths due to cold and heat conditions. Thus, separate regressions were applied for opposite ranges of temperature extremes, resulting on time windows of 14 days and 4 days, for cold and hot conditions, respectively.…”
Section: Methodsmentioning
confidence: 99%
“…Therefore, we used cumulative effects along lag 0-3 days for PM 2.5 , lag 0-2 for PM 10 , and lag 0-1 for PM 2.5-10 for subsequent analyses. We have controlled for potential nonlinear and lagged confounding effects of weather conditions, with 3 degrees of freedom natural cubic spline for 21-day moving averages [32,33] of daily mean temperature and daily mean relative humidity, respectively.…”
Section: Discussionmentioning
confidence: 99%