2017
DOI: 10.1038/srep45093
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Impact of temperature on mortality in Hubei, China: a multi-county time series analysis

Abstract: We examined the impact of extreme temperatures on mortality in 12 counties across Hubei Province, central China, during 2009–2012. Quasi-Poisson generalized linear regression combined with distributed lag non-linear model was first applied to estimate county-specific relationship between temperature and mortality. A multivariable meta-analysis was then used to pool the estimates of county-specific mortality effects of extreme cold temperature (1st percentile) and hot temperature (99th percentile). An inverse J… Show more

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Cited by 45 publications
(28 citation statements)
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“…To further eliminate the confounding effects of other meteorological variables, the current day’s mean relative humidity (MeanRh), sunshine duration (Sunshine), mean wind speed (WS), and atmospheric pressure (AP) were also introduced in the GLM as covariates using a natural cubic spline with 3 df [ 12 ]. Additionally, day of the week (DOW) and public holiday (PH) were also controlled as indicator variables [ 36 , 37 ]. Hence, the core models are given as following: and where i is the day of observation ( i = 1, 2, 3…1461), and death i and YLL i are the observed daily death counts and years of life lost, respectively; α is the intercept, and is the regression coefficient for DTR; MeanT t,l is the cross-basis matrix of mean temperature ( t ) and lag pattern ( l ) produced by DLNM, is the vector of coefficients for MeanT t,l ; NS is the natural cubic spline function; and are the regression coefficients for DOW and PH, respectively.…”
Section: Methodsmentioning
confidence: 99%
“…To further eliminate the confounding effects of other meteorological variables, the current day’s mean relative humidity (MeanRh), sunshine duration (Sunshine), mean wind speed (WS), and atmospheric pressure (AP) were also introduced in the GLM as covariates using a natural cubic spline with 3 df [ 12 ]. Additionally, day of the week (DOW) and public holiday (PH) were also controlled as indicator variables [ 36 , 37 ]. Hence, the core models are given as following: and where i is the day of observation ( i = 1, 2, 3…1461), and death i and YLL i are the observed daily death counts and years of life lost, respectively; α is the intercept, and is the regression coefficient for DTR; MeanT t,l is the cross-basis matrix of mean temperature ( t ) and lag pattern ( l ) produced by DLNM, is the vector of coefficients for MeanT t,l ; NS is the natural cubic spline function; and are the regression coefficients for DOW and PH, respectively.…”
Section: Methodsmentioning
confidence: 99%
“…They attributed this phenomenon to the urban heat island (UHI) effect [10,11], i.e., that people living in urbanized settings tend to experience higher temperatures due to the great thermal-storage capacity of heavily engineered environments, poor ventilation, and localized heat sources (e.g., vehicles, air conditioners) [12]. There are also some studies that found no significant modification effect of local SES on heat-mortality associations [13][14][15][16]. In addition to the conflicting findings, some limitations of previous studies should be noticed.…”
Section: Introductionmentioning
confidence: 99%
“…Second, most studies were conducted in developed countries; evidence from developing countries was relatively rare. Third, most studies only focused on heat-mortality associations [3][4][5][6][7][8][9][10][11][13][14][15][16], while few of them addressed inequality in heat-morbidity (e.g., hospitalization, emergency department visit) associations. The existing heat-morbidity studies also showed inconsistent findings with respect to the modification effect of regional SES [17][18][19][20], and they also had a limited number of locations (up to 158 locations [18]).…”
Section: Introductionmentioning
confidence: 99%
“…Meteorological conditions have been shown to influence daily mortality and disease burden in many studies, especially for the cardiovascular system. Weather conditions such as extreme temperature [12], diurnal temperature range [13,14], temperature variation [15], and humidity [16] have been defined as risk factors or effect modifiers that may contribute to CVD mortality.…”
Section: Introductionmentioning
confidence: 99%