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-shaped relationship was observed between temperature and mortality at the provincial level. Heat effect occurred immediately and persisted for 2–3 days, whereas cold effect was 1–2 days delayed and much longer lasting. Higher mortality risks were observed among females, the elderly aged over 75 years, persons dying outside the hospital and those with high education attainment, especially for cold effects. Our data revealed some slight differences in heat- and cold- related mortality effects on urban and rural residents. These findings may have important implications for developing locally-based preventive and intervention strategies to reduce temperature-related mortality, especially for those susceptible subpopulations. Also, urbanization should be considered as a potential influence factor when evaluating temperature-mortality association in future researches.

Diurnal Temperature Range in Relation to Daily Mortality and Years of Life Lost in Wuhan, China

“…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.…”

Socioeconomic level and associations between heat exposure and all-cause and cause-specific hospitalization in 1,814 Brazilian cities: A nationwide case-crossover study

“…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.…”

“…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]).…”

Short-Term Associations of Fine Particulate Matter and Synoptic Weather Types with Cardiovascular Mortality: An Ecological Time-Series Study in Shanghai, China

“…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.…”