The Short-Term Effect of Ambient Temperature on Mortality in Wuhan, China: A Time-Series Study Using a Distributed Lag Non-Linear Model

Yunquan Zhang, Cunlu Li, Renjie Feng, Yaohui Zhu, Kai Wu, Xiaodong Tan and Lu Ma
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Yunquan Zhang: Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan 430071, China
Cunlu Li: Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan 430071, China
Renjie Feng: Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan 430071, China
Yaohui Zhu: Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan 430071, China
Kai Wu: Jiang’an District Center for Disease Control and Prevention, Wuhan 430014, China
Xiaodong Tan: Department of Occupational and Environmental Health, School of Public Health, Wuhan University, Wuhan 430071, China
Lu Ma: Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan 430071, China

IJERPH, 2016, vol. 13, issue 7, 1-13

Abstract: Less evidence concerning the association between ambient temperature and mortality is available in developing countries/regions, especially inland areas of China, and few previous studies have compared the predictive ability of different temperature indictors (minimum, mean, and maximum temperature) on mortality. We assessed the effects of temperature on daily mortality from 2003 to 2010 in Jiang’an District of Wuhan, the largest city in central China. Quasi-Poisson generalized linear models combined with both non-threshold and double-threshold distributed lag non-linear models (DLNM) were used to examine the associations between different temperature indictors and cause-specific mortality. We found a U-shaped relationship between temperature and mortality in Wuhan. Double-threshold DLNM with mean temperature performed best in predicting temperature-mortality relationship. Cold effect was delayed, whereas hot effect was acute, both of which lasted for several days. For cold effects over lag 0–21 days, a 1 °C decrease in mean temperature below the cold thresholds was associated with a 2.39% (95% CI: 1.71, 3.08) increase in non-accidental mortality, 3.65% (95% CI: 2.62, 4.69) increase in cardiovascular mortality, 3.87% (95% CI: 1.57, 6.22) increase in respiratory mortality, 3.13% (95% CI: 1.88, 4.38) increase in stroke mortality, and 21.57% (95% CI: 12.59, 31.26) increase in ischemic heart disease (IHD) mortality. For hot effects over lag 0–7 days, a 1 °C increase in mean temperature above the hot thresholds was associated with a 25.18% (95% CI: 18.74, 31.96) increase in non-accidental mortality, 34.10% (95% CI: 25.63, 43.16) increase in cardiovascular mortality, 24.27% (95% CI: 7.55, 43.59) increase in respiratory mortality, 59.1% (95% CI: 41.81, 78.5) increase in stroke mortality, and 17.00% (95% CI: 7.91, 26.87) increase in IHD mortality. This study suggested that both low and high temperature were associated with increased mortality in Wuhan, and that mean temperature had better predictive ability than minimum and maximum temperature in the association between temperature and mortality.

Keywords: climate change; temperature; mortality; distributed lag non-linear model (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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