Estimating Temperature-Mortality Exposure-Response Relationships and Optimum Ambient Temperature at the Multi-City Level of China

Abstract

Few studies have explored temperature–mortality relationships in China, especially at the multi-large city level. This study was based on the data of seven typical, large Chinese cities to examine temperature-mortality relationships and optimum temperature of China. A generalized additive model (GAM) was applied to analyze the acute-effect of temperature on non-accidental mortality, and meta-analysis was used to merge data. Furthermore, the lagged effects of temperature up to 40 days on mortality and optimum temperature were analyzed using the distributed lag non-linear model (DLNM). We found that for all non-accidental mortality, high temperature could significantly increase the excess risk (ER) of death by 0.33% (95% confidence interval: 0.11%, 0.56%) with the temperature increase of 1 °C. Similar but non-significant ER of death was observed when temperature decreased. The lagged effect of temperature showed that the relative risk of non-accidental mortality was lowest at 21 °C. Our research suggests that high temperatures are more likely to cause an acute increase in mortality. There was a lagged effect of temperature on mortality, with an optimum temperature of 21 °C. Our results could provide a theoretical basis for climate-related public health policy.

The effect of high temperature on cause-specific mortality: A multi-county analysis in China

“…We found that all causes of death could be significantly increased under exposure to high temperature, especially extremely high temperature. This result agrees with many previous findings for single outcomes, including non-accidental mortality (Guo et al, 2014; Zeng et al, 2016), circulatory disease (Anderson and Bell, 2009; Gasparrini et al, 2012; Huang et al, 2014), acute myocardial infarction (Gasparrini et al, 2012; Schwartz et al, 2004), coronary heart disease (Tian et al, 2012), stroke (Chen et al, 2013), and respiratory disease (Anderson and Bell, 2009; Gasparrini et al, 2012; Liu et al, 2011). All causes of deaths generally increase with increasing age, and populations of 75 years or older are associated with a larger heat effect, as reported in previous studies (Anderson and Bell, 2009; Gasparrini et al, 2012).…”

“…The inclusion of 531,794 deaths provides high statistical power for our analysis and a more accurate description of heat-related effects. Compared to previous studies of multi-cities (Chen et al, 2013; Huang et al, 2015; Ma et al, 2014; Zeng et al, 2016) and global communities (Gasparrini et al, 2015; Guo et al, 2014), another strength of this study is our inclusion of non-accidental death and five different causes, which enables us to understand the responses of cause-specific diseases under exposure to high temperature and characteristics for different population groups. The findings have important public health implications for targeted disease control and prevention.…”

“…This result is consistent with earlier multi-center studies in China. In this study, a 1 °C increase of moderately high temperature (90th vs. 75th) is associated with pooled RRs of 1.046 (95% CI: 1.034, 1.058), 1.042 (95%CI: 1.030, 1.054), and 1.063 (95%CI: 1.044, 1.081) in mortality of non-accidental cause, circulatory disease, and respiratory disease, respectively, which is consistent with Ma’s study reporting that a 1 °C increase in temperature (99th vs. 75th) was associated with increases of 2.83% (95% posterior interval (PI): 1.42%, 4.24%) in non-accidental mortality, 3.02% (95% PI: 1.33%, 4.71%) in cardiovascular disease, and 4.64% (95% PI: 1.96%, 7.31%) in respiratory mortality, respectively (Ma et al, 2014), Zeng’s study reporting an increase of 0.33% (95%CI: 0.11%, 0.56%) in non-accidental mortality (Zeng et al, 2016), and Huang’s study reporting a pooled RR of 1.04 (95%CI: 0.90, 1.18) for non-accidental mortality associated with a 1 °C increase above the threshold temperature (Huang et al, 2015). A previous study of 107 US communities reported that increasing temperature (at the 99th versus 90th percentile temperature) was associated with an increase of 3.0% (95%PI: 2.4%, 3.6%) in mortality risk (Anderson and Bell, 2009), compared to a RR of 1.105 (95%CI: 1.089, 1.122) in non-accidental mortality in our study.…”

“…Furthermore, rapid urbanization in these counties may exacerbate the heat effect (Anderson and Bell, 2009). A five-city study in China also indicated that cities in subtropical climate zones such as Shanghai and Nanjing suffered more from extremely high temperatures (Sun et al, 2012; Zeng et al, 2016). Investigation in the national climate change report also validated our findings that the central and eastern areas of China have suffered and may be at the forefront for more severe extreme heat in the future (Committee of National Assessment Report on Climate Change, 2015).…”

“…In recent years, there have been an increased number of multicenter studies in China. For example: the heat effect on stroke in eight Chinese cities (Chen et al, 2013), the health effect from high temperature on non-accidental mortality in seven Chinese cities (Zeng et al, 2016) and 17 large cities (Ma et al, 2014), and the investigation of heat effects in 66 communities (Huang et al, 2015). However, all of these studies focused on only a single outcome or a couple of main outcomes (cardiovascular and respiratory diseases) and mostly adopted a pre-specified temperature or temperature pattern such as extremely high or moderately high temperatures.…”