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.

Heat waves are associated with increased mortality, however, few studies have examined the added effect of heat waves. Moreover, there is limited evidence for the influence of different heat wave definitions (HWs) on cardiovascular mortality in Beijing, the capital of China. The aim of this study was to find the best HW definitions for cardiovascular mortality, and we examined the effect modification by an individual characteristic on cardiovascular mortality in Beijing, a typical northern city in China. We applied a Poisson generalized additive approach to estimate the differences in cardiovascular mortality during heat waves (using 12 HWs) compared with non-heat-wave days in Beijing from 2006 to 2009. We also validated the model fit by checking the residuals to ensure that the autocorrelation was successfully removed. In addition, the effect modifications by individual characteristics were explored in different HWs. Our results showed that the associations between heat waves and cardiovascular mortality differed from different HWs. HWs using the 93th percentile of the daily average temperature (27.7 °C) and a duration ≥5 days had the greatest risk, with an increase of 18% (95% confidence interval (CI): 6%, 31%) in the overall population, 24% (95% CI: 10%, 39%) in an older group (ages ≥65 years), and 22% (95% CI: 3%, 44%) in a female group. The added effect of heat waves was apparent after 5 consecutive heat wave days for the overall population and the older group. Females and the elderly were at higher risk than males and younger subjects (ages <65 years). Our findings suggest that heat wave definitions play a significant role in the relationship between heat wave and cardiovascular mortality. Using a suitable definition may have implications for designing local heat early warning systems and protecting the susceptible populations during heat waves.

Temperature is increasingly understood to impact mental health. However, evidence of the long-term effect of temperature exposure on the risk of depressive symptoms is still scarce. Based on the China Health and Retirement Longitudinal Study (CHARLS), this study estimated associations between long-term apparent temperature, extreme temperature, and depressive symptoms in middle-aged and older adults. Results showed that a 1 °C increase or decrease from optimum apparent temperature (12.72 °C) was associated with a 2.7% (95% CI: 1.3%, 4.1%) and 2.3% (95% CI: 1.1%, 3.5%) increased risk of depressive symptoms, respectively. This study also found that each percent increase in annual change in ice days, cool nights, cool days, cold spell durations, and tropical nights was associated with higher risk of depressive symptoms, with HRs (95%CI) of 1.289 (1.114–1.491), 2.064 (1.507–2.825), 1.315 (1.061–1.631), 1.645 (1.306–2.072), and 1.344 (1.127–1.602), respectively. The results also indicated that people living in northern China have attenuated risk of low apparent temperature. Older people were also observed at higher risk relating to more cool nights. Middle-aged people, rural residents, and people with lower household income might have higher related risk of depressive symptoms due to increased tropical nights. Given the dual effect of climate change and global aging, these findings have great significance for policy making and adaptive strategies for long-term temperature and extreme temperature exposure.