Effect of temperature on accidental human mortality: A time-series analysis in Shenzhen, Guangdong Province in China

Abstract

Health-risk assessments of temperature are central to determine total non-accidental human mortality; however, few studies have investigated the effect of temperature on accidental human mortality. We performed a time-series study combined with a distributed lag non-linear model (DLNM) to quantify the non-linear and delayed effects of daily mean temperature on accidental human mortality between 2013 and 2017 in Shenzhen, China. The threshold for effects of temperature on accidental human mortality occurred between 5.6 °C and 18.5 °C. Cold exposures, but not hot exposures, were significantly associated with accidental human mortality. All of the observed groups were susceptible to cold effects, with the strongest effects presented in females (relative risk [RR]: 3.14, 95% confidence interval (CI) [1.44–6.84]), followed by poorly educated people (RR: 2.63, 95% CI [1.59–4.36]), males (RR: 1.79, 95% CI [1.10–2.92]), and well-educated people (RR: 1.20, 95% CI [0.58–2.51]). Pooled estimates for cold effects at a lag of 0–21 days (d) were also stronger than hot effects at a lag of 0–2 d. Our results indicate that low temperatures increased the risk of accidental human mortality. Females and poorly educated people were more susceptible to the low temperatures. These findings imply that interventions which target vulnerable populations during cold days should be developed to reduce accidental human mortality risk.