Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui Province, China.
The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, Hefei, 230022, Anhui, China.
Environ Sci Pollut Res Int. 2022 Nov;29(52):78483-78498. doi: 10.1007/s11356-022-21268-w. Epub 2022 Jun 13.
As the health impacts of climate change take on a more serious form, this study for the first time investigates the effect of meteorological factors on the risk of death from respiratory diseases (RD) in Wuhu, a representative city along the Yangtze River in subtropical humid region. Daily meteorological element data and RD deaths in Wuhu City were collected from 2014 to 2020. Time series analysis was conducted using distributed lagged nonlinear model (DLNM) combined with generalized additive model (GAM), and stratified by age and gender. In 7 years, a total of 8016 RD death cases were collected in Wuhu, China. The results demonstrated that the maximum impacts of short-term exposure to exceedingly low temperatures mean (Tmean) were at lag 9, with the maximum relative risk (RR) of 1.044 (lag 1, 95% CI: 1.001, 1.098). The risk of exceedingly high Tmean reached its maximum at lag 0 (RR = 1.070, 95% CI: 1.018, 1.125). Low relative humidity (RH) was negatively associated with the risk of RD death, with the lowest RR values occurring at lag 12 (RR = 0.987, 95% CI: 0.975, 0.999). No significant correlation was found for diurnal temperature range (DTR). Stratified analysis showed that Tmean exposure remained statistically significant for male, female and elderly, while RH and DTR only seemed to increase the mortality risk in the young. In a word, short-term exposure to extreme temperatures may increase the RD mortality risk in the population, and young people needed to be aware that exposure to exceedingly high RH and DTR also increased the risk.
随着气候变化对健康的影响变得更加严重，本研究首次调查了气象因素对位于亚热带湿润地区长江沿岸的典型城市芜湖市呼吸系统疾病（RD）死亡风险的影响。收集了 2014 年至 2020 年芜湖市的每日气象要素数据和 RD 死亡数据。采用分布式滞后非线性模型（DLNM）结合广义相加模型（GAM）进行时间序列分析，并按年龄和性别进行分层。在 7 年时间里，共收集到中国芜湖市 8016 例 RD 死亡病例。结果表明，短期暴露于极低温度均值（Tmean）的最大影响出现在滞后 9 天，最大相对风险（RR）为 1.044（滞后 1 天，95%CI：1.001，1.098）。极高 Tmean 的风险在滞后 0 天达到最大值（RR=1.070，95%CI：1.018，1.125）。相对湿度（RH）较低与 RD 死亡风险呈负相关，最低 RR 值出现在滞后 12 天（RR=0.987，95%CI：0.975，0.999）。日较差（DTR）与 RD 死亡风险无显著相关性。分层分析表明，Tmean 暴露对男性、女性和老年人仍然具有统计学意义，而 RH 和 DTR 似乎仅增加了年轻人的死亡率风险。总之，短期暴露于极端温度可能会增加人群中 RD 的死亡风险，年轻人需要注意，暴露于极高的 RH 和 DTR 也会增加风险。
