关键词: 气温, 呼吸系统疾病, 死亡, 分布滞后非线性模型

Abstract: Objective To analyze the impact of environmental temperature on daily respiratory disease mortality in Yancheng City,and provide evidence for reducing population mortality risk. Methods Meteorological data and respiratory disease mortality data for the population in Yancheng City from 2017 to 2023 were collected. The distributed lag non-linear model（DLNM） in R software was used to analyze the time-lagged effects and attributable risks of daily average temperature on respiratory disease mortality. Results From 2017 to 2023,the exposure-response curve between environmental temperature and daily respiratory disease mortality in Yancheng City showed an inverted "J" shape,with the optimal temperature at 24 ℃. Both low and high temperatures increased the risk of death,with high temperatures exhibiting acute effects and a shorter lag duration,while low temperatures had a slower onset and a longer lag duration. For the population with respiratory diseases,exposure to extreme low temperature（-1 ℃） began to show a cold effect after a 2-day lag,the relative risk（RR） was 1.12（95%CI：1.06-1.19）,and the lag effect persisted. Exposure to extreme high temperature（30 ℃） showed a heat effect on the day of exposure,with an RR of 1.22（95%CI：1.14-1.30）,and the lag effect lasted for 3 days. For the 0-<65 years age group,the cold effect appeared after a 5-day lag,lasting for 5 days,and the heat effect appeared after a 1-day lag and lasted for 3 days. The higher the daily average temperature on the day of exposure,the greater the cumulative mortality risk for the population with respiratory diseases,with a cumulative maximum risk RR of 1.50（95%CI：1.31-1.72）. In the 0-3 day lag period,the higher the daily average temperature above the optimal temperature,the greater the cumulative mortality risk,with a cumulative maximum risk RR of 2.61（95%CI：2.24-3.04）. The maximum cumulative risk in the 0-7 day lag period occurred at the highest temperature,with an RR of 2.79（95%CI：2.33-3.33）. The maximum cumulative risk in the 0-14 day lag period occurred at the lowest temperature,with an RR of 4.92（95%CI：3.03-7.99）. The maximum cumulative risk in the 0-21 day lag period also occurred at the lowest temperature,with an RR of 7.35（95%CI：3.98-13.58）. Exposure to extreme low temperature（-1 ℃） over the 0-21 day lag period showed the maximum cold effect,with an RR of 4.49（95%CI：3.45-6.65）. Exposure to extreme high temperature（30 ℃） over the 0-7 day lag period showed the maximum cumulative heat effect,with an RR of 1.54（95%CI：1.42-1.68）. The cumulative heat effect of the 0-<65 age group exposed to extreme high temperatures（30 ℃） for 0-7 days was the greatest,with an RR of 2.13（95%CI：1.38-3.26）. The attributable fraction of deaths due to environmental temperature exposure was 44.10%,with 28 683 attributed deaths. Among these,the attributable fraction of deaths due to low temperature exposure was 4.92%,with 3 202 attributed deaths,and the attributable fraction of deaths due to high temperature exposure was 1.73%,with 1 123 attributed deaths. The cumulative attributable risk of both low and high temperature exposures was relatively low. Conclusion From 2017 to 2023,environmental temperature in Yancheng City had a lagged effect on daily respiratory disease mortality. Early monitoring and warning for high-risk populations should be implemented to reduce the impact of environmental temperature changes on population mortality risk.

Key words: Atmospheric temperature, Respiratory diseases, Mortality, Distributed lag nonlinear model