2016—2019年西宁市极端气温对非意外120急救量影响的时间序列分析

廖星豪; 宁振旭; 李永红; 何淑珍; 马春光; 吴晶

doi:10.13421/j.cnki.hjwsxzz.2024.06.003

2016—2019年西宁市极端气温对非意外120急救量影响的时间序列分析

A time series analysis of the impact of extreme temperature on non-accidental 120 emergency visits in Xining, China, 2016—2019

摘要

摘要:
目的探讨西宁市极端气温与非意外120急救量的关联，识别脆弱人群。
方法 2016—2019年西宁市120急救量数据来源于青海省医疗紧急救援中心、逐日气象资料来源于青海省气象局、大气污染数据来源于西宁市生态环境局。在控制大气污染物浓度、湿度、星期几效应等混杂因素影响的基础上，采用分布滞后非线性模型拟合日均气温与急救量之间的关系及其滞后效应，并进行性别和年龄别亚组分析。
结果 2016—2019年期间，青海省医疗紧急救援中心共接诊西宁市非意外120急救量48 354人次，日急救量中位数为29人次。极端高温和极端低温均能引起非意外120急救量增加，高温当日出现效应并持续6日，而低温滞后5日后出现效应并持续11日。总人群在极端低温(P_2.5：-10 ℃)下的最大累积相对危险度(cumulative relative risk, cumRR)(lag0-19 d)为1.49(95%CI：1.09~ 2.04)，极端高温(P_97.5：21 ℃)下的最大cumRR(lag0-9 d)为1.15(95%CI：1.06~1.25)。不同人群的累积效应值略有差异，女性的累积冷效应高于男性， < 65岁人群的累积热效应高于≥65岁人群。
结论极端高温和极端低温均能增加西宁市居民非意外急救风险，且存在滞后效应。在极端高温下健康危害效应产生迅速但短暂，在极端低温下健康危害效应产生缓慢但持久。

Abstract:
Objective To investigate the association between extreme temperature and non-accidental 120 emergency visits in Xining, China, and to identify vulnerable populations.
Methods The data of daily emergency visits in Xining in 2016—2019 were obtained from Qinghai Provincial Medical Emergency Center, daily meteorological data were obtained from Qinghai Provincial Meteorological Bureau, and air pollution data were collected from Xining Ecology and Environment Bureau. After controlling for the confounding factors such as atmospheric pollutant concentration, humidity, and the day-of-the-week effect, a distributed lag non-linear model was used to fit the association between daily average temperature and emergency visits, including its lag effect, and subgroup analyses were performed based on sex and age.
Results In 2016—2019, Qinghai Provincial Medical Emergency Center handled 48 354 non-accidental emergency visits in Xining, with a median daily number of 29 visits. Both extremely high and low temperature caused an increase in non-accidental emergency visits, and the effect of high temperature appeared on the same day and lasted for 6 days, while the effect of low temperature appeared after a lag of 5 days and lasted for 11 days. The maximum cumulative relative risk (cumRR) (lag 0-19 d) for the total population under extremely low temperature (P_2.5=-10 ℃) was 1.49 (95% confidence interval CI: 1.09-2.04), while the maximum cumRR (lag 0-9 d) under extremely high temperature (P_97.5 = 21 ℃) was 1.15 (95% CI: 1.06-1.25). There was a slight difference in cumulative effect value between different populations, with female individuals showing a higher cumulative effect from cold than male individuals and the < 65 years age group showing a higher cumulative effect from heat than the ≥65 years age group.
Conclusion Both extremely high and low temperature can increase the risk of non-accidental emergency visits among the residents of Xining, with the presence of lag effects. The health hazard effect is rapid but short-lived under extremely high temperature and is slow but long-lasting under extremely low temperature.

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