摘要
目的探索高温健康风险预警的最适气象指标。方法收集2013—2019年每年5—10月浙江省宁波市户籍人口死因监测资料、气象资料和空气质量监测资料;采用时间序列分析方法结合分布滞后非线性模型分别构建日最低气温、日均气温、日最高气温、日最低热指数、日均热指数、日最高热指数、日均体感温度和炎热指数8项气象指标与死亡数、寿命损失年(YLL)的关系模型,根据赤池信息量准则(AIC)最小化原则评价模型拟合效果;分析不同性别、年龄和死因人群高温健康风险预警最适气象指标。结果研究期间宁波市共报告死亡120628例,日均死亡94例,日均YLL率为19.74人年/10万。除日最低热指数和炎热指数外,其他6项气象指标与总死亡数、总YLL率的暴露-反应关系均为“J”型。日均气温与全人群、女性、≥65岁人群、循环系统疾病患者和呼吸系统疾病患者死亡数和YLL率关系模型的AIC值均最小,拟合效果均为最优。结论日均气温与死亡数、YLL率的模型拟合效果均较好,更适用于高温健康风险预警。
Objective To identify the most appropriate meteorological variable for forecasting the health risk of high tem⁃peratures.Methods The surveillance on causes of death,meteorological data and surveillance on air quality among reg⁃istered residents in Ningbo City,Zhejiang Province during the period between May and October from 2013 to 2019 were collected.The association models of daily minimum temperature,average daily temperature,daily maximum tempera⁃ture,daily minimum heat index,average daily heat index,daily maximum heat index,average daily apparent tempera⁃ture and torridity index with deaths and years of life lost(YLL)were created using time series analysis and distributed lag non-linear models,and the model fitting effect was evaluated using the minimum Akaike information criterion(AIC)procedure.The most appropriate meteorological variable for forecasting gender-,age-and mortality-specific health risks of high temperatures was identified.Results A total of 120628 deaths were reported during the study period,with dai⁃ly deaths of 94 cases,and daily YLL rate of 19.74 person-years/105.Except for daily minimum heat index and torridity index,the exposure-response relationships between other six meteorological variables and deaths and overall YLL rate all appeared a“J”shape.The lowest AIC values and the optimal model fitting effects were measured for the associa⁃tion models between average daily temperature and whole populations,females,subjects at ages of 65 years and older,and deaths and YLL rates due to circulatory diseases and respiratory diseases.Conclusion High model fitting effects are observed between average daily temperature and deaths and YLL rates,which are more suitable for forecasting the health risk of high temperature.
作者
谷少华
陆蓓蓓
王永
金永高
王爱红
GU Shaohua;LU Beibei;WANG Yong;JIN Yonggao;WANG Aihong(Dpeartment of Environmental and Occupational Health,Ningbo Center for Disease Control and Prevention,Ningbo,Zhejiang 315010,China)
出处
《预防医学》
2022年第8期803-808,815,共7页
CHINA PREVENTIVE MEDICINE JOURNAL
基金
浙江省公益技术研究项目(LGF19H260010)
宁波市自然科学基金项目(2019A610379)
宁波市医学重点学科(2022-B18)
宁波市市级医疗品牌学科(PPXK2018-10)。
关键词
高温
气象指标
健康风险预警
死亡
寿命损失年
high temperature
meteorological variable
health risk forcast
mortality
years of life lost
