| 气候变化国家评估报告(I): 中国气候变化的历史和未来趋势 |
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| 引用本文： | 覃芳葵, 高绪芳, 鹿茸, 孙婧雯, 杜慧兰, 廖江, 孙磊, 陈曦. 成都市环境气温与人群死亡关系分析[J]. 中国公共卫生, 2021, 37(4): 719-722. DOI: 10.11847/zgggws1125887 |
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| 作者姓名： | 覃芳葵 高绪芳 鹿茸 孙婧雯 杜慧兰 廖江 孙磊 陈曦 |
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| 作者单位： | 1.成都市疾病预防控制中心，四川 610041;2.成都市气象局;3.成都市环境保护科学研究院 |
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| 基金项目： | 四川省卫健委重点研究项目（18ZD047） |
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| 摘 要： | 目的 探讨成都市气温与人群死亡的关系，为降低敏感人群死亡风险提供依据。 方法 收集2013年1月1日 — 2018年12月31日四川省成都市逐日气象、大气污染物及全死因死亡数据并进行描述分析，将 ≤ 第2.5个百分位数的气温定义为低温，≥ 第97.5个百分位数定义为高温。采用分布滞后非线性模型（DLNM），分析气温与死亡的暴露–滞后–反应关系，求得最适宜温度，并将其作为参考水平，计算气温归因死亡人数与分值。 结果 2013 — 2018年，成都市日均气温范围为 – 1.9 ℃～29.8 ℃，P50为17.5 ℃，死亡总人数484 736人，日死亡人数范围为137～375人/d，P50为214人/d，日均气压、日均相对湿度、PM10、和O3 – 8的P50分别为950.8 kPa、80 %、88.3 μg/m3和79.3 μg/m3；日均气温和死亡人数存在统计学关联（P < 0.05），暴露 – 反应关系呈近似L型，最适宜温度为25 ℃。高温效应在暴露当日出现，最长持续5 d；低温效应在暴露后1～2 d出现，最长持续13 d，高温和低温暴露0～21 d的累积死亡相对危险度分别为1.142（95 % CI = 1.087～1.199）、1.405（95 % CI = 1.244～1.587）；以最适宜温度作为基线暴露水平，累积滞后0～21 d情况下，气温归因死亡人数为60 280人（95 % CI = 30 875～85 953），归因分值为12.4 %（95 % CI = 6.5 %～17.6 %），其中低温归因死亡人数为56 794人，归因分值为11.7 %，高温归因死亡人数为3 493人，归因分值为0.72 %。 结论 成都市日均气温和人群死亡的暴露 – 反应关系呈近似L型，低温风险大于高温风险，表现在相对危险度大、效应滞后时间长，归因死亡人数多和分值高。
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| 关 键 词： | 气温 死亡 分布滞后非线性模型（DLNM） 归因风险 |
| 收稿时间： | 2019-09-05 |
Diurnal temperature range and daily mortality in Shanghai,China |
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| QIN Fang-kui, GAO Xu-fang, LU Rong, . Associate of ambient temperature with daily mortality among residents of Chengdu city[J]. Chinese Journal of Public Health, 2021, 37(4): 719-722. DOI: 10.11847/zgggws1125887 |
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| Authors: | QIN Fang-kui GAO Xu-fang LU Rong |
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| Affiliation: | 1.Chengdu Municipal Center for Disease Control and Prevention, Chengdu, Sichuan Province 610041, China |
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| Abstract: | Objective To explore the relationship between ambient temperature and mortality among residents in Chengdu municipality and to provide evidences for developing relevant interventions in vulnerable populations. Methods The population of the study were permanent residents in 20 districts/counties of Chengdu municipality, Sichuan province during the period from January 1, 2013 to December 31, 2018. We collected daily data on meteorology (mean temperature, relative humidity, and air pressure), atmospheric pollutants (average concentration of particulate matter < 10 microns in aerodynamic diameter [PM10] and ozone in 8 hours [O3 – 8], and number of all-cause deaths. The average daily temperatures equal or less than the 2.5th percentile of all the values were defined as low temperatures, and those equal or greater than to the 97.5th percentile of all the values were defined as high temperatures. Distributed lag non-linear model was used to analyze exposure-related lag-response relationship between average daily temperature and daily number of deaths. A minimum mortality temperature (MMT), at which mortality being the lowest, was adopted as the reference level to calculate attribute number and attribute fraction of deaths attributed to the exposure to a specific average daily temperature. Results During the 6 – year period, the average daily temperature for the study area ranged from – 1.9 ℃ to 29.8 ℃, and the median was 17.5 ℃; totally 484 736 deaths were registered and the daily number of deaths ranged from 137 to 375, with a median of 214; the medians were 950.8 kPa for average daily pressure, 80% for relative humidity, 88.3 μg/m3 for PM10, and 79.3 μg/m3 for O3 – 8, respectively. There was a statistical correlation between the average daily temperature and daily number of deaths (P < 0.05), with an approximate L-shape exposure-response relationship and a MMT of 25 ℃. The effect of high temperature exposure on mortality was observed at lag days 0 – 5 and that of low temperature was observed at lag days 1 – 13. The cumulative relative risk of mortality at lag days 0 – 21 was 1.142 (95% confidence interval [95% CI]: 1.087 – 1.199) for the exposure to high temperature and 1.405 (95% CI: 1.244 – 1.587) for the exposure to low temperature. For the whole population during the period and taking the baseline number of deaths under MMT as the reference, the estimated cumulative number of deaths attributed to unfavorable temperature at lag days 0 – 21 was 60 280 (95% CI: 30 875 – 85 953) and the estimated attributable fraction was 12.4% (95% CI: 6.5% – 17.6%); while, the estimated cumulative number of deaths attributed to low and high temperature at lag days 0 – 21 were 56 794 (95% CI: 28 080 – 82 993) and 3 493 (95% CI: 2 053 – 4 414) and the estimated attributable fraction were 11.7% (95% CI: 5.9% – 17.1%) and 0.72% (95% CI: 0.44% – 0.96%), respectively. Conclusion There is an approximate L-shaped exposure-response correlation between average daily temperature and daily number of deaths among residents in Chengdu municipality and the adverse effect of low temperature is greater than that of high temperature, with higher relative risk, long lag time of effect, and larger attributable death number and fraction. |
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| Keywords: | atmospheric temperature mortality distributed lag non-linear models attributable risk |
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