石家庄市日均气温与居民非意外死亡风险和负担的相关性

李梦娜; 陈思源; 康慧; 沈强; 暴磊; 马金沙; 纪逸; 陈凤格

doi:10.16462/j.cnki.zhjbkz.2025.09.004

石家庄市日均气温与居民非意外死亡风险和负担的相关性

doi: 10.16462/j.cnki.zhjbkz.2025.09.004

李梦娜^{1, 2},
陈思源^{2, 4},
康慧^{2, 3},
沈强²,
暴磊¹,
马金沙^{2, 3},
纪逸⁵,
陈凤格^{1, 2, 3, 6, ,}

1.
河北医科大学公共卫生学院，石家庄 050017
2.
石家庄市疾病预防控制中心公共卫生监测与评价科，石家庄 050011
3.
中国疾病预防控制中心环境与健康研究基地(石家庄)，石家庄 050011
4.
华北理工大学公共卫生学院，唐山 063210
5.
中国疾病预防控制中心地方病控制中心，哈尔滨 150081
6.
河北省疑难病原研究重点实验室，石家庄 050011

基金项目:

河北省医学科学研究课题 20251134

详细信息

通讯作者:
陈凤格，E-mail: chenfengge319@163.com

中图分类号: R122
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出版历程
- 收稿日期: 2025-02-21
- 修回日期: 2025-05-13
- 网络出版日期: 2025-10-10
- 刊出日期: 2025-09-10

Study on the correlation between daily average temperature and the risk and burden of non-accidental deaths among residents in Shijiazhuang City

LI Mengna^{1, 2},
CHEN Siyuan^{2, 4},
KANG Hui^{2, 3},
SHEN Qiang²,
BAO Lei¹,
MA Jinsha^{2, 3},
JI Yi⁵,
CHEN Fengge^{1, 2, 3, 6
, ,}

1.
School of Public Health, Hebei Medical University, Shijiazhuang 050017, China
2.
Department of Public Health Monitoring and Assessment, Shijiazhuang Center for Disease Control and Prevention, Shijiazhuang 050011, China
3.
Research Base for Environment and Health in Shijiazhuang, Chinese Center for Disease Control and Prevention, Shijiazhuang 050011, China
4.
School of Public Health, North China University of Science and Technology, Tangshan 063210, China
5.
Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin 150081, China
6.
Hebei Key Laboratory of Intractable Pathogens, Shijiazhuang 050011, China

Funds:

Medical Science Research Project of Hebei 20251134

More Information

Corresponding author: CHEN Fengge, E-mail: chenfengge319@163.com

摘要

摘要: 目的分析石家庄市日均气温对居民非意外死亡风险的影响，为制定针对性公共卫生防护策略提供科学依据。方法运用分布滞后非线性模型(distributed lag nonlinear model, DLNM)对2019―2023年石家庄市每日死亡人数和气象数据进行拟合，分析日均气温对非意外、循环系统疾病和呼吸系统疾病死亡影响的滞后效应和累积滞后效应。结果 2019―2023年石家庄市日均气温与非意外、循环系统疾病和呼吸系统疾病死亡率呈现非线性关联。非意外死亡和循环系统疾病死亡风险最低的气温阈值为12.0 ℃。非意外死亡(RR=2.144, 95% CI: 1.625~2.829)和循环系统疾病死亡(RR=2.083, 95% CI: 1.448~2.998)的累积低温影响在lag 0~21 d时达峰值，非意外死亡的高温累积影响(RR=1.282, 95% CI: 1.150~1.429)在lag 0~3 d时达到峰值，循环系统疾病死亡的高温累积影响在lag 0~4 d时达到最大值(RR=1.416, 95% CI: 1.210~1.658)。呼吸系统疾病死亡风险在累积低温和累积高温作用下差异均无统计学意义(均P＞0.05)。冷、热效应对男、女的死亡风险均产生影响，与＜65岁人群相比，≥65岁人群对冷、热效应更为敏感。结论石家庄市居民的非意外、循环系统疾病和呼吸系统疾病死亡率均受到极端气温影响，并存在累积效应，≥65岁人群死亡风险高于＜65岁人群死亡风险。
- 气温 /
- 非意外死亡 /
- 最低死亡气温 /
- 分布滞后非线性模型
Abstract: Objective To analyze the impact of daily average temperature on the risk of non-accidental deaths among residents, to provide scientific basis for formulating targeted public health protection strategies. Methods A distributed lag nonlinear model (DLNM) was applied to analyze the lag effects and cumulative lag effects of daily mean temperature on mortality, including deaths from non-accidental causes, circulatory system diseases, and respiratory system diseases, using daily mortality and meteorological data from Shijiazhuang City during 2019-2023. Results Shijiazhuang City showed a nonlinear association between daily mean temperature and mortality from nonaccidental deaths, circulatory diseases, and respiratory diseases. The lowest temperature threshold for the risk of non-accidental deaths and deaths from circulatory diseases was 12.0 ℃. The cumulative low-temperature effect of non accidental death (RR=2.144, 95% CI: 1.625-2.829) and death from circulatory system diseases (RR=2.083, 95% CI: 1.448-2.998) reached its peak after a lag of 0-21 days, and the cumulative high-temperature effect for non-accidental deaths (RR=1.282, 95% CI: 1.150-1.429) peaked at a lag of 0-3 days. The cumulative effect of high temperature on mortality from circulatory system diseases reaches its maximum value with a lag of 0-4 days (RR=1.416, 95% CI: 1.210-1.658). The difference in the risk of death from respiratory diseases under both cumulative cold and cumulative heat effects was not statistically significant (all P>0.05). Cold and heat effects affected the risk of death in both men and women, and those ≥65 years of age were more sensitive to cold and heat effects than those < 65 years of age. Conclusions The mortality rates for non-accidental deaths, cardiovascular disease deaths, and respiratory disease deaths among residents in Shijiazhuang are influenced by extreme temperatures, showing significant cumulative effects. The mortality risk for individuals aged ≥65 is higher than that for individuals aged < 65.
- Temperature /
- Non-accidental deaths /
- Minimum mortality temperature /
- Distributional lag nonlinear modeling

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图 1 死因数据收集与整理的流程图

Figure 1. Flowchart for the collection and collation of cause of death data

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图 2 2019―2023年石家庄市日均气温对疾病死亡的单日滞后效应

A：非意外死亡；B：循环系统疾病死亡；C：呼吸系统疾病死亡。

Figure 2. One-day lagged effect of daily mean temperature on disease mortality in Shijiazhuang City, 2019-2023

A: non-accidental death; B: death from circulatory system diseases; C: death from respiratory system diseases.

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图 3 石家庄市日均气温与居民非意外死亡、循环系统疾病死亡和呼吸系统疾病死亡的效应曲线分析

A：非意外死亡；B：循环系统疾病死亡；C：呼吸系统疾病死亡。

Figure 3. Analysis of the effect curves of the daily mean temperature in Shijiazhuang City on residents' non-accidental deaths, deaths from cardiovascular diseases, and deaths from respiratory diseases

A: non-accidental death; B: death from circulatory system diseases; C: death from respiratory system diseases.

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图 4 极端高低温对非意外死亡影响的滞后效应图

A~C：依次表示低温对非意外、循环系统疾病和呼吸系统疾病死亡的单日滞后效应；D~F：依次表示高温对非意外、循环系统疾病和呼吸系统疾病死亡的单日滞后效应。

Figure 4. Plot of lagged effects of extreme high and low temperatures on non-accidental deaths

A-C: The single-day lag effect of low temperature on non-accidental, circulatory and respiratory deaths; D-F: Represents the single-day lag effect of high temperature on non-accidental, circulatory and respiratory deaths.

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表 1 2019―2023年石家庄市居民日均非意外死亡情况

Table 1. Daily non-accidental deaths of residents in Shijiazhuang from 2019 to 2023

变量Variable	例数Number of cases^①	最小值Min	P₂₅	中位数Median	P₇₅	最大值Max
非意外死亡 Non accidental death	40.67±19.04	17	32	38	44	232
＜65岁 years old	8.44±4.73	0	6	8	10	56
≥65岁 years old	32.23±15.64	13	25	30	35	182
男 Male	23.15±11.43	6	18	22	26	145
女 Female	17.51±8.70	5	13	16	18	98
循环系统疾病死亡 Death from circulatory system diseases	22.76±11.24	7	17	21	25	130
呼吸系统疾病死亡 Death from respiratory system diseases	5.40±4.61	1	3	5	7	54
注：①以x±s表示。 Note: ① x±s.

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表 2 气温对非意外、循环系统疾病和呼吸系统疾病死亡的累积冷效应和热效应

Table 2. Temperature's cumulative cold and heat influences on deaths caused by non-accidental factors, circulatory diseases, and respiratory diseases

滞后效应期 Lag effect period	非意外死亡 Non accidental death^①		循环系统疾病死亡 Death from circulatory system diseases^①		呼吸系统疾病死亡 Death from respiratory system diseases^①
滞后效应期 Lag effect period	冷效应 Cold effect (-4.0 ℃)	热效应 Heat effect (31.0 ℃)	冷效应 Cold effect (-4.0 ℃)	热效应 Heat effect (31.0 ℃)	冷效应 Cold effect (-4.0 ℃)	热效应 Heat effect (31.0 ℃)
Lag 0 d	0.956(0.896~1.019)	1.129(1.047~1.218)^②	0.923(0.848~1.004)	1.153(1.044~1.274)^②	0.849(0.556~1.297)	0.888(0.677~1.164)
Lag 0~1 d	0.939(0.872~1.012)	1.218(1.110~1.336)^②	0.911(0.826~1.005)	1.279(1.133~1.444)^②	0.766(0.454~1.293)	0.874(0.621~1.230)
Lag 0~2 d	0.948(0.878~1.023)	1.266(1.148~1.395)^②	0.936(0.846~1.035)	1.365(1.201~1.552)^②	0.715(0.403~1.267)	0.886(0.603~1.302)
Lag 0~3 d	0.976(0.896~1.063)	1.282(1.150~1.429)^②	0.977(0.874~1.094)	1.408(1.221~1.623)^②	0.679(0.355~1.299)	0.878(0.564~1.366)
Lag 0~4 d	1.020(0.928~1.122)	1.277(1.132~1.441)^②	1.028(0.907~1.164)	1.416(1.210~1.658)^②	0.657(0.319~1.354)	0.852(0.520~1.395)
Lag 0~5 d	1.074(0.968~1.192)	1.260(1.103~1.439)^②	1.083(0.944~1.242)	1.404(1.179~1.671)^②	0.652(0.294~1.449)	0.823(0.477~1.417)
Lag 0~6 d	1.133(1.010~1.271)^②	1.238(1.069~1.434)^②	1.140(0.979~1.326)	1.381(1.139~1.675)^②	0.667(0.276~1.612)	0.803(0.440~1.464)
Lag 0~7 d	1.193(1.051~1.355)^②	1.217(1.035~1.431)^②	1.196(1.012~1.414)^②	1.358(1.097~1.680)^②	0.706(0.267~1.865)	0.800(0.412~1.551)
Lag 0~8 d	1.250(1.087~1.437)^②	1.200(1.005~1.433)^②	1.250(1.040~1.502)^②	1.339(1.061~1.690)^②	0.771(0.266~2.231)	0.817(0.395~1.688)
Lag 0~9 d	1.302(1.120~1.514)^②	1.190(0.983~1.440)	1.300(1.066~1.586)^②	1.329(1.034~1.707)^②	0.869(0.275~2.742)	0.858(0.390~1.887)
Lag 0~10 d	1.350(1.148~1.587)^②	1.185(0.967~1.453)	1.347(1.088~1.667)^②	1.325(1.015~1.731)^②	1.006(0.293~3.451)	0.923(0.394~2.160)
Lag 0~11 d	1.394(1.174~1.656)^②	1.186(0.956~1.471)	1.391(1.109~1.746)^②	1.328(1.001~1.763)^②	1.188(0.318~4.434)	1.010(0.405~2.520)
Lag 0~12 d	1.438(1.198~1.725)^②	1.190(0.948~1.494)	1.435(1.129~1.825)^②	1.336(0.991~1.800)	1.423(0.349~5.799)	1.117(0.419~2.981)
Lag 0~13 d	1.482(1.222~1.798)^②	1.197(0.943~1.520)	1.480(1.148~1.909)^②	1.345(0.983~1.842)	1.720(0.385~7.683)	1.242(0.434~3.555)
Lag 0~14 d	1.530(1.248~1.875)^②	1.206(0.938~1.550)	1.528(1.169~1.998)^②	1.356(0.975~1.886)	2.088(0.425~10.255)	1.380(0.448~4.250)
Lag 0~15 d	1.583(1.277~1.961)^②	1.216(0.935~1.580)	1.581(1.192~2.096)^②	1.366(0.968~1.929)	2.533(0.468~13.711)	1.525(0.460~5.062)
Lag 0~16 d	1.644(1.313~2.058)^②	1.225(0.932~1.610)	1.639(1.219~2.203)^②	1.375(0.960~1.970)	3.063(0.514~18.266)	1.671(0.467~5.980)
Lag 0~17 d	1.714(1.355~2.168)^②	1.235(0.930~1.639)	1.705(1.252~2.322)^②	1.381(0.951~2.007)	3.677(0.560~24.156)	1.809(0.469~6.978)
Lag 0~18 d	1.797(1.407~2.295)^②	1.243(0.927~1.666)	1.781(1.291~2.456)^②	1.384(0.940~2.037)	4.375(0.605~31.649)	1.928(0.463~8.027)
Lag 0~19 d	1.894(1.469~2.443)^②	1.249(0.923~1.691)	1.868(1.337~2.609)^②	1.382(0.927~2.062)	5.148(0.645~41.098)	2.021(0.449~9.098)
Lag 0~20 d	2.009(1.541~2.619)^②	1.254(0.917~1.715)	1.968(1.389~2.788)^②	1.376(0.910~2.082)	5.983(0.675~53.031)	2.080(0.425~10.173)
Lag 0~21 d	2.144(1.625~2.829)^②	1.257(0.909~1.740)	2.083(1.448~2.998)^②	1.366(0.889~2.099)	6.866(0.690~68.317)	2.101(0.392~11.252)
注：①以RR值(95% CI)表示; ② P＜0.05。 Note: ① RR value(95% CI); ② P＜0.05.

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表 3 日均气温对不同性别和年龄人群非意外死亡的累积效应

Table 3. Cumulative effect of daily average temperature on non-accidental deaths in different gender and age groups

组别 Group	累积冷效应Cumulative cold effect^① (-4.0 ℃，lag 0~21 d)		累积热效应Cumulative thermal effect^① (31.0 ℃，lag 0~3 d)
组别 Group	RR值value (95% CI)	P值value	RR值value (95% CI)	P值value
性别Gender
男Male	2.217(1.548~3.176)	< 0.001	1.331(1.154~1.534)	< 0.001
女Female	2.038(1.372~3.029)	< 0.001	1.222(1.049~1.425)	0.010
年龄/岁Age/years
< 65	2.046(1.192~3.512)	0.009	1.100(0.884~1.369)	0.394
≥65	2.113(1.563~2.855)	< 0.001	1.335(1.186~1.502)	< 0.001

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参考文献(23)

[1]	IPCC. Climate change 2023: synthesis report[EB/OL]. (2023-03-20)[2024-12-03]. https://www.ipcc.ch/report/sixth-assessment-report-cycle/.
[2]	Wan K, Feng ZQ, Hajat S, et al. Temperature-related mortality and associated vulnerabilities: evidence from Scotland using extended time-series datasets[J]. Environ Health, 2022, 21(1): 99. DOI: 10.1186/s12940-022-00912-5.
[3]	肖瑶. 深圳市温度变化对非意外死亡的短期效应研究[D]. 武汉: 华中科技大学, 2022. Xiao Y. Short-term effects of temperature change on non-accidental mortality in Shenzhen[D]. Wuhan: Huazhong University of Science and Technology, 2022.
[4]	Yang X, Wang JS, Zhang GM, et al. Spatiotemporal distribution and lag effect of extreme temperature exposure on mortality of residents in Jiangsu, China[J]. Heliyon, 2024, 10(9): e30538. DOI: 10.1016/j.heliyon.2024.e30538.
[5]	魏娜娜, 杨升美, 张向楠, 等. 内蒙古自治区5个典型城市可吸入颗粒物暴露对居民每日死亡的影响[J]. 中华疾病控制杂志, 2024, 28(7): 791-797. DOI: 10.16462/j.cnki.zhjbkz.2024.07.008. Wei NN, Yang SM, Zhang XN, et al. The impact of inhalable particulate matter exposure on daily mortality among residents in five typical cities in Inner Mongolia[J]. Chin J Dis Control Prev, 2024, 28(7): 791-797. DOI: 10.16462/j.cnki.zhjbkz.2024.07.008.
[6]	陈敏, 孟显峰, 金丽君, 等. 潍坊市环境温度对人群非意外死亡风险的影响[J]. 环境与健康杂志, 2024, 41(8): 706-709. DOI: 10.16241/j.cnki.1001-5914.2024.08.010. Chen M, Meng XF, Jin LJ, et al. Impact of environmental temperature on risk of non accidental deaths of the crowd in Weifang City[J]. J Environ Health, 2024, 41(8): 706-709. DOI: 10.16241/j.cnki.1001-5914.2024.08.010.
[7]	孟群, 刘爱民. 国家疾病分类与代码(ICD-10)应用指导手册[M]. 北京: 中国协和医科大学出版社, 2017: 45-48. Meng Q, Liu AM. Guidance manual for the application of national disease classification and codes (ICD-10)[M]. Beijing: Peking Union Medical College Press, 2017: 45-48.
[8]	Green H, Bailey J, Schwarz L, et al. Impact of heat on mortality and morbidity in low and middle income countries: a review of the epidemiological evidence and considerations for future research[J]. Environ Res, 2019, 171: 80-91. DOI: 10.1016/j.envres.2019.01.010.
[9]	韩志伟, 王永星, 张杰, 等. 2013―2020年郑州市气温对居民非意外死亡的影响归因风险评估[J]. 河南预防医学杂志, 2022, 33(4): 241-244, 263. DOI: 10.13515/j.cnki.hnjpm.1006-8414.2022.04.009. Han ZW, Wang YX, Zhang J, et al. Attribution risk assessment of air temperature impact on non-accidental death of residents in Zhengzhou, 2013-2020[J]. Henan J Prev Med, 2022, 33(4): 241-244, 263. DOI: 10.13515/j.cnki.hnjpm.1006-8414.2022.04.009.
[10]	唐静, 肖长春, 张俊青, 等. 合肥市2007―2016年日平均温度与居民非意外死亡人数的关系[J]. 中华疾病控制杂志, 2018, 22(4): 422-425. DOI: 10.16462/j.cnki.zhjbkz.2018.04.023. Tang J, Xiao CC, Zhang JQ, et al. The relationship between daily average temperature and non-accidental mortality in Hefei City from 2007 to 2016[J]. Chin J Dis Control Prev, 2018, 22(4): 422-425. DOI: 10.16462/j.cnki.zhjbkz.2018.04.023.
[11]	Gao S, Yang T, Zhang XH, et al. A longitudinal study on the effect of extreme temperature on non-accidental deaths in Hulunbuir City based on DLNM model[J]. Int Arch Occup Environ Health, 2023, 96(7): 1009-1014. DOI: 10.1007/s00420-023-01986-5.
[12]	陈文倩. 极端气温对深圳市居民非意外死亡风险的影响[D]. 武汉: 华中科技大学, 2022. Chen WQ. The effect of extreme temperature on health risk of non-accidental death in Shenzhen[D]. Wuhan: Huazhong University of Science and Technology, 2022.
[13]	Chen X, Su M, Yuan M, et al. Association between ambient temperature and non-accidental mortality in Guiyang, China: a time-series analysis (2013-2023)[J]. PLoS One, 2025, 20(4): e0319863. DOI: 10.1371/journal.pone.0319863.
[14]	Chen RJ, Yin P, Wang LJ, et al. Association between ambient temperature and mortality risk and burden: time series study in 272 main Chinese cities[J]. BMJ, 2018, 363: k4306. DOI: 10.1136/bmj.k4306.
[15]	陈宇, 施泉清, 张春霞, 等. 基于分布滞后非线性模型评估镇江市日均气温对居民死亡风险的影响[J]. 环境卫生学杂志, 2023, 13(6): 433-438, 464. DOI: 10.13421/j.cnki.hjwsxzz.2023.06.005. Chen Y, Shi QQ, Zhang CX, et al. Influence of daily average temperature on the risk of resident mortality in Zhenjiang, China: an assessment based on a distributed lag nonlinear model[J]. Journal of Environm ental Hygien, 2023, 13(6): 433-438, 464. DOI: 10.13421/j.cnki.hjwsxzz.2023.06.005.
[16]	刘佳, 钱云, 周伟杰, 等. 无锡市气温对非意外死亡及寿命损失年影响的时间序列分析[J]. 中华疾病控制杂志, 2019, 23(6): 673-678. DOI: 10.16462/j.cnki.zhjbkz.2019.06.011. Liu J, Qian Y, Zhou WJ, et al. Lagged effect of temperature on non-accidental mortality and years of life lost in Wuxi: a time-series study[J]. Chin J Dis Control Prev, 2019, 23(6): 673-678. DOI: 10.16462/j.cnki.zhjbkz.2019.06.011.
[17]	Yuan L, Madaniyazi L, Vicedo-Cabrera AM, et al. A nationwide comparative analysis of temperature-related mortality and morbidity in Japan[J]. Environ Health Perspect, 2023, 131(12): 127008. DOI: 10.1289/EHP12854.
[18]	Huang CR, Barnett AG, Wang XM, et al. Effects of extreme temperatures on years of life lost for cardiovascular deaths: a time series study in Brisbane, Australia[J]. Circ Cardiovasc Qual Outcomes, 2012, 5(5): 609-614. DOI: 10.1161/CIRCOUTCOMES.112.965707.
[19]	Guo YM, Punnasiri K, Tong SL. Effects of temperature on mortality in Chiang Mai City, Thailand: a time series study[J]. Environ Health, 2012, 11: 36. DOI: 10.1186/1476-069X-11-36.
[20]	Kwon BY, Lee E, Lee SJ, et al. Vulnerabilities to temperature effects on acute myocardial infarction hospital admissions in south Korea[J]. Int J Environ Res Public Health, 2015, 12(11): 14571-14588. DOI: 10.3390/ijerph121114571.
[21]	Yu DZ, Lee SB, Chen S, et al. Coupling the effects of extreme temperature and air pollution on non-accidental mortality in Rencheng, China[J]. Front Public Health, 2023, 11: 1241385. DOI: 10.3389/fpubh.2023.1241385.
[22]	Gao S, Yang T, Zhang XH, et al. A longitudinal study on the effect of extreme temperature on non-accidental deaths in Hulunbuir City based on DLNM model[J]. Int Arch Occup Environ Health, 2023, 96(7): 1009-1014. DOI: 10.1007/s00420-023-01986-5.
[23]	Gan H, Hou XQ, Zhu Z, et al. Smoking: a leading factor for the death of chronic respiratory diseases derived from global burden of disease study 2019[J]. BMC Pulm Med, 2022, 22(1): 149. DOI: 10.1186/s12890-022-01944-w.