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预防医学  2026, Vol. 38 Issue (9): 880-885    DOI: 10.19485/j.cnki.issn2096-5087.2026.09.004
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高温高湿暴露对循环系统和呼吸系统疾病住院的影响
谷少华1, 陆蓓蓓1, 鹿文涵2, 裘一丹1, 王亚奇1
1.宁波市疾病预防控制中心,浙江 宁波 315000;
2.宁波市海曙区气象局,浙江 宁波 315000
Effects of high temperature and high humidity exposure on hospitalization for circulatory system diseases and respiratory system diseases
GU Shaohua1, LU Beibei1, LU Wenhan2, Qiu Yidan1, Wang Yaqi1
1. Ningbo Center for Disease Control and Prevention, Ningbo, Zhejiang 315000, China;
2. Haishu District Meteorological Bureau, Ningbo, Zhejiang 315000, China
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摘要 目的 评估高温高湿复合暴露对循环系统、呼吸系统疾病住院风险的影响。方法 收集2022—2025年每年5—10月浙江省宁波市主城区3家医院循环系统、呼吸系统疾病住院病例资料,通过宁波市气象服务中心收集同期气象资料。采用时间分层病例交叉设计,分别构建日均气温、湿球黑球温度(WBGT)与循环系统、呼吸系统疾病住院人次的关联模型,通过比较模型拟合优度和极端高温(≥第99百分位数)效应强度筛选高温暴露评价较优指标;构建温湿度分层交互模型,评估低、高湿度(以第50百分位数划分)下极端高温与循环系统、呼吸系统疾病住院风险的暴露-反应关系。结果 2022—2025年每年5—10月宁波市循环系统疾病住院27 845人次,呼吸系统疾病住院27 023人次,日均气温、WBGT M(QR)分别为26.55(7.70)℃、25.11(7.11)℃。以WBGT为暴露指标,极端高温暴露与循环系统(RR=1.192,95%CI:1.014~1.401)、呼吸系统疾病(RR=1.278,95%CI:1.094~1.494)住院风险呈正相关;以日均气温为暴露指标,极端高温暴露与呼吸系统疾病住院风险呈正相关(RR=1.260,95%CI:1.080~1.471),与循环系统疾病住院风险的关联无统计学意义(RR=1.146,95%CI:0.974~1.349),且准赤池信息准则值较大,选择WBGT为高温暴露评价指标。温湿度分层交互分析结果显示,高湿度条件下极端高温暴露时,全人群循环系统(RR=1.480,95%CI:1.000~2.192)、呼吸系统疾病(RR=1.617,95%CI:1.102~2.374)住院风险较高;亚组分析结果显示,高湿度条件下极端高温暴露时,男性循环系统(RR=1.901,95%CI:1.187~3.044)、呼吸系统疾病(RR=1.848,95%CI:1.074~3.179)住院风险较高,45~<65岁人群呼吸系统疾病(RR=2.691,95%CI:1.127~6.426)住院风险较高。结论 高温高湿复合暴露可增加循环系统、呼吸系统疾病住院风险,男性和中年人群为易感高危人群。
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谷少华
陆蓓蓓
鹿文涵
裘一丹
王亚奇
关键词 : 高温,  高湿,  循环系统疾病,  呼吸系统疾病,  住院,  病例交叉设计    
Abstract:Objective To assess the effects of combined high temperature and high humidity exposure on the risk of hospitalization for circulatory system diseases and respiratory system diseases. Methods Data on inpatients with circulatory system diseases and respiratory system diseases from 3 hospitals in the main urban area of Ningbo City, Zhejiang Province were collected for the periods of May to October in each year from 2022 to 2025, and the meteorological data of the same period were obtained from Ningbo Meteorological Service Center. A time-stratified case-crossover design was used to separately construct association models of daily mean temperature and wet-bulb globe temperature (WBGT) with the hospitalization counts of circulatory system diseases and respiratory system diseases. The optimal high temperature exposure assessment indicator was selected by comparing the model goodness-of-fit and the effect size of extreme high temperature (≥99th percentile). Exposure-response relationships between extreme high temperature and the risk of hospitalization for circulatory system diseases and respiratory system diseases were constructed using a temperature-humidity stratified interaction model, stratified by low and high relative humidity based on the 50th percentile. Results From May to October each year during 2022-2025, a total of 27 845 hospitalizations for circulatory system diseases and 27 023 hospitalizations for respiratory system diseases were recorded in Ningbo City. The median daily mean temperature and WBGT were 26.55 (interquartile range, 7.70) ℃ and 25.11 (interquartile range, 7.11) ℃, respectively. When WBGT was used as the exposure indicator, extreme high temperature exposure was positively associated with the hospitalization risks of circulatory system diseases (RR=1.192, 95%CI: 1.014-1.401) and respiratory system diseases (RR=1.278, 95%CI: 1.094-1.494). When daily mean temperature was used as the exposure indicator, extreme high temperature exposure was positively associated with the hospitalization risk of respiratory system diseases (RR=1.260, 95%CI: 1.080-1.471), while the association with the hospitalization risk of circulatory system diseases was not statistically significant (RR=1.146, 95%CI: 0.974-1.349). Considering that the quasi-Akaike information criterion value of this model was larger than that of the WBGT-based model, WBGT was selected as the optimal high temperature exposure assessment indicator. The temperature-humidity stratified interaction analysis showed that under high-humidity conditions, extreme high-temperature exposure was associated with higher risks of hospitalization for circulatory system diseases (RR=1.480, 95%CI: 1.000-2.192) and respiratory system diseases (RR=1.617, 95%CI: 1.102-2.374) in the overall population. Subgroup analysis showed that under high-humidity conditions, extreme high-temperature exposure was associated with higher risks of hospitalization for circulatory system diseases (RR=1.901, 95%CI: 1.187-3.044) and respiratory system diseases (RR=1.848, 95%CI: 1.074-3.179) among males, and with higher risk of hospitalization for respiratory system diseases among those aged 45-<65 years (RR=2.691, 95%CI: 1.127-6.426). Conclusion Combined exposure to high temperature and high humidity can increases the hospitalization risks of circulatory system diseases and respiratory system diseases, and males and the middle-aged population are susceptible high-risk populations.
Key words: high temperature    high humidity    circulatory system diseases    respiratory system diseases    hospitalization    case-crossover design
收稿日期: 2026-06-22      修回日期: 2026-09-06     
中图分类号:  R122.2  
基金资助:浙江省自然科学基金项目(LTGY24H260008); 宁波市自然科学基金项目(2022J001); 宁波市医疗卫生品牌学科(PPXK2024-09)
作者简介: 谷少华,硕士,副主任医师,主要从事环境流行病学研究工作
通信作者: 陆蓓蓓,E-mail:493944183@qq.com   
引用本文:   
谷少华, 陆蓓蓓, 鹿文涵, 裘一丹, 王亚奇. 高温高湿暴露对循环系统和呼吸系统疾病住院的影响[J]. 预防医学, 2026, 38(9): 880-885.
GU Shaohua, LU Beibei, LU Wenhan, Qiu Yidan, Wang Yaqi. Effects of high temperature and high humidity exposure on hospitalization for circulatory system diseases and respiratory system diseases. Preventive Medicine, 2026, 38(9): 880-885.
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https://www.zjyfyxzz.com/CN/10.19485/j.cnki.issn2096-5087.2026.09.004      或      https://www.zjyfyxzz.com/CN/Y2026/V38/I9/880
[1] Raymond C,Matthews T,Horton R M.The emergence of heat and humidity too severe for human tolerance[J/OL].Sci Adv,2020,6(19)[2026-09-06].https://doi.org/10.1126/sciadv.aaw1838.
[2] He W Y,Chen H P.More extreme-heat occurrences related to humidity in China[J/OL].Atmos Ocean Sci Lett,2023,16(5)[2026-09-06].https://doi.org/10.1016/j.aosl.2023.100391.
[3] Sobolewski A,Młynarczyk M,Konarska M,et al.The influence of air humidity on human heat stress in a hot environment[J].Int J Occup Saf Ergon,2021,27(1):226-236.
[4] Armstrong B,Sera F,Vicedo-Cabrera A M,et al.The role of humidity in associations of high temperature with mortality:a multicountry,multicity study[J/OL].Environ Health Perspect,2019,127(9)[2026-09-06].https://doi.org/10.1289/EHP5430.
[5] Fang W,Li Z X,Gao J H,et al.The joint and interaction effect of high temperature and humidity on mortality in China[J/OL].Environ Int,2023,171[2026-09-06].https://doi.org/10.1016/j.envint.2022.107669.
[6] 谷少华,金永高,陆蓓蓓,等.2013—2018年宁波市高温热浪致超额死亡风险评价[J].预防医学,2021,33(9):897-901,905.
[7] Tobias A,Kim Y,Madaniyazi L.Time-stratified case-crossover studies for aggregated data in environmental epidemiology:a tutorial[J/OL].Int J Epidemiol,2024,53(2)[2026-09-06].https://doi.org/10.1093/ije/dyae020.
[8] Parsons K.Heat stress standard ISO 7243 and its global application[J].Ind Health,2006,44(3):368-379.
[9] Liang C,Yuan J C,Tang X,et al.The influence of humid heat on morbidity of megacity Shanghai in China[J/OL].Environ Int,2024,183[2026-09-06].https://doi.org/10.1016/j.envint.2024.108424.
[10] Gasparrini A,Armstrong B,Kenward M G.Distributed lag non-linear models[J].Stat Med,2010,29(21):2224-2234.
[11] 李静,王焕新,屈龙,等.昌平区PM2.5和气温对日门诊量的交互影响[J].预防医学,2019,31(6):593-596,599.
[12] 曾韦霖,马文军,刘涛,等.构建气温-死亡关系模型中温度指标的选择[J].中华预防医学杂志,2012,46(10):946-951.
[13] Chen S J,Dong H,Li M M,et al.Interactive effects between temperature and PM2.5 on mortality:a study of varying coefficient distributed lag model-Guangzhou,Guangdong Province,China,2013-2020[J].China CDC Wkly,2022,4(26):570-576.
[14] Cramer M N,Gagnon D,Laitano O,et al.Human temperature regulation under heat stress in health,disease,and injury[J].Physiol Rev,2022,102(4):1907-1989.
[15] Wang W J,Wang J H,Song G H,et al.Environmental and sensitization variations among asthma and/or rhinitis patients between2008 and 2018 in China[J/OL].Clin Transl Allergy,2022,12(2)[2026-09-06].https://doi.org/10.1002/clt2.12116.
[16] Rahman M M,McKeon K,Luglio D,et al.Compounding effects of heat and high humidity on cardiovascular morbidity in Dhaka,Bangladesh:an implication of climate crisis[J/OL].Sci Total Environ,2025,995[2026-09-06].https://doi.org/10.1016/j.scitotenv.2025.180220.
[17] Baldwin J W,Benmarhnia T,Ebi K L,et al.Humidity's role in heat-related health outcomes:a heated debate[J/OL].Environ Health Perspect,2023,131(5)[2026-09-06].https://doi.org/10.1289/EHP11807.
[18] 刘磊,宋丹丹,侯赛,等.2010—2016年安徽省高温中暑流行病学特征[J].疾病监测,2017,32(10):895-899.
[19] 刘弢,李辉,张传会.某市2013至2017年高温中暑流行病学分析[J].中华劳动卫生职业病杂志,2018,36(12):915-916.
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