Reflections on the Evolution of Heat Alert Systems into Heat Health Risk Warning Systems

Taiyuan Zhang^1,&; Yuxin Zeng^2,&; Yu Lan³; Qinghua Sun⁴; Pengran Qi¹; Min Li¹; Tiantian Li^4,#

1. Huafeng Meteorological Media Group, CMA, Beijing, China;

2. Queen Mary College, Nanchang University, Nanchang City, Jiangxi Province, China;

3. National Meteorological Center, Beijing, China;

4. National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases,  China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.

^& Joint first authors.

^# Corresponding author: Tiantian Li, litiantian@nieh.chinacdc.cn.

The frequent occurrence of extreme heat events in the context of global warming poses a serious threat to public health. Increasing evidence has highlighted the limitations of China’s traditional  heat early warning system, including an overemphasis on meteorological factors, absence of health risk assessments, limited regional adaptability, and a disconnect between observations and public perception. These shortcomings hinder the ability of the system to meet the growing demand for precise health protection warnings and initiatives. Consequently, the development of an early warning system that focuses on the health risks of high temperatures has emerged as a critical strategy for addressing climate change-related health impacts. This study systematically reviews the existing standards and service limitations of heat warning systems in China and analyzes the necessity of advancing research on and applications of health-oriented heat risk warnings. In future, the broader social scope of such meteorological warning systems is expected to transform them into health risk assessment systems that benefit the entire population.

从高温预警到高温健康风险预警的应用思考

张泰源^1,&；曾禹鑫^2,&；蓝渝³；孙庆华⁴；齐鹏然¹；李敏¹；李湉湉^4,#

1. 华风气象传媒集团有限责任公司，北京，中国；

2. 玛丽女王学院，南昌大学，南昌市，江西省，中国；

3. 国家气象中心，北京，中国；

4. 中国疾病预防控制中心传染病预防控制国家重点实验室，中国疾病预防控制中心环境与人群健康重点实验室，环境与健康相关产品安全所， 中国疾病预防控制中心，北京，中国。

^& 共同第一作者。

^# 通信作者: 李湉湉，litiantian@nieh.chinacdc.cn。

在全球气候变暖加剧的背景下，极端高温事件的频发对公众健康构成严重威胁。我国传统高温预警体系虽在气象监测与预警发布方面积累了丰富经验，但其聚焦气象特征的局限性逐渐凸显，包括健康风险评估缺失、区域适配性不足及“观测—感知”偏差等问题，难以满足精准化健康防护需求。基于此，构建高温健康风险预警体系成为应对气候变化健康风险的关键举措。本文系统梳理了我国高温预警的标准体系与服务局限，分析了高温健康风险预警研究与应用的必要性。未来随着该预警的社会化应用拓展，将推动气象“预警力”向全民“健康力”的跃迁。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2025.236

Individual-Level, Multi-Provincial Analysis of High Temperature and Heat-Related Illness Association — China, 2013–2022

Zhe Wang^1,&,#, Runmei Ma^2,&, Xiaoye Wang¹, Fei Mo¹, Yunzhang Zhao³, Yunxia Geng¹, Yirong Liu², Xiangxiang Wei^2,4, Miao He²

1. Chinese Center for Disease Control and Prevention, Beijing, China;

2. National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China;

3. National Institute for Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention, Beijing, China;

4. School of Public Health, Nanjing Medical University, Nanjing City, Jiangsu Province, China.

^& Joint first authors.

^# Corresponding author: Zhe Wang, wangzhe@chinacdc.cn; .

Climate change is intensifying extreme heat events, positioning heat-related illness as an escalating public health threat. However, multi-provincial, individual-level evidence quantifying the association between elevated temperatures and heat-related illness in China remains limited. This multi-provincial study employed a time-stratified case-crossover design. Individual heat-related illness case data (2013–2022) were obtained from the Heat-related Illness Report System, which collects reports from local healthcare facilities and CDCs across 11 provincials-level administrative divisions (PLADs). We evaluated associations between daily mean and maximum temperatures and heat-related illness risk across multiple lag periods (lag0 to lag07), with lag01 designated a priori as the primary exposure window. Effect estimates are presented as relative risks (RR) and percentage changes in RR per 1°C temperature increase. Subgroup analyses examined potential effect modification by sex, age, heat-related illness subtype, heat intensity, and geographic location. Between 2013 and 2022, 53,061 heat-related illness cases were recorded across study areas, with annual counts rising throughout the decade and reaching a peak of 14,025 in 2022. Although mild cases predominated each year (maximum 83.0% in 2015), the proportion of severe cases exhibited a concerning gradual increase. Regarding temperature associations, each 1°C increase in daily mean temperature corresponded to a 21.03% (95% CI: 20.59, 21.47) elevation in the RR of heat-related illness. Daily maximum temperature demonstrated a comparable pattern, though risk estimates were marginally lower. This study demonstrates a clear upward trend in heat-related illness incidence linked to climate change and confirms that elevated temperatures significantly increase disease risk. The escalating health burden necessitates urgent development and implementation of targeted heat-health action plans to protect vulnerable populations.

基于多省份个体水平数据研究高温与热相关疾病的关联 — 中国，2013–2022

王哲^1,&,#，马润美^2,&，王霄晔¹, 莫非¹, 赵云璋³, 耿云霞¹，刘依荣²，魏想想^2,4，贺淼²

1.  中国疾病预防控制中心，北京，中国；

2. 环境与健康相关产品安全所，中国疾病预防控制中心，北京，中国；

3. 职业卫生与中毒控制所，中国疾病预防控制中心，北京，中国；

4. 南京医科大学公共卫生学院，南京市，江苏省，中国。

^& 共同第一作者。

^# 通讯作者: 王哲，wangzhe@chinacdc.cn。

气候变化正加剧极端高温事件，使热相关疾病成为日益严峻的公共卫生威胁。然而，目前基于中国多省份、个体水平数据开展的高温与热相关疾病关联性研究证据仍较为有限。本研究采用时间分层的病例交叉研究设计，分析2013–2022年间由各地医疗机构及疾控中心上报至"高温中暑报告系统"的个案数据，覆盖全国11个省份/直辖市。研究评估了日平均温度与日最高温度在不同滞后天数（lag0–lag07）下与热相关疾病风险的关联，并设定累计滞后01天（lag01）为主要暴露窗口。效应估计值以相对风险（RR）及其每升高1℃的百分比变化表示。通过亚组分析探讨性别、年龄、疾病亚型、高温强度及地理区域等因素对关联的影响。2013—2022年研究地区共报告53,061例热相关疾病病例，年报告数总体呈上升趋势，并于2022年达到峰值（14,025例）。尽管轻症中暑病例占主导（2015年最高达83.0%），但重症中暑病例比例呈现逐步上升趋势。关联分析显示，日平均温度每升高1°C，热相关疾病的相对风险增加21.03%（95% CI: 20.59, 21.47）。日最高温度的效应模式与之相近，但风险估计值略低。本研究揭示了气候变化背景下热相关疾病发病率的明显上升趋势，证实温度升高会显著增加疾病风险。为应对日益加重的疾病负担，有必要尽快制定并实施针对性的高温健康行动计划。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2025.238

County-Level Hotspot Identification and Spatial Regression Analysis of Health Loss from Kashin–Beck Disease — China, 2019 and 2023

Ying Liu^1,2, Fang Qi^1,2, Haoyu Du^1,2, Haonan Li^1,2, Shicong Zheng^1,2, Qian Yu^1,2, Hexuan Dong^1,2, Chenxi Wang^1,2, Jiaxin Li^1,2, Yue Zhao^1,2, Jiayuan Li^1,2, Jun Yu^1,2#

1. Institute for Kashin-Beck Disease Control and Prevention, Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin City, Heilongjiang Province, China

2. National Healthy Commission Key Laboratory of Etiology and Epidemiology (Harbin Medical University), Key Laboratory of Etiology and Epidemiology, Education Bureau of Heilongjiang Province, Heilongjiang Provincial Laboratory of Trace Element and Human Health, Harbin Medical University, Harbin City, Heilongjiang Province, China.

^# Corresponding author: Jun Yu, 400049@hrbmu.edu.cn.

We analyzed the spatial distribution of years lived with disability (YLDs) among patients with Kashin–Beck disease (KBD) at the county level across the country, identified hotspot regions and the primary areas of disease burden. This provides a foundation for the prevention and control of KBD and the rational allocation of healthcare resources to regions with high disease burden. The data were obtained from the National KBD Surveillance System. Spatial autocorrelation analysis was conducted to assess spatial clustering and identify hotspots of YLDs in patients with KBD. Geographically weighted regression (GWR) models were used to identify counties with limited economic and healthcare resources and a high burden of health losses. Spatial aggregation of YLDs among patients with KBD was observed nationwide, with hotspots concentrated in diseased counties in western China, including Shaanxi, Gansu, and Sichuan, and in the northern regions of Heilongjiang and Inner Mongolia. Among the variables, the number of health technicians was negatively correlated with the YLD rate of patients with KBD across 2 years (P < 0.05). Significant geographical differences were found in the spatial distribution of YLDs, with key disease burden areas in 85 northern counties, including Heilongjiang, Jilin, and Inner Mongolia, and 145 western counties, including Shaanxi, Shanxi, and other provincial-level administrative divisions. YLDs among patients with KBD at the county level in China demonstrated spatial clustering, with hotspots primarily in the western regions. Strengthening the recruitment and training of health professionals in high-burden, underserved areas may help improve the quality of life of patients.

基于县级水平的大骨节病健康损失的热点识别与空间回归分析 — 中国, 2019和2023年

刘莹^1,2，齐芳^1,2，杜浩宇^1,2，李昊楠^1,2，郑世聪^1,2，于倩^1,2，董鹤萱^1,2，王晨曦^1,2，李佳昕^1,2，赵月^1,2，李嘉远^1,2，于钧^1,2,#

1. 大骨节病防治研究所，中国疾病预防控制中心地方病控制中心，哈尔滨医科大学，哈尔滨市，黑龙江省，中国；

2. 国家卫生健康委病因流行病学重点实验室（哈尔滨医科大学），黑龙江省教育厅病因流行病学重点实验室，黑龙江省微量元素与人类健康实验室，哈尔滨医科大学，哈尔滨市，黑龙江省，中国。

^# 通信作者：于钧，400049@hrbmu.edu.cn。

本研究旨在分析全国县级水平大骨节病（KBD）患者健康寿命损失年（YLDs）的空间聚集性，明确热点地区，定位疾病负担重点区域，为重点病区防控与医疗资源合理分配提供依据。数据来自KBD监测系统，应用空间自相关分析 KBD 患者 YLDs 的空间聚集性及热点地区，借助地理加权回归（GWR）模型定位经济、医疗水平低且健康寿命损失严重的区县。KBD 患者YLDs整体呈空间聚集性，热点区域集中在我国西部的陕西、甘肃、四川以及北部黑龙江、内蒙古等省内的多个区县，多个变量中卫生技术人员数与 KBD患者YLD率在两年中均呈负相关（P＜0.05），且在空间上存在明显的地域差异，其疾病负担重点区域集中在北部黑龙江、吉林、内蒙古等85个病区县及西部陕西、山西以及其他省份等145个病区县。全国县级水平KBD患者YLDs呈空间聚集性，其热点地区主要集中在西部地区。针对疾病负担重且医疗人员匮乏的区县应加强卫生技术人员的引进与培养，以此提升KBD患者的生存质量。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2025.237

Development of a Landscape Pattern Health Index and Association with Stroke Mortality Using GWQS Regression — Ningbo City, Zhejiang Province, China, 2001–2023

Qinsheng Kong¹, Jing Huang¹, Tianfeng He^2,#, Guoxing Li^1,3,#

1. Health Science Center, Peking University, Beijing, China

2. Ningbo Municipal Center for Disease Control and Prevention, Ningbo City, Zhejiang Province, China

3. Environmental Research Group, School of Public Health, Imperial College London, London, United Kingdom.

^# Corresponding authors: Guoxing Li, liguoxing@bjmu.edu.cn; Tianfeng He, hetf@nbcdc.org.cn .

Urban landscape patterns impact population health; however, traditional indices are limited by single-dimensional focus, multicollinearity, and weak health relevance. Developing a holistic Landscape Pattern Health Index (LPHI) is critical for planning healthy cities. Using data from Ningbo (China), this study integrated 2001–2023 land use data (reclassified into 7 types) and 2009–2016 street level stroke mortality data. A two-stage Generalized Weighted Quantile Sum (GWQS) regression addressed the temporal data discrepancy, first deriving weights from 2009-2016 health data, then calculating the LPHI for the full 2001–2023 period. Quasi-Poisson regression was used to validate the association between the LPHI and stroke mortality. LPHI included protective (grassland, waterbody, etc.) and hazard (impervious surface, bareland) indices. An interquartile range increase in the Protective Composite Index reduced stroke mortality by 20% (warm seasons) and 22% (cold seasons), while the Hazard Composite Index increased risk by 29% (warm) and 20% (cold). The LPHI demonstrated significant associations with stroke mortality, with the Protective Composite Index reducing risk and the Hazard Composite Index increasing it across both seasons. Our study suggests that the LPHI can serve as a bridge between landscape ecology and public health, with the potential to identify high-risk areas and seasonal priorities. This approach could guide targeted interventions through landscape optimization, supporting evidence-based healthy urban planning.

景观格局健康指数的构建及其与脑卒中死亡的关联性研究 — 宁波市，浙江省，中国，2001–2023年

孔钦胜¹；黄婧¹；贺天锋^2,#；李国星^1,3,#

1. 医学部，北京大学，北京，中国；

2. 宁波市疾病预防控制中心，宁波市，浙江省，中国；

3. 环境研究组，公共卫生学院，帝国理工学院，伦敦，英国。

^# 通信作者：李国星，liguoxing@bjmu.edu.cn；贺天锋，hetf@nbcdc.org.cn。

城市景观格局影响人群健康，但传统指数因局限于单一维度、存在多重共线性及与健康关联性弱而受限。开发一个综合性的景观格局健康指数（LPHI）对于健康城市规划至关重要。本研究利用中国宁波市的数据，整合了2001–2023年的土地利用数据（重分类为7种类型）和2009–2016年的街道级脑卒中死亡率数据。采用两阶段广义加权分位数和（GWQS）回归处理时间数据不一致的问题，首先利用2009-2016年健康数据推导权重，进而计算整个2001-2023年期间的LPHI。随后，采用准泊松回归验证LPHI与脑卒中死亡的关联。LPHI包含保护性指数（如草地、水体等）和危害性指数（如不透水地表、裸地）。构建的保护性综合指数每增加一个四分位距，暖季和冷季的脑卒中死亡率分别降低20%和22%；而危害性综合指数则使风险分别增加29%和20%。LPHI与脑卒中死亡率存在显著关联，保护性综合指数在两个季节均能降低风险，而危害性综合指数则增加风险。本研究提示，LPHI可作为连接景观生态学与公共卫生的桥梁，并具有识别高风险区域和季节性优先干预重点的潜力。该方法可能通过景观优化来指导针对性干预措施，从而为基于证据的健康城市规划提供支持。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2025.239