中国区域室内温度造成的死亡率负担

IF 9.7 1区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Environment International Pub Date : 2025-10-01 DOI:10.1016/j.envint.2025.109822

Zhongguo Huang , Jianxiong Hu , Jinghua Gao , Min Yu , Mengen Guo , Ruilin Meng , Chunliang Zhou , Yize Xiao , Biao Huang , Jiangmei Liu , Maigeng Zhou , Ryan J. Gainor , Ramune Reliene , Guanhao He , Tao Liu , Wenjun Ma

{"title":"中国区域室内温度造成的死亡率负担","authors":"Zhongguo Huang , Jianxiong Hu , Jinghua Gao , Min Yu , Mengen Guo , Ruilin Meng , Chunliang Zhou , Yize Xiao , Biao Huang , Jiangmei Liu , Maigeng Zhou , Ryan J. Gainor , Ramune Reliene , Guanhao He , Tao Liu , Wenjun Ma","doi":"10.1016/j.envint.2025.109822","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Despite predominant indoor occupancy patterns, mortality risks and burdens associated with regional (county/district level) indoor temperature remain underexplored in epidemiological research.</div></div><div><h3>Objective</h3><div>To construct a reliable regional indoor temperature prediction model and estimate the disease burden attributed to non-optimal regional indoor temperature.</div></div><div><h3>Design</h3><div>The outdoor meteorological parameters were from the Fifth Generation European Reanalysis dataset, while regional determinants were from national statistical yearbooks. Indoor temperature and building characteristics were collected from 99 buildings across 33 cities. Employing a random forest (RF) algorithm, we developed a prediction model of regional indoor temperatures based on outdoor meteorological parameters, regional determinants and building characteristics. Subsequently, we estimated the regional temperature-mortality associations for both indoor and outdoor temperatures using a distributed lag non-linear model (DLNM) based on cause-specific mortality data collected from 364 counties/districts in China during 2006–2017. Finally, we compared the temperature-related mortality burdens associated with both indoor and outdoor temperature.</div></div><div><h3>Results</h3><div>The RF algorithm identified outdoor meteorological parameters (temperature, relative humidity, wind speed, and precipitation) and regional determinants (green space, latitude, longitude, education attainment, penetration rate of air conditioner, and seasonal variation) as primary determinants of regional average indoor temperature, whereas building characteristics exhibited limited influence. The developed prediction model demonstrated superior predictive accuracy with performance metrics including a root mean square error (RMSE) of 1.473 °C, mean absolute error (MAE) of 1.034 °C, and R<sup>2</sup> value of 0.938. Analysis of 6.5 million non-accidental death records revealed consistent inverse J-shaped associations for both regional indoor and outdoor temperature-mortality relationships, with indoor temperature demonstrating greater mortality risks. Comparative assessment showed higher temperature-attributable fractions for indoor exposure (18.09 %, 95 %CI:17.87–18.31 %) versus outdoor exposure (14.46 %, 95 %CI:14.41–14.52 %), particularly notable for heat-related mortality burden (indoor:8.38 % vs outdoor:3.66 %).</div></div><div><h3>Conclusions</h3><div>Meteorological parameters and regional determinants emerged as primary predictors of indoor temperature. Regional indoor temperature exposure exhibited greater mortality risks and burden compared to regional outdoor temperature, particularly during heat condition.</div></div>","PeriodicalId":308,"journal":{"name":"Environment International","volume":"204 ","pages":"Article 109822"},"PeriodicalIF":9.7000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The mortality burden attributed to regional indoor temperatures in China\",\"authors\":\"Zhongguo Huang , Jianxiong Hu , Jinghua Gao , Min Yu , Mengen Guo , Ruilin Meng , Chunliang Zhou , Yize Xiao , Biao Huang , Jiangmei Liu , Maigeng Zhou , Ryan J. Gainor , Ramune Reliene , Guanhao He , Tao Liu , Wenjun Ma\",\"doi\":\"10.1016/j.envint.2025.109822\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>Despite predominant indoor occupancy patterns, mortality risks and burdens associated with regional (county/district level) indoor temperature remain underexplored in epidemiological research.</div></div><div><h3>Objective</h3><div>To construct a reliable regional indoor temperature prediction model and estimate the disease burden attributed to non-optimal regional indoor temperature.</div></div><div><h3>Design</h3><div>The outdoor meteorological parameters were from the Fifth Generation European Reanalysis dataset, while regional determinants were from national statistical yearbooks. Indoor temperature and building characteristics were collected from 99 buildings across 33 cities. Employing a random forest (RF) algorithm, we developed a prediction model of regional indoor temperatures based on outdoor meteorological parameters, regional determinants and building characteristics. Subsequently, we estimated the regional temperature-mortality associations for both indoor and outdoor temperatures using a distributed lag non-linear model (DLNM) based on cause-specific mortality data collected from 364 counties/districts in China during 2006–2017. Finally, we compared the temperature-related mortality burdens associated with both indoor and outdoor temperature.</div></div><div><h3>Results</h3><div>The RF algorithm identified outdoor meteorological parameters (temperature, relative humidity, wind speed, and precipitation) and regional determinants (green space, latitude, longitude, education attainment, penetration rate of air conditioner, and seasonal variation) as primary determinants of regional average indoor temperature, whereas building characteristics exhibited limited influence. The developed prediction model demonstrated superior predictive accuracy with performance metrics including a root mean square error (RMSE) of 1.473 °C, mean absolute error (MAE) of 1.034 °C, and R<sup>2</sup> value of 0.938. Analysis of 6.5 million non-accidental death records revealed consistent inverse J-shaped associations for both regional indoor and outdoor temperature-mortality relationships, with indoor temperature demonstrating greater mortality risks. Comparative assessment showed higher temperature-attributable fractions for indoor exposure (18.09 %, 95 %CI:17.87–18.31 %) versus outdoor exposure (14.46 %, 95 %CI:14.41–14.52 %), particularly notable for heat-related mortality burden (indoor:8.38 % vs outdoor:3.66 %).</div></div><div><h3>Conclusions</h3><div>Meteorological parameters and regional determinants emerged as primary predictors of indoor temperature. Regional indoor temperature exposure exhibited greater mortality risks and burden compared to regional outdoor temperature, particularly during heat condition.</div></div>\",\"PeriodicalId\":308,\"journal\":{\"name\":\"Environment International\",\"volume\":\"204 \",\"pages\":\"Article 109822\"},\"PeriodicalIF\":9.7000,\"publicationDate\":\"2025-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environment International\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0160412025005732\",\"RegionNum\":1,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environment International","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0160412025005732","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}

引用次数: 0

摘要

尽管室内居住模式占主导地位，但流行病学研究仍未充分探讨与区域（县/区）室内温度相关的死亡风险和负担。目的建立可靠的区域室内温度预测模型，估算非最优区域室内温度引起的疾病负担。室外气象参数来自第五代欧洲再分析数据集，而区域决定因素来自国家统计年鉴。研究人员收集了33个城市99栋建筑的室内温度和建筑特征。采用随机森林（RF）算法，基于室外气象参数、区域决定因素和建筑特征，建立了区域室内温度预测模型。随后，我们基于2006-2017年中国364个县/区收集的死因特异性死亡率数据，使用分布式滞后非线性模型（DLNM）估计了室内和室外温度的区域温度-死亡率关联。最后，我们比较了与室内和室外温度相关的温度相关的死亡率负担。结果射频算法识别出室外气象参数（温度、相对湿度、风速和降水）和区域决定因素（绿地面积、经纬度、受教育程度、空调普及率和季节变化）是区域平均室内温度的主要决定因素，而建筑特征的影响有限。所建立的预测模型具有较好的预测精度，其性能指标包括均方根误差（RMSE）为1.473 °C，平均绝对误差（MAE）为1.034 °C， R2值为0.938。对650万份非意外死亡记录的分析显示，区域室内和室外温度与死亡率的关系呈一致的反j型关系，室内温度显示出更大的死亡风险。比较评估显示，室内暴露的温度归因分数（18.09 %,95 %CI: 17.87-18.31 %）高于室外暴露（14.46 %,95 %CI: 14.41-14.52 %），特别是与热相关的死亡率负担（室内：8.38 % vs室外：3.66 %）。结论气象参数和区域因素是室内温度的主要预测因子。与区域室外温度相比，区域室内温度暴露表现出更大的死亡风险和负担，特别是在高温条件下。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

The mortality burden attributed to regional indoor temperatures in China

Background

Despite predominant indoor occupancy patterns, mortality risks and burdens associated with regional (county/district level) indoor temperature remain underexplored in epidemiological research.

Objective

To construct a reliable regional indoor temperature prediction model and estimate the disease burden attributed to non-optimal regional indoor temperature.

Design

The outdoor meteorological parameters were from the Fifth Generation European Reanalysis dataset, while regional determinants were from national statistical yearbooks. Indoor temperature and building characteristics were collected from 99 buildings across 33 cities. Employing a random forest (RF) algorithm, we developed a prediction model of regional indoor temperatures based on outdoor meteorological parameters, regional determinants and building characteristics. Subsequently, we estimated the regional temperature-mortality associations for both indoor and outdoor temperatures using a distributed lag non-linear model (DLNM) based on cause-specific mortality data collected from 364 counties/districts in China during 2006–2017. Finally, we compared the temperature-related mortality burdens associated with both indoor and outdoor temperature.

Results

The RF algorithm identified outdoor meteorological parameters (temperature, relative humidity, wind speed, and precipitation) and regional determinants (green space, latitude, longitude, education attainment, penetration rate of air conditioner, and seasonal variation) as primary determinants of regional average indoor temperature, whereas building characteristics exhibited limited influence. The developed prediction model demonstrated superior predictive accuracy with performance metrics including a root mean square error (RMSE) of 1.473 °C, mean absolute error (MAE) of 1.034 °C, and R² value of 0.938. Analysis of 6.5 million non-accidental death records revealed consistent inverse J-shaped associations for both regional indoor and outdoor temperature-mortality relationships, with indoor temperature demonstrating greater mortality risks. Comparative assessment showed higher temperature-attributable fractions for indoor exposure (18.09 %, 95 %CI:17.87–18.31 %) versus outdoor exposure (14.46 %, 95 %CI:14.41–14.52 %), particularly notable for heat-related mortality burden (indoor:8.38 % vs outdoor:3.66 %).

Conclusions

Meteorological parameters and regional determinants emerged as primary predictors of indoor temperature. Regional indoor temperature exposure exhibited greater mortality risks and burden compared to regional outdoor temperature, particularly during heat condition.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Environment International 环境科学-环境科学

CiteScore

21.90

自引率

3.40%

发文量

734

审稿时长

2.8 months

期刊介绍： Environmental Health publishes manuscripts focusing on critical aspects of environmental and occupational medicine, including studies in toxicology and epidemiology, to illuminate the human health implications of exposure to environmental hazards. The journal adopts an open-access model and practices open peer review. It caters to scientists and practitioners across all environmental science domains, directly or indirectly impacting human health and well-being. With a commitment to enhancing the prevention of environmentally-related health risks, Environmental Health serves as a public health journal for the community and scientists engaged in matters of public health significance concerning the environment.