Evidence map›Paper›PMID 39877409›Full record

ArticleThe Lancet regional health. Western Pacific2025

The graded heat-health risk forecast and early warning with full-season coverage across China: a predicting model development and evaluation study.

Qing Wang, Chen Chen, Huaiyue Xu, Yuanyuan Liu, Yu Zhong, Jing Liu, Menghan Wang, Mengxue Zhang, Yiting Liu, Jing Li and 1 more

Abstract read
In one paragraph

Article in The Lancet regional health. Western Pacific, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Qing WangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China.
Chen ChenNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China.
Huaiyue XuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China.
Yuanyuan LiuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China.
Yu ZhongNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China.
Jing LiuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China.
Menghan WangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China.
Mengxue ZhangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China.
Yiting LiuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China.
Jing LiNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China.
Tiantian LiNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Due to global climate change, high temperature and heatwaves have become critical issues that pose a threat to human health. An effective early warning system is essential to mitigate the health risks associated with high temperature and heatwaves. However, most of the current heatwave early warning systems are not adequately developed based on the heat-health risk model, and the health impact of hot weather has not been well managed in most countries. Methods: This study proposed a "full-season coverage and population health-oriented graded early-warning" concept and developed a heat-health surveillance, forecast and early warning (HHSEW) model. The exposure-response (E-R) relationship between temperature and mortality was analyzed through a two-stage approach using time-series analysis data from 323 counties across China for the period 2013-2018. The premature mortality curve at each temperature percentile was plotted and four temperature-percentile points on the curve were determined as the thresholds of the pre-warning and warning levels 1-3 based on the variations in the rates of the segmental slopes on the curve. The HHSEW model was evaluated by comparing the frequency, the mortality risk of all-cause and cause-specific diseases, the predicted numbers of premature deaths, and the heat-related health economic burden at each warning level with those of the current high temperature early warning systems. Findings: The HHSEW model determined five levels, including seasonal surveillance, pre-warning, and warning levels 1-3. There was a gradual increase in the mortality risks of all-cause and cause-specific diseases along with the increase of warning levels. The risk of all-cause mortality increased by 9.79% (95% CI: 8.59%-11.01%), 22.62% (95% CI: 19.49%-25.83%), 28.36% (95% CI: 24.72%-32.10%), and 33.87% (95% CI: 28.89%-39.06%) at the pre-warning level, warning level 1, warning level 2, and warning level 3, respectively. Through our HHSEW model, 94,008 heat-related all-cause deaths were predicted annually in the 337 major cities of China, which was much larger than the number (14,858) of the China Meteorological Administration (CMA) heatwave early-warning system currently used in China. It was estimated that the proper implementation of the HHSEW-based early warning system would save 220 billion CNY in heat-related health burden compared to the current heatwave early-warning system. Interpretation: The HHSEW model has been proven to surpass the current heatwave early warning system. With its full-season coverage and graded warning levels for heat-related health risks, the HHSEW model and system can provide timely early warnings to the public, leading to significant health benefits. This methodology, labeled "full-season coverage and population health-oriented graded early-warning", should be implemented globally to mitigate the escalating health risks associated with high temperature. Funding: National Natural Science Foundation of China (82425051, 42071433, 42305196, 82241051) and the Special Foundation of Basic Science and Technology Resources Survey of Ministry of Science and Technology of China (2017FY101204).

Indexed as

ChinaEarly warningHealth riskHeatwaveHigh temperatureSurveillance

Identifiers

PMID39877409
PMCPMC11772993

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.