Evidence map›Paper›PMID 39393374›Full record

ArticleThe Lancet. Planetary health2024

Projecting future excess deaths associated with extreme precipitation events in China under changing climate: an integrated modelling study.

Jie Ban, Kailai Lu, Yuanyuan Liu, Jiawei Zang, Zhen Zhou, Can Zhang, Zhao Liu, Jianbin Huang, Yidan Chen, Xuejie Gao and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Lancet. Planetary health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Rainfall events and mortality in Brazil.Journal of exposure science & environmental epidemiology · 2026
    Article
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

16 authors.

Jie BanNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Kailai LuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Yuanyuan LiuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Jiawei ZangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Zhen ZhouNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Can ZhangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Zhao LiuSchool of Linkong Economics and Management, Beijing Institute of Economics and Management, Beijing, China.
Jianbin HuangDepartment of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China.
Yidan ChenState Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing, China.
Xuejie GaoCollege of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China; Climate Change Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China.
Ying XuNational Climate Center, China Meteorological Administration, Beijing, China.
Can WangState Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing, China.
Wenjia CaiDepartment of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China.
Peng GongDepartment of Earth Sciences and Geography, University of Hong Kong, Hong Kong Special Administrative Region, China.
Yong LuoDepartment of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China.
Tiantian LiNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China. Electronic address: litiantian@nieh.chinacdc.cn.

Funding

Wellcome Trust
6 · The paper itself

Abstract

backgroundClimate-change-induced extreme precipitation events have attracted global attention; however, the associated excess deaths burden has been insufficiently explored and remains unclear.

methodsWe first defined an extreme precipitation event for each county when the daily total precipitation exceeded the county-specific 99·5th percentile of the daily precipitation from 1986 to 2005; then we estimated the associations between extreme precipitation events and cause-specific deaths in 280 Chinese counties using a two-stage time-series model. Second, we projected the excess deaths related to extreme precipitation events by combining the bias-corrected multi-model precipitation predictions derived under different combined emission-population scenarios of three representative concentration pathways (RCPs; RCP2·6, RCP4·5, and RCP8·5) and three shared socioeconomic pathways (SSP2, a business-as-usual scenario) populations (S1, low fertility rate; S2, medium fertility rate; and S3, high fertility rate). We quantified the climate and population contributions to the changes of future excess deaths nationwide and by climatic zones.

findingsCompared with the non-extreme precipitation days, the percentage increase of deaths associated with exposure to extreme precipitation days is 13·0% (95% CI 7·0-19·3) for accidental cause, 4·3% (2·0-6·6) for circulatory disease, and 6·8% (2·8-10·9) for respiratory disease. The number of annual average excess deaths related to extreme precipitation events during 1986-2005 was 2644 (95% CI 1496-3730) for accidental cause, 69 (33-105) for circulatory disease, and 181 (79-279) for respiratory disease. In the 2030s, the total number of excess deaths of these three causes will increase by 1244 (43%), 1756 (61%), and 2008 (69%) under RCP2·6, RCP4·5, and RCP8·5 scenarios combined with a medium-fertility-rate population (SSP2-S2), respectively, but will decrease by 3% under RCP2·6-SSP2-S2 and increase by 25% under RCP8·5-SSP2-S2 in the 2090s. Humid and water-limited regions in subtropical, middle-temperate, and plateau climate zones will face highly increased risks. Climate and population factors contributed disproportionally among the five climate zones.

interpretationThis study is the largest integrated projection exploring the disease burden associated with extreme precipitation events. The excess deaths will be amplified by climate and population changes. Improving mitigation and adaptation capacities is crucial when responding to precipitation extremes.

fundingNational Natural Science Foundation of China and Wellcome Trust.

Indexed as

Climate ChangeRainCause of DeathChinaHumansModels, TheoreticalMortality

Identifiers

PMID39393374
PMCPMC11461903

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.