Evidence map›Paper›PMID 37471118›Full record

ArticleJMIR public health and surveillance2023

The Combined Effects of Short-Term Exposure to Multiple Meteorological Factors on Unintentional Drowning Mortality: Large Case-Crossover Study.

Yingyin Liu, Xiaomei Dong, Zhixing Li, Sui Zhu, Ziqiang Lin, Guanhao He, Weiwei Gong, Jianxiong Hu, Zhulin Hou, Ruilin Meng and 12 more

Open access · goldAbstract read
In one paragraph

Article in JMIR public health and surveillance, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.6field-weighted citation impact, top 30% of its field
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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

22 authors at 5 institutions in 1 country.

Yingyin Liu *Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China.ORCID 0000-0003-0989-2351
Xiaomei Dong *Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China.ORCID 0000-0002-3832-038X
Zhixing LiDepartment of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China.ORCID 0000-0002-1636-1359
Sui ZhuDepartment of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China.ORCID 0000-0002-0012-9045
Ziqiang LinDepartment of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China.ORCID 0000-0003-1990-6788
Guanhao HeDepartment of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China.ORCID 0000-0003-4152-6132
Weiwei GongZhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China.ORCID 0000-0002-7066-4772
Jianxiong HuGuangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.ORCID 0000-0003-4180-3187
Zhulin HouJilin Provincial Center for Disease Control and Prevention, Changchun, China.ORCID 0009-0009-9860-4542
Ruilin MengGuangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.ORCID 0000-0001-7987-4269
Chunliang ZhouHunan Provincial Center for Disease Control and Prevention, Changsha, China.ORCID 0000-0003-4710-4950
Min YuZhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China.ORCID 0000-0003-3272-0013
Biao HuangJilin Provincial Center for Disease Control and Prevention, Changchun, China.ORCID 0009-0000-0497-197X
Lifeng LinGuangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.ORCID 0000-0001-7815-5991
Jianpeng XiaoGuangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.ORCID 0000-0001-9665-6036
Jieming ZhongZhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China.ORCID 0000-0001-8987-2216
Donghui JinHunan Provincial Center for Disease Control and Prevention, Changsha, China.ORCID 0000-0003-3391-3124
Yiqing XuHunan Provincial Center for Disease Control and Prevention, Changsha, China.ORCID 0000-0001-6481-7288
Lingshuang LvHunan Provincial Center for Disease Control and Prevention, Changsha, China.ORCID 0000-0001-7696-826X
Cunrui HuangVanke School of Public Health, Tsinghua University, Beijing, China.ORCID 0000-0002-9139-8354
Tao LiuDisease Control and Prevention Institute of Jinan University, School of Medicine, Jinan University, Guangzhou, China.ORCID 0000-0003-1451-5251
Wenjun MaDepartment of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China.ORCID 0000-0002-9266-4802
Jinan University · CNGuangdong Provincial Center for Disease Control and Prevention · CNHunan Provincial Center for Disease Control and Prevention · CNZhejiang Center for Disease Control and Prevention · CNVanke (China) · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDrowning is a serious public health problem worldwide. Previous epidemiological studies on the association between meteorological factors and drowning mainly focused on individual weather factors, and the combined effect of mixed exposure to multiple meteorological factors on drowning is unclear.

objectiveWe aimed to investigate the combined effects of multiple meteorological factors on unintentional drowning mortality in China and to identify the important meteorological factors contributing to drowning mortality.

methodsUnintentional drowning death data (based on International Classification of Diseases, 10th Edition, codes W65-74) from January 1, 2013, to December 31, 2018, were collected from the Disease Surveillance Points System for Guangdong, Hunan, Zhejiang, Yunnan, and Jilin Provinces, China. Daily meteorological data, including daily mean temperature, relative humidity, sunlight duration, and rainfall in the same period were obtained from the Chinese Academy of Meteorological Science Data Center. We constructed a time-stratified case-crossover design and applied a generalized additive model to examine the effect of individual weather factors on drowning mortality, and then used quantile g-computation to estimate the joint effect of the mixed exposure to meteorological factors.

resultsA total of 46,179 drowning deaths were reported in the 5 provinces in China from 2013 to 2018. In an effect analysis of individual exposure, we observed a positive effect for sunlight duration, a negative effect for relative humidity, and U-shaped associations for temperature and rainfall with drowning mortality. In a joint effect analysis of the above 4 meteorological factors, a 2.99% (95% CI 0.26%-5.80%) increase in drowning mortality was observed per quartile rise in exposure mixture. For the total population, sunlight duration was the most important weather factor for drowning mortality, with a 93.1% positive contribution to the overall effects, while rainfall was mainly a negative factor for drowning deaths (90.5%) and temperature and relative humidity contributed 6.9% and -9.5% to the overall effects, respectively.

conclusionsThis study found that mixed exposure to temperature, relative humidity, sunlight duration, and rainfall was positively associated with drowning mortality and that sunlight duration, rather than temperature, may be the most important meteorological factor for drowning mortality. These findings imply that it is necessary to incorporate sunshine hours and temperature into early warning systems for drowning prevention in the future.

Indexed as

DrowningChinaCross-Over StudiesHumansMeteorological ConceptsTemperaturedrowningenvironmental epidemiologyexposure mixturemeteorological factorquantile g-computation

Identifiers

PMID37471118
PMCPMC10401198
OpenAlexW4384819994

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LicenceCC BY
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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.