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

      research-article
      , MPH 1 , , PhD 1 , , MPH 1 , 2 , , PhD 1 , , PhD 1 , , PhD 1 , , MPH 3 , , MS 4 , , MS 6 , , PhD 7 , , BS 8 , , MS 3 , , MS 6 , , PhD 7 , , PhD 4 , , PhD 3 , , BS 8 , , MS 8 , , MS 8 , , PhD 9 , , PhD 10 , , PhD 1 ,
      ,
      (Reviewer), (Reviewer), (Reviewer)
      JMIR Public Health and Surveillance
      JMIR Publications
      drowning, exposure mixture, quantile g-computation, environmental epidemiology, meteorological factor

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          Abstract

          Background

          Drowning 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.

          Objective

          We 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.

          Methods

          Unintentional 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.

          Results

          A 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.

          Conclusions

          This 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.

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          Most cited references50

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          A Quantile-Based g-Computation Approach to Addressing the Effects of Exposure Mixtures

          Background: Exposure mixtures frequently occur in data across many domains, particularly in the fields of environmental and nutritional epidemiology. Various strategies have arisen to answer questions about exposure mixtures, including methods such as weighted quantile sum (WQS) regression that estimate a joint effect of the mixture components. Objectives: We demonstrate a new approach to estimating the joint effects of a mixture: quantile g-computation. This approach combines the inferential simplicity of WQS regression with the flexibility of g-computation, a method of causal effect estimation. We use simulations to examine whether quantile g-computation and WQS regression can accurately and precisely estimate the effects of mixtures in a variety of common scenarios. Methods: We examine the bias, confidence interval (CI) coverage, and bias–variance tradeoff of quantile g-computation and WQS regression and how these quantities are impacted by the presence of noncausal exposures, exposure correlation, unmeasured confounding, and nonlinearity of exposure effects. Results: Quantile g-computation, unlike WQS regression, allows inference on mixture effects that is unbiased with appropriate CI coverage at sample sizes typically encountered in epidemiologic studies and when the assumptions of WQS regression are not met. Further, WQS regression can magnify bias from unmeasured confounding that might occur if important components of the mixture are omitted from the analysis. Discussion: Unlike inferential approaches that examine the effects of individual exposures while holding other exposures constant, methods like quantile g-computation that can estimate the effect of a mixture are essential for understanding the effects of potential public health actions that act on exposure sources. Our approach may serve to help bridge gaps between epidemiologic analysis and interventions such as regulations on industrial emissions or mining processes, dietary changes, or consumer behavioral changes that act on multiple exposures simultaneously. https://doi.org/10.1289/EHP5838
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            Global risk of deadly heat

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              On Judging the Significance of Differences by Examining the Overlap Between Confidence Intervals

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                Author and article information

                Contributors
                Journal
                JMIR Public Health Surveill
                JMIR Public Health Surveill
                JPH
                JMIR Public Health and Surveillance
                JMIR Publications (Toronto, Canada )
                2369-2960
                2023
                20 July 2023
                : 9
                : e46792
                Affiliations
                [1 ] Department of Public Health and Preventive Medicine School of Medicine Jinan University Guangzhou China
                [2 ] Department of Nosocomial Infection Management Affiliated Nanfang Hospital of Southern Medical University Guangzhou China
                [3 ] Zhejiang Provincial Center for Disease Control and Prevention Hangzhou China
                [4 ] Guangdong Provincial Institute of Public Health Guangdong Provincial Center for Disease Control and Prevention Guangzhou China
                [5 ] Yunnan Provincial Center for Disease Control and Prevention Kunming China
                [6 ] Jilin Provincial Center for Disease Control and Prevention Changchun China
                [7 ] Guangdong Provincial Center for Disease Control and Prevention Guangzhou China
                [8 ] Hunan Provincial Center for Disease Control and Prevention Changsha China
                [9 ] Vanke School of Public Health Tsinghua University Beijing China
                [10 ] Disease Control and Prevention Institute of Jinan University School of Medicine Jinan University Guangzhou China
                Author notes
                Corresponding Author: Wenjun Ma mawj@ 123456gdiph.org.cn
                Author information
                https://orcid.org/0000-0003-0989-2351
                https://orcid.org/0000-0002-3832-038X
                https://orcid.org/0000-0002-1636-1359
                https://orcid.org/0000-0002-0012-9045
                https://orcid.org/0000-0003-1990-6788
                https://orcid.org/0000-0003-4152-6132
                https://orcid.org/0000-0002-7066-4772
                https://orcid.org/0000-0003-4180-3187
                https://orcid.org/0009-0009-9860-4542
                https://orcid.org/0000-0001-7987-4269
                https://orcid.org/0000-0003-4710-4950
                https://orcid.org/0000-0003-3272-0013
                https://orcid.org/0009-0000-0497-197X
                https://orcid.org/0000-0001-7815-5991
                https://orcid.org/0000-0001-9665-6036
                https://orcid.org/0000-0001-8987-2216
                https://orcid.org/0000-0003-3391-3124
                https://orcid.org/0000-0001-6481-7288
                https://orcid.org/0000-0001-7696-826X
                https://orcid.org/0000-0002-9139-8354
                https://orcid.org/0000-0003-1451-5251
                https://orcid.org/0000-0002-9266-4802
                Article
                v9i1e46792
                10.2196/46792
                10401198
                37471118
                fff529ff-f8cb-4e45-a782-ec6f9f3ad74b
                ©Yingyin Liu, Xiaomei Dong, Zhixing Li, Sui Zhu, Ziqiang Lin, Guanhao He, Weiwei Gong, Jianxiong Hu, Zhulin Hou, Ruilin Meng, Chunliang Zhou, Min Yu, Biao Huang, Lifeng Lin, Jianpeng Xiao, Jieming Zhong, Donghui Jin, Yiqing Xu, Lingshuang Lv, Cunrui Huang, Tao Liu, Wenjun Ma. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 20.07.2023.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.

                History
                : 25 February 2023
                : 18 April 2023
                : 5 May 2023
                : 15 June 2023
                Categories
                Original Paper
                Original Paper

                drowning,exposure mixture,quantile g-computation,environmental epidemiology,meteorological factor

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