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Tian T, Lin X, Huang T, Zhang K, Shi C, Wang P, Chen S, Guo T, Li Z, Qin P, Liang B, Zhang W, Hao Y. The risk of injuries during work and its association with precipitation: New insight from a sentinel-based surveillance and a case-crossover design. Front Public Health 2023; 11:1117948. [PMID: 36935708 PMCID: PMC10018157 DOI: 10.3389/fpubh.2023.1117948] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/09/2023] [Indexed: 03/06/2023] Open
Abstract
Background Injuries during work are often exogenous and can be easily influenced by environmental factors, especially weather conditions. Precipitation, a crucial weather factor, has been linked to unintentional injuries, yet evidence of its effect on work-related injuries is limited. Therefore, we aimed to clarify the impact of precipitation on injuries during work as well as its variation across numerous vulnerability features. Methods Records on the work-related injury during 2016-2020 were obtained from four sentinel hospitals in Guangzhou, China, and were matched with the daily weather data during the same period. We applied a time-stratified case-crossover design followed by a conditional logistic regression to evaluate the association between precipitation and work-related injuries. Covariates included wind speed, sunlight, temperature, SO 2, NO 2, and PM 2.5. Results were also stratified by multiple factors to identify the most vulnerable subgroups. Results Daily precipitation was a positive predictor of work-related injuries, with each 10 mm increase in precipitation being associated with an increase of 1.57% in the rate of injuries on the same day and 1.47-1.14% increase of injuries on subsequent 3 days. The results revealed that precipitation had a higher effect on work-related injuries in winter (4.92%; 95%CI: 1.77-8.17%). The elderly (2.07%; 95%CI: 0.64-3.51%), male (1.81%; 95%CI: 0.96-2.66%) workers or those with lower educational levels (2.58%; 95%CI: 1.59-3.54%) were more likely to suffer from injuries on rainy days. There was a higher risk for work-related injuries caused by falls (2.63%; 95%CI: 0.78-4.52%) or the use of glass products (1.75%; 95%CI: 0.49-3.02%) on rainy days. Conclusions Precipitation was a prominent risk factor for work-related injury, and its adverse effect might endure for 3 days. Certain sub-groups of workers were more vulnerable to injuries in the rain.
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Affiliation(s)
- Tian Tian
- Guangdong Key Laboratory of Medicine, Department of Medical Statistics, Center for Health Information Research, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xiao Lin
- Guangdong Key Laboratory of Medicine, Department of Medical Statistics, Center for Health Information Research, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Tingyuan Huang
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Kai Zhang
- Department of Environmental Health Sciences, University at Albany, The State University of New York, Rensselaer, NY, United States
| | - Congxing Shi
- Guangdong Key Laboratory of Medicine, Department of Medical Statistics, Center for Health Information Research, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Pengyu Wang
- Guangdong Key Laboratory of Medicine, Department of Medical Statistics, Center for Health Information Research, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Shimin Chen
- Guangdong Key Laboratory of Medicine, Department of Medical Statistics, Center for Health Information Research, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Tong Guo
- Guangdong Key Laboratory of Medicine, Department of Medical Statistics, Center for Health Information Research, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Zhiqiang Li
- Guangdong Key Laboratory of Medicine, Department of Medical Statistics, Center for Health Information Research, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Pengzhe Qin
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Boheng Liang
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China
- *Correspondence: Boheng Liang
| | - Wangjian Zhang
- Guangdong Key Laboratory of Medicine, Department of Medical Statistics, Center for Health Information Research, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
- Wangjian Zhang
| | - Yuantao Hao
- Guangdong Key Laboratory of Medicine, Department of Medical Statistics, Center for Health Information Research, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
- Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, Guangdong, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing, China
- Yuantao Hao
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