Association between meteorological factors and elderly falls in injury surveillance from 2014 to 2018 in Guangzhou, China

Wei-Quan Lin; Lin Lin; Le-Xin Yuan; Le-Le Pan; Ting-Yuan Huang; Min-Ying Sun; Fa-Ju Qin; Chang Wang; Yao-Hui Li; Qin Zhou; Di Wu; Bo-Heng Liang; Guo-Zhen Lin; Hui Liu

doi:10.1016/j.heliyon.2022.e10863

Association between meteorological factors and elderly falls in injury surveillance from 2014 to 2018 in Guangzhou, China

Heliyon. 2022 Oct 4;8(10):e10863. doi: 10.1016/j.heliyon.2022.e10863. eCollection 2022 Oct.

Authors

Wei-Quan Lin^{1

2}, Lin Lin³, Le-Xin Yuan⁴, Le-Le Pan⁵, Ting-Yuan Huang⁶, Min-Ying Sun^{1

2}, Fa-Ju Qin⁶, Chang Wang¹, Yao-Hui Li¹, Qin Zhou¹, Di Wu^{2

6}, Bo-Heng Liang⁶, Guo-Zhen Lin¹, Hui Liu¹

Affiliations

¹ Department of Basic Public Health, Center for Disease Control and Prevention of Guangzhou, Guangzhou 510440, China.
² Institute of Public Health, Guangzhou Medical University & Guangzhou Center for Disease Control and Prevention, Guangzhou 510440, China.
³ School of Public Health, Guangzhou Medical University, Guangzhou 511436, China.
⁴ Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou 510440, China.
⁵ Department of Obstetrics and Gynecology, Women and Children's Hospital of Guangdong Province, Guangzhou 511400, China.
⁶ Department of Prevention and Control of Chronic Noncommunicable Diseases, Center for Disease Control and Prevention of Guangzhou, Guangzhou 510440, China.

Abstract

Introduction: With rapid increase in the aging population, falls injuries have become an important public health problem. However, limited data have been reported on the associations between meteorological factors and falls injuries in the elderly. This study assessed the epidemiology of falls injuries and explored this association in the elderly in Guangzhou, China.

Methods: Data on elderly falls injury cases and meteorological variables from 2014 to 2018 in Guangzhou were collected from the Guangzhou Injury Monitoring System and Guangzhou Meteorological Bureau, respectively. The monthly average data on falls injuries and meteorological factors were applied to the data analysis. These correlations were conducted using Pearson correlation analysis. A multiple linear regression model was used to estimate the effects of meteorological factors on falls injuries in the elderly in Guangzhou, China.

Results: Accounting for 49.41% of causes of elderly injury were falls in the Guangzhou Injury Monitoring System from 2014 to 2018, which occupied first place for five consecutive years. The monthly number of elderly falls injury cases was lowest in April and highest in December, and had a positive correlation with monthly mean wind speed (r = 0.187, P < 0.01) and a negative correlation with monthly atmospheric pressure (r = -0.142, P < 0.05). A multiple linear regression model was constructed (F = 10.176, P < 0.01), which explained 23.7% of the variances (R ² = 0.237). Monthly mean wind speed (β = 76.85, P < 0.01) and monthly mean atmospheric pressure (β = -3.162, P < 0.01) were independent factors affecting monthly elderly falls injuries.

Conclusions: Falls are the primary cause of injury among elderly people in Guangzhou, China. Meteorological factors are related to falls injuries in the elderly population. Decreasing activity during high wind and low atmospheric pressure weather may help reduce the number of elderly falls injury cases.

Keywords: Atmospheric pressure; Elderly falls injury; Wind speed.