埃塞俄比亚南部Arba Minch纺织厂工人职业伤害患病率及相关因素:一项横断面研究

G. Gebremichael, A. Kumie, D. Ajema
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引用次数: 33

摘要

背景:职业事故和与工作有关的疾病每年造成230多万人死亡;全球每天有近86万人受伤。专门确定埃塞俄比亚纺织工人职业伤害程度和相关因素的研究有限。因此,本研究旨在评估Arba Minch纺织厂工人职业伤害的发生率及相关因素。方法:采用基于机构的横断面研究设计,于2015年3月3日至3月20日对433名Arba Minch纺织厂工人进行调查。使用阿姆哈拉语预测问卷和观察清单收集数据,由经过培训的6名数据收集者和2名主管进行数据收集。数据输入Epi Info version 7模板,使用SPSS version 21软件进行清理和分析。采用95% ci的粗比值比进行双变量logistic回归,探讨因变量与自变量之间的关系。最后,建立变量分层输入的多变量logistic回归模型,确定与职业伤害相关的独立因素。最终模型中p值<0.05的变量被认为具有统计学意义。结果:1年职业伤害发生率为31.4%。在社会人口学因素中,较低的月薪与较高的受伤几率相关[AOR (95% ci): 3.5(1.7, 7.2)]。调整各因素后,加班、健康安全培训和工作场所监管等与工作环境相关因素具有显著相关性[AOR (95% ci)分别为2.5(1.2,5.4)、0.4(0.17,0.97)和0.36(0.17,0.75)]。在行为因素中,个人防护用品使用和工作压力与伤害显著相关。结论:工厂工人职业伤害的显著发生率及其相关因素应引起工厂、从事职业健康与安全工作的政府和非政府组织的重视。
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The prevalence and associated factors of occupational injury among workers in Arba Minch textile factory, Southern Ethiopia: a cross sectional study
Background: Occupational accidents and work-related diseases cause over 2.3 million deaths annually; nearly 860,000 people are injured every day globally. There are limited studies that specifically determine the magnitude and associated factors of occupational injury in textile workers done in Ethiopia. Therefore, this study aimed to assess the prevalence and associated factors of occupational injury in Arba Minch Textile Factory workers. Methods: An institution based cross-sectional study design was implemented from March 3 to March 20, 2015 among selected 433 Arba Minch Textile Factory workers. An Amharic version pre-tested questionnaire and observation checklist were used to collect the data using trained six data collectors and two supervisors. Data was entered into Epi Info version 7 templates, cleaned and analyzed using SPSS Version 21 software. Bivariate logistic regression was used to explore the relation between the dependent and independent variables using crude odds ratio with 95% C.I. Finally, to determine the independent factors associated with occupational injury, multivariate logistic regression model with hierarchical entry of variables was done. Variables with P-value <0.05 in the final model were considered statistically significant. Results: The one year prevalence of occupational injury was 31.4%. Lower monthly salary was associated with higher odds of injury among the socio-demographic factors [AOR (95%C.I): 3.5(1.7, 7.2)]. Work environment related factors like extra hour duty, health and safety training and workplace supervision had significant association after adjusting all factors [AOR (95%C.I): 2.5 (1.2, 5.4), 0.4 (0.17, 0.97), and 0.36 (0.17, 0.75) respectively]. Among the behavioral factors, Personal Protective Equipment use and job stress showed association with injury significantly. Conclusion: The significant prevalence and associated factors of occupational injury in the factory workers should alert the factory, governmental and non-governmental organizations working on Occupational Health and Safety.
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