Christopher Edet Ekpenyong1, Udoinyang Clement Inyang2. 1. College of Health Sciences, University of Uyo, Akwa Ibom State, Nigeria. chrisvon200@yahoo.com. 2. Department of Orthopedics and Traumatology, University of Uyo, Akwa Ibom State, Nigeria.
Abstract
OBJECTIVE: This study assessed the association between worker characteristics, workplace factors, and work-related musculoskeletal disorders (WMSDs) in Nigeria's construction industry. METHODS: A cross-sectional site-by-site survey was conducted in 5 existing construction companies in Uyo, Nigeria. The subjects (n = 1200 males), aged 18-55 years, filled in the semistructured Nordic musculoskeletal questionnaire and the job content questionnaire on demographics, work and lifestyle characteristics, and workplace risk factors for WMSDs. RESULTS: The overall prevalence of WMSDs was 39.25%. Differences in age, race, weight, body mass index (BMI), education status, and employment status were significantly associated with the prevalence of WMSDs. Prevalence according to trade was as follows: ironworkers highest at 49% and administrative staff lowest at 31%. Ironworkers (55.7%), administrative staff (53.3%), and security staff (38.7%) scored higher on physical, psychosocial, and individual risk factors, respectively. Workplace factors with increased odds for WMSDs were psychological demands and mental workload, age, BMI, low work experience, low education status, awkward movement of head and arms, working against force or vibration, fast work pace, and race. CONCLUSION: The recorded high prevalence was multifactorial in etiology; hence, multi-intervention strategies are required.
OBJECTIVE: This study assessed the association between worker characteristics, workplace factors, and work-related musculoskeletal disorders (WMSDs) in Nigeria's construction industry. METHODS: A cross-sectional site-by-site survey was conducted in 5 existing construction companies in Uyo, Nigeria. The subjects (n = 1200 males), aged 18-55 years, filled in the semistructured Nordic musculoskeletal questionnaire and the job content questionnaire on demographics, work and lifestyle characteristics, and workplace risk factors for WMSDs. RESULTS: The overall prevalence of WMSDs was 39.25%. Differences in age, race, weight, body mass index (BMI), education status, and employment status were significantly associated with the prevalence of WMSDs. Prevalence according to trade was as follows: ironworkers highest at 49% and administrative staff lowest at 31%. Ironworkers (55.7%), administrative staff (53.3%), and security staff (38.7%) scored higher on physical, psychosocial, and individual risk factors, respectively. Workplace factors with increased odds for WMSDs were psychological demands and mental workload, age, BMI, low work experience, low education status, awkward movement of head and arms, working against force or vibration, fast work pace, and race. CONCLUSION: The recorded high prevalence was multifactorial in etiology; hence, multi-intervention strategies are required.
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