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Occup Med (Lond) ; 67(6): 442-447, 2017 Aug 01.
Article in English | MEDLINE | ID: mdl-28898967

ABSTRACT

BACKGROUND: The mining industry is associated with high levels of accidents, injuries and illnesses. Lost-time injuries are useful measures of health and safety in mines, and the effectiveness of its safety programmes. AIMS: To identify the type of lost-time injuries in the US mining workforce and to examine predictors of these occupational injuries. METHODS: Primary papers on lost-time injuries in the US mining sector were identified through a literature search in eight health, geology and mining databases, using a systematic review protocol tailored to each database. The Critical Appraisal Skills Programme (CASP), Framework of Quality Assurance for Administrative Data Source and the Cochrane Collaboration 'Risk of bias' assessment tools were used to assess study quality. RESULTS: A total of 1736 articles were retrieved before duplicates were removed. Fifteen articles were ultimately included with a CASP mean score of 6.33 (SD 0.62) out of 10. Predictors of lost-time injuries included slips and falls, electric injuries, use of mining equipment, working in underground mining, worker's age and occupational experience. CONCLUSIONS: This is the first systematic review of lost-time injuries in the US mining sector. The results support the need for further research on factors that contribute to workplace lost-time injuries as there is limited literature on the topic. Safety analytics should also be applied to uncover new trends and predict the likelihood of future incidents before they occur. New insights will allow employers to prevent injuries and foster a safer workplace environment by implementing successful occupational health and safety programmes.


Subject(s)
Miners/statistics & numerical data , Occupational Injuries/epidemiology , Accidental Falls/statistics & numerical data , Accidents, Occupational/statistics & numerical data , Age Factors , Electric Injuries/epidemiology , Humans , Mining/statistics & numerical data , United States/epidemiology
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