Predictive Modelling of Workplace Hazards and Accident Probabilities in Ghana’s Mining Sector

Abstract

The mining industry in Ghana, despite its economic significance, continues to grapple with occupational health and safety (OHS) issues, which include but are not limited to high accident rates and a largely reactive approach to safety. In this study, current practices in OHS management are assessed and a model is developed that reflects the interdependencies and associations between workplace hazards, accidents, health effects and preventative actions in a probabilistic fashion. A quantitative analytical design was employed and a sample of 298 workers, safety officers and supervisors from mines were surveyed. The data were analysed using Principal Component Analysis (PCA) to derive latent OHS factors, K-modes clustering and Hierarchical Clustering to classify worker safety profiles, and a Bayesian Network model was employed to investigate probabilistic dependencies. Three major OHS dimensions emerged from PCA: perceived adequacy of safety measures, formal training exposure, and safety resources. Three distinct worker profiles were identified, suggesting that the provision of physical safety equipment does not necessarily reflect perceived operational safety. Moreover, the Bayesian Network model indicated a conditional dependency between workers’ reported health issues and formal accident reporting, and that high hazard environments more than double the probability of an accident occurring (from 0.216 to 0.453). The results indicate that the industry is now operating in an incident-based manner. In order to mitigate the likelihood of accidents, management needs to move towards anticipatory safety management systems, which involve proactive health monitoring, equipment maintenance, and implementation of safety policies in practice.