Predictors and Management of Hazards in the Southwestern Nigerian Sawmilling Industrial Environment

The study examined the relationship between personal characteristics of sawmill workers and the hazard they experienced it also determined the relationship between the use of appropriate signage and disaster history as well as the extent and factors in insurance patronage by sawmilling industries in response to possible disaster occurrence. A multi-stage sampling procedure was employed to select 54 small-scale sawmilling industries from 4 states in Southwestern Nigeria from which 256 workers were selected using disproportionate stratified random sampling. Two instruments were used to elicit information on personal characteristics and hazards experienced in the course of the work and efforts that have been put in place by both the workers and their employers at preventing and minimizing such hazards. The data collected were analyzed using descriptive and inferential statistics such as percentages, chi-square and binary logistic regression model. The odds of experiencing hazard were higher among respondents who were 30-39 years (OR = 1.10, p>0.05), 40-49 years (OR = 1.21; p>0.05) and ≥50 years (OR = 4.68; p>0.05) relative to those who were below 30 years of age. Respondents whose work designation was log/plank related (OR =0.43; p>0.05) and management/administration (OR = 0.57; p > 0.05) were significantly less likely to experience hazard relative to the ones with machine related designation while that of respondents with waste/ dust activities are more likely to experience hazards. Respondents with primary education (OR = 0.05; p< 0.05), junior secondary education (OR = 0.07; P < 0.05), tertiary education (OR = 0.29; P > 0.05), were less likely to experience hazards, compared with those who did not have any education. Hazards were found to be less with respondents with more than 5 years of experience on the job. The use of „Restricted area‟ and „Maximum working load‟ signage were the significant predictors of disaster occurrence. Only 37% of the industries and 30% of the workers were insured. Significant relationship was not found between size of industries (χ 2 (1,1) =0.554,P>0.05) and insurance patronage as well as each of work designation (χ 2 ( 3,1) = 0.200,P>0.05) , income (χ 2 (4,1) = 0.453,P> 0.05) and insurance of workers.