Clustering of Korean workers according to occupational risks : using the third Korean working conditions survey (2011)

Abstract

Objective: Health problems caused by chronic exposure to various risk factors recently have been recognized as problematic in the occupational setting. To address this concern, we performed cluster analysis to provide a comprehensive examination of occupational risk factors to which individuals are exposed and to identify occupational characteristics.Methods: The current study analyzed data from the 3rd Korean Working Conditions Survey (KWCS) that were collected by the Korea Occupational Safety & Health Agency in 2011. The KWCS was conducted between June 1 and November 30, 2011. A total of 50,032 workers were surveyed (male: 28,640 (57.2%); female: 21,392 (42.8%)). Cluster analysis was performed on occupational risk factors such as chemicophysical factors, musculoskeletal factors, job stress, working hours, and age of workers. Cluster analysis was also performed by gender.Results: Workers were divided into 6 clusters: “old age,” “high stress,” “overwork,” “low risk,” “intermediate risk,” and “high risk.” Although there were slight gender differences in the degree of risk factors, clusters could be defined as above. The “old age” group had short working hours and low occupational risk factors due to age, but they also had high overall occupational illness and physical illness. Workers belonging to this cluster (52.8%) were employed in agriculture, fishery, and forestry. The “high stress” group had intermediate levels of other occupational risk factors, and they had intermediate levels of occupational illness. The “overwork” group had intermediate levels of other occupational risk factors. Of the self-employed without employees, 31.3% belonged to this cluster, whereas 33.8% of unpaid family workers belonged to the cluster. Occupational illness in this cluster was intermediate, but mental illness was high. The “high risk” group had high levels of occupational risk factors and high occupational illness. Blue-collar workers mainly belong to this cluster. With the “low risk” group as the reference, the odds ratio (OR) for occupational illness was 3.4 (95% CI, 3.15–3.62) in this cluster.Conclusions: A comprehensive plan and appropriate measures are required to prevent occupational illness. Risk factors have been examined according to occupational category and business type in the past. However, this study revealed that workers under the same occupational category can be classified into different clusters depending on the risk factors to which they are exposed. Therefore, such characteristics must be taken into consideration when implementing policies for the prevention of occupational illness.