| Literature DB >> 18437006 |
Hyeongsu Kim1, Jaewook Choi, Hwayoung Rim, Sounghoon Chang, Kunsei Lee.
Abstract
The purpose of this study was to identify factors that could be used as standardized criteria for evaluating occupational diseases in initial assessments or requests for examination. Using 100 administrative litigation cases on the work-relatedness of cerebrovascular diseases (CVDs) by the Seoul Branch of the Korea Labor Welfare Corporation (KLWC) from 1997 to 2002, we estimated the relationship between the investigated variables and designation of the work-relatedness of the CVD. As for the age, the odds ratio of the acceptance rate of a case as work-related in subjects over 60 yr of age was 0.08 (95% CI, 0.01-0.75), which was compared to subjects under 30 yr of age. Regarding working hours, the odds ratio of the acceptance rate of a case as work-related in CVDs in those over 56 hr was 9.50 (95% CI, 1.92-47.10) when compared to those less than 56 hr. As for the benefit type, the odds ratio of the acceptance rate of a case as work-related in medical benefits was 5.74 (95% CI, 1.29-25.54), compared to survivor benefits. As for the criteria for defining situations as work overload, the odds ratio of the acceptance rate of a case as work-related in injured workers was 12.06 (95% CI, 3.12-46.62), compared to that in non-injured workers. Our findings show that the criteria for defining situations of work overload played an important role in assessing the work-relatedness of CVDs in administrative litigation, and it is necessary to make the scientific evidence on judgement of work-relatedness on overwork.Entities:
Mesh:
Year: 2008 PMID: 18437006 PMCID: PMC2526438 DOI: 10.3346/jkms.2008.23.2.236
Source DB: PubMed Journal: J Korean Med Sci ISSN: 1011-8934 Impact factor: 2.153
Comparison between work-relatedness and characteristics of individuals
Comparison between work-relatedness and job characteristics
Comparison between work-relatedness and other characteristics
Odds ratios of approval rate according to independent variables using multiple logistic regression