ObjectivesThis study aims to investigate the relationship between the total injury experience rate and socioeconomic status based on the fourth Korea National Health and Nutrition Examination Survey (KNHANES).MethodsBy analyzing data from the fourth KNHANES conducted from 2007 to 2009, we estimated the injury experience rate according to socioeconomic status, including the occupational characteristics of 11,837 subjects. Setting the injury experience rate as a dependent variable and socioeconomic status as an independent variable, we performed logistic regression to calculate odds ratios reflecting the likelihood of injury according to socioeconomic status while controlling for relevant covariates.ResultsIn 797 subjects who had injury experience over the past 1 year, 290 persons (36.4%) had a work-related injury. As their income, home value, and educational status increased, their injury experiences decreased. Among occupational groups, the craft, equipment, machine operating, and assembling workers showed the highest rate (10.6%) of injury experience, and the lowest rate (5.7%) was found in the unemployed group. After adjusting for the confounding variables, the experience of injury was significantly related to several socioeconomic factors: high income (OR = 0.54; 95% CI: 0.34-0.86), high home value (OR = 0.65; 95% CI: 0.43-0.96), low education status (OR = 1.28; 95% CI: 1.07-1.52), and specific occupations such as craft, equipment, machine operating, and assembling work (OR = 1.99; 95% CI: 1.60-2.47), skilled agriculture, forestry and fishery work (OR = 1.43; 95% CI: 1.02-2.01), and simple labor (OR = 1.38; 95% CI: 1.04-1.82).ConclusionsThe injury experience rate differed depending on the socioeconomic status. A negative correlation was found between the injury experience rate and income, low home value, and education level. Moreover, a higher rate of injury experience was found in occupation groups and physical worker groups in comparison to the unemployed group and white-collar worker groups. This study would be useful in selecting appropriate priorities for injury management in Korea.

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BackgroundThe objective of this study was to measure malondialdehyde (MDA) and isoprostane which has been used as an index of lipid injury, 8-hydroxy-2′-deoxyguanosine (8-OHdG), which has been used as an index of DNA damage, and dialkyl-phosphate (DAP), which has been used to quantify pesticide exposure, and to investigate the relationship between pesticide exposure and oxidative stress.MethodsThis study was a cross-sectional study that evaluated 84 male farmers exposure to pesticide. In this study, 8-OHdG, isoprostane, and MDA were measured as oxidative stress indices, and dialkyl-phosphate (dimethylphosphate(DMP), diethylphosphate(DEP), dimethylthiophosphate(DMTP), and diethylthiophosphate (DETP)) excreted in the urine was also measured to evaluate pesticide exposure. A linear regression analysis was performed to investigate the relationship between pesticide metabolites, and oxidative stress biomarkers.ResultsA Correlation analysis was performed for pesticide exposure month (PEI), cumulative exposure index (CEI), and DAP as well as the concentration of the oxidative stress biomarkers. The PEM significantly and positively correlated to the levels of 8-OHdG, isoprostane, CEI, and DMP. CEI showed a correlation to 8-OHdG and PEM. DMP, DEP, and DETP showed a positive correlation to 8-OHdG, isoprostane, and MDA. A correlation analysis was adjusted some demographic characteristics, such as age, smoking, drinking, and exercise to determine the relationship between pesticide exposure and oxidative stress. The 8-OHdG, isoprostane, and MDA levels were significantly related to the DMP (ß = 0.320), DEP (ß = 0.390), and DETP (ß = 0.082); DMP (ß = 0.396), DEP (ß = 0.508), and DETP (ß = 0.504); and DMP (ß = 0.432), DEP (ß = 0.508), and DETP (ß = 0.329) levels, respectively.ConclusionsThe concentration between oxidative stress biomarkers and the pesticide metabolite were a positive correlation. Indicators of oxidative stress was associated with a pesticide metabolite DMP, DEP, and DETP. Therefore, Pesticide exposure and oxidative stress were relevant.