Kim and Ruger BMC Public Health 2010, 10:195 http://www.biomedcentral.com/1471-2458/10/195 Table 6: Age-adjusted Income Disparities in Behavioral Risk Factors by Gender (2005) Male Smoking Never Ex Current Alcohol No Moderate Heavy Obesity No Obesity Exercise No Regular Sleeping Not Adequate 90.27 3.96 5.77 36.03 32.49 31.48 67.80 32.20 56.39 43.61 42.42 57.58 92.66 2.83 4.51 25.62 35.67 38.71 71.77 28.23 54.71 45.29 36.58 63.42 All Lowest Low Medium High Highest Low Medium High Highest All Lowest (%) (%) (20%) (20%) (20%) (20%) (20%) (20%) (20%) (20%) (20%) (20%) χ2 = 1178.43(p < .01, df = 8) χ2 = 2021.92(p < .01, df = 8) χ2 = 788.93(p < .01, df = 8) χ2 = 540.24(p < .01, df = 8) χ2 = 221.84(p < .01, df = 4) χ2 = 39.36(p < .01, df = 4) 95.72 1.97 2.31 30.16 37.10 32.69 72.25 27.75 48.55 51.45 38.21 61.79 89.02 2.95 8.03 26.08 35.25 38.67 70.06 29.94 50.71 49.29 40.26 59.74 Page 11 of 19 Female 87.93 3.87 8.20 25.08 31.05 43.87 70.11 29.89 54.99 45.01 50.11 49.89 80.52 8.03 11.44 20.68 39.26 40.06 68.37 31.63 61.91 38.09 44.01 55.99 11.57 13.05 75.37 11.69 15.84 72.48 71.52 28.48 58.16 41.84 48.87 51.13 11.97 28.05 59.98 17.32 19.04 63.68 60.44 39.56 60.94 39.06 42.74 57.26 13.09 25.46 61.45 12.12 11.73 76.15 64.66 35.34 58.43 41.57 39.42 60.58 14.66 34.05 51.30 9.09 13.24 77.67 58.07 41.93 45.40 54.60 40.78 59.22 13.85 34.43 51.73 15.24 13.26 71.50 62.87 37.13 50.65 49.35 40.74 59.26 10.46 24.87 64.67 15.52 18.38 66.10 58.41 41.59 55.19 44.81 35.25 64.75 χ2 = 1244.23(p < .01, df = 4) χ2 = 469.30(p < .01, df = 4) χ2 = 122.68(p < .01, df = 4) χ2 = 415.16(p < .01, df = 4) income groups were smaller than among education groups, suggesting that educational disparities delineate a different socioeconomic gradient from income dispari- ties. In 2005, the education gradient for morbidity decreased significantly among women, approaching the odds ratios for men. Gradients in self-reported health, by contrast, became steeper for both genders in all socioeco- nomic variables. The gender implications of this study are significant and require further study. Some studies have found that health inequalities among different income groups affect mortality in women less than in men [11,23,24], while others have found the opposite to be true [14,23,24]. In this study, however, inequalities in both morbidity and self-reported health status were greater, and the socio- economic gradient in education and income was steeper, among women than men, although the educational gradi- ent had decreased by roughly 40% by 2005. The steeper education and income gradients compared to the relative lack of gradient in occupation, especially among women, require explanation and further study. One explanation may be the more enduring effect of income and education disparities due to the more ingrained social hierarchies in these variables, which prevail from childhood. Occupa- tional disparities, by contrast, commence primarily in adulthood [3,30]. One recent study, for example, explained gender disparities in self-rated health as result- ing from Korea's social structure [50]. Another possible explanation points to the relatively lower percentage of Korean women who are employed and participate in regular or professional job markets, compared to their western counterparts and the relative insecurity of employment. This comports with research that analyzed data from the 2001 Korean Labor and Income Survey and found that precarious employment had a significant association with worse self-reported health for women as compared to men [51]. Another study that looked at the 2001 KNHANES found a rela- tionship between non-standard employment (less job security) and health that differed by gender: Korean