Kim and Ruger BMC Public Health 2010, 10:195 http://www.biomedcentral.com/1471-2458/10/195 Table 8: Age-adjusted Occupational Disparities in Behavioral Risk Factors by Gender (2005) Male Female All (%) Class I Class II Class III Class IV Class V Class VI All (%) Class I Class II Class III Class IV Class V Class VI 13.30 33.97 52.73 7.21 13.43 79.36 58.08 41.92 43.14 56.86 41.00 59.00 11.46 27.51 61.04 11.82 13.06 45.12 60.31 39.69 58.22 41.78 35.67 64.33 11.49 26.70 61.81 7.20 13.89 78.92 59.41 40.59 52.86 47.14 40.36 59.64 15.87 33.14 50.99 16.84 12.76 70.39 61.04 38.96 67.55 32.45 33.47 66.53 10.79 22.51 66.90 9.05 10.63 80.33 70.25 29.75 68.22 31.78 49.77 50.23 90.27 3.96 5.77 36.03 32.49 31.48 67.80 32.20 56.39 43.61 42.42 57.58 92.59 4.76 2.64 22.05 37.99 39.97 83.37 16.63 43.71 56.29 35.34 64.66 93.13 2.25 4.63 21.90 37.96 40.14 85.62 14.38 51.18 48.82 42.10 57.90 88.04 4.04 7.92 23.23 31.47 45.29 63.62 36.38 52.96 47.04 48.09 51.91 88.71 3.46 7.84 20.80 33.56 45.64 73.60 26.40 66.97 33.03 44.79 55.21 χ2 = 895.44(p < .01, df = 10) χ2 = 265.51(p < .01, df = 10) χ2 = 120.68(p < .01, df = 5) χ2 = 911.05(p < .01, df = 5) χ2 = 2430.68(p < .01, df = 5) χ2 = 1065.05(p < .01, df = 5) χ2 = 2722.32(p < .01, df = 10) χ2 = 464.86(p < .01, df = 10) Page 13 of 19 98.45 0.00 1.55 27.68 47.38 24.93 71.13 28.87 70.40 29.60 37.34 62.66 85.54 3.19 11.26 26.15 34.64 39.21 65.26 34.74 64.21 35.79 46.48 53.52 Smoking Never Ex Current Alcohol No Moderate Heavy Obesity No Obesity Exercise No Regular Sleeping No Adequate 13.85 34.43 51.73 15.24 13.26 71.50 62.87 37.13 50.65 49.35 40.74 59.26 32.82 35.46 31.72 15.90 16.99 67.11 60.84 39.52 31.75 68.25 50.34 49.66 χ2 = 564.98(p < .01, df = 5) χ2 = 190.18(p < .01, df = 5) conditions, making it possible for health disparities to be larger in females than in males. Gender-specific health differentials may possibly be narrowed by health promotional activities specifically targeted towards women in lower socioeconomic classes. Behavioral risk factors did not entirely account for differ- ences in self-reported health and morbidity among socio- economic position. This result suggests that choices regarding cigarette smoking, drinking, body weight, exer- cise, and adequate sleeping may only partly change socio- economic health inequalities in Asia, although significant differences are found in terms of gender. This study has some limitations. First, lack of response and missing values for some variables might cause bias, though previous studies of the KNHANES have not detected bias [54,55]. Second, self-reports of household income could be prone to error, despite corroboration by other household members. Third, the personal interviews for the 1998 data took place at the end of 1998, directly after the Asian economic crisis. The crisis might have affected the morbidity rates in both men and women. In addition, some respondents reporting low income levels might previously have earned more and enjoyed better health care. Conclusions This study explored varying impacts of education, occu- pation, and income on health inequalities in men and women, separately. The results are consistent with those of the Whitehall studies of British civil servants, where social gradients in morbidity existed from the bottom to the top of the hierarchy [7,8,56]. An interesting finding from this study was that socioeconomic differences in health appeared to be higher among women than men. After adjusting for the respondents' behavioral risk fac- tors, the mean differences among socioeconomic classes for both male and female morbidity increased. These results demonstrate that the magnitude of health inequal- ities can differ by socioeconomic variables and by per- sonal and behavioral risk factors. Education and household income showed the most significant effect on differences among levels, while occupational groups