16.06 19.60 64.34 29.00 35.14 35.87 71.07 28.93 78.01 21.99 34.17 65.83 χ2 = 715.82(p < .01, df = 4) χ2 = 444.66(p < .01, df = 4) Male Smoking Never Ex Current Alcohol No Moderate Heavy Obesity No Obesity Exercise No Regular Sleeping Not Adequate 17.00 18.84 64.17 26.44 37.93 35.63 71.37 28.63 75.12 24.88 32.03 67.97 17.55 18.72 63.73 26.81 38.63 34.55 62.32 37.68 78.28/ 21.72 35.60 64.40 93.46 1.93 4.61 71.79 24.19 4.03 68.92 31.08 83.67 16.33 41.26 58.74 89.08 2.83 8.09 72.08 22.47 5.45 70.20 29.80 88.58 11.42 41.30 58.70 93.11 2.73 4.16 66.26 28.95 4.78 67.14 32.86 84.06 15.94 39.69 60.31 94.60 1.89 3.51 69.36 26.64 4.00 70.38 29.62 80.98 19.02 36.13 63.87 Kim and Ruger BMC Public Health 2010, 10:195 http://www.biomedcentral.com/1471-2458/10/195 Table 5: Age-adjusted Income Disparities in Behavioral Risk Factors by Gender (1998) All Lowest Low Medium High Highest Low Medium High Highest (%) (20%) (20%) (20%) (20%) (20%) (20%) (20%) (20%) (20%) (20%) Female All Lowest (%) 13.39 16.66 69.94 29.08 35.82 35.10 74.50 25.50 80.75 19.25 35.72 64.28 15.47 19.92 64.61 26.38 36.17 37.45 74.97 25.03 83.20 16.80 27.08 72.923 16.30 19.42 64.28 29.08 35.74 35.18 70.25 29.75 76.06 23.94 36.30 63.70 χ2 = 201.71(p < .01, df = 8) χ2 = 1197.22(p < .01, df = 8) χ2 = 102.83(p < .01, df = 8) χ2 = 371.57(p < .01, df = 8) χ2 = 208.62(p < .01, df = 4) χ2 = 493.38(p < .01, df = 4) χ2 = 458.29(p < .01, df = 4) χ2 = 138.00(p < .01, df = 4) have largely been limited to either self-reported health status or mortality. Using composite indices, one study found mortality risks for breast and cervical cancer was much greater in lower income groups [46]. Previous stud- ies have also typically targeted a single socioeconomic variable (e.g. education or income) and a common limita- tion is the inability to analyze behavioral risk factors alongside socioeconomic groupings in a national sample. One Korean study, for example, found that educational gradients in mortality were not consistent across causes of death and in some cases differed by sex [22] whereas another study found that a negative relationship between socioeconomic status and cardiovascular disease among men did not change by introducing cardiovascular risk factors [47]. Using three socioeconomic measures (edu- cation, occupation, and income) to capture differences in both self-reported acute and chronic disease and subjec- tive health status, this study found that differences in per- sonal characteristics and behavioral risk factors relate significantly to socioeconomic groups, but that the rela- tionship was not necessarily linear in fashion and there was no clear socioeconomic gradient with behavioral risk factors. The results did, however, confirm an association between socioeconomic position and morbidity [48,49] and self-reported health status. In 1998, health inequali- ties among socioeconomic groups were significant: age- adjusted mean differences in health outcomes were higher in self-reported health status than for morbidity, findings that are consistent with previous work in India [37]. An education gradient in morbidity was found for both men and women, but was especially steep among women; a weaker education gradient existed for self- reported health, even among women. The income gradi- ent for morbidity and self-reported health status was not as steep as for education, although a weak gradient did exist and was also more pronounced among women. Findings for occupational classification were also statisti- cally significant. Thus, in general, the differences among Page 10 of 19 96.77 1.56 1.67 69.04 28.18 2.79 73.26 26.74 83.28 16.72 40.96 59.04 95.56 1.13 3.31 39.56 25.80 4.64 73.22 26.78 80.80 19.20 38.04 61.96