Kim and Ruger BMC Public Health 2010, 10:195 http://www.biomedcentral.com/1471-2458/10/195 Self-reported Acute and Chronic Disease and Self-rated Health Outcome Measures We used morbidity as the self-reported acute and chronic measure and self-rated health condition as the subjective measure for this study. To measure morbidity, we recorded the respondent's presence or absence of illness (all acute and chronic diseases), a value of 0 or 1. Self- rated health status contained five categories: (1) very good; (2) good; (3) average; (4) poor; and (5) very poor. Independent Variables Measures of Socioeconomic Position Three variables identified socioeconomic position: household income, education, and occupation. For household income, we categorized individuals into income quintiles: (1) lowest; (2) low; (3) medium; (4) high; and (5) highest. The lowest quintile is the 20% of the population with the lowest household equivalent income in 1998. Each successive group is the fifth of the popula- tion with the next highest household equivalent income. To determine household equivalent income, which reflects total income and the number of adults and chil- dren in the household, we used the Organisation for Eco- nomic Co-operation and Development (OECD) Equivalence Scale, which equals (1 + 0.7(Na-1) + 0.5Nc), where Na = number of adults and Nc = number of chil- dren. This scale is a standard measure for international comparisons of poverty and income inequality [41]. We identified five education levels: (1) illiterate (no for- mal education); (2) elementary (1-6 years); (3) middle (7- 9 years); (4) high (10-12 years); and (5) college and above (>12 years). The occupation variables contained 13 cate- gories: (1) legislator, senior officials, and managers; (2) professionals; (3) technicians and associate professionals; (4) clerks; (5) service and sales workers; (6) skilled agri- cultural, forestry and fishery workers; (7) craft and related trades workers; (8) plant, machine operators, and assemblers; (9) manual laborers; (10) military personnel; (11) students; (12) housewives; and (13) unemployed. The occupation variables were collapsed into an occupa- tional index of six occupational classes (Class I-VI) (See Additional file 1). Other Sociodemographic Indicators and Behavioral Risk Factors Personal characteristics for the study included marital status, gender, and age. Marital status fell into three groups: never married, married, and single. Single also included widow or divorced. The behavioral risk factors included smoking (currently smoke, previously smoked, never smoked), alcohol consumption (heavy drinking: drink frequently; moderate drinking: drink once in a while; no drinking: hardly or never drink), weight (obese: BMI [weight (kg)/height (m2)] ≥ 25; not obese: BMI < 25), self-reported exercise, and sleep (adequate sleep: ≥ 7 Page 3 of 19 hours daily; inadequate sleep: < 7 hours daily). Previous studies note the use of a lower BMI cut-off point for defining overweight in Asians -- including Koreans -- compared with whites when identifying different risks [42-44]. However, the question on heavy drinking was excluded in the 2005 data. Thus, we used an alternative question which is close to its connotation: "how often do you drink more than 7 cups of Korean hard liquor (soju) or 5 cans of beer at a time?" The original answer scales ranged from no drinking (not applicable) to drinking almost everyday (no drinking: never drink; moderate drinking: drink 7 cups of soju or 5 cans of beers at one spot less than once a month; heavy drinking: drink 7 cups or soju or 5 cans of beers at one spot at least once a month). Statistical Methods We used Analysis of Covariance (ANCOVA) to assess behavioral risk factors by socioeconomic position and then the General Linear Model to compute age-adjusted mean values of health, controlling for respondents' per- sonal characteristics and behavioral risk factors. We ana- lyzed men and women separately for both morbidity and self-rated health. We estimated odds ratios, means, and 95% confidence intervals with multiple logistic regression methods, adjusting for age and other sociodemographic variables. The odds calculated from logistic regressions approximate the relative risk of ill health and morbidity respectively. The p-value for the threshold of significance was set at the 0.01 level. SAS [45] and Excel statistical software were used for all analyses. Sample weights were included for all analyses. Results Study Population and Sociodemographic Indicators Table 1 and Table 2 show the 1998 and 2005's distribution of sociodemographic indicators overall and by income quintile, including gender, age, occupation, education, and marital status. The sample included an equal distri- bution of males (48.2% in 1998, 50.6% in 2005) and females (51.9% in 1998, 49.4% in 2005). Genders were equally distributed across all five income quintiles in both years. In age, 37.0% of the sample was 30-39 and 10.6% was 60-64, with little percentage change in each age bracket from 1998 to 2005 (35.8% in age group 30-39 and 8.1% in age 60-64 in 2005). However, a significant change was found in the income distribution: in 1998, 43.6% of respondents aged 60-64 had the lowest income, while respondents aged 40-49 comprised the greatest percent- age (22.5%) of the highest income group in 2005. A small percentage of the overall population was illiterate (5.9% in 1998 and 2.1% in 2005), while a slightly higher percentage (18%) had a college or above education (18.1% in 1998 and 29.1% in 2005). In 1998 and 2005 respectively, the