Temporal trends in educational inequalities in non-communicable diseases in Korea, 2007-2015

Abstract

BackgroundSocioeconomic inequalities in non-communicable diseases are known to exist; however there is a paucity of research describing the secular trends in these inequalities. To this end, the current study aims to explore the recent time trends in social patterning of selected non-communicable diseases among Korean adults between 2007 and 2015.MethodsUsing data from the Korea National Health and Nutrition Examination Survey (KNHANES), temporal trends in socioeconomic inequalities in diabetes, arthritis, asthma and depressive symptoms were assessed across three time points. Respondents were adults aged 20 years or over (N = 47,091, 20,180 men and 26,911 women). Socioeconomic circumstance was assessed based on highest level of educational attainment. We estimated prevalence ratios with 95% confidence intervals using Poisson regression with robust variance estimation (adjusted for age, smoking status, alcohol consumption, obesity, and physical activity) separately for men and women. The magnitude of the inequalities was computed using the relative index of inequality (RII).ResultsIn men, diabetes was not associated with educational attainment, while there was evidence of a negative association in women across surveys. Similar inverse associations were found with arthritis and depressive symptoms, but these associations were less clear for asthma. RII showed a non-significant increasing trend in educational disparities in depressive symptoms. Meanwhile, relative inequalities in diabetes, arthritis and asthma have narrowed. These trends were, in general, more pronounced in women.ConclusionsThe findings of this study indicate higher burden of selected NCDs among the lower educational groups, particularly among women. In addition, our results indicated some improvements in inequalities in diabetes, arthritis and asthma in recent years. These findings have important implications for understanding the causes of social patterning of NCDs and for the targeting of effective interventions.

Educational level as a predictor of the incidences of non-communicable diseases among middle-aged Japanese: a hazards-model analysis

“…Even after controlling for baseline SES and health behaviour, Cox proportional hazards model regressions showed that lower educational level was positively associated with the incidences of diabetes and stroke among both men and women, and with hypertension only among women. The observations of the inverse educational gradients were largely consistent with those of preceding studies (for diabetes [2, 10–12, 18], stroke [2, 5], and hypertension [2, 13]).…”

“…Thus, the RII indicates the ratio of the odds of each outcome at the lowest educational level (with the ridit score = 1) to those at the highest level (with the ridit score = 0). The RII has been often used to assess educational inequalities in health outcomes by preceding studies [4, 18, 24, 39].…”

“…[5, 11, 14]), or (repeated) cross-sectional analyses, which compared the prevalence of the diseases among respondents with different levels of educational attainment (e.g. [2, 4, 6, 18]). In comparison, hazards model analyses, which aimed to relate the time passage before the incidence of the disease to educational level, have been relatively scarce [12].…”

“…If the mediating effects are limited, policy interventions to improve SES or promote healthy lifestyle after the completion of education would not be expected to be effective in moderating educational inequalities in health during later life. Indeed, some studies have provided evidence for the limited mediating effects of SES and health behaviour on worsening frailty [23] and NCD prevalence [18].…”

Measurement of Socioeconomic Position in Research on Cardiovascular Health Disparities in Korea: A Systematic Review

“…Thirty-four studies (71.0%) employed a compositional approach to measuring SEP. Of those 34 studies, 17 depended on a single parameter and classified SES level according to a continuous or dichotomous distribution or standardized categori-zation. In these studies, the principal indicators were traditional measures of education [12][13][14][15][16][17][18], household income [19][20][21], and occupation [22,23]. Some studies specified the SES level according to proxy measures such as type of health insurance or health-insurance premium levels, which are determined in proportion to one's comprehensive living status [24][25][26][27][28].…”