An evolution of socioeconomic inequalities in self-rated health in Korea: Evidence from Korea National Health and Nutrition Examination Survey (KNHANES) 1998–2018
Bibliographic record
Abstract
Reducing socioeconomic inequalities in health has become an important health policy agenda. This study aimed to measure socioeconomic inequalities in health in Korea over the past two decades and identify the contributing factors to the observed inequalities. Data from the Korea National Health and Nutrition Examination Survey (KNHANES) from 1998 to 2016/2018 were utilized. The concentration index (CI) was calculated to measure health inequalities, and decomposition analysis was applied to identify and quantify the contributing factors to the observed inequalities in health. The results indicated that health inequalities exist, suggesting that poor health was consistently more concentrated among Korean adults with lower income (1998: -0.154; 2016/2018: -0.152). Gender-stratified analyses also showed that poor health was more concentrated in lower income women and men, with the degree of inequalities slightly more pronounced among women. The decomposition approach revealed that income and educational attainment were the largest contributors to the observed health inequalities as higher income and education associated with better self-rated health. These findings suggest the importance of considering socioeconomic determinants, such as income and education, in efforts to tackling health inequalities, particularly considering that self-rated health is a predictor of future mortality and morbidity. Furthermore, it is essential to implement more egalitarian social, labour market, and health policies in order to eliminate the existing socioeconomic inequalities in health in Korea.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".