Relationship between socioeconomic status and type 2 diabetes: results from Korea National Health and Nutrition Examination Survey (KNHANES) 2010–2012
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between socioeconomic status (SES) and type 2 diabetes using the Korea National Health and Nutrition Examination Survey (KNHANES) 2010-2012. DESIGN: A pooled sample cross-sectional study. SETTING: A nationally representative population survey data. PARTICIPANTS: A total of 14,330 individuals who participated in the KNHANES 2010-2012 were included in our analysis. PRIMARY OUTCOME: Prevalence of type 2 diabetes. RESULTS: The relationship between SES and type 2 diabetes was assessed using logistic regression after adjusting for covariates including age, gender, marital status, region, body mass index, physical activity, smoking and high-risk drinking behaviour. After adjustment for covariates, our results indicated that individuals with the lowest income were more likely to have type 2 diabetes than those with the highest income (OR 1.35; 95% CI 1.08 to 1.72). In addition, lower educational attainment was an independent factor for a higher prevalence of type 2 diabetes in Korea. CONCLUSIONS: These findings suggest the need for developing a health policy to ameliorate socioeconomic inequalities, in particular income and education-related disparities in type 2 diabetes, along with risk factors at the individual level. In addition, future investigations of type 2 diabetes among Koreans should pay more attention to the social determinants of diabetes in order to understand the various causes of the condition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".