Regional Disparities in Factors Associated with Subjective Health Among Older Adults in Aging and Super-Aged Areas of Korea: Nationwide Cross-Sectional Study (Preprint)
Bibliographic record
Abstract
Background: As South Korea transitions into a super-aged society, understanding regional disparities in subjective health among older adults is critical to addressing health inequalities and supporting healthy aging. Objective: This study aimed to compare determinants of subjective health between aging and super-aged areas in South Korea and identify region-specific characteristics contributing to disparities among older adults. Methods: A cross-sectional analysis was conducted using data from the Korea Community Health Survey (2020-2023), a nationwide population-based survey at the city, county, and district levels. Adults aged 65 years and older (n=179,571) were categorized into aging (n=19,759) or super-aged (n=159,782) areas based on regional aging rates. Propensity score matching was applied to adjust for demographic differences, yielding 18,574 matched participants in each group. Subjective health was assessed using a 5-point Likert scale. Ordinal logistic regression was used to examine associations between subjective health and various exposures, including demographic characteristics, health behaviors, physical and mental health status, and health literacy indicators such as nutrition label recognition and reading. Results: Older adults in super-aged areas reported poorer subjective health than those in aging regions. Physical activity and mental health were consistently associated with better subjective health in both regions, region-specific patterns were observed. In aging regions, nutrition label recognition was significantly associated with better subjective health, whereas in super-aged areas, nutrition label reading showed a stronger association. The negative impact of hypertension and diabetes on subjective health was more pronounced in super-aged areas. Conclusions: Although key determinants of subjective health were similar across regions, regional differences underscore the importance of tailored public health strategies. Interventions that strengthen health literacy and provide nutrition education focused on disease-related nutrients may help mitigate disparities and enhance subjective health among older adults in aging and super-aged areas.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".