Trends in inequality in life expectancy at birth between 2004 and 2017 and projections for 2030 in Korea: multiyear cross-sectional differences by income from national health insurance data
Bibliographic record
Abstract
OBJECTIVES: The current status, time trends and future projections of a national health equity target are crucial elements of national health equity surveillance. This study examined time trends in inequality by income in life expectancy (LE) at birth between 2004 and 2017 and made future projections for the year 2030 in Korea. DESIGN: Using individually linked mortality data, time trends in inequality by income in LE at birth were examined. The LE projection was made with the Lee-Carter model. SETTING: Total Korean population and death data derived from the National Health Information Database of the National Health Insurance Service. PARTICIPANTS: A total of 685 773 157 subjects and 3 486 893 deaths between 2004 and 2017 were analysed. PRIMARY AND SECONDARY OUTCOME MEASURES: Annual LE and the magnitude of inequality by income in LE between 2004 and 2030. RESULTS: Inequality by income in LE among the total Korean population increased during the past 14 years, and this inequality is projected to become even greater in the future. In 2030, the magnitude of inequality by income in LE is projected to increase by 0.25 years in comparison to the magnitude in 2017. The increase in LE inequality was projected to be more prominent among women, with a projected 1.08 year increase in LE inequality between 2017 and 2030. CONCLUSION: Aggressive policies should be developed to close the increasing LE gap in Korea. LE inequalities by income should be considered as a measurable target for health equity in the process of establishing the National Health Plan 2030 in Korea.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".