Impact of economic crisis on cause-specific mortality in South Korea
Bibliographic record
Abstract
BACKGROUND: Economic changes can be powerful determinants of health. In the late 1990s, South Korea experienced a steep economic decline. This study examines whether the massive economic changes affected trends in all-cause and cause-specific mortality in South Korea. METHOD: Mid-year population estimates of 5 year age groups (denominators) and death certificate data (numerators) from the National Statistical Office of Korea were used to compute cause-specific age-standardized mortality rates before and after the economic crisis. RESULTS: All-cause mortality continued to decrease in both sexes and all age groups during the crisis. Cerebrovascular accidents, stomach cancer, and liver disease contributed most to this decline. A remarkable decrease in transport accident mortality rates was also observed. The most salient increase in mortality was suicidal death. Mortality from homicide, pneumonia, and alcohol dependence increased during the economic crisis, but these accounted for a small proportion of total mortality. CONCLUSIONS: Short-term mortality effects of the South Korean economic crisis were relatively small. It appears that any short-term effects of the economic decline were overwhelmed by the momentum of large declines in causes of death such as stroke, stomach cancer, and liver disease, which are probably related to exposures with much longer aetiological periods. However, this study focused on rather immediate mortality effects and follow-up studies are needed to elucidate any longer-term health effects of the South Korean economic crisis.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".