Consistency between education reported in health survey and recorded in death certificate
Bibliographic record
Abstract
BACKGROUND: Education level is one indicator of socioeconomic position which, in several countries including South Korea, is provided though death certificate data. Its validity determines the usefulness of death certificate data for exploring the association between socioeconomic position and mortality. This study was to compare education recorded on the death certificate with that reported before death in a nationally representative cohort of participants in the National Health and Nutrition Examination Survey (NHANES). METHODS: The 1998/2001 NHANES data contained unique 13-digit personal identification numbers that were individually linked to death certificate data from the Korean National Statistical Office. Duration of mortality follow-up was 7.1 years. The data from 513 deaths were used to determine sensitivity and specificity of education in death certificate and estimate agreement rates of education level between NHANES data and death certificate data. Odds ratios for agreement in education were also estimated. Covariates considered in the analyses were gender, age, duration between NHANES and death, and cause of death. RESULTS: The proportion of deaths without recorded education in death certificate was very low (0.2%). A total of 29.4% discordant pairs were found. Sensitivity and specificity for college or higher education were 0.84 (95% confidence interval 0.71-0.97) and 0.99 (0.98-1.00). However, sensitivity was poor for middle school education. The overall agreement rate was 70.7% (66.8%-74.6%) when education was categorized into five groups and increased up to 88.9% (86.2%-91.6%) when three education categories were used. The magnitude of validity and reliability for education did not generally vary with age, duration between health survey and death, and cause of death. However, a significantly smaller likelihood of agreement was found for middle and elementary school education after adjusting for covariates. CONCLUSION: Low percentage of missing information on education in South Korean death certificate data could provide a great potential to monitor mortality inequalities. A more collapsed categorization in education would be recommended when a more definitive conclusion on educational mortality inequality is required.
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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 | 0.019 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".