Trends in Educational Inequalities in Drug Poisoning Mortality: United States, 1994–2010
Bibliographic record
Abstract
OBJECTIVES: We estimated trends in drug poisoning death rates by educational attainment and investigated educational inequalities in drug poisoning mortality by race, gender, and region. METHODS: We linked drug poisoning death counts from the National Vital Statistics System to population denominators from the Current Population Survey to estimate drug poisoning rates by gender, race, region, and educational attainment (less than high school degree, high school degree, some college, college degree) from 1994 to 2010. RESULTS: There were 372,485 drug poisoning deaths. Education-related inequalities increased during the study among all demographic groups and varied by region. Absolute increases in educational inequalities were higher among Whites than Blacks and men than women. The age-adjusted rate difference between White men with less than a high school degree increased from 8.7 per 100,000 in 1994 to 27.4 in 2010 (change = 18.7). Among Black men, the corresponding increases were 11.7 and 18.3, respectively (change = 6.6). CONCLUSIONS: We found strong educational patterning in drug poisoning rates, chiefly by region and race. Rates are highest and increasing the fastest among groups with less education.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".