Trends in socioeconomic mortality inequalities in a southern European urban setting at the turn of the 21st century
Bibliographic record
Abstract
OBJECTIVE: To analyse trends in mortality inequalities by educational level for main causes of death among men and women in Barcelona, Spain, at the turn of the 21st century (1992-2003). METHODS: The population of reference was all Barcelona residents older than 19 years. All deaths between 1992-2003 were included. Educational level was obtained through record linkage between the mortality register and the municipal census of Barcelona city. Variables studied were age, sex, educational level, period of death (four periods of 3 years) and cause of death. Age-standardised mortality rates for each educational level, sex and period were calculated. Poisson regression models were fitted to obtain relative index of inequality (RII) for educational level, adjusted for age for the time-periods. RESULTS: RII for all causes of death was constant (around 1.5), but rate differences were higher in 1995-7 (715.6 per 100,000 in men and 352.8 in women) than in other periods and tended to decrease in men over the periods. Analysis of inequality trends by specific causes of death shows a stable trend for the majority of causes, with higher mortality among those with less education for all causes of death except lung cancer and breast cancer among women having RII below 1. CONCLUSIONS: Relative inequalities in total mortality by sex in Barcelona did not change during the 12 years studied, whereas absolute inequalities tended to decrease in men. Our study fills an important gap in southern Europe and Spanish literature on trends during this period.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".