Sex differences in mortality: results from a population-based study of 12 longitudinal cohorts
Bibliographic record
Abstract
BACKGROUND: Women generally have longer life expectancy than men but have higher levels of disability and morbidity. Few studies have identified factors that explain higher mortality in men. The aim of this study was to identify potential factors contributing to sex differences in mortality at older age and to investigate variation across countries. METHODS: This study included participants age ≥ 50 yr from 28 countries in 12 cohort studies of the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) consortium. Using a 2-step individual participant data meta-analysis framework, we applied Cox proportional hazards modelling to investigate the association between sex and mortality across different countries. We included socioeconomic (education, wealth), lifestyle (smoking, alcohol consumption), social (marital status, living alone) and health factors (cardiovascular disease, diabetes, mental disorders) as covariates or interaction terms with sex to test whether these factors contributed to the mortality gap between men and women. RESULTS: = 71.5%, HR range 1.1-2.4). Only smoking and cardiovascular diseases substantially attenuated the effect size (by about 22%). INTERPRETATION: Lifestyle and health factors may partially account for excess mortality in men compared with women, but residual variation remains unaccounted for. Variation in the effect sizes across countries may indicate contextual factors contributing to gender inequality in specific settings.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".