Risk of premature mortality due to smoking, alcohol use, obesity and physical activity varies by income: A population-based cohort study
Bibliographic record
Abstract
Background: Premature deaths are a strong population health indicator. There is a persistent and widening pattern of income inequities for premature mortality. We sought to understand the combined effect of health behaviours and income on premature mortality in a large population-based cohort. Methods: We analyzed a cohort of 121,197 adults in the 2005-2014 Canadian Community Health Surveys, linked to vital statistics data to ascertain deaths for up to 5 years following baseline. Information on household income quintile and mortality-relevant risk factors (smoking status, alcohol use, body mass index (BMI), and physical activity) was captured from the survey. Hazard ratios (HR) for combined income-risk factor groups were estimated using Cox proportional hazards models. Stratified Cox models were used to identify quintile-specific HR for each risk factor. Results: For each risk factor, HR of premature mortality was highest in the lowest-income, highest-risk group. Additionally, an income gradient was seen for premature mortality HR for every exposure level of each risk factor. In the stratified models, risk factor HRs did not vary meaningfully between income groups. All findings were consistent in the unadjusted and adjusted models. Conclusion: These findings highlight the need for targeted strategies to reduce health inequities and more careful attention to how policies and interventions are distributed at the population level. This includes targeting and tailoring resources to those in lower income groups who disproportionately experience premature mortality risk to prevent further widening health inequities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".