Trends in socioeconomic inequalities in premature and avoidable mortality in Canada, 1991–2016
Bibliographic record
Abstract
BACKGROUND: Recent epidemiologic findings suggest that socioeconomic inequalities in health may be widening over time. We examined trends in socioeconomic inequalities in premature and avoidable mortality in Canada. METHODS: We conducted a population-based repeated cohort study using the 1991, 1996, 2001, 2006 and 2011 Canadian Census Health and Environment Cohorts. We linked individual-level Census records for adults aged 25-74 years to register-based mortality data. We defined premature mortality as death before age 75 years. For each census cohort, we estimated age-standardized rates, risk differences and risk ratios for premature and avoidable mortality by level of household income and education. RESULTS: We identified 16 284 045 Census records. Between 1991 and 2016, premature mortality rates declined in all socioeconomic groups except for women without a high school diploma. Absolute income-related inequalities narrowed among men (from 2478 to 1915 deaths per 100 000) and widened among women (from 1008 to 1085 deaths per 100 000). Absolute education-related inequalities widened among men and women. Relative socioeconomic inequalities in premature mortality widened progressively over the study period. For example, the relative risk of premature mortality associated with the lowest income quintile increased from 2.10 (95% confidence interval [CI] 2.02-2.17) to 2.79 (95% CI 2.66-2.91) among men and from 1.72 (95% CI 1.63- 1.81) to 2.50 (95% CI 2.36-2.64) among women. Similar overall trends were observed for avoidable mortality. INTERPRETATION: Socioeconomically disadvantaged groups have not benefited equally from recent declines in premature and avoidable mortality in Canada. Efforts to reduce socioeconomic inequalities and associated patterns of disadvantage are necessary to prevent this pattern of widening health inequalities from persisting or worsening over time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".