Labour market income inequality and mortality in North American metropolitan areas
Bibliographic record
Abstract
OBJECTIVE: To investigate relations between labour market income inequality and mortality in North American metropolitan areas. METHODS: An ecological cross sectional study of relations between income inequality and working age (25-64 years) mortality in 53 Canadian (1991) and 282 US (1990) metropolitan areas using four measures of income inequality. Two labour market income concepts were used: labour market income for households with non-trivial attachment to the labour market and labour market income for all households, including those with zero and negative incomes. Relations were assessed with weighted and unweighted bivariate and multiple regression analyses. RESULTS: US metropolitan areas were more unequal than their Canadian counterparts, across inequality measures and income concepts. The association between labour market income inequality and working age mortality was robust in the US to both the inequality measure and income concept, but the association was inconsistent in Canada. Three of four inequality measures were significantly related to mortality in Canada when households with zero and negative incomes were included. In North American models, increases in earnings inequality were associated with hypothetical increases in working age mortality rates of between 23 and 33 deaths per 100 000, even after adjustment for median metropolitan incomes. CONCLUSIONS: This analysis of labour market inequality provides more evidence regarding the robust nature of the relation between income inequality and mortality in the US. It also provides a more refined understanding of the nature of the relation in Canada, pointing to the role of unemployment in generating Canadian metropolitan level health inequalities.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".