Income-Related Health Inequalities in Canada and the United States: A Decomposition Analysis
Bibliographic record
Abstract
OBJECTIVES: We examined income-related inequalities in self-reported health in the United States and Canada and the extent to which they are associated with individual-level risk factors and health care system characteristics. METHODS: We estimated income inequalities with concentration indexes and curves derived from comparable survey data from the 2002 to 2003 Joint Canada-US Survey of Health. Inequalities were then decomposed by regression and decomposition analysis to distinguish the contributions of various factors. RESULTS: The distribution of income accounted for close to half of income-related health inequalities in both the United States and Canada. Health care system factors (e.g., unmet needs and health insurance status) and risk factors (e.g., physical inactivity and obesity) contributed more to income-related health inequalities in the United States than to those in Canada. CONCLUSIONS: Individual-level health risk factors and health care system characteristics have similar associations with health status in both countries, but they both are far more prevalent and much more concentrated among lower-income groups in the United States than in Canada.
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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 | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 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".