How Much Might Universal Health Insurance Reduce Socioeconomic Disparities in Health? A Comparison of the US and Canada
Bibliographic record
Abstract
A strong association between lower socioeconomic status (SES) and worse health--the SES-health gradient--has been documented in many countries, but little work has compared the size of the gradient across countries.We compare the size of the income gradient in self-reported health in the US and Canada.We find that being below median income raises the likelihood that a middle aged person is in poor or fair health by about 15 percentage points in the U.S., compared to less than 8 percentage points in Canada.We also find that the 7 percentage point gradient difference between the two countries is reduced by about 4 percentage points after age 65, the age at which the virtually all U.S. citizens receive basic health insurance through Medicare.Income disparities in the probability that an individual lacks a usual source of care are also significantly larger in the US than in Canada before the age of 65, but about the same after 65.Our results are therefore consistent with the availability of universal health insurance in the U.S, or at least some other difference that occurs around the age of 65 in one country but not the other, narrowing SES differences in health between the US and Canada.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".