Universal Health Insurance and Equity in Primary Care and Specialist Office Visits: A Population-Based Study
Bibliographic record
Abstract
PURPOSE: Universal coverage of physician services should serve to reduce socioeconomic disparities in care, but the degree to which a reduction occurs is unclear. We examined equity in use of physician services in Ontario, Canada, after controlling for health status using both self-reported and diagnosis-based measures. METHODS: Ontario respondents to the 2000-2001 Canadian Community Health Survey (CCHS) were linked with physician claim files in 2002-2003 and 2003-2004. Educational attainment and income were based on self-report. The CCHS was used for self-reported health status and Johns Hopkins Adjusted Clinical Groups was used for diagnosis-based health status. RESULTS: After adjustment, higher education was not associated with at least 1 primary care visit (odds ratio [OR] = 1.05; 95% confidence interval [CI], 0.87-1.24), but it was inversely associated with frequent visits (OR = 0.77; 95% CI, 0.65-0.88). Higher education was directly associated with at least 1 specialist visit (OR = 1.20; 95% CI, 1.07-1.34), with frequent specialist visits (OR = 1.21; 95% CI, 1.03-1.39), and with bypassing primary care to reach specialists (OR = 1.23, 95% CI 1.02-1.44). The largest inequities by education were found for dermatology and ophthalmology. Income was not independently associated with inequities in physician contact or frequency of visits. CONCLUSIONS: After adjusting for health status, we found equity in contact with primary care for educational attainment but inequity in specialist contact, frequent visits, and bypassing primary care. In this setting, universal health insurance appears to be successful in achieving income equity in physician visits. This strategy alone does not eliminate education-related gradients in specialist care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".