Understanding the Influence of Single Payer Health Insurance on Socioeconomic Disparities in Total Hip Arthroplasty (THA) Utilization: A Transnational Analysis
Bibliographic record
Abstract
OBJECTIVE: Access to care varies between countries. It is theorized that income-based disparities in access may be reduced in countries with universal health insurance relative to the United States, but data are currently limited. We hypothesized that income-based differences in total hip arthroplasty (THA) utilization and outcomes would be larger in the United States than in Canada. METHODS: We retrospectively compared all patients undergoing THA from 2012 to 2018 in Pennsylvania, the United States, and Ontario, Canada. We compared age-standardized and sex-standardized per-capita THA utilization in the United States and Canada overall and across different income strata, where income strata were defined by neighborhood income quintile. We also examined income-based differences in rates of 1-year revision, 90-day mortality, and 90-day readmission. RESULTS: Overall THA utilization per 10,000 people per year was higher across all income groups in Pennsylvania compared with Ontario (15.1 versus 8.8, P < 0.001 in lowest-income quintile; 21.4 versus 12.6, P < 0.001 in highest-income quintile). Income-based differences in utilization in the highest-income vs lowest-income quintile groups were greater in Ontario (43.2%) than Pennsylvania (41.7%). The adjusted odds for the lowest-income group compared with the highest-income group of 1-year revision were greater in Ontario compared with Pennsylvania ( P = 0.03), and risk of 90-day mortality and 90-day readmission was similar between the regions. CONCLUSION: Income-based differences in THA utilization were more notable in Ontario than in Pennsylvania. In addition, patients in low-income communities in Ontario were at equal or greater risk relative to high-income community patients for adverse outcomes compared with patients in Pennsylvania. Income-based disparities in THA utilization and outcomes were smaller in the United States than in Canada, in contrast to what might be expected. LEVEL OF EVIDENCE: III.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".