Are Income-based Differences in TKA Use and Outcomes Reduced in a Single-payer System? A Large-database Comparison of the United States and Canada
Bibliographic record
Abstract
BACKGROUND: Income-based differences in the use of and outcomes in TKA have been studied; however, it is not known if different healthcare systems affect this relationship. Although Canada's single-payer healthcare system is assumed to attenuate the wealth-based differences in TKA use observed in the United States, empirical cross-border comparisons are lacking. QUESTIONS/PURPOSES: (1) Does TKA use differ between Pennsylvania, USA, and Ontario, Canada? (2) Are income-based disparities in TKA use larger in Pennsylvania or Ontario? (3) Are TKA outcomes (90-day mortality, 90-day readmission, and 1-year revision rates) different between Pennsylvania and Ontario? (4) Are income-based disparities in TKA outcomes larger in Pennsylvania or Ontario? METHODS: We identified all patients hospitalized for primary TKA in this cross-border retrospective analysis, using administrative data for 2012 to 2018, and we found a total of 161,244 primary TKAs in Ontario and 208,016 TKAs in Pennsylvania. We used data from the Pennsylvania Health Care Cost Containment Council, Harrisburg, PA, USA, and the ICES (formally the Institute for Clinical Evaluative Sciences), Toronto, Ontario, Canada. We linked patient-level data to the respective census data to determine community-level income using ZIP Code or postal code of residence and stratified patients into neighborhood income quintiles. We compared TKA use (age and gender, standardized per 10,000 population per year) for patients residing in the highest-income versus the lowest-income quintile neighborhoods. Similarly secondary outcomes 90-day mortality, 90-day readmission, and 1-year revision rates were compared between the two regions and analyzed by income groups. RESULTS: TKA use was higher in Pennsylvania than in Ontario overall and for all income quintiles (lowest income quartile: 31 versus 18 procedures per 10,000 population per year; p < 0.001; highest income quartile: 38 versus 23 procedures per 10,000 population per year; p < 0.001). The relative difference in use between the highest-income and lowest-income quintile was larger in Ontario (28% higher) than in Pennsylvania (23% higher); p < 0.001. Patients receiving TKA in Pennsylvania were more likely to be readmitted within 90 days and were more likely to undergo revision within the first year than patients in Ontario, but there was no difference in mortality at 1 year. When comparing income groups, there were no differences between the countries in 90-day mortality, readmission, or 1-year revision rates (p > 0.05). CONCLUSION: These results suggest that universal health insurance through a single-payer may not reduce the income-based differences in TKA access that are known to exist in the United States. Future studies are needed determine if our results are consistent across other geographic regions and other surgical procedures. LEVEL OF EVIDENCE: Level III, therapeutic study.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".