Disparities in Utilization Rates of Total Knee and Hip Arthroplasty Among Racially Visible Populations in Canada: A Retrospective Cohort Analysis
Bibliographic record
Abstract
BACKGROUND: Published evidence on total hip arthroplasty (THA) and knee arthroplasty (TKA) among racially visible (RV) populations suggests inequities in utilization rates. The study's aim was to compare THA and TKA utilization rates in RV populations to the general population (non-RV). Additionally, we compared rates in populations of African descent (AD) to non-African descent (non-AD) population. METHODS: The study population was identified from the 2016 Canadian long-form census, and minority status was self-reported. Statistics Canada and Canadian Institute of Health Information used personal information from multiple sources to construct a unique identifier, enabling accurate linkage across data sources. Census data captured key covariates including age, sex, and income. Procedures of THA and TKA were identified from the Discharge Abstract Database and National Ambulatory Care Reporting System. Multivariate logistic regression was employed in comparing utilization rates between groups, controlling for confounders including age, sex, and income. Chi-square statistics were used to test for statistically significant differences at a 95% confidence level. RESULTS: The observed utilization rates for TKA and THA were lower for RVs and ADs compared to non-RV and non-AD populations, respectively. Multivariate analyses revealed an adjusted odds ratio (OR) of RV individuals undergoing THA of 0.22 (P < 0.001) compared to non-RV individuals, with a lower probability for RVs. Similarly, RV individuals had a statistically lower probability of undergoing TKA compared to non-RV individuals (OR 0.72, P < 0.001). The probability of AD individuals undergoing THA (OR 0.46, P < 0.001) and TKA (OR 0.73, P < 0.0001) after adjusting for confounders was lower compared to non-AD populations. CONCLUSIONS: Disparities in THA and TKA utilization rates were pervasive among racialized populations across Canada. We advocate that future studies on access to investigate causality or potential factors driving the observed disparity, such as language barriers and sociocultural perceptions regarding surgery. 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".