Comparison of Management and Outcomes of Hip Fractures Among Low- and High-Income Patients in Six High-Income Countries
Bibliographic record
Abstract
BACKGROUND: There is a perception that income-based disparities are present in most countries but may differ in magnitude. However, there are few international comparisons that describe income-based disparities across countries and none that focus on hip fractures. OBJECTIVE: To compare treatment patterns and outcomes of high- and low-income older adults hospitalized with hip fracture across six high-income countries. DESIGN: Retrospective serial cross-sectional cohort study. PARTICIPANTS: Adults aged ≥ 66 years hospitalized with hip fracture from 2013 to 2019 in Canada, England, Israel, the Netherlands, Taiwan, and the USA using population-representative patient-level administrative data. MAIN MEASURES: Older adults in the top and bottom income quintiles within countries were compared on 30-day and 1-year mortality, treatment approaches, hospital length of stay (LOS), 30-day readmission rates, time to surgery, and discharge disposition. KEY RESULTS: Annual age- and sex-standardized incidence rates of hip fracture were higher for low- than for high-income populations in all countries except in the USA. In all countries, adjusted 1-year mortality was lower for high-income than low-income patients, with the largest difference in Israel (- 10.0 percentage points [95% confidence interval [CI], - 15.2 to - 4.8 percentage points]). Across countries, utilization of total hip arthroplasty was 0.1 (95% CI, 0.0-0.2 percentage points) to 6.9 percentage points (95% CI, 4.6-9.2 percentage points) higher among high- vs. low-income populations. With few exceptions, LOS, adjusted 30-day readmission rate, and time to surgery were shorter and lower for high-income patients. CONCLUSIONS: Income-based disparities in treatments and outcomes for older adults hospitalized for hip fractures differed in magnitude, but were present in all six high-income countries. Defying our expectations, the USA did not have consistently larger disparities than other countries suggesting that the impacts of poverty exist in vastly different healthcare systems and transcend geopolitical borders.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".