Regional variation in hip and knee arthroplasty rates in Switzerland: A population-based small area analysis
Bibliographic record
Abstract
BACKGROUND: Compared to other OECD countries, Switzerland has the highest rates of hip (HA) and knee arthroplasty (KA). OBJECTIVE: We assessed the regional variation in HA/KA rates and potential determinants of variation in Switzerland. METHODS: We conducted a population-based analysis using discharge data from all Swiss hospitals during 2013-2016. We derived hospital service areas (HSAs) by analyzing patient flows. We calculated age-/sex-standardized procedure rates and measures of variation (the extremal quotient [EQ, highest divided by lowest rate] and the systemic component of variation [SCV]). We estimated the reduction in variance of HA/KA rates across HSAs in multilevel regression models, with incremental adjustment for procedure year, age, sex, language, urbanization, socioeconomic factors, burden of disease, and the number of orthopedic surgeons. RESULTS: Overall, 69,578 HA and 69,899 KA from 55 HSAs were analyzed. The mean age-/sex-standardized HA rate was 265 (range 179-342) and KA rate was 256 (range 186-378) per 100,000 persons and increased over time. The EQ was 1.9 for HA and 2.5 for KA. The SCV was 2.0 for HA and 2.2 for KA, indicating a low variation across HSAs. When adjusted for procedure year and demographic, cultural, and sociodemographic factors, the models explained 75% of the variance in HA and 63% in KA across Swiss HSAs. CONCLUSION: Switzerland has high HA/KA rates with a modest regional variation, suggesting that the threshold to perform HA/KA may be uniformly low across regions. One third of the variation remained unexplained and may, at least in part, represent differing physician beliefs and attitudes towards joint arthroplasty.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".