Handling Regional Variation in Health State Preferences within a Country
Bibliographic record
Abstract
BACKGROUND: Health state preferences vary among countries, and country-specific value sets are important in health care reimbursement decisions. When decisions are made at the regional level, regional variation in health state preferences may be important. We propose that shrinkage analysis and Bland-Altman plots can be a helpful way to investigate regional variation. METHODS: The presence of regional variation can be investigated by introducing interactions between regions and the regression coefficients in the scoring algorithm. When variation is present, regional scoring algorithms can be derived through shrinkage analysis. The impact of using regional algorithms in place of the national algorithm can be investigated using simulation and illustrated using Bland-Altman plots. We applied this methodological approach to the Canadian EQ-5D-5L valuation study, which used time-tradeoff (TTO) tasks to elicit health state preferences from 1073 participants from 4 regions (Alberta, British Columbia, Ontario, and Quebec). RESULTS: There were statistically significant interactions between the fixed effects of the scoring algorithm and region. On computing regional scoring algorithms and applying them to the EQ-5D-5L health states reported by our population, the mean utility using the Canada-wide scoring algorithm was 0.87 (standard error, 0.0013), compared to 0.85 (0.0013) on using the algorithm for Alberta, 0.80 (0.0013) on using the algorithm for British Columbia, 0.91 (0.0013) for Ontario, and 0.89 (0.0014) for Quebec. CONCLUSIONS: When health care falls under regional jurisdiction, shrinkage estimators can be used to generate regional scoring algorithms for the EQ-5D-5L and Bland-Altman plots used to assess the importance of regional variation in health state preferences. Our results suggest that mean health state preferences vary among Canada's regions and make a sizable impact on estimates of population mean utility.
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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 | 0.039 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".