Regional Variation in Medication Adherence
Why this work is in the frame
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Bibliographic record
Abstract
Abstract An extensive literature has demonstrated geographic variation in medical services and this variation has been largely attributed to the health care system and not to regional differences in patient behavior. We use empirical Bayes shrinkage models, conditional on patient, firm, and market covariates, to investigate geographic variation in adherence to prescription medications across hospital referral regions (HRRs). Models are estimated for commercially insured patients in 11 combinations of chronic diseases and drug classes. We use factor analysis to create a market-level composite measure of adherence that we relate to adjusted market-level spending on non-drug services. We find that there is a very small amount of variation in adherence to prescription drugs across HRRs supporting the widely held assumption that geographic variation is attributable to the health system. Markets with high adherence have systematically lower medical spending, and this inverse correlation is more likely due to unobserved market traits.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it