Impact of a budget-restrictive (Germany) versus an incentive-driven (UK) reimbursement system on LDL-goal-achievement in statin-treated patients for secondary prevention: results of DYSIS
Bibliographic record
Abstract
Background: Statin treatment is widespread used for secondary prevention in Europe. However, there are large differences in LDL-Cholesterol (LDL-C) target achievement between European countries. Little is known about the impact of different reimbursement systems on achievement of lipid targets in clinical practice. Methods: Between June 2008 and February 2009, 22,063 consecutive statin-treated outpatients were enrolled in 11 European countries and Canada (DYSIS = Dyslipidemia International Study) to assess LDL-C target achievement for secondary prevention. In outpatient treatment in Germany chronic medical treatment is restricted by budget constraints (restrictive system) whereas in the UK reimbursement is linked to treatment goal achievements (incentive system). We compared the level of LDL-C goal achievement in patients enrolled in Germany versus the UK. Results: A total of 4,260 patients were enrolled in Germany, 540 patients in the UK. Patients in Germany were older, more often female, more often had diabetes, less often were obese and less often reported sedentary lifestyle. They had a higher prevalence of cerebrovascular and peripheral artery disease but less often ischemic heart disease as compared to the patients in the UK. Patients in Germany (with a restrictive reimbursement system) less often received potent statins such as atorvastatin or rosuvastatin as compared to patients in the UK. Independent of the statin used, daily dosages were significantly lower in Germany than in the UK. As a result, significantly less patients in Germany did reach the recommended treatment goal of LDL-C <100mg/dl as compared to the UK (42.0% in Germany versus 79.8% in the UK). Conclusion: In Germany, patients treated with statins for secondary prevention received less potent statins and lower dosages as compared to the UK probably due to differences in reimbursement systems (restrictive versus incentive). This led to a much worth LDL-C goal achievement in Germany as compared to the UK.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".