Impact of angiotensin receptor blocker product recalls on antihypertensive prescribing in Germany
Why this work is in the frame
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Bibliographic record
Abstract
In Germany, ~8 million patients take angiotensin receptor blockers (ARBs) and 2.25 million of them valsartan. In 2018, contamination of generic ARBs with probable carcinogenic nitrosamines resulted in more than 30 recalls. The impact of such a huge recall has never been explored in Europe. We analyzed the utilization of valsartan, all ARBs, and other alternative antihypertensive drugs in Germany. We used our database of anonymized dispensing data from >80% of community pharmacies at the expense of the statutory health insurance (SHI) funds from January 2017 to December 2019. We analyzed 290.8 million prescriptions, including all oral mono- and fixed-dose combinations of ARBs and plausible alternatives, i.e. ACE inhibitors (ACEi), beta-blockers (BB), and calcium channel blockers (CCB). Utilization was calculated by defined daily doses per 1000 SHI-insured persons per day (DID). Valsartan use decreased substantially after the recalls in July 2018 from 39.0 to 14.2 DID (-64%) in the second quarter of 2019 and to 16.9 DID (-57%) in the fourth quarter of 2019. Simultaneously, the use of alternative ARBs increased from 77.7 DID in the second quarter of 2018 to 121.9 DID (+57%) in the fourth quarter of 2019, mainly due to an increase of candesartan dispensing to 99.8 DID (+73%). There were no changes in the utilization of ACEi, BB, or CCB. The majority of recalled generic valsartan products were replaced by other ARBs, predominantly candesartan, despite documented drug shortages. In contrast to previous safety warnings/recalls, our data do not suggest an under-prescription of antihypertensives during this period.
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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) | 0.002 | 0.001 |
| 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 | 0.001 |
| 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