Population Impact of Generic Valsartan Recall
Bibliographic record
Abstract
health services research ◼ hypertension O n July 9, 2018, Health Canada announced a voluntary recall of 6 generic valsartan products because a known carcinogen N-nitrosodimethylamine was detected.1 In total, more than 22 countries, including the United States, initiated recalls.Despite an increase in drug recalls, few studies have evaluated their impact.2,3 The valsartan recall of a frequently used oral medication for high prevalence chronic conditions, such as hypertension, provides an opportunity to examine the consequences of a drug recall by patients and health care systems.We used a segmented regression analysis using multiple linked healthcare databases in Ontario, Canada, including the Ontario Drug Benefit prescription claims database to identify our cohort of recalled valsartan users and ascertain nonvalsartan medication use, the Ontario Registered Persons database for vital status, Canadian Institute for Health Information for discharge diagnoses for hospital admissions, baseline comorbidities, and clinical outcomes, National Ambulatory Care Reporting System database for emergency department (ED) visits, and the Statistics Canada census database for income datasets.These datasets were linked using unique encoded identifiers and analyzed at ICES.We included patients ≥65 years old who had been dispensed a supply of at least 1 recalled valsartan product that would cover the period up to and including the date of July 9, 2018, and were alive on this date.We characterized changes in prescription patterns in recalled valsartan users before and after the July 9, 2018, recall date, the index date at which point 100% of patients in our cohort were taking a recalled valsartan product.We computed monthly rates of ED visits and hospitalizations for hypertension, heart failure, myocardial infarction, and stroke/transient ischemic attack as primary diagnoses from January 9, 2017, through January 9, 2019.We fit segmented regression models using 3 variables: baseline monthly change in the rate of the outcome before the recall, monthly change in rate after the recall, and an indicator variable representing an immediate jump in the rate at the time of the recall; all entered into the autoregressive model with identified lags using SAS version 9.3 (SAS Institute, Cary, NC).Data use in this study was authorized under section 45 of Ontario's Personal Health Information Protection Act, which does not require Research Ethics Board review, but was approved by ICES' Privacy and Legal Office.There were 55 461 patients in our sample.The mean age was 76.3±7.7 years, 41.5% were male, and 95.0% of patients had hypertension, while 5.0% had heart failure.The majority of patients changed to a nonvalsartan angiotensin-receptorantagonist (73.8%) or a nonrecalled valsartan product (8.8%)within 1 month of the recall, with 84.4% of patients filling a new prescription for a likely alternative to their recalled valsartan.At 3 months, 10.7% of recalled valsartan users did not fill an alternative medication.( Figure)
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.018 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".