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Population Impact of Generic Valsartan Recall

2019· letter· en· W2989293495 on OpenAlexafffund
Cynthia A. Jackevicius, Harlan M. Krumholz, Alice Chong, Maria Koh, Aya Ozaki, Peter C. Austin, Jacob A. Udell, Dennis T. Ko

Bibliographic record

VenueCirculation · 2019
Typeletter
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsSunnybrook Health Science CentreWomen's College HospitalInstitute of Health EconomicsUniversity Health Network
FundersCanadian Institutes of Health Research
KeywordsMedicinePharmacyHavenHealth carePopulationFamily medicineGerontologyLibrary sciencePolitical scienceLaw

Abstract

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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)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0180.010
Insufficient payload (model declined to judge)0.0080.002

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.

Opus teacher head0.037
GPT teacher head0.304
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEditorial

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".

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Citations29
Published2019
Admission routes2
Has abstractno

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