Public Health Outcomes May Differ After Switching from Brand-Name to Generic Angiotensin II Receptor Blockers
Bibliographic record
Abstract
BACKGROUND: It is unclear whether generics are as safe as brand-name drugs in cardiology. For public health surveillance purposes, we evaluated if switching from the brand-name losartan, valsartan, or candesartan impacted the occurrence of the following outcomes: emergency room (ER) consultations, hospitalizations, or death. STUDY DESIGN: This was a retrospective cohort study. METHODS: This study was conducted in the Quebec Integrated Chronic Disease Surveillance System, including healthcare administrative data of the population of Quebec, Canada. We included brand-name users of losartan, valsartan, or candesartan aged ≥ 66 years who had undergone ≥ 30 days of stable treatment on the brand-name drug prior to cohort entry (substitution time-distribution matching was used to prevent immortal time bias). Outcomes up to 1 year were compared between groups using multivariable Cox proportional hazards regression models (validity assumptions were verified). RESULTS: In our cohorts (losartan, n =15,783; valsartan, n =16,907; candesartan, n =26,178), mean age was 76-78 years, 59-66% were female, 90-92% had hypertension, and 13-15% had heart failure. Validity assumptions were violated for losartan only. For patients switched to generic valsartan, the hazard ratio (95% confidence interval) was 1.07 (0.99-1.14) for ER consultation, 1.26 (1.14-1.39) for hospitalization, and 1.01 (0.61-1.67) for death. The corresponding rates for candesartan were 1.00 (0.95-1.05), 0.96 (0.89-1.03), and 0.57 (0.37-0.88), respectively. CONCLUSIONS: We observed an increased risk of hospitalizations for patients switched to generic valsartan, and a decreased risk of death for patients switched to generic candesartan, compared with those who continued taking the brand-name drug. The differences between generic and brand-name drugs may lead to some differences in public health outcomes, but this safety signal must be further studied using other cohorts and settings.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".