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 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) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".