Abstract 12799: Hospitalizations and Emergency Consultations Following Generic Antihypertensive Drugs Commercialization in Quebec
Bibliographic record
Abstract
Introduction: Once patent of a brand-name drug expires, generic drugs are commercialized and substitution may occur. Few studies have assessed clinical impacts of generic drugs commercialization at a population level. Objective: We evaluated the impact for three generic versions of antihypertensive drugs on adverse events: hospitalizations and emergency room consultations. Methods: This is an interrupted time series analysis using the Quebec Integrated Chronic Disease Surveillance System. The rate of adverse events for losartan (brand-name and 8 generics), valsartan (brand-name and 5 generics) and candesartan (brand-name and 3 generics) users aged ≥ 66 years (all: N=121 185 citizens) were calculated monthly, 12 months before and 12 months after generics commercialization. Periods before and after generics commercialization were compared with negative binomial segmented regression models for all users with a specific variable for generic and brand-name users. Results: There was an approximated monthly mean rate of 100 adverse events for 1000 exposed person-months to losartan, valsartan and candesartan. Monthly trend of adverse events was affected by generics commercialization. Generic users of losartan, valsartan and candesartan showed increased rate of adverse events of 11% (p=0.0034), 9% (p=0.0024) and 13% (p=0.0008) respectively right after generics commercialization (Figure 1) which was always higher compared to brand-name users (p<0.05). The monthly trend of hospitalizations was affected for losartan users up to 1 year after generics commercialization (generics: +1.2%, brand-name: -2.2, p=0.0094). Conclusion: Generics commercialization impacted population rates of adverse events. A significant adverse events increase was observed right after generics commercialization for all antihypertensive drugs studied, among generic users. More studies are required to identify differences in drugs or users, which could explain this increase.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".