Abstract P146: Public Health Surveillance Reveals an Increase in Health Care Utilization for Generic versus Brand-name Warfarin Users
Bibliographic record
Abstract
Background: Even with many direct oral anticoagulant options, brand-name or generic warfarin is still widely used to prevent atherothrombotic events in cardiology. Federal standards regulate bioequivalence of generic vs. brand-name drugs through comparative bioavailability studies but does not regulate clinical equivalence nor tolerability in a “real-life” settings. Through public health surveillance, we have evaluated the impact of the generic warfarin commercialization on health care utilization: emergency room (ER) consultations or hospitalizations. Methods: We used an interrupted time series analysis using the Quebec Integrated Chronic Disease Surveillance System, a surveillance system from the second populous province in Canada (~8.3 million in 2017). Rates of health care utilization for warfarin users (n=280,158) aged ≥ 66 years were calculated for 6-month periods, 5 years before up to 15 years after warfarin commercialization (from January 1996 to January 2016). Periods before and after generic warfarin commercialization were compared by negative binomial segmented regression models for all users with a specific variable for generic or brand-name users. Sensitivity analyses were also conducted. Results: Generic warfarin analogs (n=5) were commercialized from January 2001. There was an approximated mean rate of 1134 ER or hospitalizations for 1000 brand-name and generic users per 6-month period, similar before and after generics commercialization. After generics commercialization, there was an immediate increase in rates of health care utilization for generic (9.9%) vs. brand-name users (0%), a statistically significant difference (9.9% [95% confidence interval: 4.4% to 15.5%], p = 0.0001). Rates of health care utilization remained stable and higher for generic vs. brand-name users throughout the period after generics commercialization. Conclusion: Among generic warfarin users, we observed an increased rates of health care utilization soon after generics commercialization. Risk and survival analysis studies controlling for potential confounders are required to deepen this pharmacovigilance signal as stricter licensing process may be required.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".