A Population-Based Study of the Use of Selective Serotonin Reuptake Inhibitors before and after Introduction of Generic Equivalents
Bibliographic record
Abstract
OBJECTIVE: Generic drugs are less expensive than their branded equivalents, but receive limited promotion. This study sought to examine how user rates of individual selective serotonin reuptake inhibitors (SSRIs) changed after the introduction of their generic equivalents. METHOD: Administrative health and census data were used to examine the rates of use of all 6 SSRIs from 1996 to 2009 in the province of Manitoba (population of 1.2 million). The primary outcome measure was a comparison of the rates of use in the pre- and post-generic periods, using generalized estimating equations. Secondary analyses were stratified by specialty of physician prescriber. RESULTS: Escalating rates of use of branded SSRIs in the pre-generic period significantly decreased after generic versions became available (all Ps < 0.001). Incident use of sertraline and paroxetine continued to decrease throughout the post-generic period (1.5% and 1.9% quarterly decreasing rates, respectively). During the years when generic sertraline, fluoxetine, and fluvoxamine were available, their use declined while branded paroxetine and citalopram use continued to increase. Use of branded citalopram, sertraline, and paroxetine prescribed by general practitioners (GPs) increased at rates significantly higher than when prescribed by psychiatrists (all Ps < 0.001). CONCLUSION: The introduction of cheaper generic alternatives of SSRIs paradoxically resulted in their use diminishing rather than increasing. With the exception of escitalopram, branded SSRIs tended to be preferentially used, compared with available less expensive generic SSRIs. These patterns were more pronounced for prescriptions by GPs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".