The impact of a physician detailing and sampling program for generic atorvastatin: an interrupted time series analysis
Bibliographic record
Abstract
BACKGROUND: In 2011, Manitoba implemented a province-wide program of physician detailing and free sampling for generic atorvastatin to increase use of this generic statin. We examined the impact of this unique combined program of detailing and sampling for generic atorvastatin on the use and cost of statin medicines, market share of generic atorvastatin, the choice of starting statin for new users, and switching from a branded statin to generic atorvastatin. METHODS: We conducted a retrospective study of Manitoba insurance claims data for all continuously enrolled patients who filled one or more prescriptions for a statin between 2008 and 2013. Data were linked to physician-level data on the number of detailing visits and sample provision. We used interrupted time series analyses to assess policy-related changes in the use and cost of statin medicines, market share of generic atorvastatin, the choice of starting statin for new users, and switching from a branded statin to generic atorvastatin. RESULTS: The detailing program reached 31% (651/2103) of physicians who prescribed a statin during the study period. Collectively, these physicians prescribed 61% of statins dispensed in the province. Free sample cards were provided to 61% (394/651) of the detailed physicians. The program did not change the level or trend in the overall statin use rate and the total cost of statins or increase the number of patients switching from another branded statin to generic atorvastatin. We found the program had a small impact on atorvastatin's market share of new prescriptions, with a level increase of 2.6%. CONCLUSIONS: Though physician detailers were skilled at targeting high-prescribing physicians, a combined program of detailing visits and sample provision for generic atorvastatin did not lower overall statin costs or lead to switching from branded statins to the generic. The preceding introduction of generic atorvastatin appeared sufficient to modify prescribing patterns and decrease costs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".