Cost Impact of Switching Histamine <sub>2</sub> -Receptor Antagonists to Nonprescription Status
Bibliographic record
Abstract
BACKGROUND: There is a recent trend to switching medications from prescription to nonprescription status. Often, such switches are accompanied by dramatic changes in utilization due to increased availability or decreased insurance coverage. The histamine(2)-receptor antagonists (H(2)RAs) underwent such status change in the UK in 1994, the US in 1995, and Canada in 1996. OBJECTIVE: To examine the impact of the status change for H(2)RAs on the market for gastrointestinal (GI) agents in the US, UK, and Canada. METHODS: IMS market sales data from 1992 to 1997 were procured. All costs were converted to 1997 US dollars using the consumer price index. Per capita sales figures were determined using population data from the US Census Bureau's International Database. RESULTS: Overall spending on GI remedies increased in all 3 markets between 1992 and 1997; however, the contribution of prescription sales and number of prescriptions varied across the 3 countries. An increased market share for nonprescription H(2)RAs occurred in the US, correlating with a decline in prescription numbers for GI remedies. The opposing trend occurred in the UK, where market share of nonprescription H(2)RAs was minimal and use of prescription H(2)RAs increased. Prescription and nonprescription H(2)RA sales could not be differentiated for Canada. CONCLUSIONS: The impact of the H(2)RA status change varied across countries. Differences in utilization may be attributed to many factors such as differing healthcare systems, patient convenience, and physician prescribing practices. Further research is required to identify the reasons for differences in utilization and to quantify the potential clinical impact.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".