Investigating Generic and Brand Name Pharmaceutical’s Market Shares and Prices in Tunisia
Bibliographic record
Abstract
The purpose of this paper is to investigate how the brand name’s market shares in Tunisia are affected by generic competition during the pre-reform period of the Tunisian health insurance \nsystem following the methodological approach developed by Aronsson et al. (2001). In this study we use data for three molecules Captopril (antihypertensive) Glibenclamide (antidiabetic) and Carbamazepine (antiepileptic) from IMS Health database. The data span \nfrom the third quarter 2002 to second quarter 2007. Statistical results indicate that the impact of generic competition seems to be not different across markets (Captopril, Glibenclamide \nand Carbamazepine) in Tunisia. In addition, the relative price has a positive and significant effect on the change of market share of the brand name drug in Tunisia for the three active molecules. The higher the price of the brand name product relative to the average price of the \ngeneric substitutes, the smaller the decrease of market share of the brand name product. In Tunisian pharmaceutical market, brand- names charge a higher price than their generic versions and still obtain positive market shares. Thus, from a policy perspective, the large \nmarket share of higher priced brand-names relative to their generic versions is an \nunsatisfactory outcome taking into account that brand name drug and generics are identical products and provide similar health gains to patients.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".