Pharmaceutical Pricing and Reimbursement Policies: lessons learnt and perspectives for Brazil
Bibliographic record
Abstract
The special series “Pharmaceutical Pricing and Reimbursement Policies” published throughout 2022, was organised by invitation of the Brazilian Journal of Hospital Pharmacy and Health Services, to characterise medicines pricing and reimbursement in Brazil and other countries and discuss implementation, effects, gaps and opportunities for improvement. The series included an editorial, a perspective on the Pharmaceutical Pricing and Reimbursement Policies (PPRI) network and eight country case studies, namely Austria, Brazil, Greece, Canada, Italy, United Kingdom, Portugal and the United States. In closing the series, an overview is presented, as well as a critical appraisal of how aspects such as innovation, access, health litigation, the importance of international cooperation and networking are producing advances or setbacks for country pharmaceutical regulation, pricing and reimbursement, and in-place health technology assessment. This article also highlights the scenario in Brazil, with recent developments in pharmaceutical policies approaching medicines pricing, incorporation (reimbursement), access, and expenditure, at the same time introducing arguments that show how these same issues may intertwine with planning and budgets, straining sustainability and threatening the concept of comprehensiveness adopted by the Unified Health System. New perspectives for strengthening of pharmaceutical regulation, pricing and reimbursement policies with a life-cycle approach, aiming for improving access to essential medicines, stimulating innovation and ensuring sustainability of the health system are needed. Experiences in the series´ case studies are examples of benchmarking and best practices, opportunities for increasing collaboration and sources of inspiration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".