Medicines regulation, pricing and reimbursement in Brazil
Bibliographic record
Abstract
Brazil is an upper-middle-income country with a high human development index (HDI) of 0.765 (2019). The Unique Health System (SUS) is a universal, decentralised system, free at point-of-care, although 27% of Brazilians have voluntary supplementary health insurance. Medicines are provided free-of-charge through the SUS, though there are a few exceptions where co-payment is required. Around 87% of the country’s expenditure with medicines and medical devices corresponds to out-of-pocket, highlighting the importance of price regulation. Marketing authorisation and maximum price approval are mandatory market entry requirements for medicines. Pricing policies include maximum price approval, regulation of mark-ups, tax exemption, annual price adjustment and a mandatory discount for government procurement and enforcement mechanisms. The pricing of new drugs considers the patent status and added therapeutic benefit. It is a combination of health technology assessment and external or internal reference pricing, while drugs with active ingredients in the market follow internal reference pricing. The maximum price of generics must be up to 65% of the reference’s price. The maximum approved prices and public procurement prices are publicly available. Brazil has a value-based decision-making process for incorporating medicines and other technologies at the SUS. Current areas of work include horizon scanning, participation of patients in decision-making and re-assessment of technologies. As a decentralised system, medicines are procured by the Ministry of Health, states and municipalities, according to their level of responsibility. Pricing and reimbursement policies, including a consolidated generics policy, have been important in promoting transparency, predictability, and price stability, in turn contributing to cost-containment and access. Ongoing challenges include high rates of judicialisation, medicines with excessive prices not commensurate with their clinical benefits, no provision for pricing review, problems related to governance and politics. To address these challenges, the authors have three main recommendations. First: improving regulatory governance, second: incentivising the development and promoting access to medicines with stronger evidence, added clinical benefit and fair prices, and third: increasing awareness among stakeholders, avoiding judicialisation and minimising its impact; contributing to closing the gap between innovation and access to medicines.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".