Essential Medicines List Implementation Dynamics: A Case Study Using Brazilian Federal Medicines Expenditures
Bibliographic record
Abstract
The aim was to analyse the implementation dynamics of the essential medicines list (EML). We used the government expenditures on medicines and Brazil as a case study. Drug purchases were considered as a proxy for utilization. The essential medicines (EMs) expenditures were followed over time by Brazilian National EMLs life-time and defined by broad therapeutic categories and by specific medicines. Brazil increased the number of the medicines during the last four editions of Brazilian National EMLs and the federal government expenditures on them. The EML implementation dynamics changed the distribution of expenditures on EMs. We identified a common set of 404 EMs present in all four editions of the Brazilian National EMLs. There was a proportional decrease in expenditures on anti-infectives for systemic use, blood and blood-forming organs and alimentary tract and metabolism, and increase in expenditures on antineoplastic and immunomodulating agents. The expenditures distribution per specific medicines revealed that a small set of EMs was responsible for 50% or more of expenditures considering Brazilian National EML life-time for all four periods. The increase in expenditures on EMs in Brazil was a consequence of the newer medicines incorporated over time in the Brazilian National EMLs. The use of the medicines expenditures as a source of data and the definition of an EML life-time permitted follow-up of the implementation dynamics of different versions of the Brazilian National EMLs. Our results have implications for policymakers and stakeholders to gain a better understanding of the role EMLs play in health system sustainability and in the provision of the most beneficial heath care.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".