Essential psychiatric medicines: wrong selection, high consumption and social problems
Bibliographic record
Abstract
BACKGROUND: The World Health Organization Essential Medicines List (WHO-LIST) and national essential medicines lists differ because many countries face significant challenges, such as product availability, cost, product quality and epidemiological disease profiles. In Brazil, governments pay for drugs that are included on the federal, state and municipal government (REMUME) lists. The extent to which municipal lists differ from state and national lists and from the WHO-LIST is unclear. We investigate the use of the WHO-LISTas a tool with which to evaluate the selection process for the essential psychiatric medicines in the public system coverage list of Brazilian communities (cities) and the use of the target drugs. METHODS: Municipal health secretaries were interviewed regarding the selection process for REMUMEs and the antidepressants and benzodiazepines included in REMUMEs and reference lists. We calculated the use of REMUME drugs that appeared or did not appear on reference lists according to the defined daily dose (DDD) per 10,000 inhabitants. RESULTS: Local physicians and pharmacists without specific training or explicit criteria developed the REMUMEs. Of the 13 drugs and 24 products (i.e., the different dosages of these 13 drugs) in the REMUMEs, 8 drugs and 10 products were included in at least one reference list and in one municipal list; 4 drugs and 6 products were included in at least one reference list but in none of the municipal lists; and 7 drugs and 8 products were included in at least one municipal list but in none of the reference lists. The antidepressants that appeared in at least one municipal list but in none of the reference lists represented 25.1 % (mean 60.9 DDD/10,000 inhabitants-day) of the usage. The benzodiazepines that appeared in at least one of the municipal lists but in none of the reference lists represented 14.7 % mean 18.5 DDD/10,000 inhabitants-day) of the usage. CONCLUSIONS: Brazilian cities have no rigorous processes for selecting the drugs that appear on their lists, and drugs that do not appear on the reference lists represent a significant proportion of antidepressant and benzodiazepine use, resulting in public health and social problems.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".