Consumption of antidepressants and economic austerity in Brazil
Bibliographic record
Abstract
OBJECTIVE: To describe consumption of antidepressants in Brazil through dispensing data from pharmacy retail outlets, in between 2011 and 2017, and explore the relationship between consumption patterns and changing economic context during this period. METHODS: A time-series analysis of dispensing data from pharmacy retail outlets was carried out considering 10 commonly used antidepressants. DDDs/1000 inhabitants/year for each drug were calculated for each quarter. Ttime-series graphs were constructed to analyze the volumes of drugs purchasedRelationship between economic context and consumption was assessed using the following indicators: annual percent change in Gross Domestic Product (GDP), public debt (% of GDP), and annual net savings (in billions of Brazilian reais - BRL). RESULTS: Overall consumption of antidepressants increased over the study period despite a sharp fall of -3.55% in annual percent change in GDP, negative net annual savings of -53.568 BRL, and an increase in public debt exceeding 32% of the GDP during the economic crisis of 2015. CONCLUSION: Consumption of antidepressants from pharmacy retail outlets increased even within a context of economic crisis, which may be a reflection of the disease burden in Brazil. Health budget cuts due to the economic crisis may be directing users to out-of-pocket expenses, deepening social inequalities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".