Data sources for drug utilization research in Latin American countries—A cross‐national study: <scp>DASDUR‐LATAM</scp> study
Bibliographic record
Abstract
PURPOSE: Drug utilization research (DUR) contributes to inform policymaking and to strengthen health systems. The availability of data sources is the first step for conducting DUR. However, documents that systematize these data sources in Latin American (LatAm) countries are not known. We compiled the potential data sources for DUR in the LatAm region. METHODS: A network of DUR experts from nine LatAm countries was assembled and experts conducted: (i) a website search of the government, academic, and private health institutions; (ii) screening of eligible data sources, and (iii) liaising with national experts in pharmacoepidemiology (via an online survey). The data sources were characterized by accessibility, geographic granularity, setting, sector of the data, sources and type of the data. Descriptive analyses were performed. RESULTS: We identified 125 data sources for DUR in nine LatAm countries. Thirty-eight (30%) of them were publicly and conveniently available; 89 (71%) were accessible with limitations, and 18 (14%) were not accessible or lacked clear rules for data access. From the 125 data sources, 76 (61%) were from the public sector only; 46 (37%) were from pharmacy records; 43 (34%) came from ambulatory settings and; 85 (68%) gave access to individual patient-level data. CONCLUSIONS: Although multiple sources for DUR are available in LatAm countries, the accessibility is a major challenge. The procedures for accessing DUR data should be transparent, feasible, affordable, and protocol-driven. This inventory could permit a comparison of drug utilization between countries identifying potential medication-related problems that need further exploration.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".