Drug utilization research in Peru: Is real-world data available?
Bibliographic record
Abstract
Background: Drug utilization research (DUR) is used to provide evidence-based data to inform policies and make decisions. The aim of this study was to map and describe available data sources for drug utilization research in Peru. Methods: We performed a search of data sources providing information on medication use on the website of governmental organizations. We also conducted a literature review using PubMed, LILACs, and BVS. Independently, researchers screened eligible data sources. Data characterization included accessibility, coverage data provider, type of data sources, and setting. We performed a descriptive analysis. Results: We identified seven data sources, CENAFyT, ICI, IDI (SISMED), and ENSUSALUD from MINSA, and CRI-ESSALUD, SGSS/ESSI, and ENSSA from ESSALUD. These presented information on adverse drug reactions (n = 2), drug consumption, and distribution (n = 2), prescription and drug dispensing (n = 1), and surveys addressed to medication users (n = 2). ENSUSALUD was the only data source publicly available. VIGIFLOW and ENSUSALUD have a national granularity from the public and private sectors. The setting of the data sources was both hospital and ambulatory care. Two data sources have individual-level data on adverse drug reactions and one on prescriptions. Four studies on drug utilization research in Peru were derived from ENSUSALUD. Conclusion: In Peru, few data sources are available for drug utilization research. There is an increased need to monitor medications for decision-making purposes. Local and international initiatives and partnerships of the government with academic institutions and the private sector might be a good strategy to increase the transparency of health data and for supporting decision-making using drug utilization research.
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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 | 0.041 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.025 | 0.052 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".