Electronic data sources for drug utilization research and healthcare decision-making in Mexico
Bibliographic record
Abstract
BACKGROUND: Understanding drug utilization is essential for informed decision-making in national healthcare and for enabling comparisons across countries. In Mexico, the limited research in this field may be attributed to the lack of awareness and accessibility of existing data sources. Addressing this gap requires a comprehensive inventory of data sources for Drug Utilization Research (DUR). The purpose of this study was to develop an inventory of electronic data sources available in Mexico for DUR, outlining their characteristics, strengths, and limitations. METHODS: From 2019 to 2024, specialists in pharmacoepidemiology and healthcare systems research conducted online searches for DUR data sources, including official websites of the Mexican government and public health institutions. A literature review was also performed for country-specific data sources in articles published between 2000 and 2023. Data sources were independently searched, screened, and selected by independent reviewers, with disagreements resolved through consensus. A descriptive analysis of selected databases was conducted, focusing on accessibility, geographical coverage, data aggregation level, health sector type, data source type, and setting. RESULTS: The analysis included twenty data sources, of which only four were publicly available. These databases offer insights into various aspects of drug utilization, primarily owned by social security institutions (twelve). Only four contain data from the private healthcare sector. Regarding data source type, five focused on procurement, twelve on prescription, two on pharmacovigilance, and one on drug disposal. CONCLUSIONS: Mexico faces notable challenges in accessible data for DUR especially in non-social security institutions and the private sector. This study underscores the urgent need to improve healthcare data accessibility and research in Mexico, to drive evidence-informed decision-making regarding medicines utilization.
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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 | 0.044 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.052 | 0.061 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".