Evaluation of Rational Medicines Use Based on World Health Organization Core Indicators: A Cross-Sectional Study in Five Health Districts in Mauritania
Bibliographic record
Abstract
Introduction: The rational use of medicines is essential for preventing adverse medicine reactions, achieving therapeutic outcomes, and optimizing treatment costs. While the irrational use of medicines is frequently reported in sub-Saharan Africa, to the best of our knowledge no formal studies have taken place in Mauritania thus far. The main objective of this study was therefore to analyze the rational use of medicines in public and private not-for-profit health facilities, in five health districts in Mauritania. Methods: We conducted a cross-sectional study to assess the rational use of medicines. We used the standard indicators derived from the methodologies of the World Health Organization (WHO) and International Network for Rational Use of Drugs (INRUD). Data were prospectively collected from 1050 prescriptions/patients, in thirty-one public and private not-for-profit health posts/centers in 5 health districts. The data were analyzed using the Statistical Package for the Social Sciences. P value less than 0.05 at 95% confidence interval considered for significance of relationships for associations in statistical test. Results: The average number of medicines per prescription was 2.21; 83.1% (1931/2325) of medicines were prescribed by generic name, but only 54% (1253/2325) were on the National Essential Medicine List (NEML). Antibiotics were prescribed in 62.4% (655/1050) of the consultations, and injectable medicines were prescribed in 15.6% (164/1050) of the consultations. The average consultation time was 16.32 minutes, and the average dispensing time was 97 seconds. Dispensed medicines were correctly labeled, and 83% (871/1050) of patients met the correct administration schedule. The NEML, and the "restricted NEML" for 76 commonly-used medicines, were available in all surveyed health facilities, but the National Therapeutic Guidelines were available in only 60.26% of them. Conclusion: Our findings indicate a possible excess of antibiotics prescriptions, and a likely lack of knowledge of the National Therapeutic Guidelines. There is a need to investigate in more detail the prescription patterns versus disease-specific therapeutic guidelines, and to qualitatively investigate the factors that contribute to the observed irrational prescribing. Moreover, training local staff in the rational use of medicines seems important.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".