Core drug use indicators in Nigerian health facilities: a systematic review (1994–2024)
Bibliographic record
Abstract
Objectives We systematically reviewed the rational use of medicines using the World Health Organization/International Network of Rational Use of Drugs (WHO/INRUD) core drug use indicators. We also assessed the impact of the coronavirus disease 2019 pandemic and the National Drug Policy (NDP) 2005 on the rational use of medicines. Methods Searches were conducted in PubMed, Scopus, and Google Scholar databases to identify studies that met our eligibility criteria. Assessment of the quality of studies was conducted using the Joanna Briggs Institute criteria for analytical studies. We reported and compared the median values of WHO/INRUD core drug use indicators with standard thresholds. Data were presented with median, interquartile range (IQR), and percentages. Mann-Whitney and Kruskal-Wallis tests were conducted to assess for statistical significance ( P < 0.05) across variables. Results Thirty-one studies were included in the review, comprising 50,931 patient encounters across 268 health facilities. Within prescribing indicators, average number of medicines per patient encountered [3.4 (IQR: 3.0 to 4.0)], percentage of medicines prescribed by generic [50.4 % (IQR: 47.4 % to 65.0 %)], percentage of encounters with antibiotic prescribed [40.2 % (IQR: 30.5 % to 52.7 %)], percentage of encounters with injection prescribed [18 % (IQR: 3.2 % to 30.0 %)] and the percentage of medicines prescribed from essential medicines list [82.0 % (IQR: 66.4 % to 89.3 %)]. The median percentage of encounters with antibiotics ( P = 0.04) and the median percentage of medicines prescribed by generics ( P = 0.03) increased during and after the COVID-19 pandemic. Prescribing indicators were worse in primary and secondary health facilities, with significant differences in the median percentage of encounters with antibiotics ( P = 0.007) and injections ( P = 0.0002) across primary, secondary, and tertiary health facilities. There were improvements across all prescribing indicators after the implementation of NDP 2005. Conclusions Core drug use indicators in Nigerian health facilities deviated from the WHO/INRUD thresholds, with noticeable improvement after the implementation of NDP 2005. More efforts are needed to improve rational drug use in Nigerian hospitals.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".