Understanding the regulatory-procurement interface for medicines in Africa via publicly available information on standards, implementation, and enforcement in five countries
Bibliographic record
Abstract
Background: Substandard and falsified medicines in Africa are a major public health concern. Access to quality medical products in African countries is governed in large part by two major entities at the national level: the regulatory authority and the procurement agency. The importance of national regulators in ensuring quality medical products is well known. The interplay between the national regulator and the national procurement agency also has a significant impact on access to quality medicines but is less understood. This study's aim was to characterise the regulatory-procurement interface - the intersection of decision-making in these two spheres - using publicly available data from five African countries. Methods: to identify key national policies and practices around the nexus of medicines regulation and procurement. Results: Though legal and policy frameworks enabling best practices in procurement were often in place, implementation and enforcement of these practices appear to be key areas for strengthening. In addition, we documented a lack of publicly available information related to the role that quality plays in selecting medical products. Finally, none of the five countries have publicly published the results of their selection decisions with key product details, making it difficult to assess whether basic quality standards are being met. Conclusion: Based on these findings, one of the most important next steps for improving the effectiveness and transparency of national procurement is for procurement agencies to publish detailed quality selection criteria and an up-to-date list of the medical products they have purchased, with key product information. We hope these findings can help inform the conversation about implementing and enforcing best practices at the regulatory-procurement interface, with the goal of improving access to quality versions of medical products in Africa and globally.
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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 | 0.034 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".