Improving Pharmacovigilliance Quality Management System in the Pharmacy and Poisions Board of Kenya
Bibliographic record
Abstract
The purpose of this study was to explore ways of improving the pharmacovigilance quality system employed by the Pharmacy and Poisons Board of Kenya. The Pharmacy and Poisons Board of Kenya employs a hybrid system of pharmacovigilance that utilizes an online system of reporting pharmacovigilance incidences and a physical system, where a yellow book is physically filled by the healthcare worker and sent to the Pharmacy and Poisons Board for onward processing. This system, even though it has been relatively effective compared to other systems employed in Africa, has one major flaw. It is a slow and delayed system that captures the data much later after the fact and the agency will always be behind the curve in controlling the adverse incidents and events. This means that the incidences might continue to arise or go out of control. This project attempts to develop a system that would be more proactive in the collection of pharmacovigilance data and more predictive of pharmacovigilance incidences. The pharmacovigilance system should have the capacity to detect and analyze subtle changes in reporting frequencies and in patterns of clinical symptoms and signs that are reported as suspected adverse drug reactions. The method involved carrying out a thorough literature review of the latest trends in pharmacovigilance employed by different regulatory agencies across the world, especially the more stringent regulatory authorities. A review of the system employed by the Pharmacy and Poisons Board of Kenya was also done. Pharmacovigilance data, both primary and secondary, were collected and reviewed. Media reports on adverse drug reactions and poor-quality medicines over the period were also collected and reviewed. An appropriate predictive pharmacovigilance tool was also researched and identified. It was found that the Pharmacy and Poisons Board had a robust system of collecting historical pharmacovigilance data both from the healthcare workers and the general public. However, a more responsive data collection and evaluation system is proposed that will help the agency achieve its pharmacovigilance objectives. On analysis of the data it was found that just above half of all the product complaints, about 55%, involved poor quality medicines; 15% poor performance, 13% presentation, 8% adverse drug reactions, 7% market authorization, 2% expired drugs and 1% adulteration complaints. A regulatory pharmacovigilance prioritization tool was identified, employing a risk impact analysis was proposed for regulatory action.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".