The Detection and Causality Assessment of Adverse Events Related to Natural Health Product Use in Community Pharmacies through the Implementation of Active Surveillance.
Notice bibliographique
Résumé
Background: Natural health products (NHP) are widely used by the public. Since NHPs are pharmacologically active products, their ability to cause adverse reactions (AR) is present and well-documented. Currently employed passive surveillance systems are not well-equipped to detect NHP adverse events (AE) due to issues with significant underreporting, lack of patient disclosure of NHP use to health care providers and patients not attributing an AE to a NHP due to their perception of safety with these products. Other types of surveillance systems, such as active surveillance, may be more appropriate to detect NHP AEs as increased detection has been documented with these systems. Pharmacists are well-trained to screen for NHP use and AEs, including interactions between health products. Once an AE is detected, causality assessment is required to determine if there is a causal link between a health product and the AE. Currently, no causality tools are available, or take into consideration, the evaluation of AEs involving NHPs. Methods: The work for this thesis was derived from two studies. The first study involved the implementation of active surveillance into community pharmacies to screen for the proportion of patients taking prescription drugs and/or NHPs, as well as their respective AE rates. All AEs reported by patients who consented to, and were available for, a detailed telephone interview were adjudicated fully to assess for causality. The second study involved developing, piloting and refining an adjudication process and subsequent causality assessment tools to be used to assess AEs; these process and tools were modified for inclusion of NHP-specific factors. Important case reports resulting from the screening and causality assessment were used to translate knowledge to pharmacists. Results: We screened 1118 patients screened in 10 community pharmacies across Alberta and British Columbia, and obtained reports of 54 AEs. Of the 657 (58.8%; 95% CI: 55.5-61.6) patients who took prescription drugs and NHPs concurrently, 48 (7.3%; 95% CI: 5.6% to 9.6%) reported an AE. This AE rate is 6.4 times (OR; 95% CI: 2.5 – 16.2; p<0.001) greater than those who took prescription drugs alone. On a national level, combined with data from Ontario, Canada, 45.4% (95% CI: 43.8%-47.0%) of Canadians that visit community pharmacies take NHPs and prescription drugs concurrently and of those, 7.4% (95% CI: 6.3%-8.8%) report an AE. Three causality assessment scales, Naranjo, Horn and WHO-UMC, were modified to include the assessment of NHP AEs. The adjudication process and scales developed were piloted in 24 cases (patients reporting an AE with NHP use and available for a full interview) and were able to assess causality of all cases. The tools were then refined by the adjudication team until no further changes were deemed necessary. Two cases found through this process were submitted and will be published in a well-known national pharmacists’ journal to highlight the importance of the data found to practicing pharmacists. Conclusion: A substantial proportion of community pharmacy patients use both prescription drugs and NHPs concurrently; these patients are more likely to experience an AE than those taking prescription drugs only. Active surveillance provides a means of detecting such AEs and collecting high-quality data on which causality assessment can be based. The causality assessment tools developed allowed for full adjudication of AEs involving NHPs. Lastly, such data has clinical relevance for pharmacists in terms of raising awareness around NHP use and the potential risks for their patients.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».