Assessing the Sensitivity of the Canadian Adverse Event Following Immunization Surveillance System ( CAEFISS)
Notice bibliographique
Résumé
Background Background: Vaccines are important to public health, but because of the way they are manufactured, their mechanism of action, and their indicated population, careful monitoring of their adverse events is necessary.Canada has a national surveillance system that collects reports on adverse events that may be associated with vaccine administration.Sensitivity is one of the tools used with surveillance systems to study the extent and characteristics of reporting of a surveillance system.To date, the sensitivity of the Canadian system has not been assessed. Purpose Purpose: To assess the sensitivity of the Canadian Adverse Event Following Immunization Surveillance System (CAEFISS).Methods Methods: Based on specific adverse events following immunization (AEFI) and vaccines chosen for the study, a thorough literature search was completed to find the best source which identifies expected rates of AEFI.Studies used were assessed based on quality and sample size.The expected rates of AEFI, in combination with public health estimates of vaccine coverage rates, were used to estimate the expected number of reports.The reports provided the actual number of events used to calculate the sensitivity.Sensitivity was compared based on year of administration, age group, and type of AEFI.Results: Results: The overall sensitivity of the CAEFISS varied from 1.0% to 136.6% for various AEFI for the years 1997 to 2008.For influenza the sensitivity was found to be 93.6% and 136.3% for GBS and anaphylaxis respectively.For DTaP, the rates were found to be 15.0%, 1.0%, and 21.2% for anaphylaxis, HHE, and seizures respectively, and for MMR the rates were 16.5%, 52.7%, and 12.7% in relation to anaphylaxis, thrombocytopenia, and seizures respectively.Conclusions: Conclusions: This is the first assessment of the sensitivity of the CAEFISS, and this study found that the system has reasonable ability to detect AEFI on a national level.CAEFISS had comparable senstivity to other vaccine reporting systems.Many of the AEFI had sensitivity values higher than the 5%-10% range traditionally seen in other passive surveillance systems related to adverse events.The greatest variation of sensitivity was seen between vaccines.Rarity and timing of the AEFI may also impact the sensitivity.Variation of sensitivity and the variation found in the sensitivity analysis lend to the further development and implementations of case definitions for rarer adverse events, especially anaphylaxis.Further research of other factors that impact reporting is necessary.Many thanks to my advisor, Dr. Wang.She was extremely helpful throughout this process and guided me well.She has always responded quickly too all of my questions, at all hours.I would also like to thank my committee members, Dr. Arnold and Dr. Hoffman.They were more than flexible to schedule the times to propose and defend my thesis and provided great input as to the direction of my thesis.I would also like to show my deepest gratitude to Dr. Law, who supported me whole-heartedly although she met with an unexpected workload
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,030 | 0,083 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,006 | 0,007 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».