Background incidence rates from electronic healthcare databases for vaccine safety monitoring: review of challenges from the COVID-19 vaccination campaign and proposal for best practices
Notice bibliographique
Résumé
Background incidence rates (BIRs) are essential for contextualizing adverse event rates in vaccine safety monitoring, particularly through observed-to-expected (O/E) analyses. The unprecedented rapid development of vaccines during the coronavirus disease 2019 (COVID-19) pandemic necessitated rigorous and continuous safety monitoring. While BIRs are traditionally obtained from literature reviews, the pandemic accelerated the large-scale generation of BIRs from electronic healthcare databases through various initiatives such as the vACCine covid-19 monitoring readinESS (ACCESS) and the Biologics Effectiveness and Safety (BEST) to support COVID-19 vaccine safety surveillance strategies. The Beyond COVID-19 Monitoring Excellence (BeCOME) initiative, launched in 2022, established seven working groups, including one focused on best practices for BIR generation and utilization in pharmacovigilance activities. The BeCOME BIR working group conducted a targeted literature review to enable focused analysis of challenges that emerged during large-scale vaccination campaigns through December 2023. The group also used structured group discussion following the nominal group technique over 10 months to develop consensus-based judgments on critical factors for BIR best practices. The findings were organized into four domains: key initiatives summary, pandemic-related challenges, implications for O/E analyses, and best practice recommendations. To identify key BIR initiatives supporting COVID-19 vaccine safety assessment, the review employed multiple strategies including scientific literature examination, public health authority website reviews, and reference list searches; data on study characteristics, limitations, challenges, and recommendations were extracted. The targeted review focused on five major initiatives, such as ACCESS and BEST, that generated BIRs for adverse events of special interest (AESIs) during the pandemic. The group identified persistent challenges during the vaccination campaign including timeliness constraints during rapid vaccine deployment, substantial heterogeneity across data sources, inconsistent case definitions, limited information for key subpopulations, and difficulties addressing emerging AESIs. These challenges directly impacted O/E analyses, potentially leading to biased safety signal assessments. We propose a coordinated action plan among key stakeholders to establish sustainable mechanisms for regular BIR delivery with methodological improvements, develop consensus on best practices for BIR selection, and secure resources to ensure pandemic preparedness. Implementing these recommendations will strengthen vaccine safety monitoring systems for both routine vaccination programs and future public health emergencies.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,123 | 0,347 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,005 | 0,006 |
| Bibliométrie | 0,058 | 0,044 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,008 | 0,012 |
| Science ouverte | 0,006 | 0,005 |
| Intégrité de la recherche | 0,003 | 0,005 |
| 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 ».