Surveillance System Evaluation for COVID-19 Vaccine–Associated Adverse Events Following Immunization (AEFI), Sindh Pakistan (2021)
Notice bibliographique
Résumé
Background In February 2021, a mass vaccination campaign commenced in Sindh Province in response to the COVID-19 epidemic. An adverse-events-following-immunization surveillance system (AEFI-SS) was established to monitor the adverse events following vaccination. Objective We evaluated the AEFI-SS with the aim to identify its strengths and weaknesses and suggest recommendations. Methods In May-June 2021, a descriptive evaluation study was conducted in Sindh Province, Pakistan. The Centers for Disease Control and Prevention’s (CDC) updated guidelines for evaluation of SS-2001 were followed to measure the qualitative, quantitative, and utility attributes of the AEFI-SS. Key stakeholders were identified based on their involvement in the AEFI-SS and were interviewed. Case investigation proformas for the AEFI were randomly reviewed for data quality, timeliness, and completeness. Sensitivity was calculated. Each attribute was rated as good, fair, or poor based on a scoring legend. Results The SS was useful in effectively identifying 7147 cases of AEFIs. Timeliness of all AEFI cases was good and was found to be 100%, as all cases were reported within 24 hours. The World Health Organization (WHO)-approved case definition was used for the identification of AEFI cases and had a simple flow of information. The AEFI-SS was good in data quality and completeness (100%), and data collection tools were filled by trained medical officers. Sensitivity was 100%, and the predictive value positive (PVP) was not calculated due to the absence of a laboratory component. Good representativeness (>80%) of the population was covered by 1004 vaccination centers. The system was found to be stable as resources of the health department government of Sindh were being used. The AEFI-SS was paper based and deficient in a feedback mechanism. Conclusions Sindh Province has an appropriate surveillance mechanism for AEFI detection and management for the ongoing COVID-19 vaccination SS. The representativeness can be increased by involvement of the private health sector. Establishment of a feedback mechanism and digital data transformation and integration of the AEFI system with the Expanded Program on Immunization (EPI) are recommended.
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 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 | 0,005 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,003 | 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 ».