Developing National Information Systems to Monitor COVID-19 Vaccination: A Global Observational Study
Notice bibliographique
Résumé
Background: Strong information systems are essential for safe and effective immunization programs. The COVID-19 vaccine rollout presented all immunization information systems (IIS) with challenging demands-requiring in-depth vaccine implementation data at all health system levels in real time. The system development approaches taken by countries were heterogeneous, with some countries opting to adapt existing systems and others implementing new ones. Objective: Using data reported by Member States to the World Health Organization (WHO), we aim to develop a global understanding of (1) the types of IIS used to monitor COVID-19 vaccination implemented in 2021 and (2) the approaches taken by countries to develop these systems. Methods: We conducted a descriptive analysis of data reported through a supplemental questionnaire of the WHO/United Nations Children's Emergency Fund (UNICEF) Joint Reporting Form on Immunization, collecting data for 2021 on (1) the use of and developmental approaches taken for 7 IIS functions (appointments, aggregate reporting, individual-level reporting, reminders, home-based records, safety surveillance, and stock management), and (2) modifications needed for digital health frameworks to permit COVID-19 vaccination monitoring. Results: In total, 188 of 194 WHO Member States responded to the supplemental questionnaire, with 155 reporting on the IIS-related questions. Among those reporting, for each of the 7 IIS functions explored, greater than 85% of responding countries reported that the system was in place for COVID-19 vaccines. Among responding countries, "aggregate reporting system" was the system most frequently reported as being in place (n=116, 98.3%), while "reminder system" was the least (n=77, 89%). Among the countries reporting using a system, whether an existing system was adapted for COVID-19 vaccines or a new one was developed varied by system. Additionally, two-thirds (n=127, 67.6%) of countries reported establishing at least one new system, ranging from 72% (n=42) in high-income countries (HICs) to 62% (n=16) in low-income countries. Concurrently, 55.3% (n=104) of countries reported adapting at least one system already in place for COVID-19 vaccines, with 62% (n=36) of HICs reporting this compared to about 53% for other income groups. Of those reporting developing new systems, for each of the systems explored, more than 85% of countries reported that they intended to keep new systems specific to COVID-19 vaccines. Further, 147 of the 188 (78.2%) Member States responding to the supplemental questionnaire responded to the digital health frameworks question. Lastly, 31% (n=46) of responding countries reported needing to adapt them for COVID-19 vaccination systems. HICs had a higher percentage. Conclusions: Nearly all countries have adapted existing or developed new IIS to monitor COVID-19 vaccination. The approaches varied, notably by income group. Reflection is needed on how to sustain the investments made in IIS during the pandemic. Continued support for IIS is critical, given their essential role in program monitoring and performance.
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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 | 0,012 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,004 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».