Catch-Up of Routine School-Based Immunizations Since the COVID-19 Pandemic: 4-Year Observational Cohort Study
Notice bibliographique
Résumé
Abstract Background School-based immunization programs (SBIPs) have been widely used to efficiently deliver vaccines to children and adolescents, achieving higher and more equitable coverage than community-based approaches, though implementation varies across regions. However, the COVID-19 pandemic disrupted SBIPs, leading to significant declines in vaccine coverage and highlighting the need for long-term assessments to determine the effectiveness of recovery efforts, especially as current research has largely overlooked SBIPs or focused narrowly on select vaccines. Objective This study aimed to assess the impact of the COVID-19 pandemic and ongoing catch-up immunization efforts on vaccine coverage and administration volume among school-aged children in the Canadian province of Alberta. We also sought to determine the magnitude of any remaining vaccine deficits and estimate the duration of additional catch-up efforts required to address those deficits. Methods In this retrospective cohort study, we used population-wide administrative health data to calculate monthly cumulative vaccine coverage among Grade 1, 6, and 9 cohorts, comparing four pandemic cohorts (2019‐2020, 2020‐2021, 2021‐2022, and 2022‐2023 grade-years) to a prepandemic cohort for each grade. We also calculated vaccine administration rates as monthly count of vaccines delivered, stratified by routine (ie, received during the school year as scheduled) and catch-up (ie, received after the regularly scheduled school year) doses. For each vaccine, we calculated vaccine coverage deficits remaining at the end of the study period and estimated time to clear those deficits based on observed catch-up uptake rates. Results All Grade 6 pandemic cohorts had lower human papillomavirus (HPV) and hepatitis B vaccine coverage at the end of the school year (2.6‐60 percentage points lower and 1.2‐41.7 percentage points lower, respectively), though coverage had increased to near prepandemic levels after approximately 3 years of follow-up. Coverage among the Grade 1 and 9 cohorts remained below prepandemic levels at study end; the largest deficits were noted in the 2022‐2023 Grade 1 cohort (7.87 percentage points lower for measles-containing vaccines, 4.64 percentage points lower for pertussis-containing vaccines) and the 2020‐2021 cohort for Grade 9 (14.0 percentage points lower for meningococcal conjugate ACYW-135 vaccine, 12.9 percentage points lower for pertussis-containing vaccines). The additional catch-up time required to reach prepandemic coverage levels for all cohorts was estimated to be less than 1 year for Grade 6 cohorts, 5.6 years for Grade 1 cohorts, and 4.8 years for Grade 9 cohorts. Conclusions Partial recovery of vaccine coverage was found for cohorts impacted by interruptions to school-based programming during the COVID-19 pandemic. Further efforts to alleviate coverage deficits among Grade 1 cohorts may be addressed in routine Grade 6 programs, while targeted efforts in postsecondary institutions may be required to address delays in Grade 9 immunizations.
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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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».