The Impact of COVID‐19 Pandemic on Diabetic Children: A Systematic Review on the Current Evidence
Notice bibliographique
Résumé
Coronavirus disease 2019 (COVID-19) pandemic is a worldwide public health emergency. Children seem less likely to be infected with COVID-19 and develop milder symptoms than adults if infected. However, there is limited data regarding the impact of COVID-19 pandemic on diabetic children. Objective: This systematic review aims to summarize and compile the available evidence of COVID-19 pandemic on the pediatric diabetic population, including the incidence of newly diagnosed patients, the risk of DKA and disturbed glycemic control, the use of telemedicine, the impact of lockdown on the daily dietary and physical activity routine, and the management of diabetes during the pandemic. Method: We conducted a comprehensive search of literature published in PubMed, Google Scholar, and Cochrane databases for studies published in English language within the last year as of October 1st, 2020 on the impact of COVID-19 pandemic on diabetic children. Results: We included 20 studies (7 incidence studies, 5 cross sectional, 2 observational, 4 case reports and 2 case series) with a total population of 1989 diabetic children and adolescents. The current evidence suggesting increased incidence of newly diagnosed type 1 diabetes (T1DM) during the COVID-19 pandemic is still weak. Several studies identified delayed diagnosis of children and adolescents with new-onset T1DM leading to presentation with severe DKA. Underlying causes contributed to this observation include reduced access to primary care services, limited availability of healthcare providers, and parental fear from infection during the pandemic period. Moreover, the current pandemic affected the availability of Insulin and glucose measuring supplies leading to poor glycemic control and increasing the risk of DKA among diabetic children especially in resource limited countries. Diabetic children and adolescents had shown good coping skills as a considerable number of them maintained their eating habits and regularly practiced physical activity at home during the lockdown period. In addition, the results of studies on the use of telemedicine for diabetic children and adolescents were positive regarding the effectiveness and patient satisfaction. Conclusion: More studies are required to document the association between COVID-19 infection and the development of T1DM, and to evaluate the physical and psychological impact of the current pandemic on diabetic children and adolescents. In preparation for any potential second wave, specific strategies are essential to alleviate the negative impact of the current pandemic on the management of diabetic children. In order to avoid delayed diagnosis of patients with new onset diabetes, countries should reopen the access to essential non-COVID-19 services, and families should be encouraged about timely attendance at the ED for children with symptoms that are not related to COVID-19. Telemedicine is a promising approach for the management of diabetic patients as it provides a safe, fast and effective way of communication between patients and their diabetic teams. Keywords: adolescents; children; COVID-19; diabetes mellitus and DKA; pediatrics; SARSCoV2
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 | 0,004 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,007 |
| Bibliométrie | 0,009 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».