6 Incidence of Eating Disorders During COVID-19: A Retrospective Review
Notice bibliographique
Résumé
Abstract Background The COVID-19 pandemic has had marked effects on mental health, including in pediatric populations. Pediatric patients have faced mental health concerns at increased rates including anxiety and depression. Furthermore, patients with eating disorders represent a vulnerable group who have been negatively impacted as well, as a result of lack of support, loss of in-person follow-up and increased relapse. In our centre, and nationally, clinicians have noted a trend towards increased eating disorder referrals and increased hospitalizations during the pandemic. Objectives The objective of this study was to determine the incidence, severity and triggers for eating disorders in the adolescent population during the COVID-19 pandemic and how it compares to the year prior. As well, the subset of patients who were hospitalized for medical stabilization were further analyzed to determine severity of illness. Design/Methods A retrospective chart review compared the first year of the COVID-19 pandemic (March 2020-March 2021), to the previous 12 months. Inclusion criteria included referrals to an eating disorder clinic and inpatient admissions to pediatrics or mental health services during the specified time frame. Data collected included age of onset, triggers, comorbid mental health conditions, and weight measures. Among hospitalized patients, orthostatic vital changes, need for NG feeds, length of medical stabilization and length of mental health hospitalization were included. Results Overall, 76 patients were included in the study. 44 (57.9%) were referred after COVID, which was significantly increased from the prior year (p=0.05). On average, patients presented at a younger age (14.2 ± 2.3 vs. 14.9 ± 1.9; p=0.08). Pre-COVID, approximately 44% of referrals were from family physicians and 19% from pediatrics. During COVID, approximately 39% were from family doctors and 25% from pediatricians. There was an increase in the number of patients requiring hospitalization for treatment (16 vs. 3), with 50% of the post-COVID admissions being direct from the ED Clinic on initial assessment. The reason for hospitalization was unstable vitals/ bradycardia in 68.7% of admissions; self-harm comprised the majority of the other admissions. Conclusion Our results support national and international reports that eating disorder incidence has increased during COVID-19. Patients described loss of routine, anxiety, and isolation as triggers related to the pandemic. Disruptions to daily life including school, sports, recreation, and relationships had profound effects on the mental health of children. The effect of social media on body image has also contributed. It is important for clinicians to screen for mental health conditions, including eating disorders at all available opportunities. Furthermore, this study demonstrates the need for increased services at our centre. Limitations for this study include that it is a single-centre study with a relatively small patient population. As well, it does not capture patients who may have been referred only to psychiatry.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».