Estimating additional health and social costs in eating disorder care for young people during the COVID-19 pandemic: implications for surveillance and system transformation
Notice bibliographique
Résumé
Abstract Background The impact of the COVID-19 pandemic on young people with eating disorders (EDs) and their families was profound, with surging rates of hospitalizations and referrals reported internationally. This paper provides an account of the additional health and social costs of ED care for young people living in Canada incurred during the COVID-19 pandemic, drawing attention to the available data to inform these estimates while noting gaps in data capacities to account for a full view of the ED system of care. Methods Three methodologies were used to capture costs: (1) provincial administrative data holdings available at the Canadian Institute of Health Information (CIHI) were used by Deloitte Access Economics to conduct analyses on costs related to hospitalizations, emergency room visits, outpatient visits with physicians and loss of well-being from being on a waitlist. These were examined across three fiscal years (April 1 to March 31, 2019–2022) to compare costs from one year before to two years after the onset of the pandemic, (2) data collected on support-based community ED organizations and, (3) costs identified by young people, caregivers and health care professionals. Results Estimates of additional health care costs and social costs arising from ED care waitlists were estimated to have increased by 21% across the two years after the onset of the pandemic and is likely to represent an underestimate of costs. Costs related to some standard ED care services (e.g. day treatment programs) and support-based community ED organizations that saw a 118% increase in services during this time, are some examples of costs not captured in the current cost estimate. Conclusions This paper provides a first account of the additional health and social ED care costs associated with the pandemic, which indicate at minimum, a 21% increase. The results invite discussion for more investments in ED services for young people in Canada, as it is unclear if needs are expected to remain elevated. We suggest a call for a national surveillance strategy to improve data holdings to aid in managing services and informing policy. A robust strategy could open the door for much-needed, data-informed, system transformation efforts that can improve ED care for youth, families and clinicians.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 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 ».