Eating disorder hospitalizations among children and youth in Canada from 2010 to 2022: a population-based surveillance study using administrative data
Bibliographic record
Abstract
BACKGROUND: Eating disorders (EDs) are severe mental illnesses associated with significant morbidity and mortality. EDs are more prevalent among females and adolescents. Limited research has investigated Canadian trends of ED hospitalizations prior to the COVID-19 pandemic, however during the pandemic, rates of ED hospitalizations have increased. This study examined rates of ED hospitalizations among children and youth in Canada from 2010 to 2022, by sex, age, province/territory, length of stay, discharge disposition and ED diagnosis. METHODS: Cases of ED hospitalizations among children and youth, ages 5 to 17 years, were identified using available ICD-10 codes in the Discharge Abstract Database from the 2010/11 to 2022/23 fiscal years. The EDs examined in this study were anorexia nervosa (F50.0), atypical anorexia nervosa (F50.1), bulimia nervosa (F50.2), other EDs (F50.3, F50.8) and unspecified EDs (F50.9). Both cases of total and first-time ED hospitalizations were examined. Descriptive statistics and trend analyses were performed. RESULTS: Between 2010/11 and 2022/23, 18,740 children and youth were hospitalized for an ED, 65.9% of which were first-time hospitalizations. The most frequent diagnosis was anorexia nervosa (51.3%). Females had significantly higher rates of ED hospitalization compared to males (66.7/100,000 vs. 5.9/100,000). Youth had significantly higher rates compared to children. The average age of ED hospitalization was 14.7 years. Rates of ED hospitalizations were relatively stable pre-pandemic, however during the pandemic (2020-2021), rates increased. INTERPRETATION: Rates of pediatric ED hospitalizations in Canada increased significantly during the pandemic, suggesting that there may have been limited access to alternative care for EDs or that ED cases became more severe and required hospitalization. This emphasizes the need for continued surveillance to monitor how rates of ED hospitalizations evolve post-pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".