Acute Care Visits for Eating Disorders Among Children and Adolescents After the Onset of the COVID-19 Pandemic
Bibliographic record
Abstract
PURPOSE: Anecdotal reports suggest a significant increase in acute presentations of eating disorders among children and adolescents. Our objective was to compare the rates of emergency department visits and hospitalizations for pediatric eating disorders before and during the first 10 months of the COVID-19 pandemic. METHODS: Using linked health administrative databases, we conducted a population-based repeated cross-sectional study of emergency department visits and hospitalizations for eating disorders among all children and adolescents aged 3-17 years, residing in Ontario, Canada. We defined the pre-COVID period from January 1, 2017, to February 29, 2020, and the post-COVID period from March 1, 2020, to December 26, 2020. Poisson generalized estimating equations were used to model 3-year pre-COVID trends to predict expected post-COVID trends and estimate the relative change from expected rates. RESULTS: In our population of almost 2.5 million children and adolescents, acute care visits for eating disorders increased immediately after the onset of the pandemic, reaching a 4-week peak annualized rate of 34.6 (emergency department visits) and 43.2 per 100,000 population (hospitalizations) in October 2020. Overall, we observed a 66% (adjusted relative rate: 1.66, 95% confidence interval: 1.41-1.96) and 37% (adjusted relative rate: 1.37, 95% confidence interval: 1.25-1.50) increase in risk for emergency department visit and hospitalization, respectively. CONCLUSIONS: Acute care visits for pediatric eating disorders increased significantly in Ontario after the onset of COVID-19 pandemic and remained well above expected levels during the first 10 months of the pandemic. Further research is needed to understand the social and neurobiological mechanisms underlying the observed changes in health system utilization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".