Long-Term Trends in New and Pre-Existing Eating Disorder Acute Presentations Among Adolescents and Young Adults During and After the COVID-19 Pandemic: A Population-Based Cohort Study
Bibliographic record
Abstract
PURPOSE: To measure rates of emergency department (ED) visits and hospitalizations among adolescents and young adults with new and pre-existing eating disorders 3.5 years after the onset of the COVID-19 pandemic. METHODS: We conducted a population-based cross-sectional study using linked health administrative data for Ontario residents aged 10-17 years and 18-26 years during the prepandemic (January 1, 2017-February 29, 2020) and postpandemic periods (March 1, 2020-September 30, 2023). We used Poisson generalized estimating equations models to predict expected yearly overall and monthly age-stratified rates of eating disorder-related ED visits and hospitalizations using prepandemic trends among those with a new or pre-existing eating disorder. RESULTS: ED visit rates peaked above expected in 2021 among adolescents with new (adjusted rate ratio [aRR] 2.70, 95% confidence interval [CI; 2.34, 3.11]) and pre-existing eating disorders (aRR 3.38, 95% CI [2.70, 4.23]). Although ED visit rates decreased over time for adolescents, they remained higher than expected in 2023 for both new (aRR 1.37, 95% CI [1.16,1.63]) and pre-existing eating disorders (aRR 1.53, 95% CI [1.17, 2.01]). Hospitalization trends among adolescents mirrored these patterns. Among young adults, ED visit rates for new eating disorders peaked above expected levels in 2021 (aRR 1.21, 95% CI [1.15, 1.27]), whereas the peak for pre-existing eating disorders occurred in 2020 (aRR 1.45, 95% CI [1.22, 1.73]). By 2022, ED visit rates had returned to or dropped below expected levels. DISCUSSION: The surge in eating disorder-related acute care visits was limited to the pandemic, with rates mostly returning to baseline, although adolescent rates remain elevated. This highlights the need for continual monitoring of long-term trends to ensure that public health responses are informed by sustained data.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".