Acute presentations of eating disorders among adolescents and adults before and during the COVID-19 pandemic in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Increased rates of pediatric eating disorders have been observed during the COVID-19 pandemic, but little is known about trends among adults. We aimed to evaluate rates of emergency department visits and hospital admissions for eating disorders among adolescents and adults during the pandemic. METHODS: We conducted a population-based, repeated cross-sectional study using linked health administrative data for Ontario residents aged 10-105 years during the prepandemic (Jan. 1, 2017, to Feb. 29, 2020) and pandemic (Mar. 1, 2020, to Aug. 31, 2022) periods. We evaluated monthly rates of emergency department visits and hospital admissions for eating disorders, stratified by age. RESULTS: Compared with expected rates derived from the prepandemic period, emergency department visits for eating disorders increased during the pandemic among adolescents aged 10-17 years (7.38 v. 3.33 per 100 000; incidence rate ratio [IRR] 2.21, 95% confidence interval [CI] 2.17-2.26), young adults aged 18-26 years (2.79 v. 2.46 per 100 000; IRR 1.13, 95% CI 1.10-1.16) and older adults aged 41-105 years (0.14 v. 0.11 per 100 000; IRR 1.15, 95% CI 1.07-1.24). Hospital admissions for eating disorders increased during the pandemic for adolescents (8.82 v. 5.74 per 100 000; IRR 1.54, 95% CI 1.54-1.54) but decreased for all adult age groups, especially older adults aged 41-105 years (0.21 v. 0.30 per 100 000; IRR 0.72, 95% CI 0.64-0.80). INTERPRETATION: Emergency department visits for eating disorders increased among adolescents, young adults and older adults during the pandemic, but hospital admissions increased only for adolescents and decreased for all adult groups. Differential rates of acute care use for eating disorders by age have important implications for allocation of inpatient mental health resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| 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".