Pandemic Stringency Measures and Hospital Admissions for Eating Disorders
Bibliographic record
Abstract
Importance: Hospitalizations for eating disorders rose dramatically during the COVID-19 pandemic. Public health restrictions, or stringency, are believed to have played a role in exacerbating eating disorders. Few studies of eating disorders during the pandemic have extended to the period when public health stringency restrictions were lifted. Objective: To assess the association between hospitalization rates for eating disorders and public health stringency during the COVID-19 pandemic and after the easing of public health restrictions. Design, Setting, and Participants: This Canadian population-based cross-sectional study was performed from April 1, 2016, to March 31, 2023, and was divided into pre-COVID-19 and COVID-19-prevalent periods. Data were provided by the Canadian Institute for Health Information and the Institut National d'Excellence en Santé et Services Sociaux for all Canadian provinces and territories. Participants included all children and adolescents aged 6 to 20 years. Exposure: The exposure was public health stringency, as measured by the Bank of Canada stringency index. Main Outcomes and Measures: The primary outcome was hospitalizations for a primary diagnosis of eating disorders (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision code F50), stratified by region, age group, and sex. Interrupted time series analyses based on Poisson regression were used to estimate the association between the stringency index and the rate of hospitalizations for eating disorders. Results: During the study period, there were 11 289 hospitalizations for eating disorders across Canada, of which 8726 hospitalizations (77%) were for females aged 12 to 17 years. Due to low case counts in other age-sex strata, the time series analysis was limited to females within the 12- to 17-year age range. Among females aged 12 to 17 years, a 10% increase in stringency was associated with a significant increase in hospitalization rates in Quebec (adjusted rate ratio [ARR], 1.05; 95% CI, 1.01-1.07), Ontario (ARR, 1.05; 95% CI, 1.03-1.07), the Prairies (ARR, 1.08; 95% CI, 1.03-1.13), and British Columbia (ARR, 1.11; 95% CI, 1.05-1.16). The excess COVID-19-prevalent period hospitalizations were highest at the 1-year mark, with increases in all regions: Quebec (RR, 2.17), Ontario (RR, 2.44), the Prairies (RR, 2.39), and British Columbia (RR, 2.02). Conclusion and Relevance: In this cross-sectional study of hospitalizations for eating disorders across Canada, hospitalization rates for eating disorders in females aged 12 to 17 years were associated with public health measure stringency. The findings suggest that future pandemic preparedness should consider implications for youths at risk for eating disorders and their resource and support needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".