Hospitalizations for Anorexia Nervosa during the COVID-19 Pandemic in France: A Nationwide Population-Based Study
Bibliographic record
Abstract
The COVID-19 pandemic has had a detrimental impact on mental health, including on food-related behaviors. However, little is known about the effect of the pandemic on anorexia nervosa (AN). We sought to assess an association between the COVID-19 pandemic and a potential increase in hospitalizations for AN in France. We compared the number of hospitalizations with a diagnosis of AN during the 21-month period following the onset of the pandemic with the 21-month period before the pandemic using Poisson regression models. We identified a significant increase in hospitalizations for girls aged 10 to 19 years (+45.9%, RR = 1.46[1.43−1.49]; p < 0.0001), and for young women aged 20 to 29 (+7.0%; RR = 1.07[1.04−1.11]; p < 0.0001). Regarding markers of severity, there was an increase in hospitalizations for AN associated with a self-harm diagnosis between the two periods. Multivariate analysis revealed that the risk of being admitted for self-harm with AN increased significantly during the pandemic period among patients aged 20−29 years (aOR = 1.39[1.06−1.81]; p < 0.05 vs. aOR = 1.15[0.87−1.53]; NS), whereas it remained high in patients aged 10 to 19 years (aOR = 2.40[1.89−3.05]; p < 0.0001 vs. aOR = 3.12[2.48−3.98]; p < 0.0001). Furthermore, our results suggest that the pandemic may have had a particular effect on the mental health of young women with AN, with both a sharp increase in hospitalizations and a high risk of self-harming behaviors.
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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) | 0.001 | 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".