Prolonged impact of the COVID-19 pandemic on self-harm hospitalizations in France: A nationwide retrospective observational study
Bibliographic record
Abstract
Abstract Background The first wave of the COVID-19 pandemic in France was associated with a reduced number of hospitalizations for self-harm, with the exception of older people. The on-going pandemic may have both sustained and delayed effects. Methods Data were extracted from the French national hospital database (PMSI), a nationwide exhaustive database. The number of self-harm hospitalizations (ICD-10 codes X60–84) between September 1, 2020 and August 31, 2021 ( N = 85,679) was compared to 2019 ( N = 88,782) using Poisson regression models. Results There was a decrease in the total number of self-harm hospitalizations during the studied period versus 2019 (−3.5%; Relative Risk [RR] [95% Confidence Intervals] = 0.97 [0.96–0.97]; p < 0.0001). However, sex and age effects were identified. While adults aged 30–59-years-old showed a decrease (monthly decreases: −12.6 to −15.0%), we found an increase in adolescent girls (+27.7%, RR = 1.28 [1.25–1.31]; p < 0.0001), notably since January 2021. Moreover, the numbers were similar to 2019 in adolescent boys, in youths aged 20–29 years, and in people aged 70 and more. Hospitalizations in intensive care units decreased (−6.7%, RR = 0.93 [0.91–0.96]; p < 0.0001) and deaths at hospital following self-harm remained stable (+0.6%, Hazard Ratio = 0.99 [0.91–1.08], p = 0.79). Conclusions During this second stage, the number of self-harm hospitalizations remained at a lower level than in the prepandemic period. However, significant variations over time, age, and sex were observed. Young people (notably adolescent girls) appear to have particularly suffered from the persistence of the pandemic, while older people did not show any decrease since the beginning. Vigilance and continuing prevention are warranted.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".