Association of COVID-19 Pandemic with Newly Diagnosed Anorexia Nervosa Among Children and Adolescents in Japan
Bibliographic record
Abstract
Background and Objectives: The COVID-19 pandemic globally has negative effects on mental health. Research from Western countries, such as the US, Canada, Australia, and Europe, shows that the COVID-19 pandemic is associated with an increased trend of anorexia nervosa (AN) among children and adolescents. However, the trend after the pandemic in Eastern countries, including Japan, is not well-understood, and it remains unclear whether the pandemic is associated with the trend in these countries. This study aimed to examine the association between the COVID-19 pandemic and the newly diagnosed AN among children and adolescents in Japan. Materials and Methods: Using the nationwide multi-hospital database in Japan, we analyzed the clinical outpatient data in the departments of pediatrics, psychosomatic medicine, and psychiatry. The participants comprised children and adolescents aged 7–19 years newly diagnosed with AN from January 2017 to January 2022. An interrupted time series analysis was used to compare the trends of newly diagnosed AN before and after the COVID-19 pandemic. Estimating the changes in the trends over the pandemic was performed using a Poisson regression model. Results: The total cases of newly diagnosed AN were 41 cases diagnosed in 38 months (1.08 cases per month) before the pandemic and 34 in 23 months (1.48 cases per month) after the pandemic. Notably, in the 7–14 age group, the cases per month increased from 0.74 to 1.13 cases per month over the period. Before the pandemic, a decrease in cases was observed (Incidence Rate Ratio [IRR] = 0.961; 95% CI = 0.932–0.990). Conversely, the after-pandemic period showed a contrasting trend, with increased cases (IRR = 1.096; 95% CI = 1.032–1.176). Furthermore, the number and cases per month in boys increased over the period, from 1 to 5 cases and from 0.03 to 0.22 cases per month, compared to girls from 28 to 26 cases and from 1.05 to 1.26 cases per month. Conclusions: Our findings suggest that sociocultural differences at a national level may not affect the trend of AN after the pandemic. The pandemic and lifestyle changes after the pandemic occurred in both Western and Eastern countries. Considering that, individual, parental, and peer factors related to the pandemic and the consequent lifestyle changes may be more associated with the development of AN. Further research in different countries is needed to elucidate the mechanisms of AN and the long-term impact of the pandemic.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".