Different trends in suicide rates among foreign residents in Japan and Japanese citizens during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Suicide rates in Japan have increased during the COVID-19 pandemic, and foreign residents may be more vulnerable to mental stress during such crises. Therefore, we aimed to compare the trends in suicide rates during the COVID-19 pandemic between foreign residents and Japanese citizens. METHODS: Vital statistics of Japan data from January 1, 2016 to December 31, 2021 were used to calculate quarterly sex-specific suicide rates for foreign residents and Japanese citizens. An event-study analysis was conducted to evaluate whether suicide rates during the COVID-19 pandemic increased compared to pre-pandemic estimates; foreign residents and Japanese citizens were compared using difference-in-difference-in-differences estimates. RESULTS: Between 2016 and 2021, 1,431 foreign residents and 121,610 Japanese citizens died from suicide in Japan. Although the suicide rate for foreign residents was lower than that for Japanese citizens, Korean residents, who comprise approximately half of the foreign decedents, had largely higher suicide rates than Japanese citizens. The event-study analysis indicated that suicide rates increased among foreign residents for both men and women, and continued for men by the end of 2021. In Japanese citizens, after a decline in suicide rates in the second quarter of 2020, suicide rates increased both among men and women, and lasted for women until the fourth quarter of 2021. The difference-in-difference-in-differences analyses confirmed the initial decline in the second quarter of 2020 in suicide rate only in Japanese men and women, and the persistent increase through 2021 in foreign men. CONCLUSIONS: We found differential trends in suicide rates between foreign and Japanese men and women during the COVID-19 pandemic featuring a persistent increase in foreign men. Suicide prevention measures should be focused on these high-risk subpopulations.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".