Suicidal Ideation, Suicide Attempts, and Suicide Deaths in Adolescents and Young Adults With Type 1 Diabetes: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Evidence is lacking on the risk of suicide-related behaviors (suicidal ideation, suicide attempt, suicide death) in youth with type 1 diabetes (T1D). PURPOSE: We aimed to 1) determine the prevalence of suicidal ideation, suicide attempts, and suicide deaths in adolescents and young adults (AYA) with T1D aged 10-24 years; 2) compare suicide-related behavior prevalence in youth with and without T1D; and 3) identify factors associated with suicide-related behaviors. DATA SOURCES: A systematic search was conducted in MEDLINE, Embase, and PsycInfo up to 3 September 2023. STUDY SELECTION: We included observational studies where investigators reported the prevalence of suicide-related behaviors among AYA aged 10-24 years with T1D. DATA EXTRACTION: We collected data on study characteristics, data on prevalence of suicide-related behaviors, and data on associated factors. DATA SYNTHESIS: We included 31 studies. In AYA with versus without T1D, pooled prevalence of suicidal ideation was 15.4% (95% CI 10.0-21.7; n = 18 studies) vs. 11.5% (0.4-33.3; n = 4), respectively, and suicide attempts 3.5% (1.3-6.7; n = 8) vs. 2.0% (0.0-6.4; n = 5). Prevalence of suicide deaths ranged from 0.04% to 4.4% among youth with T1D. Difficulties with T1D self-management were frequently reported to be associated with higher rates of suicide-related behaviors. However, findings on the association of glycemic levels and suicide-related behaviors were inconsistent. LIMITATIONS: There was a considerable level of heterogeneity in meta-analysis of both suicidal ideation and suicide attempts. CONCLUSIONS: Suicidal ideation and suicide attempts are prevalent in AYA with T1D. Current evidence does not suggest that these rates are higher among AYA with T1D than rates among those without.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".