Perinatal adversity profiles and suicide attempt in adolescence and young adulthood: longitudinal analyses from two 20-year birth cohort studies
Bibliographic record
Abstract
BACKGROUND: We aimed to identify groups of children presenting distinct perinatal adversity profiles and test the association between profiles and later risk of suicide attempt. METHODS: Data were from the Québec Longitudinal Study of Child Development (QLSCD, N = 1623), and the Avon Longitudinal Study of Parents and Children (ALSPAC, N = 5734). Exposures to 32 perinatal adversities (e.g. fetal, obstetric, psychosocial, and parental psychopathology) were modeled using latent class analysis, and associations with a self-reported suicide attempt by age 20 were investigated with logistic regression. We investigated to what extent childhood emotional and behavioral problems, victimization, and cognition explained the associations. RESULTS: In both cohorts, we identified five profiles: No perinatal risk, Poor fetal growth, Socioeconomic adversity, Delivery complications, Parental mental health problems (ALSPAC only). Compared to children with No perinatal risk, children in the Poor fetal growth (pooled estimate QLSCD-ALSPAC, OR 1.89, 95% CI 1.04-3.44), Socioeconomic adversity (pooled-OR 1.42, 95% CI 1.08-1.85), and Parental mental health problems (OR 1.74, 95% CI 1.27-2.40), but not Delivery complications, profiles were more likely to attempt suicide. The proportion of this effect mediated by the putative mediators was larger for the Socioeconomic adversity profile compared to the others. CONCLUSIONS: Perinatal adversities associated with suicide attempt cluster in distinct profiles. Suicide prevention may begin early in life and requires a multidisciplinary approach targeting a constellation of factors from different domains (psychiatric, obstetric, socioeconomic), rather than a single factor, to effectively reduce suicide vulnerability. The way these factors cluster together also determined the pathways leading to a suicide attempt, which can guide decision-making on personalized suicide prevention strategies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".