The effects of parental adverse childhood experiences (ACEs) and childhood threat and deprivation on adolescent depression and anxiety: an analysis of the longitudinal study of Australian children
Bibliographic record
Abstract
AIMS: Evidence on the effects of parental Adverse Childhood Experiences (ACEs) on adolescent mental health remains limited. This study investigates the associations between parental ACEs, children's exposure to threat- and deprivation-related ACEs, and adolescent depression and anxiety using data from the Longitudinal Study of Australian Children. METHODS: We conducted a secondary analysis of the Longitudinal Study of Australian Children (LSAC), a population-based longitudinal cohort study. Parental ACEs were retrospectively reported by caregivers. Children's exposure to ACEs was assessed from ages 4-17 years and categorised as threat-related ACEs (e.g., bullying, hostile parenting, unsafe neighbourhoods, family violence) or deprivation-related ACEs (e.g., financial hardship, parental substance abuse, parental psychological distress, death of a family member, parental separation, parental legal problems). Depressive and anxiety symptoms were self-reported by adolescents at ages between 12 and 17 years. Modified Poisson regression models were used to examine the independent and combined associations of parental ACEs and children's threat- and deprivation-related ACEs (assessed before ages 12, 14, and 16 years) with depression and anxiety outcomes, including tests for interaction effects. RESULTS: The analysis included 3,956 children aged 12-13 years, 3,357 children aged 14-15 years, and 3,089 children aged 16-17 years. Males comprised 50.8-59.8% and females 40.2-49.2% across all ages. By the age of 17, 30.4% and 9.4% of the adolescents had depression and anxiety, respectively. Parental ACEs (≥2) were associated with increased depression risk at ages 12 to 13 years (RR = 1.42; 95% CI: 1.10-1.84) and at 16-17 years (RR = 1.19; 95% CI: 1.02-1.39). Exposure to ≥ 2 deprivation-related ACEs significantly increased the risk of depression across all ages, with relative risks ranging from 1.31 to 2.18. High threat-related ACEs (≥2) were associated with increased depression risk only at 12 to 13 years (RR = 2.01; 95% CI: 1.28-3.17). No significant interactions were observed. CONCLUSIONS: The findings reinforce the ACEs model by showing that, at the population level, early identification of children exposed to early life deprivations rooted in financial crisis or familial adversities, combined with targeted interventions for both children and parents and supportive social policies, can reduce long-term mental health risks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".