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. 2020 Apr;7(4):317-326.
doi: 10.1016/S2215-0366(20)30049-3. Epub 2020 Mar 12.

Risk and protective factors for childhood suicidality: a US population-based study

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Risk and protective factors for childhood suicidality: a US population-based study

Delfina Janiri et al. Lancet Psychiatry. 2020 Apr.

Abstract

Background: Childhood suicidal ideation and behaviours are poorly understood. We examined correlates of suicidality in a US population-based sample of children participating in the Adolescent Brain and Cognitive Development (ABCD) study. The ABCD study aims to examine trajectories of mental health from childhood to adulthood and collects information on multiple domains, including mental and physical wellbeing, brain imaging, behavioural and cognitive characteristics, and social and family environment. We sought to identify and rank risk and protective factors for childhood suicidal thoughts and behaviours across these multiple domains and evaluate their association with self-agreement and caregiver agreement in reporting suicidality.

Methods: The ABCD sample comprises a cohort of 11 875 children aged 9-10 years. The sociodemographic factors on which the sample was recruited were age, sex, race, socioeconomic status, and urbanicity. Participants were enrolled at 22 sites, the catchment area of which encompassed over 20% of the entire US population in this age group. Multistage sampling was used to ensure both local randomisation and representativeness of sociodemographic variation of the ABCD sample. The data used in this study were accessed from the ABCD Study Curated Annual Release 2.0. Suicidal thoughts and behaviours (suicidality) in each child were evaluated through independent child and caregiver reports based on the computerized Kiddie Schedule for Affective Disorders and Schizophrenia for DSM-5 (KSADS-5). We used bootstrapped logistic regression to quantify the association between suicidal ideation and behaviours, with measures of mental and physical wellbeing, behaviour, cognition, and social and family environment in participants from the ABCD study.

Findings: Our study sample comprised 7994 unrelated children (mean age 9·9 years [SD 0·5]; 4234 [53%] male participants) with complete data on child-reported and caregiver-reported suicidal ideas and behaviours. Overall, 673 (8·4%) children reported any past or current suicidal ideation, 75 (0·9%) had any past or current suicidal plans, and 107 (1·3%) had any past or current suicidal attempts. According to caregivers, 650 (8·1%) of the children reported any past or current suicidal ideation, 46 (0·6%) reported any past or current suicidal plans, and 39 (0·5%) reported past or current suicidal attempts. However, inter-informant agreement was low (Cohen's κ range 0·0-0·2). Regardless of informant, child psychopathology (odds ratio [OR] 1·7-4·8, 95% CI 1·5-7·4) and child-reported family conflict (OR 1·4-1·8, 95% CI 1·1-2·5) were the most robust risk factors for suicidality. The risk of child-reported suicidality increased with higher weekend screen use time (OR 1·3, 95% CI 1·2-1·7) and reduced with greater parental supervision and positive school involvement (for both OR 0·8, 95% CI 0·7-0·9). Additionally, caregiver-reported suicidality was positively associated with caregiver educational level (OR 1·3, 95% CI 1·1-1·5) and male sex in children (1·5, 1·1-2·0), and inversely associated with the number of household cohabitants (0·8, 0·7-1·0).

Interpretation: We identified risk and protective factors that show robust and generalisable associations with childhood suicidality. These factors provide actionable targets for optimising prevention and intervention strategies, support the need to identify and treat psychopathology in school-age children, and underscore the importance of school and family interventions for childhood suicidality.

Funding: National Institutes of Health.

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Figures

Figure 1:
Figure 1:. Risk and protective factors for child-reported suicidality
Mean odds ratios and 95% CIs of bootstrapped logistic regression models for child-reported suicidality
Figure 2:
Figure 2:. Stability of predictor variables for suicidality
Heatmap showing the percentage of times (columns) that the contribution of each predictor variable (rows) was statistically significant in bootstrapped logistic regression models for child-reported, caregiver-reported, and concordantly-reported suicidality. Further details in the appendix (pp 16–18).

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