Quantifying cross-sectional and longitudinal associations in mental health symptoms within families: network models applied to UK cohort data
Year: 2025
Type: article
Abstract: OBJECTIVES: Families offer promising targets for mental health interventions. Existing evidence investigates parent-child dyads or partners; we use an innovative approach to look at triads of parents and their children. This gives us more detail on mental health dimensions and individuals central to mental health transmission in families. DESIGN: Both cross-sectional and longitudinal network models SETTING: We identified triads of children (under age 16), mothers and fathers from the UK Household Longitudinal Study, between 2009 and 2022. PARTICIPANTS AND METHODS: Cross-sectional networks captured independent associations between family members' mental health (n=8795 families). Longitudinal networks examined directional temporal associations among family members' emotional symptoms (n=3757 families). PRIMARY OUTCOME MEASURES: Children's and parents' mental health dimensions were assessed using the Strengths and Difficulties Questionnaire and the General Health Questionnaire, respectively. RESULTS: Mothers' mental health, particularly emotional symptoms, was linked to children's mental health, while fathers' symptoms showed no independent association. In the longitudinal network, maternal feelings of being overwhelmed were associated with children's future worry, affecting symptoms of nervousness and unhappiness, which then fed back into worsening maternal emotional symptoms. CONCLUSIONS: Investigating family mental health using network models highlights mothers' central role. The longitudinal relationship between maternal feelings of being overwhelmed and children's anxiety, and the subsequent feedback into maternal anxiety, indicates a promising target for intervention.
Source: BMJ Open
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,839
Funder: Wellcome Trust
OpenAlex ID: W4414883189
