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P20 Cumulative effect of adverse childhood experiences on affective symptom trajectories in adulthood: evidence from a british birth cohort
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  1. EJ Thompson1,
  2. M Richards2,
  3. D Gaysina1
  1. 1School of Psychology, University of Sussex, Brighton, UK
  2. 2MRC Unit for Lifelong Health and Ageing, University College London, London, UK

Abstract

Background Previous studies have shown that specific types of adverse childhood events (ACEs), such as parental divorce and parental psychopathology, pose a risk for the development of affective symptoms in adulthood (AS). However, a majority of this evidence is based on single types of retrospectively reported ACEs. This is problematic as ACEs tend to be inter-related and often co-occur.

Methods We used the data from the MRC National Survey of Health and Development (NSHD). This is an ongoing longitudinal study of 5362 women and men who were born in Britain in1946. Later life AS were measures using the General Health Questionnaire (GHQ) at ages 53 y, 60–64 y and 69.

Multiple imputation was implemented on each ACE predictor and a cumulative risk index was derived though summing the number of adversities experienced by each participant (0, 1, 2, 3…20) before age 16 y. The effect of cumulative ACEs on AS at each time point (53, 60–64 and 69) was examined using linear regression.

Results Preliminary analyses revealed a significant association was found between cumulative ACEs and AS at ages 60–64, β(1, 2183)=0.07, p=0.002, and 69, β(1, 2110)=0.07, p=0.003, but not age 53 β(1, 2900)=0.04, p=0.058. Further to this growth mixture modelling will be used to model latent trajectories of AS between age 53 and 69 years and the effect of cumulative ACEs will be examined.

Discussion These findings will be presented in light of the growing evidence for the negative effects of ACEs on health and wellbeing in later life. Furthermore, we will discuss how this research informs prevention for the development of psychopathology across the life course.

  • cumulative risk
  • mental health
  • longitudinal data analysis

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