B949 - Mental health resilience in children of parents with depression - 03/02/2010

B number: 
B949
Principal applicant name: 
Dr Stephan Collishaw (University of Cardiff, UK)
Co-applicants: 
Prof Ricardo Araya (University of Bristol, UK), Dr Jon Heron (University of Bristol, UK), Prof Anita Thapar (University of Cardiff, UK), Prof Frances Gardner (University of Oxford, UK)
Title of project: 
Mental health resilience in children of parents with depression.
Proposal summary: 

Analytic aims

Aim 1: Identification of high-risk group

A core concern is to ensure that resilience is defined by reference to a clearly defined risk, and that variation in child outcomes does not merely reflect variation in severity of exposure to the risk factor of interest. Parent depression symptom data from pregnancy up to child age 12 will be analysed using group-based trajectory models to identify parents with chronic high levels of depressive symptoms. Additional information on severity of comorbid psychopathology, and functional impairment will be used to further characterise variation in severity of risk.

Aim 2: Definition of offspring mental health resilience

Most previous studies have focused on specific outcomes or on adaptation on a single occasion, limiting conclusions about the extent and stability of positive adaption in children defined as resilient. The second aim will be to define resilience by assessing adaptive youth outcomes over multiple mental health domains (i.e. absence of depression, anxiety, disruptive behaviour disorders, substance use, self harm, suicidality) and over multiple time points spanning adolescence.

Aim 3: Predictors of resilience in children of depressed parents

The third aim will be to evaluate the extent to which the different parent, family, child and peer factors outlined above predict resilience. Analyses will assess longitudinal associations using multivariate models to highlight the most important independent predictors of resilience in this high risk group.

Aim 4: Testing causal mechanism underlying resilient adaptation

Having identified key predictors of resilience using standard regression approaches, we will use more complex analytic techniques (e.g. structural equation modelling, latent growth models) to make full use of the multi-time point data to test causal mechanisms underlying resilience.

Aim 5: Identification of predictors of resilience of particular relevance to children of depressed parents

The ALSPAC data offers the opportunity to distinguish between general predictors of positive mental health and specific predictors of resilience in children of depressed mothers. In particular, we will test interactions between resilience factors and risk status (chronic maternal depression or not).

Date proposal received: 
Wednesday, 3 February, 2010
Date proposal approved: 
Wednesday, 3 February, 2010
Keywords: 
Depression, Mental Health
Primary keyword: