Examining the aetiology, nature and consequences of depression in young people within a biosocial framework

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My research interests are identifying why some young people go on to have poor mental health, whilst others do not. I am interested in identifying the causes of these differences and what the consequences of poor mental health will be for individuals and society. My research uses longitudinal methods that track the same individuals over time and combines these methods with social science and biological data (i.e., genetics) to identify stages of development where preventions and interventions could be implemented to boost mental wellbeing.Depression is one of the most common mental health disorders affecting over 1 in 10 people in the UK. It is expected to be the highest global burden of disease by 2030 and is currently rising in young populations. Depression during adolescence is associated with lower educational attainment, poorer social relationships and increased risk of suicide. One third of individuals with depression first develop onset in adolescence so identifying the nature and causes of depression early could have major benefits in later life. Improving mental health research is one of the ESRC's priority investments and the government has recognised the importance of reducing depression in young people through the development of the Scottish Mental Health Strategy (https://www.gov.scot/publications/mental-health-strategy-2017-2027/). Likewise, many charities and policy makers are keen to further understand the long-term impact of depression in young people. This fellowship contains an interdisciplinary programme of complex statistical research using biological and social data (biosocial) designed to further enhance our understanding of the nature and background of depression in young people. This research will ultimately facilitate better wellbeing for individuals and society.This fellowship uses big data from a longitudinal population-based cohort of children born in the 1990's and their families, followed up over the last 30 years. It will investigate how depression starts and changes through important transitions in life (childhood to adolescence and young adulthood, starting secondary school, leaving school), and what the impact of depression during these periods has on wellbeing in adulthood. Advanced Quantitative Methods (AQM) will be used to highlight these changes over time, and identify when specific risk factors like childhood trauma, parental depression and low self-esteem could be having their greatest effect on depression in young people. Likewise, this fellowship will also demonstrate that depression at different stages of youth development could be important for different outcomes in later life (such as poorer physical health or increased substance misuse). The key point here is that risk factors like childhood trauma, parental depression and low self-esteem, as well as depression itself are modifiable, but it is important to know when and how they operate in order to design well timed interventions.The central aim of this fellowship is to further enhance our understanding of depression in young people through the dissemination of research conducted within my PhD and the further publication of two research projects that were not completed during my PhD and one through my current research. This fellowship provides a critical opportunity to capitalise on this important and timely research for maximum output through high impact publications, dissemination to policy makers, health officials, charities and the general public.A further aim of this fellowship is to further integrate social and biological data within the context of AQM. The ESRC has previously identified the use of AQM in social sciences and health as a priority area, and also acknowledged there is a shortage of researchers using genetic data within social sciences. This fellowship will help develop this framework and provide a platform for training the next generation of social scientists and researchers in the use of biosocial AQM.