Investigating the differential geographical, sociodemographic and genetic predictors of mental illness and wellbeing using longitudinal survey data

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This fellowship contains an interdisciplinary programme of quantitative research to examine the differential patterning and predictors of mental ill-health and mental wellbeing. Interchangeable use of the two concepts is common in policy and in academic research with the, often unquestioned, assumption that one is the inverse of the other. This has led to many policy recommendations for wellbeing being generalised from a literature specifically addressing mental illness prediction. This assumption, whilst perhaps intuitive, has been increasingly called into question by scientific studies of population measurement. This fellowship builds upon work undertaken as part of an ESRC funded Advanced Quantitative Methods thesis in investigating the (dis)similarity between the two concepts. It uses data from a nationally representative panel study with 8 annual sweeps representing approximately 45,000 individuals per wave between 2009 and 2018.It will deploy advanced quantitative techniques to evaluate the degree to which the underpinning processes of a common mental illness measure differ from the underpinning processes of a mental wellness measure, using simultaneous responses to two questionnaires. This allows the investigation to go beyond simply comparing whether responses are the same, but whether the underpinning processes patterning the responses are the same. The research then goes on to investigate whether the geographical patterning of the two responses is similar, and whether the demographic predictors of each are similar. This also simultaneously allows insight into what spatial scale the responses behave most similarly, identifying for policymakers at which spatial scale recommendations from one may be most justifiably transferred to the other. The programme of work then goes on to longitudinally model the decomposed underpinning processes of the illness metric in order to assess which demographic groups are suffering the greatest burden of mental distress in the years since 2009.To complement this demographic and geographical understanding of the relationship between mental illness and mental wellbeing, I will investigate the to which the genetic predictors of wellbeing are the same as the genetic predictors of mental illness. This is especially pertinent in a literature which increasingly recognises that how mental health is defined can critically influence findings of the degree to which genetics influence psychological distress. This fellowship will further examine this and contribute to an ongoing academic and policy discussion about the complex and under-examined relationship between positive and negative mental health, and how interventions can be most effectively designed to maximise positive impact.