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Casual Inference Methods for Study of Dynamic Predictors of Mental Health Needs and Patients’ Outcomes (ESRC CASE Studentship)


   Lancaster Medical School


About the Project

While the importance of understanding causal mechanisms has been understood for a long time, statistical and data-driven methods to explore these relationships are still in active development. To date, theoretical models in areas such as Mental Health remain to be often descriptive. One of the reasons why a detailed assessment of causality has previously been hampered is the lack of time-rich data sources allowing investigation into the order of causes and effects dynamically. However, recently a steep rise in large longitudinal studies and availability of routinely collected health data provide new potential.

This project, in collaboration with Lancashire and South Cumbria National Health Service (NHS) Foundation Trust (LSCFT) will test, evaluate and develop the methodological tools and frameworks within specific subfields of psychopathology and population mental health to enable researchers and practitioners to harness new datasets and take advantage of temporal aspects to study causality and reciprocity, and provide answers to questions such as ‘What comes first?’ also known as ‘Chicken and Egg’ problems within the area of mental health. We will focus on at least two data-driven areas (e.g. longitudinal population studies and NHS routinely collected data) that require pressing solutions and while the characteristics of the data-sources are shared, these represent a quite diverse set of applications. With this proposal we therefore hope to enable the causal study of interacting factors over time to answer questions like a) ‘In what settings does income cause psychological distress?’ b) ’what contributes to ‘too early’ discharge of mental health patients?’. Such an extensive programme will ensure robustness, transparency, and widest impact possible on population mental health field from this methodological development.

The LSCFT Clinical Services Strategy 2021-26 report has identified that key localities covered by the trust (Fylde Coast, the Bay, Pennine, Central) that are in urgent need of attention. For example, the deprivation index within Blackpool is currently the worst in the country and the prevalence of depression among adults is the highest. The Cumbria region has a higher prevalence of suicide and A&E admittance for psychiatric disorders (45% higher that country’s average). Whilst these statistics indicate a problem, research is critically needed to understand the drivers of these patterns and help guide the way for solutions. This project will aid understanding of predictors of ill-mental health within the area and how these evolve over time; provide a comprehensive overview of determinants for patient outcomes. Once patients are registered on NHS systems, knowledge of these factors can be used to inform interventions which can then be actioned on by practitioners.

Given the nature of this project (applied methodology) we will be considering candidates who may want to focus on applied aspects of the project or have more methodological/statistical focus. Please indicate in your application which one you would be most interested with and why.

We welcome applications from candidates with training in statistics, data science, psychology, sociology, economics and related fields with strong quantitative component.

Student/s will be based in Lancaster Medical School’s Centre for Computing, Health Informatics and Statistics (https://chicas.lancaster-university.uk/index.html) and will be working towards receiving PhD in Health Data Science. 

Successful candidate/s will be supervised by Dr Anastasia Ushakova and Professor Jo Knight, and will be embedded within participating NHS trust. There will also be opportunities for various training and development targeted to candidate needs (e.g. through renown Data Science Institute https://www.lancaster.ac.uk/dsi/) Informal queries about this programme are very welcome and should be direct to Dr Ushakova

Application process: Applications should be made in writing to the lead supervisor, Dr Anastasia Ushakova (). You MUST include the following

1.     CV (max 2 A4 sides), including details of two academic references

2.     A cover letter outlining your qualifications and interest in the studentship (max 2 A4 sides) 

3. A brief, no longer than 1,000 words, research plan related to project proposal

The deadline is on the 30th of June. We encourage interested candidates to apply as soon as possible


Funding Notes

The full-time studentships are tenable up to 3 years full-time (subject to satisfactory progress) and will cover the cost of tuition fees at Home rates alongside a stipend in line with the UK Research Council is payable.
It is expected the successful applicant will commence 1st October 2022.

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