Contact: Mariana Pinto da Costa
Email: mariana.pintodacosta@kcl.ac.uk
Department: Psychological Medicine
Institution: Kings College London
Project timeline: Can be flexible.
Project duration: 13 weeks (part-time or full-time).
Closing date: TBC
Expertise required: Previous experience in statistical analysis.
Project description: Psychosis is a serious mental disorder that can greatly affect how a person thinks, feels, and behaves. Within the clinical features of this disorder, there are certain symptoms called ‘negative symptoms’ which do not respond well to existing treatments and can make it challenging for individuals to feel motivated to socialise. As a result, people with psychosis often become socially withdrawn and isolated. Social isolation, which is when someone lacks social interactions with others, can lead to feeling lonely. This can increase the risk of developing or worsening physical and mental health problems, and it can make it difficult for people with psychosis to get treatment and slow down their recovery.
Mental health research is being transformed by the very large data resources accruing from electronic health records, amongst other sources, creating exciting emerging capabilities and a rapidly evolving discipline of Mental Health Data Science. At the NIHR Maudsley Biomedical Research Centre, and particularly through our Clinical Record Interactive Search (CRIS) platform, we have created internationally leading data resources which contain unparalleled granularity (detail) of information on large clinical populations receiving routine care, including the application of natural language processing to extract structured data from text fields.
Making use of these resources, quantitative methodology will be used to investigate the link between recorded loneliness in patients with psychosis, clinical phenotypes (negative symptoms, positive symptoms, depressive symptoms, and manic symptoms) and patient characteristics, such as age and ethnicity, as well as the patterns of healthcare use, health care efficiency, and clinical outcomes. This project will contribute to a robust and novel evaluation of loneliness on health outcomes, and how it may change over time.
Description of the work involved: The student will work with a dataset on loneliness in people with psychosis and will be supported to conduct/lead on analyses to assess the: 1) prevalence of recorded loneliness in people with psychosis and 2) how recorded loneliness relates to positive and negative symptoms, depressive symptoms, manic symptoms, and individual factors such as age and ethnicity.
The student will be supervised to lead analyses using advanced quantitative methods including multivariate analysis.
Student benefits: Students will gain practical experience in working with electronic health records, using large real-world big datasets and applying advanced quantitative methods.
Students will have the opportunity to cultivate expertise in the emerging field of mental health data science, gaining hands-on experience and skills in analysing and interpreting complex mental health data.
Students will work under the supervision of experienced researchers and gain valuable exposure to the work conducted by the CRIS team at the NIHR Maudsley Biomedical Research Centre.