Individuals with severe mental illnesses face a higher risk of developing conditions such as diabetes (sugar in the blood) and heart disease (e.g., heart attacks). Consequently, they tend to live shorter lives, on average 10 to 15 years less than those without these mental health challenges. To manage their conditions, they are often prescribed antipsychotic medications for extended periods. When used over long periods, these medications can lead to weight gain and changes in blood pressure and cholesterol levels (unhealthy fat in the blood). These effects may partly contribute to the increased risk of diabetes and heart disease in this group.
My goal is to investigate how the use of antipsychotic medications impacts the physical health of individuals with severe mental illnesses and how this impact may vary depending on factors like age, gender, ethnicity, and socioeconomic status. By analysing data routinely collected in general practices from over 200,000 patients, I will study the patterns of antipsychotic medication use over nearly two decades (2000-2019). I will then compare their body weight, blood pressure, and cholesterol levels before and after starting antipsychotic treatment to understand the long-term effects on physical health. Additionally, I will explore how prescription patterns (e.g., changes in doses over time) and their effects on physical health may differ among different age groups, genders, socioeconomic backgrounds, and ethnicities.
The insights gained will provide valuable information to patients and their healthcare providers, helping them better comprehend the long-term effects of antipsychotic medications and guiding future treatment decisions.
People with severe mental illnesses (SMI) have reduced life expectancy and a higher risk of developing type-2 diabetes mellitus (T2DM) and cardiovascular diseases (CVD). I aim to examine health inequalities in terms of 1) long-term antipsychotic treatment patterns and how these vary across groups by age, gender deprivation and ethnicity; 2) relationship between long-term antipsychotic treatment and the change of HbA1c, weight, blood pressure and cholesterol over time (health indicators); 3) based on this long-term association, determine the relationship between antipsychotic treatment and the risk of developing T2DM, CVD, and all-cause mortality, and how inequalities by socio-demographics might influence this pathway. Findings will be reported for England and each area identified by the NIHR 'heat map' as being underserved in terms of mental health research. I will use primary care data on individuals aged 18-99 observed 2000-2019 who were prescribed antipsychotics. I will apply machine learning (ML) techniques to identify treatment patterns linked to specific sociodemographic groups. I will fit multivariate mixed-effects models for the interrupted time series (ITS) analysis of health indicators trajectories over time, before and after antipsychotic treatment initiation, jointly with survival models to estimate the risk of developing T2DM, CVD and mortality, stratified by socio-demographics. ML results will help identify inequalities in long-term treatment prescriptions across sociodemographic groups, including most deprived and ethnic minority groups and providing new evidence for underserved regions in England. Results from ITS will inform doctors and people-with-SMI decisions on long-term treatment, which will be able to consider evidence from minority and most deprived groups typically underrepresented in mental health research.
Intermediate outcomes: a change in low-density lipoprotein cholesterol (LDL-C); systolic blood pressure (BP); glycated haemoglobin (HbA1c); weight over time.
Final outcomes: first event after treatment initiation of antipsychotics of cardiovascular disease (CVD); type-2 diabetes mellitus (T2DM); and all-cause mortality.
Juan Carlos Bazo Alvarez - Chief Investigator - University College London ( UCL )
Juan Carlos Bazo Alvarez - Corresponding Applicant - University College London ( UCL )
Irene Petersen - Collaborator - University College London ( UCL )
Muhammad Qummer ul Arfeen - Collaborator - University College London ( UCL )
Natalie Fitzpatrick - Collaborator - University College London ( UCL )
CPRD Aurum Ethnicity Record