Evaluating physical urban features in several mental illnesses using electronic health record data

TY - JOUR

T1 - Evaluating physical urban features in several mental illnesses using electronic health record data

AU - Mahabadi, Zahra

AU - Mahabadi, Maryam

AU - Velupillai, Sumithra

AU - Roberts, Angus

AU - McGuire, Philip

AU - Ibrahim, Zina

AU - Patel, Rashmi

N1 - Funding Information: PM and AR have received funding from the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, which also supports the development and maintenance of the BRC Case Register. RP has received funding from an NIHR Advanced Fellowship (NIHR301690), a Medical Research Council (MRC) Health Data Research UK Fellowship (MR/S003118/1) and a Starter Grant for Clinical Lecturers (SGL015/1020) supported by the Academy of Medical Sciences, The Wellcome Trust, MRC, British Heart Foundation, Arthritis Research UK, the Royal College of Physicians and Diabetes UK. AR is supported by Health Data Research UK, an initiative funded by UK Research and Innovation, the Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities. ZI is supported by the NIHR Biomedical Research Centre at SLaM and King's College London and NIHR University College London Hospitals Biomedical Research Centre. Publisher Copyright: 2022 Mahabadi, Mahabadi, Velupillai, Roberts, Mcguire, Ibrahim and Patel.

PY - 2022/9/7

Y1 - 2022/9/7

N2 - Objectives: Understanding the potential impact of physical characteristics of the urban environment on clinical outcomes on several mental illnesses. Materials and Methods: Physical features of the urban environment were examined as predictors for affective and non-affective several mental illnesses (SMI), the number and length of psychiatric hospital admissions, and the number of short and long-acting injectable antipsychotic prescriptions. In addition, the urban features with the greatest weight in the predicted model were determined. The data included 28 urban features and 6 clinical variables obtained from 30,210 people with SMI receiving care from the South London and Maudsley NHS Foundation Trust (SLaM) using the Clinical Record Interactive Search (CRIS) tool. Five machine learning regression models were evaluated for the highest prediction accuracy followed by the Self-Organising Map (SOM) to represent the results visually. Results: The prevalence of SMI, number and duration of psychiatric hospital admission, and antipsychotic prescribing were greater in urban areas. However, machine learning analysis was unable to accurately predict clinical outcomes using urban environmental data. Discussion: The urban environment is associated with an increased prevalence of SMI. However, urban features alone cannot explain the variation observed in psychotic disorder prevalence or clinical outcomes measured through psychiatric hospitalisation or exposure to antipsychotic treatments. Conclusion: Urban areas are associated with a greater prevalence of SMI but clinical outcomes are likely to depend on a combination of urban and individual patient-level factors. Future mental healthcare service planning should focus on providing appropriate resources to people with SMI in urban environments.

AB - Objectives: Understanding the potential impact of physical characteristics of the urban environment on clinical outcomes on several mental illnesses. Materials and Methods: Physical features of the urban environment were examined as predictors for affective and non-affective several mental illnesses (SMI), the number and length of psychiatric hospital admissions, and the number of short and long-acting injectable antipsychotic prescriptions. In addition, the urban features with the greatest weight in the predicted model were determined. The data included 28 urban features and 6 clinical variables obtained from 30,210 people with SMI receiving care from the South London and Maudsley NHS Foundation Trust (SLaM) using the Clinical Record Interactive Search (CRIS) tool. Five machine learning regression models were evaluated for the highest prediction accuracy followed by the Self-Organising Map (SOM) to represent the results visually. Results: The prevalence of SMI, number and duration of psychiatric hospital admission, and antipsychotic prescribing were greater in urban areas. However, machine learning analysis was unable to accurately predict clinical outcomes using urban environmental data. Discussion: The urban environment is associated with an increased prevalence of SMI. However, urban features alone cannot explain the variation observed in psychotic disorder prevalence or clinical outcomes measured through psychiatric hospitalisation or exposure to antipsychotic treatments. Conclusion: Urban areas are associated with a greater prevalence of SMI but clinical outcomes are likely to depend on a combination of urban and individual patient-level factors. Future mental healthcare service planning should focus on providing appropriate resources to people with SMI in urban environments.

UR - http://www.scopus.com/inward/record.url?scp=85138349434&partnerID=8YFLogxK

U2 - 10.3389/fdgth.2022.874237

DO - 10.3389/fdgth.2022.874237

M3 - Article

SN - 2673-253X

VL - 4

JO - Frontiers in digital health

JF - Frontiers in digital health

M1 - 874237

ER -