Mapping multimorbidity in individuals with schizophrenia and bipolar disorders: evidence from the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLAM BRC) case register

ObjectivesThe first aim of this study was to design and develop a valid and replicable strategy to extract physical health conditions from clinical notes which are common in mental health services. Then, we examined the prevalence of these conditions in individuals with severe mental illness (SMI) a...

Full description

Saved in:

Bibliographic Details
Published in:	BMJ open 2022-01, Vol.12 (1), p.e054414-e054414
Main Authors:	Bendayan, Rebecca, Kraljevic, Zeljko, Shaari, Shaweena, Das-Munshi, Jayati, Leipold, Leona, Chaturvedi, Jaya, Mirza, Luwaiza, Aldelemi, Sarah, Searle, Thomas, Chance, Natalia, Mascio, Aurelie, Skiada, Naoko, Wang, Tao, Roberts, Angus, Stewart, Robert, Bean, Daniel, Dobson, Richard
Format:	Article
Language:	eng
Subjects:	Adolescent Annotations Arthritis Biomedical Research Bipolar disorder Bipolar Disorder - epidemiology Chronic illnesses Chronic obstructive pulmonary disease Comorbidity Electronic health records epidemiology Epilepsy Ethnicity health informatics Humans Ischemia London - epidemiology Mental disorders Mental Disorders - epidemiology Mental Health Mental health care Migraine Multimorbidity Multiple sclerosis Psoriasis psychiatry Public health Schizophrenia Schizophrenia - epidemiology State Medicine
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	ObjectivesThe first aim of this study was to design and develop a valid and replicable strategy to extract physical health conditions from clinical notes which are common in mental health services. Then, we examined the prevalence of these conditions in individuals with severe mental illness (SMI) and compared their individual and combined prevalence in individuals with bipolar (BD) and schizophrenia spectrum disorders (SSD).DesignObservational study.SettingSecondary mental healthcare services from South LondonParticipantsOur maximal sample comprised 17 500 individuals aged 15 years or older who had received a primary or secondary SMI diagnosis (International Classification of Diseases, 10th edition, F20-31) between 2007 and 2018.MeasuresWe designed and implemented a data extraction strategy for 21 common physical comorbidities using a natural language processing pipeline, MedCAT. Associations were investigated with sex, age at SMI diagnosis, ethnicity and social deprivation for the whole cohort and the BD and SSD subgroups. Linear regression models were used to examine associations with disability measured by the Health of Nations Outcome Scale.ResultsPhysical health data were extracted, achieving precision rates (F1) above 0.90 for all conditions. The 10 most prevalent conditions were diabetes, hypertension, asthma, arthritis, epilepsy, cerebrovascular accident, eczema, migraine, ischaemic heart disease and chronic obstructive pulmonary disease. The most prevalent combination in this population included diabetes, hypertension and asthma, regardless of their SMI diagnoses.ConclusionsOur data extraction strategy was found to be adequate to extract physical health data from clinical notes, which is essential for future multimorbidity research using text records. We found that around 40% of our cohort had multimorbidity from which 20% had complex multimorbidity (two or more physical conditions besides SMI). Sex, age, ethnicity and social deprivation were found to be key to understand their heterogeneity and their differential contribution to disability levels in this population. These outputs have direct implications for researchers and clinicians.
ISSN:	2044-6055 2044-6055