Journal article
N. Launders, J. Hayes, G. Price, D. Osborn
medRxiv, 2021
medRxiv, 2021
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APA
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Launders, N., Hayes, J., Price, G., & Osborn, D. (2021). Clustering of physical health multimorbidity in 68,392 people with severe mental illness and matched comparators: a lifetime prevalence analysis of United Kingdom primary care data. MedRxiv.
Chicago/Turabian
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Launders, N., J. Hayes, G. Price, and D. Osborn. “Clustering of Physical Health Multimorbidity in 68,392 People with Severe Mental Illness and Matched Comparators: a Lifetime Prevalence Analysis of United Kingdom Primary Care Data.” medRxiv (2021).
MLA
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Launders, N., et al. “Clustering of Physical Health Multimorbidity in 68,392 People with Severe Mental Illness and Matched Comparators: a Lifetime Prevalence Analysis of United Kingdom Primary Care Data.” MedRxiv, 2021.
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@article{n2021a,
title = {Clustering of physical health multimorbidity in 68,392 people with severe mental illness and matched comparators: a lifetime prevalence analysis of United Kingdom primary care data},
year = {2021},
journal = {medRxiv},
author = {Launders, N. and Hayes, J. and Price, G. and Osborn, D.}
}
Abstract
Objective: To investigate the clustering of physical health multimorbidity in people with severe mental illness (SMI) compared to matched comparators. Design: A cohort-nested analysis of lifetime diagnoses of physical health conditions. Setting: Over 1,800 UK general practices (GP) contributing to Clinical Practice Research DataLink (CPRD) Gold or Aurum databases. Participants: 68,392 adult patients with a diagnosis of SMI between 2000 and 2018, with at least one year of follow up data, matched 1:4 to patients without an SMI diagnosis, on age, sex, GP, and year of GP registration. Main outcome measures: Odds ratios for 24 physical health conditions derived using Elixhauser and Charlson comorbidity indices. We controlled for age, sex, region, and ethnicity; and then additionally for smoking status, alcohol and drug misuse and body mass index. We defined multimorbidity clusters using Multiple Correspondence Analysis and K-Means cluster analysis and described them based on the observed/expected ratio. Results: Patients with a diagnosis of SMI had an increased odds of 19 of 24 physical health conditions and had a higher prevalence of multimorbidity at a younger age compared to comparators (aOR: 2.47; 95%CI: 2.25 to 2.72 in patients aged 20-29). Smoking, obesity, alcohol, and drug misuse were more prevalent in the SMI group and adjusting for these reduced the odds ratio of all comorbid conditions. In patients with multimorbidity (SMI cohort: n=22,843, comparators: n=68,856), we identified six multimorbidity clusters in the SMI cohort, and five in the comparator cohort. Five profiles were common to both. The "hypertension and varied multimorbidity" cluster was most common: 49.8% in the SMI cohort, and 56.7% in comparators. 41.5% of the SMI cohort were in a "respiratory and neurological disease" cluster, compared to 28.7% of comparators. Conclusions: Physical health multimorbidity clusters similarly in people with and without SMI, though patients with SMI develop multimorbidity earlier and a greater proportion fall into a "respiratory and neurological disease" cluster. There is a need for interventions aimed at younger-age multimorbidity in those with SMI.
