aiMH Lab - Clustering of physical health multimorbidity in 68,392

Journal article

N. Launders, J. Hayes, G. Price, D. Osborn
medRxiv, 2021

Cite

APA Click to copy
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 Click to copy
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 Click to copy
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.

BibTeX Click to copy

@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.