Comorbidity patterns in people with multiple sclerosis: A latent class analysis of the Australian Multiple Sclerosis Longitudinal Study

Lara Marie Pangan Lo; Bruce V Taylor; Tania Winzenberg; Andrew J Palmer; Leigh Blizzard; Mohammad Akhtar Hussain; Ingrid van der Mei

doi:10.1111/ene.14887

Comorbidity patterns in people with multiple sclerosis: A latent class analysis of the Australian Multiple Sclerosis Longitudinal Study

Eur J Neurol. 2021 Jul;28(7):2269-2279. doi: 10.1111/ene.14887. Epub 2021 May 20.

Authors

Lara Marie Pangan Lo¹, Bruce V Taylor¹, Tania Winzenberg¹, Andrew J Palmer^{1

2}, Leigh Blizzard¹, Mohammad Akhtar Hussain¹, Ingrid van der Mei¹

Affiliations

¹ Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia.
² Centre for Health Policy, School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.

PMID: 33931923
DOI: 10.1111/ene.14887

Abstract

Background and purpose: This study was undertaken to identify clinically meaningful comorbidity patterns and their associations with the demographic/clinical characteristics of people with multiple sclerosis (MS).

Methods: We conducted latent class analysis to identify clinically distinct comorbidity patterns in MS using the 15 most common comorbidities among 1518 Australian Multiple Sclerosis Longitudinal Study participants. The associations between demographic/clinical characteristics and comorbidity patterns were examined using log-binomial and multinomial logistic regression.

Results: Five distinct comorbidity patterns were identified: "minimally diseased class" (30.8%), consisting of participants with no or one comorbidity; "metabolic class" (22.7%); "mental health-allergy class" (21.7%); "nonmetabolic class" (7.6%); and "severely diseased class" (7.0%), consisting of participants with higher prevalence of these comorbidities. The relative probabilities of being assigned to comorbidity classes compared to the minimally diseased class were significantly increased for participants who were older (metabolic: relative risk ratio [RRR] = 1.09, 95% confidence interval [CI] = 1.06-1.11; nonmetabolic: RRR = 1.07, 95% CI = 1.04-1.11; severely diseased: RRR = 1.04, 95% CI = 1.01-1.08), female (nonmetabolic: RRR = 5.35, 95% CI = 1.98-14.42; severely diseased: RRR = 2.21, 95% CI = 1.02-4.77), and obese (metabolic: RRR = 4.06, 95% CI = 2.45-6.72; mental health-allergy: RRR = 1.57, 95% CI = 1.00-2.46; severely diseased: RRR = 4.53, 95% CI = 2.21-9.29) and who had moderate disability (mental health-allergy: RRR = 2.32, 95% CI = 1.47-3.64; severely diseased: RRR = 2.65, 95% CI = 1.16-6.04).

Conclusions: Comorbidity patterns exist in MS. Women, people who were older, people who were obese, and people who had higher disability levels were more likely to be in classes with higher levels of comorbidity. These findings may offer opportunities for designing more personalised approaches to comorbidity prevention and treatment.

Keywords: clinical characteristics; comorbidity; comorbidity patterns; demographics; latent class analysis; multiple sclerosis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Australia / epidemiology
Comorbidity
Female
Humans
Latent Class Analysis
Longitudinal Studies
Multiple Sclerosis* / epidemiology