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Abstract

Background: More research is needed to understand the contribution of comorbidities to MS symptomatology. Objectives: To examine the dose-response relationship between the number of comorbidities and severity of MS symptoms and to assess the relative contribution of comorbidity groups and individual comorbidities to the severity of each symptom. Methods: We surveyed 1223 participants of the Australian MS Longitudinal Study for the presence of 30 comorbidities and the severity of 13 MS symptoms (0-10 scale). The associations between comorbidities and symptoms were assessed using negative binomial regression. The relative contributions of comorbidities to the severity of symptoms were assessed using general dominance analysis. Results: Higher number of comorbidities was most strongly associated with a higher severity of pain and feelings of anxiety and depression (ratios of means ≥ 0.12 per comorbidity increase). Comorbidities explained between 3.7% (spasticity) and 22.0% (feelings of anxiety) of the total variance of symptom severity. Mental health and musculoskeletal disorders contributed most strongly to the severity of the most common symptoms in MS. Conclusions: Our findings support that early recognition and optimal management of comorbidities, particularly of mental health and musculoskeletal disorders, could have a positive impact on the severity of symptoms of people with MS.

Item Type:	Article
Authors/Creators:	Lo, LMP and Taylor, BV and Winzenberg, T and Palmer, AJ and Blizzard, L and van der Mei, I
Keywords:	comorbidity, mental health disorder, multiple sclerosis, musculoskeletal disorder, prevalence, symptom
Journal or Publication Title:	Journal of Neurology
Publisher:	Dr Dietrich Steinkopff Verlag
ISSN:	0340-5354
DOI / ID Number:	10.1007/s00415-020-10192-z
Copyright Information:	© Springer-Verlag GmbH Germany, part of Springer Nature 2020. Post-prints are subject to Springer Nature re-use terms
Related URLs:	UTAS Profile: Taylor, BV Google Scholar: Winzenberg, T UTAS Profile: Winzenberg, T UTAS Profile: Palmer, AJ UTAS Profile: Blizzard, L Google Scholar: van der Mei, I UTAS Profile: van der Mei, I
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