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Clin Neuropsychol. 2016 Oct;30(7):1050-62. doi: 10.1080/13854046.2016.1200144. Epub 2016 Jun 21.

Latent profile analysis of regression-based norms demonstrates relationship of compounding MS symptom burden and negative work events.

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a Department of Neurology , University at Buffalo School of Medicine and Biomedical Sciences , Buffalo , NY , USA.



We endeavored to clarify how distinct co-occurring symptoms relate to the presence of negative work events in employed multiple sclerosis (MS) patients. Latent profile analysis (LPA) was utilized to elucidate common disability patterns by isolating patient subpopulations.


Samples of 272 employed MS patients and 209 healthy controls (HC) were administered neuroperformance tests of ambulation, hand dexterity, processing speed, and memory. Regression-based norms were created from the HC sample. LPA identified latent profiles using the regression-based z-scores. Finally, multinomial logistic regression tested for negative work event differences among the latent profiles.


Four profiles were identified via LPA: a common profile (55%) characterized by slightly below average performance in all domains, a broadly low-performing profile (18%), a poor motor abilities profile with average cognition (17%), and a generally high-functioning profile (9%). Multinomial regression analysis revealed that the uniformly low-performing profile demonstrated a higher likelihood of reported negative work events.


Employed MS patients with co-occurring motor, memory and processing speed impairments were most likely to report a negative work event, classifying them as uniquely at risk for job loss.


Multiple sclerosis; latent profile analysis; negative work events; regression-based norms; symptom burden

[Indexed for MEDLINE]

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