Background and Objectives: Employment is a major determinant of quality of life in people with multiple sclerosis (pwMS). This multicenter cross-sectional study aimed to identify which commonly studied demographic, disease-related, clinical, cognitive, and psychological variables, alongside the presence of lower urinary tract symptoms (LUTS), predict employment status in pwMS. Materials and Methods: Seventy-eight pwMS were classified as either full-time employed (n = 41) or non-employed (n = 37). Participants underwent clinical and neuropsychological assessment including disability status (Expanded Disability Status Scale; EDSS), fatigue (Modified Fatigue Impact Scale; MFIS), information processing speed (Symbol Digit Modalities Test; SDMT), depressive symptoms (Hospital Anxiety and Depression Scale-Depression; HADS-D), and LUTS status (presence/absence), alongside demographic and disease-related variables (sex, age, education level, relationship status, and disease duration). Results: Hierarchical binary logistic regression indicated that higher information processing speed was associated with higher odds of employment (OR = 1.11, p = 0.008), whereas the presence of LUTS was associated with lower odds of employment (OR = 0.13, p = 0.026). Disability severity, fatigue, depressive symptoms, demographic characteristics, and disease duration did not contribute in the final model (p > 0.05). Conclusions: Information processing speed and urinary dysfunction were associated with employment status in pwMS. Within the present sample, the multivariable model including these variables showed good discrimination between employed and non-employed participants. The findings should be interpreted as exploratory, and they require further confirmation in independent cohorts before any potential application is considered.
Stavrogianni et al. (Fri,) studied this question.
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