Insights Into Disability and Disability Progression in People With Multiple Sclerosis Using Large-Scale Healthcare Data

Background: Identifying predictors for disability progression is crucial for managing multiple sclerosis (MS). This study aims to explore levels of disability and informative factors for disability progression in people with MS (PwMS) using healthcare data without detailed clinical information. Methods: We conducted a case–control/cohort study on data from Bavaria's largest health insurance organization. The dataset included records of assistive devices, nursing care, sick leaves, rehabilitation, drug therapies, and diagnoses for individuals with MS, Crohn's disease (CD), rheumatoid arthritis (RA), and controls (CTR) without these diseases. We used generalized linear models to compare healthcare service utilization between MS and other cohorts. A gradient-boosting algorithm identified informative healthcare-related factors associated with disability progression in PwMS, defined by increased nursing care utilization. Results: PwMS (N = 11,961) demonstrated higher healthcare utilization than CD (N = 21,884), RA (N = 105,450), and CTR (N = 82,677) groups, even at young ages. Besides expected risk factors like age, smoking, diabetes, and psychiatric disorders, the prediction algorithm revealed that PwMS with specific gynecological disorders, upper tract infections, asthma, and thyroiditis were less likely to need higher levels of nursing care. Conclusions: Leveraging healthcare data allows for an objective assessment of disability in PwMS and can identify informative factors for disability progression. Our approach can be applied to studies on disease progression in large cohorts without detailed clinical data and can be adapted to other diseases, disability measures, and healthcare systems. Higher utilization of healthcare resources even at young ages revealed an unmet need for improved treatment and management strategies for young adults with MS.