Karen Blitz-Shabbir, Aimee M Banks, Hina Garg, Flavia Nelson, Suma Shah, Nicholas Belviso, Jason P Mendoza, Robin L Avila, Boyang Bian, Kinyee Fong
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引用次数: 0
Abstract
Background: Multiple sclerosis (MS) is a heterogeneous disease that may manifest differently among racial/ethnic groups, influencing response to disease-modifying therapy (DMT). Data on natalizumab (NTZ) effectiveness in people with MS (PwMS) based on race/ethnicity are limited.
Objective: The aim of this study was to evaluate the effectiveness of NTZ on relapse onset and rate, and to assess MS-related healthcare encounters and costs in Black, Hispanic, Asian, and White PwMS.
Methods: This was a retrospective analysis of the Komodo Health Claims Database, including adult patients in the US with one or more MS claim at index date (January 1, 2016-August 31, 2022). Patients were followed from first NTZ exposure through end of study, end of insurance eligibility, gap in index DMT > 45 days, or DMT switch. Annualized relapse rate (ARR), time to first relapse, and MS-related healthcare encounters and costs were compared in the 12 months pre/post NTZ initiation and while on treatment across racial strata.
Results: The study included 3244 PwMS (Black, n = 632; Hispanic, n = 382; Asian, n = 49; White, n = 2181). Mean post-index NTZ exposure was 15.5-19.2 months. Post-index ARRs were significantly reduced across racial/ethnic groups (p < 0.001). The adjusted Kaplan-Meier estimated proportion of relapse-free patients at 2 years for all racial/ethnic groups was not significantly different from the White group. Significant differences were observed in annualized MS-related healthcare cost rates but not in annualized MS-related encounter rates before and after NTZ initiation across the racial/ethnic groups.
Conclusion: NTZ was effective across racial/ethnic groups although not significantly different between groups.
期刊介绍:
Drugs - Real World Outcomes targets original research and definitive reviews regarding the use of real-world data to evaluate health outcomes and inform healthcare decision-making on drugs, devices and other interventions in clinical practice. The journal includes, but is not limited to, the following research areas: Using registries/databases/health records and other non-selected observational datasets to investigate: drug use and treatment outcomes prescription patterns drug safety signals adherence to treatment guidelines benefit : risk profiles comparative effectiveness economic analyses including cost-of-illness Data-driven research methodologies, including the capture, curation, search, sharing, analysis and interpretation of ‘big data’ Techniques and approaches to optimise real-world modelling.