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Published on: June 30, 2014
Socioeconomic status and disease-modifying therapy prescribing patterns in people with multiple sclerosis
Saúl Reyes1, Sebastian Suarez2, Kimberley Allen-Philbey3
1Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, 4 Newark St, Whitechapel, London, E1 2AT, United Kingdom; Department of Neurology, Royal London Hospital, Barts Health NHS Trust, 7000 Whitechapel Road, Whitechapel, London, E1 1FR, United Kingdom.
Aims:
To examine the association between socioeconomic status (SES) and disease-modifying therapy (DMT) prescribing patterns in people with relapsing-remitting multiple sclerosis (pwRRMS).
Methods:
A cross-sectional analysis was conducted among pwRRMS treated with a DMT in the neuroinflammation service at The Royal London Hospital (Barts Health NHS Trust). Study data were collected between July and September 2017. SES was determined by patient income and education extracted from the English Index of Multiple Deprivation. Based on their efficacy, DMTs were categorized as moderate efficacy (Glatiramer Acetate and Beta-Interferons), high efficacy (Cladribine, Fingolimod and Dimethyl Fumarate) and very-high efficacy therapies (Natalizumab and Alemtuzumab). Multinomial logistic regressions were performed for univariate and multivariate models to assess differences between SES and DMT prescribing patterns.
Results:
Treatment consisted of moderate efficacy (n = 76, 12%), high efficacy (n = 325, 51.3%) and very-high efficacy therapies (n = 232, 36.7%). Medians for income and education deciles were 4 (IQR 3-7) and 6 (IQR 4-8), respectively. After multinomial logistic regression analysis, patient income was not associated with increased odds of being treated with high efficacy (OR, 0.92; 95% CI, 0.82-1.04; p = 0.177) or very-high efficacy DMTs (OR, 0.95; 95% CI, 0.85-1.06; p = 0.371). Similarly, patient education was not associated with being treated with high efficacy (OR, 0.91; 95% CI, 0.80-1.03; p = 0.139) or very-high efficacy therapies (OR, 0.92; 95% CI, 0.81-1.04; p = 0.188).
Conclusions:
SES was not predictive of DMT prescribing patterns in pwRRMS. Whilst this appears reassuring within this universal health care setting, the same methodology needs to be applied to other MS services for comparison. Data could then be further interrogated to explore potential socioeconomic inequities in DMT prescribing patterns across the UK.
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