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01.12.2017 | Research article | Ausgabe 1/2017 Open Access

BMC Neurology 1/2017

Development and validation of a claims-based measure as an indicator for disease status in patients with multiple sclerosis treated with disease-modifying drugs

BMC Neurology > Ausgabe 1/2017
Michael Munsell, Molly Frean, Joseph Menzin, Amy L. Phillips
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Electronic supplementary material

The online version of this article (doi:10.​1186/​s12883-017-0887-1) contains supplementary material, which is available to authorized users.



Administrative healthcare claims data provide a mechanism for assessing and monitoring multiple sclerosis (MS) disease status across large, clinically representative “real-world” populations. The estimation of MS disease status using administrative claims can be a challenge, however, due to a lack of detailed clinical information. Retrospective claims analyses in MS have traditionally used rates of MS relapses to approximate disease status. Healthcare costs may be alternate, broader claims-based indicators of disease activity because costs reflect multiple facets of care of patients with MS, and there is a strong correlation between quality of life of patients with MS and costs of the disease. This study developed, tested, and validated a healthcare cost-based measure to serve as an indicator of overall disease status in patients with MS treated with disease-modifying drugs (DMDs) utilizing administrative claims.


Using IMS Health Real World Data Adjudicated Claims – US data (January 2006June 2013), a negative binomial regression predicted annual all-cause medical costs. Coefficients reaching statistical significance (p < 0.05) and increasing costs by ≥5% were selected for inclusion into an MS-specific severity score (scale of 0 to 100). Components of the score included rehabilitation services, altered mental state, pain, disability, stiffness, balance disorder, urinary incontinence, numbness, malaise/fatigue, and infections. Coefficient weights represented each predictor’s contribution. The predictive model was derived using 50% of a random sample and tested/validated using the remaining 50%.


Average overall predicted annual total medical cost was $11,134 (development sample, n = 11,384, vs. $10,528 actual) and $11,303 (validation sample, n = 11,385, vs. $10,620 actual). The model had consistent bias (approximately +$600 or +6% of actual costs) for both samples. In the validation sample, mean MS disease status scores were 0.24, 8.95, and 21.77 for low, medium, and high tertiles, respectively. Mean costs were most accurately predicted among less severe patients ($5243 predicted vs. $5233 actual cost for lowest tertile).


The algorithm developed in this study provides an initial step to helping understand and potentially predict cost changes for a commercially insured MS population.
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