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

Overview of attention for article published in BMC Neurology, June 2017
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  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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3 tweeters

Citations

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5 Dimensions

Readers on

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59 Mendeley
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Title
Development and validation of a claims-based measure as an indicator for disease status in patients with multiple sclerosis treated with disease-modifying drugs
Published in
BMC Neurology, June 2017
DOI 10.1186/s12883-017-0887-1
Pubmed ID
Authors

Michael Munsell, Molly Frean, Joseph Menzin, Amy L. Phillips

Abstract

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 2006-June 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.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 59 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 10 17%
Student > Ph. D. Student 10 17%
Researcher 9 15%
Student > Master 5 8%
Other 4 7%
Other 9 15%
Unknown 12 20%
Readers by discipline Count As %
Medicine and Dentistry 11 19%
Nursing and Health Professions 10 17%
Pharmacology, Toxicology and Pharmaceutical Science 4 7%
Neuroscience 3 5%
Biochemistry, Genetics and Molecular Biology 3 5%
Other 12 20%
Unknown 16 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 June 2017.
All research outputs
#6,244,649
of 11,337,680 outputs
Outputs from BMC Neurology
#571
of 1,343 outputs
Outputs of similar age
#117,604
of 267,064 outputs
Outputs of similar age from BMC Neurology
#17
of 28 outputs
Altmetric has tracked 11,337,680 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,343 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has gotten more attention than average, scoring higher than 56% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 267,064 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.