Abstract Details

Title Use of an Early Adherence Measure To Predict Future Disease Modifying Drugs Adherence in Patients with Multiple Sclerosis

Topic MS and Related Diseases

Presentation(s) P01 - (-)

Poster/Presentation
Number 214

Objective

Background BACKGROUND: Models using administrative claims to predict adherence have typically included demographic characteristics, comorbidities or previous consumption of healthcare resources. Addition of a measure of early adherence may improve the ability to predict future adherence outcomes.

Design/Methods DESIGN/METHODS: The first DMD claim (i.e., index event) for adult MS patients (?18 and ?65 years) who received self-injected DMDs between 1/1/2006 and 5/31/2010 was identified in a national US managed care database. Patients were required to have continuous eligibility for 12 months pre- and 24 months post-index. Multivariate regression was used to predict future adherence as measured by the proportion of days covered (PDC). The base model included age, gender, a medication intensity measure, presence of a non-MS-related hospitalization pre-index, and markers for physical difficulty, forgetfulness or depression/stress. Models for early DMD adherence were analyzed using incrementing 30-day periods predicting the subsequent 360 days.

Results RESULTS: There were 4,606 patients included with an average age of 46.0 (SD 9.4) years and 78.7% were female. Average PDC in the first 360 days post-index was 80.0% (SD 26.0). Using only the first 60 days of early adherence with no additional variables showed an r-squared of 20.6%. The base model yielded an adjusted r-squared of only 2.3%. As the time period of early adherence is increased (from 60 to 360 days), the explained variance as measured by adjusted r-squared values increased from 20.6% to 53.5%. Addition of the covariates from the base model increased the r-squared by 1 to 2%.

Conclusions CONCLUSIONS: Predictive models that include early adherence with DMDs were able to explain the variance in future adherence outcomes to a greater extent than models based solely on baseline characteristics.

Authors/Disclosures
Chris M. Kozma PRESENTER	No disclosure on file
Amy L. Phillips, PharmD (EMD Serono)	Amy L. Phillips, PharmD has received personal compensation for serving as an employee of EMD Serono, Inc.
Dennis Meletiche (EMD Serono)	No disclosure on file
	No disclosure on file

��ɫ��

��ɫ��