Jonathan D Campbell1, Vahram Ghushchyan2, R Brett McQueen3, Sharon Cahoon-Metzger4, Terrie Livingston5, Timothy Vollmer6, John Corboy7, Augusto Miravalle8, Teri Schreiner9, Victoria Porter10, Kavita Nair11. 1. University of Colorado, Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO, USA. Electronic address: Jon.Campbell@ucdenver.edu. 2. University of Colorado, Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO, USA. Electronic address: Vahram.Ghushchyan@ucdenver.edu. 3. University of Colorado, Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO, USA. Electronic address: Robert.McQueen@ucdenver.edu. 4. Biogen Idec, Medical and Outcomes Science, Weston, MA, USA. Electronic address: sharon.cahoon-metzger@biogenidec.com. 5. Biogen Idec, Medical and Outcomes Science, Weston, MA, USA. Electronic address: terrie.pang.livingston@biogenidec.com. 6. University of Colorado School of Medicine, Department of Neurology, Aurora, CO, USA. Electronic address: Timothy.Vollmer@ucdenver.edu. 7. University of Colorado School of Medicine, Department of Neurology, Aurora, CO, USA. Electronic address: John.Corboy@ucdenver.edu. 8. University of Colorado School of Medicine, Department of Neurology, Aurora, CO, USA. Electronic address: Augusto.Miravalle@ucdenver.edu. 9. University of Colorado School of Medicine, Department of Neurology, Aurora, CO, USA. Electronic address: Teri.Schreiner@ucdenver.edu. 10. Mastic Beach, NY, USA. Electronic address: veepjean@msn.com. 11. University of Colorado, Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO, USA. Electronic address: kavita.nair@ucdenver.edu.
Abstract
BACKGROUND: MS imposes a significant burden on patients, caregivers, employers, and the healthcare system. OBJECTIVE: To comprehensively evaluate the US MS burden using nationally representative data from the Medical Expenditure Panel Survey. METHODS: We identified non-institutionalized patients aged ≥18 with MS (ICD-9 code 340) from 1998 to 2009 and compared them to individuals without an MS diagnosis (non-MS) during the interview year. The cohorts were compared using multivariate regression on direct costs, indirect costs (measured in terms of employment status, annual wages, and workdays missed), and health-related quality of life (HRQoL; measured using Short Form 12, SF-6 Dimensions, and quality-adjusted life years [QALYs]). RESULTS: MS prevalence was 572,312 (95% CI: 397,004, 747,619). Annual direct costs were $24,327 higher for the MS population (n=526) vs. the non-MS population (n=270,345) (95% CI: $22,320, $26,333). MS patients had an adjusted 3.3-fold (95% CI: 2.4, 4.5) increase in the odds of not being employed vs. non-MS individuals and a 4.4-fold higher adjusted number of days in bed (95% CI 2.97, 6.45). On average, MS patients lost 10.04 QALYs vs. non-MS cohort. CONCLUSIONS: MS was associated with higher healthcare costs across all components, reduced productivity due to unemployment and days spent in bed, and lower HRQoL.
BACKGROUND: MS imposes a significant burden on patients, caregivers, employers, and the healthcare system. OBJECTIVE: To comprehensively evaluate the US MS burden using nationally representative data from the Medical Expenditure Panel Survey. METHODS: We identified non-institutionalized patients aged ≥18 with MS (ICD-9 code 340) from 1998 to 2009 and compared them to individuals without an MS diagnosis (non-MS) during the interview year. The cohorts were compared using multivariate regression on direct costs, indirect costs (measured in terms of employment status, annual wages, and workdays missed), and health-related quality of life (HRQoL; measured using Short Form 12, SF-6 Dimensions, and quality-adjusted life years [QALYs]). RESULTS: MS prevalence was 572,312 (95% CI: 397,004, 747,619). Annual direct costs were $24,327 higher for the MS population (n=526) vs. the non-MS population (n=270,345) (95% CI: $22,320, $26,333). MS patients had an adjusted 3.3-fold (95% CI: 2.4, 4.5) increase in the odds of not being employed vs. non-MS individuals and a 4.4-fold higher adjusted number of days in bed (95% CI 2.97, 6.45). On average, MS patients lost 10.04 QALYs vs. non-MS cohort. CONCLUSIONS: MS was associated with higher healthcare costs across all components, reduced productivity due to unemployment and days spent in bed, and lower HRQoL.
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