The Economic Burden of Multiple Sclerosis in the United States

Data Sources

We relied on a variety of primary and secondary data sources to estimate different components of the costs of MS, including existing national survey data, public and private claims data, national death records, and a self-administered survey (Figure 1). We relied on MS prevalence estimates for 2010 that were published in the literature in 2019.¹ To obtain the 2019 MS estimates, we used the strategy described by the MS Prevalence Workgroup¹ and applied an annual growth rate of 2.3% to the 2010 estimates.

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Figure 1 Flowchart of Cost Calculation and Data Sources

CDC = Centers for Disease Control and Prevention¹⁶; dNHI = Optum de-identified Normative Health Information system¹⁴; MCBS = Medicare Current Beneficiary Survey¹⁵; Medicare SAF = Medicare Standard Analytical File 5% sample¹³; MS = multiple sclerosis; WONDER = Wide-ranging Online Data for Epidemiologic Research.

To estimate the direct medical costs of MS, we used the 2017–2019 Medicare Standard Analytical File (Medicare 5%) and the 2017–2019 Optum de-identified Normative Health Information (dNHI) System.¹³,14 The 2018 Medicare Current Beneficiary Survey (MCBS)¹⁵ was used for the estimation of prescription drugs and long-term care costs for the Medicare population, as these costs were not reported in the Medicare 5%. Future earnings loss due to premature deaths attributable to MS was estimated using death certificates from the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) Detailed Mortality Database¹⁶ for 2015–2017, 2018 Medicare 5% sample claims data, Bureau of Labor Statistics earnings data¹⁷, and CDC National Vital Statistics Report.¹⁸ Finally, we implemented a web-based survey to estimate the caregiver burden utilizing a set of comprehensive measures of labor market consequences for PwMS and their caregivers. It also included cost of medical treatments that were not typically covered by insurance and were paid out-of-pocket. For indirect cost estimates, we also utilized data from the American Community Survey (ACS) public use microdata,¹⁹ Current Population Survey (CPS) Volunteer Supplement,²⁰ and data published by the Independent Sector.²¹

Study Samples

For the dNHI and the Medicare files, PwMS were identified if they had continuous coverage for both medical and pharmacy benefits in the study year and MS diagnostic code (ICD-9 code 340 or ICD-10 code G35) at any time during the year at either the primary or the secondary diagnosis positions. Also, for the dNHI claims, they must have had ≥3 MS-related inpatient visits, outpatient visits, or prescription claims for an MS disease-modifying therapy (DMT) in any combination within a 1-year period. For Medicare 5% claims that did not include Medicare Part D drug claims, we required ≥2 MS-related inpatient or outpatient visits in any combination. For the 2018 MCBS file, we required ≥2 MS-related medical claims, any drug claim for a DMT, or 1 answer in the MCBS survey file indicating that the person had MS. For the questionnaire (see Supplementary materials, links.lww.com/WNL/B879), a total of 946 PwMS completed the survey. eTables 1–5 show the characteristics of our survey respondents.

A comparison of the characteristics of the total MS population as calculated from the prevalence estimates and that of the survey respondents found that the survey sample is slightly younger than the prevalent MS population¹ and has slightly more female participants. Therefore, we stratified the survey sample and the MS population into both age group and sex strata and applied weights for each survey respondent to represent the underlying population.

Estimation of Direct Costs

We used dNHI, Medicare claims, and the MCBS to estimate medical costs related to MS. Per-person medical costs included primary payer paid amount, out-of-pocket expenses, and third party paid amounts. We calculated the per-person direct medical costs for 2017, 2018, and 2019 as well as a 3-year average cost. The direct excess medical costs were calculated as the difference in the annual per-person costs between the MS samples and matched controls by age, sex, race/ethnicity, and insurance (10:1 control-to-case ratio). We also estimated the direct medical costs of MS by insurance types, age, sex, and types of health care services, including cost of hospital inpatient stays, physician office visits, prescription medications, administration of prescription medication in the outpatient setting, durable medical equipment, outpatient services, and nonacute institutional care (e.g., skilled nursing facility, nursing home, or hospice). The prescription medication costs, identified from the pharmacy claims, included DMT costs. The administration of medication in the clinic setting was identified from outpatient/physician claims. All costs were expressed in 2019 dollars.

Estimations of Indirect and Nonmedical Costs

Indirect costs included future earnings loss due to premature mortality, reduced labor market participation because of early retirement, productivity loss for those in the labor force, productivity loss from reduced participation in social activities, and nonmedical costs of MS. To estimate the loss in future earnings, we first estimated the number of premature deaths associated with MS and then multiplied that number by an estimate of average current value of future earnings (by age group). We computed the net current value of future earnings for men and women by age group to estimate the national productivity loss of early mortality associated with MS. We assumed 0 loss in earnings from MS for those aged 75 and older. This approach incorporated information on average annual earnings, labor force participation rates, and mortality rates for men and women in the United States and assumed a productivity growth rate of 1% and a discount rate of 3%, a rate often used in public health studies.^22,23,24

All other indirect and nonmedical cost categories were estimated from the survey. Among working age (18–64 years) PwMS, 58.7% were in the labor market, compared to the US labor force participation rate of 63.1%. The labor market employment-related earnings loss due to MS was calculated as the counts of PwMS who had retired or stopped working in the past 12 months and indicated that MS played a major role in their early retirement decision, multiplied with the median annual earnings by age, sex, and job status obtained from the 2019 ACS public use microdata.¹⁹ Because the job status of PwMS before retirement was unknown, we used the allocation of full-time to part-time job status among currently working PwMS.

Based on the number of days in an average working month during 2019 that the PwMS and the caregivers missed work or felt less productive while at work because of MS and the average daily earnings calculated from the self-reported annual earnings, we calculated the annualized productivity loss due to absenteeism and presenteeism (with an adjustment factor applied to each day felt unproductive).

We evaluated the plausibility of reported hours by comparing the reported volunteering hours with the average national annual volunteering hours obtained from the CPS Volunteer Supplement (2017).²⁰ We took a conservative approach by calculating the percentage of people volunteered and average hours volunteered from CPS and multiplied it with the estimated percentage productivity loss from our survey (calculated as the difference between before and after hours divided by before hours). Productivity loss due to forgone volunteering activities was calculated as volunteering hours affected per year times $27.20, the estimated dollar value per hour volunteering according to the Independent Sector.²¹

Nonmedical costs included expenses for purchasing formal daily care, modification to homes, purchases of special motor vehicles, food, or dietary supplements, and increased travel costs for medical visits, as well as medical tourism. We estimated the costs of nonmedical components and medical out-of-pocket costs by multiplying the weighted percentage of families who responded as having incurred such expenses and the median expense per family per year with the total MS population in 2019 by age and sex.

We also asked whether the PwMS had received Supplemental Security Income, SSDI, or other types of disability income (e.g., income from state disability insurance, VA benefits, long-term disability benefits) in 2019.

Finally, we projected the number of people with MS and the economic burden over the next 20 years assuming current population growth and mortality trends. Specifically, we applied the estimated age- and sex-specific MS prevalence rate to US Census population projections for 2020–2039.²⁵ We assumed that MS incidence increased 2.3% annually and mortality rates and per-person burden remained constant during this period.

Standard Protocol Approvals, Registrations, and Patient Consents

The study was reviewed and exempted by the New England Institutional Review Board. Standard contracts and data use agreements were obtained for the analysis of all datasets.

Data Availability

The private and Medicare claims datasets for this study are proprietary to Optum and CMS and therefore cannot be shared without a data use agreement. Parties interested in the survey data should contact the National Multiple Sclerosis Society.