• Aylin Yucel, Andrew Thach, Sharanya Kumar, Catherine Loden, Mark Bensink, and Neil Goldfarb.
• Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, 915 Greene St, Ste 354, Columbia, SC 29208. Email: bchen@mailbox.sc.edu.
• Am J Manag Care. 2020 Dec 1; 26 (12): e403-e408.

AbstractMigraine is a leading cause of disability globally. Individuals with migraine experience reduced work productivity and greater health care costs compared with the general population. The Migraine Impact Model (MIM) is an interactive calculator developed to estimate the economic burden of migraine on United States-based employers. We demonstrate use of the model with 4 case studies from different industries (education, manufacturing, retail/trade, and entertainment). The MIM estimates migraine prevalence among employees and the employer's annual migraine-associated costs by applying published, licensed, and publicly available data and several underlying assumptions to employee population information that is inputted by users. User inputs include the employer's industry, geographic location, and workforce characteristics (number of employees, gender distribution, and average age and compensation). Model outputs include estimated migraine prevalence, annual workdays affected by migraine, and annual migraine-associated indirect and direct costs to the employer. In the case studies presented, workforce size ranged from 18,800 to 250,000, representing midsized to larger employers. Employee gender distribution ranged from 29% to 74% women, and mean employee age was either 41 or 44 years. The model-estimated percentage of employees with migraine ranged from 14% to 19%. The model projected approximately 60,000 to 686,000 annual workdays to be affected by lost productive time due to migraine (often referred to as "absenteeism" and "presenteeism") and estimated annual indirect costs to total between 6.2 and 8.5 times the annual direct costs. The MIM estimates the economic burden of migraine on a company's workforce, which may aid employers in making data-driven decisions to reduce that burden for employees and business.

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