Announcement

CCHE Seminar: Economics of Prescription Drug Insurance in Canada

Paul Grootendorst
University of Toronto, McMaster University

Friday, February 1st, 2019, 10 AM – 12 PM, HS 412 (155 College Street)

Abstract: Everyone agrees that all Canadians should have access to needed prescription drugs. Unfortunately, many individuals — those working part time, or on contract, or in temporary positions, or are self-employed, or working in low wage industries do not qualify for either private employer-sponsored coverage nor comprehensive public coverage. In principle, individuals can purchase prescription drug insurance coverage but adverse selection and other problems means that this coverage is either non-comprehensive or very expensive. Thus, we need to expand public coverage. The question is how. In his presentation, Professor Grootendorst will review two approaches to do so. One is a tax financed system (i.e. 100% public coverage), an approach recommended by many civil society groups. The other is a mixed system, where those with adequate private employer coverage would retain this coverage and all others would automatically qualify for public coverage. This is the approach advocated by the Canadian Life and Health Insurance Association, and some of its member organizations (such as Sunlife), and the Canadian Pharmacists Association. Professor Grootendorst will explore the pros and cons of each approach and consider how the expansion of public coverage could be financed.

Paul Grootendorst is an associate professor in the Faculty of Pharmacy, and the School of Public Policy and Governance, University of Toronto. He is also an adjunct associate professor in the Department of Economics, McMaster University in Hamilton, Canada. His research interests are on the economic aspects of the pharmaceutical industry, including drug development; pharmaceuticals use, insurance and reimbursement; and interactions between innovative (brand) and generic drug firms. He also has an interest in program evaluation using observational data.