General population versus disease-specific event rate and cost estimates: potential bias for economic appraisals

Ron Goeree Programs for the Assessment of Technology in Health (PATH) Research Institute, 25 Main St. W., Suite 2000, Hamilton, ON L8P 1H1, Canada; Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada;[email protected]

Daria O’Reilly Programs for the Assessment of Technology in Health (PATH) Research Institute, 25 Main St. W., Suite 2000, Hamilton, ON L8P 1H1, Canada; Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada; Programs for the Assessment of Technology in Health (PATH) Research Institute, 25 Main St. W., Suite 2000, Hamilton, ON L8P 1H1, Canada; Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada

Robert Hopkins Programs for the Assessment of Technology in Health (PATH) Research Institute, 25 Main St. W., Suite 2000, Hamilton, ON L8P 1H1, Canada; Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada; Programs for the Assessment of Technology in Health (PATH) Research Institute, 25 Main St. W., Suite 2000, Hamilton, ON L8P 1H1, Canada; Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada

Gordon Blackhouse Programs for the Assessment of Technology in Health (PATH) Research Institute, 25 Main St. W., Suite 2000, Hamilton, ON L8P 1H1, Canada; Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada; Programs for the Assessment of Technology in Health (PATH) Research Institute, 25 Main St. W., Suite 2000, Hamilton, ON L8P 1H1, Canada; Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada

Jean-Eric Tarride Programs for the Assessment of Technology in Health (PATH) Research Institute, 25 Main St. W., Suite 2000, Hamilton, ON L8P 1H1, Canada; Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada; Programs for the Assessment of Technology in Health (PATH) Research Institute, 25 Main St. W., Suite 2000, Hamilton, ON L8P 1H1, Canada; Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada

Feng Xie Programs for the Assessment of Technology in Health (PATH) Research Institute, 25 Main St. W., Suite 2000, Hamilton, ON L8P 1H1, Canada; Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada; Programs for the Assessment of Technology in Health (PATH) Research Institute, 25 Main St. W., Suite 2000, Hamilton, ON L8P 1H1, Canada; Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada

Morgan Lim Programs for the Assessment of Technology in Health (PATH) Research Institute, 25 Main St. W., Suite 2000, Hamilton, ON L8P 1H1, Canada; Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada; Programs for the Assessment of Technology in Health (PATH) Research Institute, 25 Main St. W., Suite 2000, Hamilton, ON L8P 1H1, Canada; Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada

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Abstract

Economic appraisals are increasingly being used for reimbursement decision making. Differences exist in the population data sources used in different studies and these differences may result in errors or biased estimates. A review of the literature suggests that very little has been written on this topic and guidelines and good practice documents are silent on the issue. Using illustrative examples, it was found that the population chosen for event/complication costing did not have a large impact on a cost–effectiveness analysis; however, the choice of population did have a large impact for cost-of-illness (COI) estimation. It was found that not controlling for event/complication rates in a nondiseased population resulted in a 15% inflated COI estimate and using event costs from the general population underestimated COI by 20–32%. Our analysis suggests that using event costs from the general population instead of a diseased population may not have a significant impact on cost–effectiveness estimates; however, COI studies should only use excess event/complication rates and should also only use event costs from populations with the disease.

Keywords::

Financial & competing interests disclosure

Funding for this project was provided by the Ontario Ministry of Health and Long Term Care and the Ontario Health Technology Advisory Committee. Daria O’Reilly and Jean-Eric Tarride are supported by an Ontario Ministry of Health and Long Term Care Career Scientist Award. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

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General population versus disease-specific event rate and cost estimates: potential bias for economic appraisals

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General population versus disease-specific event rate and cost estimates: potential bias for economic appraisals

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