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Abstract #161060

M. Anne Harris, MSc, Adrian R. Levy, PhD, and Kay Teschke, PhD. Department of Health Care and Epidemiology, University of British Columbia, James Mather Building, 5804 Fairview Cr., Vancouver, BC V6T2G9, Canada, 604-827-5454, aharris7@interchange.ubc.ca

Introduction: While privacy laws vary between Canadian provinces, and among U.S. states, increasing legislative protection of personal privacy has imposed restrictions on health research across North America. Previously observed effects of these restrictions on patient recruitment include increased study costs, durations, and decreased participation rates. In addition to loss of statistical power, low participation rates can jeopardize the validity of research findings and the accuracy of measures of association by introducing non-response, or participation bias. Methods: After reviewing the legislation in selected North American jurisdictions, we constructed simulations to assess potential effects of non-response bias on the accuracy of measures of association in a hypothetical case-control study. Results: Small biases that alter the probability of selecting an exposed case can lead to dramatic inflation or attrition of the odds ratio (OR) in case-control studies. ORs are more unstable and subject to error when the true probability of selecting an exposed case is greater, such that strong positive associations are subject to substantial error at low levels of bias. Discussion: Well-powered, population-based epidemiological research is a cornerstone of public health. Therefore, when weighing the benefits of protecting personal privacy, the benefits of valid and robust health research must also be considered. Options might include special legislative treatment of health research, or the use of an opt-out construct for consent in confidential research.

Learning Objectives:

• Specify pertinent details of common provisions of privacy legislation in North America.
• Describe likely sources of methodological bias introduced by such provisions.
• Identify details of a statistical model to capture the effects of such bias on estimated effect sizes.
• Identify estimates of these effects for different levels of bias.
• List suggestions for ways to maintain privacy while avoiding loss of research validity.

Keywords: Privacy, Epidemiology

Presenting author's disclosure statement:

Any relevant financial relationships? No
Any institutionally-contracted trials related to this submission?

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.