Individualised risk estimation and the nature of prevention

Abstract

Statistical risk models hold substantial promise for the practice of cancer prevention by helping to identify high risk populations and subsequently guide decisions about surveillance, further testing and treatments. They have come under criticism for creating new categories of disease-free but ‘at risk’ individuals. We analysed the debate over the interpretation of risk estimates to assess the importance of these models for the practice of cancer prevention. In particular, we focused on the Gail model for breast cancer risk assessment as a case study, because it is widely used and has been promoted directly to consumers. We describe the critiques that have been offered of the Gail model for individualised risk assessment, such as that classification of a new ‘high risk’ category may increase the use of medical intervention in otherwise healthy individuals. We then analyse the primary methodological limitations of individualised risk models, which are often overlooked in the application of these models and interpretation of their results. In particular, the application of statistical risk models like the Gail model to individuals fails to acknowledge the uncertainty surrounding estimates of individual risk. Moreover, putting the focus of risk management at the individual level minimises the influence of important environmental factors on risk, such as social influences and policies that may impact behaviour or outcomes. Overall, the increasing use of individualised risk estimates for individual decision-making may obscure the fact that successful disease prevention requires intervention on all levels, including the political, social, economic and individual level.