Abstract

Objectives The working definition of overdiagnosis is that it is the diagnosis of a condition that would not otherwise have become clinically significant. This definition highlights prognostic questions. To inform policy and clinical decision-making, epidemiologists have focused on estimating the extent of overdiagnosis, reaching widely diverging conclusions. Our objectives in this conceptual research are to: analyze existing conceptual definitions of overdiagnosis, in order to identify additional factors that help to put methodological disagreements into perspective. Method Conceptual analysis based on philosophical methods for identifying phenomena, analysing definitions, challenging assumptions and refining concepts. Results Conceptual definitions differ about what is overdiagnosed (harmless disease or ‘indicative phenomena’ not themselves disease) and how treatment-related harms and benefits matter for identifying overdiagnosis. The disagreement usefully highlights three factors that are important in describing and communicating overdiagnosis. 1. The wide variation in overdiagnosis estimates arises in a circular fashion from assumptions about disease incidence and dynamics. Escaping the circle requires addressing the plausibility of basic causal disease mechanisms and criteria for setting disease boundaries. 2. There is a focus on variations in estimates of overdiagnosis. However, these contested estimates are an order of magnitude greater than the potential benefits against which they are weighed. 3. The focus on potential benefits from early intervention leads to neglect of the nature of overdiagnosis harms. While information about the rates of overdiagnosis is important, the seriousness of overdiagnosis harms is also critical for all levels of decision making. Conclusions Existing efforts to refine epidemiological estimates should continue and will help to inform patients; this analysis of conceptual definitions of overdiagnosis highlights additional issues that will complement advances in epidemiology by placing the estimates in ethical and practical context. We need to consider how estimates depend on assumptions about underlying disease incidence and dynamics, whether the range of disagreement matters when placed in perspective with corresponding benefits and harms, and how to weigh and act on the harms.

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