Overdiagnosis, or the detection by screening of cases that would never become clinically diagnosed, and associated treatment are the greatest potential harms of screening. Overdiagnosed cases cannot be helped by treatment and overtreatment of these cases carries a high price in terms of patient morbidity and economic costs. Knowledge about overdiagnosis is critical for well-formed screening policies and for well-informed patient decision making. However, overdiagnosis depends on screening practices and personal factors and many published studies are biased or do not apply to populations that differ from those used for estimation.
This work will advance knowledge about how to validly estimate overdiagnosis and to provide concrete information about overdiagnosis associated with specific cancer screening settings to inform screening policy development and clinical decision making. We will conduct a mathematical modeling study to identify acceptable approaches and key characteristics of valid overdiagnosis studies, apply these lessons to estimate overdiagnosis rates associated with breast and prostate cancer screening under different screening policies and for different population subgroups defined by patient and tumor characteristics, and make these estimates available to policy makers and clinicians via online calculators.
This activity is supported by a grant from the National Cancer Institute (R01 CA192402).
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