Helical CT screening examinations are detecting asymptomatic, early-stage lung cancer not seen on conventional chest radiographs. These findings are fueling direct-to-consumer marketing of CT lung cancer screening targeted to individuals who are able to pay out-of-pocket costs for the hope of significantly dropping their risk of death from lung cancer. Lung cancer screening with CT has not been shown to reduce lung cancer mortality, but has been shown to generate unnecessary, life-threatening biopsies, and large downstream health care costs that are paid out by insurers. Even without evidence of the effectiveness of CT screening, policy makers are questioning its potential cost-effectiveness. We propose a framework for evaluating the potential cost-effectiveness of CT lung cancer screening that will have the flexibility to be updated with an improved understanding of the technology and changes in clinical practice patterns. At the core of our proposal is a novel model of the natural history of lung cancer that will be used to estimate the stage-shift and the mortality reduction due to lung cancer screening. Our natural history model will describe the growth rate of lung cancer and the propensity of the disease to progress to more advanced stages as a function of the primary tumor size. It will be embedded into a larger Monte-Carlo simulation model of the US population undergoing lung cancer screening. The Monte-Carlo model will simulate health and economic outcomes under alternative screening scenarios and predict annual US lung cancer incidence and mortality trends, 5-year survival statistics, as well as quantify the impact of screening in terms life years saved, resource utilization and health care costs. An incremental cost-effectiveness analysis will be used to identify dominant screening strategies in terms of target population risk, ages to start and stop screening, and screening interval. ? ? ? ? ?
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