Dynamics of Medical Screening: A Simulation Model of PSA Screening for Early Detection of Prostate Cancer

In this study, we present a novel simulation model and case study to explore the long-term dynamics of early detection of disease, also known as routine population screening. We introduce a realistic and portable modeling framework that can be used for most cases of cancer, including a natural disease history and a realistic yet generic structure that allows keeping track of critical stocks that have been generally overlooked in previous modeling studies. Our model is specific to prostate-specific antigen (PSA) screening for prostate cancer (PCa), including the natural progression of the disease, respective changes in population size and composition, clinical detection, adoption of the PSA screening test by medical professionals, and the dissemination of the screening test. The key outcome measures for the model are selected to show the fundamental tradeoff between the main harms and benefits of screening, with the main harms including (i) overdiagnosis, (ii) unnecessary biopsies, and (iii) false positives. The focus of this study is on building the most reliable and flexible model structure for medical screening and keeping track of its main harms and benefits. We show the importance of some metrics which are not readily measured or considered by existing medical literature and modeling studies. While the model is not primarily designed for making inferences about optimal screening policies or scenarios, we aim to inform modelers and policymakers about potential levers in the system and provide a reliable model structure for medical screening that may complement other modeling studies designed for cancer interventions. Our simulation model can offer a formal means to improve the development and implementation of evidence-based screening, and its future iterations can be employed to design policy recommendations to address important policy areas, such as the increasing pool of cancer survivors or healthcare spending in the U.S.