This proposal will generate evidence to reduce the overdiagnosis of thyroid cancer in the United States. Overdiagnosis is the identification of a disease that, had it not been detected, would be unlikely to cause symptoms or death during a patient?s lifetime. Overdiagnosis has significant consequences, such as overtreatment with associated side effects and complications, patient anxiety, and increased healthcare costs. Despite a three-fold increase in thyroid cancer diagnoses since the late 1980s, the mortality rate remains stable. Small papillary thyroid cancers, which are rarely lethal, are responsible for virtually the entire increase in incidence. However, it is not safe to assume that all small thyroid cancers are overdiagnosed; some small thyroid cancers can be aggressive and do need treatment. Effective methods are urgently needed to understand the key factors contributing to thyroid cancer overdiagnosis, so that directed solutions can be developed and implemented to reduce overdiagnosis. We propose the innovative use of systems engineering and simulation modeling to address this knowledge gap and provide a nuanced understanding of the natural history of thyroid tumors. We will use our model to identify the effect of reducing referrals for and use of thyroid imaging on overdiagnosis; the effect of changing the size threshold for biopsy on overdiagnosis; and the downstream impact of reducing overdiagnosis on harms and benefits of treatment. This approach also accounts for differential use and improved precision of ultrasound over time. Our goal is to create and validate a simulation model that quantifies overdiagnosis in thyroid cancer. We will engage stakeholders at all stages of development, from model conception to validation, to elicit clinical guidance and inform our model inputs, outcomes, and dissemination strategies. Our research team comprises an industrial-systems engineer with expertise in cancer modeling, as well as experts in thyroid cancer, cancer epidemiology, health services research, and communication. The multidisciplinary team is highly qualified to complete the three specific aims: (1) Develop and validate a simulation model to quantify overdiagnosis of thyroid cancer in the US; (2) Identify healthcare utilization patterns (e.g., provider encounters and referral decisions) that expose patients to increased thyroid imaging, biopsies, and the overdiagnosis of thyroid cancer; (3) Engage key stakeholders throughout the duration of the project to ensure that the model has face validity, and that the output can be applied to questions important to both clinicians and policy makers. The proposed research aligns with the National Cancer Institute?s mission to help people live longer and healthier lives. Results from this innovative model will help to inform clinical practice guidelines and referral practice recommendations to improve the quality of health care, while reducing inappropriate testing, to minimize overdiagnosis and overtreatment.

Public Health Relevance

(Public Health Relevance) Overdiagnosis of thyroid cancer is a significant problem that compromises patients? quality of life, leading to overtreatment, exposure to unnecessary risk, and high medical costs. The objective of this research is to identify factors that predispose people to being diagnosed with thyroid cancer that would never cause symptoms or death if undetected, so the healthcare system can instead focus resources on thyroid cancers that would result in morbidity if untreated. Results of the model will help to inform clinical practice guidelines and referral practice recommendations to improve the quality of health care while reducing inappropriate testing, to minimize overdiagnosis and overtreatment.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Health Services Organization and Delivery Study Section (HSOD)
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Divi, Rao L
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University of Wisconsin Madison
Schools of Medicine
United States
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