Dr. Karen Hicklin
Post Doctoral Trainee
Department of Health Behavior
University of North Carolina Chapel Hill
Friday, January 17, 2020 2:30-3:30pm
Tickle Building 410
Increased utilization of colorectal cancer (CRC) screening has contributed to the declining rates of CRC incidence and mortality in the United States. However, the burden of CRC remains relatively high with over 141,000 new cases diagnosed and more than 52,000 deaths in 2016 alone. Healthy People 2020 and the National Colorectal Cancer Roundtable have set national CRC screening targets at 70.5% and 80%, respectively. While CRC screening has increased in recent years, with approximately two-thirds of age-eligible individuals screened, the feasibility of these targets at the population level remains uncertain. In this talk, I will present results of an individual-based micro simulation model used to (i) estimate the effectiveness of diverse types of multi component interventions in increasing CRC screening statewide and among disparate demographic groups in North Carolina and (ii) to determine if the estimated gains will achieve the 70.5% and 80% screening targets over a 5-year intervention period.
Karen Hicklin is a Postdoctoral Trainee in the NCI-funded T32 Cancer Health Disparities Training Program through the Department of Health Behavior at the University of North Carolina at Chapel. She received her PhD in Industrial Engineering from the Edward P. Fitts Department of Industrial and Systems Engineering at North Carolina State University. Before her current appointment, she was a Postdoctoral Trainee within the Department of Statistics and Operations Research as a part of Carolina Postdoctoral Program for Faculty Diversity. Her research interests are mathematical modeling of stochastic systems with an emphasis on statistical and decision analysis as applied to healthcare and service environments. Her current research projects include population modeling of interventions for colorectal cancer outcomes using simulation, cost-effectiveness modeling of genetic testing for pediatric patients on a diagnostic odyssey, and stochastic decision modeling to estimate the impact incentives have on increasing the rate of HIV screening and treatment.