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Comparative Modeling to Inform Cervical Cancer Control Policies


Biography

Overview
? DESCRIPTION (provided by applicant): Despite successful cervical cancer screening in the United States, over 12,000 women develop and 4,000 women die from cervical cancer each year. New technologies, including screening tests and vaccines against human papillomavirus (HPV), a sexually-transmitted virus known to cause cervical cancer, represent tremendous opportunities for innovative and efficient cervical cancer programs in the U.S. but also pose significant challenges for decision making, due to the complex and long natural history of disease and the different time points along the disease spectrum at which interventions are applied. Consequently, policy makers are uniquely reliant on mathematical modeling to provide evidence on the optimal cervical cancer control strategies. These models can be used to integrate the most up-to-date data, extrapolate current short- term findings into long-term outcomes, and evaluate what-if scenarios that would otherwise be impractical or infeasible to conduct in clinical studies. There has been a growing number of independent mathematical models that have been developed to address clinical and policy questions with respect to cervical cancer prevention and control but no formal efforts to compare assumptions and results across models. As part of the CISNET consortium, a group of established cervical cancer modelers from the U.S., Australia, and the Netherlands will engage in a formal collaboration of comparative modeling using a series of state-of-the-art mathematical models of HPV and cervical carcinogenesis. We will pursue analyses related to the historical impact of screening, the comparative effectiveness of current and anticipated HPV vaccination and screening strategies, and optimal routes for reducing cervical cancer disparities.
U01CA199334
KIM, JANE JOOYUN

Time
2015-09-09
2021-08-31
Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.