Mathematical models of cervical cancer have been widely used to evaluate the comparative effectiveness and cost-effectiveness of preventive strategies. Major advances in the understanding of cervical carcinogenesis motivate the creation of a new disease paradigm in such models. To keep pace with the most recent evidence, we updated a previously developed microsimulation model of human papillomavirus (HPV) infection and cervical cancer to reflect 1) a shift towards health states based on HPV rather than poorly reproducible histological diagnoses and 2) HPV clearance and progression to precancer as a function of infection duration and genotype, as derived from the control arm of the Costa Rica Vaccine Trial (2004-2010). The model was calibrated leveraging empirical data from the New Mexico Surveillance, Epidemiology, and End Results Registry (1980-1999) and a state-of-the-art cervical cancer screening registry in New Mexico (2007-2009). The calibrated model had good correspondence with data on genotype- and age-specific HPV prevalence, genotype frequency in precancer and cancer, and age-specific cancer incidence. We present this model in response to a call for new natural history models of cervical cancer intended for decision analysis and economic evaluation at a time when global cervical cancer prevention policy continues to evolve and evidence of the long-term health effects of cervical interventions remains critical.
Keywords: decision analysis; human papillomavirus; mathematical models; uterine cervical neoplasms.
Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2014. This work is written by (a) US Government employee(s) and is in the public domain in the US.