Burger EA, de Kok IMCM, Groene E, Killen J, Canfell C, Kulasingam S, Kuntz KM, Matthijsse S, Regan C, Simms K, Sy S, Alarid-Escudero F, Vaidyanathan V, van Ballegooijen M, Kim JJ. Estimating the Natural History of Cervical Carcinogenesis Using Simulation Models: A CISNET Comparative Analysis. JNCI Journal of the National Cancer Institute, 2020;112(9):955-963.
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Abstract
Background
The natural history of human papillomavirus (HPV)-induced cervical cancer (CC) is not directly observable, yet the age of HPV acquisition and duration of preclinical disease (dwell time) influences the effectiveness of alternative preventive policies. We performed a Cancer Intervention and Surveillance Modeling Network (CISNET) comparative modeling analysis to characterize the age of acquisition of cancer-causing HPV infections and implied dwell times for distinct phases of cervical carcinogenesis.
Methods
Using four CISNET-cervical models with varying underlying structures but fit to common US epidemiological data, we estimated the age of acquisition of causal HPV infections and dwell times associated with three phases of cancer development: HPV, high-grade precancer, and cancer sojourn time. We stratified these estimates by HPV genotype under both natural history and CC screening scenarios, because screening prevents cancer development that affects the mix of detected cancers.
Results
The median time from HPV acquisition to cancer detection ranged from 17.5 to 26.0 years across the four models. Three models projected that 50% of unscreened women acquired their causal HPV infection between ages 19 and 23 years, whereas one model projected these infections occurred later (age 34 years). In the context of imperfect compliance with US screening guidelines, the median age of causal infection was 4.4–15.9 years later compared with model projections in the absence of screening.
Conclusions
These validated CISNET-CC models, which reflect some uncertainty in the development of CC, elucidate important drivers of HPV vaccination and CC screening policies and emphasize the value of comparative modeling when evaluating public health policies.