UMN-Cervical (Minnesota) University of Minnesota

Model Overview

The University of Minnesota (UMN) models are a group of models that reflect HPV-induced cervical carcinogenesis stratified by human papillomavirus (HPV)-16, 18, Hi-5 HPV, and other high-risk types.^1-5

HPV Transmission

Transmission of HPV infections in males and females is modeled with a dynamic individual-based model, with individual partnerships characterized by sex, age, and sexual activity. Females and males form heterosexual partnerships as they age, and transmission of type-specific HPV can occur as a function of sexual behavior patterns in the population, prevalence of HPV in the population, and female-to-male or male-to-female transmission probabilities of HPV per susceptible-infected partnership. Following clearance of HPV, individuals may develop natural immunity, reducing future risk of that same type of infection.

Cervical Carcinogenesis

The UMN models include health states that reflect cervical carcinogenesis associated with HPV-16, 18, Hi-5 HPV, and/or other high-risk types. In these models, women transition between health states, which reflect the individual’s underlying true health and include HPV infection status, grade of CIN (CIN 1, CIN 2, and CIN 3), and stage of invasive cancer (I through IV). Individuals transition between health states according to probabilities that depend on age, HPV type, type-specific natural immunity, CIN status, and treatment history. Natural immunity is modeled as a reduction in future type-specific infection. Death can occur each year from non-cervical cancer causes from all health states, or from cervical cancer after its onset. Hysterectomy is modeled as a competing risk.^1,2,5

Vaccination

The microsimulation model and the compartmental model are used to project the effects of HPV vaccination in reducing HPV-16, HPV-18, Hi-5 HPV, and other high-risk type infections over time, capturing both direct and indirect benefits.

Screening, Diagnosis and Treatment of CIN

The microsimulation model and the cohort models can accommodate detailed features of screening strategies, including algorithms that are based on a single test or multiple tests (either in parallel or serial). The models reflect screening, follow-up, and treatment recommendations based on the American Cancer Society (ACS), U.S. Preventive Services Task Force (USPSTF), and American Society for Colposcopy and Cervical Pathology (ASCCP) guidelines, but assumptions can be modified. The models both incorporate a detailed post-treatment surveillance component.^1,2 The compartmental model utilizes a simplified screening, triage and post-treatment surveillance algorithm.⁵

Cancer Treatment and Survival

The models include cancer states by stage (I through IV) and conditional probabilities of survival based on the stage of detection. The models also include a separate state for survivors and cancer-related deaths based on data from the Surveillance, Epidemiology, and End Results (SEER) Program.⁶

Calibration and Validation

The UMN models were calibrated by varying HPV incidence, CIN progression and regression rates, and probability of symptoms by cancer stage. The parameter set that achieved the best visual fit to historical data (in the absence of screening) was selected for analysis. The face validity of the models is assessed by comparing model-projected estimates of age-specific HPV prevalence and age-specific cervical cancer incidence, as well as the lifetime risk of cervical cancer, to empirically observed values from SEER.^1,2

References

1. Burger EA, de Kok IMCM, Groene E, et al. Estimating the Natural History of Cervical Carcinogenesis Using Simulation Models: A CISNET Comparative Analysis. J Natl Cancer Inst. 09 01 2020;112(9):955-963. doi:10.1093/jnci/djz227
2. de Kok IMCM, Burger EA, Naber SK, et al. The Impact of Different Screening Model Structures on Cervical Cancer Incidence and Mortality Predictions: The Maximum Clinical Incidence Reduction (MCLIR) Methodology. Med Decis Making. 05 2020;40(4):474-482. doi:10.1177/0272989X20924007
3. Sawaya GF, Sanstead E, Alarid-Escudero F, et al. Estimated Quality of Life and Economic Outcomes Associated With 12 Cervical Cancer Screening Strategies: A Cost-effectiveness Analysis. JAMA Intern Med. Jul 01 2019;179(7):867-878. doi:10.1001/jamainternmed.2019.0299
4. Easterly CW, Alarid-Escudero F, Enns EA, Kulasingam S. Revisiting assumptions about age-based mixing representations in mathematical models of sexually transmitted infections. Vaccine. Sep 05 2018;36(37):5572-5579. doi:10.1016/j.vaccine.2018.07.058
5. Alarid-Escudero F, Gracia V, Wolf M, et al. State-level disparities in cervical cancer prevention and outcomes in the U.S.: a modeling study. J Natl Cancer Inst. Published online November 21, 2024:djae298. doi:10.1093/jnci/djae298
6. SEER: Surveillance, Epidemiology, and End Results Program. SEER*Stat database: mortality—all COD, aggregated with state, total U.S. (1969-2015). {Katrina/Rita population adjustment}, National Cancer Institute, DCCPS, Surveillance Research Program, released December 2017. Underlying mortality data provided by NCHS.