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 and/or 18 and other high-risk types. (1-3)
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 develop natural immunity, reducing future risk of that same-type of infection. Women with high-risk infection can develop precancerous lesions (i.e., cervical intraepithelial neoplasia (CIN)1, CIN2 or CIN 3), which may regress naturally, and those with CIN 2 or 3 may develop invasive cancer. Death can occur from age- and sex-specific background mortality or excess mortality in women with invasive cervical cancer.
Cervical carcinogenesis. Both the individual-level and Markov cohort models include health states that reflect cervical carcinogenesis associated with HPV-16 and/or 18 and other high-risk types). In these models, women transition between health states, which reflect the cohort’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). In the cohort model, women enter the model before sexual debut and 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.
Vaccination.The dynamic model is used to project the effects of HPV vaccination in reducing HPV-16, HPV-18 and other high-risk type infections over time, capturing both direct and indirect benefits. The dynamic model also accounts for the impact of these effects on CIN and cancer.
Screening, diagnosis and treatment of CIN. The dynamic and 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 American Cancer Society (ACS), US Preventive Services Task Force (USPSTF) and American Society for Colposcopy and Cervical Pathology (ASCCP) guidelines, but assumptions can be modified flexibly. The models both incorporate a detailed post-treatment surveillance component. (3)
Cancer treatment and survival. The models include cancer states by stage (I through IV) and conditional probabilities of survival based on 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.
Calibration and validation. The Markov model was calibrated by varying HPV incidence, CIN progression and regression rates, and probability of symptoms by cancer stage. The parameter set that achieved best visual fit to historic 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.
Tip: Hover your cursor over the dashed attribute links below for more information. View the details of this model in a grid with other cervical models.
You may be interested in these publications by this modeling group, which were supported by a funding source other than the CISNET grant.