UW-Cervical (UWash)

Dynamic, compartmental, HPV 16 model

Basic Model Attributes
Cancer site	Cervical
Host institution	University of Washington
Purpose	A dynamic compartmental model of HPV 16 infection and progression to cervical cancer (including LSIL and HSIL progression and regression). The model includes both men and women, sexual behavior, smoking, and vaccination strategies of varying coverage and duration as well as screening and treatment. This model was used to estimate the reduction of cervical cancer associated with varying assumptions of HPV 16 vaccine efficacy, duration, and population coverage. Comparative CC reductions from vaccinating men and women vs women only are evaluated.
Contact	Ruanne Barnabas (rbarnaba@uw.edu)

Model overview. The UW model is an open-cohort, age-structured, compartmental, deterministic human papillomavirus (HPV) transmission model stratified by HPV-16/18 and other high-risk HPV types. It also includes cervical carcinogenesis, HPV vaccination and cervical cancer screening.

Cervical carcinogenesis. Women and men enter the susceptible pool upon sexual debut, and with each partnership, face a risk of HPV acquisition depending on the number of new partners, the prevalence of HPV in the population (generated “dynamically” each cycle in the model), and the probability of HPV transmission per susceptible-infected partnership. Women transition from the uninfected to the HPV-infected state, to low- (cervical intraepithelial neoplasia [CIN I]) and high-grade (CIN II and III) pre-cancer and cervical cancer. The model allows for hysterectomy (including reasons other than cervical cancer treatment) at any stage and accounts for the impact of smoking on cervical cancer rates. (1) Men transition between the susceptible and the HPV-infected state.

HPV transmission, vaccination and screening. As described previously (2-3), the model captures the transmission of HPV by estimating the force of infection, which is a function of sexual mixing (by age and activity class), the proportion of infected individuals in the population (generated internally in the model each cycle) and the HPV transmission probability. (4) Vaccination and screening decreases the incidence of HPV according to demonstrated clinical efficacy, leading to the decrease in incidence of subsequent disease states. The screening function accounts for sensitivity and specificity of the screening tool, loss-to-follow-up, and treatment success. Screening and treatment returns a proportion of individuals to the HPV-negative susceptible state, but a proportion experience treatment failure and remain in the disease compartment. The model is able to explore synergies between vaccination and screening, such as a decrease in recurrence of CIN after vaccination.

Calibration and validation. The model was calibrated using data on sexual behavior, national data for HPV seropositivity (5), hysterectomy rates, smoking (1), and uptake of screening by age. HPV progression and regression rates converted to transition probabilities (6) were based on a review the literature (7-8). Independent data on age-specific cervical cancer incidence rates over time were used to validate the model.

References

Roura E, Castellsague X, Pawlita M, Travier N, Waterboer T, Margall N, et al. Smoking as a major risk factor for cervical cancer and pre-cancer: results from the EPIC cohort. Int J Cancer. 2014;135(2):453-66. [Abstract]
Barnabas RV, Laukkanen P, Koskela P, Kontula O, Lehtinen M, Garnett GP. Epidemiology of HPV 16 and cervical cancer in Finland and the potential impact of vaccination: mathematical modelling analyses. PLoS Med. 2006;3(5):e138. [Abstract]
French KM, Barnabas RV, Lehtinen M, Kontula O, Pukkala E, Dillner J, et al. Strategies for the introduction of human papillomavirus vaccination: modelling the optimum age- and sex-specific pattern of vaccination in Finland. Br J Cancer. 2007;96(3):514-8. [Abstract]
Garnett GP, Anderson RM. Sexually transmitted diseases and sexual behavior: insights from mathematical models. J Infect.Dis. 1996;174 Suppl 2:S150-S61. [Abstract]
Dillner J, Kallings I, Brihmer C, Sikstrom B, Koskela P, Lehtinen M, et al. Seropositivities to human papillomavirus types 16, 18, or 33 capsids and to Chlamydia trachomatis are markers of sexual behavior. J Infect Dis. 1996;173(6):1394-8. [Abstract]
Miller DK, Homan SM. Determining transition probabilities: confusion and suggestions. Med Decis Making. 1994;14(1):52-8. [Abstract]
Myers ER, McCrory DC, Nanda K, Bastian L, Matchar DB. Mathematical model for the natural history of human papillomavirus infection and cervical carcinogenesis. Am J Epidemiol. 2000;151(12):1158-71. [Abstract]
Syrjänen KJ, Syrjänen S. Papillomavirus infections in human pathology. Chichester (United Kingdom): Wiley; 2000.

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.

Detailed Package Attributes
Attribute Category	Attribute
Approach
Primary Purpose	Epidemiological analysis (The model was used to estimate the per partnership transition probability of HPV 16 using sexual behavior data and HPV prevalence), Policy evaluation (The model was used to estimate the impact of HPV vaccation on ICC incidence under varying assumptions),
Features
Intervention	Vaccination (The model estimates the impact of HPV 16 vaccination on ICC incidence),
Natural History	Recurrence (Individuals are immune to future infections after treatment), Precancer (LSIL and HSIL), Virus (HPV 16 only),
Construction
Approach	Macro Simulation (Model is compartmental and stratified by age category, sexual activity category, sex, and disease states.), Dynamic Transmission (Men are modeled but can only aquire and clear infections without pathogenisis.),
Methods	Likelihood optimization,
Unit of Analysis	Population,
Data Source
Census
Cancer Registry	FCR,
Linked
Clinical Trial
Survey
Meta Analysis
Assumptions
Benefit Factors
Screening	Cure Rate, Lead Time (Fast progression from LSIL to cancer is modeled), Prevention,
Treatment
Vaccination	Prevention,
Inputs	Incidence, Disease, Grade Distribution, Stage Distribution,
Screening	Effect (Screened and treated women have improved survival),
Diagnosis
Precancer
Treatment
Precancer
Survival
Mortality	Smoking History (Smoking is assumed to increase cancer progression.),
Risk Factor	Age, Sexual behavior (Men and women are divided into 4 sexual activity classes.),
Vaccination
Outputs	Incidence (Cervical Cancer incidence),
Disease	Grade Distribution (Low-grade squamous intraepithelial lesion (LSIL) and High-grade squamous intraepithelial lesion (HSIL)), HPV Infection (HPV 16),
Prevalence
Treatment	Effect,
Precancer
Screening
Risk Factor	Smoking,
Outcomes	Cause-specific Mortality, All-cause Mortality, Temporal trends, Vaccination effect,
Screening
Treatment
Implementation
Development
Tested Platforms	Windows, Mac OS X,
Language	C or C++,

2021

Rao DW, Wheatley MM, Goodreau SM, Enns EA, Partnership dynamics in mathematical models and implications for representation of sexually transmitted infections: a review., Ann Epidemiol, July 1, 2021 [Abstract]
Shin MB, Liu G, Mugo N, Garcia PJ, Rao DW, Bayer CJ, Eckert LO, Pinder LF, Wasserheit JN, Barnabas RV, A Framework for Cervical Cancer Elimination in Low-and-Middle-Income Countries: A Scoping Review and Roadmap for Interventions and Research Priorities., Front Public Health, July 1, 2021 [Abstract]

2018

Tan N, Sharma M, Winer R, Galloway D, Rees H, Barnabas RV, Model-estimated effectiveness of single dose 9-valent HPV vaccination for HIV-positive and HIV-negative females in South Africa, Vaccine, Aug. 6, 2018 [Abstract]
Liu G, Sharma M, Tan N, Barnabas RV, HIV-positive women have higher risk of human papilloma virus infection, precancerous lesions, and cervical cancer, AIDS, March 27, 2018 [Abstract]

Additional publications by this modeling group

You may also be interested in these publications by this modeling group, which were not supported by the CISNET grant.

2014

Roura E, Castellsagué X, Pawlita M, Travier N, Waterboer T, Margall N, Bosch FX, de Sanjosé S, Dillner J, Gram IT, Tjønneland A, et al., Smoking as a major risk factor for cervical cancer and pre-cancer: results from the EPIC cohort, Int J Cancer, July 15, 2014 [Abstract]

2007

French KM, Barnabas RV, Lehtinen M, Kontula O, Pukkala E, Dillner J, Garnett GP, Strategies for the introduction of human papillomavirus vaccination: modelling the optimum age- and sex-specific pattern of vaccination in Finland, Br J Cancer, Feb. 12, 2007 [Abstract]

2006

Barnabas RV, Laukkanen P, Koskela P, Kontula O, Lehtinen M, Garnett GP, Epidemiology of HPV 16 and cervical cancer in Finland and the potential impact of vaccination: mathematical modelling analyses, PLoS Med, May 1, 2006 [Abstract]

2000

Myers ER, McCrory DC, Nanda K, Bastian L, Matchar DB, Mathematical model for the natural history of human papillomavirus infection and cervical carcinogenesis, Am J Epidemiol, June 15, 2000 [Abstract]

1996

Garnett GP, Anderson RM, Sexually transmitted diseases and sexual behavior: insights from mathematical models, J Infect Dis, Oct. 1, 1996 [Abstract]
Dillner J, Kallings I, Brihmer C, Sikström B, Koskela P, Lehtinen M, Schiller JT, Sapp M, Mårdh PA, Seropositivities to human papillomavirus types 16, 18, or 33 capsids and to Chlamydia trachomatis are markers of sexual behavior, J Infect Dis, June 1, 1996 [Abstract]

1994

Miller DK, Homan SM, Determining transition probabilities: confusion and suggestions, Med Decis Making, Jan. 1, 1994 [Abstract]