HSPH-Cervical (Harvard)

Cervical Cancer Model

Basic Model Attributes
Cancer site	Cervical
Host institution	Harvard T.H. Chan School of Public Health
Purpose	The purpose of the Harvard HPV and cervical cancer models are: to integrate the most up-to-date evidence on the epidemiology of HPV and cervical cancer, as well as clinical practice and delivery of vaccination and screening interventions; to simulate the burden of HPV infection and cervical cancer in populations of interest; to respond to priority policy questions regarding the effectiveness and cost-effectiveness of prevention and control strategies against HPV-related cancers in the US and in low- and middle-income countries. The HSPH modeling group uses two distinct models: (1) a dynamic agent-based model of HPV sexual transmission between males and females, and (2) a static, individual-based model of HPV-induced cervical carcinogenesis. The models can be used as stand-alone models or can be linked to allow for the inclusion of direct and indirect benefits from vaccination, potential synergies between vaccination and screening strategies, and vaccine cross-protection of non-16/18 types.
Contact	Jane Kim (jkim@hsph.harvard.edu)

Model overview. The Harvard School of Public Health (HSPH) modeling group uses two distinct models: 1) a dynamic compartmental model of human papillomavirus (HPV) sexual transmission between males and females; and 2) a static, individual-based model of HPV-induced cervical carcinogenesis. The models can be used as stand-alone models or can be linked to allow for the inclusion of direct and indirect benefits from vaccination, potential synergies between vaccination and screening strategies, and vaccine cross-protection of non-16/18 types.

HPV transmission. Transmission of HPV infections in males and females is modeled using an agent-based simulation of sexual mixing. The agents are characterized by static attributes of sex, age, and sexual activity (none, low, medium, high). (1-3) Every month, females and males form heterosexual partnerships, and transmission of type-specific HPV can dynamically occur as a function of sexual behavior patterns in the population, prevalence of HPV in the population (generated each cycle in the model), and female-to-male or male-to-female transmission probabilities of HPV per susceptible-infected partnership. Following clearance of HPV, individuals develop partial type- and gender-specific natural immunity, reducing future risk of that same type of infection. Deaths and births occur monthly using age- and sex-specific background mortality and fertility rates, respectively. HPV types are stratified by the nine types in the nonavalent (9v) vaccine (i.e. 16, 18, 31, 33, 45, 52, 58, 6, 11), and infection of each HPV type occurs independently.

Cervical carcinogenesis. The static model is a microsimulation (i.e., individual-based) model of HPV-induced cervical carcinogenesis, in which individual women representative of a single birth cohort enter the model at an early age (e.g., 9 years) and are followed over their lifetimes. (4-5) Each woman undergoes monthly transitions between health states that describe underlying true health, including HPV infection, pre-cancer (i.e., cervical intraepithelial neoplasia (CIN)2, CIN3), and invasive cancer (i.e., local, regional, distant). CIN2 and CIN3 are modeled as non-sequential precancerous health states with distinct probabilities of progression to cancer, whereas CIN1 is interpreted as a microscopic manifestation of acute HPV infection and is therefore incorporated into the HPV-infected state (5). States are further stratified into oncogenic HPV types 16, 18, 31, 33, 45, 52, and 58, each considered separately; other pooled oncogenic types; and pooled non-oncogenic types. Transition probabilities can vary by age, HPV type, duration of infection or lesion status, and a woman’s history of prior HPV infection and CIN treatment. Cancer detection can occur through symptoms or screening. Each month, all women are subjected to background mortality and hysterectomy, as well as excess mortality from cervical cancer.

Vaccination. The dynamic model is used to project the effects of HPV vaccination in reducing HPV-16 and -18 infections over time, capturing both direct and indirect benefits. These estimates of reductions in HPV-16 and 18 incidence are applied to the corresponding input parameters of HPV-16 and -18 incidence in the static cervical model to account for the impact of vaccination on cervical cancer outcomes in the context of complex screening strategies. The model also captures health and cost outcomes related to non-cervical HPV-related cancers (i.e., vulvar, vaginal, penile, anal, and oropharyngeal), genital warts, and juvenile-onset recurrent respiratory papillomatosis (JORRP).

Screening, diagnosis, and treatment of pre-cancer. Screening assumptions in the static model can vary by screening start age, stop age, frequency, coverage, triage testing, and compliance to recommended follow-up. Tests for primary screening and triage include cytology (conventional, liquid-based), HPV DNA testing (pooled or genotyping), as well as cytology and HPV co-testing. Management of screen-positive women can vary by age, follow-up test, time to follow-up test(s), and number of negative follow-up tests required to return to routine screening. The downstream consequences of CIN treatment on pregnancy outcomes (i.e., pre-term birth) are modeled using a separate open-cohort model. The model simulates US birth rates by age and gestational weight, birth order, and age-specific rates of CIN treatment projected from the model to estimate the number of pre-term births due to CIN excisional treatments under different screening scenarios.

Cancer treatment and survival. Cancer staging (i.e., local to regional to distant) and progression is modeled, accounting for symptomatic detection and the possibility of downstaging at diagnosis due to screening. In addition to background mortality, women with cervical cancer are subject to excess mortality, based on 5-year survival estimates depending on cancer stage, age, and time since diagnosis according to Surveillance, Epidemiology, and End Results (SEER) data.

Calibration and validation. A likelihood-based approach is used to calibrate highly uncertain parameters to fit corresponding empirical data (e.g., HPV prevalence). In the static model, parameters include: 1) age- and type-specific incidence of HPV infection; and 2) natural immunity following type-specific HPV infection. Baseline values for each of the uncertain parameters are randomly selected from a pre-determined plausible range, creating a unique natural history parameter set. Goodness of fit is ascertained by calculating the likelihood of model-projected outcomes from each parameter set against corresponding calibration targets (e.g., age- and type-specific prevalence of HPV). Parameter calibration of the dynamic transmission model followed a similar approach for the following uncertain model parameter categories: 1) sex-specific transmission probabilities of HPV per infected-susceptible partnership for each of the nine individual HPV infection types; 2) HPV type- and sex-specific infection clearance rates; and 3) progression rate of CIN 2,3 to invasive cancer. For model validation, model-projected outcomes of cervical cancer incidence rates by age in the absence of any intervention are compared against those reported historically in SEER cancer registries prior to widespread Pap smear screening, as well as current SEER incidence and mortality rates with screening operational in the model. Analyses are underway to compare model projections of cumulative risks of CIN2, CIN3, and cancer over 5 years following an index Pap result with and without HPV status against empirical data from Kaiser Permanente Northern California (6-7) and the New Mexico HPV Pap Registry (8).

References

Kim JJ, Andres-Beck B, Goldie SJ. The value of including boys in an HPV vaccination programme: a cost-effectiveness analysis in a low-resource setting. Br J Cancer. 2007;97(9):1322-8. [Abstract]
Kim JJ, Goldie SJ. Health and economic implications of HPV vaccination in the United States. N Engl J Med. 2008;359(8):821-32. [Abstract]
Kim JJ, Goldie SJ. Cost effectiveness analysis of including boys in a human papillomavirus vaccination programme in the United States. BMJ. 2009;339:b3884. [Abstract]
Kim JJ, Kuntz KM, Stout NK, Mahmud S, Villa LL, Franco EL, et al. Multiparameter calibration of a natural history model of cervical cancer. Am J Epidemiol. 2007;166(2):137-50. [Abstract]
Campos NG, Burger EA, Sy S, Sharma M, Schiffman M, Rodriguez AC, et al. An updated natural history model of cervical cancer: derivation of model parameters. Am J Epidemiol. 2014;180(5):545-55. [Abstract]
Katki HA, Kinney WK, Fetterman B, Lorey T, Poitras NE, Cheung L, et al. Cervical cancer risk for women undergoing concurrent testing for human papillomavirus and cervical cytology: a populationbased study in routine clinical practice. Lancet Oncol. 2011;12(7):663-72. [Abstract]
Katki HA, Schiffman M, Castle PE, Fetterman B, Poitras NE, Lorey T, et al. Five-year risks of CIN 3+ and cervical cancer among women with HPV testing of ASC-US Pap results. J Low Genit Tract Dis. 2013;17(5 Suppl 1):S36-42. [Abstract]
Wheeler CM, Hunt WC, Cuzick J, Langsfeld E, Robertson M, Castle PE, et al. The influence of typespecific human papillomavirus infections on the detection of cervical precancer and cancer: A population-based study of opportunistic cervical screening in the United States. Int J Cancer. 2014;135(3):624-34. [Abstract]

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	Screening evaluation, Epidemiological analysis, Policy evaluation, Population trends,
Features
Intervention	Prevention, Screening, Treatment, Vaccination,
Natural History	Metastases (We model cancer progression to more advanced stages for cancers that have not yet been detected (and therefore not yet treated), including metastases, which is modelled by incorporating poorer survival for later stages of disease detection.), Tumor Growth (We include different stages of cancer in our model with different survival probabilities), Precancer, Dysplasia (CIN2, CIN3), Virus (HPV-16, -18, -31, -33, -45, -52 and -58, pooled other high-risk HPV, and pooled low-risk HPV),
Construction
Approach	Micro Simulation, Agent-based, Dynamic Transmission,
Methods	Longitudinal, Likelihood optimization, Stochastic process, State Transition,
Unit of Analysis	Person,
Data Source	PROSPR, KPNC,
Census	US,
Cancer Registry	SEER, NMHPVPR,
Linked
Clinical Trial
Survey	NHIS, NHANES, BRFSS, NHDS,
Meta Analysis
Assumptions
Benefit Factors
Screening	Stage Shift, Temporal Trends, Modality (HPV genotype stratification), Prevention,
Treatment	Modality (Survival of cancer after a cancer diagnosis is based on the stage at diagnosis and can also be setting-specific. We assume different survival in different regions, taking into account resources available in each setting.), Precancer,
Vaccination	Prevention,
Inputs	Incidence, Disease, Grade Distribution (CIN2, CIN3), Stage Distribution, Other Conditions (Hysterectomies), Prevalence,
Screening	Attendance, Dissemination, Test Performance, Effect, Risk Adaptive Factors, Incidental Finding Surveillance,
Diagnosis
Precancer	Attendance, Test Performance,
Treatment	Dissemination (The model has the capability to have varying success on treatment procedures based on geographic region, which is modelled by applying different survival by stage based on the region being simulated), Efficacy (The model has the capability to incorporating varying degrees of success in treatment procedures due to emerging technologies or other situations where treatment success/failure may vary),
Precancer	Attendance, Efficacy,
Survival	Relative,
Mortality	Other cause, Disease-specific, Tumor Attributes (Treatability depends on cancer stage),
Risk Factor	Age, Personal History (E.g., sexual behavior, prior HPV infection), Sexual behavior, Demography (E.g., hysterectomy), Natural History, Cost,
Vaccination	Quadrivalent, Bivalent, Nonavalent, Efficacy,
Outputs	Cost, Incidence,
Disease	Stage Distribution, Grade Distribution, HPV Infection, Other Conditions (Resource use (e.g., number of test, CIN treatments, preterm births)),
Prevalence	Other Conditions (HPV infection),
Treatment	Effect,
Precancer	Effect,
Screening	Test Performance, Tumor Attributes (Stage distribution of cancers),
Risk Factor	Demography, Natural History (E.g., dwell time),
Outcomes	Survival, Life years, QALY, Cause-specific Mortality, All-cause Mortality, Temporal trends, Vaccination effect (We model detailed vaccination parameters, e.g. vaccine type, efficacy against vaccine-included types, efficacy against non-vaccine included types, as well as duration of protection against vaccine-included types and duration of cross-protection. We also model vaccine coverage (catch-up, uptake).),
Screening	False Positives, True Positives, False Negatives, True Negatives, Biopsies performed, Unnecessary biopsies, Overdiagnoses, History,
Treatment	Overtreatment, Pregnancy,
Implementation
Development
Tested Platforms	Windows,
Language	C or C++,

2023

Spencer JC, Burger EA, Campos NG, Regan MC, Sy S, Kim JJ, Adapting a model of cervical carcinogenesis to self-identified Black women to evaluate racial disparities in the United States., J Natl Cancer Inst Monogr, Nov. 8, 2023 [Abstract]

2022

Rao DW, Bayer CJ, Liu G, Chikandiwa A, Sharma M, Hathaway CL, Tan N, Mugo N, Barnabas RV, Modelling cervical cancer elimination using single-visit screening and treatment strategies in the context of high HIV prevalence: estimates for KwaZulu-Natal, South Africa., J Int AIDS Soc, Oct. 1, 2022 [Abstract]
Burger EA, de Kok IMCM, O'Mahony JF, Rebolj M, Jansen EEL, de Bondt DD, Killen J, Hanley SJ, Castanon A, Kim JJ, et al., Health impacts of COVID-19 disruptions to primary cervical screening by time since last screen: A model-based analysis for current and future disruptions., medRxiv, July 25, 2022 [Abstract]

2021

Smith MA, Burger EA, Castanon A, de Kok IMCM, Hanley SJB, Rebolj M, Hall MT, Jansen EEL, Killen J, O'Farrell X, et al., Impact of disruptions and recovery for established cervical screening programs across a range of high-income country program designs, using COVID-19 as an example: A modelled analysis., Prev Med, Oct. 1, 2021 [Abstract]
Castanon A, Rebolj M, Burger EA, de Kok IMCM, Smith MA, Hanley SJB, Carozzi FM, Peacock S, O'Mahony JF, Cervical screening during the COVID-19 pandemic: optimising recovery strategies., Lancet Public Health, July 1, 2021 [Abstract]
Burger EA, Jansen EE, Killen J, Kok IM, Smith MA, Sy S, Dunnewind N, G Campos N, Haas JS, Kobrin S, et al., Impact of COVID-19-related care disruptions on cervical cancer screening in the United States., J Med Screen, June 1, 2021 [Abstract]
Kim JJ, Simms KT, Killen J, Smith MA, Burger EA, Sy S, Regan C, Canfell K, Human papillomavirus vaccination for adults aged 30 to 45 years in the United States: A cost-effectiveness analysis., PLoS Med, March 11, 2021 [Abstract]

2020

Simms KT, Yuill S, Killen J, Smith MA, Kulasingam S, de Kok IMCM, van Ballegooijen M, Burger EA, Regan C, Kim JJ, et al., Historical and projected hysterectomy rates in the USA: Implications for future observed cervical cancer rates and evaluating prevention interventions., Gynecol Oncol, Sept. 1, 2020 [Abstract]
de Kok IMCM, Burger EA, Naber SK, Canfell K, Killen J, Simms K, Kulasingam S, Groene E, Sy S, Kim JJ, 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, May 1, 2020 [Abstract]
Burger EA, Smith MA, Killen J, Sy S, Simms KT, Canfell K, Kim JJ, Projected time to elimination of cervical cancer in the USA: a comparative modelling study., Lancet Public Health, April 1, 2020 [Abstract]

2019

Burger EA, de Kok IMCM, Groene E, Killen J, Canfell K, Kulasingam S, Kuntz KM, Matthijsse S, Regan C, Simms K, Smith M, et al., Estimating the Natural History of Cervical Carcinogenesis Using Simulation Models: A CISNET Comparative Analysis, J Natl Cancer Inst, Dec. 10, 2019 [Abstract]

2018

Campos NG, Lince-Deroche N, Chibwesha CJ, Firnhaber C, Smith JS, Michelow P, Meyer-Rath G, Jamieson L, Jordaan S, Sharma M, Regan C, et al., Cost-Effectiveness of Cervical Cancer Screening in Women Living With HIV in South Africa: A Mathematical Modeling Study, J Acquir Immune Defic Syndr, Oct. 1, 2018 [Abstract]
Kim JJ, Burger EA, Regan C, Sy S, Screening for Cervical Cancer in Primary Care: A Decision Analysis for the US Preventive Services Task Force, JAMA, Aug. 21, 2018 [Abstract]

2017

Rutter CM, Kim JJ, Meester RGS, Sprague BL, Burger EA, Zauber AG, Ergun MA, Campos NG, Doubeni CA, Trentham-Dietz A, Sy S, et al., Effect of Time to Diagnostic Testing for Breast, Cervical, and Colorectal Cancer Screening Abnormalities on Screening Efficacy: A Modeling Study, Cancer Epidemiol Biomarkers Prev, Nov. 17, 2017 [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.

2015

Kim JJ, Campos NG, Sy S, Burger EA, Cuzick J, Castle PE, Hunt WC, Waxman A, Wheeler CM, Inefficiencies and High-Value Improvements in U.S. Cervical Cancer Screening Practice: A Cost-Effectiveness Analysis, Ann Intern Med, Oct. 20, 2015 [Abstract]

2014

Campos NG, Burger EA, Sy S, Sharma M, Schiffman M, Rodriguez AC, Hildesheim A, Herrero R, Kim JJ, An updated natural history model of cervical cancer: derivation of model parameters, Am J Epidemiol, Sept. 1, 2014 [Abstract]
Wheeler CM, Hunt WC, Cuzick J, Langsfeld E, Robertson M, Castle PE, The influence of type-specific human papillomavirus infections on the detection of cervical precancer and cancer: A population-based study of opportunistic cervical screening in the United States, Int J Cancer, Aug. 1, 2014 [Abstract]

2013

Katki HA, Schiffman M, Castle PE, Fetterman B, Poitras NE, Lorey T, Cheung LC, Raine-Bennett T, Gage JC, Kinney WK, Five-year risks of CIN 3+ and cervical cancer among women with HPV testing of ASC-US Pap results, J Low Genit Tract Dis, April 1, 2013 [Abstract]

2011

Katki HA, Kinney WK, Fetterman B, Lorey T, Poitras NE, Cheung L, Demuth F, Schiffman M, Wacholder S, Castle PE, Cervical cancer risk for women undergoing concurrent testing for human papillomavirus and cervical cytology: a population-based study in routine clinical practice, Lancet Oncol, July 1, 2011 [Abstract]

2009

Kim JJ, Goldie SJ, Cost effectiveness analysis of including boys in a human papillomavirus vaccination programme in the United States, BMJ, Oct. 8, 2009 [Abstract]

2008

Kim JJ, Goldie SJ, Health and economic implications of HPV vaccination in the United States, N Engl J Med, Aug. 21, 2008 [Abstract]

2007

Kim JJ, Andres-Beck B, Goldie SJ, The value of including boys in an HPV vaccination programme: a cost-effectiveness analysis in a low-resource setting, Br J Cancer, Nov. 5, 2007 [Abstract]
Kim JJ, Kuntz KM, Stout NK, Mahmud S, Villa LL, Franco EL, Goldie SJ, Multiparameter calibration of a natural history model of cervical cancer, Am J Epidemiol, July 15, 2007 [Abstract]