STDSIM-MISCAN-cervix (Erasmus) Erasmus University Medical Center

Model Overview

The STDSIM-MISCAN-cervix (Erasmus) modeling approach combines a microsimulation model (STDSIM) to simulate the transmission of human papillomavirus (HPV) and vaccination with a microsimulation model (MISCAN) to simulate the natural history of cervical carcinogenesis and screening.

HPV Transmission

HPV-16 and -18 transmission is modeled in the well-established STDSIM dynamic microsimulation model for sexually transmitted diseases developed at the Department of Public Health in the Netherlands.^1-3 The model is parameterized using publicly available information on demography^4-5 and sexual behavior (currently for the Dutch population)^6-8; type-specific durations of infection are based on the literature.^9-11 The model allows for natural immunity after HPV infection clearance, either full immunity for some time (Weibull distributed) or cumulatively reduced after each subsequent infection.

Cervical Carcinogenesis

In the static MISCAN model, acquired HPV infection can progress to pre-invasive cervical intraepithelial neoplasia (CIN) lesions. The progression of cervical disease is subdivided into six sequential stages: three pre-invasive stages (CIN grade 1, 2 and 3), and four invasive stages: microinvasive (FIGO IA), local (FIGO IB), regional (FIGO II/III) and distant (FIGO IV). Cancer may be detected clinically (stages IB+) or through screening (all stages). In the model, most HPV infections will clear without ever resulting in neoplasia, and lesions in pre-invasive stages can regress spontaneously.¹² CIN grades 1 and 2 can also develop in the absence of a high-risk HPV infection; these lesions will never progress to cancer. CIN grade 3 and cancer can only develop if a high-risk HPV infection is present.

Vaccination

The vaccination component of both the dynamic and static models allows variation by target groups, coverage rates, vaccine efficacy, and duration of protection. Vaccine efficacy is modeled as reduced susceptibility to vaccine-type infections, assumed to be lifelong in the base case. The dynamic model also captures the indirect (herd immunity) effects of vaccination. Herd immunity is a phenomenon where vaccination of a significant proportion of the population can reduce the prevalence of the vaccine-targeted HPV types in the population, thereby providing some protection for individuals who are not vaccinated. The resulting HPV incidence estimates in unvaccinated women are incorporated into the MISCAN model, to also account for herd immunity.

Screening, Diagnosis, and Treatment of Pre-cancer

The MISCAN-cervix screening component enables a test to detect an HPV-infection (with or without neoplasia), CIN and preclinical cancers. Screening tests can be applied at certain ages and intervals. Per-lesion test sensitivity can vary by presence or absence of an HPV-infection, by the grade of CIN and the presence of a preclinical cancer. Based on the test result, a woman can be referred to undergo another test, or to colposcopy and eventual CIN treatment (and prevention of cancer). Screening can result in over-diagnosis, over-treatment (by the detection of CIN and cancers that would not have led to clinical disease without screening).

Cancer Treatment and Survival

For invasive cancer, age- and stage-specific survival probabilities are determined by year since diagnosis based on recent data from the Netherlands Cancer Registry (NCR). Because the NCR data include all cases country-wide, and both adenocarcinoma and squamous cell carcinoma, the estimated survival is a weighted average of nationwide treatment practice of these two types of cervical cancer. Screen-detection of cancer allows for within-stage shift, consequently improving the probability of cure compared to the woman's situation without screening.

Calibration and Validation

Partner change rates in the HPV transmission model have originally calibrated against sexual behavior data from The Netherlands¹³ and validated using chlamydia prevalence. For the US, we first compared sexual behavior outputs of the model with data^14-15 and concluded that this model can be used to describe HPV transmission in United States sexual networks.¹⁶ Next, HPV transmission probabilities per sexual contact, the Weibull scale and shape parameter for the durations of infection and the reduction in susceptibility after infection have been calibrated to the observed age- and type-specific HPV prevalence in the US.¹⁷ The age-specific onset and progression of HPV to CIN and from CIN to cervical cancer, were simultaneously calibrated to US pre-vaccination age-specific data on HPV-positivity in women,¹⁷ CIN and cancer detection rates,¹⁸ cancer incidence in 2008-2012 [SEER], and stage distribution in 2005-2012 [SEER], using (previously calibrated) cytology test characteristics of the Dutch model. The Dutch calibrated model was validated on the population-based case-control study by Landy and colleagues in 2016.¹⁹ This study was selected because it was the best available evidence for Western Europe in a systematic review on cervical screening effectiveness on cervical cancer mortality.²⁰ To validate our model, we simulated the screening protocol that was in place during the study and found that the resulting cervical cancer mortality reduction (91%) was within the 95% confidence interval of the cervical cancer mortality reduction reported in the study (92% [95%CI 91-93%]). Furthermore, the US model was validated by comparing model-projected outcomes of cervical cancer incidence rates by age in the absence of any intervention 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.

References

1. Van der Ploeg CPB, Van Vliet C, De Vlas SJ, Ndinya-Achola JO, Fransen L, Van Oortmarssen GJ, et al. STDSIM: A microsimulation model for decision support in STD control. Interfaces. 1998;28(3):84100.
2. Orroth KK, Freeman EE, Bakker R, Buve A, Glynn JR, Boily MC, et al. Understanding the differences between contrasting HIV epidemics in east and west Africa: results from a simulation model of the Four Cities Study. Sex Transm Infect. 2007;83 Suppl 1:i5-16. [Abstract]
3. Hontelez JA, Lurie MN, Barnighausen T, Bakker R, Baltussen R, Tanser F, et al. Elimination of HIV in South Africa through expanded access to antiretroviral therapy: a model comparison study. PLoS Med. 2013;10(10):e1001534. [Abstract]
4. World Population Prospects: The Revision, CD-ROM Edition. In: United Nations DoEaSA, Population Division, ed; 2011.
5. Centraal Bureau voor de Statistiek. Population: sex, age and nationality, 1 January. Statistics Netherlands, Den Haag/Heerlen. 2012. Accessed at http://statline.cbs.nl/statweb/?LA=en; November 13, 2014.
6. Bakker F, De Graaf H, De Haas S, Kedde H, Kruijer H, C W. Sexual health in the Netherlands 2009 (in Dutch). Delft: the Netherlands; 2009.
7. Bakker F, Vanwesenbeeck, I. Sexual health in the Netherlands in 2006 (in Dutch). Delft: the Netherlands; 2006.
8. de Graaf H, Meijer S, Poelman J. Sex below the age of 25: Sexual health of youth in the Netherlands in 2005 (in Dutch). Delft, The Netherlands: Rutgers Nisso Group; 2006.
9. Goodman MT, Shvetsov YB, McDuffie K, Wilkens LR, Zhu X, Thompson PJ, et al. Prevalence, acquisition, and clearance of cervical human papillomavirus infection among women with normal cytology: Hawaii Human Papillomavirus Cohort Study. Cancer Res. 2008;68(21):8813-24. [Abstract]
10. Trottier H, Mahmud S, Prado JC, Sobrinho JS, Costa MC, Rohan TE, et al. Type-specific duration of human papillomavirus infection: implications for human papillomavirus screening and vaccination. J Infect Dis. 2008;197(10):1436-47. [Abstract]
11. Giuliano AR, Lee JH, Fulp W, Villa LL, Lazcano E, Papenfuss MR, et al. Incidence and clearance of genital human papillomavirus infection in men (HIM): a cohort study. Lancet. 2011;377(9769):932-40. [Abstract]
12. Nobbenhuis MA, Helmerhorst TJ, van den Brule AJ, Rozendaal L, Voorhorst FJ, Bezemer PD, et al. Cytological regression and clearance of high-risk human papillomavirus in women with an abnormal cervical smear. Lancet. 2001;358(9295):1782-3. [Abstract]
13. Matthijsse SM, van Rosmalen J, Hontelez JA, Bakker R, de Kok IM, van Ballegooijen M, de Vlas SJ. The role of acquired immunity in the spread of human papillomavirus (HPV): explorations with a microsimulation model. PLoS One. 2015 Feb 2;10(2):e0116618. doi: 10.1371/journal.pone.0116618. PMID: 25642941; PMCID: PMC4314063.
14. Centers for Disease Control and Prevention National Survey of Family Growth (NSFG) Available from https://www.cdc.gov/nchs/nsfg/index.htm. 2010–2011
15. Centers for Disease Control and Prevention National Health and Nutrition Examination Survey (NHANES) Available from https://www.cdc.gov/nchs/nhanes/index.htm. 2011–2012
16. Naslazi E, Hontelez JAC, Naber SK, van Ballegooijen M, de Kok IMCM. The Differential Risk of Cervical Cancer in HPV-Vaccinated and -Unvaccinated Women: A Mathematical Modeling Study. Cancer Epidemiol Biomarkers Prev. 2021 May;30(5):912-919. doi: 10.1158/1055-9965.EPI-20-1321. Epub 2021 Apr 9. PMID: 33837119; PMCID: PMC8102319.
17. Wheeler CM, Hunt WC, Cuzick J, Langsfeld E, Pearse A, Montoya GD, et al A population-based study of human papillomavirus genotype prevalence in the United States: baseline measures prior to mass human papillomavirus vaccination. Int J Cancer 2013;132:198–207.
18. Kinney W, Hunt WC, Dinkelspiel H, Robertson M, Cuzick J, Wheeler CM; New Mexico HPV Pap Registry Steering Committee. Cervical excisional treatment of young women: a population-based study. Gynecol Oncol. 2014 Mar;132(3):628-35.
19. Landy R, Pesola F, Castanon A, Sasieni P. Impact of cervical screening on cervical cancer mortality: estimation using stage-specific results from a nested case-control study. Br J Cancer. 2016;115(9):1140-6.
20. Jansen EEL, Zielonke N, Gini A, Anttila A, Segnan N, Voko Z, et al. Effect of organised cervical cancer screening on cervical cancer mortality in Europe: a systematic review. Eur J Cancer. 2020;127:207-23.