MISCAN-STDSIM (Erasmus)

MIcrosimulation SCreening Analysis (MISCAN) Sexually Transmitted Diseases Simulation (STDSIM) Model

Basic Model Attributes
Cancer site	Cervical
Host institution	Erasmus University Medical Center
Purpose	MISCAN-STDSIM describes the transmission, consequences, and intervention possibilities for multiple sexually transmitted infections. This microsimulation model was originally developed for decision support in STD control. MISCAN-STDSIM was originally developed to model the natural history of cervical disease. It can be used to estimate the costs and effects of screening policies.
Contact	Inge de Kok (i.dekok@erasmusmc.nl)

Model overview. The MISCAN-STDSIM (Erasmus) modeling approach combines a microsimulation model (STDSIM) to simulate the transmission of human papillomavirus (HPV) and vaccination with the 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 three invasive stages (International Federation of Gynecology and Obstetrics [FIGO] stages IA, IB and II+). Cancer may be detected clinically (stages IB and II) 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-STDSIM 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. 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), and lead to complications.

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. The HPV transmission probabilities per sexual contact, the Weibull shape parameter for the durations of infection, the mean duration of acquired immunity and its Weibull shape parameter (or the reduction in susceptibility after an infection in the MISCAN model) have been calibrated to the observed age- and type-specific HPV prevalence in the Netherlands. (13-15) The progression of HPV to CIN and from CIN to cervical cancer, and the cytology test characteristics, were simultaneously calibrated to Dutch pre-vaccination age-specific data on screening HPV-positivity in women with negative and positive cytology, CIN and cancer detection rates by HPV positivity, cancer incidence, stage distribution, and interval cancers. (13, 16-20) Prevalence of chlamydia provided a validation metric for sexual risk behavior. (21) To check the external validity of the MISCAN model, the predicted incidence without screening was compared to observed data in selected parts of the Netherlands (Rotterdam, the Hague, and Friesland) in the period 1965-1969 (22), before organized screening started. The projected interval carcinoma rates were also compared with those collected by the International Agency for Research on Cancer (IARC) in the early screening decades. (23-24)

References

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.
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]
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]
World Population Prospects: The Revision, CD-ROM Edition. In: United Nations DoEaSA, Population Division, ed; 2011.
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.
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.
Bakker F, Vanwesenbeeck, I. Sexual health in the Netherlands in 2006 (in Dutch). Delft: the Netherlands; 2006.
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.
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]
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]
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]
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]
Bulkmans NW, Rozendaal L, Snijders PJ, Voorhorst FJ, Boeke AJ, Zandwijken GR, et al. POBASCAM, a population-based randomized controlled trial for implementation of high-risk HPV testing in cervical screening: design, methods and baseline data of 44,102 women. Int J Cancer. 2004;110(1):94-101. [Abstract]
Lenselink CH, Melchers WJ, Quint WG, Hoebers AM, Hendriks JC, Massuger LF, et al. Sexual behaviour and HPV infections in 18 to 29 year old women in the pre-vaccine era in the Netherlands. PLoS One. 2008;3(11):e3743. [Abstract]
Coupé VM, Berkhof J, Bulkmans NW, Snijders PJ, Meijer CJ. Age-dependent prevalence of 14 highrisk HPV types in the Netherlands: implications for prophylactic vaccination and screening. Br J Cancer. 2008;98(3):646-51. [Abstract]
Netherlands Cancer Registry. Incidence, survival and mortality of cervical cancer. 2009. Accessed at www.cijfersoverkanker.nl, November 13, 2014.
Casparie M, Tiebosch AT, Burger G, Blauwgeers H, van de Pol A, van Krieken JH, et al. Pathology databanking and biobanking in The Netherlands, a central role for PALGA, the nationwide histopathology and cytopathology data network and archive. Cell Oncol. 2007;29(1):19-24. [Abstract]
Bulk S, Bulkmans NW, Berkhof J, Rozendaal L, Boeke AJ, Verheijen RH, et al. Risk of high-grade cervical intra-epithelial neoplasia based on cytology and high-risk HPV testing at baseline and at 6months. Int J Cancer. 2007;121(2):361-7. [Abstract]
Lonky NM, Mahdavi A, Wolde-Tsadik G, Bajamundi K, Felix JC. Evaluation of the clinical performance of high-risk human papillomavirus testing for primary screening: a retrospective review of the Southern California Permanente Medical Group experience. J Low Genit Tract Dis. 2010;14(3):200-5. [Abstract]
Ronco G, Giorgi-Rossi P, Carozzi F, Dalla Palma P, Del Mistro A, De Marco L, et al. Human papillomavirus testing and liquid-based cytology in primary screening of women younger than 35 years: results at recruitment for a randomised controlled trial. Lancet Oncol. 2006;7(7):547-55. [Abstract]
van den Broek IV, van Bergen JE, Brouwers EE, Fennema JS, Gotz HM, Hoebe CJ, et al. Effectiveness of yearly, register based screening for chlamydia in the Netherlands: controlled trial with randomised stepped wedge implementation. BMJ. 2012;345:e4316. [Abstract]
Central Cancer Registry. Overzicht incidentie van carcinoma van de cervix uteri van Friesland, Rotterdam en Den Haag over de jaren 1960 t/m 1970. 1973.
Day NE. Screening for squamous cervical cancer: duration of low risk after negative results of cervical cytology and its implication for screening policies. BMJ. 1986:659-64. [Abstract]
van Oortmarssen GJ, Habbema JD. Duration of preclinical cervical cancer and reduction in incidence of invasive cancer following negative pap smears. Int J Epidemiol. 1995;24(2):300-7. [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 (When a cancer is not detected in an early stage, it will progress to more advanced stages, including metastases. We incorporated 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 (CIN1, CIN2 and CIN3 that can either progress or regress.), Virus (In STDSIM, we model explicitly HPV-16, HPV-18, natural immunity by type, transmission probability per sexual act by type. STDSIM is linked to MISCAN, in which we model HPV-16, HPV-18 and pooled other hr-HPV.),
Construction
Approach	Micro Simulation, Agent-based, Dynamic Transmission (Our model of HPV transmission and vaccination is a dynamic microsimulation model. The transmission probability is modeled per sexual contact.),
Methods	Longitudinal, Likelihood optimization, Stochastic process (The model is stochastic but probability distributions do not change over time.), State Transition, Time to Event,
Unit of Analysis	Tumor, Person, Population,
Data Source	PALEBA,
Census
Cancer Registry	NCR,
Linked
Clinical Trial	POBASCAM,
Survey	Other (Sexual Health Surveys (Netherlands)),
Meta Analysis	Observational Studies, Other,
Assumptions
Benefit Factors
Screening	Stage Shift, Temporal Trends (Age), Cure Rate (Within stage shift), Modality (While HPV-test detects HPV-infections, cytology detects cytological abnormalities. Screen-detected cancers have higher survival probabilities than cancers that are diagnosed because of symptoms.), Prevention,
Treatment	Temporal Trends (Higher survival probability in younger women),
Vaccination	Prevention,
Inputs	Disease (We calibrate age-specific probabilities of acquiring a progressive or regressive infection.), Other Conditions (Hysterectomies),
Screening	Attendance, Dissemination, Test Performance, Effect (Improvement of prognosis), Risk Adaptive Factors, Incidental Finding Surveillance (Women with BMD test results are offered triage tests after e.g. 6 and 18 months (routine surveillance)),
Diagnosis
Precancer	Attendance, Test Performance,
Treatment	Effect (Based on observed survival rates),
Precancer
Survival	Relative,
Mortality	Other cause, Tumor Attributes (Treatability depends on cancer stage),
Risk Factor	Age, Personal History (Natural immunity after HPV-infection and sexual behavior influence the probability of acquiring an infection. Screening history influences the risk of developing cervical cancer.), Sexual behavior, Demography, Natural History, Cost,
Vaccination	Quadrivalent (We can include this vaccine as well, but have been working with the bivalent vaccine so far. We do not have HPV-6 and -11 in the model.), Bivalent (We can include this vaccine as well, but have been working with the bivalent vaccine so far.), Nonavalent, Efficacy,
Outputs	Cost, Incidence,
Disease	Stage Distribution, Grade Distribution, HPV Infection,
Prevalence	Disease (We can perform runs in which all prevalent lesions / cancers are detected.), Other Conditions (HPV infection),
Treatment
Precancer
Screening	Effect, Tumor Attributes (Stage distribution of cancers),
Risk Factor	Demography,
Outcomes	Life years, QALY, Cause-specific Mortality, All-cause Mortality, Temporal trends, Vaccination effect (All these parameters are input, but in the output we can see the coverage in the population (by year and age group) as well.),
Screening	False Positives, True Positives, False Negatives, True Negatives, Biopsies performed (Same as number of colposcopies performed), Unnecessary biopsies (Same as number of false-positive colposcopies), Overdiagnoses, History (Number of screening tests, triage tests and referrals; CIN detection),
Treatment	History, Overtreatment (Regressive CIN lesions),
Implementation
Development
Tested Platforms	Windows,
Language	Delphi, Java,

2022

Burger EA, de Kok IMCM, O'Mahony JF, Rebolj M, Jansen EEL, de Bondt DD, Killen J, Hanley SJ, Castanon A, Regan MC, et al., A model-based analysis of the health impacts of COVID-19 disruptions to primary cervical screening by time since last screen for current and future disruptions., Elife, Oct. 12, 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

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]
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, May 1, 2021 [Abstract]
Taksler GB, Peterse EFP, Willems I, Ten Haaf K, Jansen EEL, de Kok IMCM, van Ravesteyn NT, de Koning HJ, Lansdorp-Vogelaar I, Modeling Strategies to Optimize Cancer Screening in USPTSF Guideline-Noncompliant Women., JAMA Oncol, April 29, 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]

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]

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.

2013

Hontelez JA, Lurie MN, Bärnighausen T, Bakker R, Baltussen R, Tanser F, Hallett TB, Newell ML, de Vlas SJ, Elimination of HIV in South Africa through expanded access to antiretroviral therapy: a model comparison study, PLoS Med, Oct. 1, 2013 [Abstract]

2012

van den Broek IV, van Bergen JE, Brouwers EE, Fennema JS, Götz HM, Hoebe CJ, Koekenbier RH, Kretzschmar M, Over EA, Schmid BV, Pars LL, et al., Effectiveness of yearly, register based screening for chlamydia in the Netherlands: controlled trial with randomised stepped wedge implementation, BMJ, July 5, 2012 [Abstract]

2011

Giuliano AR, Lee JH, Fulp W, Villa LL, Lazcano E, Papenfuss MR, Abrahamsen M, Salmeron J, Anic GM, Rollison DE, Smith D, et al., Incidence and clearance of genital human papillomavirus infection in men (HIM): a cohort study, Lancet, March 12, 2011 [Abstract]

2010

Lonky NM, Mahdavi A, Wolde-Tsadik G, Bajamundi K, Felix JC, Evaluation of the clinical performance of high-risk human papillomavirus testing for primary screening: a retrospective review of the Southern California Permanente Medical Group experience, J Low Genit Tract Dis, July 1, 2010 [Abstract]

2008

Goodman MT, Shvetsov YB, McDuffie K, Wilkens LR, Zhu X, Thompson PJ, Ning L, Killeen J, Kamemoto L, Hernandez BY, Prevalence, acquisition, and clearance of cervical human papillomavirus infection among women with normal cytology: Hawaii Human Papillomavirus Cohort Study, Cancer Res, Nov. 1, 2008 [Abstract]
Trottier H, Mahmud S, Prado JC, Sobrinho JS, Costa MC, Rohan TE, Villa LL, Franco EL, Type-specific duration of human papillomavirus infection: implications for human papillomavirus screening and vaccination, J Infect Dis, May 15, 2008 [Abstract]
Coupé VM, Berkhof J, Bulkmans NW, Snijders PJ, Meijer CJ, Age-dependent prevalence of 14 high-risk HPV types in the Netherlands: implications for prophylactic vaccination and screening, Br J Cancer, Feb. 12, 2008 [Abstract]
Lenselink CH, Melchers WJ, Quint WG, Hoebers AM, Hendriks JC, Massuger LF, Bekkers RL, Sexual behaviour and HPV infections in 18 to 29 year old women in the pre-vaccine era in the Netherlands, PLoS One, Jan. 1, 2008 [Abstract]

2007

Orroth KK, Freeman EE, Bakker R, Buvé A, Glynn JR, Boily MC, White RG, Habbema JD, Hayes RJ, 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, Aug. 1, 2007 [Abstract]
Bulk S, Bulkmans NW, Berkhof J, Rozendaal L, Boeke AJ, Verheijen RH, Snijders PJ, Meijer CJ, Risk of high-grade cervical intra-epithelial neoplasia based on cytology and high-risk HPV testing at baseline and at 6-months, Int J Cancer, July 15, 2007 [Abstract]
Casparie M, Tiebosch AT, Burger G, Blauwgeers H, van de Pol A, van Krieken JH, Meijer GA, Pathology databanking and biobanking in The Netherlands, a central role for PALGA, the nationwide histopathology and cytopathology data network and archive, Cell Oncol, Jan. 1, 2007 [Abstract]

2006

Ronco G, Giorgi-Rossi P, Carozzi F, Dalla Palma P, Del Mistro A, De Marco L, De Lillo M, Naldoni C, Pierotti P, Rizzolo R, Segnan N, et al., Human papillomavirus testing and liquid-based cytology in primary screening of women younger than 35 years: results at recruitment for a randomised controlled trial, Lancet Oncol, July 1, 2006 [Abstract]

2004

Bulkmans NW, Rozendaal L, Snijders PJ, Voorhorst FJ, Boeke AJ, Zandwijken GR, van Kemenade FJ, Verheijen RH, v Groningen K, Boon ME, Keuning HJ, et al., POBASCAM, a population-based randomized controlled trial for implementation of high-risk HPV testing in cervical screening: design, methods and baseline data of 44,102 women, Int J Cancer, May 20, 2004 [Abstract]

2001

Nobbenhuis MA, Helmerhorst TJ, van den Brule AJ, Rozendaal L, Voorhorst FJ, Bezemer PD, Verheijen RH, Meijer CJ, Cytological regression and clearance of high-risk human papillomavirus in women with an abnormal cervical smear, Lancet, Nov. 24, 2001 [Abstract]

1995

van Oortmarssen GJ, Habbema JD, Duration of preclinical cervical cancer and reduction in incidence of invasive cancer following negative pap smears, Int J Epidemiol, April 1, 1995 [Abstract]

1986

[No authors listed] Screening for squamous cervical cancer: duration of low risk after negative results of cervical cytology and its implication for screening policies. IARC Working Group on evaluation of cervical cancer screening programmes, Br Med J (Clin Res Ed), Sept. 13, 1986 [Abstract]