2005
DOI: 10.1002/ijc.20846
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Natural history and screening model for high‐risk human papillomavirus infection, neoplasia and cervical cancer in the Netherlands

Abstract: A simulation model is presented that assumes that persistent infection with high-risk human papillomavirus (hrHPV) is a necessary cause of cervical cancer. For the estimation of the model parameters, data of recent Dutch follow-up studies were reanalyzed. The predicted incidences of cervical cancer, cervical intraepithelial neoplasia (CIN1, CIN2 and CIN3) and abnormal cytology were validated with nationwide figures and population-based screening results. The model predicted a lifetime risk for cervical cancer … Show more

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Cited by 35 publications
(30 citation statements)
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“…The multiple high-risk HPV types in Long-term Impact of HPV Vaccination the model may invade, persist, and clear independently, with type-specific progression rates up to CIN3 that were informed by observations from a population-based screening trial of HPV DNA testing. 25,26 We acknowledge the uncertainty that remains in the model parameterization, but feel that our approach represents an improvement (in terms of estimating the vaccination impact on cancer incidence) over existing dynamic models that focus solely on HPV-16, 30 that model a combined HPV-16/18 type, 31 or that group all high-risk HPV types other than 16 and 18. [27][28][29]32 Second, our multitype model accounts for multiple type-specific infections without the need for an explicit hierarchical classification of high-risk HPV types by oncogenicity, thereby naturally admitting the possibility that elimination of vaccine-type HPV can unmask underlying high-risk HPV infections or lesions caused by nonvaccine types.…”
Section: Discussionmentioning
confidence: 97%
See 1 more Smart Citation
“…The multiple high-risk HPV types in Long-term Impact of HPV Vaccination the model may invade, persist, and clear independently, with type-specific progression rates up to CIN3 that were informed by observations from a population-based screening trial of HPV DNA testing. 25,26 We acknowledge the uncertainty that remains in the model parameterization, but feel that our approach represents an improvement (in terms of estimating the vaccination impact on cancer incidence) over existing dynamic models that focus solely on HPV-16, 30 that model a combined HPV-16/18 type, 31 or that group all high-risk HPV types other than 16 and 18. [27][28][29]32 Second, our multitype model accounts for multiple type-specific infections without the need for an explicit hierarchical classification of high-risk HPV types by oncogenicity, thereby naturally admitting the possibility that elimination of vaccine-type HPV can unmask underlying high-risk HPV infections or lesions caused by nonvaccine types.…”
Section: Discussionmentioning
confidence: 97%
“…The individual-based simulation model of cervical carcinogenesis considers 14 high-risk HPV types (16,18,31,33,35,39,45, 51, 52, 56, 58, 59, 66, and 68). 10,26 For the purpose of the current analysis, the model was adjusted to include type-specific resistance to reinfection and to allow for switching among sexual-activity categories. This ensures that the individual-based model has similar structure and parameterization as the transmission model.…”
Section: Methodsmentioning
confidence: 99%
“…As it takes at least 8 -10 years to develop invasive carcinoma of the cervix after infection with hrHPV, we repeated the analyses matching cases with cancer with normal controls 10 years younger (van Oortmarssen and Habbema, 1995;Zielinski et al, 2001;Berkhof et al, 2005). For these analyses, we included women with normal cytology who were sampled during the enrolment phase of the POBASCAM trial, but did not meet the age criteria for population-based cervical screening (n ¼ 58).…”
Section: Discussionmentioning
confidence: 99%
“…The model simulates health trajectories of a cohort of 12-year old girls until they are deceased. The preinvasive part of the model consists of 14 parallel Markov chains corresponding to an infection with HPV 16,18,31,33,35,39,45,51,52, 56, 58, 59, 66, 68. In the model, a woman may have multiple infections at a certain point in time and for example have a HPV18-positive CIN2 and a HPV16-positive CIN3.…”
Section: Modelmentioning
confidence: 99%