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Estimating Progression Rates for Human Papillomavirus Infection From Epidemiological Data

Author

Listed:
  • Mark Jit

    (Modelling and Economics Unit, Health Protection Agency Centre for Infections, London, United Kingdom, mark.jit@hpa.org.uk, Immunisation Department, Health Protection Agency Centre for Infections, London, United Kingdom)

  • Nigel Gay

    (Modelling and Economics Unit, Health Protection Agency Centre for Infections, London, United Kingdom)

  • Kate Soldan

    (the HIV/STI Department Health Protection Agency Centre for Infections, London, United Kingdom)

  • Yoon Hong Choi

    (Modelling and Economics Unit, Health Protection Agency Centre for Infections, London, United Kingdom, Immunisation Department, Health Protection Agency Centre for Infections, London, United Kingdom)

  • William John Edmunds

    (Modelling and Economics Unit, Health Protection Agency Centre for Infections, London, United Kingdom)

Abstract

A Markov model was constructed in order to estimate typespecific rates of cervical lesion progression and regression in women with high-risk human papillomavirus (HPV). The model was fitted to age- and type-specific data regarding the HPV DNA and cytological status of women undergoing cervical screening in a recent screening trial, as well as cervical cancer incidence. It incorporates different assumptions about the way lesions regress, the accuracy of cytological screening, the specificity of HPV DNA testing, and the age-specific prevalence of HPV infection. Combinations of assumptions generate 162 scenarios for squamous cell carcinomas and 54 scenarios for adenocarcinomas. Simulating an unscreened cohort of women infected with high-risk HPV indicates that the probability of an infection continuing to persist and to develop into invasive cancer depends on the length of time it has already persisted. The scenarios and parameter sets that produce the best fit to available epidemiological data provide a basis for modeling the natural history of HPV infection and disease.

Suggested Citation

  • Mark Jit & Nigel Gay & Kate Soldan & Yoon Hong Choi & William John Edmunds, 2010. "Estimating Progression Rates for Human Papillomavirus Infection From Epidemiological Data," Medical Decision Making, , vol. 30(1), pages 84-98, January.
    • Handle: RePEc:sae:medema:v:30:y:2010:i:1:p:84-98
    DOI: 10.1177/0272989X09336140
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    Cited by:

    1. Tazio Vanni & Jonathan Karnon & Jason Madan & Richard White & W. Edmunds & Anna Foss & Rosa Legood, 2011. "Calibrating Models in Economic Evaluation," PharmacoEconomics, Springer, vol. 29(1), pages 35-49, January.
    2. Jonathan Karnon & Tazio Vanni, 2011. "Calibrating Models in Economic Evaluation," PharmacoEconomics, Springer, vol. 29(1), pages 51-62, January.
    3. Jing Voon Chen & Julia L. Higle & Michael Hintlian, 2018. "A systematic approach for examining the impact of calibration uncertainty in disease modeling," Computational Management Science, Springer, vol. 15(3), pages 541-561, October.

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