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A Cervical Cancer Decision Model to Inform Recommendations About Preventive Services. Perspective of the Decision Modeler Shalini Kulasingam, PhD Duke University Durham, NC. The Natural History of Cervical Cancer. Clearance. HPV infected Cervix. Regression. Invasion. Normal Cervix.

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A cervical cancer decision model to inform recommendations about preventive services

A Cervical Cancer Decision Model to Inform Recommendations About Preventive Services

Perspective of the Decision ModelerShalini Kulasingam, PhDDuke UniversityDurham, NC

The natural history of cervical cancer
The Natural History of Cervical Cancer About Preventive Services
















Self-limited Infection

(CIN 1, CIN 2?)

Why a decision model for cervical cancer
Why a Decision Model for Cervical Cancer? About Preventive Services

  • Natural history

  • New screening tests

    • HPV tests

    • Cervical cytology tests

  • Vaccination

  • Guidelines

    • What age to begin screening

    • What age to end screening

    • Screening frequency

  • An rct for every combination is impossible
    An RCT for Every Combination is Impossible About Preventive Services

    3 screening tests *

    15 different ages to start screening *

    8 different ages to end screening =

    1 big headache + insufficient funds

    What is a model
    What is a Model? About Preventive Services

    State transition model

    Year About Preventive Services



    CIN 1

    CIN 2-3







    CIN 1

    CIN 2-3









    CIN 1

    CIN 2-3








    State Transition Model

    Screening affects transitions

    for CIN 1, CIN 2-3

    and cancer (Stage I)


    The duke cervical cancer model
    The Duke Cervical Cancer Model About Preventive Services

    • Markov state transition model of HPV, cervical pre-cancer and cancer

      • Can account for impact of screening and vaccination

    • Original model developed for 1999 AHRQ evidence report on new cervical cancer screening technologies by Evan Myers, MD, MPH (Professor, Duke University)

    • Validated by comparing outcomes to

      • Reported outcomes (e.g., SEER)

      • Outcomes predicted by other independently developed models

    • Used by a number of different academic groups and by government agencies and pharmaceutical companies

    • Limitations

      • Reflects clinical practice and includes CIN 1 as a state

      • Scientifically moving toward defining CIN 3 as the only true pre-cancer state

      • Data are grouped into age categories that may be blunt to one-year age differences

    How do we use the model to calculate an outcome
    How Do We Use the Model to Calculate an Outcome? About Preventive Services

    • Life-years gained

      • With screening and treatment, more women

      • survive for a longer time

      • Model calculates average life-expectancy for the cohort with and without screening and treatment

      • LYG is difference between these two

    • Colposcopies – Task Force measure of burden of screening

    • Cost – traditional measure of resources used

    Current recommendations 2003
    Current Recommendations (2003) About Preventive Services

    • Direct evidence to determine the optimal starting and stopping age and interval for screening is limited. Indirect evidence suggests most of the benefit can be obtained by beginning screening within 3 years of onset of sexual activity or age 21 (whichever comes first) and screening at least every 3 years

    • The USPSTF recommends against routinely screening women older than age 65 for cervical cancer if they have had adequate recent screening with normal Pap smears and are not otherwise at high risk for cervical cancer

    • The USPSTF concludes that the evidence is insufficient to recommend for or against the routine use of new technologies to screen for cervical cancer

    Questions posed by uspstf
    Questions posed by USPSTF About Preventive Services

    • Age to begin cervical cancer screening

    • Age to end cervical cancer screening

    • Role of HPV tests in primary screening and triage of abnormal cytology results

    • Role of liquid-based cytology

    Communicating with the tf
    Communicating with the TF…. About Preventive Services

    Issues in answering the tf questions
    Issues in Answering the TF Questions About Preventive Services

    • Evidence Report for Screening Tests

      • Oregon EPC

        • Use the data from this report for the model

        • Need to coordinate so that the findings are consistent

    • Short time frame

      • Original time frame of 3 months

    • The “oh you have a model” syndrome

      • Change in model structure

      • Change in questions and output requested

      • Keeping up with an onslaught of HPV and cervical cancer studies

    Results age to begin screening
    Results: Age to Begin Screening About Preventive Services

    Results age to end screening
    Results: Age to End Screening About Preventive Services

    Results age to end screening1
    Results: Age to End Screening About Preventive Services

    Results hpv dna tests
    Results: HPV DNA Tests About Preventive Services

    Summary of model results
    Summary of Model Results About Preventive Services

    • Age 21, screening q3 depends on measure used

    • Little benefit to screening well screened women after age 65

    • HPV testing for women with ASCUS confirmed; role in primary screening remains unclear

    • Preference for screening using conventional or LBC depends on classification of CIN 1

    Shortcomings of the current approach
    Shortcomings of the Current Approach About Preventive Services

    • What outcome?

      • Colposcopies similar to colonoscopies?

    • How do current guidelines affect findings?

      • ASCCP guidelines for Age 21

    • How do we compare our results with others?

      • Cost per life-year

    Shortcomings of the current model
    Shortcomings (?) of the Current Model About Preventive Services

    • Natural history

      • Role of CIN 1

    • Vaccination

      • Need to change/construct new model(s)

    Acknowledgements About Preventive Services

    • Laura Havrilesky, MD, Duke University

    • Evan Myers, MD, Duke University

    • Julian Irvine, Duke University

    • Task Force esp. George Sawaya, MD and Diana Petitti MD, PhD

    • AHRQ: Tracy Wolff, MD, Tess Miller DrPh and Mary Barton, MD; CDC: Mona Saraiya, MD, MPH

    • Funded by the United States Centers for Disease Control and Prevention and the Agency for Healthcare Research and Quality

    • Shalini Kulasingam is supported by NCI grant K07-CA113773