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A Cervical Cancer Decision Model to Inform Recommendations About Preventive Services: Perspective of the Decision Modeler

Slide Presentation from the AHRQ 2008 Annual Conference

On September 8, 2008, Shalini Kulasingam, Ph.D., made this presentation at the 2008 Annual Conference. Select to access the PowerPoint® presentation (555 KB).

Slide 1

A Cervical Cancer Decision Model to Inform Recommendations About Preventive Services: Perspective of the Decision Modeler

Shalini Kulasingam, Ph.D.
Duke University
Durham, NC

Slide 2

The Natural History of Cervical Cancer

The diagram presents the natural history of cervical cancer starting with a normal cervix. Two arrows pointing to and from "HPV [human papillomavirus ] Infected Cervix" show either "Infection" or "Clearance". With HPV Infected Cervix, two arrows pointing up and down to "Self-Limited Infection (CIN [cervical intraepithelial neoplasia] 1, CIN 2?)" show either "Regression" or "Progression". From HPV Infected Cervix, two arrows pointing to and from "Pre-Cancer CIN 3" show either "Progression" or "Regression". From Pre-Cancer CIN 3, an arrow stating "Invasion" points to "Cancer".

Slide 3

Why a Decision Model for Cervical Cancer?

  • Natural history.
  • New screening tests.
    • HPV tests.
    • Cervical cytology tests.
  • Vaccination.
  • Guidelines.
    • What age to begin screening.
    • What age to end screening.
    • Screening frequency.

Slide 4

A Randomized Controlled Trial (RCT) for Every Combination is Impossible

  • 3 screening tests.*
  • 15 different ages to start screening.*
  • 8 different ages to end screening.=
  • 1 big headache + insufficient funds.

Slide 5

What is a Model?

The colored photograph in the upper left hand corner depicts a stylish woman struggling to hold onto a dog. The cartoon in the lower right hand corner depicts a man working at a computer with both man and computer thinking, "Can't you do anything right?"

Slide 6

State Transition Model

The diagram shows an image of stick figure women in a box with an arrow pointing down to three rows of ovals containing the various stages of cervical cancer for Year 1-3. The data shows:

  • Year 1.
    • Nml: 100%.
  • Year 2.
    • Nml: 94%.
    • HPV: 5%.
    • D: 1%.
  • Year 3.
    • Nml: 88%.
    • HPV: 8%.
    • CIN 1: 2%.
    • D: 2%..
  • Screening affects transitions for CIN 1, CIN 2-3, and cancer (Stage I).
  • The progression shown for women in the diagram is "Normal", Year 1; "HPV", Year 2; and "CIN 1", Year 3.

Slide 7

The Duke Cervical Cancer Model

  • 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., Surveillance, Epidemiology, and End Results Program [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.

Slide 8

How Do We Use the Model to Calculate an Outcome?

  • 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.

Slide 9

Current Recommendations (2003)

  • 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 U.S. Preventive Services Task Force (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.

Slide 10

Questions posed by USPSTF

  • 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.

Slide 11

Communicating with the Task Force (TF)...

The cartoon depicts a man speaking to a dog.

  • The top cell reads, "What we say to dogs," with the man saying, "Okay, Ginger! I've had it! You stay out of the garbage! Understand, Ginger? Stay out of the garbage, or else!"
  • The bottom cell reads, "What they hear," with the man saying, "blah blah Ginger blah blah blah blah blah blah blah blah Ginger blah blah blah blah blah."

Slide 12

Issues in Answering the TF Questions

  • Evidence Report for Screening Tests.
    • Oregon Evidence-based Practice Center (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.

Slide 13

Results: Age to Begin Screening

The table shows the results for the strategies Colpos., Increased Colpos., Life Years-LY, Increased LY-No Intervention, Increased LY, and Increased Colpos. per LY for No Intervention and then for ages 20, q5 through 15, q1.

Slide 14

Results: Age to End Screening

The table shows the results for the strategies Colpos., Increased Colpos., Life Years-LY, Increased LY-No Intervention, Increased LY, and Increased Colpos. per LY for No Intervention and then for ages 65, q5, Age 70 through 65, q1, Age 95.

Slide 15

Results: Age to End Screening

The table shows the results for the strategies Colpos., Increased Colpos., Life Years-LY, Increased LY-No Intervention, Increased LY, and Increased Colpos. per LY for ages 65 through 65, q1, Age 95.

Slide 16

Results: HPV DNA Tests

The table shows the results for the strategies Colpos., Increased Colpos., Life Years-LY, Increased LY-No Intervention, Increased LY, and Increased Colpos. per LY for No Intervention; CC, q5; HPV and PAP, CC, q5; HPV and Pap, CC, q3; HPV and Pap, CC q2; HPV and Pap, CC, q1; and CC, q1.

Slide 17

Results: Liquid vs. Conventional Cytology

The table shows the results for the strategies Colpos., Increased Colpos., Life Years-LY, Increased LY-No Intervention, Increased LY, and Increased Colpos. per LY for No Intervention; CC, HPV for ASC-US, q5; LBC, HPV for ASC-US, q5; CC, HPV for ASC-US, q3; CC, HPV for ASC-US, q2; LBC, HPV for ASC-US, q2; CC, HPV for ASC-US, q1; and LBC, HPV for ASC-US, q1.

Slide 18

Summary of Model Results

  • Age 21, screening q3 depends on measure used.
  • Little benefit to screening well screened women after age 65.
  • HPV testing for women with atypical squamous cells of uncertain significance (ASCUS) confirmed; role in primary screening remains unclear.
  • Preference for screening using conventional or LBC depends on classification of CIN 1.

Slide 19

Shortcomings of the Current Approach

  • What outcome?
    • Colposcopies similar to colonoscopies?
  • How do current guidelines affect findings?
    • American Society for Colposcopy and Cervical Pathology (ASCCP) guidelines for Age 21.
  • How do we compare our results with others?
    • Cost per life-year.

Slide 20

Shortcomings (?) of the Current Model

  • Natural history.
    • Role of CIN 1.
  • Vaccination.
    • Need to change/construct new model(s).

Slide 21

Acknowledgements

  • Laura Havrilesky, M.D., Duke University.
  • Evan Myers, M.D., Duke University.
  • Julian Irvine, Duke University.
  • Task Force esp. George Sawaya, M.D. and Diana Petitti M.D., Ph.D.
  • AHRQ: Tracy Wolff, M.D., Tess Miller Dr.Ph. and Mary Barton, M.D.; CDC: Mona Saraiya, M.D., M.P.H.
  • Funded by the United States Centers for Disease Control and Prevention (CDC) and the Agency for Healthcare Research and Quality (AHRQ).
  • Shalini Kulasingam is supported by National Cancer Institute (NCI) grant K07-CA113773.

Current as of January 2009

Internet Citation:

A Cervical Cancer Decision Model to Inform Recommendations About Preventive Services: Perspective of the Decision Modeler. Slide Presentation from the AHRQ 2008 Annual Conference (Text Version). January 2009. Agency for Healthcare Research and Quality, Rockville, MD. http://www.ahrq.gov/about/annualmtg08/090808slides/Kulasingam.htm

 
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