1. A Cervical Cancer Decision Model to Inform Recommendations About Preventive Services Perspective of the Decision Modeler Shalini Kulasingam, PhD Duke University Durham, NC

2. The Natural History of Cervical Cancer Normal Cervix HPV infected Cervix Pre-cancer CIN 3 Cancer Invasion Regression Progression Clearance Infection Self-limited Infection (CIN 1, CIN 2?) Progression Regression

3. Natural history Natural history New screening tests New screening tests HPV tests HPV tests Cervical cytology tests Cervical cytology tests Vaccination Vaccination Guidelines Guidelines –What age to begin screening –What age to end screening –Screening frequency Why a Decision Model for Cervical Cancer?

4. 3 screening tests * 15 different ages to start screening * 8 different ages to end screening = 1 big headache + insufficient funds An RCT for Every Combination is Impossible

5. What is a Model?

6. Year 1 2 3 NmlHPVCIN 1 CIN 2-3 CAD NmlHPVCIN 1 CIN 2-3 CAD NmlHPVCIN 1 CIN 2-3 CAD 100% 94%5% 88%8%2% 1% 2% State Transition Model Nml=Normal Screening affects transitions for CIN 1, CIN 2-3 and cancer (Stage I)

7. The Duke Cervical Cancer Model Markov state transition model of HPV, cervical pre-cancer and cancer 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) 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 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 Used by a number of different academic groups and by government agencies and pharmaceutical companies Limitations 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

8. Life-years gained 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 Colposcopies – Task Force measure of burden of screening Cost – traditional measure of resources used Cost – traditional measure of resources used How Do We Use the Model to Calculate an Outcome?

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

10. Questions posed by USPSTF Age to begin cervical cancer screening Age to begin cervical cancer screening Age to end cervical cancer screening Age to end cervical cancer screening Role of HPV tests in primary screening and triage of abnormal cytology results Role of HPV tests in primary screening and triage of abnormal cytology results Role of liquid-based cytology Role of liquid-based cytology

11. Communicating with the TF….

12. Issues in Answering the TF Questions Evidence Report for Screening Tests Evidence Report for Screening Tests –Oregon EPC Use the data from this report for the model Use the data from this report for the model Need to coordinate so that the findings are consistent Need to coordinate so that the findings are consistent Short time frame Short time frame –Original time frame of 3 months The “oh you have a model” syndrome 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

13. Results: Age to Begin Screening StrategyColpos. Incr. Colpos. Life Years (LY) Incr. LY (No Inter.)Incr. LY Incr. Colpos per LY No Intervention068431.4 Age 20, q5389 68643.0211.59211.62 Age 18, q5397868644.5213.181.65 Age 17, q356016368671.7240.2927.16 Age 18, q275219268685.4254.0113.714 Age 16, q2757568685.6254.270.319 Age 17, q2766968685.9254.540.333 Age 17, q1 125649068696.8265.4310.945 Age 16, q11261568696.8265.440.0146 Age 15, q11266568696.8265.460.02309

14. Results: Age to End Screening StrategyColpos. Incr. Colpos Life Years (LY) Incr. LY (No Inter.)Incr. LY Incr. Colpos per LY No Intervention068431.4 Age 65, q5, Age 70 343468442.010.6110.613 Age 65, q3, Age 70 562268445.714.253.646 Age 65, q2, Age 70 751968447.616.191.9410 Age 65, q1, Age 70 1174268449.818.422.2319 Age 65, q1, Age 75 1816468450.819.431.0163 Age 65, q1, Age 80 2345368451.219.810.38139 Age 65, q1,Age 85 2744068451.319.940.13308 Age 65, q1, Age 90 2962268451.419.960.021100 Age 65, q1, Age 95 3081268451.419.970.011200

15. Results: Age to End Screening StrategyColpos. Incr. Colpos. Life Years (LY) Incr. LY No Inter. Incr. LY Incr. Colpos per LY Age 65 100068690.18 Age 65, q3, Age 70 10212168691.221.041.0420 Age 65, q5, Age 75 1025468691.361.180.1429 Age 65, q3, Age 75 10542968692.312.130.9531 Age 65, q3, Age 80 10691568692.572.390.2658 Age 65, q2,Age 80 11073868692.992.810.4290 Age 65, q2,Age 85 11352868693.172.990.18156 Age 65, q1, Age 80 11945968693.383.20.21281 Age 65, q1, Age 85 12354168693.513.330.13315 Age 65, q1, Age90 12582368693.533.350.021150 Age 65, q1, Age95 12701268693.543.360.011200

16. Results: HPV DNA Tests StrategyColpos. Incr. Colpos. Life Years (LY) Incr. LY No Int.Incr. LY Incr. Colpos per LY No Intervention068431.36 CC, q539839868644.52213.2213.22 HPV and Pap, CC, q54727468666.15234.821.63 HPV and Pap, CC, q359011868680.61249.314.58 HPV and Pap, CC, q276717768687.08255.76.527 HPV and Pap, CC, q197921268693.34262.06.334 CC, q1123525668693.51262.20.21506

17. Results: Liquid vs. Conventional Cytology StrategyColpos. Incr. Colpos. Life Years (LY) Incr. LY No Inter.Incr. LY Incr. Colpos per LY No Intervention068431.36 CC, HPV for ASC-US, q539839868644.82213.46213.462 LBC, HPV for ASC-US, q552813068664.77233.4119.957 CC, HPV for ASC-US, q35673968670.61239.255.847 CC, HPV for ASC-US, q275218568683.24251.8812.6315 LBC, HPV for ASC-US,q296721568689.82258.466.5833 CC, HPV for ASC-US, q1123026368693.59262.233.7770 LBC, HPV for ASC-US, q1156933968695.76264.42.17156

18. Summary of Model Results Age 21, screening q3 depends on measure used Age 21, screening q3 depends on measure used Little benefit to screening well screened women after age 65 Little benefit to screening well screened women after age 65 HPV testing for women with ASCUS confirmed; role in primary screening remains unclear 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 Preference for screening using conventional or LBC depends on classification of CIN 1

19. What outcome? What outcome? –Colposcopies similar to colonoscopies? How do current guidelines affect findings? How do current guidelines affect findings? –ASCCP guidelines for Age 21 How do we compare our results with others? How do we compare our results with others? –Cost per life-year Shortcomings of the Current Approach

20. Natural history Natural history –Role of CIN 1 Vaccination Vaccination –Need to change/construct new model(s) Shortcomings (?) of the Current Model

21. Acknowledgements Laura Havrilesky, MD, Duke University Laura Havrilesky, MD, Duke University Evan Myers, MD, Duke University Evan Myers, MD, Duke University Julian Irvine, Duke University Julian Irvine, Duke University Task Force esp. George Sawaya, MD and Diana Petitti MD, PhD 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 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 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 Shalini Kulasingam is supported by NCI grant K07-CA113773