A Cervical Cancer A Cervical Cancer Decision Model to Inform Decision Model to Inform Recommendations About Recommendations About Preventive ServicesPreventive ServicesPerspective of the Decision Perspective of the Decision ModelerModeler

Shalini Kulasingam, PhDShalini Kulasingam, PhDDuke UniversityDuke UniversityDurham, NCDurham, NC

The Natural History of The Natural History of Cervical CancerCervical Cancer

NormalCervix

HPV infectedCervix

Pre-cancerCIN 3

CancerInvasionRegression

Progression

Clearance

Infection

Self-limited Infection(CIN 1, CIN 2?)

ProgressionRegression

Natural historyNatural history New screening testsNew screening tests

HPV testsHPV tests Cervical cytology testsCervical cytology tests

VaccinationVaccination GuidelinesGuidelines

– What age to begin screeningWhat age to begin screening– What age to end screeningWhat age to end screening– Screening frequencyScreening frequency

Why a Decision Model for Why a Decision Model for Cervical Cancer?Cervical Cancer?

3 screening tests * 3 screening tests *

15 different ages to start screening * 15 different ages to start screening *

8 different ages to end screening = 8 different ages to end screening =

1 big headache + insufficient funds1 big headache + insufficient funds

An RCT for Every An RCT for Every Combination is Combination is ImpossibleImpossible

What is a Model?What is a Model?

Year1

2

3

Nml HPV CIN 1 CIN 2-3 CA D

Nml HPV CIN 1 CIN 2-3 CA D

Nml HPV CIN 1 CIN 2-3 CA D

100%

94% 5%

88% 8% 2%

1%

2%

State Transition ModelState Transition Model

Nml=Normal

Screening affects transitions for CIN 1, CIN 2-3 and cancer (Stage I)

The Duke Cervical The Duke Cervical Cancer ModelCancer Model Markov state transition model of HPV, cervical pre-cancer Markov state transition model of HPV, cervical pre-cancer

and cancerand cancer– Can account for impact of screening and vaccinationCan account for impact of screening and vaccination

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

Validated by comparing outcomes to Validated by comparing outcomes to – Reported outcomes (e.g., SEER)Reported outcomes (e.g., SEER)– Outcomes predicted by other independently developed modelsOutcomes predicted by other independently developed models

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

LimitationsLimitations– Reflects clinical practice and includes CIN 1 as a state Reflects clinical practice and includes CIN 1 as a state – Scientifically moving toward defining CIN 3 as the only true pre-Scientifically moving toward defining CIN 3 as the only true pre-

cancer statecancer state– Data are grouped into age categories that may be blunt to one-Data are grouped into age categories that may be blunt to one-

year age differencesyear age differences

Life-years gainedLife-years gained– With screening and treatment, more women With screening and treatment, more women – survive for a longer timesurvive for a longer time– Model calculates average life-expectancy for the Model calculates average life-expectancy for the

cohort with and without screening and treatmentcohort with and without screening and treatment– LYG is difference between these twoLYG is difference between these two

Colposcopies – Task Force measure of burden Colposcopies – Task Force measure of burden of screeningof screening

Cost – traditional measure of resources usedCost – traditional measure of resources used

How Do We Use the How Do We Use the Model to Calculate an Model to Calculate an Outcome?Outcome?

Current Current Recommendations Recommendations (2003)(2003) Direct evidence to determine the optimal starting and Direct evidence to determine the optimal starting and

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

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

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

Questions posed by Questions posed by USPSTFUSPSTF Age to begin cervical cancer Age to begin cervical cancer

screeningscreening Age to end cervical cancer Age to end cervical cancer

screeningscreening Role of HPV tests in primary Role of HPV tests in primary

screening and triage of abnormal screening and triage of abnormal cytology resultscytology results

Role of liquid-based cytologyRole of liquid-based cytology

Communicating with Communicating with the TF….the TF….

Issues in Answering the TF Issues in Answering the TF QuestionsQuestions

Evidence Report for Screening TestsEvidence Report for Screening Tests– Oregon EPCOregon EPC

Use the data from this report for the modelUse the data from this report for the model Need to coordinate so that the findings are Need to coordinate so that the findings are

consistent consistent Short time frameShort time frame

– Original time frame of 3 monthsOriginal time frame of 3 months The “oh you have a model” syndromeThe “oh you have a model” syndrome

– Change in model structureChange in model structure– Change in questions and output requestedChange in questions and output requested– Keeping up with an onslaught of HPV and Keeping up with an onslaught of HPV and

cervical cancer studiescervical cancer studies

Results: Age to Begin Results: Age to Begin ScreeningScreening

Strategy Colpos.Incr.Colpos.

Life Years (LY)

Incr. LY (No Inter.) Incr. LY

Incr. Colpos per LY

No Intervention 0 68431.4

Age 20, q5 389 389 68643.0 211.59 211.6 2

Age 18, q5 397 8 68644.5 213.18 1.6 5

Age 17, q3 560 163 68671.7 240.29 27.1 6

Age 18, q2 752 192 68685.4 254.01 13.7 14

Age 16, q2 757 5 68685.6 254.27 0.3 19

Age 17, q2 766 9 68685.9 254.54 0.3 33

Age 17, q1Age 17, q1 12561256 490 68696.868696.8 265.43 10.9 4545

Age 16, q1 1261 5 68696.8 265.44 0.01 4646

Age 15, q1 1266 5 68696.8 265.46 0.02 309

Results: Age to End Results: Age to End ScreeningScreening

Strategy Colpos.Incr.Colpos

Life Years (LY)

Incr. LY(No Inter.) Incr. LY

Incr. Colpos per LY

No Intervention 00 68431.468431.4

Age 65, q5, Age Age 65, q5, Age 7070 3434 3434 68442.068442.0 10.6110.61 10.6110.61 33

Age 65, q3, Age Age 65, q3, Age 7070 5656 2222 68445.768445.7 14.2514.25 3.643.64 66

Age 65, q2, Age Age 65, q2, Age 7070 7575 1919 68447.668447.6 16.1916.19 1.941.94 1010

Age 65, q1, Age Age 65, q1, Age 7070 117117 4242 68449.868449.8 18.4218.42 2.232.23 1919

Age 65, q1, Age Age 65, q1, Age 7575 181181 6464 68450.868450.8 19.4319.43 1.011.01 6363

Age 65, q1, Age Age 65, q1, Age 8080 234234 5353 68451.268451.2 19.8119.81 0.380.38 139139

Age 65, q1,Age Age 65, q1,Age 8585 274274 4040 68451.368451.3 19.9419.94 0.130.13 308308

Age 65, q1, Age Age 65, q1, Age 9090 296296 2222 68451.468451.4 19.9619.96 0.020.02 11001100

Age 65, q1, Age Age 65, q1, Age 9595 308308 1212 68451.468451.4 19.9719.97 0.010.01 12001200

Results: Age to End Results: Age to End ScreeningScreening

Strategy Colpos.Incr.Colpos.

Life Years (LY)

Incr. LYNo Inter.

Incr. LY

Incr.Colpos per

LY

Age 65Age 65 10001000 68690.1868690.18

Age 65, q3, Age Age 65, q3, Age 7070 10211021 2121 68691.2268691.22 1.041.04 1.041.04 2020

Age 65, q5, Age Age 65, q5, Age 7575 10251025 44 68691.3668691.36 1.181.18 0.140.14 2929

Age 65, q3, Age Age 65, q3, Age 7575 10541054 2929 68692.3168692.31 2.132.13 0.950.95 3131

Age 65, q3, Age Age 65, q3, Age 8080 10691069 1515 68692.5768692.57 2.392.39 0.260.26 5858

Age 65, q2,Age 80Age 65, q2,Age 80 11071107 3838 68692.9968692.99 2.812.81 0.420.42 9090

Age 65, q2,Age 85Age 65, q2,Age 85 11351135 2828 68693.1768693.17 2.992.99 0.180.18 156156

Age 65, q1, Age Age 65, q1, Age 8080 11941194 5959 68693.3868693.38 3.23.2 0.210.21 281281

Age 65, q1, Age Age 65, q1, Age 8585 12351235 4141 68693.5168693.51 3.333.33 0.130.13 315315

Age 65, q1, Age90Age 65, q1, Age90 12581258 2323 68693.5368693.53 3.353.35 0.020.02 11501150

Age 65, q1, Age95Age 65, q1, Age95 12701270 1212 68693.5468693.54 3.363.36 0.010.01 12001200

Results: HPV DNA Results: HPV DNA TestsTests

Strategy Colpos.Incr.Colpos.

Life Years(LY)

Incr. LYNo Int. Incr. LY

Incr. Colpos per LY

No Intervention 00 68431.3668431.36

CC, q5 398398 398398 68644.5268644.52 213.2213.2 213.2213.2 22

HPV and Pap, CC, q5 472472 7474 68666.1568666.15 234.8234.8 21.621.6 33

HPV and Pap, CC, q3 590590 118118 68680.6168680.61 249.3249.3 14.514.5 88

HPV and Pap, CC, q2 767767 177177 68687.0868687.08 255.7255.7 6.56.5 2727

HPV and Pap, CC, q1 979979 212212 68693.3468693.34 262.0262.0 6.36.3 3434

CC, q1 12351235 256256 68693.5168693.51 262.2262.2 0.20.2 15061506

Results: Liquid vs. Results: Liquid vs. Conventional CytologyConventional Cytology

Strategy Colpos.Incr.Colpos.

Life Years

(LY)

Incr. LY No

Inter. Incr. LY

Incr. Colpos per LY

No Intervention 00 68431.3668431.36

CC, HPV for ASC-US, q5 398398 398398 68644.8268644.82 213.46213.46 213.46213.46 22

LBC, HPV for ASC-US, q5 528528 130130 68664.7768664.77 233.41233.41 19.9519.95 77

CC, HPV for ASC-US, q3 567567 3939 68670.6168670.61 239.25239.25 5.845.84 77

CC, HPV for ASC-US, q2 752752 185185 68683.2468683.24 251.88251.88 12.6312.63 1515

LBC, HPV for ASC-US,q2 967967 215215 68689.8268689.82 258.46258.46 6.586.58 3333

CC, HPV for ASC-US, q1 12301230 263263 68693.5968693.59 262.23262.23 3.773.77 7070

LBC, HPV for ASC-US, q1 15691569 339339 68695.7668695.76 264.4264.4 2.172.17 156156

Summary of Model Summary of Model ResultsResults Age 21, screening q3 depends on Age 21, screening q3 depends on

measure usedmeasure used Little benefit to screening well Little benefit to screening well

screened women after age 65screened women after age 65 HPV testing for women with ASCUS HPV testing for women with ASCUS

confirmed; role in primary screening confirmed; role in primary screening remains unclearremains unclear

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

What outcome?What outcome?– Colposcopies similar to colonoscopies?Colposcopies similar to colonoscopies?

How do current guidelines affect How do current guidelines affect findings?findings?– ASCCP guidelines for Age 21ASCCP guidelines for Age 21

How do we compare our results How do we compare our results with others?with others?– Cost per life-yearCost per life-year

Shortcomings of the Shortcomings of the Current ApproachCurrent Approach

Natural historyNatural history– Role of CIN 1Role of CIN 1

VaccinationVaccination– Need to change/construct new Need to change/construct new

model(s)model(s)

Shortcomings (?) of Shortcomings (?) of the Current Model the Current Model

AcknowledgementsAcknowledgements

Laura Havrilesky, MD, Duke UniversityLaura Havrilesky, MD, Duke University Evan Myers, MD, Duke UniversityEvan Myers, MD, Duke University Julian Irvine, Duke UniversityJulian Irvine, Duke University Task Force esp. George Sawaya, MD and Diana Petitti Task Force esp. George Sawaya, MD and Diana Petitti

MD, PhDMD, PhD AHRQ: Tracy Wolff, MD, Tess Miller DrPh and Mary AHRQ: Tracy Wolff, MD, Tess Miller DrPh and Mary

Barton, MD; CDC: Mona Saraiya, MD, MPHBarton, MD; CDC: Mona Saraiya, MD, MPH Funded by the United States Centers for Disease Funded by the United States Centers for Disease

Control and Prevention and the Agency for Healthcare Control and Prevention and the Agency for Healthcare Research and QualityResearch and Quality

Shalini Kulasingam is supported by NCI grant K07-Shalini Kulasingam is supported by NCI grant K07-CA113773CA113773