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