Predictors of Preference for Colorectal CancerGenetic and Environmenta - PDF document

Predictors of Preference for Colorectal
Predictors of Preference for Colorectal CancerGenetic and Environmental Risk Assessment(R01-CA11230)Ronald E. Myers, PhDProfessor and Director, Division of Population Science,Department of Medical Oncology, Associate Director of PopulationScience, Kimmel Cancer Center, Thomas Jefferson UniversityAnett Petrich, MSNHeidi Swan, MPHJames Cocro

ft, MAJocelyn Andrel, MSPHRanda Sifri, M
ft, MAJocelyn Andrel, MSPHRanda Sifri, MDEileen KeenanSharon Manne, PhDDavid WeinbergMDThomas Wolf, MA1. Thomas Jefferson University2. Fox Chase Cancer Center3. Cancer Institute of New JerseyConceptualizing Genetic and Environmental Risk Assessment (GERA)Study Design and Preference Clarification in the GERA StudyIdentifying Predictors of Pr

eference for GERADecision Factors in GER
eference for GERADecision Factors in GERAConclusionsNo conflicts of interest to declare concerning the presentation,“Predictors of Preference for Colorectal CancerGenetic and Environmental Risk Assessment.”OutlineStudyDesignBehavioral Outcome Arkadianos et al., 2007Genomic risk feedbackIncreased diet changevs Usual careand

weight lossChao et al., 2008)Genomic ris
weight lossChao et al., 2008)Genomic risk feedbackIncreased prev. behaviorsvs Family/gender risk feedback(diet, exercise, vitamins)(AD and alipoprotein E (APOE) gene)Lerman et al., 1997Biomarker +genetic risk feedbackNo impact on smokingvs biomarker feedback vs counselingHamajima et al., 2006)Genomic risk feedbackDecreased smokingvs Genomic

risk feedback(high v low number of varia
risk feedback(high v low number of variants)Beery and Williams, 2007Genomic risk feedbackIncreased cancer(Review of high-risk studies)vs ControlsscreeningRisk Feedback and Preventive Health BehaviorGene-Environment Risk Assessment GERAMTHFR& Folate) and CRC Screening: Study Design
 
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 \r  Study Flowchart
 \r  Study Flowchart \n\n  \n\n\r     \r  \r\n !" #\n  $%&!'( \r )   !" #\n \r &*'+\r,(\n *" #\n \r &!'( \r )   !" #\n %&*'+\r,(\n *" #\n %

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&!')\n  "\n \n \n\n -\n)#\n  $%&*'( \r )   -\n)#\n  $%& \r ( \nResearch Design*Decision CounselingReview informationAssess preferenceFacilitate shared decisionStudy Aims1.Determine if CRC screening is higher in the Intervention Group than in the Control Gro

upH1: CRC screening will be higher in th
upH1: CRC screening will be higher in the Intervention Group than the Control Group2. Determine if GERA feedback has an impact on CRC screeningH2: CRC screening will be higher among Intervention Group participants who receive GERA+ feedback than those who receive GERA-feedbackCompleted Baseline Survey(N=755)Focus on Preference Related to G

ERAControl Group(n=257)Intervention Gr
ERAControl Group(n=257)Intervention Group(n=498)DecisionCounseling(n=343)*No DecisionCounseling(n=115)*Missed (n=89)Ineligible (n=23)Declined (n=43)* Participants who were African American or had HS education were more likely to undergo decision counseling than those who were white or had � HS education (p0.001 and p=0.009,

respectively) Study Population and Proc
respectively) Study Population and ProceduresEligible patients: 50 to 79 years of age and eligible for CRC screening, consented, and completed a baseline survey. Control Group:Usual careIntervention Group: Decision CounselingReview GERA brochureIdentify top decision factors (pros and cons)Rank factors and determine factor weightsCompute p

reference score (0.000-1.000)Assess agre
reference score (0.000-1.000)Assess agreement with preference measureEducation and Preference ClarificationProConWeight Decision FactorsFactor 1 Select WeightFactor 2 Select WeightFactor 3 Select WeightCompare Decision FactorsFactor 1-2 Select WeightFactor 2-3 Select WeightFactor 1-3 Select WeightWeight of Influence:N

one, A Little, Some, Much,Very Much, Ove
one, A Little, Some, Much,Very Much, OverwhelmingRelative Weight of Influence:About the Same, A LittleMore, Somewhat MoreMuch More, Very MuchMore, Overwhelmingly MoreAbove Average Risk:-MTHFR 677/1298-Low FolateAverage Risk:-No MTHFR 677/1298-Normal FolateComputing a Decision Preference ScoreDecision Factor DirectionScoreand Level of Fac

tor Influence Range Preferen
tor Influence Range PreferenceCon–Overwhelming 1.90.000 –0.333–Very Much1.70.334 -0.356–Much1.50.357 -0.383–Somewhat1.30.384 -0.416–A little1.10.417 -0.454Neutral1.00.455 -0.545Pro–A little1.10.546 -0.583–Somewhat1.30.584 -0.616–Much1.50.617 -0.643–Very Much1.70.644 -0.666–Overwhelming1.90.667 -1.000NeutralMo

derateModerateHighHighLowLowMethods:
derateModerateHighHighLowLowMethods: Analysis of GERA Preference•GERA preference scores for participants in the intervention group were determined (N=343) •Preference scores were dichotomized as low to moderate (0.00-0.666) versus high (0.667-1.00)•Univariable and multivariable analyses were performed to identify predictors of high prefe

rence•Decision factors were coded and ta
rence•Decision factors were coded and tallied.GERA Preference Scores (n=343)Participants & Decision Factors•Pros (Altruism, Knowledge, Worry, Convenience)–“The test will help make find out what I can do to prevent colon cancer.”–“I want to contribute to science.”–“A blood test is a quick, and painless, safe .”–“It makes sense. I'm concer

ned about my health.”•Cons (Fear, Worry,
ned about my health.”•Cons (Fear, Worry, Trust, Discomfort)–“I’m afraid of finding out I’m at higher risk.”–“I don't like blood tests.”–“I’m worried about ulterior motives of research institutions.”–“I’m concerned about my privacy.”Decision Factors (n=557)96% Pros4% ConsGERA Preference Scores (n=343)Who is in the 24%(High Preference for G

ERA)?Low/ModHighKnowledge about CRC Scr
ERA)?Low/ModHighKnowledge about CRC Screening(n=324)(n=243)(n=81)n%n%P-value505020.582530.860.0573� 5019379.425669.14(n=203)(n=69)Knowledge about GERA (n=272)n%n%P-value508039.412942.030.7012� 5012360.594057.97Univariable Analysis of Preference for GERALow/ModHigh(n=260)(n=83)Variablen%n%P-valueRace0.0001White

16563.953340.24non-White9336.0549
16563.953340.24non-White9336.054959.76Age0.709450 –59 years17667.695869.8860 –79 years8432.312530.12Gender0.4604Male10941.923137.35Female15158.085262.65Univariable Analysis of Preference for GERALow/ModHigh(n=260)(n=83)Variablen%n%P-valueEducation0.0015= High School Graduate6023.083440.96&#

x0000; High School Graduate20076.9249
x0000; High School Graduate20076.924959.04Marital Status0.4602Living as Couple12849.233744.58Living Alone13250.774655.42Baseline Decision Stage0.0214 DTD3714.2344.82DTD22385.777995.18Univariable Analysis of Preference for GERAUnivariable Analysis of Preference for GERALow/ModHighTotal(n=260)(n=83)(n=343)Variabl

en%n%n%P-valueSalience & Coherence1.0000
en%n%n%P-valueSalience & Coherence1.00003135.0044.82174.96�324795.007995.1832695.04Susceptibility0.4829321582.696579.2728081.87�34517.311720.736218.13Worries and Concerns0.4703313451.543946.9917350.44�312648.464453.0117049.56Univariable Analysis of Preference for GERALow/ModHigh(n=260)(n=83)Variablen%n%P-valueResponse

Efficacy0.12093207.6922.44�324092
Efficacy0.12093207.6922.44�324092.318097.56Social Support & Influence0.229233915.0089.76�322185.007490.24VariableOR95% Confidence IntervalP-valueRace0.0028White1.00non-White2.25(1.32, 3.82)Education0.0145=HS 1.00&#x-1.3;斑HS0.50(0.29, 0.87)Baseline Decision Stage0.0398 DTD1.00DTD3.17(1.06, 9.52)Multivariable Analysis of Pref

erence for GERA Results24% of particip
erence for GERA Results24% of participants had a high preference for GERAPredictors of high preferenceBeing nonwhiteHigh school educationDecided to do CRC screeningFrequently expressed reasons for high preferenceDesire for knowledge about risk for CRCWorry about developing CRC in the futureConclusionsPeople differ in terms of their stren

gth of preference for genetic and enviro
gth of preference for genetic and environmental risk testingStrength of preference for such testing may vary in population subgroupsResearch is needed to learn about factors that motivate subgroup preference for such testingMediated decision support should be provided to facilitate informed, shared decision making about testing and preventiv

e health behaviorObservation“Genetic i
e health behaviorObservation“Genetic information based on single-gene variants with low risk probabilities has little impact –either positive or negative –on emotions, cognitions, or behavior . . . There is a need to accelerate research in evaluating whether new understandings of genetic risk can favorably influence health behavior.”McBrid

e et al., 2010Patient-centered care is
e et al., 2010Patient-centered care is “care that is respectful of and responsive to individual patient preferences, needs, and values (and ensures) that patient values guide all clinical decisions.”(Crossing the Quality Chasm, IOM, 2001)the most important attribute of patient-centered care is the active engagement of patients whenfateful h

ealth care decisions must be made –when
ealth care decisions must be made –when an individual patient arrives at a crossroads of medical options, where the diverging pathshave different and important consequences withlasting implications.”(Barry and Edgman-Levitan, NEJM, 2012)Patient-Centered CareInfluence of Pro and Con Decision Factors (n=343)(1=No Influence, 6=Overwhelming In

fluence) Factor 1Factor 2Con(n=257)(n=2
fluence) Factor 1Factor 2Con(n=257)(n=23Factor 3Low/Mod HighDecision Support Interventions Defined“Decision support interventions help people think about choices they face; they describe where and why choice exists; (and) they provide information about options, including where reasonable, the option of taking no action.”Decisio

n support interventions can be used for
n support interventions can be used for one-way delivery of information to patients (non-mediated) or in the context of a two-way interaction between a patient and a health care provider (mediated)(Elwyn et al., 2010)Center for Health Decisions(http://www.jefferson.edu/jmc/medical _oncology/divisions/population_science/)Research in the Cen

ter focuses on informed/shared decision
ter focuses on informed/shared decision making; patient, provider, and population response to mediated decision support; and the impact of decision counseling on patient behavior, provider practice patterns, population health, disparities in cancer care, and patient-centered outcomes.Decision CounselingReport for Patient.08;肐Center f

or Health DecisionsDetermine Preference
or Health DecisionsDetermine Preference and Produce ReportNAMEDecision Counseling Program Data FlowSpecified FactorsInfluencing DecisionPatient/Client ID, Name,and Contact InformationScore, Validation Status, Comments and NotesDemographicsComparisons of FactorRelative ImportanceDecision DomainDecision SituationDCP DataRepositoryPrima

ry, Secondary, & Tertiary Factor Strengt
ry, Secondary, & Tertiary Factor StrengthsDistribution of Preference ScoresConPreference ScoreTotal–Overwhelming 0.000 –0.3331–Very Much0.334 -0.356-–Much0.357 -0.383-–Somewhat0.384 -0.416–A little0.417 -0.454-Neutral0.455 -0.5454Pro–A little0.546 -0.58318–Somewhat0.584 -0.61619–Much0.617 -0.64360–Very Much0.644 -0.6661-