grant

Addressing racial disparities in lung cancer screening

Organization VANDERBILT UNIVERSITY MEDICAL CENTERLocation NASHVILLE, UNITED STATESPosted 5 Apr 2021Deadline 31 Mar 2027
NIHUS FederalResearch GrantFY2025AccountingAddressAfrican AmericanAfrican American groupAfrican American individualAfrican American peopleAfrican American populationAfrican AmericansAfro AmericanAfroamericanAgeAlgorithmsBiometricsBiometryBiostatisticsCalibrationCancersCausalityCessation of lifeClinicalClinical DataCohort StudiesCommunitiesConcurrent StudiesDataDeathDecision AidDevelopmentDiagnosisDiagnosticEligibilityEligibility DeterminationEquityEtiologyExclusionFailureFemale HealthFocus GroupsGuidelinesHealth InformaticsIndividualIndividualized risk predictionKnowledgeLifeLungLung Respiratory SystemMachine LearningMalignant NeoplasmsMalignant TumorMalignant Tumor of the LungMalignant neoplasm of lungMeasuresMissionModelingNCI OrganizationNational Cancer InstituteNational Health Interview SurveyNatural HistoryOn-Line SystemsOnline SystemsPatientsPopulationPrecision HealthPredictive Cancer ModelProbabilistic ModelsProbability ModelsPropertyProspective cohortProtocol ScreeningProviderPublic Health InformaticsPublishingPulmonary CancerPulmonary malignant NeoplasmRaceRacesRacial EquityResearchRiskRisk FactorsRuralScreening for cancerSmokingSmoking BehaviorSmoking HistoryStatistical MethodsStatistical ModelsSurvey InstrumentSurveysTechniquesTestingTranslatingU.S. Preventative Services Task ForceU.S. Preventative Task ForceU.S. Preventive Services Task ForceU.S. Preventive Task ForceUS Preventative Services Task ForceUS Preventative Task ForceUS Preventive Health Services Task ForceUS Preventive Services Task ForceUS Preventive Task ForceUSPSTFUnited StatesUnited States Preventative Services Task ForceUnited States Preventative Task ForceUnited States Preventive Services Task ForceUnited States Preventive Task ForceValidationVariantVariationVisitWomanWomen's HealthWorkage groupagesblack femaleblack womencancer epidemiologycancer riskcausationclinical implementationco-morbidco-morbiditycohortcommunity advisory boardcommunity advisory committeecommunity advisory panelcommunity engaged approachcommunity engaged approachescommunity engaged strategiescommunity engaged strategycommunity engagementcommunity partnered approachcommunity partnered strategycomorbiditycomputer based predictionconsumer informaticsdata harmonizationdesigndesigningdevelopmentaldifferences due to racedifferences in racediffers by racediffers in racedisease causationdisparities in racedisparity due to racedisparity in healthearly cancer detectionengagement with communitiesexperienceharmonized datahealth disparityhigh riskimprovedindividualized predictionsinequality due to raceinequity due to raceinnovateinnovationinnovativelow SESlow socio-economic positionlow socio-economic statuslow socioeconomic positionlow socioeconomic statuslung cancerlung cancer early detectionlung cancer screeningmachine based learningmachine learned algorithmmachine learning algorithmmachine learning based algorithmmachine statistical learningmalignancymortalitymulti-ethnicmultidisciplinarymultiethnicneoplasm/cancernovelonline computeronline decision aidpack/yearpatient centeredpatient orientedpersonalized predictionspersonalized risk predictionpopulation basedpredictive modelingpredictive toolsprospectiverace based differencesrace based disparityrace based inequalityrace based inequityrace differencesrace disparityrace related differencesrace related disparityrace related inequalityrace related inequityracialracial backgroundracial differenceracial disparityracial diversityracial inequalityracial inequityracial originracially differentracially diverseracially unequalreal world applicationrecommended screeningrisk prediction algorithmrisk prediction modelrural arearural locationrural regionscreeningscreening cancer patientsscreening guidelinesscreening programscreening recommendationsscreeningsshared decision makingsimulationstatistic methodsstatistical and machine learningstatistical linear mixed modelsstatistical linear modelsstudy populationurban areaurban locationurban regionvalidationsweb app decision aidweb basedweb based decision aidweb toolweb-based tool
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Full Description

PROJECT SUMMARY/ABSTRACT
Screening promotes early detection of cancer to decrease mortality. Unfortunately, significant racial disparities

exist in lung cancer screening. Recently published findings by our team show that under current national

screening guidelines African Americans have reduced eligibility for lung cancer screening compared to whites.

These screening guidelines are based on a combination of age and smoking pack-year criteria derived from a

national lung screening trial that was primarily (91%) white. Importantly, smoking behaviors and baseline risks

for lung cancer differ greatly between African Americans and whites. Because of this, a risk-based screening

strategy may provide a more equitable assessment of eligibility than current screening guidelines. However, the

development of personalized risk prediction models for lung cancer in African Americans has been limited. To

address this gap and to improve equity in screening eligibility, we propose building a personalized prediction tool

using the combined data from three large-scale population-based prospective cohorts with substantial African

American representation. The combined cohorts have over 336,000 individuals (44% African American) and

9,132 incident lung cancer cases from across the United States. We propose the following three aims: 1)

construct a well-calibrated natural-history model of lung cancer risk for screening in African Americans, 2)

evaluate lung cancer screening strategies by simulation and identify sub-populations who would benefit from

screening, accounting for comorbidities and false-positives, and 3) develop a web-based decision aid for

screening that is culturally appropriate. We will employ innovative machine learning techniques and state-of-the-

art statistical methods to build a well-calibrated lung cancer prediction model for African Americans. Careful

examination will identify sub-populations (such as women, low socioeconomic status, rural, age groups, etc.)

that will benefit from screening. A key innovative aspect of this proposal is its community-engaged approach and

partnership with a Community Advisory Board, both of which will help translate our empirical findings into the

design of a patient-oriented decision aid. This project is relevant to the mission of the National Cancer Institute

since it focuses on establishing equity in lung cancer screening eligibility. Our findings will have sustained impact

on precision health and motivate improved clinical strategies for the early detection of lung cancer for African

Americans.

Grant Number: 5R01CA251758-05
NIH Institute/Center: NIH

Principal Investigator: Melinda Aldrich

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