2024
Using a Bayesian analytic approach to identify county-level ecological factors associated with survival among individuals with early-onset colorectal cancer
Siddique S, Baum L, Deziel N, Kelly J, Warren J, Ma X. Using a Bayesian analytic approach to identify county-level ecological factors associated with survival among individuals with early-onset colorectal cancer. PLOS ONE 2024, 19: e0311540. PMID: 39471191, PMCID: PMC11521299, DOI: 10.1371/journal.pone.0311540.Peer-Reviewed Original ResearchConceptsAge-of-onset colorectal cancerEarly-onset colorectal cancerEnd Results Program dataCenters for Disease Control and Prevention dataCounty-level factorsColorectal cancerHealth risk behaviorsIdentified principal componentsOdds of survivalPreventive servicesSurvival disparitiesLinear mixed modelsEOCRCChronic diseasesPreventive factorsUS countiesSalt Lake CountyCA residentsRisk behaviorsUnited StatesProgram dataCounty-levelOlder ageBayesian analytical approachYounger ageUSPSTF Colorectal Cancer Screening Recommendation and Uptake for Individuals Aged 45 to 49 Years
Siddique S, Wang R, Yasin F, Gaddy J, Zhang L, Gross C, Ma X. USPSTF Colorectal Cancer Screening Recommendation and Uptake for Individuals Aged 45 to 49 Years. JAMA Network Open 2024, 7: e2436358. PMID: 39361285, PMCID: PMC11450516, DOI: 10.1001/jamanetworkopen.2024.36358.Peer-Reviewed Original ResearchConceptsUS Preventive Services Task ForceUS Preventive Services Task Force recommendationsColorectal cancer screening uptakeAverage-risk individualsScreening uptakeHigher socioeconomic statusSocioeconomic statusScreening recommendationsColorectal cancerColorectal cancer screening recommendationsPreventive Services Task ForceCohort studyCancer screening recommendationsScreening uptake ratesInterrupted time series analysisLow socioeconomic statusPrivate insurance beneficiariesScreening ratesSocioeconomic disparitiesRetrospective cohort studyMain OutcomesPotential disparitiesEvaluate changesClaims dataAbsolute changeLong-Term Outcomes of Prostate-Specific Membrane Antigen–PET Imaging of Recurrent Prostate Cancer
Kunst N, Long J, Westvold S, Sprenkle P, Kim I, Saperstein L, Rabil M, Ghaffar U, Karnes R, Ma X, Gross C, Wang S, Leapman M. Long-Term Outcomes of Prostate-Specific Membrane Antigen–PET Imaging of Recurrent Prostate Cancer. JAMA Network Open 2024, 7: e2440591. PMID: 39441595, DOI: 10.1001/jamanetworkopen.2024.40591.Peer-Reviewed Original ResearchConceptsProstate-specific antigenProstate-specific antigen levelPSMA-PETRecurrent prostate cancerBiochemical recurrenceProstate cancerLong-term outcomesProstate-specific membrane antigen positron emission tomographyEvaluation of biochemical recurrenceDetection of biochemical recurrenceLife yearsConventional imagingDefinitive local therapyPSMA PET imagingProstate cancer deathDetection of metastasesRetrospective cohort studyBase case analysisIncremental life-yearsPositron emission tomographyDecision-analytic modelLocal therapyConventional imaging strategiesDelayed treatmentDisease course
2023
Parent Priorities in End-of-Life Care for Children With Cancer
Ananth P, Lindsay M, Mun S, McCollum S, Shabanova V, de Oliveira S, Pitafi S, Kirch R, Ma X, Gross C, Boyden J, Feudtner C, Wolfe J. Parent Priorities in End-of-Life Care for Children With Cancer. JAMA Network Open 2023, 6: e2313503. PMID: 37184834, PMCID: PMC10878399, DOI: 10.1001/jamanetworkopen.2023.13503.Peer-Reviewed Original Research
2022
Oral Cyanobacteria and Hepatocellular Carcinoma
Hernandez BY, Zhu X, Risch HA, Lu L, Ma X, Irwin ML, Lim JK, Taddei TH, Pawlish KS, Stroup AM, Brown R, Wang Z, Wong LL, Yu H. Oral Cyanobacteria and Hepatocellular Carcinoma. Cancer Epidemiology Biomarkers & Prevention 2022, 31: 221-229. PMID: 34697061, PMCID: PMC8755591, DOI: 10.1158/1055-9965.epi-21-0804.Peer-Reviewed Original ResearchConceptsHepatitis B virusHepatitis C virusHepatocellular carcinomaRisk factorsLiver diseaseHCC casesOral microbiomeU.S. case-control studyIndependent risk factorChronic liver diseaseFatty liver diseaseHCC risk factorsGut microbial alterationsType 2 diabetesCase-control studyLiver cancer developmentNSAID useAspirin useC virusB virusHCC riskNegative historyOral samplesSignificant associationCancer developmentChanges in Prostate-Specific Antigen Testing Relative to the Revised US Preventive Services Task Force Recommendation on Prostate Cancer Screening
Leapman MS, Wang R, Park H, Yu JB, Sprenkle PC, Cooperberg MR, Gross CP, Ma X. Changes in Prostate-Specific Antigen Testing Relative to the Revised US Preventive Services Task Force Recommendation on Prostate Cancer Screening. JAMA Oncology 2022, 8: 41-47. PMID: 34762100, PMCID: PMC8587214, DOI: 10.1001/jamaoncol.2021.5143.Peer-Reviewed Original ResearchConceptsProstate cancer screeningUS Preventive Services Task ForcePSA testingCancer screeningInterrupted time series analysisCohort studyUS Preventive Services Task Force (USPSTF) recommendationDraft statementLarge national cohort studyEligible beneficiariesPSA testing ratesRetrospective cohort studyRate of PSANational cohort studyProstate-specific antigenAge-adjusted ratesTask Force recommendationsUSPSTF guidelinesMedian ageGuideline changesProstate cancerMedian numberMAIN OUTCOMEClaims dataTesting rates
2021
Adoption of New Risk Stratification Technologies Within US Hospital Referral Regions and Association With Prostate Cancer Management
Leapman MS, Wang R, Park HS, Yu JB, Sprenkle PC, Dinan MA, Ma X, Gross CP. Adoption of New Risk Stratification Technologies Within US Hospital Referral Regions and Association With Prostate Cancer Management. JAMA Network Open 2021, 4: e2128646. PMID: 34623406, PMCID: PMC8501394, DOI: 10.1001/jamanetworkopen.2021.28646.Peer-Reviewed Original ResearchConceptsProstate magnetic resonance imagingMagnetic resonance imagingHospital referral regionsProportion of patientsProstate cancerGenomic testingCohort studyReferral regionsRetrospective cohort studyProstate cancer carePatient-level analysisCommercial insurance claimsProstate cancer managementUS hospital referral regionsYears of ageProportion of menPatients 40Definitive treatmentCancer careTesting uptakeHRR levelMAIN OUTCOMECancer managementPatientsRegional uptakeGenetic determinants of blood-cell traits influence susceptibility to childhood acute lymphoblastic leukemia
Kachuri L, Jeon S, DeWan AT, Metayer C, Ma X, Witte JS, Chiang CWK, Wiemels JL, de Smith AJ. Genetic determinants of blood-cell traits influence susceptibility to childhood acute lymphoblastic leukemia. American Journal Of Human Genetics 2021, 108: 1823-1835. PMID: 34469753, PMCID: PMC8546033, DOI: 10.1016/j.ajhg.2021.08.004.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedBiomarkers, TumorBlood PlateletsCase-Control StudiesChildFemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansLymphocytesMaleMendelian Randomization AnalysisMiddle AgedMonocytesNeutrophilsPrecursor Cell Lymphoblastic Leukemia-LymphomaPrognosisProspective StudiesQuantitative Trait LociUnited KingdomConceptsTrait-associated variantsMulti-trait GWASBlood cell homeostasisWide association studyGenetic risk lociTrait variationHematologic traitsRisk lociAssociation studiesCell typesGenetic determinantsLociInfluence susceptibilityUK BiobankMendelian randomization analysisGWASEtiological relevanceRandomization analysisTraitsHomeostasisSusceptibilityAcute lymphoblastic leukemiaAnalysis of Survival Among Adults With Early-Onset Colorectal Cancer in the National Cancer Database
Cheng E, Blackburn HN, Ng K, Spiegelman D, Irwin ML, Ma X, Gross CP, Tabung FK, Giovannucci EL, Kunz PL, Llor X, Billingsley K, Meyerhardt JA, Ahuja N, Fuchs CS. Analysis of Survival Among Adults With Early-Onset Colorectal Cancer in the National Cancer Database. JAMA Network Open 2021, 4: e2112539. PMID: 34132794, PMCID: PMC8209612, DOI: 10.1001/jamanetworkopen.2021.12539.Peer-Reviewed Original ResearchConceptsEarly-onset colorectal cancerOnset colorectal cancerNational Cancer DatabaseColorectal cancerAge 51Overall survivalCancer DatabaseIncidence of CRCCox proportional hazards regressionPrimary colorectal cancerKaplan-Meier analysisProportional hazards regressionAge 50 yearsAge 25 yearsAnalysis of survivalCohort studySurvival benefitHazards regressionUnadjusted analysesCancer incidenceMAIN OUTCOMEAge 35Survival advantageLower riskStage IRegional Adoption of Commercial Gene Expression Testing for Prostate Cancer
Leapman MS, Wang R, Ma S, Gross CP, Ma X. Regional Adoption of Commercial Gene Expression Testing for Prostate Cancer. JAMA Oncology 2021, 7: 52-58. PMID: 33237277, PMCID: PMC7689565, DOI: 10.1001/jamaoncol.2020.6086.Peer-Reviewed Original ResearchConceptsProstate cancerCohort studyGenomic testingTissue-based genomic testsProportion of patientsDynamic cohort studyProstate cancer careProstate cancer screeningHospital referral region levelCommercial health insuranceGene expression testingEligible patientsMedian ageCancer careCancer screeningInclusion criteriaFindings highlight factorsHRR levelMAIN OUTCOMEAdministrative claimsGroup 2Group 1Medicare beneficiariesPatientsMedian household income
2020
Diet and Risk of Myeloproliferative Neoplasms in Older Individuals from the NIH-AARP Cohort
Podoltsev NA, Wang X, Wang R, Hofmann JN, Liao LM, Zeidan AM, Mesa RA, Ma X. Diet and Risk of Myeloproliferative Neoplasms in Older Individuals from the NIH-AARP Cohort. Cancer Epidemiology Biomarkers & Prevention 2020, 29: 2343-2350. PMID: 32868318, PMCID: PMC8895351, DOI: 10.1158/1055-9965.epi-20-0592.Peer-Reviewed Original ResearchConceptsRisk of MPNPolycythemia veraEssential thrombocythemiaMyeloproliferative neoplasmsHazard ratioSugar intakeDietary factorsMultivariable Cox proportional hazards modelsRisk of PVCox proportional hazards modelHigh sugar intakeNIH-AARP DietIntake of fatConfidence intervalsNIH-AARP cohortRole of dietProportional hazards modelPotential confounding variablesParticipants ages 50Warrants further investigationProspective cohortHigh intakePV riskAge 50Health StudyNational trends in the management of patients with positive surgical margins at radical prostatectomy
Ghabili K, Park HS, Yu JB, Sprenkle PC, Kim SP, Nguyen KA, Ma X, Gross CP, Leapman MS. National trends in the management of patients with positive surgical margins at radical prostatectomy. World Journal Of Urology 2020, 39: 1141-1151. PMID: 32562045, DOI: 10.1007/s00345-020-03298-6.Peer-Reviewed Original ResearchConceptsAndrogen deprivation therapyPositive surgical marginsUse of ADTPost-prostatectomy radiation therapyRadiation therapySurgical marginsRadical prostatectomyInitial courseNode-negative prostate cancerPost-operative radiation therapyMultivariable logistic regression modelNational Cancer DatabaseAdverse pathologic featuresManagement of patientsPost-operative managementLogistic regression modelsDeprivation therapySurgical cancersPrimary endpointRT useSecondary endpointsPathologic characteristicsPathologic featuresUninsured statusCancer DatabasePatterns of care and clinical outcomes with cytarabine-anthracycline induction chemotherapy for AML patients in the United States
Zeidan AM, Podoltsev NA, Wang X, Zhang C, Bewersdorf JP, Shallis RM, Huntington SF, Neparidze N, Giri S, Gore SD, Davidoff AJ, Ma X, Wang R. Patterns of care and clinical outcomes with cytarabine-anthracycline induction chemotherapy for AML patients in the United States. Blood Advances 2020, 4: 1615-1623. PMID: 32311013, PMCID: PMC7189301, DOI: 10.1182/bloodadvances.2020001728.Peer-Reviewed Original ResearchConceptsIntensive induction chemotherapyAcute myeloid leukemiaHospital deathInduction chemotherapyAdult patientsMultivariable logistic regression modelLow hospital volumePremier Healthcare DatabasePredictors of deathHealthcare resource utilizationIntensive care unitPatterns of careStandard of careLogistic regression modelsFit patientsRemission inductionFirst hospitalizationHospital volumeInpatient deathInpatient mortalityOlder patientsSupportive careMedian ageAML patientsCare unitRisk factors for hepatocellular carcinoma (HCC) in the northeast of the United States: results of a case–control study
Shen Y, Risch H, Lu L, Ma X, Irwin ML, Lim JK, Taddei T, Pawlish K, Stroup A, Brown R, Wang Z, Jia W, Wong L, Mayne ST, Yu H. Risk factors for hepatocellular carcinoma (HCC) in the northeast of the United States: results of a case–control study. Cancer Causes & Control 2020, 31: 321-332. PMID: 32060838, PMCID: PMC7136513, DOI: 10.1007/s10552-020-01277-1.Peer-Reviewed Original ResearchConceptsRisk of HCCCase-control studyHepatocellular carcinomaRisk factorsHCV infectionHCC riskOdds ratioHepatitis C virus antibodyUnconditional logistic regression modelsElevated HCC riskRapid case ascertainmentC virus antibodyHeavy alcohol intakeConfidence intervalsFamily cancer historyImportant risk factorRandom digit dialingLow socioeconomic statusUnhealthy lifestyle choicesLower household incomeLogistic regression modelsNSAID useAlcohol intakeCigarette smokingHigher BMILifestyle factors and risk of myeloproliferative neoplasms in the NIH‐AARP diet and health study
Podoltsev NA, Wang X, Wang R, Hofmann JN, Liao LM, Zeidan AM, Mesa R, Ma X. Lifestyle factors and risk of myeloproliferative neoplasms in the NIH‐AARP diet and health study. International Journal Of Cancer 2020, 147: 948-957. PMID: 31904114, PMCID: PMC8919268, DOI: 10.1002/ijc.32853.Peer-Reviewed Original ResearchConceptsNIH-AARP DietPolycythemia veraMyeloproliferative neoplasmsEssential thrombocythemiaHazard ratioLifestyle factorsCaffeine intakeHealth StudyMultivariable Cox proportional hazards regression modelsCox proportional hazards regression modelRisk of PVProportional hazards regression modelsRisk of MPNLarge prospective studiesHazards regression modelsConfidence intervalsPhiladelphia chromosome-negative myeloproliferative neoplasmsMPN riskOverall cohortProspective cohortProspective studyInverse associationRisk factorsPV riskProtective effect
2019
Tobacco use increases risk of food insecurity: An analysis of continuous NHANES data from 1999 to 2014
Mayer M, Gueorguieva R, Ma X, White MA. Tobacco use increases risk of food insecurity: An analysis of continuous NHANES data from 1999 to 2014. Preventive Medicine 2019, 126: 105765. PMID: 31260724, DOI: 10.1016/j.ypmed.2019.105765.Peer-Reviewed Original ResearchConceptsLow food securityTobacco useTobacco productsNutrition Examination Survey dataSingle product useAlternative tobacco productsUse of cigarettesMagnitude of associationLogistic regression modelsMultiple tobacco productsMultinomial logistic regression modelsFood insecurityNational HealthHigh prevalenceHealth disparity issuesNHANES dataOnly cigarettesSignificant associationTobacco product typesProduct useHealth risksCigarettesAssociationRegression modelsOdds
2018
Selection of patients with myelodysplastic syndromes from a large electronic medical records database and a study of the use of disease-modifying therapy in the United States
Ma X, Steensma DP, Scott BL, Kiselev P, Sugrue MM, Swern AS. Selection of patients with myelodysplastic syndromes from a large electronic medical records database and a study of the use of disease-modifying therapy in the United States. BMJ Open 2018, 8: e019955. PMID: 30037860, PMCID: PMC6059277, DOI: 10.1136/bmjopen-2017-019955.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overAnemiaAzacitidineDatabases, FactualDecitabineElectronic Health RecordsEnzyme InhibitorsFemaleHematinicsHumansImmunologic FactorsIron Chelating AgentsLenalidomideMaleMiddle AgedMultivariate AnalysisMyelodysplastic SyndromesProportional Hazards ModelsRecurrenceRetrospective StudiesThalidomideTreatment OutcomeUnited StatesConceptsDisease-modifying therapiesErythropoiesis-stimulating agentsElectronic medical record databaseMyelodysplastic syndromeMedical record databasePatient characteristicsTreatment patternsGE Centricity Electronic Medical Records databaseRecord databaseLow baseline hemoglobin levelMultivariable Cox regression modelsLarge electronic medical record databaseBaseline hemoglobin levelAbsence of comorbiditiesFirst-line treatmentMajority of patientsSelection of patientsCox regression modelIron chelation therapyAgent azacitidineMore comorbiditiesHemoglobin levelsMale genderClinical trialsChelation therapyCounseling patients with higher-risk MDS regarding survival with azacitidine therapy: are we using realistic estimates?
Zeidan AM, Stahl M, DeVeaux M, Giri S, Huntington S, Podoltsev N, Wang R, Ma X, Davidoff AJ, Gore SD. Counseling patients with higher-risk MDS regarding survival with azacitidine therapy: are we using realistic estimates? Blood Cancer Journal 2018, 8: 55. PMID: 29891916, PMCID: PMC5995881, DOI: 10.1038/s41408-018-0081-8.Peer-Reviewed Original Research
2017
Long-term survival of older patients with MDS treated with HMA therapy without subsequent stem cell transplantation
Zeidan AM, Stahl M, Hu X, Wang R, Huntington SF, Podoltsev NA, Gore SD, Ma X, Davidoff AJ. Long-term survival of older patients with MDS treated with HMA therapy without subsequent stem cell transplantation. Blood 2017, 131: 818-821. PMID: 29259002, PMCID: PMC6410557, DOI: 10.1182/blood-2017-10-811729.Peer-Reviewed Original ResearchCost‐effectiveness analysis of consolidation with brentuximab vedotin for high‐risk Hodgkin lymphoma after autologous stem cell transplantation
Hui L, von Keudell G, Wang R, Zeidan AM, Gore SD, Ma X, Davidoff AJ, Huntington SF. Cost‐effectiveness analysis of consolidation with brentuximab vedotin for high‐risk Hodgkin lymphoma after autologous stem cell transplantation. Cancer 2017, 123: 3763-3771. PMID: 28640385, PMCID: PMC5610636, DOI: 10.1002/cncr.30818.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsIncremental cost-effectiveness ratioPrice reductionHealth care costsProbabilistic sensitivity analysesMarkov decision-analytic modelCost-effectiveness ratioIndication-specific pricingLife-time costsCare costsAutologous stem cell transplantationDecision analytic modelStandard discountingQALYBrentuximab vedotinHigher health care costsActive surveillancePricingConsolidative settingConsolidation therapyCostSensitivity analysis