2024
Evaluating differences in receipt of survivorship care plan among cancer survivors with and without disabilities
Sarkar S, Zaidi M, Raziani Y, Poghosyan H. Evaluating differences in receipt of survivorship care plan among cancer survivors with and without disabilities. Supportive Care In Cancer 2024, 32: 637. PMID: 39235704, DOI: 10.1007/s00520-024-08796-6.Peer-Reviewed Original ResearchConceptsSurvivorship care plan receiptSurvivorship care plansDisability countCancer survivorsCare planningReceipt of survivorship care plansMethodsWe analyzed cross-sectional dataBehavioral Risk Factor Surveillance SystemFollow-up care instructionsRisk Factor Surveillance SystemSelf-reported cancer historyTreatment adherenceSelf-care difficultiesMultinomial multivariable logistic regressionIndependent living difficultiesPromote treatment adherenceMultivariate logistic regressionCancer survivorshipCross-sectional dataTreatment summarySelf-careCancer historyNo disabilityLiving difficultiesResultsThe sampleAssociation between adverse childhood experiences and self-reported health-risk behaviors among cancer survivors: A population-based study
Sarkar S, Jackson B, Manzo L, Jeon S, Poghosyan H. Association between adverse childhood experiences and self-reported health-risk behaviors among cancer survivors: A population-based study. PLOS ONE 2024, 19: e0299918. PMID: 38512934, PMCID: PMC10956880, DOI: 10.1371/journal.pone.0299918.Peer-Reviewed Original ResearchConceptsHealth risk behaviorsSelf-reported health risk behaviorsAdverse childhood experiencesHistory of adverse childhood experiencesOdds of reportingCancer survivorsE-cigarette useIncreased odds of reportingReport adverse childhood experiencesPrevalence of adverse childhood experiencesSelf-reportAlcohol drinkingHigher odds of reportingAdult cancer survivorsE-cigarettesSurvivor self-reportPrevent health risk behaviorsAdverse childhood experience historyChildhood experiencesAssociated with health risk behaviorsCigarette smokingHistory of cancerPopulation-based studyPrimary independent variableMultivariate logistic regression
2023
Investigating Racial and Ethnic Differences in Diabetes Self-Management Education Among Adults With Diabetes
Akyirem S, Choa E, Poghosyan H. Investigating Racial and Ethnic Differences in Diabetes Self-Management Education Among Adults With Diabetes. The Science Of Diabetes Self-Management And Care 2023, 49: 206-216. PMID: 37129292, DOI: 10.1177/26350106231169693.Peer-Reviewed Original ResearchConceptsDiabetes self-management educationDiabetes self-management education participantsSelf-management educationNon-Hispanic blacksNon-Hispanic whitesSocial determinants of health factorsEthnic differencesReduce health disparitiesImprove diabetes outcomesSelf-reported diabetesWeighted descriptive statisticsLower-level educationMultivariate logistic regressionCross-sectional dataHealth disparitiesDiabetes careDiabetes outcomesDiabetes ModuleHealth factorsPopulation-basedHispanic participantsReport participationDescriptive statisticsHispanic populationLogistic regression
2021
Association between daily and non-daily cannabis use and depression among United States adult cancer survivors
Poghosyan H, Noonan E, Badri P, Braun I, Young G. Association between daily and non-daily cannabis use and depression among United States adult cancer survivors. Nursing Outlook 2021, 69: 672-685. PMID: 33581859, DOI: 10.1016/j.outlook.2021.01.012.Peer-Reviewed Original ResearchConceptsCancer survivorsBehavioral Risk Factor Surveillance System surveyIncreased odds of depressionAdult cancer survivorsOdds of depressionCannabis useWeighted descriptive statisticsEvidence-informed discussionsSelf-reported depressionNationally representative sampleMultivariate logistic regressionPopulation-basedSystem surveyIncreased oddsDescriptive statisticsRepresentative sampleOpen communicationLogistic regressionIncreased riskNone-usersSurvivorsDepressionPrevalenceCancerAssociation
2019
Food insecure cancer survivors continue to smoke after their diagnosis despite not having enough to eat: implications for policy and clinical interventions
Poghosyan H, Scarpino S. Food insecure cancer survivors continue to smoke after their diagnosis despite not having enough to eat: implications for policy and clinical interventions. Cancer Causes & Control 2019, 30: 241-248. PMID: 30729359, DOI: 10.1007/s10552-019-01137-7.Peer-Reviewed Original ResearchConceptsCancer survivorsAssociated with smoking statusSmoking statusFood insecurityBehavioral Risk Factor Surveillance SystemRisk Factor Surveillance SystemFood insecurity screeningAdult cancer survivorsWeighted multivariable logistic regression modelsSocial Context ModuleSmoking cessation interventionsConfidence intervalsMultivariate logistic regression modelCross-sectional studyProgression of careIndividual-level characteristicsLogistic regression modelsCessation interventionsConclusionsFood insecurityQuit smokingQuit attemptsOdds ratioSmoking behaviorIndividual-levelOutcome variables
2018
Social and Structural Determinants of Smoking Status and Quit Attempts Among Adults Living in 12 US States, 2015
Poghosyan H, Moen E, Kim D, Manjourides J, Cooley M. Social and Structural Determinants of Smoking Status and Quit Attempts Among Adults Living in 12 US States, 2015. American Journal Of Health Promotion 2018, 33: 498-506. PMID: 30071738, DOI: 10.1177/0890117118792827.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAge FactorsAgedBehavioral Risk Factor Surveillance SystemCross-Sectional StudiesFemaleHumansLogistic ModelsMaleMiddle AgedMultivariate AnalysisPsychologyRisk FactorsSex FactorsSmokingSmoking CessationSocial Determinants of HealthSocioeconomic FactorsUnited StatesYoung AdultConceptsBehavioral Risk Factor Surveillance SystemQuit attemptsSmoking statusIntermediary determinantsMental distressSecondary data analysis of cross-sectional dataHousing insecurityRisk Factor Surveillance SystemAnalysis of cross-sectional dataFrequent mental distressAdult smoking statusSmoking cessation interventionsSocial Context ModuleNon-Hispanic blacksRacially diverse adultsSmoking cessation ratesDetermination of smoking statusSecondary data analysisEnhance smoking cessation ratesHazardous alcohol useUS statesMultinomial logistic regressionCross-sectional dataCessation interventionsCurrent smoking
2016
Right ventricular endomyocardial biopsy in children and adolescents with drug-refractory arrhythmia
Vasichkina E, Poghosyan H, Mitrofanova L, Tatarsky R, Lebedev D. Right ventricular endomyocardial biopsy in children and adolescents with drug-refractory arrhythmia. Cardiology In The Young 2016, 27: 435-442. PMID: 27211482, DOI: 10.1017/s1047951116000688.Peer-Reviewed Original ResearchConceptsDrug-refractory arrhythmiasEndomyocardial biopsyRadiofrequency ablationPolymerase chain reactionResults of endomyocardial biopsyRight ventricular endomyocardial biopsyArrhythmogenic right ventricular dysplasiaImplantable cardioverter defibrillator implantationStructurally normal heartsRight ventricular dysplasiaConsecutive young patientsNo significant complicationsArrhythmia-induced cardiomyopathyChain reactionCardioverter defibrillator implantationMyocarditis patientsVentricular dysplasiaSignificant complicationsRight ventricleYounger patientsHolter monitoringDefibrillator implantationBiopsyPhysical examinationImmunohistological analysis
2014
The association between having a first-degree family history of cancer and smoking status
Poghosyan H, Bell J, Joseph J, Cooley M. The association between having a first-degree family history of cancer and smoking status. Preventive Medicine 2014, 66: 12-16. PMID: 24875232, DOI: 10.1016/j.ypmed.2014.05.013.Peer-Reviewed Original ResearchConceptsFirst-degree family history of cancerFamily history of cancerFirst-degree family historyHistory of cancerCalifornia Health Interview SurveyHealth-promoting behaviorsHealth Interview SurveyPoor health outcomesSmoking cessation interventionsNon-institutionalized adultsPopulation-based dataDiagnosis of cancerCross-sectional dataCessation interventionsHealth outcomesInterview SurveyFormer smokersCurrent-smokersSmoking statusNever-smokersTarget populationSurvey designSmokingSample weightDiverse sample