2023
Racial and Ethnic Bias in the Diagnosis of Alcohol Use Disorder in Veterans
Vickers-Smith R, Justice A, Becker W, Rentsch C, Curtis B, Fernander A, Hartwell E, Ighodaro E, Kember R, Tate J, Kranzler H. Racial and Ethnic Bias in the Diagnosis of Alcohol Use Disorder in Veterans. American Journal Of Psychiatry 2023, 180: 426-436. PMID: 37132202, PMCID: PMC10238581, DOI: 10.1176/appi.ajp.21111097.Peer-Reviewed Original ResearchConceptsAlcohol use disorderAlcohol consumptionAUD diagnosisHispanic veteransWhite veteransUse disordersPrevalence of AUDAlcohol Use Disorders Identification TestUnhealthy alcohol useICD-10 codesAUDIT-C scoresSelf-reported alcohol consumptionAlcohol-related disordersDiagnosis of AUDDisorders Identification TestMaximum scoreSelf-reported raceElectronic health recordsPrimary outcomeAlcohol consumption levelsPotential confoundersHigh prevalenceMillion Veteran ProgramGreater oddsICD-9
2022
Potentially inappropriate medication use by level of polypharmacy among US Veterans 49–64 and 65–70 years old
Guillot J, Rentsch CT, Gordon KS, Justice AC, Bezin J. Potentially inappropriate medication use by level of polypharmacy among US Veterans 49–64 and 65–70 years old. Pharmacoepidemiology And Drug Safety 2022, 31: 1056-1074. PMID: 35780391, PMCID: PMC9464694, DOI: 10.1002/pds.5506.Peer-Reviewed Original ResearchConceptsLevel of polypharmacyRace/ethnicityPIM prevalencePrevalence of PIMsInappropriate medication useElectronic health recordsCommon PIMsPharmacy fillsPROMPT criteriaInappropriate medicationsOlder patientsMedication usePsychotropic medicationsRefill recordsPolypharmacyPatientsVeterans AffairsMedicationsPrevalenceHealth recordsFiscal year 2016AgeMeaningful differencesSexTarget ageUsing the biomarker cotinine and survey self-report to validate smoking data from United States Veterans Health Administration electronic health records
McGinnis K, Skanderson M, Justice A, Tindle H, Akgün K, Wrona A, Freiberg M, Goetz M, Rodriguez-Barradas M, Brown S, Crothers K. Using the biomarker cotinine and survey self-report to validate smoking data from United States Veterans Health Administration electronic health records. JAMIA Open 2022, 5: ooac040. PMID: 37252267, PMCID: PMC9154288, DOI: 10.1093/jamiaopen/ooac040.Peer-Reviewed Original ResearchICD-10 codesClinical remindersCurrent smokingSmoking dataSelf-reported smoking statusVeterans Health Administration electronic health recordsCohort Study participantsElectronic health record dataHealth record dataElectronic health recordsSmoking informationSmoking statusSalivary cotinineEpidemiologic studiesInternational ClassificationSmokingCotinineStudy participantsICD-10Health systemHealth recordsRecord dataKappa statisticsAfrican AmericansReminders
2021
COVID-19 Evidence Accelerator: A parallel analysis to describe the use of Hydroxychloroquine with or without Azithromycin among hospitalized COVID-19 patients
Stewart M, Rodriguez-Watson C, Albayrak A, Asubonteng J, Belli A, Brown T, Cho K, Das R, Eldridge E, Gatto N, Gelman A, Gerlovin H, Goldberg SL, Hansen E, Hirsch J, Ho YL, Ip A, Izano M, Jones J, Justice AC, Klesh R, Kuranz S, Lam C, Mao Q, Mataraso S, Mera R, Posner DC, Rassen JA, Siefkas A, Schrag A, Tourassi G, Weckstein A, Wolf F, Bhat A, Winckler S, Sigal EV, Allen J. COVID-19 Evidence Accelerator: A parallel analysis to describe the use of Hydroxychloroquine with or without Azithromycin among hospitalized COVID-19 patients. PLOS ONE 2021, 16: e0248128. PMID: 33730088, PMCID: PMC7968637, DOI: 10.1371/journal.pone.0248128.Peer-Reviewed Original ResearchConceptsHospitalized COVID-19 patientsCOVID-19 patientsUse of hydroxychloroquineElectronic health recordsAdverse eventsTreatment groupsCOVID-19Administration of hydroxychloroquineReagan-Udall FoundationProportional hazards modelHealth systems researchHospitalized patientsElevated riskPropensity score methodsHazards modelHydroxychloroquinePatientsSignificant global threatPharmaceutical interventionsAzithromycinHealth recordsMortalityCOVID-19 pandemicCancer researchTreatment
2020
Health System‐Based Unhealthy Alcohol Use Screening and Treatment Comparing Demographically Matched Participants With and Without HIV
Silverberg MJ, Levine‐Hall T, Hood N, Anderson AN, Alexeeff SE, Lam JO, Slome SB, Flamm JA, Hare C, Ross T, Justice A, Sterne JAC, Williams A, Bryant KJ, Weisner CM, Horberg MA, Sterling SA, Satre DD. Health System‐Based Unhealthy Alcohol Use Screening and Treatment Comparing Demographically Matched Participants With and Without HIV. Alcohol Clinical And Experimental Research 2020, 44: 2545-2554. PMID: 33067802, PMCID: PMC7725961, DOI: 10.1111/acer.14481.Peer-Reviewed Original ResearchConceptsUnhealthy alcohol useAlcohol use screeningHIV statusBrief interventionRace/ethnicityAlcohol useSpecialty visitsHazard ratioPrevalence ratiosLarge integrated healthcare systemCharlson Comorbidity IndexSpecialty care visitsProportional hazards modelIntegrated healthcare systemOutcomes of timeSubstance use disordersAddiction specialty careNeighborhood deprivation indexElectronic health recordsComorbidity indexAdult PLWHCare visitsCohort studyOutpatient visitsSignificant morbidityEstimating Aspirin Overuse for Primary Prevention of Atherosclerotic Cardiovascular Disease (from a Nationwide Healthcare System)
Ong SY, Chui P, Bhargava A, Justice A, Hauser RG. Estimating Aspirin Overuse for Primary Prevention of Atherosclerotic Cardiovascular Disease (from a Nationwide Healthcare System). The American Journal Of Cardiology 2020, 137: 25-30. PMID: 32991852, DOI: 10.1016/j.amjcard.2020.09.042.Peer-Reviewed Original ResearchConceptsLow-dose aspirinElectronic health recordsSelf-reported useEHR dataPatients' self-reported useLow-dose aspirin useAtherosclerotic cardiovascular diseaseAmerican Heart AssociationEHR recordsNon-specific terminologyAspirin usePrimary preventionHeart AssociationCardiovascular diseaseAmerican CollegeAspirinPatientsHealth recordsEHR implementationEHR searchesImplementable guidelinesGuidelinesRecordsDiseaseCardiologyValidating a non-invasive, ALT-based non-alcoholic fatty liver phenotype in the million veteran program
Serper M, Vujkovic M, Kaplan DE, Carr RM, Lee KM, Shao Q, Miller DR, Reaven PD, Phillips LS, O’Donnell C, Meigs JB, Wilson PWF, Vickers-Smith R, Kranzler HR, Justice AC, Gaziano JM, Muralidhar S, Pyarajan S, DuVall SL, Assimes TL, Lee JS, Tsao PS, Rader DJ, Damrauer SM, Lynch JA, Saleheen D, Voight BF, Chang KM, . Validating a non-invasive, ALT-based non-alcoholic fatty liver phenotype in the million veteran program. PLOS ONE 2020, 15: e0237430. PMID: 32841307, PMCID: PMC7447043, DOI: 10.1371/journal.pone.0237430.Peer-Reviewed Original ResearchMeSH Keywords17-Hydroxysteroid DehydrogenasesAbdomenAdaptor Proteins, Signal TransducingAgedAlanine TransaminaseElectronic Health RecordsFemaleGenetic LociGenetic Predisposition to DiseaseGenetic VariationHumansLipaseLiverLysophospholipaseMaleMembrane ProteinsMiddle AgedNon-alcoholic Fatty Liver DiseasePhenotypeRisk FactorsVeteransConceptsMetabolic risk factorsNAFLD phenotypeAlanine aminotransferaseUnits/LElectronic health recordsAdvanced fibrosisRisk factorsMillion Veteran ProgramAlcohol consumptionNon-invasive criteriaNormal alanine aminotransferaseNAFLD fibrosis scorePopulation-based studyGenetic variantsFatty liver phenotypeVeteran ProgramPNPLA3 locusNAFLD riskLiver biopsyLiver diseaseFibrosis scoreEHR reviewUS veteransBiopsy dataAbdominal imagingValidation for using electronic health records to identify community acquired pneumonia hospitalization among people with and without HIV
Rodriguez-Barradas MC, McGinnis KA, Akgün K, Tate JP, Brown ST, Butt AA, Fine M, Goetz MB, Graber CJ, Huang L, Rimland D, Justice A, Crothers K. Validation for using electronic health records to identify community acquired pneumonia hospitalization among people with and without HIV. Pneumonia 2020, 12: 6. PMID: 32724760, PMCID: PMC7382068, DOI: 10.1186/s41479-020-00068-1.Peer-Reviewed Original ResearchICD-9 codesPharmacy dataUninfected groupEHR algorithmPrimary positionOverall positive predictive valueResultsFive hundred fortyGroup of patientsElectronic health record dataICD-9 diagnosisHealth record dataPositive predictive valueCode-based algorithmsElectronic health recordsCAP hospitalizationsCOPD exacerbationsMicrobiologic workupOverall PPVUninfected patientsAspiration pneumoniaPneumonia hospitalizationsHospital admissionHIV statusCaP diagnosisEtiologic diagnosisPolypharmacy in HIV: recent insights and future directions.
Edelman EJ, Rentsch CT, Justice AC. Polypharmacy in HIV: recent insights and future directions. Current Opinion In HIV And AIDS 2020, 15: 126-133. PMID: 31833963, PMCID: PMC7543953, DOI: 10.1097/coh.0000000000000608.Commentaries, Editorials and LettersConceptsSeverity of illnessInappropriate medicationsDrug interactionsActual adverse eventsSubstance useTotal drug burdenMechanism of injuryElectronic health recordsNonantiretroviral medicationsUpdate findingsMore medicationsAdverse eventsDrug burdenClinical managementPolypharmacyDirect biomarkerMedicationsModifiable mechanismsPWHSicker individualsHealth recordsStrong associationHIVInjuryIllnessExtracting lung function measurements to enhance phenotyping of chronic obstructive pulmonary disease (COPD) in an electronic health record using automated tools
Akgün KM, Sigel K, Cheung KH, Kidwai-Khan F, Bryant AK, Brandt C, Justice A, Crothers K. Extracting lung function measurements to enhance phenotyping of chronic obstructive pulmonary disease (COPD) in an electronic health record using automated tools. PLOS ONE 2020, 15: e0227730. PMID: 31945115, PMCID: PMC6964890, DOI: 10.1371/journal.pone.0227730.Peer-Reviewed Original ResearchMeSH KeywordsCohort StudiesData MiningElectronic Health RecordsForced Expiratory VolumeHealth Information SystemsHospitalizationHumansLungNatural Language ProcessingPulmonary Disease, Chronic ObstructiveSeverity of Illness IndexSoftwareUnited StatesUnited States Department of Veterans AffairsVeteransVital CapacityConceptsChronic obstructive pulmonary diseaseVeterans Aging Cohort StudyObstructive pulmonary diseaseFEV1 valuesPulmonary diseasePhenotyping of patientsPulmonary function testsAging Cohort StudyLung function measurementsElectronic health record dataHealth record dataStructured electronic health record dataPositive predictive valueElectronic health recordsAirflow obstructionChart reviewCohort studyExpiratory volumeCOPD phenotypesFunction testsVital capacityFEV1 measurementsDATA SOURCESPredictive valueClinical notes
2019
Measuring Exposure to Incarceration Using the Electronic Health Record
Wang EA, Long JB, McGinnis KA, Wang KH, Wildeman CJ, Kim C, Bucklen KB, Fiellin DA, Bates J, Brandt C, Justice AC. Measuring Exposure to Incarceration Using the Electronic Health Record. Medical Care 2019, 57: s157-s163. PMID: 31095055, PMCID: PMC8352066, DOI: 10.1097/mlr.0000000000001049.Peer-Reviewed Original ResearchMeSH KeywordsAdministrative Claims, HealthcareAdultCohort StudiesElectronic Health RecordsEthnicityFemaleHumansInformation Storage and RetrievalMaleMedicareMiddle AgedNatural Language ProcessingPrisonersSelf ReportSensitivity and SpecificityUnited StatesUnited States Department of Veterans AffairsVeteransConceptsVeterans Aging Cohort StudyElectronic health recordsHuman immunodeficiency virus-infected patientsVHA electronic health recordsNational observational cohortVirus-infected patientsHealth recordsAging Cohort StudyEHR dataHealth care disparitiesAdministrative dataRace/ethnicityIncarceration exposureObservational cohortUninfected patientsCohort studySpecificity 99.3DATA SOURCESCare disparitiesSpecificity 100Specificity 98.9Social determinantsMedicaid ServicesSpecificity 95.9Health informationLongitudinal Drinking Patterns and Their Clinical Correlates in Million Veteran Program Participants
Smith R, Kranzler HR, Justice AC, Tate JP, Program T. Longitudinal Drinking Patterns and Their Clinical Correlates in Million Veteran Program Participants. Alcohol Clinical And Experimental Research 2019, 43: 465-472. PMID: 30592535, PMCID: PMC6691890, DOI: 10.1111/acer.13951.Peer-Reviewed Original ResearchConceptsMean AUDIT-C scoreAUDIT-C scoresAlcohol Use Disorders Identification Test-ConsumptionTrajectory groupsU.S. Preventive Services Task ForceDrinking trajectoriesAge-adjusted mean scoresVeterans Affairs Health SystemPrimary care patientsLarger patient populationMillion Veteran Program cohortAlcohol use disorder diagnosisHigh trajectory groupPosttraumatic stress disorderElectronic health recordsHepatitis CCare patientsLiver cirrhosisClinical correlatesPatient populationLongitudinal drinking patternsHigher oddsClinical relevanceHarmful drinkingAnnual administration
2018
Provider verification of electronic health record receipt and nonreceipt of direct-acting antivirals for the treatment of hepatitis C virus infection
Rentsch CT, Cartwright EJ, Gandhi NR, Brown ST, Rodriguez-Barradas MC, Goetz MB, Marconi VC, Gibert CL, Re VL, Fiellin DA, Justice AC, Tate JP. Provider verification of electronic health record receipt and nonreceipt of direct-acting antivirals for the treatment of hepatitis C virus infection. Annals Of Epidemiology 2018, 28: 808-811. PMID: 30195616, PMCID: PMC6318448, DOI: 10.1016/j.annepidem.2018.08.007.Peer-Reviewed Original ResearchConceptsHepatitis C virus infectionCorporate Data WarehouseChronic HCV infectionC virus infectionPositive predictive valuePredictive valueHCV infectionHCV treatmentVirus infectionVeterans Health Administration Corporate Data WarehouseChronic hepatitis C virus (HCV) infectionStudy periodModern treatment eraRetrospective cohort studyElectronic health record dataPharmacy fill recordsHealth record dataNegative predictive valueElectronic health recordsAntiviral regimenHCV therapyTreatment eraChart reviewCohort studyAntiviral treatment
2017
Utilizing patient data from the veterans administration electronic health record to support web-based clinical decision support: informatics challenges and issues from three clinical domains
Rajeevan N, Niehoff KM, Charpentier P, Levin FL, Justice A, Brandt CA, Fried TR, Miller PL. Utilizing patient data from the veterans administration electronic health record to support web-based clinical decision support: informatics challenges and issues from three clinical domains. BMC Medical Informatics And Decision Making 2017, 17: 111. PMID: 28724368, PMCID: PMC5517800, DOI: 10.1186/s12911-017-0501-x.Peer-Reviewed Original ResearchConceptsClinical decision supportElectronic health recordsCDS systemsDecision supportPatient-specific clinical decision supportEHR environmentInformatics challengesWeb-based clinical decision supportHealth recordsPatient dataWeb technologiesData accessComputational infrastructureParticular architectureIssues/challengesDesign issuesSuch systemsInformatics methodsPowerful setData availabilityVeterans AdministrationInfrastructureVA electronic health recordClinical domainsUS Veterans AdministrationHealth-adjusted life expectancy in HIV-positive and HIV-negative men and women in British Columbia, Canada: a population-based observational cohort study
Hogg RS, Eyawo O, Collins AB, Zhang W, Jabbari S, Hull MW, Lima VD, Ahmed T, Kendall CE, Althoff KN, Justice AC, Barrios R, Shoveller J, Montaner JSG, study C. Health-adjusted life expectancy in HIV-positive and HIV-negative men and women in British Columbia, Canada: a population-based observational cohort study. The Lancet HIV 2017, 4: e270-e276. PMID: 28262574, PMCID: PMC5761654, DOI: 10.1016/s2352-3018(17)30029-2.Peer-Reviewed Original ResearchConceptsHealth-adjusted life expectancyActive antiretroviral therapySelect comorbiditiesHIV statusLife expectancyGeneral populationPopulation-based observational cohort studyShorter overall life expectancyHIV-negative counterpartsCase-finding algorithmHIV-negative menHIV-negative populationObservational cohort studyComplex care needsService delivery interventionsAge 20 yearsYears of ageCauses of comorbidityShort life expectancyOverall life expectancyHealthy stateElectronic health recordsAntiretroviral therapyCohort studyRetrospective cohort
2016
Incidence of Mental Health Diagnoses in Veterans of Operations Iraqi Freedom, Enduring Freedom, and New Dawn, 2001-2014.
Ramsey C, Dziura J, Justice AC, Altalib HH, Bathulapalli H, Burg M, Decker S, Driscoll M, Goulet J, Haskell S, Kulas J, Wang KH, Mattocks K, Brandt C. Incidence of Mental Health Diagnoses in Veterans of Operations Iraqi Freedom, Enduring Freedom, and New Dawn, 2001-2014. American Journal Of Public Health 2016, 107: 329-335. PMID: 27997229, PMCID: PMC5227942, DOI: 10.2105/ajph.2016.303574.Peer-Reviewed Original ResearchConceptsMental health diagnosesMajor depressive disorderPosttraumatic stress disorderHealth diagnosisIncidence rateDrug use disorder diagnosisIncident posttraumatic stress disorderOperation Iraqi FreedomAge 18Veterans Health Administration electronic health recordsDisorder diagnosisIncident bipolar disorderHigh-risk groupSociodemographic risk factorsAlcohol use disorder diagnosisMental health conditionsEnduring FreedomIraqi FreedomRace/ethnicityElectronic health recordsIncident schizophreniaRisk factorsDepressive disorderAge 45MDD diagnosisEstimating healthcare mobility in the Veterans Affairs Healthcare System
Wang KH, Goulet JL, Carroll CM, Skanderson M, Fodeh S, Erdos J, Womack JA, Abel EA, Bathulapalli H, Justice AC, Nunez-Smith M, Brandt CA. Estimating healthcare mobility in the Veterans Affairs Healthcare System. BMC Health Services Research 2016, 16: 609. PMID: 27769221, PMCID: PMC5075153, DOI: 10.1186/s12913-016-1841-4.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedAged, 80 and overDelivery of Health CareElectronic Health RecordsEmigration and ImmigrationFemaleHospitals, VeteransHumansMaleMental DisordersMiddle AgedPatient Acceptance of Health CareRetrospective StudiesUnited StatesUnited States Department of Veterans AffairsVeteransVeterans HealthYoung AdultConceptsHealthcare systemVeterans Health Administration electronic health recordsVeterans Affairs Healthcare SystemHealthcare mobilityRetrospective cohort studyHepatitis C virusOutcomes of careDifferent healthcare systemsDistinct healthcare systemsElectronic health recordsClinical characteristicsCohort studyHealthcare utilizationC virusSpecialty carePsychiatric disordersYounger veteransDisease preventionYounger agePopulation healthHealth recordsVeteransStatus changesCareYear periodThe musculoskeletal diagnosis cohort
Goulet JL, Kerns RD, Bair M, Becker W, Brennan P, Burgess DJ, Carroll CM, Dobscha S, Driscoll M, Fenton BT, Fraenkel L, Haskell S, Heapy A, Higgins D, Hoff RA, Hwang U, Justice AC, Piette JD, Sinnott P, Wandner L, Womack J, Brandt CA. The musculoskeletal diagnosis cohort. Pain 2016, 157: 1696-1703. PMID: 27023420, PMCID: PMC4949131, DOI: 10.1097/j.pain.0000000000000567.Peer-Reviewed Original ResearchConceptsMSD diagnosisMusculoskeletal disordersMSD cohortIndex dateVeterans Health Administration (VHA) careNumeric rating scale scoreICD-9-CM codesCohort inclusion criteriaNontraumatic joint disordersPain-related treatmentsMore outpatient visitsVeterans Health AdministrationMental health diagnosesRating Scale scoresHigher NRS scoresHealth services researchElectronic health recordsDiagnosis cohortSevere painInpatient visitsNRS scoresOutpatient visitsNeck disordersFirst diagnosisMean age