2025
Quantifying disruptions to tuberculosis case detection in Brazilian states during the COVID-19 pandemic
Chitwood M, Menzies N, Bartholomay P, Pelissari D, de Barros Silva Júnior J, Harada L, Johansen F, Maciel E, Castro M, Sanchez M, Warren J, Cohen T. Quantifying disruptions to tuberculosis case detection in Brazilian states during the COVID-19 pandemic. International Journal Of Epidemiology 2025, 54: dyaf146. PMID: 40838691, DOI: 10.1093/ije/dyaf146.Peer-Reviewed Original ResearchMeSH KeywordsBayes TheoremBrazilCOVID-19Disease NotificationFemaleHumansIncidenceMalePandemicsSARS-CoV-2TuberculosisConceptsPre-pandemic levelsTB casesCase detectionCOVID-19 pandemicBrazilian healthcare systemTB notification ratesTB case detectionHealth system disruptionsTuberculosis case detectionCase detection rateMortality dataHealthcare systemIncident TBMeta-regression frameworkTB incidenceNotification ratesSymptomatic TBCase notificationCOVID-19TB controlTB diagnosisBrazilian statesPandemicCareIndividualsA Bayesian Approach to the G‐Formula via Iterative Conditional Regression
Liu R, Hu L, Wilson F, Warren J, Li F. A Bayesian Approach to the G‐Formula via Iterative Conditional Regression. Statistics In Medicine 2025, 44: e70123. PMID: 40476299, PMCID: PMC12184534, DOI: 10.1002/sim.70123.Peer-Reviewed Original ResearchConceptsCausal effect estimationTime-varying covariatesModel misspecification biasBayesian approachReal world data examplesG-formulaAverage causal effect estimationTime-varying treatmentsBayesian additive regression treesAverage causal effectAdditive regression treesConditional expectationOutcome regressionConditional distributionJoint distributionData examplesPosterior distributionMisspecification biasParametric regressionSimulation studyEffect estimatesSampling algorithmAlgorithm formulaCausal effectsFlexible machine learning techniquesFactors associated with tuberculosis treatment initiation among bacteriologically negative individuals evaluated for tuberculosis: An individual patient data meta-analysis.
Kim S, Can M, Agizew T, Auld A, Balcells M, Bjerrum S, Dheda K, Dorman S, Esmail A, Fielding K, Garcia-Basteiro A, Hanrahan C, Kebede W, Kohli M, Luetkemeyer A, Mita C, Reeve B, Silva D, Sweeney S, Theron G, Trajman A, Vassall A, Warren J, Yotebieng M, Cohen T, Menzies N. Factors associated with tuberculosis treatment initiation among bacteriologically negative individuals evaluated for tuberculosis: An individual patient data meta-analysis. PLOS Medicine 2025, 22: e1004502. PMID: 39804959, PMCID: PMC11729971, DOI: 10.1371/journal.pmed.1004502.Peer-Reviewed Original ResearchMeSH KeywordsAdultAntitubercular AgentsBayes TheoremFemaleHumansMaleMycobacterium tuberculosisSputumTuberculosis, PulmonaryConceptsIndividual Patient Data Meta-AnalysisPatient data meta-analysisTreatment initiationData Meta-AnalysisBacteriological test resultsTB treatmentFactors associated with treatment initiationMultiple factors influence decisionsAssociated with treatment initiationTuberculosis treatment initiationMeta-analysisNegative test resultsPositive test resultsFactors influence decisionsHIV infectionPulmonary tuberculosisSmear microscopyNight sweatsClinical examinationMale sexClinical criteriaHierarchical Bayesian logistic regressionCohort studySystematic reviewTreatment decisionsDemographic inequities and cumulative environmental burdens within communities near superfund sites on Long Island, New York
Mooney F, Kelly J, Warren J, Deziel N. Demographic inequities and cumulative environmental burdens within communities near superfund sites on Long Island, New York. Health & Place 2025, 91: 103409. PMID: 39799904, PMCID: PMC11792615, DOI: 10.1016/j.healthplace.2024.103409.Peer-Reviewed Original ResearchConceptsLow-income residentsSuperfund siteEnvironmental burdenSocio-DemographicHigh PM2.5 concentrationsEnvironmental variablesCensus tractsLong IslandPoisson regression modelsBlack residentsCumulative environmental burdenDemographic inequalitiesNew YorkDistribution inequalitySuperfundCommunity demographicsHispanic/Latino residentsToxic releasesExposure potentialCounty residentsExposed communitiesStratified analysis
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 ageIdentifying local foci of tuberculosis transmission in Moldova using a spatial multinomial logistic regression model
Lan Y, Crudu V, Ciobanu N, Codreanu A, Chitwood M, Sobkowiak B, Warren J, Cohen T. Identifying local foci of tuberculosis transmission in Moldova using a spatial multinomial logistic regression model. EBioMedicine 2024, 102: 105085. PMID: 38531172, PMCID: PMC10987885, DOI: 10.1016/j.ebiom.2024.105085.Peer-Reviewed Original ResearchConceptsPatterns of spatial aggregationMTB strainsMDR-TBLogistic regression modelsGenome Epidemiology StudySpecific strainsMultidrug-resistant tuberculosisTreated TB casesNational Institute of AllergyMDR phenotypeRegression modelsM. tuberculosisInstitute of AllergyMultinomial logistic regression modelUS National Institutes of HealthNational Institutes of HealthMDR diseasePublic health concernAssociated with local transmissionIncident TBInstitutes of HealthMtbResistant tuberculosisStrainDiagnosing TB
2023
Investigation of Sources of Fluorinated Compounds in Private Water Supplies in an Oil and Gas-Producing Region of Northern West Virginia
Siegel H, Nason S, Warren J, Prunas O, Deziel N, Saiers J. Investigation of Sources of Fluorinated Compounds in Private Water Supplies in an Oil and Gas-Producing Region of Northern West Virginia. Environmental Science And Technology 2023, 57: 17452-17464. PMID: 37923386, PMCID: PMC10653085, DOI: 10.1021/acs.est.3c05192.Peer-Reviewed Original ResearchConceptsNorthern West VirginiaMaximum contaminant levelWater supplySurface water concentrationsNearby point sourcesSurface water samplesGas-producing regionsSurface water sourcesPFAS concentrationsPoint sourcesPrivate wellsPublic water supplyGeochemical controlsGroundwater chemistryRecharge zoneWest VirginiaWater wellsInvestigation of sourcesModel resultsPrivate water suppliesPFAS sourcesTopographic characteristicsPFAS transportWater receptorPolyfluoroalkyl substancesGlobal, regional, and national estimates of tuberculosis incidence and case detection among incarcerated individuals from 2000 to 2019: a systematic analysis
Martinez L, Warren J, Harries A, Croda J, Espinal M, Olarte R, Avedillo P, Lienhardt C, Bhatia V, Liu Q, Chakaya J, Denholm J, Lin Y, Kawatsu L, Zhu L, Horsburgh C, Cohen T, Andrews J. Global, regional, and national estimates of tuberculosis incidence and case detection among incarcerated individuals from 2000 to 2019: a systematic analysis. The Lancet Public Health 2023, 8: e511-e519. PMID: 37393090, PMCID: PMC10323309, DOI: 10.1016/s2468-2667(23)00097-x.Peer-Reviewed Original ResearchConceptsTuberculosis incidenceCase detection ratioIncidence rateCase detectionHigh tuberculosis incidence ratesGlobal tuberculosis control effortsIncident tuberculosis casesTuberculosis incidence rateIncarcerated individualsTuberculosis control effortsTuberculosis case detectionTuberculosis casesNotification ratesNational incidenceTuberculosis notificationsGlobal incidenceHigh riskPrevalence estimatesNational estimatesWHO regionsIncidenceStudy periodMeta-regression frameworkTuberculosisNational InstitutePatterns of Infectious Disease Associated With Injection Drug Use in Massachusetts
Gonsalves G, Paltiel A, Thornhill T, DeMaria A, Cranston K, Klevens R, Warren J. Patterns of Infectious Disease Associated With Injection Drug Use in Massachusetts. Clinical Infectious Diseases 2023, 76: 2134-2139. PMID: 36757712, PMCID: PMC10273381, DOI: 10.1093/cid/ciad073.Peer-Reviewed Original ResearchConceptsSoft tissue infectionsHuman immunodeficiency virusHepatitis C virusInjection drug useHCV casesInfective endocarditisHIV casesDrug useSubstance use disordersLogistic regression modelsClinical sequalaeSSTI casesPublic health crisisTissue infectionsImmunodeficiency virusDisease AssociatedHIV outbreakAbscess incisionC virusUse disordersInfectious diseasesInfectionMultiple outbreaksMedical proceduresSuch associationsSpatial Modeling of Mycobacterium Tuberculosis Transmission with Dyadic Genetic Relatedness Data
Warren J, Chitwood M, Sobkowiak B, Colijn C, Cohen T. Spatial Modeling of Mycobacterium Tuberculosis Transmission with Dyadic Genetic Relatedness Data. Biometrics 2023, 79: 3650-3663. PMID: 36745619, PMCID: PMC10404301, DOI: 10.1111/biom.13836.Peer-Reviewed Original Research
2022
Using multi-sourced big data to correlate sleep deprivation and road traffic noise: A US county-level ecological study
Tong H, Warren J, Kang J, Li M. Using multi-sourced big data to correlate sleep deprivation and road traffic noise: A US county-level ecological study. Environmental Research 2022, 220: 115029. PMID: 36495963, DOI: 10.1016/j.envres.2022.115029.Peer-Reviewed Original ResearchMeSH KeywordsBayes TheoremBig DataEnvironmental ExposureHumansNoise, TransportationSleepSleep DeprivationConceptsRoad traffic noiseCounty-level ecological studyMultiple socioeconomic factorsTraffic noisePublic health problemNo significant associationSound pressure levelGeographic information systemSocioeconomic factorsHealth problemsSleep deprivationSignificant associationSleep problemsSleep surveyDBA increaseAverage sound pressure levelCounty levelOddsIndividual levelCountyUrban sprawl patternsEcological studiesAssociationSleepMulti-source big dataA Bayesian framework for incorporating exposure uncertainty into health analyses with application to air pollution and stillbirth
Comess S, Chang HH, Warren JL. A Bayesian framework for incorporating exposure uncertainty into health analyses with application to air pollution and stillbirth. Biostatistics 2022, 25: 20-39. PMID: 35984351, PMCID: PMC10724312, DOI: 10.1093/biostatistics/kxac034.Peer-Reviewed Original ResearchConceptsFull conditional distributionsEfficient model fittingStatistical modeling approachDensity estimation approachBayesian settingKernel density estimation approachPosterior outputBayesian frameworkConditional distributionModel fittingEstimation approachAccurate inferenceKDE approachModeling approachComparison metricsExposure uncertaintyUncertaintySecond stageApproachFittingInferencePredictionSimulationsModel comparison metricsFirst stage
2019
Critical window variable selection: estimating the impact of air pollution on very preterm birth
Warren JL, Kong W, Luben TJ, Chang HH. Critical window variable selection: estimating the impact of air pollution on very preterm birth. Biostatistics 2019, 21: 790-806. PMID: 30958877, PMCID: PMC7422642, DOI: 10.1093/biostatistics/kxz006.Peer-Reviewed Original ResearchConceptsHierarchical Bayesian frameworkBayesian frameworkStatistical modelVariable selectionImproved estimationCritical windowPreterm birthRisk parametersVery preterm birthAdverse birth outcomesControl analysisExposure-disease relationshipsDifferent reproductive outcomesBirth outcomesPregnant womenReproductive outcomesCase/control analysis
2017
A Dirichlet process mixture model for clustering longitudinal gene expression data
Sun J, Herazo‐Maya J, Kaminski N, Zhao H, Warren JL. A Dirichlet process mixture model for clustering longitudinal gene expression data. Statistics In Medicine 2017, 36: 3495-3506. PMID: 28620908, PMCID: PMC5583037, DOI: 10.1002/sim.7374.Peer-Reviewed Original ResearchConceptsLongitudinal gene expression profilesDirichlet process prior distributionRegression coefficientsExtensive simulation studyLongitudinal gene expression dataBayesian settingPrior distributionClustering methodFactor analysis modelDimensionality challengeStatistical methodsSimulation studyNovel clustering methodHigh dimensionality challengeSubgroup identificationImportant problemGene expression dataInteresting subgroupsClusteringCoefficientAnalysis modelModelExpression dataA Bayesian semiparametric factor analysis model for subtype identification
Sun J, Warren JL, Zhao H. A Bayesian semiparametric factor analysis model for subtype identification. Statistical Applications In Genetics And Molecular Biology 2017, 16: 145-158. PMID: 28343169, PMCID: PMC5545128, DOI: 10.1515/sagmb-2016-0051.Peer-Reviewed Original Research
2016
Bayesian multinomial probit modeling of daily windows of susceptibility for maternal PM2.5 exposure and congenital heart defects
Warren JL, Stingone JA, Herring AH, Luben TJ, Fuentes M, Aylsworth AS, Langlois PH, Botto LD, Correa A, Olshan AF, Study B. Bayesian multinomial probit modeling of daily windows of susceptibility for maternal PM2.5 exposure and congenital heart defects. Statistics In Medicine 2016, 35: 2786-2801. PMID: 26853919, PMCID: PMC4899303, DOI: 10.1002/sim.6891.Peer-Reviewed Original ResearchConceptsGestational weeks 2Week 2PM2.5 exposureCongenital heartAmbient air pollution exposureMaternal PM2.5 exposureTetralogy of FallotNational Birth Defect Prevention StudyAir pollution exposureCritical periodDaily PM2.5 exposurePrevention StudyExposure modelEpidemiologic studiesFetal developmentHealth outcomesSignificant associationPollution exposureDay 53PregnancyDay 50Adverse effectsElevated exposureExposureHeart
2015
Assessment of critical exposure and outcome windows in time-to-event analysis with application to air pollution and preterm birth study
Chang HH, Warren JL, Darrow LA, Reich BJ, Waller LA. Assessment of critical exposure and outcome windows in time-to-event analysis with application to air pollution and preterm birth study. Biostatistics 2015, 16: 509-521. PMID: 25572998, PMCID: PMC5963471, DOI: 10.1093/biostatistics/kxu060.Peer-Reviewed Original ResearchConceptsPreterm birthAir pollution exposureGestational air pollution exposureRisk of PTBPollution exposurePreterm Birth StudyCourse of pregnancyTime-varying exposureOngoing pregnancyBirth StudyGestational exposureReproductive epidemiologyWeek 30PregnancyWeekly exposureBirth recordsVulnerable periodPopulation studiesPositive associationCritical periodExposureInconsistent findingsAssociationExposure lengthFine particulate matter
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