2022
Global Prevalence of Post-Coronavirus Disease 2019 (COVID-19) Condition or Long COVID: A Meta-Analysis and Systematic Review
Chen C, Haupert S, Zimmermann L, Shi X, Fritsche L, Mukherjee B. Global Prevalence of Post-Coronavirus Disease 2019 (COVID-19) Condition or Long COVID: A Meta-Analysis and Systematic Review. The Journal Of Infectious Diseases 2022, 226: 1593-1607. PMID: 35429399, PMCID: PMC9047189, DOI: 10.1093/infdis/jiac136.Peer-Reviewed Original ResearchMeSH KeywordsCoronavirus InfectionsCOVID-19HumansPandemicsPneumonia, ViralPost-Acute COVID-19 SyndromePrevalenceConceptsPost-COVID-19 conditionCondition prevalenceMeta-analysisGlobal prevalenceHealth effects of COVID-19Prevalence of post-COVID-19 conditionRegional prevalence estimationHealthcare systemPrevalence estimatesPooled prevalencePost-COVID-19Systematic reviewDerSimonian-Laird estimatorMeta-analyzedMemory problemsHealth effectsPrevalenceEffects of COVID-19Post-coronavirus disease 2019Long COVIDCOVID-19COVID-19 conditionsNonhospitalized patientsUnited States
2018
Associations of cumulative exposure to heavy metal mixtures with obesity and its comorbidities among U.S. adults in NHANES 2003–2014
Wang X, Mukherjee B, Park S. Associations of cumulative exposure to heavy metal mixtures with obesity and its comorbidities among U.S. adults in NHANES 2003–2014. Environment International 2018, 121: 683-694. PMID: 30316184, PMCID: PMC6268112, DOI: 10.1016/j.envint.2018.09.035.Peer-Reviewed Original ResearchConceptsEnvironmental risk scoreAssociated with obesityHeavy metal mixturesHeavy metalsMetal mixturesAssociation of cumulative exposureCumulative exposureComplex survey designEffects of cumulative exposurePhysical activityObesity measuresAdaptive elastic-netChronic conditionsExposure to heavy metalsInteraction of heavy metalsWaist circumferenceTotal body fatNHANES cyclesU.S. adultsEpidemiological researchType 2 diabetes mellitusCorrelated heavy metalSmoking statusHigher BMISkinfold thickness
2017
Construction of environmental risk score beyond standard linear models using machine learning methods: application to metal mixtures, oxidative stress and cardiovascular disease in NHANES
Park S, Zhao Z, Mukherjee B. Construction of environmental risk score beyond standard linear models using machine learning methods: application to metal mixtures, oxidative stress and cardiovascular disease in NHANES. Environmental Health 2017, 16: 102. PMID: 28950902, PMCID: PMC5615812, DOI: 10.1186/s12940-017-0310-9.Peer-Reviewed Original ResearchConceptsEnvironmental risk scoreBayesian kernel machine regressionNational Health and Nutrition Examination SurveyHealth and Nutrition Examination SurveyRisk scoreAssociated with odds ratiosNutrition Examination SurveyAssociated with systolicExamination SurveyMulti-pollutant approachKernel machine regressionPollutant mixturesSD increaseEpidemiological researchDiastolic blood pressureMortality outcomesOdds ratioBayesian additive regression treesDisease endpointsHealth endpointsCumulative riskPositive associationEnvironmental exposuresIntermediate markersCardiovascular disease
2013
Bayesian Analysis of Time-Series Data under Case-Crossover Designs: Posterior Equivalence and Inference
Li S, Mukherjee B, Batterman S, Ghosh M. Bayesian Analysis of Time-Series Data under Case-Crossover Designs: Posterior Equivalence and Inference. Biometrics 2013, 69: 925-936. PMID: 24289144, PMCID: PMC4108592, DOI: 10.1111/biom.12102.Peer-Reviewed Original ResearchConceptsSemi-parametric Bayesian approachLikelihood-based approachRandom nuisance parametersTime series analysisFrequentist literatureNuisance parametersDirichlet processInferential issuesConditional likelihoodPosterior distributionRisk functionTime seriesBayesian workFrequentist approachCase-crossover designSimulation studyRestrictive assumptionsBayesian approachTime Series DataLikelihood formulationBayesian methodsEquivalent resultsBayesian analysisCase-crossoverBayesian framework
2012
Hypertension: Development of a prediction model to adjust self-reported hypertension prevalence at the community level
Mentz G, Schulz A, Mukherjee B, Ragunathan T, Perkins D, Israel B. Hypertension: Development of a prediction model to adjust self-reported hypertension prevalence at the community level. BMC Health Services Research 2012, 12: 312. PMID: 22967264, PMCID: PMC3483283, DOI: 10.1186/1472-6963-12-312.Peer-Reviewed Original ResearchConceptsHealthy Environments PartnershipSelf-reported hypertension prevalenceEstimates of hypertension prevalenceSelf-reported dataHypertension prevalenceNHANES sampleUrban sampleNational Health and Nutrition ExaminationSelf-reportAssessment of population healthPopulation-based interventionsSelf-reported hypertensionUnderreporting of hypertensionEstimates of hypertensionAccuracy of self-reported dataHealth care programsPrevalence of hypertensionMethodsWe analyzed dataPopulation level estimatesModerate to goodSelf-reported survey dataEthnically diverse urban samplePopulation healthCare programNutrition Examination
2011
Bayesian Modeling for Genetic Anticipation in Presence of Mutational Heterogeneity: A Case Study in Lynch Syndrome
Boonstra P, Mukherjee B, Taylor J, Nilbert M, Moreno V, Gruber S. Bayesian Modeling for Genetic Anticipation in Presence of Mutational Heterogeneity: A Case Study in Lynch Syndrome. Biometrics 2011, 67: 1627-1637. PMID: 21627626, PMCID: PMC3176998, DOI: 10.1111/j.1541-0420.2011.01607.x.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAge of OnsetAgedAnticipation, GeneticBayes TheoremChildChild, PreschoolColorectal Neoplasms, Hereditary NonpolyposisComputer SimulationDenmarkFemaleHumansInfantInfant, NewbornMaleMiddle AgedModels, GeneticModels, StatisticalMutationPolymorphism, Single NucleotidePrevalenceRisk AssessmentRisk FactorsYoung AdultConceptsLynch syndromeBirth cohortGenetic anticipationHereditary nonpolyposis colorectal cancerCancer registry dataNonpolyposis colorectal cancerDanish Cancer RegisterGenetic counseling clinicAge-specific incidenceHigh-risk familiesRandom-effects modelCancer RegisterRegistry dataCounseling clinicMismatch repairRandom effectsSecular trendsMedical practiceColorectal cancerSurvival analysis methodsEffects modelConfounding effectsLynchFlexible random effects modelModel fit diagnostics