2019
Does Information on Blood Heavy Metals Improve Cardiovascular Mortality Prediction?
Wang X, Mukherjee B, Park S. Does Information on Blood Heavy Metals Improve Cardiovascular Mortality Prediction? Journal Of The American Heart Association 2019, 8: e013571. PMID: 31631727, PMCID: PMC6898859, DOI: 10.1161/jaha.119.013571.Peer-Reviewed Original ResearchMeSH KeywordsAgedBiomarkersCadmiumCardiovascular DiseasesFemaleHumansLeadMaleMercuryMiddle AgedPredictive Value of TestsRisk FactorsConceptsCardiovascular diseaseNational Health and Nutrition Examination SurveyHealth and Nutrition Examination SurveyRisk factorsStudy sampleCardiovascular disease risk factorsCardiovascular disease mortalityCardiovascular disease risk assessmentImprove CVD risk predictionC-statisticNutrition Examination SurveyCardiovascular mortality predictionCVD risk predictionCox modelBlood markersExamination SurveyPrecision healthRisk scorePairwise interaction termsBlood metalsIntegrated discrimination improvementRisk predictionReclassification improvementMortality predictionInteraction terms
2018
Evaluating the Risk of Noise-Induced Hearing Loss Using Different Noise Measurement Criteria
Roberts B, Seixas N, Mukherjee B, Neitzel R. Evaluating the Risk of Noise-Induced Hearing Loss Using Different Noise Measurement Criteria. Annals Of Work Exposures And Health 2018, 62: 295-306. PMID: 29415217, DOI: 10.1093/annweh/wxy001.Peer-Reviewed Original ResearchConceptsHearing threshold levelsHearing outcomesHearing lossOccupational Safety and Health AdministrationRisk of noise-induced hearing lossHealth AdministrationNoise exposureOccupational Safety and HealthNoise-induced hearing lossInstitute of Occupational Safety and HealthSafety and HealthNational Institute of Occupational Safety and HealthCohort of construction workersMixed modelsDuration of participationAssessment of noise exposureMeasures of exposureHearing levelAkaike’s information criterion differencesConstruction workersAverage noise levelLinear mixed modelsLAVGContinuous averagingOSHA
2014
A space-time point process model for analyzing and predicting case patterns of diarrheal disease in northwestern Ecuador
Ahn J, Johnson T, Bhavnani D, Eisenberg J, Mukherjee B. A space-time point process model for analyzing and predicting case patterns of diarrheal disease in northwestern Ecuador. Spatial And Spatio-temporal Epidemiology 2014, 9: 23-35. PMID: 24889991, PMCID: PMC4044631, DOI: 10.1016/j.sste.2014.02.001.Peer-Reviewed Original ResearchMeSH KeywordsCase-Control StudiesDiarrheaEcuadorFemaleHumansMaleModels, StatisticalPredictive Value of TestsConceptsSampled communitiesNorthwestern EcuadorLog Gaussian Cox processRiver BasinRisk-related parametersTemporal variationSpace-time modelDiarrheal diseaseLongitudinal sampling designSampling designPoint process modelNatural environmentSampling regionSpatial clusteringSampling cycleCase eventsPoint patterns
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