2022
Predicting cumulative lead (Pb) exposure using the Super Learner algorithm
Wang X, Bakulski K, Mukherjee B, Hu H, Park S. Predicting cumulative lead (Pb) exposure using the Super Learner algorithm. Chemosphere 2022, 311: 137125. PMID: 36347347, PMCID: PMC10160242, DOI: 10.1016/j.chemosphere.2022.137125.Peer-Reviewed Original ResearchConceptsPatella leadNational Health and Nutrition Examination SurveyHealth and Nutrition Examination SurveyNutrition Examination SurveyLong-term health effectsPopulation-based studyK-shell X-ray fluorescenceNormative Aging StudyCumulative lead exposureEvaluate health effectsExamination SurveyLead concentrationsBone lead measurementsAging StudyTibia leadPositive associationStudy populationHealth effectsRegression-based predictive modelBone lead concentrationsBlood pressureFlexible machine learning approachCorrelation coefficientX-ray fluorescence techniqueLead measurements
2017
Opportunities and Challenges for Environmental Exposure Assessment in Population-Based Studies
Patel C, Kerr J, Thomas D, Mukherjee B, Ritz B, Chatterjee N, Jankowska M, Madan J, Karagas M, McAllister K, Mechanic L, Fallin M, Ladd-Acosta C, Blair I, Teitelbaum S, Amos C. Opportunities and Challenges for Environmental Exposure Assessment in Population-Based Studies. Cancer Epidemiology Biomarkers & Prevention 2017, 26: 1370-1380. PMID: 28710076, PMCID: PMC5581729, DOI: 10.1158/1055-9965.epi-17-0459.Peer-Reviewed Original ResearchConceptsFollow-up of study participantsInfluence cancer riskExposure and behaviourPopulation-based studyExposure assessmentCase-control studyPhysical activityCancer riskCorrelated exposuresStudy participantsEpidemiological studiesGenetic susceptibilityEnvironmental exposure assessmentFollow-upData collectionMultidimensional indicatorsCancer developmentEvaluated 1Indicators of exposureComplex effects of environmental factorsEpidemiological investigationsOccupational exposure assessmentAssessmentEnvironmental factorsDisease development