2024
Identifying 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
Global, 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 InstituteChanges in Population Immunity Against Infection and Severe Disease From Severe Acute Respiratory Syndrome Coronavirus 2 Omicron Variants in the United States Between December 2021 and November 2022
Klaassen F, Chitwood M, Cohen T, Pitzer V, Russi M, Swartwood N, Salomon J, Menzies N. Changes in Population Immunity Against Infection and Severe Disease From Severe Acute Respiratory Syndrome Coronavirus 2 Omicron Variants in the United States Between December 2021 and November 2022. Clinical Infectious Diseases 2023, 77: 355-361. PMID: 37074868, PMCID: PMC10425195, DOI: 10.1093/cid/ciad210.Peer-Reviewed Original ResearchConceptsSevere diseasePopulation immunityOmicron infectionOmicron variantUS populationSevere acute respiratory syndrome coronavirus 2Acute respiratory syndrome coronavirus 2SARS-CoV-2 infectionRespiratory syndrome coronavirus 2SARS-CoV-2 Omicron variantRestoration of immunitySyndrome coronavirus 2Bayesian evidence synthesis modelInfection-acquired immunityEvidence synthesis modelSARS-CoV-2Prior immunological exposureCoronavirus 2Additional infectionsImmunological exposureInfectionDiseaseImmunityVaccinationUnited StatesTransmission modeling to infer tuberculosis incidence prevalence and mortality in settings with generalized HIV epidemics
Dodd P, Shaweno D, Ku C, Glaziou P, Pretorius C, Hayes R, MacPherson P, Cohen T, Ayles H. Transmission modeling to infer tuberculosis incidence prevalence and mortality in settings with generalized HIV epidemics. Nature Communications 2023, 14: 1639. PMID: 36964130, PMCID: PMC10037365, DOI: 10.1038/s41467-023-37314-1.Peer-Reviewed Original ResearchConceptsHigh HIV prevalence settingsEstimation of burdenHIV prevalence settingsGeneralized HIV epidemicsTB transmission modelAntiretroviral therapyTB infectionTB incidenceHIV prevalenceTB prevalencePrevalence settingsTB epidemicHIV epidemicHigh burdenBurden estimatesNotification dataAnnual riskSingle pathogenIntervention impactTherapy effectsTuberculosisPrevalenceEpidemicBurdenAfrican countriesSpatial 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
Reconstructing the course of the COVID-19 epidemic over 2020 for US states and counties: Results of a Bayesian evidence synthesis model
Chitwood MH, Russi M, Gunasekera K, Havumaki J, Klaassen F, Pitzer VE, Salomon JA, Swartwood NA, Warren JL, Weinberger DM, Cohen T, Menzies NA. Reconstructing the course of the COVID-19 epidemic over 2020 for US states and counties: Results of a Bayesian evidence synthesis model. PLOS Computational Biology 2022, 18: e1010465. PMID: 36040963, PMCID: PMC9467347, DOI: 10.1371/journal.pcbi.1010465.Peer-Reviewed Original ResearchConceptsSARS-CoV-2 infectionCOVID-19 casesIncident SARS-CoV-2 infectionCOVID-19 disease burdenSymptomatic COVID-19 casesLocal epidemiological trendsSARS-CoV-2 transmissionBayesian evidence synthesis modelCOVID-19 outcomesEvidence synthesis modelMagnitude of infectionCOVID-19 deathsCumulative incidenceDisease burdenExcess mortalityCase ascertainmentEpidemiological trendsSeroprevalence estimatesUnderlying incidenceUS populationDisease trendsViral transmission dynamicsInfectionEpidemiological driversCOVID-19 epidemicPopulation Immunity to Pre-Omicron and Omicron Severe Acute Respiratory Syndrome Coronavirus 2 Variants in US States and Counties Through 1 December 2021
Klaassen F, Chitwood MH, Cohen T, Pitzer VE, Russi M, Swartwood NA, Salomon JA, Menzies NA. Population Immunity to Pre-Omicron and Omicron Severe Acute Respiratory Syndrome Coronavirus 2 Variants in US States and Counties Through 1 December 2021. Clinical Infectious Diseases 2022, 76: e350-e359. PMID: 35717642, PMCID: PMC9214178, DOI: 10.1093/cid/ciac438.Peer-Reviewed Original ResearchConceptsSARS-CoV-2 infectionSARS-CoV-2Immunological exposureOmicron variantSevere diseaseAcute respiratory syndrome coronavirus 2 infectionSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infectionFuture SARS-CoV-2 infectionSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variantsUS populationSyndrome coronavirus 2 infectionCoronavirus disease 2019 (COVID-19) vaccinationCoronavirus 2 infectionPopulation-level immunityPrior immunological exposurePopulation immunityImmune escapeVaccination dataInfectionInfection statusVaccinationUS statesEffective protectionDiseaseImmunityNeighbourhood prevalence-to-notification ratios for adult bacteriologically-confirmed tuberculosis reveals hotspots of underdiagnosis in Blantyre, Malawi
Khundi M, Carpenter JR, Corbett EL, Feasey HRA, Soko RN, Nliwasa M, Twabi H, Chiume L, Burke RM, Horton KC, Dodd PJ, Cohen T, MacPherson P. Neighbourhood prevalence-to-notification ratios for adult bacteriologically-confirmed tuberculosis reveals hotspots of underdiagnosis in Blantyre, Malawi. PLOS ONE 2022, 17: e0268749. PMID: 35605004, PMCID: PMC9126376, DOI: 10.1371/journal.pone.0268749.Peer-Reviewed Original ResearchMeSH KeywordsAdultBayes TheoremHumansMalawiMass ScreeningMycobacterium tuberculosisPrevalenceSputumTuberculosisConceptsCase notification ratesPrevalence surveyNotification ratioNeighbourhood prevalenceTB case notification ratesXpert MTB/RIFCase-finding interventionsTrue disease burdenChest X-ray screeningTB prevalence surveyTB surveillance systemMTB/RIFDiagnosis of tuberculosisSputum smear microscopyTB clinicTB patientsRespiratory infectionsTB prevalenceDisease burdenNotification ratesSmear microscopyX-ray screeningTuberculosisPrevalenceUrban tuberculosisRacial/Ethnic Segregation and Access to COVID-19 Testing: Spatial Distribution of COVID-19 Testing Sites in the Four Largest Highly Segregated Cities in the United States
Asabor EN, Warren JL, Cohen T. Racial/Ethnic Segregation and Access to COVID-19 Testing: Spatial Distribution of COVID-19 Testing Sites in the Four Largest Highly Segregated Cities in the United States. American Journal Of Public Health 2022, 112: 518-526. PMID: 35196059, PMCID: PMC8887160, DOI: 10.2105/ajph.2021.306558.Peer-Reviewed Original ResearchSpatially targeted digital chest radiography to reduce tuberculosis in high-burden settings: A study of adaptive decision making
de Villiers AK, Dye C, Yaesoubi R, Cohen T, Marx FM. Spatially targeted digital chest radiography to reduce tuberculosis in high-burden settings: A study of adaptive decision making. Epidemics 2022, 38: 100540. PMID: 35093849, PMCID: PMC8983993, DOI: 10.1016/j.epidem.2022.100540.Peer-Reviewed Original ResearchConceptsHigh-burden settingsTB casesTB prevalenceChest radiographyAdditional TB casesCase-finding yieldTB prevalence estimatesHigh-burden populationsCommunity-randomized trialNumber of screeningsTB controlXpert UltraScreening roundNotification ratesPrevalence estimatesTuberculosisPrevalenceDigital chest radiographyScreeningTrialsInterventionRadiography
2021
Global estimates of paediatric tuberculosis incidence in 2013–19: a mathematical modelling analysis
Yerramsetti S, Cohen T, Atun R, Menzies NA. Global estimates of paediatric tuberculosis incidence in 2013–19: a mathematical modelling analysis. The Lancet Global Health 2021, 10: e207-e215. PMID: 34895517, PMCID: PMC8800006, DOI: 10.1016/s2214-109x(21)00462-9.Peer-Reviewed Original ResearchConceptsPediatric tuberculosisTuberculosis incidenceStudy periodIncident tuberculosis casesTuberculosis incidence rateReporting systemHigh-burden settingsGlobal tuberculosis burdenTuberculosis natural historyPediatric incidenceInfectious exposurePrompt diagnosisSubstantial morbidityTuberculosis burdenTuberculosis casesIncidence rateRisk factorsCase detectionGlobal incidenceProbability of infectionInfected individualsAge groupsNatural historyTuberculosisIncidenceEvolution and emergence of multidrug-resistant Mycobacterium tuberculosis in Chisinau, Moldova
Brown TS, Eldholm V, Brynildsrud O, Osnes M, Levy N, Stimson J, Colijn C, Alexandru S, Noroc E, Ciobanu N, Crudu V, Cohen T, Mathema B. Evolution and emergence of multidrug-resistant Mycobacterium tuberculosis in Chisinau, Moldova. Microbial Genomics 2021, 7: 000620. PMID: 34431762, PMCID: PMC8549355, DOI: 10.1099/mgen.0.000620.Peer-Reviewed Original ResearchConceptsDrug-resistant TB casesMultidrug-resistant Mycobacterium tuberculosisDrug-resistant tuberculosisDrug resistance mutationsPopulation size expansionPublic health practiceSoviet UnionSocial turmoilTB patientsTB casesTB controlRepublic of MoldovaInpatient hospitalizationMigration historyInpatient treatmentEastern EuropeNational guidelinesEpidemiological historyResistance mutationsHealth practicesGenomic surveillance effortsCapital cityMycobacterium tuberculosisTuberculosisMoldovaBayesian evidence synthesis to estimate subnational TB incidence: An application in Brazil
Chitwood MH, Pelissari DM, da Silva G, Bartholomay P, Rocha MS, Sanchez M, Arakaki-Sanchez D, Glaziou P, Cohen T, Castro MC, Menzies NA. Bayesian evidence synthesis to estimate subnational TB incidence: An application in Brazil. Epidemics 2021, 35: 100443. PMID: 33676092, PMCID: PMC8252152, DOI: 10.1016/j.epidem.2021.100443.Peer-Reviewed Original ResearchConceptsTB incidenceFraction of casesUntreated active diseaseIncident TB casesTB control programsLocal disease burdenIncident TBActive diseaseTB burdenTB casesTB outcomesTB notificationsDisease burdenBayesian evidence synthesisCase detectionIncidenceEvidence synthesisAverage annual increaseControl programsBurdenAnnual rateAnnual increaseFraction of individualsAverage annual rateSources of bias
2018
Tuberculosis control interventions targeted to previously treated people in a high-incidence setting: a modelling study
Marx FM, Yaesoubi R, Menzies NA, Salomon JA, Bilinski A, Beyers N, Cohen T. Tuberculosis control interventions targeted to previously treated people in a high-incidence setting: a modelling study. The Lancet Global Health 2018, 6: e426-e435. PMID: 29472018, PMCID: PMC5849574, DOI: 10.1016/s2214-109x(18)30022-6.Peer-Reviewed Original ResearchConceptsHigh-incidence settingsIsoniazid preventive therapyPreventive therapyTuberculosis treatmentActive casesHIV prevalenceTuberculosis controlControl interventionsIncident tuberculosis casesPrevious tuberculosis treatmentTuberculosis control interventionsTB case notificationHigh-risk groupTransmission dynamic modelTuberculosis deathsHigh tuberculosisRecurrent diseasePrevalent tuberculosisTuberculosis casesTuberculosis incidenceCase notificationTreatment outcomesTuberculosis morbidityTuberculosis epidemicAdditional interventions
2017
A Multistrain Mathematical Model To Investigate the Role of Pyrazinamide in the Emergence of Extensively Drug-Resistant Tuberculosis
Fofana MO, Shrestha S, Knight GM, Cohen T, White RG, Cobelens F, Dowdy DW. A Multistrain Mathematical Model To Investigate the Role of Pyrazinamide in the Emergence of Extensively Drug-Resistant Tuberculosis. Antimicrobial Agents And Chemotherapy 2017, 61: 10.1128/aac.00498-16. PMID: 27956422, PMCID: PMC5328532, DOI: 10.1128/aac.00498-16.Peer-Reviewed Original ResearchMeSH KeywordsAntitubercular AgentsBayes TheoremBiological AvailabilityComputer SimulationDrug Administration ScheduleDrug Resistance, Multiple, BacterialExtensively Drug-Resistant TuberculosisFluoroquinolonesHumansMicrobial Sensitivity TestsModels, StatisticalMycobacterium tuberculosisPyrazinamideRifampinTuberculosis, PulmonaryConceptsCompanion drugsExtensively Drug-Resistant TuberculosisSecond-line treatmentFirst-line treatmentSecond-line regimensDrug-resistant tuberculosisUse of pyrazinamideExtensive drug resistanceDrug resistance dataEmergence of strainsEmergence of mutationsXDR-TBSequential regimensHIV infectionAlternative drugsResistance amplificationPyrazinamide resistanceProlonged treatmentCombination antimicrobialsDrug resistanceInfectious diseasesPrevalenceProportion of simulationsAppropriate useRegimens
2013
Bayesian Estimation of Mixture Models with Prespecified Elements to Compare Drug Resistance in Treatment-Naïve and Experienced Tuberculosis Cases
Izu A, Cohen T, DeGruttola V. Bayesian Estimation of Mixture Models with Prespecified Elements to Compare Drug Resistance in Treatment-Naïve and Experienced Tuberculosis Cases. PLOS Computational Biology 2013, 9: e1002973. PMID: 23555210, PMCID: PMC3605089, DOI: 10.1371/journal.pcbi.1002973.Peer-Reviewed Original ResearchConceptsTreatment-experienced patientsDrug-resistant strainsMultiple drug-resistant strainsTreatment-naïve patientsDrug-resistant tuberculosisMycobacterium tuberculosis isolatesWorld Health OrganizationDrug resistance pathwaysTreatment-naïveTuberculosis casesTuberculosis isolatesWorldwide surveillanceDrug resistancePatientsHealth OrganizationResistant strainsResistant pathogensResistance pathwaysLow transmissibilityPathwayTuberculosis
2012
The impact of new tuberculosis diagnostics on transmission: why context matters
Lin HH, Dowdy D, Dye C, Murray M, Cohen T. The impact of new tuberculosis diagnostics on transmission: why context matters. Bulletin Of The World Health Organization 2012, 90: 739-747. PMID: 23109741, PMCID: PMC3471051, DOI: 10.2471/blt.11.101436.Peer-Reviewed Original ResearchConceptsNew tuberculosis diagnosticsNew diagnostic toolsPatient lossHuman immunodeficiency virus (HIV) infectionTuberculosis diagnosticsSmear-negative pulmonary tuberculosisDiagnostic toolImmunodeficiency virus infectionTreatment success rateSmear-negative casesIncidence of tuberculosisEpidemiology of tuberculosisPatient defaultPulmonary tuberculosisTuberculosis careDiagnostic pathwayTuberculosis transmissionSymptomatic individualsVirus infectionSmear microscopyTuberculosisAnnual declineDiagnosisAbsolute changeSuccess rate
2011
Bayesian methods for fitting mixture models that characterize branching tree processes: An application to development of resistant TB strains
Izu A, Cohen T, Mitnick C, Murray M, De Gruttola V. Bayesian methods for fitting mixture models that characterize branching tree processes: An application to development of resistant TB strains. Statistics In Medicine 2011, 30: 2708-2720. PMID: 21717491, PMCID: PMC3219798, DOI: 10.1002/sim.4287.Peer-Reviewed Original ResearchConceptsCharacterization of uncertaintyBayesian approachBayesian methodsBranching tree modelStatistical methodsMixture modelBranching treeNatural wayPrior informationDrug resistance-conferring mutationsSuch cross-sectional dataDrug-resistant TBResistant TB strainsCombination of antibioticsDrug resistance mutationsMeasurement errorResistance-conferring mutationsTB strainsSingle patientTreatment policyPatientsMultiple drugsDiagnostic specimensCross-sectional dataGenetic mutations