2024
Combining genomic data and infection estimates to characterize the complex dynamics of SARS-CoV-2 Omicron variants in the US
Lopes R, Pham K, Klaassen F, Chitwood M, Hahn A, Redmond S, Swartwood N, Salomon J, Menzies N, Cohen T, Grubaugh N. Combining genomic data and infection estimates to characterize the complex dynamics of SARS-CoV-2 Omicron variants in the US. Cell Reports 2024, 43: 114451. PMID: 38970788, DOI: 10.1016/j.celrep.2024.114451.Peer-Reviewed Original ResearchCorrigendum to—“Identifying local foci of tuberculosis transmission in Moldova using a spatial multinomial logistic regression model” [eBioMedicine 102(2024) 105085]
Lan Y, Crudu V, Ciobanu N, Codreanu A, Chitwood M, Sobkowiak B, Warren J, Cohen T. Corrigendum to—“Identifying local foci of tuberculosis transmission in Moldova using a spatial multinomial logistic regression model” [eBioMedicine 102(2024) 105085]. EBioMedicine 2024, 105: 105225. PMID: 38943727, DOI: 10.1016/j.ebiom.2024.105225.Peer-Reviewed Original ResearchPredictors of unsuccessful tuberculosis treatment outcomes in Brazil: an analysis of 259,484 patient records
Ryuk D, Pelissari D, Alves K, Oliveira P, Castro M, Cohen T, Sanchez M, Menzies N. Predictors of unsuccessful tuberculosis treatment outcomes in Brazil: an analysis of 259,484 patient records. BMC Infectious Diseases 2024, 24: 531. PMID: 38802744, PMCID: PMC11129366, DOI: 10.1186/s12879-024-09417-7.Peer-Reviewed Original ResearchConceptsTreatment outcomesFactors associated with unsuccessful treatment outcomesHigher risk of poor treatment outcomeTB treatmentRisk of poor treatment outcomesUnsuccessful treatmentEffectiveness of TB treatmentAssociated with unsuccessful treatmentUnsuccessful treatment outcomesTuberculosis treatment outcomesDrug-susceptible TBHealth system levelMultivariate logistic regression modelService-related factorsTB drug resistanceComorbid health conditionsHealth-related behaviorsCategorizing treatment outcomesPatient-level factorsClinical examination resultsNational Disease Notification SystemPoor treatment outcomesDisease notification systemHIV infectionLogistic regression modelsImpact and cost-effectiveness of the 6-month BPaLM regimen for rifampicin-resistant tuberculosis in Moldova: A mathematical modeling analysis.
James L, Klaassen F, Sweeney S, Furin J, Franke M, Yaesoubi R, Chesov D, Ciobanu N, Codreanu A, Crudu V, Cohen T, Menzies N. Impact and cost-effectiveness of the 6-month BPaLM regimen for rifampicin-resistant tuberculosis in Moldova: A mathematical modeling analysis. PLOS Medicine 2024, 21: e1004401. PMID: 38701084, PMCID: PMC11101189, DOI: 10.1371/journal.pmed.1004401.Peer-Reviewed Original ResearchQuality-adjusted life yearsStandard of careDrug susceptibility testingRifampicin-resistant tuberculosisRR-TBEnd-of-treatmentLonger regimensTreatment strategiesTreatment outcomesBurden of drug-resistant TBCost-effective treatment strategyResistance to amikacinDrug-resistant TBSevere adverse eventsHistory of TBResistance to delamanidTB drug resistanceAnti-TB drugsLifetime costsAssociated treatment outcomesFQ-R.Average timeNatural history of TBFluoroquinolone resistanceFQ-RThe recent rapid expansion of multidrug resistant Ural lineage Mycobacterium tuberculosis in Moldova
Chitwood M, Colijn C, Yang C, Crudu V, Ciobanu N, Codreanu A, Kim J, Rancu I, Rhee K, Cohen T, Sobkowiak B. The recent rapid expansion of multidrug resistant Ural lineage Mycobacterium tuberculosis in Moldova. Nature Communications 2024, 15: 2962. PMID: 38580642, PMCID: PMC10997638, DOI: 10.1038/s41467-024-47282-9.Peer-Reviewed Original ResearchConceptsMDR M. tuberculosisGenome sequenceResistance-conferring mutationsBeijing sublineageMDR strainsReproductive fitnessBeijing strainsCulture-positive casesLineagesMtb strainsMultidrug-resistant tuberculosisMDRMtbStrainMDR-TBMutationsResistant tuberculosisMDR-MTBSubstantial riskSublineagesTuberculosisSequenceDisparities in Tuberculosis Incidence by Race and Ethnicity Among the U.S.-Born Population in the United States, 2011 to 2021 : An Analysis of National Disease Registry Data.
Li Y, Regan M, Swartwood N, Barham T, Beeler Asay G, Cohen T, Hill A, Horsburgh C, Khan A, Marks S, Myles R, Salomon J, Self J, Menzies N. Disparities in Tuberculosis Incidence by Race and Ethnicity Among the U.S.-Born Population in the United States, 2011 to 2021 : An Analysis of National Disease Registry Data. Annals Of Internal Medicine 2024, 177: 418-427. PMID: 38560914, DOI: 10.7326/m23-2975.Peer-Reviewed Original ResearchConceptsIncidence rate ratiosNon-Hispanic white personsTB incidence rateRate ratiosIncidence rateTB incidenceSocial determinants of healthRelated disparitiesWhite personsHealth equity goalsU.S.-born populationAbsolute disparityDeterminants of healthTB registry dataRacial/ethnic minority populationsCenters for Disease Control and PreventionDisease Control and PreventionIncidence rate differenceTB casesYoung personControl and PreventionState of residenceAI/AN personsSocial determinantsRacial/ethnic disparitiesIdentifying 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 TBQuantifying gaps in the tuberculosis care cascade in Brazil: A mathematical model study using national program data
Emani S, Alves K, Alves L, da Silva D, Oliveira P, Castro M, Cohen T, de Macedo Couto R, Sanchez M, Menzies N. Quantifying gaps in the tuberculosis care cascade in Brazil: A mathematical model study using national program data. PLOS Medicine 2024, 21: e1004361. PMID: 38512968, PMCID: PMC10994550, DOI: 10.1371/journal.pmed.1004361.Peer-Reviewed Original ResearchDisability-adjusted life yearsLoss to follow-upTreatment loss to follow-upCare cascadeHealth lossDelay to diagnosisTB diseaseTB diagnosisReduce delays to diagnosisHuman immunodeficiency virusContribution of social factorsTotal health system costsCare cascade outcomesTB care cascadeMortality Information SystemBurden of TB diseaseHealth system costsLifetime health outcomesNotifiable Diseases Information SystemBurden of diseaseIncident TB casesState of residenceNational program dataFollow-upHealth outcomesRacial and ethnic disparities in diagnosis and treatment outcomes among US-born people diagnosed with tuberculosis, 2003–19: an analysis of national surveillance data
Regan M, Li Y, Swartwood N, Barham T, Asay G, Cohen T, Hill A, Horsburgh C, Khan A, Marks S, Myles R, Salomon J, Self J, Menzies N. Racial and ethnic disparities in diagnosis and treatment outcomes among US-born people diagnosed with tuberculosis, 2003–19: an analysis of national surveillance data. The Lancet Public Health 2024, 9: e47-e56. PMID: 38176842, DOI: 10.1016/s2468-2667(23)00276-1.Peer-Reviewed Original ResearchConceptsNational surveillance dataNon-Hispanic White peopleTreatment outcomesEthnic disparitiesSurveillance dataTuberculosis diagnosisUS National Tuberculosis Surveillance SystemIndex of disparityLog-binomial regression modelsNational Tuberculosis Surveillance SystemNon-Hispanic black peopleOverall high riskTuberculosis Surveillance SystemAlaska Native peopleTuberculosis patientsDiagnostic delayAdverse outcomesTuberculosis diseaseTuberculosis incidence
2023
Spatially-targeted tuberculosis screening has limited impact beyond household contact tracing in Lima, Peru: A model-based analysis
Havumaki J, Warren J, Zelner J, Menzies N, Calderon R, Contreras C, Lecca L, Becerra M, Murray M, Cohen T. Spatially-targeted tuberculosis screening has limited impact beyond household contact tracing in Lima, Peru: A model-based analysis. PLOS ONE 2023, 18: e0293519. PMID: 37903091, PMCID: PMC10615320, DOI: 10.1371/journal.pone.0293519.Peer-Reviewed Original ResearchPredictive power of wastewater for nowcasting infectious disease transmission: A retrospective case study of five sewershed areas in Louisville, Kentucky
Klaassen F, Holm R, Smith T, Cohen T, Bhatnagar A, Menzies N. Predictive power of wastewater for nowcasting infectious disease transmission: A retrospective case study of five sewershed areas in Louisville, Kentucky. Environmental Research 2023, 240: 117395. PMID: 37838198, PMCID: PMC10863376, DOI: 10.1016/j.envres.2023.117395.Peer-Reviewed Original ResearchConceptsDeath dataSurveillance dataSARS-CoV-2 casesClinical surveillance dataLow-resource settingsRetrospective case studyInfectious disease transmissionTrue infectionEpidemiologic dataSerosurvey dataDeath reportsTraditional surveillance dataDisease trendsInfectious diseasesWastewater dataDisease transmissionPredictive performanceGlobal burden of disease due to rifampicin-resistant tuberculosis: a mathematical modeling analysis
Menzies N, Allwood B, Dean A, Dodd P, Houben R, James L, Knight G, Meghji J, Nguyen L, Rachow A, Schumacher S, Mirzayev F, Cohen T. Global burden of disease due to rifampicin-resistant tuberculosis: a mathematical modeling analysis. Nature Communications 2023, 14: 6182. PMID: 37794037, PMCID: PMC10550952, DOI: 10.1038/s41467-023-41937-9.Peer-Reviewed Original ResearchConceptsDisability-adjusted life yearsRifampicin-resistant tuberculosisRR-TBGlobal burdenSubstantial short-term morbidityRifampicin-susceptible tuberculosisShort-term morbidityOverall disease burdenLong-term health impactsPost-treatment careTB survivorsDisease burdenTreatment outcomesTuberculosis survivorsCase detectionLife yearsRifampicin resistanceTuberculosisHealth impactsBurdenHealth expenditureDiseaseSurvivorsMathematical modeling analysisFormer Soviet Union countriesTabby2: a user-friendly web tool for forecasting state-level TB outcomes in the United States
Swartwood N, Testa C, Cohen T, Marks S, Hill A, Beeler Asay G, Cochran J, Cranston K, Randall L, Tibbs A, Horsburgh C, Salomon J, Menzies N. Tabby2: a user-friendly web tool for forecasting state-level TB outcomes in the United States. BMC Medicine 2023, 21: 331. PMID: 37649031, PMCID: PMC10469407, DOI: 10.1186/s12916-023-02785-y.Peer-Reviewed Original ResearchConceptsLatent tuberculosis infectionTB outcomesDistrict of ColumbiaTreatment of LTBIIncremental cost-effectiveness ratioTB incidence rateTuberculosis disease burdenQuality-adjusted life yearsCost-effectiveness ratioPublic health agenciesTB casesTB preventionTB incidenceTuberculosis infectionTB epidemiologyDisease burdenIncidence rateService utilizationAdditional interventionsIncidence projectionsLife yearsDemographic dataSocietal perspectiveHealth agenciesPrevention approachesGlobal, 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 StatesBedaquiline and clofazimine resistance in Mycobacterium tuberculosis: an in-vitro and in-silico data analysis
Sonnenkalb L, Carter J, Spitaleri A, Iqbal Z, Hunt M, Malone K, Utpatel C, Cirillo D, Rodrigues C, Nilgiriwala K, Fowler P, Merker M, Niemann S, Consortium C, Barilar I, Battaglia S, Borroni E, Brandao A, Brankin A, Cabibbe A, Carter J, Cirillo D, Claxton P, Clifton D, Cohen T, Coronel J, Crook D, Dreyer V, Earle S, Escuyer V, Ferrazoli L, Fowler P, Gao G, Gardy J, Gharbia S, Ghisi K, Ghodousi A, Cruz A, Grandjean L, Grazian C, Groenheit R, Guthrie J, He W, Hoffmann H, Hoosdally S, Hunt M, Iqbal Z, Ismail N, Jarrett L, Joseph L, Jou R, Kambli P, Khot R, Knaggs J, Koch A, Kohlerschmidt D, Kouchaki S, Lachapelle A, Lalvani A, Lapierre S, Laurenson I, Letcher B, Lin W, Liu C, Liu D, Malone K, Mandal A, Mansjö M, Matias D, Meintjes G, de Freitas Mendes F, Merker M, Mihalic M, Millard J, Miotto P, Mistry N, Moore D, Musser K, Ngcamu D, Hoang N, Niemann S, Nilgiriwala K, Nimmo C, Okozi N, Oliveira R, Omar S, Paton N, Peto T, Pinhata J, Plesnik S, Puyen Z, Rabodoarivelo M, Rakotosamimanana N, Rancoita P, Rathod P, Rodger G, Rodrigues C, Rodwell T, Roohi E, Santos-Lazaro D, Shah S, Kohl T, Smith G, Solano W, Spitaleri A, Supply P, Surve U, Tahseen S, Thuong N, Thwaites G, Todt K, Trovato A, Utpatel C, Van Rie A, Vijay S, Walker T, Walker S, Warren R, Werngren J, Wijkander M, Wilkinson R, Wilson D, Wintringer P, Yu X, Yang Y, Zhao Y, Yao S, Zhu B. Bedaquiline and clofazimine resistance in Mycobacterium tuberculosis: an in-vitro and in-silico data analysis. The Lancet Microbe 2023, 4: e358-e368. PMID: 37003285, PMCID: PMC10156607, DOI: 10.1016/s2666-5247(23)00002-2.Peer-Reviewed Original ResearchConceptsMutation catalogueIn silico data analysisBedaquiline resistanceClofazimine resistanceResistance mechanismsProtein modelsClinical Mycobacterium tuberculosis complex isolatesImpaired DNA bindingClinically resistant strainsMinimum inhibitory concentrationVariants in vitroPacBio sequencingGenome sequenceGenomic rearrangementsGenomic variantsIn vitroExperimental evolutionGenotype dataTranscriptional repressorDrug resistance mechanismsClinical isolatesPhenotypic dataResistance determinantsDNA bindingProtein dimerisationTransmission 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 countriesDevelopment of treatment-decision algorithms for children evaluated for pulmonary tuberculosis: an individual participant data meta-analysis
Gunasekera K, Marcy O, Muñoz J, Lopez-Varela E, Sekadde M, Franke M, Bonnet M, Ahmed S, Amanullah F, Anwar A, Augusto O, Aurilio R, Banu S, Batool I, Brands A, Cain K, Carratalá-Castro L, Caws M, Click E, Cranmer L, García-Basteiro A, Hesseling A, Huynh J, Kabir S, Lecca L, Mandalakas A, Mavhunga F, Myint A, Myo K, Nampijja D, Nicol M, Orikiriza P, Palmer M, Sant'Anna C, Siddiqui S, Smith J, Song R, Thuong Thuong N, Ung V, van der Zalm M, Verkuijl S, Viney K, Walters E, Warren J, Zar H, Marais B, Graham S, Debray T, Cohen T, Seddon J. Development of treatment-decision algorithms for children evaluated for pulmonary tuberculosis: an individual participant data meta-analysis. The Lancet Child & Adolescent Health 2023, 7: 336-346. PMID: 36924781, PMCID: PMC10127218, DOI: 10.1016/s2352-4642(23)00004-4.Peer-Reviewed Original ResearchConceptsTreatment decision algorithmsPrimary health care settingsIndividual participant dataHigh tuberculosis incidencePulmonary tuberculosisManagement of tuberculosisHealth care centersComposite reference standardHealth care settingsClinical featuresTuberculosis incidenceClinical evaluationParticipant dataTreatment decisionsChest X-ray featuresPrimary health care centersFuture prospective evaluationTuberculosis-related mortalityEvidence-based algorithmChest X-rayVariable diagnostic performanceMultivariable prediction modelReference standardEvidence-based approachTuberculosis expertsSpatial 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 ResearchEstimated rates of progression to tuberculosis disease for persons infected with Mycobacterium tuberculosis in the United States
Ekramnia M, Li Y, Haddad M, Marks S, Kammerer J, Swartwood N, Cohen T, Miller J, Horsburgh C, Salomon J, Menzies N. Estimated rates of progression to tuberculosis disease for persons infected with Mycobacterium tuberculosis in the United States. Epidemiology 2023, 35: 164-173. PMID: 38290139, PMCID: PMC10832387, DOI: 10.1097/ede.0000000000001707.Peer-Reviewed Original ResearchConceptsInterferon-gamma release assayNational Health and Nutrition Examination SurveyHealth and Nutrition Examination SurveyUS-born statusNational TB Surveillance SystemReactivation TBNutrition Examination SurveyInterferon-gammaIGRA sensitivityTB surveillance systemProgression to tuberculosis diseaseNationally representative dataEnd-stage renal diseaseReactivation rateAmerican Community SurveyExamination SurveyPerson yearsRace-ethnicityUS populationTB ratesTB incidenceCommunity SurveyRepresentative dataSurveillance systemRelease assay