2024
Potential of Pan-Tuberculosis Treatment to Drive Emergence of Novel Resistance - Volume 30, Number 8—August 2024 - Emerging Infectious Diseases journal - CDC
McQuaid C, Ryckman T, Menzies N, White R, Cohen T, Kendall E. Potential of Pan-Tuberculosis Treatment to Drive Emergence of Novel Resistance - Volume 30, Number 8—August 2024 - Emerging Infectious Diseases journal - CDC. Emerging Infectious Diseases 2024, 30: 1571-1579. PMID: 39043388, PMCID: PMC11286077, DOI: 10.3201/eid3008.240541.Peer-Reviewed Original ResearchMeSH KeywordsAntitubercular AgentsDrug Resistance, BacterialHumansMicrobial Sensitivity TestsMycobacterium tuberculosisRifampinTreatment OutcomeTuberculosisTuberculosis, Multidrug-ResistantConceptsResistance to novel drugsStandard of careAntimicrobial resistanceIncreasing prevalence of antimicrobial resistancePrevalence of antimicrobial resistanceIncreased prevalenceDrug susceptibility testingCompare treatment outcomesFirst-line drugsNovel drugsTB patient cohortTreatment regimensPatient cohortNew tuberculosisRegimensTreatment outcomesComponent drugsDrugCohortTuberculosisCDCUniversal useClinicCarePrevalence
2022
Budget impact of next-generation sequencing for diagnosis of TB drug resistance in Moldova
Cates L, Codreanu A, Ciobanu N, Fosburgh H, Allender C, Centner H, Engelthaler D, Crudu V, Cohen T, Menzies N. Budget impact of next-generation sequencing for diagnosis of TB drug resistance in Moldova. The International Journal Of Tuberculosis And Lung Disease 2022, 26: 963-969. PMID: 36163669, DOI: 10.5588/ijtld.22.0104.Peer-Reviewed Original ResearchMeSH KeywordsDrug ResistanceHigh-Throughput Nucleotide SequencingHumansMicrobial Sensitivity TestsMoldovaMycobacterium tuberculosisTuberculosis, Multidrug-ResistantConceptsPhenotypic drug susceptibility testingConventional phenotypic drug susceptibility testingTB drug resistanceDrug resistanceNext-generation sequencingTB treatment regimensNational TB ProgrammeDrug resistance testingMTB/RIFDrug susceptibility testingBudget impact analysisMajority of costsFeasibility of NGSTB programsTreatment regimensBudget impactSusceptibility testingRoutine useResistance testingStudy periodTesting volumeA crowd of BashTheBug volunteers reproducibly and accurately measure the minimum inhibitory concentrations of 13 antitubercular drugs from photographs of 96-well broth microdilution plates
Fowler P, Wright C, Spiers H, Zhu T, Baeten E, Hoosdally S, Cruz A, Roohi A, Kouchaki S, Walker T, Peto T, Miller G, Lintott C, Clifton D, Crook D, Walker A, 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, Earle S, Escuyer V, Ferrazoli L, Fowler P, Gao G, Gardy J, Gharbia S, Ghisi K, Ghodousi A, Cruz A, Grazian C, Guthrie J, He W, Hoffmann H, Hoosdally S, Hunt M, Iqbal Z, Ismail N, Jarrett L, Joseph L, Jou R, Kambli P, 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, Matias D, Meintjes G, Mendes F, Merker M, Mihalic M, Millard J, Miotto P, Mistry N, Moore D, Dreyer V, Chetty D, Musser K, Ngcamu D, Nhung H, Grandjean L, 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, Robinson E, Rodger G, Rodrigues C, Rodwell T, Roohi A, Santos-Lazaro D, Shah S, Kohl T, Smith G, Solano W, Spitaleri A, Supply P, Steyn A, Surve U, Tahseen S, Thuong N, Thwaites G, Todt K, Trovato A, Utpatel C, Van Rie A, Vijay S, Walker T, Walker A, Warren R, Werngren J, Groenheit R, Wijkander M, Wilkinson R, Wilson D, Wintringer P, Xiao Y, Yang Y, Yanlin Z, Yao S, Zhu B, Niemann S, O'Donnell M. A crowd of BashTheBug volunteers reproducibly and accurately measure the minimum inhibitory concentrations of 13 antitubercular drugs from photographs of 96-well broth microdilution plates. ELife 2022, 11: e75046. PMID: 35588296, PMCID: PMC9286738, DOI: 10.7554/elife.75046.Peer-Reviewed Original ResearchMeSH KeywordsAntitubercular AgentsHumansMicrobial Sensitivity TestsMycobacterium tuberculosisTuberculosisVolunteersConceptsMinimum inhibitory concentrationIncreasing prevalence of resistanceBroth microdilution platesAntibiotic susceptibility testingPrevalence of resistanceInhibitory concentrationPanel of antibioticsClinical samplesSusceptibility testingMicrodilution platesDilution platingTreatment outcomesIncreased prevalenceAntitubercular drugsAntibioticsInhibited growthDrugCitizen science platformRespiratory diseaseLaboratory scientistsVolunteersPre-determined concentrationsThe 2021 WHO catalogue of Mycobacterium tuberculosis complex mutations associated with drug resistance: a genotypic analysis
Walker T, Miotto P, Köser C, Fowler P, Knaggs J, Iqbal Z, Hunt M, Chindelevitch L, Farhat M, Cirillo D, Comas I, Posey J, Omar S, Peto T, Suresh A, Uplekar S, Laurent S, Colman R, Nathanson C, Zignol M, Walker A, Crook D, Ismail N, Rodwell T, Consortium T, Walker A, Steyn A, Lalvani A, Baulard A, Christoffels A, Mendoza-Ticona A, Trovato A, Skrahina A, Lachapelle A, Brankin A, Piatek A, Cruz A, Koch A, Cabibbe A, Spitaleri A, Brandao A, Chaiprasert A, Suresh A, Barbova A, Van Rie A, Ghodousi A, Bainomugisa A, Mandal A, Roohi A, Javid B, Zhu B, Letcher B, Rodrigues C, Nimmo C, NATHANSON C, Duncan C, Coulter C, Utpatel C, Liu C, Grazian C, Kong C, Köser C, Wilson D, Cirillo D, Matias D, Jorgensen D, Zimenkov D, Chetty D, Moore D, Clifton D, Crook D, van Soolingen D, Liu D, Kohlerschmidt D, Barreira D, Ngcamu D, Lazaro E, Kelly E, Borroni E, Roycroft E, Andre E, Böttger E, Robinson E, Menardo F, Mendes F, Jamieson F, Coll F, Gao G, Kasule G, Rossolini G, Rodger G, Smith E, Meintjes G, Thwaites G, Hoffmann H, Albert H, Cox H, Laurenson I, Comas I, Arandjelovic I, Barilar I, Robledo J, Millard J, Johnston J, Posey J, Andrews J, Knaggs J, Gardy J, Guthrie J, Taylor J, Werngren J, Metcalfe J, Coronel J, Shea J, Carter J, Pinhata J, Kus J, Todt K, Holt K, Nilgiriwala K, Ghisi K, Malone K, Faksri K, Musser K, Joseph L, Rigouts L, Chindelevitch L, Jarrett L, Grandjean L, Ferrazoli L, Rodrigues M, Farhat M, Schito M, Fitzgibbon M, Loembé M, Wijkander M, Ballif M, Rabodoarivelo M, Mihalic M, WILCOX M, Hunt M, ZIGNOL M, Merker M, Egger M, O'Donnell M, Caws M, Wu M, Whitfield M, Inouye M, Mansjö M, Thi M, Joloba M, Kamal S, Okozi N, ISMAIL N, Mistry N, Hoang N, Rakotosamimanana N, Paton N, Rancoita P, Miotto P, Lapierre P, Hall P, Tang P, Claxton P, Wintringer P, Keller P, Thai P, Fowler P, Supply P, Srilohasin P, Suriyaphol P, Rathod P, Kambli P, Groenheit R, Colman R, Ong R, Warren R, Wilkinson R, Diel R, Oliveira R, Khot R, Jou R, Tahseen S, Laurent S, Gharbia S, Kouchaki S, Shah S, Plesnik S, Earle S, Dunstan S, Hoosdally S, Mitarai S, Gagneux S, Omar S, Yao S, Lapierre S, Battaglia S, Niemann S, Pandey S, Uplekar S, Halse T, Cohen T, Cortes T, Prammananan T, Kohl T, Thuong N, Teo T, Peto T, Rodwell T, William T, Walker T, Rogers T, Surve U, Mathys V, Furió V, Cook V, Vijay S, Escuyer V, Dreyer V, Sintchenko V, Saphonn V, Solano W, Lin W, van Gemert W, He W, Yang Y, Zhao Y, Qin Y, Xiao Y, Hasan Z, Iqbal Z, Puyen Z. The 2021 WHO catalogue of Mycobacterium tuberculosis complex mutations associated with drug resistance: a genotypic analysis. The Lancet Microbe 2022, 3: e265-e273. PMID: 35373160, PMCID: PMC7612554, DOI: 10.1016/s2666-5247(21)00301-3.Peer-Reviewed Original ResearchMeSH KeywordsAntitubercular AgentsDrug ResistanceEthambutolMicrobial Sensitivity TestsMutationMycobacterium tuberculosisWorld Health OrganizationConceptsConsistent with susceptibilityAssociated with resistancePhenotypic resistanceDrug susceptibility testingSusceptibility testingPositive predictive valueAssociated with phenotypic resistanceWhole-genome sequencingPrevalence of phenotypic resistanceMolecular diagnosticsDrug susceptibility testing dataUniversal drug susceptibility testingMutations associated with drug resistanceOdds ratioSusceptibility testing dataDrug resistance predictionImplementation of molecular diagnosticsBinary phenotypesPredictive valueGene approachMTBC isolatesUnique mutationsFisher's exact testAnti-tuberculosis drugsGenotype analysis
2020
Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones
Clarelli F, Palmer A, Singh B, Storflor M, Lauksund S, Cohen T, Abel S, Wiesch P. Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones. PLOS Computational Biology 2020, 16: e1008106. PMID: 32797079, PMCID: PMC7449454, DOI: 10.1371/journal.pcbi.1008106.Peer-Reviewed Original ResearchConceptsDose levelsAntibiotic efficacyDrug-target bindingDose-response relationshipBeta-lactams ampicillinTime-kill curvesDose-response curveNovel resistance mutationsMeasurable biochemical parametersClinical isolatesAntibiotic dose-response curvesAntibiotic actionResistance mutationsBiochemical parameters
2019
Transmissibility and potential for disease progression of drug resistant Mycobacterium tuberculosis: prospective cohort study
Becerra MC, Huang CC, Lecca L, Bayona J, Contreras C, Calderon R, Yataco R, Galea J, Zhang Z, Atwood S, Cohen T, Mitnick CD, Farmer P, Murray M. Transmissibility and potential for disease progression of drug resistant Mycobacterium tuberculosis: prospective cohort study. The BMJ 2019, 367: l5894. PMID: 31649017, PMCID: PMC6812583, DOI: 10.1136/bmj.l5894.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedAntitubercular AgentsChildChild, PreschoolContact TracingDisease ProgressionFemaleFollow-Up StudiesHumansIncidenceInfantInfant, NewbornIsoniazidKaplan-Meier EstimateMaleMicrobial Sensitivity TestsMiddle AgedMycobacterium tuberculosisPeruProspective StudiesRifampinSputumTuberculin TestTuberculosis, Multidrug-ResistantTuberculosis, PulmonaryYoung AdultConceptsDrug-sensitive tuberculosisMultidrug-resistant tuberculosisHousehold contactsProspective cohort studySensitive tuberculosisTuberculosis infectionResistant tuberculosisCohort studyTuberculosis diseaseHigh riskDrug-resistant Mycobacterium tuberculosisIncident tuberculosis diseasePhenotypic drug resistanceDistrict Health CenterResistant Mycobacterium tuberculosisDrug-susceptible strainsDrug resistance profilesActive diseasePulmonary tuberculosisDisease progressionGuideline producersHealth centersIndex patientsEffective treatmentPatients
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 useRegimensUsing Chemical Reaction Kinetics to Predict Optimal Antibiotic Treatment Strategies
Wiesch P, Clarelli F, Cohen T. Using Chemical Reaction Kinetics to Predict Optimal Antibiotic Treatment Strategies. PLOS Computational Biology 2017, 13: e1005321. PMID: 28060813, PMCID: PMC5257006, DOI: 10.1371/journal.pcbi.1005321.Peer-Reviewed Original ResearchMeSH KeywordsAnti-Bacterial AgentsBacterial InfectionsBacterial Physiological PhenomenaComputational BiologyDrug Resistance, BacterialHumansKineticsMicrobial Sensitivity TestsModels, BiologicalConceptsOptimal dosingTreatment strategiesOnset of actionAntibiotic treatment strategiesAntibiotic concentrationsOptimal therapyFrequent dosingHigh dosesBacterial infectionsDrug concentrationsDosingBacterial replicationAntibioticsPhysiological fluctuationsAntibiotic effectCellsHIVBacterial growthTherapyCancerInfectionMalariaDoses
2016
Second line drug susceptibility testing to inform the treatment of rifampin-resistant tuberculosis: a quantitative perspective
Kendall EA, Cohen T, Mitnick CD, Dowdy DW. Second line drug susceptibility testing to inform the treatment of rifampin-resistant tuberculosis: a quantitative perspective. International Journal Of Infectious Diseases 2016, 56: 185-189. PMID: 28007660, PMCID: PMC5576040, DOI: 10.1016/j.ijid.2016.12.010.Peer-Reviewed Original ResearchMeSH KeywordsAntitubercular AgentsDirectly Observed TherapyHumansMicrobial Sensitivity TestsMycobacterium tuberculosisPrevalenceRifampinTreatment OutcomeTuberculosis, Multidrug-ResistantConceptsSecond-line drug susceptibility testingRifampin-resistant tuberculosisDrug susceptibility testingSecond-line drug resistanceDrug resistanceSusceptibility testingHigh-burden settingsSecond-line drugsDrug-resistant tuberculosisEffective regimensTreatment failureTreatment outcomesSmall incremental costEpidemiologic benefitsResistance amplificationPatientsTuberculosisIncremental costMost settingsWidespread implementationSettingRegimensPrevalenceRapid Drug Susceptibility Testing of Drug-Resistant Mycobacterium tuberculosis Isolates Directly from Clinical Samples by Use of Amplicon Sequencing: a Proof-of-Concept Study
Colman RE, Anderson J, Lemmer D, Lehmkuhl E, Georghiou SB, Heaton H, Wiggins K, Gillece JD, Schupp JM, Catanzaro DG, Crudu V, Cohen T, Rodwell TC, Engelthaler DM. Rapid Drug Susceptibility Testing of Drug-Resistant Mycobacterium tuberculosis Isolates Directly from Clinical Samples by Use of Amplicon Sequencing: a Proof-of-Concept Study. Journal Of Clinical Microbiology 2016, 54: 2058-2067. PMID: 27225403, PMCID: PMC4963505, DOI: 10.1128/jcm.00535-16.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedAged, 80 and overChildChild, PreschoolFemaleGenotyping TechniquesHigh-Throughput Nucleotide SequencingHumansMaleMicrobial Sensitivity TestsMiddle AgedMycobacterium tuberculosisPharmaceutical PreparationsPilot ProjectsSequence Analysis, DNASpecimen HandlingSputumTime FactorsYoung AdultConceptsDrug-resistant tuberculosisPatient sputum samplesDrug resistance profilesSputum samplesDrug-resistant Mycobacterium tuberculosis isolatesPhenotypic drug susceptibility testing resultsDrug susceptibility testing resultsEvidence-based treatment plansResistance profilesMajor global health concernRapid drug susceptibility testingMycobacterium tuberculosis isolatesNext-generation sequencingClinical samplesAmplification of resistanceDrug susceptibility testingTargeted Next-Generation SequencingMycobacterium tuberculosis DNAGlobal health concernSusceptibility testing resultsSame clinical samplePhenotypic DSTInfectious causesTreatment outcomesTuberculosis isolatesAssessing the utility of Xpert® MTB/RIF as a screening tool for patients admitted to medical wards in South Africa
Heidebrecht CL, Podewils LJ, Pym AS, Cohen T, Mthiyane T, Wilson D. Assessing the utility of Xpert® MTB/RIF as a screening tool for patients admitted to medical wards in South Africa. Scientific Reports 2016, 6: 19391. PMID: 26786396, PMCID: PMC4726405, DOI: 10.1038/srep19391.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedAged, 80 and overCoinfectionDrug Resistance, BacterialFemaleHIV InfectionsHumansMaleMass ScreeningMicrobial Sensitivity TestsMiddle AgedMycobacterium tuberculosisNucleic Acid Amplification TechniquesReproducibility of ResultsRifampinSouth AfricaTuberculosis, Multidrug-ResistantYoung AdultConceptsChest X-rayMTB/RIFMedical wardsScreening toolAdditional TB casesInfection control actionsUtility of GeneXpertTB/HIVConsecutive adult patientsProportion of patientsRifampicin-resistant tuberculosisDrug-resistant tuberculosisLarge public hospitalTB diseaseAdult patientsStandard careTB casesTB screeningMedical admissionsMedical chartsHospital inpatientsSputum specimensGeneXpertPatientsRifampicin resistance
2015
Classic reaction kinetics can explain complex patterns of antibiotic action
Abel Zur Wiesch P, Abel S, Gkotzis S, Ocampo P, Engelstädter J, Hinkley T, Magnus C, Waldor MK, Udekwu K, Cohen T. Classic reaction kinetics can explain complex patterns of antibiotic action. Science Translational Medicine 2015, 7: 287ra73. PMID: 25972005, PMCID: PMC4554720, DOI: 10.1126/scitranslmed.aaa8760.Peer-Reviewed Original ResearchMeSH KeywordsAnti-Bacterial AgentsEscherichia coliKineticsMicrobial Sensitivity TestsVibrio choleraeConceptsOptimal dosing strategiesAntibiotic treatment strategiesTime-kill curvesAntibiotic effectGrowth suppressionCell formationChemotherapeutic regimensClinical trialsDosing strategiesTreatment strategiesTuberculosis therapyPersister cell formationDrug effectsBacterial infectionsVariety of antibiotics
2014
Impact and cost-effectiveness of current and future tuberculosis diagnostics: the contribution of modelling
Dowdy DW, Houben R, Cohen T, Pai M, Cobelens F, Vassall A, Menzies NA, Gomez GB, Langley I, Squire SB, White R, for the TB MAC meeting participants. Impact and cost-effectiveness of current and future tuberculosis diagnostics: the contribution of modelling. The International Journal Of Tuberculosis And Lung Disease 2014, 18: 1012-1018. PMID: 25189546, PMCID: PMC4436823, DOI: 10.5588/ijtld.13.0851.Peer-Reviewed Original ResearchHigh Rates of Potentially Infectious Tuberculosis and Multidrug-Resistant Tuberculosis (MDR-TB) among Hospital Inpatients in KwaZulu Natal, South Africa Indicate Risk of Nosocomial Transmission
Bantubani N, Kabera G, Connolly C, Rustomjee R, Reddy T, Cohen T, Pym AS. High Rates of Potentially Infectious Tuberculosis and Multidrug-Resistant Tuberculosis (MDR-TB) among Hospital Inpatients in KwaZulu Natal, South Africa Indicate Risk of Nosocomial Transmission. PLOS ONE 2014, 9: e90868. PMID: 24625669, PMCID: PMC3953209, DOI: 10.1371/journal.pone.0090868.Peer-Reviewed Original ResearchConceptsMultidrug-resistant tuberculosisCurrent coughXDR-TBNosocomial transmissionInfectious tuberculosisInfectious TBHospital inpatientsClinical dataAnti-tuberculosis drug susceptibility testingMDR/XDR-TBPrevious TB treatmentDrug susceptibility testingTB treatmentHospital admissionMale sexTB inpatientsScotland hospitalsInpatient settingHigh burdenSputum samplesInfection controlInpatientsKwaZulu-NatalCoughDrug resistance
2013
Treatment outcome of multi-drug resistant tuberculosis in the United Kingdom: retrospective-prospective cohort study from 2004 to 2007.
Anderson LF, Tamne S, Watson JP, Cohen T, Mitnick C, Brown T, Drobniewski F, Abubakar I. Treatment outcome of multi-drug resistant tuberculosis in the United Kingdom: retrospective-prospective cohort study from 2004 to 2007. Eurosurveillance 2013, 18 PMID: 24128699, DOI: 10.2807/1560-7917.es2013.18.40.20601.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedAntibiotics, AntitubercularAntitubercular AgentsChildChild, PreschoolDrug Resistance, Multiple, BacterialFemaleFluoroquinolonesHumansInfantInfant, NewbornMaleMicrobial Sensitivity TestsMiddle AgedMycobacterium tuberculosisPatient ComplianceProspective StudiesRetrospective StudiesSurveys and QuestionnairesTreatment OutcomeTuberculosis, Multidrug-ResistantUnited KingdomYoung AdultConceptsMultidrug-resistant tuberculosisTreatment outcomesRetrospective-prospective cohort studyWorld Health Organization targetMulti-drug resistant tuberculosisMDR-TB patientsMDR-TB treatmentMDR-TB casesFurther drug resistanceTreatment completion ratesSuccessful treatment outcomeUnited Kingdom guidelinesIndividualised regimensCohort studyMonths treatmentResistant tuberculosisTreatment completionInfectious casesDrug resistanceDrug sensitivitySuccessful outcomeOrganization targetBacteriostatic drugsOutcomesTreatmentBayesian 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
Linking Surveillance with Action against Drug-Resistant Tuberculosis
Cohen T, Manjourides J, Hedt-Gauthier B. Linking Surveillance with Action against Drug-Resistant Tuberculosis. American Journal Of Respiratory And Critical Care Medicine 2012, 186: 399-401. PMID: 22592806, PMCID: PMC3443807, DOI: 10.1164/rccm.201203-0394pp.Peer-Reviewed Original ResearchMeSH KeywordsAntitubercular AgentsDeveloping CountriesGlobal HealthHealth PolicyHealth SurveysHumansMicrobial Sensitivity TestsPopulation SurveillanceTuberculosis, Multidrug-ResistantConceptsMultidrug-resistant tuberculosisForms of TBDrug-resistant TBManagement of patientsDrug-resistant tuberculosisSecond-line drugsEffective public health responseDrug susceptibility testingPublic health responseQuality-assured treatmentMDRTB treatmentIncident casesHigh burdenProgrammatic dataHealth responseDrug resistanceSusceptibility testingImproved surveillance methodsPopulation subgroupsSurveillance methodsSurveillance activitiesTuberculosisClear roleTreatmentRecent global estimatesOutcomes among tuberculosis patients with isoniazid resistance in Georgia, 2007-2009.
Gegia M, Cohen T, Kalandadze I, Vashakidze L, Furin J. Outcomes among tuberculosis patients with isoniazid resistance in Georgia, 2007-2009. The International Journal Of Tuberculosis And Lung Disease 2012, 16: 812-6. PMID: 22507372, PMCID: PMC3786434, DOI: 10.5588/ijtld.11.0637.Peer-Reviewed Original ResearchMeSH KeywordsAdultAntitubercular AgentsChi-Square DistributionDrug Resistance, BacterialDrug Therapy, CombinationEthambutolFemaleGeorgia (Republic)HumansIsoniazidLogistic ModelsMaleMicrobial Sensitivity TestsMiddle AgedMultivariate AnalysisMycobacterium tuberculosisPredictive Value of TestsPyrazinamideRetrospective StudiesRifampinRisk AssessmentRisk FactorsSputumTime FactorsTreatment OutcomeTuberculosisConceptsPulmonary tuberculosisPrevious treatmentOutcomes of patientsPulmonary TB patientsSubset of patientsRetrospective record reviewForms of tuberculosisFirst-line drugsHigh TB ratesDrug-resistant formsLower ratesLow tuberculosisTB patientsTuberculosis patientsOptimal management strategyTreatment regimenRecord reviewTB ratesTreatment successTreatment outcomesCountry of GeorgiaPatientsIsoniazid resistanceTuberculosisDrug resistanceIdentifying multidrug resistant tuberculosis transmission hotspots using routinely collected data
Manjourides J, Lin HH, Shin S, Jeffery C, Contreras C, Santa Cruz J, Jave O, Yagui M, Asencios L, Pagano M, Cohen T. Identifying multidrug resistant tuberculosis transmission hotspots using routinely collected data. Tuberculosis 2012, 92: 273-279. PMID: 22401962, PMCID: PMC3323731, DOI: 10.1016/j.tube.2012.02.003.Peer-Reviewed Original ResearchConceptsDrug sensitivity testTransmission hotspotsRetreatment casesDrug-resistant tuberculosis epidemicRisk of MDRTime of diagnosisDrug-resistant diseaseTB casesResistant diseaseTuberculosis epidemicHigh riskUntreated casesProgrammatic dataMDRTBRiskMDRHigh levelsTargeted investigationGeographic areasCasesDiseaseDiagnosisSensitivity testsMultidrug Resistance Among New Tuberculosis Cases
Hedt BL, van Leth F, Zignol M, Cobelens F, van Gemert W, Nhung NV, Lyepshina S, Egwaga S, Cohen T. Multidrug Resistance Among New Tuberculosis Cases. Epidemiology 2012, 23: 293-300. PMID: 22249242, PMCID: PMC3276714, DOI: 10.1097/ede.0b013e3182459455.Peer-Reviewed Original Research