2024
Impact 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-R
2023
Global 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 countries
2022
Predicting resistance to fluoroquinolones among patients with rifampicin-resistant tuberculosis using machine learning methods
You S, Chitwood MH, Gunasekera KS, Crudu V, Codreanu A, Ciobanu N, Furin J, Cohen T, Warren JL, Yaesoubi R. Predicting resistance to fluoroquinolones among patients with rifampicin-resistant tuberculosis using machine learning methods. PLOS Digital Health 2022, 1: e0000059. PMID: 36177394, PMCID: PMC9518704, DOI: 10.1371/journal.pdig.0000059.Peer-Reviewed Original ResearchDrug susceptibility testXpert MTB/RIFMachine learning-based modelsLearning-based modelsMachine learning methodsRifampicin-resistant tuberculosisTime of diagnosisRifampin-resistant tuberculosisMTB/RIFNeural network modelLearning methodsNetwork modelMulti-drug resistant tuberculosisNational TB surveillanceDrug-resistant tuberculosisOptimism-corrected areaSelection of antibioticsAnti-TB agentsDistrict-level prevalenceLow-resource settingsPatient characteristicsResistant tuberculosisTB surveillanceAppropriate treatmentDST results
2020
Ongoing challenges to understanding multidrug- and rifampicin-resistant tuberculosis in children versus adults
McQuaid CF, Cohen T, Dean AS, Houben RMGJ, Knight GM, Zignol M, White RG. Ongoing challenges to understanding multidrug- and rifampicin-resistant tuberculosis in children versus adults. European Respiratory Journal 2020, 57: 2002504. PMID: 32855219, DOI: 10.1183/13993003.02504-2020.Peer-Reviewed Original ResearchConceptsMDR/RR-TBRR-TBOdds ratioPediatric TB casesGlobal TB epidemicRifampicin-resistant tuberculosisPopulation-representative surveyTB casesBurden countriesTB epidemicMost settingsTuberculosisDrug resistanceTransmission riskCountry-specific estimatesAdultsChildrenAgeFurther investigationMultidrugOddsFormer Soviet Union countriesSufficient dataSettingSoviet Union countries
2016
Assessing Local Risk of Rifampicin-Resistant Tuberculosis in KwaZulu-Natal, South Africa Using Lot Quality Assurance Sampling
Heidebrecht CL, Podewils LJ, Pym A, Mthiyane T, Cohen T. Assessing Local Risk of Rifampicin-Resistant Tuberculosis in KwaZulu-Natal, South Africa Using Lot Quality Assurance Sampling. PLOS ONE 2016, 11: e0153143. PMID: 27050561, PMCID: PMC4822784, DOI: 10.1371/journal.pone.0153143.Peer-Reviewed Original ResearchConceptsIncident TB casesTB casesResistant TBDrug-resistant TB casesRifampicin-resistant tuberculosisMultidrug-resistant tuberculosisDrug susceptibility testingRIF-resistant TBLot Quality Assurance SamplingQuality assurance samplingResistant diseaseHigh burdenRIF resistanceKwaZulu-NatalSusceptibility testingHigh-risk areasTuberculosisRiskGreater proportionBurdenTBDiseaseGeographic heterogeneityCasesRifampicinAssessing 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