2023
Machine learning to predict bacteriologic confirmation of Mycobacterium tuberculosis in infants and very young children
Smith J, Milligan K, McCarthy K, Mchembere W, Okeyo E, Musau S, Okumu A, Song R, Click E, Cain K. Machine learning to predict bacteriologic confirmation of Mycobacterium tuberculosis in infants and very young children. PLOS Digital Health 2023, 2: e0000249. PMID: 37195976, PMCID: PMC10191346, DOI: 10.1371/journal.pdig.0000249.Peer-Reviewed Original ResearchMicrobial confirmationTB diseaseYoung childrenNoninvasive procedureLarge prospective cohortCases of tuberculosisChest X-rayDiagnosis of tuberculosisClinical decision makingBacteriologic confirmationHousehold contactsTB infectionProspective cohortRadiologic factorsPaucibacillary natureClinical similaritiesDiagnostic cohortClinical diseaseChildhood diseasesInvasive proceduresNovel biomarkersTuberculosisClinical researchM. tuberculosisDisease
2022
Zambia Assessment of Tuberculosis (TB) and HIV in the Mines (ZATHIM): implications for programs and policies
Podewils LJ, Long EF, Fuller TJ, Mwakazanga D, Kapungu K, Tembo M, Mwanza S, Curran KG, Smith JP, Tobias JL, Kasongo W. Zambia Assessment of Tuberculosis (TB) and HIV in the Mines (ZATHIM): implications for programs and policies. BMC Public Health 2022, 22: 791. PMID: 35439984, PMCID: PMC9018205, DOI: 10.1186/s12889-022-13053-8.Peer-Reviewed Original ResearchConceptsHealthcare workersTB symptomsMore TB symptomsFocus group discussionsDiagnosis of tuberculosisTB diseaseTB careTB statusMixed-method evaluationAppropriate careKey populationsReporting 2TuberculosisKAP surveyAssess knowledgeHigh-quality healthcare servicesHealthcare servicesCareHealthy livingHIVHigh rateSymptomsDiagnosisMajor barrierCurrent evaluation