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Gunasekera KS, Marcy O, Muñoz J, Lopez-Varela E, Sekadde MP, Franke MF, Bonnet M, Ahmed S, Amanullah F, Anwar A, Augusto O, Aurilio RB, Banu S, Batool I, Brands A, Cain KP, Carratalá-Castro L, Caws M, Click ES, Cranmer LM, García-Basteiro AL, Hesseling AC, Huynh J, Kabir S, Lecca L, Mandalakas A, Mavhunga F, Myint AA, Myo K, Nampijja D, Nicol MP, Orikiriza P, Palmer M, Sant'Anna CC, Siddiqui SA, Smith JP, Song R, Thuong Thuong NT, Ung V, van der Zalm MM, Verkuijl S, Viney K, Walters EG, Warren JL, Zar HJ, Marais BJ, Graham SM, Debray TPA, Cohen T, Seddon JA. Development of treatment-decision algorithms for children evaluated for pulmonary tuberculosis: an individual participant data meta-analysis. Lancet Child Adolesc Health 2023; 7:336-346. [PMID: 36924781 PMCID: PMC10127218 DOI: 10.1016/s2352-4642(23)00004-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/06/2023] [Accepted: 01/10/2023] [Indexed: 03/14/2023]
Abstract
BACKGROUND Many children with pulmonary tuberculosis remain undiagnosed and untreated with related high morbidity and mortality. Recent advances in childhood tuberculosis algorithm development have incorporated prediction modelling, but studies so far have been small and localised, with limited generalisability. We aimed to evaluate the performance of currently used diagnostic algorithms and to use prediction modelling to develop evidence-based algorithms to assist in tuberculosis treatment decision making for children presenting to primary health-care centres. METHODS For this meta-analysis, we identified individual participant data from a WHO public call for data on the management of tuberculosis in children and adolescents and referral from childhood tuberculosis experts. We included studies that prospectively recruited consecutive participants younger than 10 years attending health-care centres in countries with a high tuberculosis incidence for clinical evaluation of pulmonary tuberculosis. We collated individual participant data including clinical, bacteriological, and radiological information and a standardised reference classification of pulmonary tuberculosis. Using this dataset, we first retrospectively evaluated the performance of several existing treatment-decision algorithms. We then used the data to develop two multivariable prediction models that included features used in clinical evaluation of pulmonary tuberculosis-one with chest x-ray features and one without-and we investigated each model's generalisability using internal-external cross-validation. The parameter coefficient estimates of the two models were scaled into two scoring systems to classify tuberculosis with a prespecified sensitivity target. The two scoring systems were used to develop two pragmatic, treatment-decision algorithms for use in primary health-care settings. FINDINGS Of 4718 children from 13 studies from 12 countries, 1811 (38·4%) were classified as having pulmonary tuberculosis: 541 (29·9%) bacteriologically confirmed and 1270 (70·1%) unconfirmed. Existing treatment-decision algorithms had highly variable diagnostic performance. The scoring system derived from the prediction model that included clinical features and features from chest x-ray had a combined sensitivity of 0·86 [95% CI 0·68-0·94] and specificity of 0·37 [0·15-0·66] against a composite reference standard. The scoring system derived from the model that included only clinical features had a combined sensitivity of 0·84 [95% CI 0·66-0·93] and specificity of 0·30 [0·13-0·56] against a composite reference standard. The scoring system from each model was placed after triage steps, including assessment of illness acuity and risk of poor tuberculosis-related outcomes, to develop treatment-decision algorithms. INTERPRETATION We adopted an evidence-based approach to develop pragmatic algorithms to guide tuberculosis treatment decisions in children, irrespective of the resources locally available. This approach will empower health workers in primary health-care settings with high tuberculosis incidence and limited resources to initiate tuberculosis treatment in children to improve access to care and reduce tuberculosis-related mortality. These algorithms have been included in the operational handbook accompanying the latest WHO guidelines on the management of tuberculosis in children and adolescents. Future prospective evaluation of algorithms, including those developed in this work, is necessary to investigate clinical performance. FUNDING WHO, US National Institutes of Health.
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Affiliation(s)
- Kenneth S Gunasekera
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA.
| | - Olivier Marcy
- Inserm UMR1219, Institut de Recherche pour le Développement EMR 271, GHiGS, University of Bordeaux, Bordeaux, France
| | - Johanna Muñoz
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Elisa Lopez-Varela
- ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain; Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique; Desmond Tutu TB Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa
| | | | - Molly F Franke
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
| | - Maryline Bonnet
- University of Montpellier, TransVIHMI, Institut de Recherche pour le Développement, Inserm, Montpellier, France; Epicentre, Mbarara, Uganda
| | - Shakil Ahmed
- Department of Paediatrics, Dhaka Medical College Hospital, Dhaka, Bangladesh
| | - Farhana Amanullah
- Indus Hospital & Health Network, Karachi, Pakistan; The Aga Khan University Hospital, Karachi, Pakistan
| | - Aliya Anwar
- Indus Hospital & Health Network, Karachi, Pakistan
| | - Orvalho Augusto
- Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique
| | - Rafaela Baroni Aurilio
- Instituto de Puericultura e Pediatria Martagao Gesteira, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Sayera Banu
- Programme on Emerging Infections, Infectious Disease Division, icddr,b, Dhaka, Bangladesh
| | - Iraj Batool
- Indus Hospital & Health Network, Karachi, Pakistan
| | | | - Kevin P Cain
- US Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Lucía Carratalá-Castro
- ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain; Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique
| | - Maxine Caws
- Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK; Birat Nepal Medical Trust, Lazmipat, Kathmandu, Nepal
| | - Eleanor S Click
- US Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Lisa M Cranmer
- Department of Pediatrics, Emory School of Medicine, Atlanta, GA, USA; Department of Epidemiology, Emory Rollins School of Public Health, Atlanta, GA, USA; Children's Healthcare of Atlanta, Atlanta, GA, USA
| | - Alberto L García-Basteiro
- ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain; Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique; Centro de Investigación Biomédica en Red de Enfermedades Infecciosas, Barcelona, Spain
| | - Anneke C Hesseling
- Desmond Tutu TB Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa
| | - Julie Huynh
- Oxford University Clinical Research Unit, Centre for Tropical Diseases, Ho Chi Minh City, Viet Nam; Nuffield Department of Medicine, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Senjuti Kabir
- Programme on Emerging Infections, Infectious Disease Division, icddr,b, Dhaka, Bangladesh
| | - Leonid Lecca
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA; Socios En Salud Surcursal Perú, Lima, Perú
| | - Anna Mandalakas
- Global TB Program, Baylor College of Medicine and Texas Children's Hospital, Houston, TX, USA; Clinical Infectious Disease Group, German Center for Infectious Research, Clinical TB Unit, Research Center Borstel, Borstel, Germany
| | | | - Aye Aye Myint
- Department of Paediatrics, University of Medicine, Mandalay, Myanmar
| | - Kyaw Myo
- Department of Paediatrics, University of Medicine, Magway, Myanmar
| | - Dorah Nampijja
- Department of Paediatrics, Mbarara University of Science and Technology, Mbarara, Uganda
| | - Mark P Nicol
- Division of Infection and Immunity, Department of Biomedical Sciences, University of Western Australia, Perth, WA, Australia
| | - Patrick Orikiriza
- Epicentre, Mbarara, Uganda; Department of Microbiology, Division of Basic Medical Sciences, School of Medicine, University of Global Health Equity, Kigali, Rwanda
| | - Megan Palmer
- Desmond Tutu TB Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa
| | | | - Sara Ahmed Siddiqui
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA; Indus Hospital & Health Network, Karachi, Pakistan
| | - Jonathan P Smith
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA; US Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Rinn Song
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA; Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford, UK
| | - Nguyen Thuy Thuong Thuong
- Oxford University Clinical Research Unit, Centre for Tropical Diseases, Ho Chi Minh City, Viet Nam; Nuffield Department of Medicine, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Vibol Ung
- University of Health Sciences, Phnom Penh, Cambodia; National Pediatric Hospital, Phnom Penh, Cambodia
| | - Marieke M van der Zalm
- Desmond Tutu TB Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa
| | | | - Kerri Viney
- Global Tuberculosis Programme, WHO, Geneva, Switzerland; School of Public Health, University of Sydney, Sydney, NSW, Australia
| | - Elisabetta G Walters
- Desmond Tutu TB Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa; Directorate of Integrated Laboratory Medicine, Institute of Human Genetics, Newcastle upon Tyne NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Joshua L Warren
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Heather J Zar
- Department of Paediatrics and Child Health, Red Cross Children's Hospital, and SA-MRC Unit on Child and Adolescent Health, University of Cape Town, Cape Town, South Africa
| | - Ben J Marais
- The Children's Hospital at Westmead Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Stephen M Graham
- Department of Paediatrics and Murdoch Children's Research Institute, Royal Children's Hospital, University of Melbourne, Melbourne, VIC, Australia; Burnet Institute, Melbourne, VIC, Australia
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | - James A Seddon
- Desmond Tutu TB Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa; Department of Infectious Diseases, Imperial College London, London, UK
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Palmer M, Gunasekera KS, van der Zalm MM, Morrison J, Simon Schaaf H, Goussard P, Hesseling AC, Walters E, Seddon JA. The Diagnostic Accuracy of Chest Radiographic Features for Pediatric Intrathoracic Tuberculosis. Clin Infect Dis 2022; 75:1014-1021. [PMID: 35015857 PMCID: PMC9522424 DOI: 10.1093/cid/ciac011] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Indexed: 01/09/2023] Open
Abstract
INTRODUCTION The chest radiograph (CR) remains a key tool in the diagnosis of pediatric tuberculosis (TB). In children with presumptive intrathoracic TB, we aimed to identify CR features that had high specificity for, and were strongly associated with, bacteriologically confirmed TB. METHODS We analyzed CR data from children with presumptive intrathoracic TB prospectively enrolled in a cohort study in a high-TB burden setting and who were classified using standard clinical case definitions as "confirmed," "unconfirmed," or "unlikely" TB. We report the CR features and inter-reader agreement between expert readers who interpreted the CRs. We calculated the sensitivity and specificity of the CR features with at least moderate inter-reader agreement and analyzed the relationship between these CR
features and the classification of TB in a multivariable regression model. RESULTS Of features with at least moderate inter-reader agreement, enlargement of perihilar and/or paratracheal lymph nodes, bronchial deviation/compression, cavities, expansile pneumonia, and pleural effusion had a specificity of > 90% for confirmed TB, compared with unlikely TB. Enlargement of perihilar (adjusted odds ratio [aOR]: 6.6; 95% confidence interval [CI], 3.80-11.72) and/or paratracheal lymph nodes (aOR: 5.14; 95% CI, 2.25-12.58), bronchial deviation/compression (aOR: 6.22; 95% CI, 2.70-15.69), pleural effusion (aOR: 2.27; 95% CI, 1.04-4.78), and cavities (aOR: 7.45; 95% CI, 3.38-17.45) were associated with confirmed TB in the multivariate regression model, whereas alveolar opacification (aOR: 1.16; 95% CI, .76-1.77) and expansile pneumonia (aOR: 4.16; 95% CI, .93-22.34) were not. CONCLUSIONS In children investigated for intrathoracic TB enlargement of perihilar or paratracheal lymph nodes, bronchial compression/deviation, pleural effusion, or cavities on CR strongly support the diagnosis.
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Affiliation(s)
- Megan Palmer
- Desmond Tutu TB Centre, Department of Pediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Kenneth S Gunasekera
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA
| | - Marieke M van der Zalm
- Desmond Tutu TB Centre, Department of Pediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Julie Morrison
- Department of Pediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - H Simon Schaaf
- Desmond Tutu TB Centre, Department of Pediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- Department of Pediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Pierre Goussard
- Department of Pediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Anneke C Hesseling
- Desmond Tutu TB Centre, Department of Pediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Elisabetta Walters
- Desmond Tutu TB Centre, Department of Pediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- Great North Children’s Hospital, Newcastle upon Tyne Hospitals Trust, Newcastle, United Kingdom
| | - James A Seddon
- Desmond Tutu TB Centre, Department of Pediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- Department of Infectious Diseases, Imperial College London, London, United Kingdom
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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 Digit Health 2022; 1:e0000059. [PMID: 36177394 PMCID: PMC9518704 DOI: 10.1371/journal.pdig.0000059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Background Limited access to drug-susceptibility tests (DSTs) and delays in receiving DST results are challenges for timely and appropriate treatment of multi-drug resistant tuberculosis (TB) in many low-resource settings. We investigated whether data collected as part of routine, national TB surveillance could be used to develop predictive models to identify additional resistance to fluoroquinolones (FLQs), a critical second-line class of anti-TB agents, at the time of diagnosis with rifampin-resistant TB. Methods and findings We assessed three machine learning-based models (logistic regression, neural network, and random forest) using information from 540 patients with rifampicin-resistant TB, diagnosed using Xpert MTB/RIF and notified in the Republic of Moldova between January 2018 and December 2019. The models were trained to predict the resistance to FLQs based on demographic and TB clinical information of patients and the estimated district-level prevalence of resistance to FLQs. We compared these models based on the optimism-corrected area under the receiver operating characteristic curve (OC-AUC-ROC). The OC-AUC-ROC of all models were statistically greater than 0.5. The neural network model, which utilizes twelve features, performed best and had an estimated OC-AUC-ROC of 0.87 (0.83,0.91), which suggests reasonable discriminatory power. A limitation of our study is that our models are based only on data from the Republic of Moldova and since not externally validated, the generalizability of these models to other populations remains unknown. Conclusions Models trained on data from phenotypic surveillance of drug-resistant TB can predict resistance to FLQs based on patient characteristics at the time of diagnosis with rifampin-resistant TB using Xpert MTB/RIF, and information about the local prevalence of resistance to FLQs. These models may be useful for informing the selection of antibiotics while awaiting results of DSTs.
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Affiliation(s)
- Shiying You
- Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut, United States of America
- Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Melanie H. Chitwood
- Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, United States of America
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Kenneth S. Gunasekera
- Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, United States of America
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Valeriu Crudu
- Phthisiopneumology Institute, Chisinau, Republic of Moldova
| | | | - Nelly Ciobanu
- Phthisiopneumology Institute, Chisinau, Republic of Moldova
| | - Jennifer Furin
- Department of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Ted Cohen
- Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, United States of America
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Joshua L. Warren
- Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, United States of America
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Reza Yaesoubi
- Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut, United States of America
- Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut, United States of America
- * E-mail:
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Gunasekera KS, Vonasek B, Oliwa J, Triasih R, Lancioni C, Graham SM, Seddon JA, Marais BJ. Diagnostic Challenges in Childhood Pulmonary Tuberculosis-Optimizing the Clinical Approach. Pathogens 2022; 11:pathogens11040382. [PMID: 35456057 PMCID: PMC9032883 DOI: 10.3390/pathogens11040382] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 02/08/2022] [Accepted: 03/14/2022] [Indexed: 12/25/2022] Open
Abstract
The management of childhood tuberculosis (TB) is hampered by the low sensitivity and limited accessibility of microbiological testing. Optimizing clinical approaches is therefore critical to close the persistent gaps in TB case detection and prevention necessary to realize the child mortality targets of the End TB Strategy. In this review, we provide practical guidance summarizing the evidence and guidelines describing the use of symptoms and signs in decision making for children being evaluated for either TB preventive treatment (TPT) or TB disease treatment in high-TB incidence settings. Among at-risk children being evaluated for TPT, a symptom screen may be used to differentiate children who require further investigation for TB disease before receiving TPT. For symptomatic children being investigated for TB disease, an algorithmic approach can inform which children should receive TB treatment, even in the absence of imaging or microbiological confirmation. Though clinical approaches have limitations in accuracy, they are readily available and can provide valuable guidance for decision making in resource-limited settings to increase treatment access. We discuss the trade-offs in using them to make TB treatment decisions.
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Affiliation(s)
- Kenneth S. Gunasekera
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
- Correspondence:
| | - Bryan Vonasek
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI 53726, USA;
| | - Jacquie Oliwa
- Health Services Unit, KEMRI-Wellcome Trust Research Programme, Nairobi P.O. Box 43640-00100, Kenya;
- Department of Paediatrics and Child Health, School of Medicine, University of Nairobi, Nairobi P.O. Box 30197-00100, Kenya
| | - Rina Triasih
- Department of Pediatrics, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada/Dr. Sardjito Hospital, Yogyakarta 55284, Indonesia;
| | - Christina Lancioni
- Department of Pediatrics, School of Medicine, Oregon Health and Science University, Portland, OR 97239, USA;
| | - Stephen M. Graham
- Centre for International Child Health, University of Melbourne and Murdoch Children’s Research Institute, Royal Children’s Hospital, Melbourne, VIC 3052, Australia;
- Burnet Institute, Melbourne, VIC 3004, Australia
| | - James A. Seddon
- Department of Infectious Diseases, Imperial College London, London W2 1PG, UK;
- Desmond Tutu TB Centre, Department of Paediatrics and Child Health, Stellenbosch University, Cape Town 8000, South Africa
| | - Ben J. Marais
- University of Sydney and The Children’s Hospital at Westmead, Sydney, NSW 2145, Australia;
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Yang C, Sobkowiak B, Naidu V, Codreanu A, Ciobanu N, Gunasekera KS, Chitwood MH, Alexandru S, Bivol S, Russi M, Havumaki J, Cudahy P, Fosburgh H, Allender CJ, Centner H, Engelthaler DM, Menzies NA, Warren JL, Crudu V, Colijn C, Cohen T. Phylogeography and transmission of M. tuberculosis in Moldova: A prospective genomic analysis. PLoS Med 2022; 19:e1003933. [PMID: 35192619 PMCID: PMC8903246 DOI: 10.1371/journal.pmed.1003933] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 03/08/2022] [Accepted: 01/31/2022] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND The incidence of multidrug-resistant tuberculosis (MDR-TB) remains critically high in countries of the former Soviet Union, where >20% of new cases and >50% of previously treated cases have resistance to rifampin and isoniazid. Transmission of resistant strains, as opposed to resistance selected through inadequate treatment of drug-susceptible tuberculosis (TB), is the main driver of incident MDR-TB in these countries. METHODS AND FINDINGS We conducted a prospective, genomic analysis of all culture-positive TB cases diagnosed in 2018 and 2019 in the Republic of Moldova. We used phylogenetic methods to identify putative transmission clusters; spatial and demographic data were analyzed to further describe local transmission of Mycobacterium tuberculosis. Of 2,236 participants, 779 (36%) had MDR-TB, of whom 386 (50%) had never been treated previously for TB. Moreover, 92% of multidrug-resistant M. tuberculosis strains belonged to putative transmission clusters. Phylogenetic reconstruction identified 3 large clades that were comprised nearly uniformly of MDR-TB: 2 of these clades were of Beijing lineage, and 1 of Ural lineage, and each had additional distinct clade-specific second-line drug resistance mutations and geographic distributions. Spatial and temporal proximity between pairs of cases within a cluster was associated with greater genomic similarity. Our study lasted for only 2 years, a relatively short duration compared with the natural history of TB, and, thus, the ability to infer the full extent of transmission is limited. CONCLUSIONS The MDR-TB epidemic in Moldova is associated with the local transmission of multiple M. tuberculosis strains, including distinct clades of highly drug-resistant M. tuberculosis with varying geographic distributions and drug resistance profiles. This study demonstrates the role of comprehensive genomic surveillance for understanding the transmission of M. tuberculosis and highlights the urgency of interventions to interrupt transmission of highly drug-resistant M. tuberculosis.
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Affiliation(s)
- Chongguang Yang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | | | - Vijay Naidu
- Department of Mathematics, Simon Fraser University, Burnaby, Canada
| | | | - Nelly Ciobanu
- Phthisiopneumology Institute, Chisinau, Republic of Moldova
| | - Kenneth S. Gunasekera
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Melanie H. Chitwood
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | | | - Stela Bivol
- Center for Health Policies and Studies, Chisinau, Republic of Moldova
| | - Marcus Russi
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Joshua Havumaki
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Patrick Cudahy
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Heather Fosburgh
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | | | - Heather Centner
- Translational Genomics Research Institute, Flagstaff, Arizona, United States of America
| | - David M. Engelthaler
- Translational Genomics Research Institute, Flagstaff, Arizona, United States of America
| | - Nicolas A. Menzies
- Department of Global Health and Population, and Center for Health Decision Science, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Joshua L. Warren
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Valeriu Crudu
- Phthisiopneumology Institute, Chisinau, Republic of Moldova
| | - Caroline Colijn
- Department of Mathematics, Simon Fraser University, Burnaby, Canada
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
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Gunasekera KS, Walters E, van der Zalm MM, Palmer M, Warren JL, Hesseling AC, Cohen T, Seddon JA. Development of a Treatment-decision Algorithm for Human Immunodeficiency Virus-uninfected Children Evaluated for Pulmonary Tuberculosis. Clin Infect Dis 2021; 73:e904-e912. [PMID: 33449999 PMCID: PMC8366829 DOI: 10.1093/cid/ciab018] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Limitations in the sensitivity and accessibility of diagnostic tools for childhood tuberculosis contribute to the substantial gap between estimated cases and cases notified to national tuberculosis programs. Thus, tools to make accurate and rapid clinical diagnoses are necessary to initiate antituberculosis treatment in more children. METHODS We analyzed data from a prospective cohort of children <13 years old being routinely evaluated for pulmonary tuberculosis in Cape Town, South Africa, from March 2012 to November 2017. We developed a regression model to describe the contributions of baseline clinical evaluation to the diagnosis of tuberculosis using standardized, retrospective case definitions. We included baseline chest radiographic and Xpert MTB/RIF assay results to the model to develop an algorithm with ≥90% sensitivity in predicting tuberculosis. RESULTS Data from 478 children being evaluated for pulmonary tuberculosis were analyzed (median age, 16.2 months; interquartile range, 9.8-30.9 months); 242 (50.6%) were retrospectively classified with tuberculosis, bacteriologically confirmed in 104 (43.0%). The area under the receiver operating characteristic curve for the final model was 0.87. Clinical evidence identified 71.4% of all tuberculosis cases in this cohort, and inclusion of baseline chest radiographic results increased the proportion to 89.3%. The algorithm was 90.1% sensitive and 52.1% specific, and maintained a sensitivity of >90% among children <2 years old or with low weight for age. CONCLUSIONS Clinical evidence alone was sufficient to make most clinical antituberculosis treatment decisions. The use of evidence-based algorithms may improve decentralized, rapid treatment initiation, reducing the global burden of childhood mortality.
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Affiliation(s)
- Kenneth S Gunasekera
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA
| | - Elisabetta Walters
- Desmond Tutu Tuberculosis Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa
| | - Marieke M van der Zalm
- Desmond Tutu Tuberculosis Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa
| | - Megan Palmer
- Desmond Tutu Tuberculosis Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa
| | - Joshua L Warren
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
| | - Anneke C Hesseling
- Desmond Tutu Tuberculosis Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA
| | - James A Seddon
- Desmond Tutu Tuberculosis Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa
- Department of Infectious Diseases, Imperial College London, London, United Kingdom
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Gunasekera KS, Zelner J, Becerra MC, Contreras C, Franke MF, Lecca L, Murray MB, Warren JL, Cohen T. Children as sentinels of tuberculosis transmission: disease mapping of programmatic data. BMC Med 2020; 18:234. [PMID: 32873309 PMCID: PMC7466499 DOI: 10.1186/s12916-020-01702-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 07/09/2020] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Identifying hotspots of tuberculosis transmission can inform spatially targeted active case-finding interventions. While national tuberculosis programs maintain notification registers which represent a potential source of data to investigate transmission patterns, high local tuberculosis incidence may not provide a reliable signal for transmission because the population distribution of covariates affecting susceptibility and disease progression may confound the relationship between tuberculosis incidence and transmission. Child cases of tuberculosis and other endemic infectious disease have been observed to provide a signal of their transmission intensity. We assessed whether local overrepresentation of child cases in tuberculosis notification data corresponds to areas where recent transmission events are concentrated. METHODS We visualized spatial clustering of children < 5 years old notified to Peru's National Tuberculosis Program from two districts of Lima, Peru, from 2005 to 2007 using a log-Gaussian Cox process to model the intensity of the point-referenced child cases. To identify where clustering of child cases was more extreme than expected by chance alone, we mapped all cases from the notification data onto a grid and used a hierarchical Bayesian spatial model to identify grid cells where the proportion of cases among children < 5 years old is greater than expected. Modeling the proportion of child cases allowed us to use the spatial distribution of adult cases to control for unobserved factors that may explain the spatial variability in the distribution of child cases. We compare where young children are overrepresented in case notification data to areas identified as transmission hotspots using molecular epidemiological methods during a prospective study of tuberculosis transmission conducted from 2009 to 2012 in the same setting. RESULTS Areas in which childhood tuberculosis cases are overrepresented align with areas of spatial concentration of transmission revealed by molecular epidemiologic methods. CONCLUSIONS Age-disaggregated notification data can be used to identify hotspots of tuberculosis transmission and suggest local force of infection, providing an easily accessible source of data to target active case-finding intervention.
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Affiliation(s)
- Kenneth S Gunasekera
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, 60 College Street, New Haven, CT, 06520, USA
| | - Jon Zelner
- Department of Epidemiology, University of Michigan School of Public Health, 267 SPH Tower, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Mercedes C Becerra
- Department of Global Health and Social Medicine, Harvard Medical School, 641 Huntington Avenue, Boston, MA, 02115, USA
| | | | - Molly F Franke
- Department of Global Health and Social Medicine, Harvard Medical School, 641 Huntington Avenue, Boston, MA, 02115, USA
| | - Leonid Lecca
- Department of Global Health and Social Medicine, Harvard Medical School, 641 Huntington Avenue, Boston, MA, 02115, USA
- Socios En Salud, Lima, Peru
| | - Megan B Murray
- Department of Global Health and Social Medicine, Harvard Medical School, 641 Huntington Avenue, Boston, MA, 02115, USA
| | - Joshua L Warren
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, 06520, USA
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, 60 College Street, New Haven, CT, 06520, USA.
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