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James LP, Klaassen F, Sweeney S, Furin J, Franke MF, Yaesoubi R, Chesov D, Ciobanu N, Codreanu A, Crudu V, Cohen T, Menzies NA. Impact and cost-effectiveness of the 6-month BPaLM regimen for rifampicin-resistant tuberculosis in Moldova: A mathematical modeling analysis. PLoS Med 2024; 21:e1004401. [PMID: 38701084 PMCID: PMC11101189 DOI: 10.1371/journal.pmed.1004401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 05/17/2024] [Accepted: 04/10/2024] [Indexed: 05/05/2024] Open
Abstract
BACKGROUND Emerging evidence suggests that shortened, simplified treatment regimens for rifampicin-resistant tuberculosis (RR-TB) can achieve comparable end-of-treatment (EOT) outcomes to longer regimens. We compared a 6-month regimen containing bedaquiline, pretomanid, linezolid, and moxifloxacin (BPaLM) to a standard of care strategy using a 9- or 18-month regimen depending on whether fluoroquinolone resistance (FQ-R) was detected on drug susceptibility testing (DST). METHODS AND FINDINGS The primary objective was to determine whether 6 months of BPaLM is a cost-effective treatment strategy for RR-TB. We used genomic and demographic data to parameterize a mathematical model estimating long-term health outcomes measured in quality-adjusted life years (QALYs) and lifetime costs in 2022 USD ($) for each treatment strategy for patients 15 years and older diagnosed with pulmonary RR-TB in Moldova, a country with a high burden of TB drug resistance. For each individual, we simulated the natural history of TB and associated treatment outcomes, as well as the process of acquiring resistance to each of 12 anti-TB drugs. Compared to the standard of care, 6 months of BPaLM was cost-effective. This strategy was estimated to reduce lifetime costs by $3,366 (95% UI: [1,465, 5,742] p < 0.001) per individual, with a nonsignificant change in QALYs (-0.06; 95% UI: [-0.49, 0.03] p = 0.790). For those stopping moxifloxacin under the BPaLM regimen, continuing with BPaL plus clofazimine (BPaLC) provided more QALYs at lower cost than continuing with BPaL alone. Strategies based on 6 months of BPaLM had at least a 93% chance of being cost-effective, so long as BPaLC was continued in the event of stopping moxifloxacin. BPaLM for 6 months also reduced the average time spent with TB resistant to amikacin, bedaquiline, clofazimine, cycloserine, moxifloxacin, and pyrazinamide, while it increased the average time spent with TB resistant to delamanid and pretomanid. Sensitivity analyses showed 6 months of BPaLM to be cost-effective across a broad range of values for the relative effectiveness of BPaLM, and the proportion of the cohort with FQ-R. Compared to the standard of care, 6 months of BPaLM would be expected to save Moldova's national TB program budget $7.1 million (95% UI: [1.3 million, 15.4 million] p = 0.002) over the 5-year period from implementation. Our analysis did not account for all possible interactions between specific drugs with regard to treatment outcomes, resistance acquisition, or the consequences of specific types of severe adverse events, nor did we model how the intervention may affect TB transmission dynamics. CONCLUSIONS Compared to standard of care, longer regimens, the implementation of the 6-month BPaLM regimen could improve the cost-effectiveness of care for individuals diagnosed with RR-TB, particularly in settings with a high burden of drug-resistant TB. Further research may be warranted to explore the impact and cost-effectiveness of shorter RR-TB regimens across settings with varied drug-resistant TB burdens and national income levels.
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Affiliation(s)
- Lyndon P. James
- PhD Program in Health Policy, Harvard University, Cambridge, Massachusetts, United States of America
- Center for Health Decision Science, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Fayette Klaassen
- Department of Global Health and Population, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Sedona Sweeney
- Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Jennifer Furin
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Molly F. Franke
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Reza Yaesoubi
- Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Dumitru Chesov
- Discipline of Pneumology and Allergology, Nicolae Testemitanu State University of Medicine and Pharmacy, Chişinǎu, Moldova
- Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany
| | - Nelly Ciobanu
- Chiril Draganiuc Institute of Phthisiopneumology, Chișinǎu, Moldova
| | | | - Valeriu Crudu
- Chiril Draganiuc Institute of Phthisiopneumology, Chișinǎu, Moldova
| | - Ted Cohen
- Department of Epidemiology and Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Nicolas A. Menzies
- Center for Health Decision Science, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Global Health and Population, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
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Chitwood MH, 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. Nat Commun 2024; 15:2962. [PMID: 38580642 PMCID: PMC10997638 DOI: 10.1038/s41467-024-47282-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 03/26/2024] [Indexed: 04/07/2024] Open
Abstract
The projected trajectory of multidrug resistant tuberculosis (MDR-TB) epidemics depends on the reproductive fitness of circulating strains of MDR M. tuberculosis (Mtb). Previous efforts to characterize the fitness of MDR Mtb have found that Mtb strains of the Beijing sublineage (Lineage 2.2.1) may be more prone to develop resistance and retain fitness in the presence of resistance-conferring mutations than other lineages. Using Mtb genome sequences from all culture-positive cases collected over two years in Moldova, we estimate the fitness of Ural (Lineage 4.2) and Beijing strains, the two lineages in which MDR is concentrated in the country. We estimate that the fitness of MDR Ural strains substantially exceeds that of other susceptible and MDR strains, and we identify several mutations specific to these MDR Ural strains. Our findings suggest that MDR Ural Mtb has been transmitting efficiently in Moldova and poses a substantial risk of spreading further in the region.
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Affiliation(s)
- Melanie H Chitwood
- Department of Epidemiology of Microbial Disease, Yale School of Public Health, 60 College Street, New Haven, CT, USA.
| | - Caroline Colijn
- Department of Mathematics, Simon Fraser University, 8888 University Drive West, Burnaby, BC, Canada
| | - Chongguang Yang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, No. 132 Outer Ring East Road, Guangzhou University Town Guangdong, Guangdong, PR China
| | - Valeriu Crudu
- Phthisiopneumology Institute, Strada Constantin Vârnav 13, Chisinau, Republic of Moldova
| | - Nelly Ciobanu
- Phthisiopneumology Institute, Strada Constantin Vârnav 13, Chisinau, Republic of Moldova
| | - Alexandru Codreanu
- Phthisiopneumology Institute, Strada Constantin Vârnav 13, Chisinau, Republic of Moldova
| | - Jaehee Kim
- Department of Computational Biology, Cornell University, 237 Tower Road, Ithaca, NY, USA
| | - Isabel Rancu
- Department of Epidemiology of Microbial Disease, Yale School of Public Health, 60 College Street, New Haven, CT, USA
| | - Kyu Rhee
- Department of Medicine, Weill Cornell Medicine, 1300 York Ave, New York, NY, USA
| | - Ted Cohen
- Department of Epidemiology of Microbial Disease, Yale School of Public Health, 60 College Street, New Haven, CT, USA.
| | - Benjamin Sobkowiak
- Department of Epidemiology of Microbial Disease, Yale School of Public Health, 60 College Street, New Haven, CT, USA
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Lan Y, Crudu V, Ciobanu N, Codreanu A, Chitwood MH, Sobkowiak B, Warren JL, 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] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 03/08/2024] [Accepted: 03/11/2024] [Indexed: 03/28/2024] Open
Abstract
BACKGROUND Multidrug resistant tuberculosis (MDR-TB) represents a major public health concern in the Republic of Moldova, with an estimated 31% of new and 56% of previously treated TB cases having MDR disease in 2022. A recent genomic epidemiology study of incident TB occurring in 2018 and 2019 found that 92% of MDR-TB was the result of transmission. The MDR phenotype was concentrated among two M. tuberculosis (Mtb) lineages: L2.2.1 (Beijing) and L4.2.1 (Ural). METHODS We developed and applied a hierarchical Bayesian multinominal logistic regression model to Mtb genomic, spatial, and epidemiological data collected from all individuals with diagnosed TB in Moldova in 2018 and 2019 to identify locations in which specific Mtb strains are being transmitted. We then used a logistic regression model to estimate locality-level factors associated with local transmission. FINDINGS We found differences in the spatial distribution and degree of local concentration of disease due to specific strains of Beijing and Ural lineage Mtb. Foci of transmission for four strains of Beijing lineage Mtb, predominantly of the MDR-TB phenotype, were located in several regions, but largely concentrated in Transnistria. In contrast, transmission of Ural lineage Mtb had less marked patterns of spatial aggregation, with a single strain (also of the MDR phenotype) spatially clustered in southern Transnistria. We found a 30% (95% credible interval 2%-80%) increase in odds of a locality being a transmission cluster for each increase of 100 persons per square kilometer, while higher local tuberculosis incidence and poverty were not associated with a locality being a transmission focus. INTERPRETATION Our results identified localities where specific Mtb transmission networks were concentrated and quantified the association between locality-level factors and focal transmission. This analysis revealed Transnistria as the primary area where specific Mtb strains (predominantly of the MDR-TB phenotype) were locally transmitted and suggests that targeted intensified case finding in this region may be an attractive policy option. FUNDING Funding for this work was provided by the National Institute of Allergy and Infectious Diseases at the US National Institutes of Health.
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Affiliation(s)
- Yu Lan
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | - Valeriu Crudu
- Phthisiopneumology Institute, Chisinau, Republic of Moldova
| | - Nelly Ciobanu
- Phthisiopneumology Institute, Chisinau, Republic of Moldova
| | | | - Melanie H Chitwood
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | - Benjamin Sobkowiak
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | - Joshua L Warren
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA.
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Song Z, He W, Cao X, Ma A, He P, Zhao B, Wang S, Liu C, Zhao Y. The Recent Transmission and Associated Risk Factor of Mycobacterium tuberculosis in Golmud City, China. Infect Drug Resist 2024; 17:417-425. [PMID: 38318210 PMCID: PMC10840525 DOI: 10.2147/idr.s437026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 12/05/2023] [Indexed: 02/07/2024] Open
Abstract
Background Tuberculosis (TB) remains a severe public health problem globally, and it is essential to comprehend the transmission pattern to control tuberculosis. Herein, we evaluated the drug-resistant characteristics, recent transmission, and associated risk factors of TB in Golmud, Qinghai, China. Methods In this study, we performed a population-based study of patients diagnosed with TB in Golmud from 2013 to 2018. Drug-susceptibility testing and whole-genome sequencing were performed on 133 Mycobacterium tuberculosis strains. The genomic clustering rate was calculated to evaluate the level of recent transmission. Risk factors were identified by logistic regression analysis. Results Our results showed that 46.97% (62/132) of strains were phylogenetically clustered and formed into 23 transmission clusters, suggesting a high recent transmission of TB in the area. 12.78% (17/133) strains were multidrug-resistant/rifampicin tuberculosis (MDR/RR-TB), with a high drug-resistant burden. Based on drug resistance gene analysis, we found 23 strains belonging to genotype MDR/RR-TB, where some strains may have borderline mutations. Among these strains, 65.2% (15/23) were found within putative transmission clusters. Additionally, risk factor analysis showed that recent transmission of TB happened more in patients with Tibetan nationality or older age. Conclusion Overall our study indicates that the recent transmissions of MTB strains, especially genotypic MDR/RR strains, drive the tuberculosis epidemic in Golmud, which could contribute to developing effective TB prevention and control strategies.
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Affiliation(s)
- Zexuan Song
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
| | - Wencong He
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
| | - Xiaolong Cao
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
| | - Aijing Ma
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
| | - Ping He
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
| | - Bing Zhao
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
| | - Shengfen Wang
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
| | - Chunfa Liu
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
- Animal Science and Technology College, Beijing University of Agriculture, Huilongguan, Changping, Beijing, 102206, People’s Republic of China
| | - Yanlin Zhao
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
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Warren JL, Chitwood MH, 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] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 01/31/2023] [Indexed: 02/07/2023]
Abstract
Understanding factors that contribute to the increased likelihood of pathogen transmission between two individuals is important for infection control. However, analyzing measures of pathogen relatedness to estimate these associations is complicated due to correlation arising from the presence of the same individual across multiple dyadic outcomes, potential spatial correlation caused by unmeasured transmission dynamics, and the distinctive distributional characteristics of some of the outcomes. We develop two novel hierarchical Bayesian spatial methods for analyzing dyadic pathogen genetic relatedness data, in the form of patristic distances and transmission probabilities, that simultaneously address each of these complications. Using individual-level spatially correlated random effect parameters, we account for multiple sources of correlation between the outcomes as well as other important features of their distribution. Through simulation, we show the limitations of existing approaches in terms of estimating key associations of interest, and the ability of the new methodology to correct for these issues across datasets with different levels of correlation. All methods are applied to Mycobacterium tuberculosis data from the Republic of Moldova, where we identify previously unknown factors associated with disease transmission and, through analysis of the random effect parameters, key individuals, and areas with increased transmission activity. Model comparisons show the importance of the new methodology in this setting. The methods are implemented in the R package GenePair.
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Affiliation(s)
| | - Melanie H. Chitwood
- Department of Epidemiology of Microbial Diseases, Yale University, Connecticut, USA
| | | | - Caroline Colijn
- Department of Mathematics, Simon Fraser University, Burnaby, Canada
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases, Yale University, Connecticut, USA
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Vīksna A, Sadovska D, Berge I, Bogdanova I, Vaivode A, Freimane L, Norvaiša I, Ozere I, Ranka R. Genotypic and phenotypic comparison of drug resistance profiles of clinical multidrug-resistant Mycobacterium tuberculosis isolates using whole genome sequencing in Latvia. BMC Infect Dis 2023; 23:638. [PMID: 37770850 PMCID: PMC10540372 DOI: 10.1186/s12879-023-08629-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 09/19/2023] [Indexed: 09/30/2023] Open
Abstract
BACKGROUND Multidrug-resistant tuberculosis (MDR-TB) remains a major public health problem in many high tuberculosis (TB) burden countries. Phenotypic drug susceptibility testing (DST) take several weeks or months to result, but line probe assays and Xpert/Rif Ultra assay detect a limited number of resistance conferring gene mutations. Whole genome sequencing (WGS) is an advanced molecular testing method which theoretically can predict the resistance of M. tuberculosis (Mtb) isolates to all anti-TB agents through a single analysis. METHODS Here, we aimed to identify the level of concordance between the phenotypic and WGS-based genotypic drug susceptibility (DS) patterns of MDR-TB isolates. Overall, data for 12 anti-TB medications were analyzed. RESULTS In total, 63 MDR-TB Mtb isolates were included in the analysis, representing 27.4% of the total number of MDR-TB cases in Latvia in 2012-2014. Among them, five different sublineages were detected, and 2.2.1 (Beijing group) and 4.3.3 (Latin American-Mediterranean group) were the most abundant. There were 100% agreement between phenotypic and genotypic DS pattern for isoniazid, rifampicin, and linezolid. High concordance rate (> 90%) between phenotypic and genotypic DST results was detected for ofloxacin (93.7%), pyrazinamide (93.7%) and streptomycin (95.4%). Phenotypic and genotypic DS patterns were poorly correlated for ethionamide (agreement 56.4%), ethambutol (85.7%), amikacin (82.5%), capreomycin (81.0%), kanamycin (85.4%), and moxifloxacin (77.8%). For capreomycin, resistance conferring mutations were not identified in several phenotypically resistant isolates, and, in contrary, for ethionamide, ethambutol, amikacin, kanamycin, and moxifloxacin the resistance-related mutations were identified in several phenotypically sensitive isolates. CONCLUSIONS WGS is a valuable tool for rapid genotypic DST for all anti-TB agents. For isoniazid and rifampicin phenotypic DST potentially can be replaced by genotypic DST based on 100% agreement between the tests. However, discrepant results for other anti-TB agents limit their prescription based solely on WGS data. For clinical decision, at the current level of knowledge, there is a need for combination of genotypic DST with modern, validated phenotypic DST methodologies for those medications which did not showed 100% agreement between the methods.
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Affiliation(s)
- Anda Vīksna
- Riga East Clinical University Hospital, Centre of Tuberculosis and Lung Diseases, Ropaži Municipality, Stopiņi Parish, Upeslejas, Latvia
- Rīga Stradiņš University, Riga, Latvia
| | - Darja Sadovska
- Latvian Biomedical Research and Study Centre, Ratsupites Str. 1, Riga, LV-1067, Latvia
| | - Iveta Berge
- Riga East Clinical University Hospital, Centre of Tuberculosis and Lung Diseases, Ropaži Municipality, Stopiņi Parish, Upeslejas, Latvia
| | - Ineta Bogdanova
- Riga East Clinical University Hospital, Centre of Tuberculosis and Lung Diseases, Ropaži Municipality, Stopiņi Parish, Upeslejas, Latvia
| | - Annija Vaivode
- Latvian Biomedical Research and Study Centre, Ratsupites Str. 1, Riga, LV-1067, Latvia
| | - Lauma Freimane
- Latvian Biomedical Research and Study Centre, Ratsupites Str. 1, Riga, LV-1067, Latvia
| | - Inga Norvaiša
- Riga East Clinical University Hospital, Centre of Tuberculosis and Lung Diseases, Ropaži Municipality, Stopiņi Parish, Upeslejas, Latvia
| | - Iveta Ozere
- Riga East Clinical University Hospital, Centre of Tuberculosis and Lung Diseases, Ropaži Municipality, Stopiņi Parish, Upeslejas, Latvia
- Rīga Stradiņš University, Riga, Latvia
| | - Renāte Ranka
- Latvian Biomedical Research and Study Centre, Ratsupites Str. 1, Riga, LV-1067, Latvia.
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Guo H, An J, Li S, Ding B, Zhang Z, Shu W, Shang Y, Wang Y, Cheng K, Wang Y, Xue Z, Ren W, Pan J, Luo T, Pang Y. Transmission and resistome of extremely drug-resistant tuberculosis in Beijing, China: A retrospective population-based epidemiological study. J Infect Public Health 2023; 16:1193-1200. [PMID: 37271100 DOI: 10.1016/j.jiph.2023.05.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 01/10/2023] [Accepted: 05/17/2023] [Indexed: 06/06/2023] Open
Abstract
BACKGROUND In this study, we utilized whole genome sequencing (WGS) of clinical extremely drug-resistant tuberculosis (EDR-TB) strains collected during 2014-2020 in Beijing to detect clustered strains. METHODS A retrospective cohort study was conducted by inclusion of EDR-TB patients with positive cultures in Beijing between 2014 and 2020. RESULTS A total of 95 EDR-TB patients were included in our analysis. Up on the WGS based genotyping, 94 (94/95, 98.9%) out of 95 were identified as lineage 2 (East Asia). The pairwise genomic distance analysis identified 7 clusters, ranging in size from 2 to 5 isolates. The clustering rate of EDR-TB was 21.1%; while no patients had significantly higher odds of clustering. All isolates harbor rpoB RRDR mutations that confer RIF resistance and katG or inhA promoter mutations that confer INH resistance. Of 95 EDR-TB isolates, a total of 15 mutation types were recorded in the transcriptional regulator mmpR5. In vitro susceptibility testing results revealed that 14 (14/15, 93.3%) out of 15 mutation types were resistant to CFZ; whereas only 3 (3/15, 20.0%) showed resistance to BDQ. Interestingly, 12 isolates harbored mutations within rrl locus, whereas only mutations at positions 2294 and 2296 conferred CLA resistance. Favorable outcomes of EDR-TB patients were positively associated with more effective drugs in the regimes. CONCLUSION WGS data demonstrate limited transmission of EDR-TB in this metropolis city. WGS-based drug susceptibility predictions will bring benefits to EDR-TB patients to formulate optimal therapeutic regimens.
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Affiliation(s)
- Haiping Guo
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Postal No 9, Beiguan Street, Tongzhou District, Beijing 101149, PR China
| | - Jun An
- Medical Record Department, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing 101149, PR China
| | - Shanshan Li
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Postal No 9, Beiguan Street, Tongzhou District, Beijing 101149, PR China
| | - Beichuan Ding
- Clinical Center on TB, Beijing Institute of Tuberculosis Control, Beijing 101149, PR China
| | - Zhiguo Zhang
- Clinical laboratory, Beijing Changping District Tuberculosis Prevention and Control Institute, Beijing 101149, PR China
| | - Wei Shu
- Clinical Center on TB, Beijing Chest Hospital, Capital Medical University/ Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing 101149, PR China
| | - Yuanyuan Shang
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Postal No 9, Beiguan Street, Tongzhou District, Beijing 101149, PR China
| | - Yi Wang
- Laboratory of Infection and Immunity, Department of Pathogen Biology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Postal No 17, People's South Road, Chengdu 610041, PR China
| | - Ken Cheng
- Laboratory of Infection and Immunity, Department of Pathogen Biology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Postal No 17, People's South Road, Chengdu 610041, PR China
| | - Yufeng Wang
- Department of Laboratory Quality Control, Innovation Alliance on Tuberculosis Diagnosis and Treatment (Beijing), Beijing 101149, PR China
| | - Zhongtan Xue
- Department of Laboratory Quality Control, Innovation Alliance on Tuberculosis Diagnosis and Treatment (Beijing), Beijing 101149, PR China
| | - Weicong Ren
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Postal No 9, Beiguan Street, Tongzhou District, Beijing 101149, PR China
| | - Junhua Pan
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Postal No 9, Beiguan Street, Tongzhou District, Beijing 101149, PR China.
| | - Tao Luo
- Laboratory of Infection and Immunity, Department of Pathogen Biology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Postal No 17, People's South Road, Chengdu 610041, PR China.
| | - Yu Pang
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Postal No 9, Beiguan Street, Tongzhou District, Beijing 101149, PR China.
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Noroc E, Chesov D, Merker M, Gröschel MI, Barilar I, Dreyer V, Ciobanu N, Reimann M, Crudu V, Lange C. Limited Nosocomial Transmission of Drug-Resistant Tuberculosis, Moldova. Emerg Infect Dis 2023; 29:1046-1050. [PMID: 37081601 PMCID: PMC10124655 DOI: 10.3201/eid2905.230035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/22/2023] Open
Abstract
Applying whole-genome-sequencing, we aimed to detect transmission events of multidrug-resistant/rifampin-resistant strains of Mycobacterium tuberculosis complex at a tuberculosis hospital in Chisinau, Moldova. We recorded ward, room, and bed information for each patient and monitored in-hospital transfers over 1 year. Detailed molecular and patient surveillance revealed only 2 nosocomial transmission events.
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Li M, Lu L, Guo M, Jiang Q, Xia L, Jiang Y, Zhang S, Qiu Y, Yang C, Chen Y, Hong J, Guo X, Takiff H, Shen X, Chen C, Gao Q. Discrepancy in the transmissibility of multidrug-resistant Mycobacterium tuberculosis in urban and rural areas in China. Emerg Microbes Infect 2023; 12:2192301. [PMID: 36924242 DOI: 10.1080/22221751.2023.2192301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
The fitness of multidrug-resistant tuberculosis (MDR-TB) is thought to be an important determinant of a strain's ability to be transmitted and cause outbreaks. Studies in the laboratory have demonstrated that MDR-TB strains have reduced fitness but the relative transmissibility of MDR-TB versus drug-susceptible (DS) TB strains in human populations remains unresolved. We used data on genomic clustering from our previous molecular epidemiological study in Songjiang (2011-2020) and Wusheng (2009-2020), China, to compare the relative transmissibility of MDR-TB versus DS-TB. Genomic clusters were defined with a threshold distance of 12-single-nucleotide-polymorphisms and the risk for MDR-TB clustering was analyzed by logistic regression. In total, 2212 culture-positive pulmonary TB patients were enrolled in Songjiang and 1289 in Wusheng. The clustering rates of MDR-TB and DS-TB strains were 19.4% (20/103) and 26.3% (509/1936), respectively in Songjiang, and 43.9% (29/66) and 26.0% (293/1128) in Wusheng. The risk of MDR-TB clustering was 2.34 (95% CI 1.38-3.94) times higher than DS-TB clustering in Wusheng and 0.64 (95% CI 0.38-1.06) times lower in Songjiang. Neither lineage 2, compensatory mutations nor rpoB S450L were significantly associated with MDR-TB transmission, and katG S315T increased MDR-TB transmission only in Wusheng (OR 5.28, 95% CI 1.42-19.21). MDR-TB was not more transmissible than DS-TB in either Songjiang or Wusheng. It appears that the different transmissibility of MDR-TB in Songjiang and Wusheng is likely due to differences in the quality of the local TB control programs. These results suggest that the most effective way to control MDR-TB is by improving local TB control programs.
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Affiliation(s)
- Meng Li
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Science, Shanghai Medical College, Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China.,National Clinical Research Center for Infectious Diseases, Shenzhen Third People's Hospital, Shenzhen, Guangdong, China
| | - Liping Lu
- Department of Tuberculosis Control, Songjiang District Center for Disease Control and Prevention, Shanghai, China
| | - Mingcheng Guo
- Department of Tuberculosis Control, Wusheng County Center for Disease Control and Prevention, Guang'an, China
| | - Qi Jiang
- School of Public Health, Renmin Hospital Public Health Research Institute, Wuhan University, Wuhan, China
| | - Lan Xia
- Institution for Tuberculosis Prevention and Control, Sichuan Provincial Center for Disease Control and Prevention, Chengdu, China
| | - Yuan Jiang
- Tuberculosis Laboratory, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Shu Zhang
- Institution for Tuberculosis Prevention and Control, Sichuan Provincial Center for Disease Control and Prevention, Chengdu, China
| | - Yong Qiu
- Department of Tuberculosis Control, Wusheng County Center for Disease Control and Prevention, Guang'an, China
| | - Chongguang Yang
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Science, Shanghai Medical College, Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China.,School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Yiwang Chen
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Science, Shanghai Medical College, Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China.,National Clinical Research Center for Infectious Diseases, Shenzhen Third People's Hospital, Shenzhen, Guangdong, China
| | - Jianjun Hong
- Department of Tuberculosis Control, Songjiang District Center for Disease Control and Prevention, Shanghai, China
| | - Xiaoqin Guo
- Department of Tuberculosis Control, Songjiang District Center for Disease Control and Prevention, Shanghai, China
| | - Howard Takiff
- Laboratorio de Genética Molecular, CMBC, IVIC, Caracas, Venezuela
| | - Xin Shen
- Tuberculosis Laboratory, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Chuang Chen
- Institution for Tuberculosis Prevention and Control, Sichuan Provincial Center for Disease Control and Prevention, Chengdu, China
| | - Qian Gao
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Science, Shanghai Medical College, Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China.,National Clinical Research Center for Infectious Diseases, Shenzhen Third People's Hospital, Shenzhen, Guangdong, China
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10
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Bakuła Z, Marczak M, Bluszcz A, Proboszcz M, Kościuch J, Krenke R, Stakėnas P, Mokrousov I, Jagielski T. Phylogenetic relationships of Mycobacterium tuberculosis isolates in Poland: The emergence of Beijing genotype among multidrug-resistant cases. Front Cell Infect Microbiol 2023; 13:1161905. [PMID: 37009494 PMCID: PMC10061152 DOI: 10.3389/fcimb.2023.1161905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 02/28/2023] [Indexed: 03/18/2023] Open
Abstract
IntroductionThe epidemiological situation of tuberculosis (TB) in Poland urges for its continuous and scrupulous monitoring. The objective of this study was to explore the genetic diversity of multidrug-resistant (MDR) and drug-susceptible (DS) Mycobacterium tuberculosis isolates from Poland with a combination of spoligotyping and high-resolution mycobacterial interspersed repetitive unit-variable number tandem repeat (MIRU-VNTR) analysis. The results were placed in the Northern and Eastern Europe context.MethodsThe study included 89 (39 MDR and 50 DS) M. tuberculosis isolates collected from as many patients between 2018 and 2021 in Poland. The analysis was done using spoligotyping, and MIRU-VNTR typing at 24 standard loci. The data were compared to those available on Poland and neighbors and global M. tuberculosis datasets.ResultsThe main identified families were Beijing (28.1%) and Haarlem (16.8%) while 34.8% of isolates were in the heterogeneous L4-unclassified group. Although the Beijing family was the most prevalent (61.5%) among MDR-TB cases, it accounted for only 2% of DS isolates. Among foreign-born patients, a higher ratio of MDR isolates were observed when compared with those who Poland-born (64.3% vs. 40%). Furthermore, all patients from the Former Soviet Union (FSU) countries were infected with MDR-TB.DiscussionWhereas DS M. tuberculosis population in Poland is dominated by L4 isolates, MDR isolates are mostly of the Beijing genotype. The rise in the prevalence of the Beijing isolates in Poland, coupled with high proportion of the Beijing genotype among foreign-born TB patients may reflect an ongoing transmission of this family, imported to Poland mainly from FSU countries.
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Affiliation(s)
- Zofia Bakuła
- Department of Medical Microbiology, Institute of Microbiology, University of Warsaw, Faculty of Biology, Warsaw, Poland
| | - Mateusz Marczak
- Department of Medical Microbiology, Institute of Microbiology, University of Warsaw, Faculty of Biology, Warsaw, Poland
| | - Agata Bluszcz
- Department of Medical Microbiology, Institute of Microbiology, University of Warsaw, Faculty of Biology, Warsaw, Poland
| | - Małgorzata Proboszcz
- Department of Internal Medicine, Pulmonary Diseases & Allergy, Medical University of Warsaw, Warsaw, Poland
| | - Justyna Kościuch
- Department of Internal Medicine, Pulmonary Diseases & Allergy, Medical University of Warsaw, Warsaw, Poland
| | - Rafał Krenke
- Department of Internal Medicine, Pulmonary Diseases & Allergy, Medical University of Warsaw, Warsaw, Poland
| | - Petras Stakėnas
- Department of Immunology and Cell Biology, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Igor Mokrousov
- Laboratory of Molecular Epidemiology and Evolutionary Genetics, St. Petersburg Pasteur Institute, St. Petersburg, Russia
- Henan International Joint Laboratory of Children’s Infectious Diseases, Henan Children’s Hospital, Zhengzhou Children’s Hospital, Zhengzhou, China
- *Correspondence: Tomasz Jagielski, ; Igor Mokrousov,
| | - Tomasz Jagielski
- Department of Medical Microbiology, Institute of Microbiology, University of Warsaw, Faculty of Biology, Warsaw, Poland
- *Correspondence: Tomasz Jagielski, ; Igor Mokrousov,
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11
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Susvitasari K, Tupper PF, Cancino-Muños I, Lòpez MG, Comas I, Colijn C. Epidemiological cluster identification using multiple data sources: an approach using logistic regression. Microb Genom 2023; 9. [PMID: 36867086 PMCID: PMC10132077 DOI: 10.1099/mgen.0.000929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/04/2023] Open
Abstract
In the management of infectious disease outbreaks, grouping cases into clusters and understanding their underlying epidemiology are fundamental tasks. In genomic epidemiology, clusters are typically identified either using pathogen sequences alone or with sequences in combination with epidemiological data such as location and time of collection. However, it may not be feasible to culture and sequence all pathogen isolates, so sequence data may not be available for all cases. This presents challenges for identifying clusters and understanding epidemiology, because these cases may be important for transmission. Demographic, clinical and location data are likely to be available for unsequenced cases, and comprise partial information about their clustering. Here, we use statistical modelling to assign unsequenced cases to clusters already identified by genomic methods, assuming that a more direct method of linking individuals, such as contact tracing, is not available. We build our model on pairwise similarity between cases to predict whether cases cluster together, in contrast to using individual case data to predict the cases' clusters. We then develop methods that allow us to determine whether a pair of unsequenced cases are likely to cluster together, to group them into their most probable clusters, to identify which are most likely to be members of a specific (known) cluster, and to estimate the true size of a known cluster given a set of unsequenced cases. We apply our method to tuberculosis data from Valencia, Spain. Among other applications, we find that clustering can be predicted successfully using spatial distance between cases and whether nationality is the same. We can identify the correct cluster for an unsequenced case, among 38 possible clusters, with an accuracy of approximately 35 %, higher than both direct multinomial regression (17 %) and random selection (< 5 %).
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Affiliation(s)
| | - Paul F Tupper
- Department of Mathematics, Simon Fraser University, Burnaby, Canada
| | - Irving Cancino-Muños
- I2SysBio, University of Valencia-CSIC, Valencia, Spain.,FISABIO Public Health, Valencia, Spain
| | - Mariana G Lòpez
- Tuberculosis Genomics Unit, Instituto de Biomedicina de Valencia (IBV-CSIC), Valencia, Spain
| | - Iñaki Comas
- Tuberculosis Genomics Unit, Instituto de Biomedicina de Valencia (IBV-CSIC), Valencia, Spain.,Ciber en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Caroline Colijn
- Department of Mathematics, Simon Fraser University, Burnaby, Canada
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12
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Sanabria GE, Sequera G, Aguirre S, Méndez J, Dos Santos PCP, Gustafson NW, Godoy M, Ortiz A, Cespedes C, Martínez G, García-Basteiro AL, Andrews JR, Croda J, Walter KS. Phylogeography and transmission of Mycobacterium tuberculosis spanning prisons and surrounding communities in Paraguay. Nat Commun 2023; 14:303. [PMID: 36658111 PMCID: PMC9849832 DOI: 10.1038/s41467-023-35813-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Accepted: 01/04/2023] [Indexed: 01/20/2023] Open
Abstract
Recent rises in incident tuberculosis (TB) cases in Paraguay and the increasing concentration of TB within prisons highlight the urgency of targeting strategies to interrupt transmission and prevent new infections. However, whether specific cities or carceral institutions play a disproportionate role in transmission remains unknown. We conducted prospective genomic surveillance, sequencing 471 Mycobacterium tuberculosis complex genomes, from inside and outside prisons in Paraguay's two largest urban areas, Asunción and Ciudad del Este, from 2016 to 2021. We found genomic evidence of frequent recent transmission within prisons and transmission linkages spanning prisons and surrounding populations. We identified a signal of frequent M. tuberculosis spread between urban areas and marked recent population size expansion of the three largest genomic transmission clusters. Together, our findings highlight the urgency of strengthening TB control programs to reduce transmission risk within prisons in Paraguay, where incidence was 70 times that outside prisons in 2021.
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Affiliation(s)
| | - Guillermo Sequera
- Instituto de Salud Global de Barcelona (ISGLOBAL), Barcelona, Spain
- Programa Nacional de Control de la Tuberculosis, Ministerio de Salud Pública y Bienestar Social (MSPyBS), Asunción, Paraguay
| | - Sarita Aguirre
- Programa Nacional de Control de la Tuberculosis, Ministerio de Salud Pública y Bienestar Social (MSPyBS), Asunción, Paraguay
| | - Julieta Méndez
- Instituto Regional de Investigación en Salud, Caaguazú, Paraguay
| | - Paulo César Pereira Dos Santos
- Postgraduate Program in Infectious and Parasitic Diseases, Federal University of Mato Grosso do Sul, Mato Grosso do Sul, Brazil
| | - Natalie Weiler Gustafson
- Laboratorio Central de Salud Pública (LCSP), Ministerio de Salud Publica y Bienestar Social (MSPyBS), Asunción, Paraguay
| | - Margarita Godoy
- Laboratorio Central de Salud Pública (LCSP), Ministerio de Salud Publica y Bienestar Social (MSPyBS), Asunción, Paraguay
| | - Analía Ortiz
- Instituto Regional de Investigación en Salud, Caaguazú, Paraguay
| | - Cynthia Cespedes
- Programa Nacional de Control de la Tuberculosis, Ministerio de Salud Pública y Bienestar Social (MSPyBS), Asunción, Paraguay
| | - Gloria Martínez
- Instituto Regional de Investigación en Salud, Caaguazú, Paraguay
| | - Alberto L García-Basteiro
- Instituto de Salud Global de Barcelona (ISGLOBAL), Barcelona, Spain
- Centro de Investigação em Saude de Manhiça (CISM), Maputo, Mozambique
- Centro de Investigación Biomédica en Red de Enfermedades Infecciosas (CIBERINFEC), Barcelona, Spain
| | - Jason R Andrews
- Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Julio Croda
- Federal University of Mato Grosso do Sul - UFMS, Campo Grande, MS, Brazil
- Oswaldo Cruz Foundation Mato Grosso do Sul, Mato Grosso do Sul, Brazil
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, USA
| | - Katharine S Walter
- Division of Epidemiology, University of Utah, Salt Lake City, UT, 84105, USA.
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13
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Ji L, Tao FX, Yu YF, Liu JH, Yu FH, Bai CL, Wan ZY, Yang XB, Ma J, Zhou P, Niu Z, Zhou P, Xiang H, Chen M, Xiang Z, Zhang FQ, Jiang Q, Liu XJ. Whole-genome sequencing to characterize the genetic structure and transmission risk of Mycobacterium tuberculosis in Yichang city of China. Front Public Health 2023; 10:1047965. [PMID: 36699912 PMCID: PMC9868839 DOI: 10.3389/fpubh.2022.1047965] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 12/19/2022] [Indexed: 01/11/2023] Open
Abstract
Objective The burden of both general and drug-resistant tuberculosis in rural areas is higher than that in urban areas in China. To characterize the genetic structure and transmission risk of Mycobacterium tuberculosis in rural China, we used whole genome sequencing to analyze clinical strains collected from patients in two counties of Yichang for three consecutive years. Methods From 2018 to 2020, sputum samples were collected for cultures from patients with suspected tuberculosis in Yidu and Zigui county, and DNA was extracted from the positive strains for genome sequencing. The online SAM-TB platform was used to identify the genotypes and drug resistance-related mutations of each strain, establish a phylogenetic tree, and calculated the genetic distances between pairwise strains. Twelve single nucleotide polymorphisms (SNPs) were used as thresholds to identify transmission clusters. The risk of related factors was estimated by univariable and multivariable logistic regression. Results A total of 161 out of the collected 231 positive strains were enrolled for analysis, excluding non-tuberculous mycobacterium and duplicate strains from the same patient. These strains belonged to Lineage 2 (92, 57.1%) and Lineage 4 (69, 42.9%), respectively. A total of 49 (30.4%) strains were detected with known drug resistance-related mutations, including 6 (3.7%) multidrug-resistant-TB (MDR-TB) strains and 11 (6.8%) RIF-resistant INH-susceptible TB (Rr-TB) strains. Six of the MDR/Rr-TB (35.3%) were also resistant to fluoroquinolones, which made them pre-extensively drug-resistant TB (pre-XDR-TB). There were another seven strains with mono-resistance to fluoroquinolones and one strain with resistance to both INH and fluoroquinolones, making the overall rate of fluoroquinolones resistance 8.7% (14/161). A total of 50 strains (31.1%) were identified as transmission clusters. Patients under 45 years old (adjusted odds ratio 3.46 [95% confidential intervals 1.28-9.35]), treatment-naive patients (6.14 [1.39-27.07]) and patients infected by lineage 4 strains (2.22 [1.00-4.91]) had a higher risk of transmission. Conclusion The drug resistance of tuberculosis in rural China, especially to the second-line drug fluoroquinolones, is relatively serious. The standardized treatment for patients and the clinical use of fluoroquinolones warrant attention. At the same time, the recent transmission risk of tuberculosis is high, and rapid diagnosis and treatment management at the primary care needs to be strengthened.
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Affiliation(s)
- Lv Ji
- Institute of Public Health Inspection, Yichang Center for Disease Control and Prevention, Yichang, Hubei, China
| | - Feng-Xi Tao
- School of Public Health, Wuhan University, Wuhan, Hubei, China
| | - Yun-Fang Yu
- Institute of Public Health Inspection, Yichang Center for Disease Control and Prevention, Yichang, Hubei, China
| | - Jian-Hua Liu
- Institute of Infectious Disease Prevention and Control, Yichang Center for Disease Control and Prevention, Yichang, Hubei, China
| | - Feng-Hua Yu
- Institute of Public Health Inspection, Yichang Center for Disease Control and Prevention, Yichang, Hubei, China
| | - Chun-Lin Bai
- Institute of Public Health Inspection, Yichang Center for Disease Control and Prevention, Yichang, Hubei, China
| | - Zheng-Yang Wan
- Institute of Public Health Inspection, Yichang Center for Disease Control and Prevention, Yichang, Hubei, China
| | - Xiao-Bo Yang
- Institute of Public Health Inspection, Yichang Center for Disease Control and Prevention, Yichang, Hubei, China
| | - Jing Ma
- Institute of Public Health Inspection, Yichang Center for Disease Control and Prevention, Yichang, Hubei, China
| | - Pan Zhou
- Institute of Public Health Inspection, Yichang Center for Disease Control and Prevention, Yichang, Hubei, China
| | - Zhao Niu
- Institute of Public Health Inspection, Yichang Center for Disease Control and Prevention, Yichang, Hubei, China
| | - Ping Zhou
- Institute of Infectious Disease Prevention and Control, Yichang Center for Disease Control and Prevention, Yichang, Hubei, China
| | - Hong Xiang
- Institute of Infectious Disease Prevention and Control, Yidu Center for Disease Control and Prevention, Yidu, Hubei, China
| | - Ming Chen
- Clinical Laboratory, Yidu First People's Hospital, Yidu, Hubei, China
| | - Zhou Xiang
- Institute of Infectious Disease Prevention and Control, Zigui Center for Disease Control and Prevention, Zigui, Hubei, China
| | - Fang-Qiong Zhang
- Clinical Laboratory, Zigui County People's Hospital, Zigui, Hubei, China
| | - Qi Jiang
- School of Public Health, Wuhan University, Wuhan, Hubei, China,*Correspondence: Qi Jiang ✉
| | - Xiao-Jun Liu
- Institute of Public Health Inspection, Yichang Center for Disease Control and Prevention, Yichang, Hubei, China,Institute of Infectious Disease Prevention and Control, Yichang Center for Disease Control and Prevention, Yichang, Hubei, China,Xiao-Jun Liu ✉
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14
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Cancino-Muñoz I, López MG, Torres-Puente M, Villamayor LM, Borrás R, Borrás-Máñez M, Bosque M, Camarena JJ, Colijn C, Colomer-Roig E, Colomina J, Escribano I, Esparcia-Rodríguez O, García-García F, Gil-Brusola A, Gimeno C, Gimeno-Gascón A, Gomila-Sard B, Gónzales-Granda D, Gonzalo-Jiménez N, Guna-Serrano MR, López-Hontangas JL, Martín-González C, Moreno-Muñoz R, Navarro D, Navarro M, Orta N, Pérez E, Prat J, Rodríguez JC, Ruiz-García MM, Vanaclocha H, Comas I. Population-based sequencing of Mycobacterium tuberculosis reveals how current population dynamics are shaped by past epidemics. eLife 2022; 11:76605. [PMID: 35880398 PMCID: PMC9323001 DOI: 10.7554/elife.76605] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 07/07/2022] [Indexed: 11/13/2022] Open
Abstract
Transmission is a driver of tuberculosis (TB) epidemics in high-burden regions, with assumed negligible impact in low-burden areas. However, we still lack a full characterization of transmission dynamics in settings with similar and different burdens. Genomic epidemiology can greatly help to quantify transmission, but the lack of whole genome sequencing population-based studies has hampered its application. Here, we generate a population-based dataset from Valencia region and compare it with available datasets from different TB-burden settings to reveal transmission dynamics heterogeneity and its public health implications. We sequenced the whole genome of 785 Mycobacterium tuberculosis strains and linked genomes to patient epidemiological data. We use a pairwise distance clustering approach and phylodynamic methods to characterize transmission events over the last 150 years, in different TB-burden regions. Our results underscore significant differences in transmission between low-burden TB settings, i.e., clustering in Valencia region is higher (47.4%) than in Oxfordshire (27%), and similar to a high-burden area as Malawi (49.8%). By modeling times of the transmission links, we observed that settings with high transmission rate are associated with decades of uninterrupted transmission, irrespective of burden. Together, our results reveal that burden and transmission are not necessarily linked due to the role of past epidemics in the ongoing TB incidence, and highlight the need for in-depth characterization of transmission dynamics and specifically tailored TB control strategies.
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Affiliation(s)
- Irving Cancino-Muñoz
- Tuberculosis Genomics Unit, Instituto de Biomedicina de Valencia (IBV-CSIC), Valencia, Spain
| | - Mariana G López
- Tuberculosis Genomics Unit, Instituto de Biomedicina de Valencia (IBV-CSIC), Valencia, Spain
| | - Manuela Torres-Puente
- Tuberculosis Genomics Unit, Instituto de Biomedicina de Valencia (IBV-CSIC), Valencia, Spain
| | - Luis M Villamayor
- Unidad Mixta "Infección y Salud Pública" (FISABIO-CSISP), Valencia, Spain
| | - Rafael Borrás
- Microbiology Service, Hospital Clínico Universitario, Valencia, Spain
| | - María Borrás-Máñez
- Microbiology and Parasitology Service, Hospital Universitario de La Ribera, Alzira, Spain
| | | | - Juan J Camarena
- Microbiology Service, Hospital Universitario Dr Peset, Valencia, Spain
| | - Caroline Colijn
- Department of Mathematics, Faculty of Science, Simon Fraser University, Burnaby, Canada
| | - Ester Colomer-Roig
- Unidad Mixta "Infección y Salud Pública" (FISABIO-CSISP), Valencia, Spain.,Microbiology Service, Hospital Universitario Dr Peset, Valencia, Spain
| | - Javier Colomina
- Microbiology Service, Hospital Clínico Universitario, Valencia, Spain
| | - Isabel Escribano
- Microbiology Laboratory, Hospital Virgen de los Lirios, Alcoy, Spain
| | | | | | - Ana Gil-Brusola
- Microbiology Service, Hospital Universitari i Politècnic La Fe, Valencia, Spain
| | - Concepción Gimeno
- Microbiology Service, Hospital General Universitario de Valencia, Valencia, Spain
| | | | - Bárbara Gomila-Sard
- Microbiology Service, Hospital General Universitario de Castellón, Castellón, Spain
| | | | | | | | | | - Coral Martín-González
- Microbiology Service, Hospital Universitario de San Juan de Alicante, Alicantes, Spain
| | - Rosario Moreno-Muñoz
- Microbiology Service, Hospital General Universitario de Castellón, Castellón, Spain
| | - David Navarro
- Microbiology Service, Hospital Clínico Universitario, Valencia, Spain
| | - María Navarro
- Microbiology Service, Hospital de la Vega Baixa, Orihuela, Spain
| | - Nieves Orta
- Microbiology Service, Hospital Universitario de San Juan de Alicante, Alicantes, Spain
| | - Elvira Pérez
- Subdirección General de Epidemiología y Vigilancia de la Salud y Sanidad Ambiental de Valencia (DGSP), Valencia, Spain
| | - Josep Prat
- Microbiology Service, Hospital de Sagunto, Sagunto, Spain
| | | | | | - Hermelinda Vanaclocha
- Subdirección General de Epidemiología y Vigilancia de la Salud y Sanidad Ambiental de Valencia (DGSP), Valencia, Spain
| | | | - Iñaki Comas
- Tuberculosis Genomics Unit, Instituto de Biomedicina de Valencia (IBV-CSIC), Valencia, Spain.,CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain
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15
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Menardo F. Understanding drivers of phylogenetic clustering and terminal branch lengths distribution in epidemics of Mycobacterium tuberculosis. eLife 2022; 11:76780. [PMID: 35762734 PMCID: PMC9239681 DOI: 10.7554/elife.76780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 06/15/2022] [Indexed: 11/13/2022] Open
Abstract
Detecting factors associated with transmission is important to understand disease epidemics, and to design effective public health measures. Clustering and terminal branch lengths (TBL) analyses are commonly applied to genomic data sets of Mycobacterium tuberculosis (MTB) to identify sub-populations with increased transmission. Here, I used a simulation-based approach to investigate what epidemiological processes influence the results of clustering and TBL analyses, and whether differences in transmission can be detected with these methods. I simulated MTB epidemics with different dynamics (latency, infectious period, transmission rate, basic reproductive number R0, sampling proportion, sampling period, and molecular clock), and found that all considered factors, except for the length of the infectious period, affect the results of clustering and TBL distributions. I show that standard interpretations of this type of analyses ignore two main caveats: (1) clustering results and TBL depend on many factors that have nothing to do with transmission, (2) clustering results and TBL do not tell anything about whether the epidemic is stable, growing, or shrinking, unless all the additional parameters that influence these metrics are known, or assumed identical between sub-populations. An important consequence is that the optimal SNP threshold for clustering depends on the epidemiological conditions, and that sub-populations with different epidemiological characteristics should not be analyzed with the same threshold. Finally, these results suggest that different clustering rates and TBL distributions, that are found consistently between different MTB lineages, are probably due to intrinsic bacterial factors, and do not indicate necessarily differences in transmission or evolutionary success.
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Affiliation(s)
- Fabrizio Menardo
- Department of Plant and Microbial Biology, University of Zurich, Zurich, Switzerland
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