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Farhat M, Cox H, Ghanem M, Denkinger CM, Rodrigues C, Abd El Aziz MS, Enkh-Amgalan H, Vambe D, Ugarte-Gil C, Furin J, Pai M. Drug-resistant tuberculosis: a persistent global health concern. Nat Rev Microbiol 2024:10.1038/s41579-024-01025-1. [PMID: 38519618 DOI: 10.1038/s41579-024-01025-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/12/2024] [Indexed: 03/25/2024]
Abstract
Drug-resistant tuberculosis (TB) is estimated to cause 13% of all antimicrobial resistance-attributable deaths worldwide and is driven by both ongoing resistance acquisition and person-to-person transmission. Poor outcomes are exacerbated by late diagnosis and inadequate access to effective treatment. Advances in rapid molecular testing have recently improved the diagnosis of TB and drug resistance. Next-generation sequencing of Mycobacterium tuberculosis has increased our understanding of genetic resistance mechanisms and can now detect mutations associated with resistance phenotypes. All-oral, shorter drug regimens that can achieve high cure rates of drug-resistant TB within 6-9 months are now available and recommended but have yet to be scaled to global clinical use. Promising regimens for the prevention of drug-resistant TB among high-risk contacts are supported by early clinical trial data but final results are pending. A person-centred approach is crucial in managing drug-resistant TB to reduce the risk of poor treatment outcomes, side effects, stigma and mental health burden associated with the diagnosis. In this Review, we describe current surveillance of drug-resistant TB and the causes, risk factors and determinants of drug resistance as well as the stigma and mental health considerations associated with it. We discuss recent advances in diagnostics and drug-susceptibility testing and outline the progress in developing better treatment and preventive therapies.
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Affiliation(s)
- Maha Farhat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Helen Cox
- Institute of Infectious Disease and Molecular Medicine, Wellcome Centre for Infectious Disease Research and Division of Medical Microbiology, University of Cape Town, Cape Town, South Africa
| | - Marwan Ghanem
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Claudia M Denkinger
- Division of Infectious Disease and Tropical Medicine, Heidelberg University Hospital, Heidelberg, Germany
- German Center for Infection Research (DZIF), partner site Heidelberg University Hospital, Heidelberg, Germany
| | | | - Mirna S Abd El Aziz
- Division of Infectious Disease and Tropical Medicine, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Debrah Vambe
- National TB Control Programme, Manzini, Eswatini
| | - Cesar Ugarte-Gil
- School of Public and Population Health, University of Texas Medical Branch, Galveston, TX, USA
| | - Jennifer Furin
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
| | - Madhukar Pai
- McGill International TB Centre, McGill University, Montreal, Quebec, Canada.
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Spies R, Hong HN, Trieu PP, Lan LK, Lan K, Hue NN, Huong NTL, Thao TTLN, Quang NL, Anh TDD, Vinh TV, Ha DTM, Dat PT, Hai NP, Van LH, Thwaites GE, Thuong NTT, Watson JA, Walker TM. Spatial Analysis of Drug-Susceptible and Multidrug-Resistant Cases of Tuberculosis, Ho Chi Minh City, Vietnam, 2020-2023. Emerg Infect Dis 2024; 30:499-509. [PMID: 38407176 PMCID: PMC10902525 DOI: 10.3201/eid3003.231309] [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] [Indexed: 02/27/2024] Open
Abstract
We characterized the spatial distribution of drug-susceptible (DS) and multidrug-resistant (MDR) tuberculosis (TB) cases in Ho Chi Minh City, Vietnam, a major metropolis in southeastern Asia, and explored demographic and socioeconomic factors associated with local TB burden. Hot spots of DS and MDR TB incidence were observed in the central parts of Ho Chi Minh City, and substantial heterogeneity was observed across wards. Positive spatial autocorrelation was observed for both DS TB and MDR TB. Ward-level TB incidence was associated with HIV prevalence and the male proportion of the population. No ward-level demographic and socioeconomic indicators were associated with MDR TB case count relative to total TB case count. Our findings might inform spatially targeted TB control strategies and provide insights for generating hypotheses about the nature of the relationship between DS and MDR TB in Ho Chi Minh City and the wider southeastern region of Asia.
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Mutai CK, McSharry PE, Ngaruye I, Musabanganji E. Use of unsupervised machine learning to characterise HIV predictors in sub-Saharan Africa. BMC Infect Dis 2023; 23:482. [PMID: 37468851 DOI: 10.1186/s12879-023-08467-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 07/17/2023] [Indexed: 07/21/2023] Open
Abstract
INTRODUCTION Significant regional variations in the HIV epidemic hurt effective common interventions in sub-Saharan Africa. It is crucial to analyze HIV positivity distributions within clusters and assess the homogeneity of countries. We aim at identifying clusters of countries based on socio-behavioural predictors of HIV for screening. METHOD We used an agglomerative hierarchical, unsupervised machine learning, approach for clustering to analyse data for 146,733 male and 155,622 female respondents from 13 sub-Saharan African countries with 20 and 26 features, respectively, using Population-based HIV Impact Assessment (PHIA) data from the survey years 2015-2019. We employed agglomerative hierarchical clustering and optimal silhouette index criterion to identify clusters of countries based on the similarity of socio-behavioural characteristics. We analyse the distribution of HIV positivity with socio-behavioural predictors of HIV within each cluster. RESULTS Two principal components were obtained, with the first describing 62.3% and 70.1% and the second explaining 18.3% and 20.6% variance of the total socio-behavioural variation in females and males, respectively. Two clusters per sex were identified, and the most predictor features in both sexes were: relationship with family head, enrolled in school, circumcision status for males, delayed pregnancy, work for payment in last 12 months, Urban area indicator, known HIV status and delayed pregnancy. The HIV positivity distribution with these variables was significant within each cluster. CONCLUSIONS /FINDINGS The findings provide a potential use of unsupervised machine learning approaches for substantially identifying clustered countries based on the underlying socio-behavioural characteristics.
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Affiliation(s)
- Charles K Mutai
- African Center of Excellence in Data Science, University of Rwanda, Kigali, BP 4285, Rwanda.
- Department of Mathematics, Physics and Computing, Moi University, Eldoret, Kenya.
| | - Patrick E McSharry
- African Center of Excellence in Data Science, University of Rwanda, Kigali, BP 4285, Rwanda
- College of Engineering, Carnegie Mellon University Africa, Kigali, BP 6150, Rwanda
- Oxford-Man Institute of Quantitative Finance, Oxford University, Oxford, OX2 6ED, UK
| | - Innocent Ngaruye
- College of Science and Technology, University of Rwanda, Kigali, Rwanda
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Dias S, Castro S, Ribeiro AI, Krainski ET, Duarte R. Geographic patterns and hotspots of pediatric tuberculosis: the role of socioeconomic determinants. J Bras Pneumol 2023; 49:e20230004. [PMID: 37341241 PMCID: PMC10578936 DOI: 10.36416/1806-3756/e20230004] [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] [Received: 02/08/2023] [Accepted: 04/24/2023] [Indexed: 06/22/2023] Open
Abstract
OBJECTIVE Children are an important demographic group for understanding overall tuberculosis epidemiology, and monitoring of childhood tuberculosis is essential for appropriate prevention. The present study sought to characterize the spatial distribution of childhood tuberculosis notification rates in continental Portugal; identify high-risk areas; and evaluate the association between childhood tuberculosis notification rates and socioeconomic deprivation. METHODS Using hierarchical Bayesian spatial models, we analyzed the geographic distribution of pediatric tuberculosis notification rates across 278 municipalities between 2016 and 2020 and determined high-risk and low-risk areas. We used the Portuguese version of the European Deprivation Index to estimate the association between childhood tuberculosis and area-level socioeconomic deprivation. RESULTS Notification rates ranged from 1.8 to 13.15 per 100,000 children under 5 years of age. We identified seven high-risk areas, the relative risk of which was significantly above the study area average. All seven high-risk areas were located in the metropolitan area of Porto or Lisbon. There was a significant relationship between socioeconomic deprivation and pediatric tuberculosis notification rates (relative risk = 1.16; Bayesian credible interval, 1.05-1.29). CONCLUSIONS Identified high-risk and socioeconomically deprived areas should constitute target areas for tuberculosis control, and these data should be integrated with other risk factors to define more precise criteria for BCG vaccination.
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Affiliation(s)
- Sara Dias
- . Hospital Pedro Hispano, Matosinhos, Portugal
| | - Sofia Castro
- . Centro Hospitalar do Baixo Vouga, Hospital Infante D. Pedro, Aveiro, Portugal
| | - Ana Isabel Ribeiro
- . EPIUnit, Instituto de Saúde Pública - ISPUP - Universidade do Porto, Porto, Portugal
- . Laboratório para a Investigação Integrativa e Translacional em Saúde Populacional - ITR - Porto, Portugal
- . Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina, Universidade do Porto, Porto, Portugal
| | - Elias T Krainski
- . Departamento de Estatística, Universidade Federal do Paraná - UFPR -Curitiba (PR) Brasil
- . King Abdullah University of Science and Technology - KAUST - Tuwal, Saudi Arabia
| | - Raquel Duarte
- . EPIUnit, Instituto de Saúde Pública - ISPUP - Universidade do Porto, Porto, Portugal
- . Instituto de Ciências Biomédicas Abel Salazar - ICBAS - Universidade do Porto, Porto, Portugal
- . Unidade de Investigação Clínica da ARS Norte, Porto, Portugal
- . Serviço de Pneumologia, Centro Hospitalar de Vila Nova de Gaia/Espinho, Vila Nova de Gaia, Portugal
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Lin CH, Wen TH. How Spatial Epidemiology Helps Understand Infectious Human Disease Transmission. Trop Med Infect Dis 2022; 7:tropicalmed7080164. [PMID: 36006256 PMCID: PMC9413673 DOI: 10.3390/tropicalmed7080164] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/15/2022] [Accepted: 07/15/2022] [Indexed: 02/06/2023] Open
Abstract
Both directly and indirectly transmitted infectious diseases in humans are spatial-related. Spatial dimensions include: distances between susceptible humans and the environments shared by people, contaminated materials, and infectious animal species. Therefore, spatial concepts in managing and understanding emerging infectious diseases are crucial. Recently, due to the improvements in computing performance and statistical approaches, there are new possibilities regarding the visualization and analysis of disease spatial data. This review provides commonly used spatial or spatial-temporal approaches in managing infectious diseases. It covers four sections, namely: visualization, overall clustering, hot spot detection, and risk factor identification. The first three sections provide methods and epidemiological applications for both point data (i.e., individual data) and aggregate data (i.e., summaries of individual points). The last section focuses on the spatial regression methods adjusted for neighbour effects or spatial heterogeneity and their implementation. Understanding spatial-temporal variations in the spread of infectious diseases have three positive impacts on the management of diseases. These are: surveillance system improvements, the generation of hypotheses and approvals, and the establishment of prevention and control strategies. Notably, ethics and data quality have to be considered before applying spatial-temporal methods. Developing differential global positioning system methods and optimizing Bayesian estimations are future directions.
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Affiliation(s)
- Chia-Hsien Lin
- Department of Health Promotion and Health Education, National Taiwan Normal University, Taipei City 10610, Taiwan
- Department of Geography, National Taiwan University, Taipei City 10617, Taiwan;
- Correspondence:
| | - Tzai-Hung Wen
- Department of Geography, National Taiwan University, Taipei City 10617, Taiwan;
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Sy KTL, Leavitt SV, de Vos M, Dolby T, Bor J, Horsburgh CR, Warren RM, Streicher EM, Jenkins HE, Jacobson KR. Spatial heterogeneity of extensively drug resistant-tuberculosis in Western Cape Province, South Africa. Sci Rep 2022; 12:10844. [PMID: 35760977 PMCID: PMC9237070 DOI: 10.1038/s41598-022-14581-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 06/09/2022] [Indexed: 02/04/2023] Open
Abstract
Tuberculosis (TB) remains a leading infectious disease killer globally. Treatment outcomes are especially poor among people with extensively drug-resistant (XDR) TB, until recently defined as rifampicin-resistant (RR) TB with resistance to an aminoglycoside (amikacin) and a fluoroquinolone (ofloxacin). We used laboratory TB test results from Western Cape province, South Africa between 2012 and 2015 to identify XDR-TB and pre-XDR-TB (RR-TB with resistance to one second-line drug) spatial hotspots. We mapped the percentage and count of individuals with RR-TB that had XDR-TB and pre-XDR-TB across the province and in Cape Town, as well as amikacin-resistant and ofloxacin-resistant TB. We found the percentage of pre-XDR-TB and the count of XDR-TB/pre-XDR-TB highly heterogeneous with geographic hotspots within RR-TB high burden areas, and found hotspots in both percentage and count of amikacin-resistant and ofloxacin-resistant TB. The spatial distribution of percentage ofloxacin-resistant TB hotspots was similar to XDR-TB hotspots, suggesting that fluoroquinolone-resistace is often the first step to additional resistance. Our work shows that interventions used to reduce XDR-TB incidence may need to be targeted within spatial locations of RR-TB, and further research is required to understand underlying drivers of XDR-TB transmission in these locations.
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Affiliation(s)
- Karla Therese L Sy
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Sarah V Leavitt
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Margaretha de Vos
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research/South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Tania Dolby
- National Health Laboratory Service, Cape Town, South Africa
| | - Jacob Bor
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - C Robert Horsburgh
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Global Health, Boston University School of Public Health, Boston, MA, USA
- Section of Infectious Diseases, School of Medicine and Boston Medical Center, Boston University, Boston, MA, USA
| | - Robin M Warren
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research/South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Elizabeth M Streicher
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research/South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Helen E Jenkins
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Karen R Jacobson
- Section of Infectious Diseases, School of Medicine and Boston Medical Center, Boston University, Boston, MA, USA.
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Guerra SS, Seixas E, Ribeiro AI, Duarte R. Tell me where you went, I may tell who you infected. JORNAL BRASILEIRO DE PNEUMOLOGIA : PUBLICACAO OFICIAL DA SOCIEDADE BRASILEIRA DE PNEUMOLOGIA E TISILOGIA 2022; 48:e20220099. [PMID: 35703673 PMCID: PMC9262441 DOI: 10.36416/1806-3756/e20220099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
- Sónia Silva Guerra
- . Serviço de Pneumologia, Centro Hospitalar Tondela-Viseu, Viseu, Portugal
| | - Eduarda Seixas
- . Serviço de Pneumologia, Centro Hospitalar Baixo-Vouga, Aveiro, Portugal
| | - Ana Isabel Ribeiro
- . EPIUnit - Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal.,. Laboratório para a Investigação Integrativa e Translacional em Saúde Populacional, Porto, Portugal.,. Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina, Universidade do Porto, Porto, Portugal
| | - Raquel Duarte
- . EPIUnit - Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal.,. ICBAS-UP - Instituto de Ciências Biomédicas de Abel Salazar, Universidade do Porto, Porto, Portugal.,. Serviço de Pneumologia, Centro Hospitalar de Vila Nova de Gaia, Vila Nova de Gaia, Portugal.,. Unidade de Investigação Clínica, Administração Regional de Saúde do Norte, Porto, Portugal
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Intra-urban variation in tuberculosis and community socioeconomic deprivation in Lisbon metropolitan area: a Bayesian approach. Infect Dis Poverty 2022; 11:24. [PMID: 35321758 PMCID: PMC8942608 DOI: 10.1186/s40249-022-00949-1] [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: 11/25/2021] [Accepted: 02/18/2022] [Indexed: 11/13/2022] Open
Abstract
Background Multidrug resistant tuberculosis (MDR-TB) is a recognized threat to global efforts to TB control and remains a priority of the National Tuberculosis Programs. Additionally, social determinants and socioeconomic deprivation have since long been associated with worse health and perceived as important risk factors for TB. This study aimed to analyze the spatial distribution of non-MDR-TB and MDR-TB across parishes of the Lisbon metropolitan area of Portugal and to estimate the association between non-MDR-TB and MDR-TB and socioeconomic deprivation. Methods In this study, we used hierarchical Bayesian spatial models to analyze the spatial distribution of notification of non-MDR-TB and MDR-TB cases for the period from 2000 to 2016 across 127 parishes of the seven municipalities of the Lisbon metropolitan area (Almada, Amadora, Lisboa, Loures, Odivelas, Oeiras, Sintra), using the Portuguese TB Surveillance System (SVIG-TB). In order to characterise the populations, we used the European Deprivation Index for Portugal (EDI-PT) as an indicator of poverty and estimated the association between non-MDR-TB and MDR-TB and socioeconomic deprivation. Results The notification rates per 10,000 population of non-MDR TB ranged from 18.95 to 217.49 notifications and that of MDR TB ranged from 0.83 to 3.70. We identified 54 high-risk areas for non-MDR-TB and 13 high-risk areas for MDR-TB. Parishes in the third [relative risk (RR) = 1.281, 95% credible interval (CrI): 1.021–1.606], fourth (RR = 1.786, 95% CrI: 1.420–2.241) and fifth (RR = 1.935, 95% CrI: 1.536–2.438) quintile of socioeconomic deprivation presented higher non-MDR-TB notifications rates. Parishes in the fourth (RR = 2.246, 95% CrI: 1.374–3.684) and fifth (RR = 1.828, 95% CrI: 1.049–3.155) quintile of socioeconomic deprivation also presented higher MDR-TB notifications rates. Conclusions We demonstrated significant heterogeneity in the spatial distribution of both non-MDR-TB and MDR-TB at the parish level and we found that socioeconomically disadvantaged parishes are disproportionally affected by both non-MDR-TB and MDR-TB. Our findings suggest that the emergence of MDR-TB and transmission are specific from each location and often different from the non-MDR-TB settings. We identified priority areas for intervention for a more efficient plan of control and prevention of non-MDR-TB and MDR-TB. Graphical Abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1186/s40249-022-00949-1.
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Oliveira O, Gaio R, Correia-Neves M, Rito T, Duarte R. Evaluation of drug-resistant tuberculosis treatment outcome in Portugal, 2000-2016. PLoS One 2021; 16:e0250028. [PMID: 33878119 PMCID: PMC8057584 DOI: 10.1371/journal.pone.0250028] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 03/29/2021] [Indexed: 11/18/2022] Open
Abstract
Treatment of drug-resistant tuberculosis (TB), which is usually less successful than that of drug-susceptible TB, represents a challenge for TB control and elimination. We aimed to evaluate treatment outcomes and to identify the factors associated with death among patients with MDR and XDR-TB in Portugal. We assessed MDR-TB cases reported for the period 2000-2016, using the national TB Surveillance System. Treatment outcomes were defined according to WHO recommendations. We identified the factors associated with death using logistic regression. We evaluated treatment outcomes of 294 MDR- and 142 XDR-TB patients. The treatment success rate was 73.8% among MDR- and 62.7% among XDR-TB patients (p = 0.023). The case-fatality rate was 18.4% among MDR- and 23.9% among XDR-TB patients. HIV infection (OR 4.55; 95% CI 2.31-8.99; p < 0.001) and resistance to one or more second-line injectable drugs (OR 2.73; 95% CI 1.26-5.92; p = 0.011) were independently associated with death among MDR-TB patients. HIV infection, injectable drug use, past imprisonment, comorbidities, and alcohol abuse are conditions that were associated with death early on and during treatment. Early diagnosis of MDR-TB and further monitoring of these patients are necessary to improve treatment outcome.
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Affiliation(s)
- Olena Oliveira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B, PT Government Associate Laboratory, University of Minho, Braga/Guimarães, Portugal
- EPIUnit, Instituto de Saúde Pública da Universidade do Porto, Porto, Portugal
| | - Rita Gaio
- Department of Mathematics, Faculty of Sciences, University of Porto, Porto, Portugal
- Centre of Mathematics, University of Porto, Porto, Portugal
| | - Margarida Correia-Neves
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B, PT Government Associate Laboratory, University of Minho, Braga/Guimarães, Portugal
| | - Teresa Rito
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B, PT Government Associate Laboratory, University of Minho, Braga/Guimarães, Portugal
- Centre of Molecular and Environmental Biology (CBMA), University of Minho, Braga, Portugal
| | - Raquel Duarte
- EPIUnit, Instituto de Saúde Pública da Universidade do Porto, Porto, Portugal
- Clinical Epidemiology, Predictive Medicine and Public Health Department, Faculty of Medicine, University of Porto, Porto, Portugal
- Pulmonology Unit, Centro Hospitalar de Vila Nova de Gaia/Espinho EPE, Vila Nova de Gaia, Portugal
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