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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 DIGITAL 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] [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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Carrasco-Escobar G, Schwalb A, Tello-Lizarraga K, Vega-Guerovich P, Ugarte-Gil C. Spatio-temporal co-occurrence of hotspots of tuberculosis, poverty and air pollution in Lima, Peru. Infect Dis Poverty 2020; 9:32. [PMID: 32204735 PMCID: PMC7092495 DOI: 10.1186/s40249-020-00647-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 03/05/2020] [Indexed: 12/03/2022] Open
Abstract
Growing evidence suggests pollution and other environmental factors have a role in the development of tuberculosis (TB), however, such studies have never been conducted in Peru. Considering the association between air pollution and specific geographic areas, our objective was to determine the spatial distribution and clustering of TB incident cases in Lima and their co-occurrence with clusters of fine particulate matter (PM2.5) and poverty. We found co-occurrences of clusters of elevated concentrations of air pollutants such as PM2.5, high poverty indexes, and high TB incidence in Lima. These findings suggest an interplay of socio-economic and environmental in driving TB incidence.
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Affiliation(s)
- Gabriel Carrasco-Escobar
- Health Innovation Lab, Instituto de Medicina Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru
- Division of Infectious Diseases, Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Alvaro Schwalb
- Instituto de Medicina Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Kelly Tello-Lizarraga
- School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Lima, Peru
| | | | - Cesar Ugarte-Gil
- Instituto de Medicina Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru.
- School of Medicine, Universidad Peruana Cayetano Heredia, Lima, Peru.
- TB Centre, London School of Hygiene and Tropical Medicine, London, UK.
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Shaweno D, Karmakar M, Alene KA, Ragonnet R, Clements AC, Trauer JM, Denholm JT, McBryde ES. Methods used in the spatial analysis of tuberculosis epidemiology: a systematic review. BMC Med 2018; 16:193. [PMID: 30333043 PMCID: PMC6193308 DOI: 10.1186/s12916-018-1178-4] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 09/20/2018] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Tuberculosis (TB) transmission often occurs within a household or community, leading to heterogeneous spatial patterns. However, apparent spatial clustering of TB could reflect ongoing transmission or co-location of risk factors and can vary considerably depending on the type of data available, the analysis methods employed and the dynamics of the underlying population. Thus, we aimed to review methodological approaches used in the spatial analysis of TB burden. METHODS We conducted a systematic literature search of spatial studies of TB published in English using Medline, Embase, PsycInfo, Scopus and Web of Science databases with no date restriction from inception to 15 February 2017. The protocol for this systematic review was prospectively registered with PROSPERO ( CRD42016036655 ). RESULTS We identified 168 eligible studies with spatial methods used to describe the spatial distribution (n = 154), spatial clusters (n = 73), predictors of spatial patterns (n = 64), the role of congregate settings (n = 3) and the household (n = 2) on TB transmission. Molecular techniques combined with geospatial methods were used by 25 studies to compare the role of transmission to reactivation as a driver of TB spatial distribution, finding that geospatial hotspots are not necessarily areas of recent transmission. Almost all studies used notification data for spatial analysis (161 of 168), although none accounted for undetected cases. The most common data visualisation technique was notification rate mapping, and the use of smoothing techniques was uncommon. Spatial clusters were identified using a range of methods, with the most commonly employed being Kulldorff's spatial scan statistic followed by local Moran's I and Getis and Ord's local Gi(d) tests. In the 11 papers that compared two such methods using a single dataset, the clustering patterns identified were often inconsistent. Classical regression models that did not account for spatial dependence were commonly used to predict spatial TB risk. In all included studies, TB showed a heterogeneous spatial pattern at each geographic resolution level examined. CONCLUSIONS A range of spatial analysis methodologies has been employed in divergent contexts, with all studies demonstrating significant heterogeneity in spatial TB distribution. Future studies are needed to define the optimal method for each context and should account for unreported cases when using notification data where possible. Future studies combining genotypic and geospatial techniques with epidemiologically linked cases have the potential to provide further insights and improve TB control.
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Affiliation(s)
- Debebe Shaweno
- Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia.
- Victorian Tuberculosis Program at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia.
| | - Malancha Karmakar
- Victorian Tuberculosis Program at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
- Department of Microbiology and Immunology, University of Melbourne, Melbourne, Victoria, Australia
| | - Kefyalew Addis Alene
- Research School of Population Health, College of Health and Medicine, The Australian National University, Canberra, Australia
- Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Romain Ragonnet
- Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia
- Burnet Institute, Melbourne, Australia
| | | | - James M Trauer
- Victorian Tuberculosis Program at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Justin T Denholm
- Victorian Tuberculosis Program at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
- Department of Microbiology and Immunology, University of Melbourne, Melbourne, Victoria, Australia
| | - Emma S McBryde
- Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia
- Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Queensland, Australia
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Auld SC, Shah NS, Cohen T, Martinson NA, Gandhi NR. Where is tuberculosis transmission happening? Insights from the literature, new tools to study transmission and implications for the elimination of tuberculosis. Respirology 2018; 23:10.1111/resp.13333. [PMID: 29869818 PMCID: PMC6281783 DOI: 10.1111/resp.13333] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2018] [Revised: 05/14/2018] [Accepted: 05/20/2018] [Indexed: 12/12/2022]
Abstract
More than 10 million new cases of tuberculosis (TB) are diagnosed worldwide each year. The majority of these cases occur in low- and middle-income countries where the TB epidemic is predominantly driven by transmission. Efforts to 'end TB' will depend upon our ability to halt ongoing transmission. However, recent studies of new approaches to interrupt transmission have demonstrated inconsistent effects on reducing population-level TB incidence. TB transmission occurs across a wide range of settings, that include households and hospitals, but also community-based settings. While home-based contact investigations and infection control programmes in hospitals and clinics have a successful track record as TB control activities, there is a gap in our knowledge of where, and between whom, community-based transmission of TB occurs. Novel tools, including molecular epidemiology, geospatial analyses and ventilation studies, provide hope for improving our understanding of transmission in countries where the burden of TB is greatest. By integrating these diverse and innovative tools, we can enhance our ability to identify transmission events by documenting the opportunity for transmission-through either an epidemiologic or geospatial connection-alongside genomic evidence for transmission, based upon genetically similar TB strains. A greater understanding of locations and patterns of transmission will translate into meaningful improvements in our current TB control activities by informing targeted, evidence-based public health interventions.
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Affiliation(s)
- Sara C Auld
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - N Sarita Shah
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Division of Global HIV and TB, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | - Neil A Martinson
- Perinatal HIV Research Unit, University of the Witwatersrand, Johannesburg, South Africa
- Center for TB Research, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Neel R Gandhi
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Department of Global Health, Emory University Rollins School of Public Health, Atlanta, GA, USA
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Spatial patterns of multidrug resistant tuberculosis and relationships to socio-economic, demographic and household factors in northwest Ethiopia. PLoS One 2017; 12:e0171800. [PMID: 28182726 PMCID: PMC5300134 DOI: 10.1371/journal.pone.0171800] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2016] [Accepted: 01/26/2017] [Indexed: 11/19/2022] Open
Abstract
Background Understanding the geographical distribution of multidrug-resistant tuberculosis (MDR-TB) in high TB burden countries such as Ethiopia is crucial for effective control of TB epidemics in these countries, and thus globally. We present the first spatial analysis of multidrug resistant tuberculosis, and its relationship to socio-economic, demographic and household factors in northwest Ethiopia. Methods An ecological study was conducted using data on patients diagnosed with MDR-TB at the University of Gondar Hospital MDR-TB treatment centre, for the period 2010 to 2015. District level population data were extracted from the Ethiopia National and Regional Census Report. Spatial autocorrelation was explored using Moran’s I statistic, Local Indicators of Spatial Association (LISA), and the Getis-Ord statistics. A multivariate Poisson regression model was developed with a conditional autoregressive (CAR) prior structure, and with posterior parameters estimated using a Bayesian Markov chain Monte Carlo (MCMC) simulation approach with Gibbs sampling, in WinBUGS. Results A total of 264 MDR-TB patients were included in the analysis. The overall crude incidence rate of MDR-TB for the six-year period was 3.0 cases per 100,000 population. The highest incidence rate was observed in Metema (21 cases per 100,000 population) and Humera (18 cases per 100,000 population) districts; whereas nine districts had zero cases. Spatial clustering of MDR-TB was observed in districts located in the Ethiopia-Sudan and Ethiopia-Eritrea border regions, where large numbers of seasonal migrants live. Spatial clustering of MDR-TB was positively associated with urbanization (RR: 1.02; 95%CI: 1.01, 1.04) and the percentage of men (RR: 1.58; 95% CI: 1.26, 1.99) in the districts; after accounting for these factors there was no residual spatial clustering. Conclusion Spatial clustering of MDR-TB, fully explained by demographic factors (urbanization and percent male), was detected in the border regions of northwest Ethiopia, in locations where seasonal migrants live and work. Cross-border initiatives including options for mobile TB treatment and follow up are important for the effective control of MDR-TB in the region.
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Girón S. Primary health care: a necessary, current and profitable investment. Colomb Med (Cali) 2015; 46:88-9. [PMID: 26600622 PMCID: PMC4640429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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Zelner JL, Murray MB, Becerra MC, Galea J, Lecca L, Calderon R, Yataco R, Contreras C, Zhang Z, Manjourides J, Grenfell BT, Cohen T. Identifying Hotspots of Multidrug-Resistant Tuberculosis Transmission Using Spatial and Molecular Genetic Data. J Infect Dis 2015; 213:287-94. [PMID: 26175455 DOI: 10.1093/infdis/jiv387] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2015] [Accepted: 07/08/2015] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND We aimed to identify and determine the etiology of "hotspots" of concentrated multidrug-resistant tuberculosis (MDR-tuberculosis) risk in Lima, Peru. METHODS From 2009 to 2012, we conducted a prospective cohort study among households of tuberculosis cases from 106 health center (HC) areas in Lima, Peru. All notified tuberculosis cases and their household contacts were followed for 1 year. Symptomatic individuals were screened by microscopy and culture; positive cultures were tested for drug susceptibility (DST) and genotyped by 24-loci mycobacterial interspersed repetitive units-variable-number tandem repeats (MIRU-VNTR). RESULTS 3286 individuals with culture-confirmed disease, DST, and 24-loci MIRU-VNTR were included in our analysis. Our analysis reveals: (1) heterogeneity in annual per-capita incidence of tuberculosis and MDR-tuberculosis by HC, with a rate of MDR-tuberculosis 89 times greater (95% confidence interval [CI], 54,185) in the most-affected versus the least-affected HC; (2) high risk for MDR-tuberculosis in a region spanning several HCs (odds ratio = 3.19, 95% CI, 2.33, 4.36); and (3) spatial aggregation of MDR-tuberculosis genotypes, suggesting localized transmission. CONCLUSIONS These findings reveal that localized transmission is an important driver of the epidemic of MDR-tuberculosis in Lima. Efforts to interrupt transmission may be most effective if targeted to this area of the city.
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Affiliation(s)
- Jonathan L Zelner
- Robert Wood Johnson Foundation Health and Society Scholars Program, Interdisciplinary Center for Innovative Theory and Empirics (INCITE) & Mailman School of Public Health, Columbia University, New York, New York
| | - Megan B Murray
- Department of Global Health and Social Medicine, Harvard Medical School, Department of Epidemiology, Harvard School of Public Health
| | - Mercedes C Becerra
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts
| | | | | | | | | | | | - Zibiao Zhang
- Division of Global Health Equity, Brigham and Women's Hospital
| | - Justin Manjourides
- Department of Health Sciences, Bouvé College of Health Sciences, Northeastern University, Boston, Massachusetts
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, New Jersey Fogarty International Center, National Institutes of Health, Bethesda, Maryland
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut
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Chang TS, Gangnon RE, David Page C, Buckingham WR, Tandias A, Cowan KJ, Tomasallo CD, Arndt BG, Hanrahan LP, Guilbert TW. Sparse modeling of spatial environmental variables associated with asthma. J Biomed Inform 2015; 53:320-9. [PMID: 25533437 PMCID: PMC4355087 DOI: 10.1016/j.jbi.2014.12.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2014] [Revised: 11/04/2014] [Accepted: 12/12/2014] [Indexed: 12/18/2022]
Abstract
Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors.
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Affiliation(s)
- Timothy S Chang
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin, 5795 Medical Sciences Center, 1300 University Ave, Madison, WI 53706, USA.
| | - Ronald E Gangnon
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin, 603 Warf Office Building, 610 Walnut St, Madison, WI 53706, USA.
| | - C David Page
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin, 6743 Medical Sciences Center, 1300 University Ave, Madison, WI 53706, USA.
| | - William R Buckingham
- Applied Population Laboratory, Department of Rural Sociology, University of Wisconsin, 308b Agricultural Hall, 1450 Linden Dr, Madison, WI 53706, USA.
| | - Aman Tandias
- Department of Family Medicine, School of Medicine and Public Health, University of Wisconsin, 1100 Delaplaine Ct, Madison, WI 53715, USA.
| | - Kelly J Cowan
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, WI 53706, USA.
| | - Carrie D Tomasallo
- Division of Public Health, Bureau of Environmental and Occupational Health, Wisconsin Department of Health Services, Room 150, 1 West Wilson Street, Madison, WI 53703, USA.
| | - Brian G Arndt
- Department of Family Medicine, School of Medicine and Public Health, University of Wisconsin, 1100 Delaplaine Ct, Madison, WI 53715, USA.
| | - Lawrence P Hanrahan
- Department of Family Medicine, School of Medicine and Public Health, University of Wisconsin, 1100 Delaplaine Ct, Madison, WI 53715, USA.
| | - Theresa W Guilbert
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, WI 53706, USA.
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Cohen T, Jenkins HE, Lu C, McLaughlin M, Floyd K, Zignol M. On the spread and control of MDR-TB epidemics: an examination of trends in anti-tuberculosis drug resistance surveillance data. Drug Resist Updat 2014; 17:105-23. [PMID: 25458783 DOI: 10.1016/j.drup.2014.10.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND Multidrug resistant tuberculosis (MDR-TB) poses serious challenges for tuberculosis control in many settings, but trends of MDR-TB have been difficult to measure. METHODS We analyzed surveillance and population-representative survey data collected worldwide by the World Health Organization between 1993 and 2012. We examined setting-specific patterns associated with linear trends in the estimated per capita rate of MDR-TB among new notified TB cases to generate hypotheses about factors associated with trends in the transmission of highly drug resistant tuberculosis. RESULTS 59 countries and 39 sub-national settings had at least three years of data, but less than 10% of the population in the WHO-designated 27-high MDR-TB burden settings were in areas with sufficient data to track trends. Among settings in which the majority of MDR-TB was autochthonous, we found 10 settings with statistically significant linear trends in per capita rates of MDR-TB among new notified TB cases. Five of these settings had declining trends (Estonia, Latvia, Macao, Hong Kong, and Portugal) ranging from decreases of 3% to 14% annually, while five had increasing trends (four individual oblasts of the Russian Federation and Botswana) ranging from 14% to 20% annually. In unadjusted analysis, better surveillance indicators and higher GDP per capita were associated with declining MDR-TB, while a higher existing absolute burden of MDR-TB was associated with an increasing trend. CONCLUSIONS Only a small fraction of countries in which the burden of MDR-TB is concentrated currently have sufficient surveillance data to estimate trends in drug-resistant TB. Where trend analysis was possible, smaller absolute burdens of MDR-TB and more robust surveillance systems were associated with declining per capita rates of MDR-TB among new notified cases.
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Affiliation(s)
- Ted Cohen
- Brigham and Women's Hospital, Division of Global Health Equity, Boston, MA 02115, USA; Harvard School of Public Health, Department of Epidemiology, Boston, MA 02115, USA.
| | - Helen E Jenkins
- Brigham and Women's Hospital, Division of Global Health Equity, Boston, MA 02115, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Chunling Lu
- Brigham and Women's Hospital, Division of Global Health Equity, Boston, MA 02115, USA; Harvard Medical School, Department of Global Health and Social Medicine, Boston, MA 02115, USA
| | | | - Katherine Floyd
- Global TB Programme, TB Monitoring and Evaluation, World Health Organization, Geneva, Switzerland
| | - Matteo Zignol
- Global TB Programme, TB Monitoring and Evaluation, World Health Organization, Geneva, Switzerland
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