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Wang J, Liu X, Jing Z, Yang J. Spatial and temporal clustering analysis of pulmonary tuberculosis and its associated risk factors in southwest China. GEOSPATIAL HEALTH 2023; 18. [PMID: 37246542 DOI: 10.4081/gh.2023.1169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/30/2023] [Indexed: 05/30/2023]
Abstract
Pulmonary tuberculosis (PTB) remains a serious public health problem, especially in areas of developing countries. This study aimed to explore the spatial-temporal clusters and associated risk factors of PTB in south-western China. Space-time scan statistics were used to explore the spatial and temporal distribution characteristics of PTB. We collected data on PTB, population, geographic information and possible influencing factors (average temperature, average rainfall, average altitude, planting area of crops and population density) from 11 towns in Mengzi, a prefecture-level city in China, between 1 January 2015 and 31 December 2019. A total of 901 reported PTB cases were collected in the study area and a spatial lag model was conducted to analyse the association between these variables and the PTB incidence. Kulldorff's scan results identified two significant space-time clusters, with the most likely cluster (RR = 2.24, p < 0.001) mainly located in northeastern Mengzi involving five towns in the time frame June 2017 - November 2019. A secondary cluster (RR = 2.09, p < 0.05) was located in southern Mengzi, covering two towns and persisting from July 2017 to December 2019. The results of the spatial lag model showed that average rainfall was associated with PTB incidence. Precautions and protective measures should be strengthened in high-risk areas to avoid spread of the disease.
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Affiliation(s)
- Jianjiao Wang
- Institution of Health Statistics and Epidemiology, School of Public Health, Lanzhou University, Gansu.
| | - Xiaoning Liu
- Institution of Health Statistics and Epidemiology, School of Public Health, Lanzhou University, Gansu.
| | - Zhengchao Jing
- Mengzi Center for Disease Control and Prevention, Yunnan.
| | - Jiawai Yang
- Mengzi Center for Disease Control and Prevention, Yunnan.
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Guerrero-Vadillo M, Peñuelas M, Domínguez Á, Godoy P, Gómez-Barroso D, Soldevila N, Izquierdo C, Martínez A, Torner N, Avellón A, Rius C, Varela C. Epidemiological Characteristics and Spatio-Temporal Distribution of Hepatitis A in Spain in the Context of the 2016/2017 European Outbreak. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16775. [PMID: 36554666 PMCID: PMC9778781 DOI: 10.3390/ijerph192416775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/02/2022] [Accepted: 12/09/2022] [Indexed: 06/17/2023]
Abstract
The aim of our study was to describe the results of the epidemiological surveillance of hepatitis A infections in Spain in the context of the 2016/2017 European outbreak, particularly of hepatitis A outbreaks reported in the MSM population, incorporating the results of a spatio-temporal analysis of cases. Hepatitis A cases and outbreaks reported in 2016-2017 to the National Epidemiological Surveillance Network were reviewed: outbreaks in which some of the cases belonged to the MSM group were described, and clusters of hepatitis A cases in men and women were analysed using a space-time scan statistic. Twenty-six outbreaks were identified, with a median size of two cases per outbreak, with most of the outbreak-related cases belonging to the 15-44 years-old group. Nearly 85% occurred in a household setting, and in all outbreaks, the mode of transmission was direct person-to-person contact. Regarding space-time analysis, twenty statistically significant clusters were identified in the male population and eight in the female population; clusters in men presented a higher number of observed cases and affected municipalities, as well as a higher percentage of municipalities classified as large urban areas. The elevated number of cases detected in clusters of men indicates that the number of MSM-related outbreaks may be higher than reported, showing that spatio-temporal analysis is a complementary, useful tool which may improve the detection of outbreaks in settings where epidemiological investigation may be more challenging.
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Affiliation(s)
- María Guerrero-Vadillo
- Doctorate Programme in Biomedical Sciences and Public Health, National University of Distance Education (UNED), 28015 Madrid, Spain
- National Centre for Epidemiology, Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Marina Peñuelas
- National Centre for Epidemiology, Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Ángela Domínguez
- Departament de Medicina, Universitat de Barcelona (UB), 08036 Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
| | - Pere Godoy
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
- Department of Medicine, Institut de Recerca Biomédica de Lleida (IRBLLeida)-Universidad de Lleida, 25008 Lleida, Spain
| | - Diana Gómez-Barroso
- National Centre for Epidemiology, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
| | - Nuria Soldevila
- Departament de Medicina, Universitat de Barcelona (UB), 08036 Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
| | | | - Ana Martínez
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
- Agència de Salut Pública de Catalunya, 08005 Barcelona, Spain
| | - Nuria Torner
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
| | - Ana Avellón
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
- Hepatitis Unit, National Centre of Microbiology, Instituto de Salud Carlos III, 28222 Majadahonda, Spain
| | - Cristina Rius
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
- Agència de Salut Pública de Barcelona, 08023 Barcelona, Spain
| | - Carmen Varela
- National Centre for Epidemiology, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
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Althomsons SP, Winglee K, Heilig CM, Talarico S, Silk B, Wortham J, Hill AN, Navin TR. Using Machine Learning Techniques and National Tuberculosis Surveillance Data to Predict Excess Growth in Genotyped Tuberculosis Clusters. Am J Epidemiol 2022; 191:1936-1943. [PMID: 35780450 PMCID: PMC10790200 DOI: 10.1093/aje/kwac117] [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: 10/01/2021] [Revised: 05/05/2022] [Accepted: 06/28/2022] [Indexed: 02/01/2023] Open
Abstract
The early identification of clusters of persons with tuberculosis (TB) that will grow to become outbreaks creates an opportunity for intervention in preventing future TB cases. We used surveillance data (2009-2018) from the United States, statistically derived definitions of unexpected growth, and machine-learning techniques to predict which clusters of genotype-matched TB cases are most likely to continue accumulating cases above expected growth within a 1-year follow-up period. We developed a model to predict which clusters are likely to grow on a training and testing data set that was generalizable to a validation data set. Our model showed that characteristics of clusters were more important than the social, demographic, and clinical characteristics of the patients in those clusters. For instance, the time between cases before unexpected growth was identified as the most important of our predictors. A faster accumulation of cases increased the probability of excess growth being predicted during the follow-up period. We have demonstrated that combining the characteristics of clusters and cases with machine learning can add to existing tools to help prioritize which clusters may benefit most from public health interventions. For example, consideration of an entire cluster, not only an individual patient, may assist in interrupting ongoing transmission.
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Affiliation(s)
- Sandy P. Althomsons
- Division of TB Elimination, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, United States
| | - Kathryn Winglee
- Division of TB Elimination, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, United States
| | - Charles M. Heilig
- Center for Surveillance, Epidemiology, and Laboratory Services, Centers for Disease Control and Prevention, Atlanta, Georgia, United States
| | - Sarah Talarico
- Division of TB Elimination, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, United States
| | - Benjamin Silk
- Division of TB Elimination, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, United States
| | - Jonathan Wortham
- Division of TB Elimination, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, United States
| | - Andrew N. Hill
- Division of TB Elimination, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, United States
| | - Thomas R. Navin
- Division of TB Elimination, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, United States
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Shrestha S, Winglee K, Hill AN, Shaw T, Smith JP, Kammerer JS, Silk BJ, Marks SM, Dowdy D. Model-based Analysis of Tuberculosis Genotype Clusters in the United States Reveals High Degree of Heterogeneity in Transmission and State-level Differences Across California, Florida, New York, and Texas. Clin Infect Dis 2022; 75:1433-1441. [PMID: 35143641 PMCID: PMC9412192 DOI: 10.1093/cid/ciac121] [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: 10/01/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Reductions in tuberculosis (TB) transmission have been instrumental in lowering TB incidence in the United States. Sustaining and augmenting these reductions are key public health priorities. METHODS We fit mechanistic transmission models to distributions of genotype clusters of TB cases reported to the Centers for Disease Control and Prevention during 2012-2016 in the United States and separately in California, Florida, New York, and Texas. We estimated the mean number of secondary cases generated per infectious case (R0) and individual-level heterogeneity in R0 at state and national levels and assessed how different definitions of clustering affected these estimates. RESULTS In clusters of genotypically linked TB cases that occurred within a state over a 5-year period (reference scenario), the estimated R0 was 0.29 (95% confidence interval [CI], .28-.31) in the United States. Transmission was highly heterogeneous; 0.24% of simulated cases with individual R0 >10 generated 19% of all recent secondary transmissions. R0 estimate was 0.16 (95% CI, .15-.17) when a cluster was defined as cases occurring within the same county over a 3-year period. Transmission varied across states: estimated R0s were 0.34 (95% CI, .3-.4) in California, 0.28 (95% CI, .24-.36) in Florida, 0.19 (95% CI, .15-.27) in New York, and 0.38 (95% CI, .33-.46) in Texas. CONCLUSIONS TB transmission in the United States is characterized by pronounced heterogeneity at the individual and state levels. Improving detection of transmission clusters through incorporation of whole-genome sequencing and identifying the drivers of this heterogeneity will be essential to reducing TB transmission.
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Affiliation(s)
- Sourya Shrestha
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Kathryn Winglee
- Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Andrew N Hill
- Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Tambi Shaw
- California Department of Public Health, Richmond, California, USA
| | - Jonathan P Smith
- Department of Policy and Administration, Yale University, New Haven, Connecticut, USA
| | - J Steve Kammerer
- Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Benjamin J Silk
- Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Suzanne M Marks
- Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - David Dowdy
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
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5
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Raz KM, Talarico S, Althomsons SP, Kammerer JS, Cowan LS, Haddad MB, McDaniel CJ, Wortham JM, France AM, Powell KM, Posey JE, Silk BJ. Molecular surveillance for large outbreaks of tuberculosis in the United States, 2014-2018. Tuberculosis (Edinb) 2022; 136:102232. [PMID: 35969928 PMCID: PMC9530005 DOI: 10.1016/j.tube.2022.102232] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 06/29/2022] [Accepted: 07/13/2022] [Indexed: 11/30/2022]
Abstract
OBJECTIVE This study describes characteristics of large tuberculosis (TB) outbreaks in the United States detected using novel molecular surveillance methods during 2014-2016 and followed for 2 years through 2018. METHODS We developed 4 genotype-based detection algorithms to identify large TB outbreaks of ≥10 cases related by recent transmission during a 3-year period. We used whole-genome sequencing and epidemiologic data to assess evidence of recent transmission among cases. RESULTS There were 24 large outbreaks involving 518 cases; patients were primarily U.S.-born (85.1%) racial/ethnic minorities (84.1%). Compared with all other TB patients, patients associated with large outbreaks were more likely to report substance use, homelessness, and having been diagnosed while incarcerated. Most large outbreaks primarily occurred within residences among families and nonfamilial social contacts. A source case with a prolonged infectious period and difficulties in eliciting contacts were commonly reported contributors to transmission. CONCLUSION Large outbreak surveillance can inform targeted interventions to decrease outbreak-associated TB morbidity.
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Affiliation(s)
- Kala M Raz
- Centers for Disease Control and Prevention, Atlanta, GA, USA.
| | - Sarah Talarico
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | | | | | - Lauren S Cowan
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Maryam B Haddad
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | | | | | | | - Krista M Powell
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - James E Posey
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Benjamin J Silk
- Centers for Disease Control and Prevention, Atlanta, GA, USA
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Detection of space–time clusters using a topological hierarchy for geospatial data on COVID-19 in Japan. JAPANESE JOURNAL OF STATISTICS AND DATA SCIENCE 2022; 5:279-301. [PMID: 35578605 PMCID: PMC9097570 DOI: 10.1007/s42081-022-00159-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 03/15/2022] [Accepted: 04/01/2022] [Indexed: 01/04/2023]
Abstract
In this paper, we detected space–time clusters using data on coronavirus disease 2019 (COVID-19) collected daily by each prefecture in Japan. COVID-19 has spread globally since the first confirmed case in China, in December 2019. Several people have to date been infected in Japan since the first confirmed case in January 2020. The outbreak of COVID-19 has had a significant impact on many people’s lives. Studies are being conducted to detect regions, called clusters, which pose a significantly higher risk of infection than their surrounding areas, based on a spatial scan statistics of COVID-19 infections. Among these studies, space–time cluster detection has to date been actively performed to gain knowledge regarding infection status. Based on the spatial scan statistic, the cylindrical scan method is a widely used space–time cluster detection method. This method enables concurrent detection of the location and time of a cluster occurrence. However, this method cannot capture spatial changes in a cluster over time. When applying the existing method to a cluster whose shape changes over time, the number of calculations required becomes extremely large, and the analysis may become difficult. In this study, we focused on the hierarchical structure of the data obtained by conducting an echelon analysis and applied the space–time cluster detection method based on this structure to enable the capture of changes in a cluster’s shape. Furthermore, we visualized the location and period of a cluster’s occurrence and considered the cause of the cluster.
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Wortham JM, Li R, Althomsons SP, Kammerer S, Haddad MB, Powell KM. Tuberculosis Genotype Clusters and Transmission in the U.S., 2009-2018. Am J Prev Med 2021; 61:201-208. [PMID: 33992497 PMCID: PMC9254502 DOI: 10.1016/j.amepre.2021.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 01/15/2021] [Accepted: 02/09/2021] [Indexed: 10/21/2022]
Abstract
INTRODUCTION In the U.S., universal genotyping of culture-confirmed tuberculosis cases facilitates cluster detection. Early recognition of the small clusters more likely to become outbreaks can help prioritize public health resources for immediate interventions. METHODS This study used national surveillance data reported during 2009-2018 to describe incident clusters (≥3 tuberculosis cases with matching genotypes not previously reported in the same county); data were analyzed during 2020. Cox proportional hazards regression models were used to examine the patient characteristics associated with clusters doubling in size to ≥6 cases. RESULTS During 2009-2018, a total of 1,516 incident clusters (comprising 6,577 cases) occurred in 47 U.S. states; 231 clusters had ≥6 cases. Clusters of ≥6 cases disproportionately included patients who used substances, who had recently experienced homelessness, who were incarcerated, who were U.S. born, or who self-identified as being of American Indian or Alaska Native race or of Black race. A median of 54 months elapsed between the first and the third cases in clusters that remained at 3-5 cases compared with a median of 9.5 months in clusters that grew to ≥6 cases. The longer time between the first and third cases and the presence of ≥1 patient aged ≥65 years among the first 3 cases predicted a lower hazard for accumulating ≥6 cases. CONCLUSIONS Clusters accumulating ≥3 cases within a year should be prioritized for intervention. Effective response strategies should include plans for targeted outreach to U.S.-born individuals, incarcerated people, those experiencing homelessness, people using substances, and individuals self-identifying as being of American Indian or Alaska Native race or of Black race.
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Affiliation(s)
- Jonathan M Wortham
- Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia.
| | - Rongxia Li
- Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Sandy P Althomsons
- Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Steve Kammerer
- Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Maryam B Haddad
- Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Krista M Powell
- Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia
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Harrist AV, McDaniel CJ, Wortham JM, Althomsons SP. Developing National Genotype-Independent Indicators for Recent Mycobacterium Tuberculosis Transmission Using Pediatric Cases-United States, 2011-2017. Public Health Rep 2021; 137:81-86. [PMID: 33606947 PMCID: PMC8721760 DOI: 10.1177/0033354920985215] [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: 01/03/2023] Open
Abstract
INTRODUCTION Pediatric tuberculosis (TB) cases are sentinel events for Mycobacterium tuberculosis transmission in communities because children, by definition, must have been infected relatively recently. However, these events are not consistently identified by genotype-dependent surveillance alerting methods because many pediatric TB cases are not culture-positive, a prerequisite for genotyping. METHODS We developed 3 potential indicators of ongoing TB transmission based on identifying counties in the United States with relatively high pediatric (aged <15 years) TB incidence: (1) a case proportion indicator: an above-average proportion of pediatric TB cases among all TB cases; (2) a case rate indicator: an above-average pediatric TB case rate; and (3) a statistical model indicator: a statistical model based on a significant increase in pediatric TB cases from the previous 8-quarter moving average. RESULTS Of the 249 US counties reporting ≥2 pediatric TB cases during 2009-2017, 240 and 249 counties were identified by the case proportion and case rate indicators, respectively. The statistical model indicator identified 40 counties with a significant increase in the number of pediatric TB cases. We compared results from the 3 indicators with an independently generated list of 91 likely transmission events involving ≥2 pediatric cases (ie, known TB outbreaks or case clusters with reported epidemiologic links). All counties with likely transmission events involving multiple pediatric cases were identified by ≥1 indicator; 23 were identified by all 3 indicators. PRACTICE IMPLICATIONS This retrospective analysis demonstrates the feasibility of using routine TB surveillance data to identify counties where ongoing TB transmission might be occurring, even in the absence of available genotyping data.
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Affiliation(s)
- Alexia V. Harrist
- National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Clinton J. McDaniel
- National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Jonathan M. Wortham
- National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Sandy P. Althomsons
- National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA,Sandy P. Althomsons, MA, MHS, Centers for Disease Control and Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, 1600 Clifton Rd NE, US 12-4, Atlanta, GA 30329, USA.
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Stimson J, Gardy J, Mathema B, Crudu V, Cohen T, Colijn C. Beyond the SNP Threshold: Identifying Outbreak Clusters Using Inferred Transmissions. Mol Biol Evol 2019; 36:587-603. [PMID: 30690464 DOI: 10.1093/molbev/msy242] [Citation(s) in RCA: 77] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Whole-genome sequencing (WGS) is increasingly used to aid the understanding of pathogen transmission. A first step in analyzing WGS data is usually to define "transmission clusters," sets of cases that are potentially linked by direct transmission. This is often done by including two cases in the same cluster if they are separated by fewer single-nucleotide polymorphisms (SNPs) than a specified threshold. However, there is little agreement as to what an appropriate threshold should be. We propose a probabilistic alternative, suggesting that the key inferential target for transmission clusters is the number of transmissions separating cases. We characterize this by combining the number of SNP differences and the length of time over which those differences have accumulated, using information about case timing, molecular clock, and transmission processes. Our framework has the advantage of allowing for variable mutation rates across the genome and can incorporate other epidemiological data. We use two tuberculosis studies to illustrate the impact of our approach: with British Columbia data by using spatial divisions; with Republic of Moldova data by incorporating antibiotic resistance. Simulation results indicate that our transmission-based method is better in identifying direct transmissions than a SNP threshold, with dissimilarity between clusterings of on average 0.27 bits compared with 0.37 bits for the SNP-threshold method and 0.84 bits for randomly permuted data. These results show that it is likely to outperform the SNP-threshold method where clock rates are variable and sample collection times are spread out. We implement the method in the R package transcluster.
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Affiliation(s)
- James Stimson
- Department of Mathematics, Imperial College London, London, UK
| | - Jennifer Gardy
- British Columbia Centre for Disease Control, Communicable Disease Prevention and Control Services, Vancouver, Canada.,School of Population and Public Health, University of British Columbia, Vancouver, Canada
| | - Barun Mathema
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, USA
| | - Valeriu Crudu
- Phthisiopneumology Institute, Chisinau, Republic of Moldova
| | - Ted Cohen
- Yale University School of Public Health, New Haven
| | - Caroline Colijn
- Department of Mathematics, Imperial College London, London, UK.,Department of Mathematics, Simon Fraser University, Vancouver, Canada
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Althomsons SP, Hill AN, Harrist AV, France AM, Powell KM, Posey JE, Cowan LS, Navin TR. Statistical Method to Detect Tuberculosis Outbreaks among Endemic Clusters in a Low-Incidence Setting. Emerg Infect Dis 2019; 24:573-575. [PMID: 29460749 PMCID: PMC5823347 DOI: 10.3201/eid2403.171613] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
We previously reported use of genotype surveillance data to predict outbreaks among incident tuberculosis clusters. We propose a method to detect possible outbreaks among endemic tuberculosis clusters. We detected 15 possible outbreaks, of which 10 had epidemiologic data or whole-genome sequencing results. Eight outbreaks were corroborated.
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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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Worrell MC, Kramer M, Yamin A, Ray SM, Goswami ND. Use of Activity Space in a Tuberculosis Outbreak: Bringing Homeless Persons Into Spatial Analyses. Open Forum Infect Dis 2017; 4:ofw280. [PMID: 28480272 PMCID: PMC5414060 DOI: 10.1093/ofid/ofw280] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Accepted: 01/03/2017] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Tuberculosis (TB) causes significant morbidity and mortality in US cities, particularly in poor, transient populations. During a TB outbreak in Fulton County, Atlanta, GA, we aimed to determine whether local maps created from multiple locations of personal activity per case would differ significantly from traditional maps created from single residential address. METHODS Data were abstracted for patients with TB disease diagnosed in 2008-2014 and receiving care at the Fulton County Health Department. Clinical and activity location data were abstracted from charts. Kernel density methods, activity space analysis, and overlay with homeless shelter locations were used to characterize case spatial distribution when using single versus multiple addresses. RESULTS Data were collected for 198 TB cases, with over 30% homeless US-born cases included. Greater spatial dispersion of cases was found when utilizing multiple versus single addresses per case. Activity spaces of homeless and isoniazid (INH)-resistant cases were more spatially congruent with one another than non-homeless and INH-susceptible cases (P < .0001 and P < .0001, respectively). CONCLUSIONS Innovative spatial methods allowed us to more comprehensively capture the geography of TB-infected homeless persons, who made up a large portion of the Fulton County outbreak. We demonstrate how activity space analysis, prominent in exposure science and chronic disease, supports that routine capture of multiple location TB data may facilitate spatially different public health interventions than traditional surveillance maps.
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Affiliation(s)
| | - Michael Kramer
- Department of Epidemiology, Rollins School of Public Health and
| | - Aliya Yamin
- Fulton County Health Department Tuberculosis Clinic, Atlanta, Georgia
| | - Susan M Ray
- Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia
| | - Neela D Goswami
- Department of Epidemiology, Rollins School of Public Health and
- Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia
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13
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Teeter LD, Vempaty P, Nguyen DTM, Tapia J, Sharnprapai S, Ghosh S, Kammerer JS, Miramontes R, Cronin WA, Graviss EA. Validation of genotype cluster investigations for Mycobacterium tuberculosis: application results for 44 clusters from four heterogeneous United States jurisdictions. BMC Infect Dis 2016; 16:594. [PMID: 27769182 PMCID: PMC5075185 DOI: 10.1186/s12879-016-1937-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2016] [Accepted: 10/18/2016] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND Tracking the dissemination of specific Mycobacterium tuberculosis (Mtb) strains using genotyped Mtb isolates from tuberculosis patients is a routine public health practice in the United States. The present study proposes a standardized cluster investigation method to identify epidemiologic-linked patients in Mtb genotype clusters. The study also attempts to determine the proportion of epidemiologic-linked patients the proposed method would identify beyond the outcome of the conventional contact investigation. METHODS The study population included Mtb culture positive patients from Georgia, Maryland, Massachusetts and Houston, Texas. Mtb isolates were genotyped by CDC's National TB Genotyping Service (NTGS) from January 2006 to October 2010. Mtb cluster investigations (CLIs) were conducted for patients whose isolates matched exactly by spoligotyping and 12-locus MIRU-VNTR. CLIs were carried out in four sequential steps: (1) Public Health Worker (PHW) Interview, (2) Contact Investigation (CI) Evaluation, (3) Public Health Records Review, and (4) CLI TB Patient Interviews. Comparison between patients whose links were identified through the study's CLI interviews (Step 4) and patients whose links were identified earlier in CLI (Steps 1-3) was conducted using logistic regression. RESULTS Forty-four clusters were randomly selected from the four study sites (401 patients in total). Epidemiologic links were identified for 189/401 (47 %) study patients in a total of 201 linked patient-pairs. The numbers of linked patients identified in each CLI steps were: Step 1 - 105/401 (26.2 %), Step 2 - 15/388 (3.9 %), Step 3 - 41/281 (14.6 %), and Step 4 - 28/119 (30 %). Among the 189 linked patients, 28 (14.8 %) were not identified in previous CI. No epidemiologic links were identified in 13/44 (30 %) clusters. CONCLUSIONS We validated a standardized and practical method to systematically identify epidemiologic links among patients in Mtb genotype clusters, which can be integrated into the TB control and prevention programs in public health settings. The CLI interview identified additional epidemiologic links that were not identified in previous CI. One-third of the clusters showed no epidemiologic links despite being extensively investigated, suggesting that some improvement in the interviewing methods is still needed.
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Affiliation(s)
- Larry D. Teeter
- Houston Methodist Research Institute, Houston, TX USA
- Present Address: Texas Department of State Health Services, HSR 6/5, South Houston, TX USA
| | | | | | - Jane Tapia
- Emory School of Medicine, Atlanta, GA USA
| | | | - Smita Ghosh
- Centers for Disease Control and Prevention, Atlanta, GA USA
| | - J. Steven Kammerer
- Centers for Disease Control and Prevention, Atlanta, GA USA
- Northrop Grumman Corporation, Centers for Disease Control and Prevention Programs, Atlanta, GA USA
| | | | - Wendy A. Cronin
- Maryland Department of Health and Mental Hygiene, Baltimore, MD USA
| | - Edward A. Graviss
- Houston Methodist Research Institute, Houston, TX USA
- Department of Pathology and Genomic Medicine, Houston Methodist Research Institute, Mail Station: R6-414, 6670 Bertner, Houston, TX 77030 USA
| | - on behalf of the Tuberculosis Epidemiologic Studies Consortium
- Houston Methodist Research Institute, Houston, TX USA
- Centers for Disease Control and Prevention, Atlanta, GA USA
- Emory School of Medicine, Atlanta, GA USA
- Massachusetts Department of Public Health, Jamaica Plain, MA USA
- Northrop Grumman Corporation, Centers for Disease Control and Prevention Programs, Atlanta, GA USA
- Maryland Department of Health and Mental Hygiene, Baltimore, MD USA
- Present Address: Texas Department of State Health Services, HSR 6/5, South Houston, TX USA
- Department of Pathology and Genomic Medicine, Houston Methodist Research Institute, Mail Station: R6-414, 6670 Bertner, Houston, TX 77030 USA
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14
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Yeboah-Manu D, Asare P, Asante-Poku A, Otchere ID, Osei-Wusu S, Danso E, Forson A, Koram KA, Gagneux S. Spatio-Temporal Distribution of Mycobacterium tuberculosis Complex Strains in Ghana. PLoS One 2016; 11:e0161892. [PMID: 27564240 PMCID: PMC5001706 DOI: 10.1371/journal.pone.0161892] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Accepted: 08/12/2016] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND There is a perception that genomic differences in the species/lineages of the nine species making the Mycobacterium tuberculosis complex (MTBC) may affect the efficacy of distinct control tools in certain geographical areas. We therefore analyzed the prevalence and spatial distribution of MTBC species and lineages among isolates from pulmonary TB cases over an 8-year period, 2007-2014. METHODOLOGY Mycobacterial species isolated by culture from consecutively recruited pulmonary tuberculosis patients presenting at selected district/sub-district health facilities were confirmed as MTBC by IS6110 and rpoß PCR and further assigned lineages and sub lineages by spoligotyping and large sequence polymorphism PCR (RDs 4, 9, 12, 702, 711) assays. Patient characteristics, residency, and risks were obtained with a structured questionnaire. We used SaTScan and ArcMap analyses to identify significantly clustered MTBC lineages within selected districts and spatial display, respectively. RESULTS Among 2,551 isolates, 2,019 (79.1%), 516 (20.2%) and 16 (0.6%) were identified as M. tuberculosis sensu stricto (MTBss), M. africanum (Maf), 15 M. bovis and 1 M. caprae, respectively. The proportions of MTBss and Maf were fairly constant within the study period. Maf spoligotypes were dominated by Spoligotype International Type (SIT) 331 (25.42%), SIT 326 (15.25%) and SIT 181 (14.12%). We found M. bovis to be significantly higher in Northern Ghana (1.9% of 212) than Southern Ghana (0.5% of 2339) (p = 0.020). Using the purely spatial and space-time analysis, seven significant MTBC lineage clusters (p< 0.05) were identified. Notable among the clusters were Ghana and Cameroon sub-lineages found to be associated with north and south, respectively. CONCLUSION This study demonstrated that overall, 79.1% of TB in Ghana is caused by MTBss and 20% by M. africanum. Unlike some West African Countries, we did not observe a decline of Maf prevalence in Ghana.
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Affiliation(s)
- Dorothy Yeboah-Manu
- Noguchi Memorial Institute for Medical Research, University of Ghana, Legon, Accra, Ghana
- * E-mail:
| | - P. Asare
- Noguchi Memorial Institute for Medical Research, University of Ghana, Legon, Accra, Ghana
| | - A. Asante-Poku
- Noguchi Memorial Institute for Medical Research, University of Ghana, Legon, Accra, Ghana
| | - I. D. Otchere
- Noguchi Memorial Institute for Medical Research, University of Ghana, Legon, Accra, Ghana
| | - S. Osei-Wusu
- Noguchi Memorial Institute for Medical Research, University of Ghana, Legon, Accra, Ghana
| | - E. Danso
- Noguchi Memorial Institute for Medical Research, University of Ghana, Legon, Accra, Ghana
| | - A. Forson
- Department of Chest Diseases, Korle-Bu Teaching Hospital, Korle-bu, Accra, Ghana
| | - K. A. Koram
- Noguchi Memorial Institute for Medical Research, University of Ghana, Legon, Accra, Ghana
| | - Sebastien Gagneux
- Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
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15
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Shak EB, France AM, Cowan L, Starks AM, Grant J. Representativeness of Tuberculosis Genotyping Surveillance in the United States, 2009-2010. Public Health Rep 2016; 130:596-601. [PMID: 26556930 DOI: 10.1177/003335491513000607] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Genotyping of Mycobacterium tuberculosis isolates contributes to tuberculosis (TB) control through detection of possible outbreaks. However, 20% of U.S. cases do not have an isolate for testing, and 10% of cases with isolates do not have a genotype reported. TB outbreaks in populations with incomplete genotyping data might be missed by genotyping-based outbreak detection. Therefore, we assessed the representativeness of TB genotyping data by comparing characteristics of cases reported during January 1, 2009-December 31, 2010, that had a genotype result with those cases that did not. Of 22,476 cases, 14,922 (66%) had a genotype result. Cases without genotype results were more likely to be patients <19 years of age, with unknown HIV status, of female sex, U.S.-born, and with no recent history of homelessness or substance abuse. Although cases with a genotype result are largely representative of all reported U.S. TB cases, outbreak detection methods that rely solely on genotyping data may underestimate TB transmission among certain groups.
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Affiliation(s)
- Emma B Shak
- Centers for Disease Control and Prevention, Scientific Education and Professional Development Program Office, Atlanta, GA ; Centers for Disease Control and Prevention, Division of Tuberculosis Elimination, Atlanta, GA
| | - Anne Marie France
- Centers for Disease Control and Prevention, Division of Tuberculosis Elimination, Atlanta, GA
| | - Lauren Cowan
- Centers for Disease Control and Prevention, Division of Tuberculosis Elimination, Atlanta, GA
| | - Angela M Starks
- Centers for Disease Control and Prevention, Division of Tuberculosis Elimination, Atlanta, GA
| | - Juliana Grant
- Centers for Disease Control and Prevention, Division of Tuberculosis Elimination, Atlanta, GA
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16
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IMANISHI M, NEWTON AE, VIEIRA AR, GONZALEZ-AVILES G, KENDALL SCOTT ME, MANIKONDA K, MAXWELL TN, HALPIN JL, FREEMAN MM, MEDALLA F, AYERS TL, DERADO G, MAHON BE, MINTZ ED. Typhoid fever acquired in the United States, 1999-2010: epidemiology, microbiology, and use of a space-time scan statistic for outbreak detection. Epidemiol Infect 2015; 143:2343-54. [PMID: 25427666 PMCID: PMC5207021 DOI: 10.1017/s0950268814003021] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2014] [Revised: 09/25/2014] [Accepted: 10/20/2014] [Indexed: 01/04/2023] Open
Abstract
Although rare, typhoid fever cases acquired in the United States continue to be reported. Detection and investigation of outbreaks in these domestically acquired cases offer opportunities to identify chronic carriers. We searched surveillance and laboratory databases for domestically acquired typhoid fever cases, used a space-time scan statistic to identify clusters, and classified clusters as outbreaks or non-outbreaks. From 1999 to 2010, domestically acquired cases accounted for 18% of 3373 reported typhoid fever cases; their isolates were less often multidrug-resistant (2% vs. 15%) compared to isolates from travel-associated cases. We identified 28 outbreaks and two possible outbreaks within 45 space-time clusters of ⩾2 domestically acquired cases, including three outbreaks involving ⩾2 molecular subtypes. The approach detected seven of the ten outbreaks published in the literature or reported to CDC. Although this approach did not definitively identify any previously unrecognized outbreaks, it showed the potential to detect outbreaks of typhoid fever that may escape detection by routine analysis of surveillance data. Sixteen outbreaks had been linked to a carrier. Every case of typhoid fever acquired in a non-endemic country warrants thorough investigation. Space-time scan statistics, together with shoe-leather epidemiology and molecular subtyping, may improve outbreak detection.
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Affiliation(s)
- M. IMANISHI
- Division of Foodborne, Waterborne, and Environmental Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - A. E. NEWTON
- Division of Foodborne, Waterborne, and Environmental Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - A. R. VIEIRA
- Division of Foodborne, Waterborne, and Environmental Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - G. GONZALEZ-AVILES
- Division of Foodborne, Waterborne, and Environmental Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - M. E. KENDALL SCOTT
- Division of Foodborne, Waterborne, and Environmental Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - K. MANIKONDA
- Division of Foodborne, Waterborne, and Environmental Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - T. N. MAXWELL
- Division of Foodborne, Waterborne, and Environmental Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - J. L. HALPIN
- Division of Foodborne, Waterborne, and Environmental Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - M. M. FREEMAN
- Division of Foodborne, Waterborne, and Environmental Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - F. MEDALLA
- Division of Foodborne, Waterborne, and Environmental Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - T. L. AYERS
- Division of Foodborne, Waterborne, and Environmental Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - G. DERADO
- Division of Foodborne, Waterborne, and Environmental Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - B. E. MAHON
- Division of Foodborne, Waterborne, and Environmental Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - E. D. MINTZ
- Division of Foodborne, Waterborne, and Environmental Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
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17
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Teeter LD, Ha NP, Ma X, Wenger J, Cronin WA, Musser JM, Graviss EA. Evaluation of large genotypic Mycobacterium tuberculosis clusters: contributions from remote and recent transmission. Tuberculosis (Edinb) 2014; 93 Suppl:S38-46. [PMID: 24388648 DOI: 10.1016/s1472-9792(13)70009-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Tuberculosis genotypic clustering is used as a proxy for recent transmission. The association between clustering and recent transmission becomes problematic when the genotyping method lacks specificity in defining a cluster, as well as for clusters with extensive jurisdictional histories and/or common genotypes. We investigated the four largest spoligotype/12 loci MIRU-VNTR-defined clusters in Harris County, Texas from 2006-2012 to determine their historical contribution to tuberculosis morbidity, estimate the contributions from recent and remote transmission, and determine the impact of secondary genotyping on cluster definition. The clusters contained 189, 64, 51 and 38 cases. Each cluster was linked to cluster(s) previously identified by Houston Tuberculosis Initiative; 3 since 1995 and the fourth in 2002. Among cases for which timing of Mycobacterium tuberculosis transmission relative to tuberculosis disease could be ascertained, nearly equal proportions were associated with recent and remote transmission. The extent to which genotyping with an additional 12 MIRU-VNTR loci modified the cluster definition varied from little or no impact for the two smaller clusters to moderate impact for the larger clusters. Tuberculosis control measures to reduce morbidity associated with large clusters must involve strategies to identify and treat individuals who recently acquired infection, as well as persons infected for years.
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Affiliation(s)
- Larry D Teeter
- Department of Pathology and Genomic Medicine, Center for Molecular and Translational Human Infectious Disease Research, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Ngan P Ha
- Department of Pathology and Genomic Medicine, Center for Molecular and Translational Human Infectious Disease Research, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Xin Ma
- Department of Pathology and Genomic Medicine, Center for Molecular and Translational Human Infectious Disease Research, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Jane Wenger
- Mycobacteriology and Mycology Section, Microbial Diseases Laboratory, Division of Communicable Disease Control, Center for Infectious Diseases, California Department of Public Health, Richmond, CA, USA
| | - Wendy A Cronin
- Center for TB Control and Prevention, Maryland Department of Health and Mental Hygiene, Baltimore, MD 21202, USA
| | - James M Musser
- Department of Pathology and Genomic Medicine, Center for Molecular and Translational Human Infectious Disease Research, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Edward A Graviss
- Department of Pathology and Genomic Medicine, Center for Molecular and Translational Human Infectious Disease Research, Houston Methodist Research Institute, Houston, TX 77030, USA.
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18
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Accuracy of prospective space–time surveillance in detecting tuberculosis transmission. Spat Spatiotemporal Epidemiol 2014; 8:47-54. [DOI: 10.1016/j.sste.2014.01.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2013] [Revised: 01/11/2014] [Accepted: 01/14/2014] [Indexed: 11/16/2022]
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