A genotypic and spatial epidemiologic analysis of Massachusetts' Mycobacterium tuberculosis cases from 2012 to 2015
Massachusetts had a rate of 2.8 cases of tuberculosis (TB) per 100,000 individuals in 2015. Although TB in Massachusetts is on the decline, the case rate remains far above the 2020 National TB Target of 1.4 per 100,000. To reduce the TB case rate in Massachusetts, it is necessary to understand the l...
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| Veröffentlicht in: | Tuberculosis (Edinburgh, Scotland) Scotland), 2018-09, Vol.112, p.20-26 |
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| creator | Vindenes, T. Jordan, M.R. Tibbs, A. Stopka, T.J. Johnson, D. Cochran, J. |
| description | Massachusetts had a rate of 2.8 cases of tuberculosis (TB) per 100,000 individuals in 2015. Although TB in Massachusetts is on the decline, the case rate remains far above the 2020 National TB Target of 1.4 per 100,000. To reduce the TB case rate in Massachusetts, it is necessary to understand the local epidemiology and transmission risks.
We used an existing TB case database of Massachusetts TB cases in the time frame from 2012 to 2015, which links de-identified patient demographic information with TB genotypes obtained from the United States Centers for Disease Control and Prevention's (CDC) TB Genotyping Information Management System database. Two or more cases with identical genotypes, which were close in space (within 50 km), as determined in a geographic information system (GIS), and time (3 years), were considered TB clusters.
We analyzed 543 genotyped cases. We identified a total of 85 cases that met the TB cluster criteria, and a total of 33 clusters. US-born individuals (p = 0.003), homeless individuals (p = 0.001) and those reporting illicit substance use (p = 0.001) and alcohol use (p = 0.001) were more likely to appear in a TB cluster.
Through a combined genotypic and spatial epidemiological approach, we identified populations and individuals more likely to be in a TB cluster. Testing populations identified as at risk for being in a TB cluster, and providing appropriate treatment, may decrease the overall TB case rate and support efforts to achieve national 2020 TB targets. |
| doi_str_mv | 10.1016/j.tube.2018.07.002 |
| format | Article |
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We used an existing TB case database of Massachusetts TB cases in the time frame from 2012 to 2015, which links de-identified patient demographic information with TB genotypes obtained from the United States Centers for Disease Control and Prevention's (CDC) TB Genotyping Information Management System database. Two or more cases with identical genotypes, which were close in space (within 50 km), as determined in a geographic information system (GIS), and time (3 years), were considered TB clusters.
We analyzed 543 genotyped cases. We identified a total of 85 cases that met the TB cluster criteria, and a total of 33 clusters. US-born individuals (p = 0.003), homeless individuals (p = 0.001) and those reporting illicit substance use (p = 0.001) and alcohol use (p = 0.001) were more likely to appear in a TB cluster.
Through a combined genotypic and spatial epidemiological approach, we identified populations and individuals more likely to be in a TB cluster. Testing populations identified as at risk for being in a TB cluster, and providing appropriate treatment, may decrease the overall TB case rate and support efforts to achieve national 2020 TB targets.</description><identifier>ISSN: 1472-9792</identifier><identifier>EISSN: 1873-281X</identifier><identifier>DOI: 10.1016/j.tube.2018.07.002</identifier><identifier>PMID: 30205965</identifier><language>eng</language><publisher>Scotland: Elsevier Ltd</publisher><subject>Adult ; Aged ; Alcohol-Related Disorders - epidemiology ; Alcoholic beverages ; Bacteria ; Cluster Analysis ; Data base management systems ; Databases, Factual ; Demographics ; Disease control ; Disease transmission ; Epidemiology ; Female ; Genotype ; Genotype & phenotype ; Genotypes ; Genotypic analysis ; Genotyping ; Geographic Information Systems ; GIS ; Homeless Persons ; Homelessness ; Humans ; Information management ; Male ; Massachusetts - epidemiology ; Middle Aged ; Molecular Epidemiology ; Mycobacterium tuberculosis - genetics ; Phenotype ; Populations ; Remote sensing ; Risk Factors ; Satellite navigation systems ; Spatial analysis ; Spatial epidemiologic analysis ; Substance use ; Substance-Related Disorders - epidemiology ; Time Factors ; Tuberculosis ; Tuberculosis - diagnosis ; Tuberculosis - epidemiology ; Tuberculosis - microbiology ; Tuberculosis - transmission</subject><ispartof>Tuberculosis (Edinburgh, Scotland), 2018-09, Vol.112, p.20-26</ispartof><rights>2018 Elsevier Ltd</rights><rights>Copyright © 2018 Elsevier Ltd. All rights reserved.</rights><rights>Copyright Elsevier Science Ltd. Sep 2018</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c384t-496f7cfcd8101d6771efbe6a0912188655f5e929fadf609aaf140a051b352463</citedby><cites>FETCH-LOGICAL-c384t-496f7cfcd8101d6771efbe6a0912188655f5e929fadf609aaf140a051b352463</cites><orcidid>0000-0003-2314-8924</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.tube.2018.07.002$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>315,782,786,3554,27933,27934,46004</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30205965$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Vindenes, T.</creatorcontrib><creatorcontrib>Jordan, M.R.</creatorcontrib><creatorcontrib>Tibbs, A.</creatorcontrib><creatorcontrib>Stopka, T.J.</creatorcontrib><creatorcontrib>Johnson, D.</creatorcontrib><creatorcontrib>Cochran, J.</creatorcontrib><title>A genotypic and spatial epidemiologic analysis of Massachusetts' Mycobacterium tuberculosis cases from 2012 to 2015</title><title>Tuberculosis (Edinburgh, Scotland)</title><addtitle>Tuberculosis (Edinb)</addtitle><description>Massachusetts had a rate of 2.8 cases of tuberculosis (TB) per 100,000 individuals in 2015. Although TB in Massachusetts is on the decline, the case rate remains far above the 2020 National TB Target of 1.4 per 100,000. To reduce the TB case rate in Massachusetts, it is necessary to understand the local epidemiology and transmission risks.
We used an existing TB case database of Massachusetts TB cases in the time frame from 2012 to 2015, which links de-identified patient demographic information with TB genotypes obtained from the United States Centers for Disease Control and Prevention's (CDC) TB Genotyping Information Management System database. Two or more cases with identical genotypes, which were close in space (within 50 km), as determined in a geographic information system (GIS), and time (3 years), were considered TB clusters.
We analyzed 543 genotyped cases. We identified a total of 85 cases that met the TB cluster criteria, and a total of 33 clusters. US-born individuals (p = 0.003), homeless individuals (p = 0.001) and those reporting illicit substance use (p = 0.001) and alcohol use (p = 0.001) were more likely to appear in a TB cluster.
Through a combined genotypic and spatial epidemiological approach, we identified populations and individuals more likely to be in a TB cluster. Testing populations identified as at risk for being in a TB cluster, and providing appropriate treatment, may decrease the overall TB case rate and support efforts to achieve national 2020 TB targets.</description><subject>Adult</subject><subject>Aged</subject><subject>Alcohol-Related Disorders - epidemiology</subject><subject>Alcoholic beverages</subject><subject>Bacteria</subject><subject>Cluster Analysis</subject><subject>Data base management systems</subject><subject>Databases, Factual</subject><subject>Demographics</subject><subject>Disease control</subject><subject>Disease transmission</subject><subject>Epidemiology</subject><subject>Female</subject><subject>Genotype</subject><subject>Genotype & phenotype</subject><subject>Genotypes</subject><subject>Genotypic analysis</subject><subject>Genotyping</subject><subject>Geographic Information Systems</subject><subject>GIS</subject><subject>Homeless Persons</subject><subject>Homelessness</subject><subject>Humans</subject><subject>Information management</subject><subject>Male</subject><subject>Massachusetts - epidemiology</subject><subject>Middle Aged</subject><subject>Molecular Epidemiology</subject><subject>Mycobacterium tuberculosis - genetics</subject><subject>Phenotype</subject><subject>Populations</subject><subject>Remote sensing</subject><subject>Risk Factors</subject><subject>Satellite navigation systems</subject><subject>Spatial analysis</subject><subject>Spatial epidemiologic analysis</subject><subject>Substance use</subject><subject>Substance-Related Disorders - epidemiology</subject><subject>Time Factors</subject><subject>Tuberculosis</subject><subject>Tuberculosis - diagnosis</subject><subject>Tuberculosis - epidemiology</subject><subject>Tuberculosis - microbiology</subject><subject>Tuberculosis - transmission</subject><issn>1472-9792</issn><issn>1873-281X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kUtv1TAQhSMEog_4AyyQJRZlk3TsvGyJTVWVgtSKTRfdWY4zLr5KroPHQbr_HodbWLBgNZb8naM5c4riHYeKA-8ud1VaB6wEcFlBXwGIF8Upl31dCskfX-Z304tS9UqcFGdEO8gikPC6OKlBQKu69rSgK_aE-5AOi7fM7EdGi0neTAwXP-LswxSefv-Y6UCeWHDs3hAZ-30lTIku2P3BhsHYhNGvM9s2inadwgZbQ0jMxTCzvKRgKWyzfVO8cmYifPs8z4uHzzcP11_Ku2-3X6-v7kpbyyaVjepcb50dZU47dn3P0Q3YGVBccCm7tnUtKqGcGV0HyhjHGzDQ8qFuRdPV58XHo-0Sw48VKenZk8VpMnsMK2nBQSiuhKgz-uEfdBfWmDNvFFeqltk6U-JI2RiIIjq9RD-beNAc9NaI3uktv94a0dDr3EgWvX-2XocZx7-SPxVk4NMRwHyKnx6jJutxb3H0EW3SY_D_8_8FM_Cb5Q</recordid><startdate>201809</startdate><enddate>201809</enddate><creator>Vindenes, T.</creator><creator>Jordan, M.R.</creator><creator>Tibbs, A.</creator><creator>Stopka, T.J.</creator><creator>Johnson, D.</creator><creator>Cochran, J.</creator><general>Elsevier Ltd</general><general>Elsevier Science Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QL</scope><scope>C1K</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-2314-8924</orcidid></search><sort><creationdate>201809</creationdate><title>A genotypic and spatial epidemiologic analysis of Massachusetts' Mycobacterium tuberculosis cases from 2012 to 2015</title><author>Vindenes, T. ; Jordan, M.R. ; Tibbs, A. ; Stopka, T.J. ; Johnson, D. ; Cochran, J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c384t-496f7cfcd8101d6771efbe6a0912188655f5e929fadf609aaf140a051b352463</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Alcohol-Related Disorders - epidemiology</topic><topic>Alcoholic beverages</topic><topic>Bacteria</topic><topic>Cluster Analysis</topic><topic>Data base management systems</topic><topic>Databases, Factual</topic><topic>Demographics</topic><topic>Disease control</topic><topic>Disease transmission</topic><topic>Epidemiology</topic><topic>Female</topic><topic>Genotype</topic><topic>Genotype & phenotype</topic><topic>Genotypes</topic><topic>Genotypic analysis</topic><topic>Genotyping</topic><topic>Geographic Information Systems</topic><topic>GIS</topic><topic>Homeless Persons</topic><topic>Homelessness</topic><topic>Humans</topic><topic>Information management</topic><topic>Male</topic><topic>Massachusetts - epidemiology</topic><topic>Middle Aged</topic><topic>Molecular Epidemiology</topic><topic>Mycobacterium tuberculosis - genetics</topic><topic>Phenotype</topic><topic>Populations</topic><topic>Remote sensing</topic><topic>Risk Factors</topic><topic>Satellite navigation systems</topic><topic>Spatial analysis</topic><topic>Spatial epidemiologic analysis</topic><topic>Substance use</topic><topic>Substance-Related Disorders - epidemiology</topic><topic>Time Factors</topic><topic>Tuberculosis</topic><topic>Tuberculosis - diagnosis</topic><topic>Tuberculosis - epidemiology</topic><topic>Tuberculosis - microbiology</topic><topic>Tuberculosis - transmission</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Vindenes, T.</creatorcontrib><creatorcontrib>Jordan, M.R.</creatorcontrib><creatorcontrib>Tibbs, A.</creatorcontrib><creatorcontrib>Stopka, T.J.</creatorcontrib><creatorcontrib>Johnson, D.</creatorcontrib><creatorcontrib>Cochran, J.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Environmental Sciences and Pollution Management</collection><collection>MEDLINE - Academic</collection><jtitle>Tuberculosis (Edinburgh, Scotland)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Vindenes, T.</au><au>Jordan, M.R.</au><au>Tibbs, A.</au><au>Stopka, T.J.</au><au>Johnson, D.</au><au>Cochran, J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A genotypic and spatial epidemiologic analysis of Massachusetts' Mycobacterium tuberculosis cases from 2012 to 2015</atitle><jtitle>Tuberculosis (Edinburgh, Scotland)</jtitle><addtitle>Tuberculosis (Edinb)</addtitle><date>2018-09</date><risdate>2018</risdate><volume>112</volume><spage>20</spage><epage>26</epage><pages>20-26</pages><issn>1472-9792</issn><eissn>1873-281X</eissn><abstract>Massachusetts had a rate of 2.8 cases of tuberculosis (TB) per 100,000 individuals in 2015. Although TB in Massachusetts is on the decline, the case rate remains far above the 2020 National TB Target of 1.4 per 100,000. To reduce the TB case rate in Massachusetts, it is necessary to understand the local epidemiology and transmission risks.
We used an existing TB case database of Massachusetts TB cases in the time frame from 2012 to 2015, which links de-identified patient demographic information with TB genotypes obtained from the United States Centers for Disease Control and Prevention's (CDC) TB Genotyping Information Management System database. Two or more cases with identical genotypes, which were close in space (within 50 km), as determined in a geographic information system (GIS), and time (3 years), were considered TB clusters.
We analyzed 543 genotyped cases. We identified a total of 85 cases that met the TB cluster criteria, and a total of 33 clusters. US-born individuals (p = 0.003), homeless individuals (p = 0.001) and those reporting illicit substance use (p = 0.001) and alcohol use (p = 0.001) were more likely to appear in a TB cluster.
Through a combined genotypic and spatial epidemiological approach, we identified populations and individuals more likely to be in a TB cluster. Testing populations identified as at risk for being in a TB cluster, and providing appropriate treatment, may decrease the overall TB case rate and support efforts to achieve national 2020 TB targets.</abstract><cop>Scotland</cop><pub>Elsevier Ltd</pub><pmid>30205965</pmid><doi>10.1016/j.tube.2018.07.002</doi><tpages>7</tpages><orcidid>https://orcid.org/0000-0003-2314-8924</orcidid></addata></record> |
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| ispartof | Tuberculosis (Edinburgh, Scotland), 2018-09, Vol.112, p.20-26 |
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| subjects | Adult Aged Alcohol-Related Disorders - epidemiology Alcoholic beverages Bacteria Cluster Analysis Data base management systems Databases, Factual Demographics Disease control Disease transmission Epidemiology Female Genotype Genotype & phenotype Genotypes Genotypic analysis Genotyping Geographic Information Systems GIS Homeless Persons Homelessness Humans Information management Male Massachusetts - epidemiology Middle Aged Molecular Epidemiology Mycobacterium tuberculosis - genetics Phenotype Populations Remote sensing Risk Factors Satellite navigation systems Spatial analysis Spatial epidemiologic analysis Substance use Substance-Related Disorders - epidemiology Time Factors Tuberculosis Tuberculosis - diagnosis Tuberculosis - epidemiology Tuberculosis - microbiology Tuberculosis - transmission |
| title | A genotypic and spatial epidemiologic analysis of Massachusetts' Mycobacterium tuberculosis cases from 2012 to 2015 |
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