Programmatic utility of tuberculosis cluster investigation using a social network approach in Birmingham, United Kingdom
SETTING: Birmingham, United Kingdom, 2010-2014.OBJECTIVE: To investigate predictors for clustering of tuberculosis (TB) cases and cluster size and to evaluate the impact of cluster investigation using social network data.DESIGN: Retrospective observational cohort study. Prioritised cases linked usin...
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| Veröffentlicht in: | The international journal of tuberculosis and lung disease 2016-10, Vol.20 (10), p.1300-1305 |
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| container_title | The international journal of tuberculosis and lung disease |
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| creator | Munang, M. L. Browne, C. Evans, J. T. Smith, E. G. Hawkey, P. M. Welch, S. B Kaur, H. Dedicoat, M. J. |
| description | SETTING: Birmingham, United Kingdom, 2010-2014.OBJECTIVE: To investigate predictors for clustering of tuberculosis (TB) cases and cluster size and to evaluate the impact of cluster investigation using social network data.DESIGN: Retrospective observational cohort study.
Prioritised cases linked using 24-locus mycobacterial interspersed repetitive units-variable number of tandem repeats (MIRU-VNTR) were interviewed using a social network approach to find epidemiological links.RESULTS: Of 2055 TB cases notified, 56% could be typed. Clustering was
associated with younger age, UK birth, Black Caribbean ethnicity, social risk factors, pulmonary TB and negative human immunodeficiency virus status. Only UK birth and presence of more than one social risk factor were associated with larger cluster size, while drug resistance was associated
with smaller cluster size. Social network data from 139/431 clustered cases found new epidemiological links in 11/19 clusters with 5 members (undirected median network density 0.09, interquartile range 0.05-0.4). Ninety-eight additional contacts were assessed, with one case of active
TB and 24 with latent tuberculous infection diagnosed.CONCLUSION: A social network approach increased knowledge of likely transmission events, but few additional TB cases were diagnosed. Obtaining social network data for all typed and untyped TB cases may improve contact tracing and reduce unexpected transmission detected from molecular data. |
| doi_str_mv | 10.5588/ijtld.16.0161 |
| format | Article |
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Prioritised cases linked using 24-locus mycobacterial interspersed repetitive units-variable number of tandem repeats (MIRU-VNTR) were interviewed using a social network approach to find epidemiological links.RESULTS: Of 2055 TB cases notified, 56% could be typed. Clustering was
associated with younger age, UK birth, Black Caribbean ethnicity, social risk factors, pulmonary TB and negative human immunodeficiency virus status. Only UK birth and presence of more than one social risk factor were associated with larger cluster size, while drug resistance was associated
with smaller cluster size. Social network data from 139/431 clustered cases found new epidemiological links in 11/19 clusters with 5 members (undirected median network density 0.09, interquartile range 0.05-0.4). Ninety-eight additional contacts were assessed, with one case of active
TB and 24 with latent tuberculous infection diagnosed.CONCLUSION: A social network approach increased knowledge of likely transmission events, but few additional TB cases were diagnosed. Obtaining social network data for all typed and untyped TB cases may improve contact tracing and reduce unexpected transmission detected from molecular data.</description><identifier>ISSN: 1027-3719</identifier><identifier>EISSN: 1815-7920</identifier><identifier>DOI: 10.5588/ijtld.16.0161</identifier><identifier>PMID: 27725038</identifier><language>eng</language><publisher>France: International Union Against Tuberculosis and Lung Disease</publisher><subject>Adult ; Bacterial Typing Techniques ; Cluster Analysis ; Contact Tracing ; Female ; Humans ; Latent Tuberculosis - diagnosis ; Latent Tuberculosis - epidemiology ; Male ; Middle Aged ; Molecular Epidemiology ; Mycobacterium tuberculosis - classification ; Mycobacterium tuberculosis - isolation & purification ; Retrospective Studies ; Risk Factors ; Social Environment ; Social Network ; Tuberculosis ; Tuberculosis - diagnosis ; Tuberculosis - epidemiology ; Tuberculosis, Pulmonary - diagnosis ; Tuberculosis, Pulmonary - epidemiology ; United Kingdom - epidemiology</subject><ispartof>The international journal of tuberculosis and lung disease, 2016-10, Vol.20 (10), p.1300-1305</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c461t-8b0b8f1b4f648d3f0c426e37c9ba961e11cde812b4bdc929ebf19fb6d099e8683</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27725038$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Munang, M. L.</creatorcontrib><creatorcontrib>Browne, C.</creatorcontrib><creatorcontrib>Evans, J. T.</creatorcontrib><creatorcontrib>Smith, E. G.</creatorcontrib><creatorcontrib>Hawkey, P. M.</creatorcontrib><creatorcontrib>Welch, S. B</creatorcontrib><creatorcontrib>Kaur, H.</creatorcontrib><creatorcontrib>Dedicoat, M. J.</creatorcontrib><title>Programmatic utility of tuberculosis cluster investigation using a social network approach in Birmingham, United Kingdom</title><title>The international journal of tuberculosis and lung disease</title><addtitle>Int J Tuberc Lung Dis</addtitle><description>SETTING: Birmingham, United Kingdom, 2010-2014.OBJECTIVE: To investigate predictors for clustering of tuberculosis (TB) cases and cluster size and to evaluate the impact of cluster investigation using social network data.DESIGN: Retrospective observational cohort study.
Prioritised cases linked using 24-locus mycobacterial interspersed repetitive units-variable number of tandem repeats (MIRU-VNTR) were interviewed using a social network approach to find epidemiological links.RESULTS: Of 2055 TB cases notified, 56% could be typed. Clustering was
associated with younger age, UK birth, Black Caribbean ethnicity, social risk factors, pulmonary TB and negative human immunodeficiency virus status. Only UK birth and presence of more than one social risk factor were associated with larger cluster size, while drug resistance was associated
with smaller cluster size. Social network data from 139/431 clustered cases found new epidemiological links in 11/19 clusters with 5 members (undirected median network density 0.09, interquartile range 0.05-0.4). Ninety-eight additional contacts were assessed, with one case of active
TB and 24 with latent tuberculous infection diagnosed.CONCLUSION: A social network approach increased knowledge of likely transmission events, but few additional TB cases were diagnosed. Obtaining social network data for all typed and untyped TB cases may improve contact tracing and reduce unexpected transmission detected from molecular data.</description><subject>Adult</subject><subject>Bacterial Typing Techniques</subject><subject>Cluster Analysis</subject><subject>Contact Tracing</subject><subject>Female</subject><subject>Humans</subject><subject>Latent Tuberculosis - diagnosis</subject><subject>Latent Tuberculosis - epidemiology</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Molecular Epidemiology</subject><subject>Mycobacterium tuberculosis - classification</subject><subject>Mycobacterium tuberculosis - isolation & purification</subject><subject>Retrospective Studies</subject><subject>Risk Factors</subject><subject>Social Environment</subject><subject>Social Network</subject><subject>Tuberculosis</subject><subject>Tuberculosis - diagnosis</subject><subject>Tuberculosis - epidemiology</subject><subject>Tuberculosis, Pulmonary - diagnosis</subject><subject>Tuberculosis, Pulmonary - epidemiology</subject><subject>United Kingdom - epidemiology</subject><issn>1027-3719</issn><issn>1815-7920</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkktv1DAURi0EoqWwZIu8ZEEGXyfxYwnlKSoBEl1btuNMPSTx4Eeh_HqczpQdEt74Wjo-vvJ3EXoKZNP3Qrz0uzwNG2AbAgzuoVMQ0DdcUnK_1oTypuUgT9CjlHaEUADgD9EJ5Zz2pBWn6NeXGLZRz7PO3uKS_eTzDQ4jzsW4aMsUkk_YTiVlF7Ffrl3KflvhsOCS_LLFGqdgvZ7w4vLPEL9jvd_HoO1VpfFrH-cKXen5Bb5cfHYD_lTPQ5gfowejnpJ7ctzP0OW7t9_OPzQXn99_PH910diOQW6EIUaMYLqRdWJoR2I7ylzLrTRaMnAAdnACqOnMYCWVzowgR8MGIqUTTLRn6PnBW5v6UWr3avbJumnSiwslKRAtbykFDv-D9i3vuFjR5oDaGFKKblT76GcdbxQQteaibnNRwNSaS-WfHdXFzG74S98FUYE3B6D-jluyVrtQ4lI_RvmiV9PBR8lqvF2UHIv6oo55rWTVfP2Xxt6Z1qlYh0JdU7LUy7SOBRG0V1A7UYMbdZmyyjqq7W-VZPsHajO9Yg</recordid><startdate>20161001</startdate><enddate>20161001</enddate><creator>Munang, M. L.</creator><creator>Browne, C.</creator><creator>Evans, J. T.</creator><creator>Smith, E. G.</creator><creator>Hawkey, P. M.</creator><creator>Welch, S. B</creator><creator>Kaur, H.</creator><creator>Dedicoat, M. J.</creator><general>International Union Against Tuberculosis and Lung Disease</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>7X8</scope><scope>7QL</scope><scope>C1K</scope></search><sort><creationdate>20161001</creationdate><title>Programmatic utility of tuberculosis cluster investigation using a social network approach in Birmingham, United Kingdom</title><author>Munang, M. L. ; Browne, C. ; Evans, J. T. ; Smith, E. G. ; Hawkey, P. M. ; Welch, S. B ; Kaur, H. ; Dedicoat, M. 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T.</au><au>Smith, E. G.</au><au>Hawkey, P. M.</au><au>Welch, S. B</au><au>Kaur, H.</au><au>Dedicoat, M. J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Programmatic utility of tuberculosis cluster investigation using a social network approach in Birmingham, United Kingdom</atitle><jtitle>The international journal of tuberculosis and lung disease</jtitle><addtitle>Int J Tuberc Lung Dis</addtitle><date>2016-10-01</date><risdate>2016</risdate><volume>20</volume><issue>10</issue><spage>1300</spage><epage>1305</epage><pages>1300-1305</pages><issn>1027-3719</issn><eissn>1815-7920</eissn><abstract>SETTING: Birmingham, United Kingdom, 2010-2014.OBJECTIVE: To investigate predictors for clustering of tuberculosis (TB) cases and cluster size and to evaluate the impact of cluster investigation using social network data.DESIGN: Retrospective observational cohort study.
Prioritised cases linked using 24-locus mycobacterial interspersed repetitive units-variable number of tandem repeats (MIRU-VNTR) were interviewed using a social network approach to find epidemiological links.RESULTS: Of 2055 TB cases notified, 56% could be typed. Clustering was
associated with younger age, UK birth, Black Caribbean ethnicity, social risk factors, pulmonary TB and negative human immunodeficiency virus status. Only UK birth and presence of more than one social risk factor were associated with larger cluster size, while drug resistance was associated
with smaller cluster size. Social network data from 139/431 clustered cases found new epidemiological links in 11/19 clusters with 5 members (undirected median network density 0.09, interquartile range 0.05-0.4). Ninety-eight additional contacts were assessed, with one case of active
TB and 24 with latent tuberculous infection diagnosed.CONCLUSION: A social network approach increased knowledge of likely transmission events, but few additional TB cases were diagnosed. Obtaining social network data for all typed and untyped TB cases may improve contact tracing and reduce unexpected transmission detected from molecular data.</abstract><cop>France</cop><pub>International Union Against Tuberculosis and Lung Disease</pub><pmid>27725038</pmid><doi>10.5588/ijtld.16.0161</doi><tpages>6</tpages></addata></record> |
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| subjects | Adult Bacterial Typing Techniques Cluster Analysis Contact Tracing Female Humans Latent Tuberculosis - diagnosis Latent Tuberculosis - epidemiology Male Middle Aged Molecular Epidemiology Mycobacterium tuberculosis - classification Mycobacterium tuberculosis - isolation & purification Retrospective Studies Risk Factors Social Environment Social Network Tuberculosis Tuberculosis - diagnosis Tuberculosis - epidemiology Tuberculosis, Pulmonary - diagnosis Tuberculosis, Pulmonary - epidemiology United Kingdom - epidemiology |
| title | Programmatic utility of tuberculosis cluster investigation using a social network approach in Birmingham, United Kingdom |
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