Programmatic utility of tuberculosis cluster investigation using a social network approach in Birmingham, United Kingdom

Melinda L. Munang; C. Browne; J. T. Evans; E. G. Smith; Peter Hawkey; S. B. Welch; H. Kaur; M. J. Dedicoat

doi:10.5588/ijtld.16.0161

Programmatic utility of tuberculosis cluster investigation using a social network approach in Birmingham, United Kingdom

Melinda L. Munang^*, C. Browne, J. T. Evans, E. G. Smith, Peter Hawkey, S. B. Welch, H. Kaur, M. J. Dedicoat

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

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.

Original language	English
Pages (from-to)	1300-1305
Number of pages	6
Journal	International Journal of Tuberculosis and Lung Disease
Volume	20
Issue number	10
DOIs	https://doi.org/10.5588/ijtld.16.0161
Publication status	Published - 1 Oct 2016

Bibliographical note

Funding Information:
Mycobacterial interspersed repetitive unit-variable number of tandem repeats typing was supported by the Public Health England National TB Strain Typing Project. The authors wish to thank the tuberculosis nurse specialists and the administration team at the Birmingham Chest Clinic, Birmingham, UK, whose patients are described in the study; and the UK Clinical Research Collaboration Modernising Medical Microbiology Consortium, Heart of Birmingham Primary Care Trust and Public Health England Birmingham, UK, for funding support for this work.

Publisher Copyright:
© 2016 The Union.

Keywords

Contact tracing
Molecular epidemiology
Social network
Tuberculosis

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.5588/ijtld.16.0161

Cite this

@article{2e051f429a8b492684ebd2f7bbc14dce,

title = "Programmatic utility of tuberculosis cluster investigation using a social network approach in Birmingham, United Kingdom",

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.",

keywords = "Contact tracing, Molecular epidemiology, Social network, Tuberculosis",

author = "Munang, {Melinda L.} and C. Browne and Evans, {J. T.} and Smith, {E. G.} and Peter Hawkey and Welch, {S. B.} and H. Kaur and Dedicoat, {M. J.}",

note = "Funding Information: Mycobacterial interspersed repetitive unit-variable number of tandem repeats typing was supported by the Public Health England National TB Strain Typing Project. The authors wish to thank the tuberculosis nurse specialists and the administration team at the Birmingham Chest Clinic, Birmingham, UK, whose patients are described in the study; and the UK Clinical Research Collaboration Modernising Medical Microbiology Consortium, Heart of Birmingham Primary Care Trust and Public Health England Birmingham, UK, for funding support for this work. Publisher Copyright: {\textcopyright} 2016 The Union.",

year = "2016",

month = oct,

day = "1",

doi = "10.5588/ijtld.16.0161",

language = "English",

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AU - Munang, Melinda L.

AU - Browne, C.

AU - Evans, J. T.

AU - Smith, E. G.

AU - Hawkey, Peter

AU - Welch, S. B.

AU - Kaur, H.

AU - Dedicoat, M. J.

N1 - Funding Information: Mycobacterial interspersed repetitive unit-variable number of tandem repeats typing was supported by the Public Health England National TB Strain Typing Project. The authors wish to thank the tuberculosis nurse specialists and the administration team at the Birmingham Chest Clinic, Birmingham, UK, whose patients are described in the study; and the UK Clinical Research Collaboration Modernising Medical Microbiology Consortium, Heart of Birmingham Primary Care Trust and Public Health England Birmingham, UK, for funding support for this work. Publisher Copyright: © 2016 The Union.

PY - 2016/10/1

Y1 - 2016/10/1

N2 - 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.

AB - 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.

KW - Contact tracing

KW - Molecular epidemiology

KW - Social network

KW - Tuberculosis

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JO - International Journal of Tuberculosis and Lung Disease

JF - International Journal of Tuberculosis and Lung Disease

IS - 10

ER -

Programmatic utility of tuberculosis cluster investigation using a social network approach in Birmingham, United Kingdom

Abstract

Bibliographical note

Keywords

UN SDGs

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