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

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

doi:10.5588/ijtld.16.0161

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

M. L. Munang, C. Browne, Jason T. Evans, Gillian E. Smith, Peter Hawkey, S. B. Welch, H. Kaur, Martin J. Dedicoat

Microbiology and Infection

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

4 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 (IJTLD)
Volume	20
Issue number	10
DOIs	https://doi.org/10.5588/ijtld.16.0161
Publication status	Published - 1 Oct 2016

Keywords

Adult
Bacterial Typing Techniques
Cluster Analysis
Female
Humans
Latent Tuberculosis/diagnosis
Male
Middle Aged
Mycobacterium tuberculosis/classification
Retrospective Studies
Risk Factors
Social Environment
Tuberculosis/diagnosis
Tuberculosis, Pulmonary/diagnosis
United Kingdom/epidemiology

UN SDGs

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

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Cite this

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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 = "Adult, Bacterial Typing Techniques, Cluster Analysis, Female, Humans, Latent Tuberculosis/diagnosis, Male, Middle Aged, Mycobacterium tuberculosis/classification, Retrospective Studies, Risk Factors, Social Environment, Tuberculosis/diagnosis, Tuberculosis, Pulmonary/diagnosis, United Kingdom/epidemiology",

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

year = "2016",

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doi = "10.5588/ijtld.16.0161",

language = "English",

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journal = "International Journal of Tuberculosis and Lung Disease (IJTLD)",

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publisher = "International Union Against Tuberculosis and Lung Disease",

number = "10",

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T1 - Programmatic utility of tuberculosis cluster investigation using a social network approach in Birmingham, United Kingdom

AU - Munang, M. L.

AU - Browne, C.

AU - Evans, Jason T.

AU - Smith, Gillian E.

AU - Hawkey, Peter

AU - Welch, S. B.

AU - Kaur, H.

AU - Dedicoat, Martin J.

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.

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KW - Cluster Analysis

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KW - Humans

KW - Latent Tuberculosis/diagnosis

KW - Male

KW - Middle Aged

KW - Mycobacterium tuberculosis/classification

KW - Retrospective Studies

KW - Risk Factors

KW - Social Environment

KW - Tuberculosis/diagnosis

KW - Tuberculosis, Pulmonary/diagnosis

KW - United Kingdom/epidemiology

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JO - International Journal of Tuberculosis and Lung Disease (IJTLD)

JF - International Journal of Tuberculosis and Lung Disease (IJTLD)

IS - 10

ER -

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

Abstract

Keywords

UN SDGs

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