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

Research output: Contribution to journalArticlepeer-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 languageEnglish
Pages (from-to)1300-1305
Number of pages6
JournalInternational Journal of Tuberculosis and Lung Disease
Volume20
Issue number10
DOIs
Publication statusPublished - 1 Oct 2016

Keywords

  • Contact tracing
  • Molecular epidemiology
  • Social network
  • Tuberculosis

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