Multiple large clusters of tuberculosis in London: A cross-sectional analysis of molecular and spatial data

Catherine M. Smith*, Helen Maguire, Charlotte Anderson, Neil Macdonald, Andrew C. Hayward

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


Large outbreaks of tuberculosis (TB) represent a particular threat to disease control because they reflect multiple instances of active transmission. The extent to which long chains of transmission contribute to high TB incidence in London is unknown. We aimed to estimate the contribution of large clusters to the burden of TB in London and identify risk factors. We identified TB patients resident in London notified between 2010 and 2014, and used 24-locus mycobacterial interspersed repetitive units–variable number tandem repeat strain typing data to classify cases according to molecular cluster size. We used spatial scan statistics to test for spatial clustering and analysed risk factors through multinomial logistic regression. TB isolates from 7458 patients were included in the analysis. There were 20 large molecular clusters (with n>20 cases), comprising 795 (11%) of all cases; 18 (90%) large clusters exhibited significant spatial clustering. Cases in large clusters were more likely to be UK born (adjusted odds ratio 2.93, 95% CI 2.28–3.77), of black-Caribbean ethnicity (adjusted odds ratio 3.64, 95% CI 2.23–5.94) and have multiple social risk factors (adjusted odds ratio 3.75, 95% CI 1.96–7.16). Large clusters of cases contribute substantially to the burden of TB in London. Targeting interventions such as screening in deprived areas and social risk groups, including those of black ethnicities and born in the UK, should be a priority for reducing transmission.

Original languageEnglish
Article number00098-2016
JournalERS Monograph
Issue number1
Publication statusPublished - 1 Jan 2017

Bibliographical note

Publisher Copyright:
© ERS 2017.


Dive into the research topics of 'Multiple large clusters of tuberculosis in London: A cross-sectional analysis of molecular and spatial data'. Together they form a unique fingerprint.

Cite this