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TB Notes Newsletter

No. 4, 2005

TB Epidemiologic Studies Consortium Updates

Use of Network Analysis Methods to Characterize M. tuberculosis Transmission Patterns Among Women and Other High-Risk Populations

The science of network analysis (NA) includes mathematical and visual strategies to examine nodes (people and places) and the connections (relationships) among the nodes. For a respiratory infection spread via droplet nuclei, NA aims to identify the most critical nodes responsible for transmission and predict the contacts most likely infected. In the presence of ongoing M. tuberculosis transmission, networks of individual TB patients and contacts will begin to converge over time and provide early evidence of an outbreak.†

Much of the data collected in a routine TB contact investigation provides the fundamental basis for NA. Many investigators believe network analysis can be used to complement local health departmentsí current contact investigation practices, not supplant these practices.†

TBESC task order 7 had the following objectives: 1) to determine whether routine contact investigation data could be abstracted from local health department records on an ongoing basis and analyzed by commercially available network analysis software, and 2) to test the hypothesis that contacts prioritized with network analysis were more likely to have LTBI than nonprioritized contacts.

Task order 7 included three consortium sites (California Dept of Health, Emory University, and University of British Columbia), three local TB control program collaborators (Contra Costa County TB Control, CA; DeKalb County TB Control, GA; and Downtown Eastside Chest Clinic, Vancouver), two independent network analysis experts, plus extensive collaboration with DTBE staff. Over a 6-month period, each site abstracted routine demographic and clinical data from TB case records and contact investigation records and entered these data into a Microsoft Access database. Each site also employed a 1-page form to collect data from patients and contacts regarding places of frequent social aggregation during the 6-month period preceding TB diagnosis or evaluation as a contact. The database was designed to flag repeatedly-named patients and repeatedly-named places of social aggregation.

Over the 6-month data collection period, the three sites enrolled 42, 36, and 9 TB cases and 179, 218, and 56 contacts, respectively. NA did not detect any clustering of TB cases (no overlapping or repeatedly-named contacts or places of social aggregation) in Contra Costa despite the presence of three 2-case PCR genotype clusters (RFLP pending). However, NA did detect clustering in both DeKalb County and Vancouver. Repeatedly-named places helped connect TB cases in both DeKalb and Vancouver, which were later confirmed through genotyping. In Vancouver, contacts linked to multiple repeatedly-named places of social aggregation were more likely to be diagnosed with LTBI than contacts linked to TB cases only. Despite very different demographic composition of cases and contacts across the three sites, there was remarkable consistency in the distribution of reported daytime, evening, and night places of social aggregation. The number of repeatedly-named places was inversely related to the geographic size of the TB control jurisdiction, with 2%, 8%, and 16% in Contra Costa, DeKalb, and Vancouver, respectively.

NA assisted investigators in identifying disease clusters in two of three study sites and provided information related to core environments in which M. tuberculosis transmission could have potentially taken place. NA may be a valuable adjunct to traditional contact investigation activities.

óReported by Peter McElroy, PhD, MPH
Div of HIV/AIDS Prevention


Released October 2008
Centers for Disease Control and Prevention
National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention
Division of Tuberculosis Elimination -

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