BACKGROUND: Social network analysis (SNA) is an innovative approach to the collection and analysis of infectious disease transmission data. We studied whether this approach can detect patterns of Mycobacterium tuberculosis transmission and play a helpful role in the complex process of prioritizing tuberculosis (TB) contact investigations. METHODS: We abstracted routine demographic and clinical variables from patient medical records and contact interview forms. We also administered a structured questionnaire about places of social aggregation to TB patients and their contacts. All case-contact, contact-contact, case-place, and contact-place dyads (pairs and links) were considered in order to analyze the structure of a social network of TB transmission. Molecular genotyping was used to confirm SNA-detected clusters of TB. RESULTS: TB patients not linked through conventional contact-investigation data were connected through mutual contacts or places of social aggregation, using SNA methods. In some instances, SNA detected connected groups prior to the availability of genotyping results. A positive correlation between positive results of contacts' tuberculin skin test (TST) and location in denser portions of the person-place network was observed (P<.01). CONCLUSIONS: Correlation between TST-positive status and dense subgroup occurrence supports the value of collecting place data to help prioritize TB contact investigations. TB controllers should consider developing social network analysis capacity to facilitate the systematic collection, analysis, and interpretation of contact-investigation data.
BACKGROUND: Social network analysis (SNA) is an innovative approach to the collection and analysis of infectious disease transmission data. We studied whether this approach can detect patterns of Mycobacterium tuberculosis transmission and play a helpful role in the complex process of prioritizing tuberculosis (TB) contact investigations. METHODS: We abstracted routine demographic and clinical variables from patient medical records and contact interview forms. We also administered a structured questionnaire about places of social aggregation to TB patients and their contacts. All case-contact, contact-contact, case-place, and contact-place dyads (pairs and links) were considered in order to analyze the structure of a social network of TB transmission. Molecular genotyping was used to confirm SNA-detected clusters of TB. RESULTS: TB patients not linked through conventional contact-investigation data were connected through mutual contacts or places of social aggregation, using SNA methods. In some instances, SNA detected connected groups prior to the availability of genotyping results. A positive correlation between positive results of contacts' tuberculin skin test (TST) and location in denser portions of the person-place network was observed (P<.01). CONCLUSIONS: Correlation between TST-positive status and dense subgroup occurrence supports the value of collecting place data to help prioritize TB contact investigations. TB controllers should consider developing social network analysis capacity to facilitate the systematic collection, analysis, and interpretation of contact-investigation data.
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