A social networks-driven approach to understand the unique alcohol mixing patterns of tuberculosis patients

Individuals who consume alcohol have a higher chance of contracting tuberculosis (TB) due to their social mixing patterns. We aimed to study the social mixing patterns of TB patients who consume alcohol on a regular basis using a quantitative social network approach. In a high-TB prevalence context in India, a social network survey of 300 newly diagnosed pulmonary drug-sensitive TB patients was done. The survey found 52 (17%) male TB patients who shared alcohol on a regular basis with 106 (4%) of their first-degree social contacts. Alcohol sharing happened in 16 neighborhood venues. When compared to contacts who did not use alcohol, a higher proportion of contacts with regular alcohol use were diagnosed with TB (12.3%; 95% CI: 6.6–20.00 vs. 3.5%; 95% CI: 2.8–4.3). Social network analysis showed that the network consisting of patients and contacts was less dense and less connected (with density ratio of 0.009, and degree centrality of 1.3, and betweenness centrality of 0.5), indicating weaker transmission potential of the network. Comparatively the network consisting of patients, contacts and their alcohol sharing venues was more dense and more connected (with density ratio of 0.018, higher degree centrality of 3.1 and betweenness centrality of 154.2) indicating stronger transmission potential of the network. Regular alcohol sharing in four venues created a giant network component, that linked a higher proportion of contacts without TB (72.3%) to a higher proportion of TB patients (67.3%) and their contacts with TB (38.4%). When examined from a network perspective, the pooled TB transmission exposure of contacts with regular alcohol use grew by a factor of 10, which helped explain the unfavorable social mixing of patients and contacts with regular alcohol use

Alcohol · Betweenness centrality · Centrality · Geography · Mixing patterns · Pathology · Physics · Social network analysis · Sociology · Statistics · Tuberculosis · Chemistry · Computer Science · Data-Driven Disease Surveillance · Demography · Mathematics · Medicine · Psychology · Schizophrenia research and treatment · Tuberculosis Research and Epidemiology

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