Association between Binge Drinking Behaviors and Comorbidities in Brazil: Network Analysis of a National Health Survey
Zadey, S.; Franca, D.; Coelho Pessoa Santos, P.; Pereira, N. D.; Tchuisseu, Y. P.; Andrade, L.; Nunes, B. P.; Machado, W. D. L.; Staton, C. A.; Vissoci, J. R. N.
Show abstract
Alcohol consumption is the sixth leading cause of death globally. Brazil ranks second in alcohol-related mortality within the American regions with a notable increase in binge drinking behavior from 2013 (5.9%) to 2019 (17.1%). Binge drinking, a form of alcohol misuse, is a known risk factor for several diseases. We aimed to understand the differences in binge drinking across various sociodemographic groups and the patterns of comorbidities in a national-level dataset by doing a cross-sectional network analysis of the 2013 Brazilian National Health Survey. Binge drinking was defined as a binary variable based on alcohol consumption of >5 (4) doses in a month for male (female) responders. Weighted undirected network analysis using Ising models was performed to discover the strength of inter-relations between nineteen chronic conditions. In the network, the nodes represented the conditions and the edges were formed by statistical associations derived using logistic regression. Community analyses identified the clusters within networks. A nationally representative sample of 60,202 people revealed the prevalence of binge drinking to be about 13.5%. The study revealed a less connected network among binge drinkers, potentially impacting disease associations. Binge drinking demonstrated unique correlations with comorbidities across age, gender, ethnicity, and education levels, highlighting the complex interplay between binge drinking and health outcomes. Recognizing the specific comorbidities associated with binge drinking, such as hypertension and chronic spinal problems, allows healthcare professionals to tailor preventive measures and early interventions. In this sense, differences in binge drinking and its direct association with comorbidities as well as in comorbidity structures across sociodemographic characteristics point to the utility of network models to identify specific populations at various health risks.
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