The application of clique percolation method in health disparities research.

Abstract

Clique percolation, one of the joint community detection algorithms in network science, is a novel and efficient approach to detecting overlapping communities in real networks. The current study illustrated how clique percolation can help to identify overlapping communities within the complex networks underlying health disparities, particularly highlighting nodes with strong associations with more than one community. A cross-sectional study. The study used a dataset on Latinx populations (N = 1654; mean age = 43.3 years; 53.1% women) as an example to demonstrate the role of such overlapping nodes in the network of syndemic conditions and their common risk factors. Syndemic conditions in the network included HIV risk, substance abuse (smoking, heavy alcohol consumption and marijuana use) and poor mental health. Moreover, the risk factors encompassed individual (education and income) and sociostructural (adverse childhood experiences [ACEs] and access to services) factors. The network was estimated using the R-package bootnet. Clique percolation was conducted on the estimated network using the R-package CliquePercolation. A total of three communities were detected, with HIV risk and poor mental health not being assigned to any community. In general, Community 1 was comprised of ACE categories, Community 2 included education, income and access to services and Community 3 included other syndemic conditions. Of note, two nodes were assigned to two communities: 'household dysfunction' to Communities 1 and 2 and 'smoking' to Communities 2 and 3. Household dysfunction might be the key connector, among other ACEs, to individual and structural barriers. Such barriers further exposed Latinx individuals to risky behaviours, especially smoking, which further linked to marijuana use and heavy alcohol consumption. Clique percolation facilitated our understanding of the complex systems of factors shaping health disparities. The overlapping nodes are promising intervention targets for reducing health disparities in this historically marginalized population. No patient or public contribution.