Analyzing Municipal Patterns of Suicide and Depression in Mexico: A Multilayer Network Approach

Abstract

This study employs a multilayer network approach to analyze the spatial and temporal patterns of suicide and depression across Mexican municipalities from 2015 to 2020. Using a panel dataset of mental health cases, substance use, and healthcare infrastructure, we constructed a multilayer graph based on cosine similarity. The Infomap clustering algorithm was then applied to identify communities of municipalities with similar mental health profiles. Our results reveal five distinct clusters with significant variations in the levels and temporal dynamics of the analyzed indicators. Notably, two clusters consistently exhibited higher rates of substance use and adverse mental health outcomes. These findings demonstrate the efficacy of network-based methods for identifying at-risk
municipal groupings, thereby informing targeted public health interventions.