Authors

Junyi Li, Hongying Wu, Shenghan Lyu

Research Question

How do different urban features influence depression rates through causal mechanisms?

Background

Depression is a critical public health concern, particularly in urban environments where dynamic and nonlinear interactions shape mental health outcomes. Despite extensive research, the causal mechanisms linking urban features to depression remain debated.

Methodology

This study employs Multi-Method Causation Analysis to uncover relationships between urban features and depression. Transfer Entropy is used to detect the directional flow of influence between socioeconomic factors and mental health, and the PC Algorithm is employed to infer causal structures from statistical dependencies. Additionally, spatial and scale-adjusted metropolitan indicators were used to remove extraneous noise from measurements related to population, spatial autocorrelation, and area under the assumption all indicators are temporally independent. These approaches move beyond static correlations to identify key urban characteristics—such as socioeconomic networks and infrastructure—that exacerbate or mitigate depression. The research delivers robust, actionable insights to inform urban planning and mental health interventions that could be implemented by the Centers for Disease Control and Prevention (CDC). These findings can help policymakers design cities that promote mental well-being, emphasizing infrastructure and policies that reduce stressors contributing to depression.

Deliverables

• Technical Report

Datasets