Detection of potential causal pathways among social determinants of health: A data-informed framework

Understanding social determinants of health (SDOH) as a complex system is necessary for designing effective public health interventions. Traditional expert-driven approaches to mapping SDOH relationships, when used in isolation, are susceptible to subjective biases, incomplete knowledge, and inconsistencies across different domains of expertise. Additionally, SDOH variables often contain overlapping information, making it difficult to isolate unique SDOH constructs. A data-driven approach integrating dimensionality reduction and causal discovery can provide a more objective framework for identifying and mapping SDOH factors within a causal system. The data-driven method may serve as a starting point to overcome potential research biases in the development of causal structures.

An observational study was conducted using census tract-level SDOH data from the 2020 Agency for Healthcare Research and Quality (AHRQ) database. Principal Component Analysis (PCA) was applied to derive latent constructs from 157 SDOH variables across 85,528 U.S. census tracts. The Greedy Equivalence Search (GES) algorithm was then used to identify dominant causal pathways between these constructs.

PCA-derived components explained substantial variance within each domain, with food access (71.1 %) and income (50.0 %) explaining the most within-domain variance. The causal graph revealed economic stability as a central determinant influencing education, employment, housing, and healthcare access. Education, access to care, and access to technology mediated many pathways.

Findings highlight the interconnected nature of SDOH, emphasizing financial stability as a foundational determinant. The role of digital equity in health outcomes is increasingly significant. The data-driven approach may serve as an important tool to support researchers in the mapping of SDOH causal structures.

This study demonstrates the utility of combining PCA and GES to uncover causal pathways among SDOH constructs. Developing causal systems using data-driven methods provides an enhanced method for conducting public health assessments, identify optimal intervention points, and informing policy development.