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

Author

Listed:
  • Korvink, Michael
  • Biondolillo, Madeleine
  • Van Dijk, Julie Willems
  • Banerjee, Anjishnu
  • Simenz, Christopher
  • Nelson, David

Abstract

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.

Suggested Citation

  • Korvink, Michael & Biondolillo, Madeleine & Van Dijk, Julie Willems & Banerjee, Anjishnu & Simenz, Christopher & Nelson, David, 2025. "Detection of potential causal pathways among social determinants of health: A data-informed framework," Social Science & Medicine, Elsevier, vol. 373(C).
    • Handle: RePEc:eee:socmed:v:373:y:2025:i:c:s0277953625003557
    DOI: 10.1016/j.socscimed.2025.118025
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