Causal modeling of chronic kidney disease in a participatory framework for informing the inclusion of social drivers in health algorithms

Abstract

Objectives: Incomplete or incorrect causal theories are a key source of bias in machine learning (ML) algorithms. Community-engaged methodologies provide an avenue for mitigating this bias through incorporating causal insights from community stakeholders into ML development. In health applications, community-engaged approaches can enable the study of social drivers of health (SDOH), which are known to shape health inequities. However, it remains challenging for SDOH to inform ML algorithms, partially because SDOH variables are known to be interrelated, yet it is difficult to elucidate the causal relationships between them. Community-based system dynamics is a community-engaged methodology that can be used to cocreate formal causal graphs, called causal loop diagrams, with patients. Materials and Methods: We used community-based system dynamics to create a causal graph representing the impacts of SDOH on the progression of chronic kidney disease, a chronic condition with SDOH-driven health disparities. We conducted focus groups with 42 participants and a day-long model building workshop with 11 participants. Results: Our model building workshop resulted in a final graph comprising 16 variables, 42 causal links, and 5 subsystems of semantically related SDOH variables. Conclusion: This final graph, representing the causal relationships between social variables relevant to chronic kidney disease, can inform the development of clinical ML algorithms and other technological interventions.