Subtyping Social Determinants of Health in : Network Analysis and Visualization Approach.

Background: Social determinants of health (SDoH), such as financial resources and housing stability, account for between 30-55% of people's health outcomes. While many studies have identified strong associations among specific SDoH and health outcomes, most people experience multiple SDoH that impact their daily lives. Analysis of this complexity requires the integration of personal, clinical, social, and environmental information from a large cohort of individuals that have been traditionally underrepresented in research, which is only recently being made available through the research program. However, little is known about the range and response of SDoH in , and how they co-occur to form subtypes, which are critical for designing targeted interventions.

Objective: To address two research questions: (1) What is the range and response to survey questions related to SDoH in the dataset? (2) How do SDoH co-occur to form subtypes, and what are their risk for adverse health outcomes?

Methods: For Question-1, an expert panel analyzed the range of SDoH questions across the surveys with respect to the 5 domains in (, and analyzed their responses across the full data (n=372,397, V6). For Question-2, we used the following steps: (1) due to the missingness across the surveys, selected all participants with valid and complete SDoH data, and used inverse probability weighting to adjust their imbalance in demographics compared to the full data; (2) an expert panel grouped the SDoH questions into SDoH factors for enabling a more consistent granularity; (3) used bipartite modularity maximization to identify SDoH biclusters, their significance, and their replicability; (4) measured the association of each bicluster to three outcomes (depression, delayed medical care, emergency room visits in the last year) using multiple data types (surveys, electronic health records, and zip codes mapped to Medicaid expansion states); and (5) the expert panel inferred the subtype labels, potential mechanisms that precipitate adverse health outcomes, and interventions to prevent them.

Results: For Question-1, we identified 110 SDoH questions across 4 surveys, which covered all 5 domains in . However, the results also revealed a large degree of missingness in survey responses (1.76%-84.56%), with later surveys having significantly fewer responses compared to earlier ones, and significant differences in race, ethnicity, and age of participants of those that completed the surveys with SDoH questions, compared to those in the full dataset. Furthermore, as the SDoH questions varied in granularity, they were categorized by an expert panel into 18 SDoH factors. For Question-2, the subtype analysis (n=12,913, d=18) identified 4 biclusters with significant biclusteredness (Q=0.13, random-Q=0.11, z=7.5, <0.001), and significant replication (Real-RI=0.88, Random-RI=0.62, <.001). Furthermore, there were statistically significant associations between specific subtypes and the outcomes, and with Medicaid expansion, each with meaningful interpretations and potential targeted interventions. For example, the subtype included the SDoH factors , and , and had a significantly higher odds ratio (OR=4.2, CI=3.5-5.1, -corr<.001) for depression, when compared to the subtype . Individuals that match this subtype profile could be screened early for depression and referred to social services for addressing combinations of SDoH such as and . Finally, the identified subtypes spanned one or more domains revealing the difference between the current knowledge-based SDoH domains, and the data-driven subtypes.

Conclusions: The results revealed that the SDoH subtypes not only had statistically significant clustering and replicability, but also had significant associations with critical adverse health outcomes, which had translational implications for designing targeted SDoH interventions, decision-support systems to alert clinicians of potential risks, and for public policies. Furthermore, these SDoH subtypes spanned multiple SDoH domains defined by revealing the complexity of SDoH in the real-world, and aligning with influential SDoH conceptual models such as by Dahlgren-Whitehead. However, the high-degree of missingness warrants repeating the analysis as the data becomes more complete. Consequently we designed our machine learning code to be generalizable and scalable, and made it available on the workbench, which can be used to periodically rerun the analysis as the dataset grows for analyzing subtypes related to SDoH, and beyond.