Polysocial Risk Scores: Implications for Cardiovascular Disease Risk Assessment and Management

Javed, Zulqarnain; Kundi, Harun; Chang, Ryan; Titus, Anoop; Arshad, Hassaan

doi:10.1007/s11883-023-01173-4

Cited by 1 publication

(1 citation statement)

References 28 publications

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“…Due to the collinearity of SDoH factors, researchers have grappled with how to approach dealing with these measures, including clustering methods, creating indices using PCA, and mostly recently, polysocial risks scores [33]. The decision to use exploratory factor analysis, hierarchical clustering, and latent class analysis in our research reflects a deliberate effort to approach patient clustering from multiple angles, acknowledging the diverse aspects of the underlying data.…”

Section: Discussionmentioning

confidence: 99%

Analyzing Multiple Social Determinants of Health Using Different Clustering Methods

Zhang,

Clay,

Lee

et al. 2024

IJERPH

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Social determinants of health (SDoH) have become an increasingly important area to acknowledge and address in healthcare; however, dealing with these measures in outcomes research can be challenging due to the inherent collinearity of these factors. Here we discuss our experience utilizing three statistical methods—exploratory factor analysis (FA), hierarchical clustering, and latent class analysis (LCA)—to analyze data collected using an electronic medical record social risk screener called Protocol for Responding to and Assessing Patient Assets, Risks, and Experience (PRAPARE). The PRAPARE tool is a standardized instrument designed to collect patient-reported data on SDoH factors, such as income, education, housing, and access to care. A total of 2380 patients had complete PRAPARE and neighborhood-level data for analysis. We identified a total of three composite SDoH clusters using FA, along with four clusters identified through hierarchical clustering, and four latent classes of patients using LCA. Our results highlight how different approaches can be used to handle SDoH, as well as how to select a method based on the intended outcome of the researcher. Additionally, our study shows the usefulness of employing multiple statistical methods to analyze complex SDoH gathered using social risk screeners such as the PRAPARE tool.

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Section: Discussionmentioning

confidence: 99%