Clustering of Social Determinants of Health as an Indicator of Meaningful Subgroups within an African American Population: Application of Latent Class Analysis.

Abstract

Health disparities between people who are African American (AA) versus their White counterparts have been well established, but disparities among AA people have not. The current study introduces a systematic method to determine subgroups within a sample of AA people based on their social determinants of health. Health screening data collected in the West Side of Chicago, an underserved predominantly AA area, in 2018 were used. Exploratory latent class analysis was used to determine subgroups of participants based on their responses to 16 variables, each pertaining to a specific social determinant of health. Four unique clusters of participants were found, corresponding to those with "many unmet needs", "basic unmet needs", "unmet healthcare needs", and "few unmet needs". The findings support the utility of analytically determining meaningful subgroups among a sample of AA people and their social determinants of health. Understanding the differences within an underserved population may contribute to future interventions to eliminate health disparities.