  As interest in implementing artificial intelligence (AI) in medical systems
grows, discussion continues on how to evaluate the fairness of these systems,
or the disparities they may perpetuate. Socioeconomic status (SES) is commonly
included in machine learning models to control for health inequities, with the
underlying assumption that increased SES is associated with better health. In
this work, we considered a large cohort of patients from the Mount Sinai Health
System in New York City to investigate the effect of patient SES, race, and sex
on schizophrenia (SCZ) diagnosis rates via a logistic regression model. Within
an intersectional framework, patient SES, race, and sex were found to have
significant interactions. Our findings showed that increased SES is associated
with a higher probability of obtaining a SCZ diagnosis in Black Americans
($\beta=4.1\times10^{-8}$, $SE=4.5\times10^{-9}$, $p < 0.001$). Whereas high
SES acts as a protective factor for SCZ diagnosis in White Americans
($\beta=-4.1\times10^{-8}$, $SE=6.7\times10^{-9}$, $p < 0.001$). Further
investigation is needed to reliably explain and quantify health disparities.
Nevertheless, we advocate that building fair AI tools for the health care space
requires recognizing the intersectionality of sociodemographic factors.
