Understanding racial bias in health using the Medical Expenditure Panel Survey data

TL;DR

This paper investigates racial bias in health indicators within the US Medical Expenditure Panel Survey data, showing that predictive models inherit this bias and can be mitigated with simple techniques.

Contribution

It highlights the presence of racial bias in health data and demonstrates effective mitigation methods for predictive models trained on this data.

Findings

Predictive models inherit racial bias from MEPS data

Simple mitigation techniques can significantly reduce bias

Bias reduction improves fairness in healthcare predictions

Abstract

Over the years, several studies have demonstrated that there exist significant disparities in health indicators in the United States population across various groups. Healthcare expense is used as a proxy for health in algorithms that drive healthcare systems and this exacerbates the existing bias. In this work, we focus on the presence of racial bias in health indicators in the publicly available, and nationally representative Medical Expenditure Panel Survey (MEPS) data. We show that predictive models for care management trained using this data inherit this bias. Finally, we demonstrate that this inherited bias can be reduced significantly using simple mitigation techniques.