Chronic kidney disease affects over 37 million Americans, with racial and ethnic minorities bearing a disproportionate burden. Black and Hispanic patients face significantly higher risks of kidney failure compared to their non-Hispanic white counterparts, partly due to delayed specialist referrals.
Despite recognizing these disparities in the 1980s, progress has been minimal. The use of race in clinical algorithms, particularly in assessing kidney disease severity, has come under scrutiny. Until recently, two widely adopted equations incorporated a Black or non-Black race variable, potentially propagating racial bias in medical decision-making.
In response to these concerns, professional organizations developed a new clinical algorithm in 2021 that eliminates race as a factor. However, a groundbreaking study by Stanford Health Care researchers suggests that this change alone may not be sufficient to address health inequities.
The study, titled “Algorithmic Changes Are Not Enough: Evaluating the Removal of Race Adjustment from the eGFR Equation,” is the first to assess the impact of the new race-neutral equation on care decisions for chronic kidney disease patients. While focused on a single medical center and disease, the findings have broader implications for healthcare.
As the field increasingly turns to AI and machine learning algorithms, this research underscores the need for comprehensive health equity studies. It highlights the limitations of technical solutions in addressing deeply rooted health disparities.
The study serves as a crucial reminder that achieving health equity requires more than algorithmic adjustments. It calls for a holistic approach that addresses the multifaceted structural inequities pervasive in healthcare systems.
See “Policy Brief: The Complexities of Race Adjustment in Health Algorithms” (September 26, 2024)
