The coronavirus pandemic and resulting sharp recession put a glaring spotlight on the importance of data disaggregation. During the early stages of the pandemic, most states were not reporting coronavirus infections and COVID-19 fatalities by race. Consequently, policymakers did not know—but do now—that Black people in the United States died at 1.4 times the rate of White people from COVID-19, and that in certain states, Latinx people were 3.7 times more likely to have tested positive for the virus than their White neighbors. This lack of data disaggregation made it more difficult for policymakers to understand the contours of the pandemic and design policies to mitigate these disparities.

Public health in general benefits from disaggregating data. Recent studies on the expanding use of artificial intelligence in health and medicine find that these new technologies may, in fact, exacerbate existing health inequities. Just one case in point: Data from pulse oximeters—devices used to measure oxygen levels without drawing blood—are fed into algorithms that increasingly help determine medical decisions, such as patients receiving supplemental oxygen. Studies show, however, that these devices are three times more likely to report incorrect blood gas levels in Black patients compared to White patients. This racially disparate inaccuracy can have devastating effects, leading to patients not receiving proper treatment.

Data disaggregation is likewise critically important for better understanding the many racially disparate aspects of the U.S. economy and considering policies to address those disparities. Racial and ethnic discrepancies in economic outcomes have long been known, but improvements to data disaggregated by race and ethnicity by federal statistical agencies can help improve policymakers’ understanding of economic and social outcomes for all communities of color.

Read the full article about racial equity in economic data by Shaun Harrison at Equitable Growth.