The Environmental Justice Case for Racially Disaggregated Data: Part 2
This is part two of two, click here to read part one.
By Alexander Ian Passe
It is no secret that Black, Indigenous, and People of Color (BIPOC) on average live in a more polluted, more dangerous world than white people in the United States. This is, of course, by design. Hundreds of years of racist land use, transportation, development, waste, and manufacturing decisions have created a situation where communities of color are more likely to be located next to the worst polluters and face the worst health outcomes.
Time and time again we have witnessed BIPOC communities in Minnesota disproportionately harmed by the decisions made by those in power to turn their home environments against them. As Dr. Samuel L. Meyers Jr. wrote in his monumental book on racial disparities in Minnesota, The Minnesota Paradox, “Measured by racial gaps in unemployment rates, wage and salary incomes, incarceration rates, arrest rates, home ownership rates, mortgage lending rates, test scores, reported child maltreatment rates, school disciplinary and suspension rates, and even drowning rates, African Americans are worse off in Minnesota than they are in virtually every other state in the nation.”
Notably, these disparities do not mention health. That is because in Minnesota, health data is not typically disaggregated by race and, as a result, environmental health disparities are often only known about by overlaying existing data about income and race in geographic areas with data about health outcomes in the same areas.
Our approach to fighting the injustices of systemic racism must be systemic, however, it must also be targeted. In order to effectively address the most insidious health disparities in the state, we first must know where they lie. This is not to say that we are completely ignorant of racial health disparities but that without this information glaring knowledge gaps remain, many of which can have devastating consequences.
In the 2015 report Counting a Diverse Nation: Disaggregating data on Race and Ethnicity to Advance a Culture of Health, by the Robert Wood Johnson Foundation, the authors point out that “Racial and ethnic health disparities and inequities can only be eliminated if high-quality information is available by which to track immediate problems and the underlying social determinants of health.”
Data disaggregation is not the most exciting topic, however, it must be practiced if we are to fight for environmental justice. The concept is simple, just break down the information that we already have into small subpopulations. In this case, Minnesota would take the already collected data on health issues such as asthma, lead poisoning, and life expectancy, and categorize those numbers (cases) by racial group. Doing so would allow policymakers, advocates, and, most importantly, members of those racial groups to have a firm understanding of where there are disparities so that we can work to end them.
Not convinced? Data disaggregation has changed the world, in both large and small ways.
One example, made famous by Caroline Criado-Perez's 2019 book Invisible Women: Exposing Data Bias in a World Designed for Men, is the Swedish snow plowing experiment. Sweden, a famously snowy country, has plowed roads like most of the rest of the world for centuries. They started with the main roads then moved to the smaller roads, and then the sidewalks. However, in 2016 that all changed when the government mandated that all decisions should be reassessed through an equity lens. In doing so, the municipal governments were forced to look at the efficacy of the traditional road clearing order. When they looked at the data, disaggregated by gender, they found that women were being hospitalized at rates much higher than men in the winter due to accidents while walking on unplowed sidewalks and through unplowed neighborhoods, which normally got plowed last.
The data showed that men were more likely to commute to work by car and that they, on average, only made that one trip per day. Women, on the other hand, typically commuted via public transportation and walking, and made multiple trips per day to run errands and take care of family members, forcing them to traverse treacherous, snowy, unplowed terrain. So they switched the order. First they plowed sidewalks and neighborhood streets, then major roadways. The disparate rate of hospitalizations due to snow and ice related accidents decreased dramatically, commutes went unimpeded, and the city saved money.
Sweden is not the only country that has recognized the need for using disaggregated data to promote health equity. In 1994, President Clinton issued Executive Order 12898 - Federal Actions to Address Environmental Justice in Minority Populations and Low-Income Populations. As part of the order, federal agencies were directed to “identify and address the disproportionately high and adverse human health or environmental effects of their actions on minority and low-income populations, to the greatest extent practicable and permitted by law.” In order to achieve the goal that President Clinton set forth, United States Commission on Civil Rights, determined that, “Federal agencies should disaggregate data on risks and exposures by race, ethnicity, gender, age, income, and geographic location if communities are to have the tools they need to defend environmental and human health and if agencies are to fulfill their obligations under Executive Order 12898 and Title VI.”
A one-size-fits-all approach to decision making just doesn’t cut it because it fails to take into account the unique and historic impacts that past decision making has had on certain communities. The first lines of the 2014 Advancing Health Equity in Minnesota report by the Minnesota Department of Health (MDH), are “Minnesota ranks, on average, among the healthiest states in the nation. But the averages do not tell the whole story. Too many people in Minnesota are not as healthy as they could and should be, and the health disparities that exist are significant, persistent and cannot be explained by bio-genetic factors.” This is because White Minnesotans are among the healthiest people in the country and boast some of the longest life expectancies in the world. However, non-White Minnesotans are underrepresented in those statistics. The report found that among other disparities, Black and American Indian babies were twice as likely to die in their first year of life than White babies. Shockingly, a resident of the affluent suburbs of the Twin Cities has an average life expectancy of over 15 years greater than those living in “an inner city neighborhood of either [Minneapolis or St. Paul.]”
Later in that same report, MDH acknowledges that disaggregated data is essential for an equitable public health response but adds the caveat that, “changes in systems and processes for the collection, analysis, and dissemination of health equity data will be time consuming, labor intensive, and require an investment of resources.” There is no doubt that disaggregating data is a challenge, but one that is not insurmountable. More importantly, the consequences of failing to overcome this challenge is, for some people, a matter of life and death.
This article is the second of two parts, released as part of an environmental justice social media week of action. Part one was published on Monday, February 1.
Alexander Ian Passe is an Environmental Epidemiology Master of Public Health student at the University of Minnesota. They served as a Climate Resilience and Environmental Justice intern at MCEA and currently serve as a Public Health consultant for MCEA.
Image credit: Minnesota Pollution Control Agency CC-BY-SA 2.0