Where we live determines how long we live. Read that again.
Health disparities, in large part, are determined by where we live. In Nashville—a city that prides itself on being a renowned healthcare hub—life expectancy increases 5 years by moving to the neighboring Williamson County. Similar patterns hold true in other cities all over the U.S.
For those of us in public health, this unfortunate reality is not surprising. Structural racism—the category of racism that stems from the very infrastructure of our communities—has long determined unjust resource allocation. Inequitable access to things like quality education, nutritional foods, and healthcare services can lead to poorer health outcomes.
Connecting the dots, it’s easy to see how ZIP code can be more predictive of health than genetic code.
Health disparities, especially those stemming from the location of our homes, were only heightened by the COVID-19 pandemic. Throughout the pandemic, ZIP code determined access to testing sites, personal protective equipment, and vaccine availability.
While most communities were mindful of marginalized populations when making key decisions regarding limited pandemic resources, efforts fell short and left already vulnerable populations to disproportionately suffer the repercussions.
How did we miss the mark? Because the data being used to make these important decisions were inadequate and misrepresented the needs of our communities.
Missing data makes it difficult to appropriately assess the needs of the diverse populations that make up our communities. Ultimately, this contributes to a racist data infrastructure and the continuation of underlying disparities that impact individual health and beyond.
For example, Dr. Stella Yi , an assistant professor at the NYU Grossman School of Medicine Department of Population Health, noted:
“As of April 28th, 2021, race/ethnicity data were missing from 39 percent of reported COVID-19 cases and 17 percent of deaths nationally. An even larger proportion of race/ethnicity data were missing for vaccine recipients with up to 58 percent of fully vaccinated people missing race/ethnicity information.”
These deficiencies had very real and detrimental consequences.
During COVID-19 vaccine rollout, data played a pivotal role in determining which communities and individuals received the vaccine first. These decisions, however, were made using the collected data with much of the race and ethnicity data missing. Important decisions were made without proper representation. And, the lack of equity within our public health data infrastructure led to a disproportionate impact on marginalized populations.
This is unacceptable.
For the best policy, we need the best data: data that accurately depict the varying needs of our communities. Good data should be both comprehensive and granular so that particular groups within communities can have their specific health challenges assessed. Most importantly, good data should be actionable, building a bridge between inequities and their downstream health impacts.
By this definition, our current public health data system is not producing good data. We need to reimagine how we collect, analyze, and use health data. We need data equity.
To improve our current infrastructure, we need to promote data equity across all sectors—healthcare, housing, income, employment, and education—painting a more comprehensive picture of our communities. We can start by revising our current race and ethnicity reporting standards at the federal level and by investing the time and money to update electronic health records.
Currently, the Office of Management and Budget requires 5 race and ethnicity subgroups be listed: American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Other Pacific Islander, or White. Despite these being mandated, many counties, states, and agencies fail to report these data due to a lack of enforcement measures.
The federal government should update the current minimum standards to account for the significant diversity that exists within broad race and ethnicity categories. These standards should be consistently enforced.
Moreover, collecting good data will take time and money, especially in updating electronic health records. Current systems are relatively limited and many health departments, including the one in Metro Nashville, still rely on paper records. Updating these records and collection methods will allow for data to establish a more realistic and real time assessment of community needs. This will in turn lead to more informed policy and outreach.
An equitable data infrastructure is not only vital to improving the health of every person within our communities but also to combatting racism and discrimination.
There is much work to be done, but revision of our current methods will allow us insights into our communities that can inform more effective and beneficial policy decisions. While our focus is on improving the public health data system itself, our motivation should be on using these data to combat the actual inequities that lead to worse health outcomes.