Using pandemic data incidents to improve health outcomes
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 center, life expectancy increases 5 years moving to neighboring Williamson County. Similar trends hold true in other cities in the United States
For those of us in public health, this sad reality is not surprising. Structural racism – the category of racism that stems from the very infrastructure of our communities – has long determined the unfair allocation of resources. Inequitable access to things like quality education, nutritional foods and health services can lead to poorer health outcomes.
By connecting the dots, it’s easy to see how the zip code can be more predictive of health than the genetic code.
Health disparities, especially those arising from the location of our homes, have only been exacerbated by the COVID-19 pandemic. Throughout the pandemic, the postal code determined access to testing sites, personal protective equipment and vaccine availability.
While most communities were aware of marginalized populations when making key decisions about scarce resources in the event of a pandemic, efforts failed and left already vulnerable populations disproportionately impacted.
How did we miss the mark? Because the data used to make these important decisions was inadequate and misrepresented the needs of our communities.
Missing data makes it difficult to properly 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 , assistant professor at the NYU Grossman School of Medicine Department of Population Health, noted:
“As of April 28e, 2021, race / ethnicity data were missing for 39% of reported COVID-19 cases and 17% of deaths nationwide. An even larger proportion of race / ethnicity data was missing for those vaccinated, with up to 58% of fully vaccinated people missing information on race / ethnicity. “
These shortcomings have had very real and harmful consequences.
When deploying the COVID-19 vaccine, data played a central role in determining which communities and individuals received the vaccine first. These decisions, however, were made using the data collected, with much of the data on race and ethnicity missing. Important decisions have been made without adequate representation. Additionally, the lack of equity within our public health data infrastructure has had a disproportionate impact on marginalized populations.
This is unacceptable.
For the best policy, we need the best data: data that accurately describes the different needs of our communities. Good data needs to be both comprehensive and granular so that particular groups within communities can have their specific health issues assessed. Most importantly, good data must be actionable, bridging inequalities and their downstream health impacts.
By this definition, our current public health data system does not produce good data. We need to reinvent the way we collect, analyze and use health data. We need data fairness.
To improve our current infrastructure, we need to promote data fairness across all sectors (health, housing, income, employment and education) by providing a more complete picture of our communities. We can start by revising our current race and ethnicity reporting standards at the federal level and investing time and money to update electronic health records.
Currently, the Office of Management and Budget requires that 5 subgroups of race and ethnicity be listed: American Indian or Native Alaskan, Asian, Black or African American, Native Hawaiian, or another Pacific Islander, or White. Although these are mandatory, many counties, states and agencies do not report this data due to a lack of enforcement measures.
The federal government should update the current minimum standards to reflect the significant diversity that exists within broad categories of race and ethnicity. These standards must be systematically applied.
In addition, collecting good data will take time and money, especially for updating electronic health records. Current systems are relatively small, and many health services, including Metro Nashville’s, still rely on paper records. Updating these records and collection methods will allow the data to establish a more realistic and real-time assessment of the community’s needs. This in turn will lead to more informed policy and awareness.
A fair data infrastructure is not only vital for improving the health of every person in our communities, but also for combating racism and discrimination.
There is a lot of work to do, but revising our current methods will allow us to gain insight into our communities that can inform more effective and beneficial policy decisions. As we focus on improving the public health data system itself, our motivation should be to use that data to tackle the real inequalities that lead to worse health outcomes.