Data is critical in addressing COVID-19 racial and ethnic health disparities
The COVID pandemic has brought a bright spotlight to the impact of health disparities on underserved populations
COVID-19 has hit underserved and minority communities across the U.S. particularly hard. Several systemic health inequities have conspired, leading to significantly greater rates of COVID-19 illness, hospitalization, and death, paired with below-average COVID-19 vaccination rates. For instance, according to recent CDC data, American Indian and Alaskan Native individuals are 3.3 times more likely to be hospitalized and 2.2 times more likely to die due to COVID-19 infection compared to non-Hispanic White people. These estimates, however sobering, are likely gross underestimations of the true health inequities.
A dearth of data on social determinants of health (SDOH) has made it much harder for public health authorities to understand and address the pandemic’s disparate impact on underserved communities. Existing data are plagued by systemic gaps. For instance, almost 1 in 3 COVID cases reported to CDC are missing data on race or ethnicity. Even so, despite imperfect and incomplete data, innovative analytical approaches can offer help to bridge these gaps and provide valuable insights into health equity.
Data challenges are pervasive, but innovative analytics can help uncover and address health inequities
Recently, I had the opportunity to sponsor an insightful discussion of this topic at the National Association of Health Data Organization’s (NAHDO) annual symposium as part of the Health Databases and Covid-19 session. The NAHDO symposium showcased three specific examples that highlight how even imperfect data can provide valuable insights for health equity.
- Traci Treasure analyzed publicly available data to show disparate COVID-19 case and case-fatality rates in counties with high percentages of AI/AN individuals; conversely, her data also demonstrated higher vaccine uptake in these communities compared to surrounding counties. Together, this example showcased that publicly available data can give deep insights into health inequities, help prioritize interventions, and demonstrate when interventions such as vaccination promotions do work well - even if the available data are not perfect and lack the level of granularity and coverage we would ideally want.
- Alyssa Harris analyzed the correlation between SDOH and COVID in clinical data; the data clearly show that adoption of SDOH codes by clinicians remains limited, and the available data, therefore, is plagued by systemic gaps. However, the available data provide valuable insights into the critical unmet needs in COVID-affected communities, including homelessness, social context, and other SDOH dimensions. A focus on strategies to increase the adoption of SDOH codes is needed to close existing data gaps, which will grow even more important as USCDI v2 is rolled out and expands the SDOH information clinicians are expected to routinely capture in electronic health records.
- Carrie Baker and Bill Balderaz showcased how GIS technology and data visualizations can help make complex COVID data actionable and help with issues like access to local public health resources. Their presentation also highlighted how important it is to understand local needs and tailor analyzes and dashboards accordingly.
As greater emphasis is placed on SDOH, including efforts to more consistently capture this data, innovative analyses will allow increasingly incisive health equity insights in the future.
Going forward: good data will continue to improve health outcomes
The presenters each spoke to points that we at Maximus have observed first-hand. Throughout our pandemic response work, we have seen time and again how hard the COVID-19 pandemic has hit our nation's most vulnerable individuals – and how inequities continue to disproportionately affect lives and livelihoods in underserved communities. We have also seen how public health authorities and stakeholders have struggled with persistent data challenges around race, ethnicity, and social determinants of health. Our scope of work has given us a keen understanding of the shortfalls in data collection and urgent needs for modernizing the nation’s public health infrastructure. Finally, we have also seen how good data, made accessible through data analytics, visualizations, and dashboards can drive efforts towards a more equitable response to the pandemic and better health outcomes.
The Maximus Center for Health Innovation is helping federal, state, and local governments to serve the public and end the pandemic.
Maximus Health Data Analytics provides federal, state, and local government clients with the data systems, analytical tools, and technical expertise needed to promote population health. We develop integrated solutions to ensure public health professionals have the data, analytics, and expertise at their fingertips to ensure program success. We bring technical experience in public health surveillance system modernization, advanced data analytics and emerging technologies, and industry-leading robotic automation, Agile, and DevSecOpspractice, led by public health experts with deep programmatic insights and decades of experience implementing population health programs.