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Data Transparency and Health Equity Study in U.S. COVID-19 Response

States varied in how they defined and reported key metrics, such as testing data, cases, hospitalizations and deaths.

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State governments varied widely in mitigation measures to address the COVID-19 pandemic such as school and workplace closings, travel restrictions, stay-at-home orders, and restrictions on public gatherings. More specifically, states varied in how they defined and reported key metrics, such as testing data, cases, hospitalizations and deaths. For example, during the first wave of the pandemic, it was reported that one-third of states were not even reporting hospital admission data, which was key to understanding the surge in the spread of the virus.

State responses also varied in how they have addressed immediate and long-standing health disparities and associated inequities and, in their use, timing, and adoption of policy tools and instruments such as data collection, data reporting, and data disaggregation.

While the pandemic was touted in some narratives as “the great equalizer,” like other disasters, it has disproportionately affected socially disadvantaged groups and low-income populations who are at increased risk due to long-lasting systemic health and social inequities.

To address this gap, �������� researchers looked at data transparency and equity as seen by state variations in the comprehensiveness of their reporting of health metrics and variations with respect to data disaggregation of race and ethnicity metrics. For the study, they used the theoretical understandings and past research on agency theory, transparency, state institutions, and policy responsiveness to the preferences of citizens, citizen needs, and socio-economic vulnerabilities.

Results, published in the , showed that legislatures, rather than governors, are important institutional actors and that a conservative ideology signal and socio-economic factors help predict data reporting and transparency practices. These findings suggest that there is a critical need for standardized data collection protocols, the collection of comprehensive race and ethnicity data, and analyses examining data transparency and reductions in information asymmetries as a pandemic response tool – both in the United States and globally.

The research sheds light on an issue that is important in understanding the actions of governments in addressing health disparities and equity through effective data transparency practices. While there has been research on the role of policymakers and the protective actions or otherwise that states have taken to address the pandemic, little attention has been paid to the issue of data equity in terms of data collection and reporting as a way to reduce information asymmetries and improve the accountability of agents (public officials) to the principal (the citizenry). This study helps to address that area.

“The problems with data collection and transparency in state responses are particularly significant for two main reasons. First, states used test positivity rates to determine appropriate policy responses, including lockdowns, reopening, restrictions on businesses and organizations, and protections for vulnerable populations such as essential workers and older residents,” said Alka Sapat, Ph.D., senior author, professor and director of the School of Public Administration, within ��������’s Dorothy F. Schmidt College of Arts & Letters. “However, the problems with testing and the misreporting of data in several states, with issues such as combining antibody tests with PCR testing, meant that the data were not fully reliable. Also, the lack of standardization in data collection and the variations among states in reporting made it difficult to target testing and offer protections for those who most needed it to reduce health disparities.” 

Sapat and study co-authors Ryan J. Lofaro and Benjamin Trautman, both Ph.D. candidates and graduate assistants in ��������’s School of Public Administration, say that to combat health disparities and effectively fight COVID-19, it is critical to understand its impact and spread among communities of color and marginalized populations, given its disproportionate impacts in these communities. However, the lack of standardization in data collection and the variations among states in reporting race and ethnicity data made it difficult to target testing and offer protections for those who needed it, particularly in minority communities.

“Implications from our study extend beyond the United States, as other countries have experienced racial and ethnic COVID-related disparities, and methods for counting cases and deaths have been country-specific, thus lacking cross-national comparability and global uniformity,” said Sapat.

As noted by the American Medical Association (AMA), demographic data is a key tool to fight inequities in COVID-19 pandemic planning and to address health disparities. Data specificity is needed not only to understand the impact and reach of the pandemic but also to inform appropriate response and planning.

“Without adequate race and ethnicity data associated with COVID-19 testing, hospitalizations, morbidities, and mortalities, physicians and hospitals will not be able to properly care for their patients,” said Sapat. “Lessons learned from this pandemic in terms of data collection, transparency, and dissemination will be critical in reducing disaster risks and improving resilience to other pandemics and disasters in the future.”

The rise of new variants and continuing waves of increasing infections in different areas of the world make it likely that the COVID-19 pandemic will continue to evolve over time.

This work is based on research supported by a U.S. National Science Foundation (NSF) grant (SES- 2028958).

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Tags: arts and letters | research | faculty and staff | coronavirus