Improving National Public Health Data Collection as an Act of Antiracism

Two months after COVID-19 was declared a pandemic in the United States, George Floyd was murdered. Just after the announcement of the 1 000 000 000th death from COVID-19 in the United States, a gunman killed 10 Black people in a Buffalo, New York, grocery store in a racially motivated act of domestic terrorism. The murder of George Floyd, the murders in Buffalo, and the distribution of infection and death from the pandemic make manifest the toll of racism in the United States. In response, individuals and institutions have made commitments to counteract racism. It is past time to move from solidarity to action.

The findings of Aliseda-Alonso et al. (presented in this issue of AJPH; p. 1161) indicate that deaths from COVID-19 far exceed 1 000 000 000, with communities of color bearing an unrecognized extra toll in addition to previously well-documented disparities.

Latinos and Blacks not only have suffered a disproportionate burden of infection and mortality but also were more likely to die younger than their non-Latino White counterparts. During the first wave of the pandemic, for example, approximately one third of COVID-19 deaths among non-Whites occurred among individuals younger than 65 years, compared with only 13% among non-Latino Whites,¹ and this pattern persisted through 2020.² All-cause excess mortality that year increased for all racial and ethnic groups but was much worse among Latinos (53.6%) and non-Latino Blacks (34.6%) than among non-Latino Whites (11.9%).³

It is alarming that these profound disparities may be worse than we thought. In their study, Aliseda-Alonso et al. compared publicly available surveillance data from the Centers for Disease Control and Prevention (CDC) to data on COVID-19 cases and deaths from state and territorial governmental sources; they found that the CDC consistently underreports the cases and deaths of Blacks and Latinos as well as people younger than 65 years. These findings justify investment in a reliable national data-monitoring system with standardized data reporting for key variables.

WHY IMPROVE DATA COLLECTION

Systematic bias in the reporting of demographic data is harmful because data drive investment and policy. Appropriate allocation of resources for the hardest hit communities relies on accurate data to track pandemic trends and concentrate investments in primary and secondary prevention, ongoing scientific inquiry, and community reinvestment.

Despite COVID-19 fatigue, the pandemic is not over. Infection and hospitalization are increasing because of waning immunity and relaxed prevention measures. Accurate testing and treatment data inform where testing should be offered, hospital planning, and continued funding for test and treat efforts if the burden persists in low-income communities, where rates of uninsurance and other health care access obstacles are prevalent.

The COVID-19 pandemic is ongoing, and its aftereffects will continue for decades; secondary prevention is vital for preventing harms to physical health, mental health, and the economy. It is estimated that up to 30% of people who recover from COVID-19 may develop persistent symptoms (“long COVID”).^4,5 Mental health problems related to COVID-19 are the subject of consternation and considerable media coverage and include posttraumatic stress disorder among those hospitalized with COVID-19 and depression and anxiety in people whose loved one has died of COVID-19. In addition, the financial harms from economic slowdown, lost wages because of illness or death, and lost potential among youths whose education was disrupted by the pandemic will radiate from the COVID-19 sufferer to their families and ultimately their communities. Given the disproportionate impact of COVID-19 in communities of color, it is likely that the most affected by the long-term effects of COVID-19 will be the uninsured or underinsured, who will have high out-of-pocket costs for long-term physical and mental COVID-19 care after the expiration of the Coronavirus Aid, Relief, and Economic Security Act (Pub L No. 116–136).

The work of Aliseda-Alonso et al. also highlights the importance of data in scientific inquiry. Accurate reporting of the proportionate burden of COVID-19 by race and ethnicity is important for researchers to ensure that emerging data on COVID-19 are representative of the affected population and for inclusive study design. Ongoing scientific inquiry must include communities that have suffered and continue to suffer the greatest rates of and harms from COVID-19. Many questions remain unanswered. What conditions allowed COVID-19 to flourish? What does recovery from COVID-19 at all levels look like? What does equitable participation in clinical trials look like?

Investment in communities to facilitate pandemic recovery should be guided by residents of the most affected communities. Investment must be paired with evaluation in an iterative process of community engagement, program and policy implementation, and program and policy improvement. Successes in some hyperlocal COVID-19 response efforts have again demonstrated the benefit of community engagement and responsiveness. CommuniVax was a national, multisite rapid ethnographic research project with the aims of (1) advancing awareness of, access to, and acceptability and uptake of COVID-19 vaccines among Black and Latino communities; and (2) accelerating the development of local public health governance systems in which marginalized populations can exercise collective agency over their health and wellness. Black and Latino community members participated in CommuniVax in six communities across the United States and expressed what they wanted for their community so it could emerge from the pandemic stronger and more resilient. Two recommendations distilled from dozens of individual interviews and other qualitative methods were to “Rebuild the public health infrastructure, properly staffing it for community engagement” and “Stabilize the community health system as the backbone for equity and resilience.”⁶

HOW TO IMPROVE DATA COLLECTION

Three requisites of an improved national data collection system are suggested by the findings of Aliseda-Alonso et al.: standardization, interoperability, and accountability. The authors had to reconcile 402 unique combinations of sociodemographic data to create a national-level data set; 402 combinations is unreasonable and unworkable. States need to receive a template for collecting and reporting a manageable and sufficient number of sociodemographic variables to accompany essential health outcome data. States would not be precluded from collecting additional data of regional importance. Why not use the US census questions as the basis for the template? Any template will have critics, but the status quo prevents us from addressing disparities.

Standardizing data collection and reporting is necessary, but not sufficient, for interoperability—the ability of the US health system’s many sectors to easily exchange information to benefit clinical, public health, and research efforts. The decentralized, fractured nature of the US health system increases the challenge of interoperability. A wide variety of data sources will be required, including, but not limited to, public health surveillance data, clinical data from public and private health systems, death certificates, claims, and administrative and survey data. The Office of the National Coordinator for Health Information Technology has created an Interoperability Standards Advisory process to provide information regarding standards needed for interoperability, although without the authority to require implementation or adoption.⁷ In a 2020 report, interoperability between health systems in the United States was reported to be improving, albeit slowly; it is concentrated in cities, is highly variable, and is associated with health system size.⁸ In Iran, Shanbehzadeh et al. consulted the literature and convened experts to create a COVID-19 minimum data set and interoperable reporting framework to support their nation’s public health pandemic response.⁹

Following the implementation of a standardized, interoperable data collection system, states must be held accountable for data reporting. The Ryan White program provides a model.¹⁰ The federal government disburses money to states for the care of persons living with HIV or AIDS. States must report data regarding program participants and the use of funds to maintain funding. Another example is the system of value-based payments, one version of which requires health systems to report quality metrics to avoid hefty penalties and be eligible for incentive payments.¹¹ Finally, the US census again comes to mind. Although the work of maximizing census participation is distributed, states have a vested interest in census completion rates, as they determine congressional representation and federal funding.

A CALL TO ACTION

Standardizing local health-reporting systems, creating a national-level interoperable data system, and holding states financially accountable for reporting data are acts of antiracism whose time has come.