Health Policy$ense

Social Determinants Among Communities Receiving Early COVID-19 Relief Funds

Tracking 'High Impact' Relief to Hospitals

COVID-19 has placed an immense burden on the US health care system, particularly hospitals serving communities with the highest number of cases. To alleviate financial strain, the U.S. Department of Health and Human Services distributed $12 billion of “high impact” relief funds to 395 hospitals that individually cared for 100 or more COVID-19 patients. Collectively, this group accounted for 71% of all U.S. COVID-related hospitalizations through April 10, 2020.

However, despite being located in areas with high COVID-19 case counts, hospitals receiving relief funds may serve communities that differ with respect to certain characteristics, including social determinants of health. Unfortunately, it remains unknown whether the approach used to distribute initial relief funds resulted in equitable distribution of funds based on social determinants – a key knowledge gap given concerns about COVID-19 disparities by age, race/ethnicity, and socioeconomic status.


(r) Joshua Liao, MD, MSc, is an Associate Professor in the Department of Medicine at the University of Washington School of Medicine and an LDI Adjunct Senior Fellow;
(l) Amol Navathe, MD, PhD, is an Assistant Professor of Health Policy and Medicine at Penn and an LDI Senior Fellow.

Communities Receiving High Impact Relief Funds

We used data from the Centers for Disease Control and Prevention to identify hospitals receiving relief funds, aggregate funds at the community-level (county), and categorize communities as top relief (those in the top quartile of funds received) and other relief (those in the bottom three quartiles of funds received). Data from the Johns Hopkins Coronavirus Resource Center were used to obtain county-level COVID-19 burden (confirmed cases per 1,000 individuals).

To evaluate social needs and determinants among communities receiving relief funds, we used U.S. Census Bureau data to obtain county-level information about population size, age (proportion of individuals age 65 or older), socioeconomic status (median household income, proportion of individuals with some college education), and housing (severe housing cost burden, or proportion of households spending >50% of their income on housing).

We also included the proportion of Black and Latinx (defined as “Hispanic” in underlying data) individuals in each county, as well as several measures of residential segregation. These measures captured segregation by quantifying the percentage of Black and non-white individuals in a county that would need to relocate, as compared to white individuals, so that the composition of smaller regions within a county (in our analysis, census tract) matched the overall composition across the county. Measures ranged from 0% (no residential segregation) to 100% (complete residential segregation).

Relief funds were distributed to hospitals across 135 US counties, with county-level payments ranging from $7,697,578 to $1,158,670,378. Compared to other relief counties, top relief counties were more likely to be large (52.9% versus 15.8% being counties in the top quartile of population) and have white/Black geographic segregation (41.2% versus 19.8% being counties in the top quartile of white/Black residential segregation), with higher COVID-19 case burden (61.8% versus 11.9% were counties in the top quarter of confirmed cases), more Latinx individuals (44.1% versus 17.8% being counties in the top quartile of Latinx population), and greater financial housing challenges (64.7% versus 10.9% being counties in the top quartile of severe housing cost burden). Regression analysis controlling for other county characteristics – total population; proportion of individuals age 65 years and older; median household income; proportion of individuals with some college education or severe housing cost burden – largely corroborated these associations while also demonstrating an association between greater amounts of relief funds and lower white/non-white segregation.

Policy Implications

These findings pose several policy implications amid the COVID-19 era. First, by distributing funds to hospitals with high numbers of admitted patients with COVID-19, policymakers also sent funds to communities with higher case burden. While these results are reassuring, policymakers should also recognize that a significant proportion of COVID-19 patients will be managed outside of the hospital through primary care practices that are also reeling from COVID-related financial strain. Therefore, overcoming the pandemic may require policymakers to offer financial relief to primary care practices, not just hospitals. One approach would be to increase testing capacity, track data about the community spread, and – like was done in the first wave of high impact relief funds  target primary care practices serving communities with the highest need.

Second, it is notable that geographic segregation and housing cost burden were associated with relief funds. On one hand, these findings support overarching concerns expressed by elected officials about how social determinants could adversely impact COVID-19 outcomes and exacerbate health care disparities. For instance, research analyses1 and data released via a Freedom of Information Act request highlight higher rates of infection among Black and Latinx versus white individuals, as well as higher rates of hospitalization and death in areas with the most racial/ethnic minorities, individuals living in poverty, and those with lower educational levels. On the other, our results highlight complexity in the underlying dynamics (e.g., more extensively Black/white segregated areas received more relief funds, but so did areas with less white/non-white segregation) and the need to carefully contextualize data that suggest disparities.2

Our analysis indicates that more work is needed to determine if relief funds were equitably distributed to communities based on social determinants of health. As we face protracted and future waves of viral resurgence in different parts of the country, policymakers could consider social determinants in decisions about providing financial relief. It is particularly important to weigh these factors since case counts alone may not reflect the resources needed in health care organizations (e.g., other relevant factors include hospitals’ existing financial health) or surrounding communities to support an adequate response and minimize COVID-19’s health effects at a national level.

Conclusion

Data about early COVID-19 relief funds suggests that communities receiving these funds varied with respect to several measures of social determinants of health. Policymakers should urgently support future work to elucidate these dynamics and ensure that social determinants are appropriately considered in future financial relief policies.

References

  1. Wadhera RK, Wadhera P, Gaba P, et al. Variation in COVID-19 Hospitalizations and Deaths Across New York City Boroughs. JAMA. 2020;323(21):2192-2195.
  2. Chowkwanyun M, Reed Jr AL. Racial Health Disparities and Covid-19 – Caution and Context. N Engl J Med. 2020. DOI: 10.1056/NEJMp2012910