You are looking at a map that links the effects of COVID-19 in the United States with a wide range of social, economic, and environmental conditions.
It compares four indexes of vulnerability alongside COVID-19 data and presents multiple options for addressing the effects of the pandemic with a Community Health Corps.
These conditions and vulnerabilities predated the pandemic and created the conditions for the virus to flourish in this country. The map displays the acute inequalities embedded in the social and political landscape of the United States. This pandemic is not simply biological. It is a symptom of an illness in our body politic. As SARS-CoV-2 roars across the country, it is following the fault lines of social vulnerability.
As the New York Times reported in July 2020, “Black and Latino people have been disproportionately affected by the coronavirus in a widespread manner that spans the country, throughout hundreds of counties in urban, suburban and rural areas, and across all age groups.”1
Repairing the wounds of this pandemic means confronting the policy decisions made long ago that have led us to this moment. As we try to combat this disease, we have to think more broadly about rebuilding health from the ground up in the United States.
We have proposed a New Deal for Public Health, with a Community Health Corps that goes beyond testing and tracing to confront the underlying conditions that have sickened and killed so many people in the United States.2 A new Community Health Corps must be integrated into our communities, providing economic support and social services to start to undo the vulnerabilities that plague us. We call this a new politics of care.
The Community Health Corps should be deployed across the United States. This map poses a series of questions and demands a set of options about how these care workers might be distributed to states and to counties within each state. Making these decisions responsibly requires confronting and addressing not just the virus and the disease but also the inequalities and vulnerabilities that underlie and propel this pandemic.
Two general challenges run throughout this project.
The first challenge is epistemological. Which population(s) will benefit the most from community health workers (CHWs)? While the empirical research on the effects of CHWs shows that they are largely beneficial, these studies are few in number and examine interventions for specific conditions with limited generalizability. In addition, while there is much literature on the systemic disparities of health care associated with social and economic factors, the publicly available data for these factors has poor spatial and temporal resolution. Finally, there are few studies that look at the impact of CHWs on critical outcomes like deaths averted3 or health costs minimized.4 This is a challenge for the field of health policy and our political leaders: we need more rigorous evaluations of the effects of CHW programs in the United States, but such evaluations require data that, at this point, is not in the public domain or accessible to researchers even by data use agreements with government agencies.
The second challenge is ethical. Let’s assume that our epistemological problems were satisfied, such that we could, with absolute certainty, both locate and quantify the marginal benefit an individual would gain from a CHW. Given a limited number of CHWs, how should we prioritize individuals and communities to whom they are allocated? Throughout our project, we have assumed an approach that seeks to improve health care by reducing health disparities. That said, without knowing how different populations would benefit differently from CHWs, we are unable to quantify the impact of each allocation method.
A consequence of these two challenges is that there is no single correct choice for how to allocate CHWs. Each choice has both benefits and limitations, giving a larger number of resources to certain types of locations and giving fewer resources to others. In what follows, we outline these differences.
We can make better choices with better data. This project begins with what data is available now and paints a picture of how different kinds of vulnerability shape the landscape of the United States in different ways. What would a Community Health Corps look like if those working for it followed the fault lines of each of these metrics for defining vulnerability and inequity in the United States?
We have chosen seven metrics to identify health-related vulnerabilities by county and have organized them under two broad themes: (1) social and economic vulnerability and (2) COVID-19. Social and economic vulnerability can be measured by the CDC’s Social Vulnerability Index (SVI),5 number of Medicaid enrollees, 6 Years of Potential Life Lost (YPLL), 7 and unemployment rates. 8 As we know, the pandemic is not playing out randomly across the country; it is following trajectories of social and economic vulnerability at the local level. Thus, among our metrics, we also include the following COVID-19 metrics by county: COVID-19 cases (last 14 days), COVID-19 cases per 100,000 residents (last 14 days), and cumulative deaths per 100,000 residents attributable to COVID-19. 9 The more recent data tell us about the pandemic now; the cumulative figures show us the path of destruction since the virus arrived in the United States.
Earlier this year, proposals for a national health corps were promoted by several members of Congress, including Senators Kirsten Gillibrand (D-NY), Michael Bennet (D-CO), Dick Durbin (D-IL), Chris Coons (D-DE), Brian Schatz (D-HI), Ed Markey (D-MA), Chris Van Hollen (D-MD), Elizabeth Warren (D-MA), Jeff Merkley (D-OR), Tina Smith (D-MN), and Cory Booker (D-NJ). We have called directly for a million-person community health corps to address the myriad needs presented by the COVID-19 pandemic and the preexisting health disparities in communities hardest hit by the coronavirus.
We allocate the proposed one million CHWs to states in proportion to their respective Medicaid enrollment numbers, which we update monthly as we have seen enrollment increasing as a result of rising unemployment.10 We then use the vulnerability metrics above—COVID-19 cases, COVID-19 cases per capita, SVI, YPLL, unemployment, and number of Medicaid enrollees—to outline multiple ways to allocate CHW resources to counties within states.
Again, none of these choices is the “right” one—they all depend on your vision for how you conceptualize vulnerability. This project visualizes the differences and trade-offs between each of these choices.
Each method of quantifying vulnerability captures some populations and excludes others. As well, key data are limited by their geographic resolution and the frequency with which they are updated. In what follows, we outline what factors are included in each metric and differences in the availability of data on vulnerability, and we discuss advantages and limitations of each dataset.
The Social Vulnerability Index (SVI) is a metric published by the Centers for Disease Control (CDC) to spatially identify communities that are likely to be the most vulnerable to the adverse impacts of disasters and disease outbreaks. SVI is a composite score of 15 social factors equally weighted and organized under four themes derived from the US Census Bureau’s American Community Survey (ACS).
Our study uses a modified version of the CDC SVI index to facilitate the proportional allocation of CHWs. The CDC generates their SVI index by calculating percentile rankings for each county within states across 15 individual variables, summing these percentile rankings and then calculating an overall percentile ranking for each county within its state. In our method (SVI*), we first calculate the ratio between each county’s value and overall state value for each of the 15 variables. We then take the average of these ratios across the 15 individual variable values for each county. We use the resulting value as the SVI* for each county in our model.
The table below highlights places that would receive the most CHW resources when prioritizing by SVI. As a composite index, locations with high values across more than one of these four categories would receive the most resources.
| Socioeconomic Status | Household Composition and Disability | Race/Ethnicity and Language | Housing Type and Transportation |
|---|---|---|---|
| Population below federal poverty level Unemployed population Income Population without a high school diploma |
Population over 65 Population under 17 Population over 5 years old with a disability Single-parent households |
Population of color Population that speaks english “less than well” |
Number of housing structures with more than ten units Number of mobile homes Crowding (more people than rooms in home) Households without a vehicle Population living in group quarters |
Years of Potential Life Lost measures the rate of premature deaths by region. YPLL is calculated as the sum of the estimated number of years that individuals would have lived if they had not died before the age of 75 per 100,000 people.
Using YPLL to allocate CHWs would send the most resources to counties where a large proportion of the population dies before the age of 75.
Strength of YPLL Data as an Indicator of Vulnerability
Limitations of YPLL Data as an Indicator of Vulnerability
Data Sources for YPLL
YPLL rankings for the United States (including Washington, DC) were provided by the County Health Rankings YPLL data for 2016–2018. (15, 7) YPLL for Puerto Rico municipios was calculated using Puerto Rico mortality records for 2015–2016 16 in conjunction with the US Census population estimates. 17.
Medicaid is a means-tested health insurance program for low-income children, pregnant women, adults, seniors, and people with disabilities. Medicaid is jointly funded by federal and state governments and managed by states within federal standards and a wide range of state options.
Using Medicaid enrollment to allocate CHWs would send the most resources to counties that had the greatest share of Medicaid enrollees as of the 2018 ACS 5-year estimates.
Strengths of Medicaid Enrollment Data as an Indicator of Vulnerability
Limitations of Medicaid Enrollment Data as an Indicator of Vulnerability
Data Source for Medicaid Enrollment
State-level Medicaid enrollees: Centers for Medicaid and Medicare Services6 County-level Medicaid enrollee estimates: US Census Bureau’s 2014–2018 ACS 5-year estimates 18
The US Bureau of Labor Statistics releases monthly estimates of the number of people in the civilian labor force who are without jobs.8
Using unemployment figures to allocate CHWs would send the most resources to counties that have the greatest share of each state’s total unemployed residents.
Strength of Unemployment Data as an Indicator of Vulnerability
Limitations of Unemployment Data as an Indicator of Vulnerability
Data Source for Unemployment Rate
US Bureau of Labor Statistics—Local Area Unemployment Statistics (LAUS)19
The number of COVID-19 cases is an absolute metric of the total number of COVID-19 cases in a county over the last 14 days. COVID-19 cases per 100,000 is a relative metric calculated by dividing the number of COVID-19 cases by the estimated county population and multiplying by 100,000. Cases include both confirmed cases, based on viral testing, and probable cases, based on specific criteria for symptoms and epidemiological exposure.20
Using COVID-19 cases in the past 14 days to allocate CHWs would target resources to counties that have the largest overall epidemic within the state. Using COVID-19 cases per 100,000 residents would send resources to counties where the epidemic is largest in proportion to the population size compared with the state overall.
Strengths of COVID-19 Cases as a Measure of Vulnerability
Limitations of COVID-19 Cases as a Measure of Vulnerability
Data Sources for COVID-19 Cases and COVID-19 Cases per 100,000
COVID-19 case data: New York Times Coronavirus (COVID-19) Data in the United States9
County population data: US Census Bureau’s 2014–2018 ACS 5-year estimates18
What Are COVID-19 Deaths per 100,000 Residents?
The number of COVID-19 deaths per capita is the total number of deaths per county population where COVID-19 is listed on the death certificate as either the cause of death or a significant contributing condition to death. Similar to COVID-19 cases, COVID-19 deaths include both confirmed deaths, based on viral testing, and probable deaths, based on specific criteria for symptoms and epidemiological exposure.20
Using cumulative COVID-19 deaths per 100,000 residents to allocate CHWs would send resources to counties that have seen the highest rate of death compared to each state overall.
Strength of COVID-19 Deaths as a Measure of Vulnerability
Limitations of COVID-19 Deaths as a Measure of Vulnerability
Data Sources for COVID-19 Deaths per 100,000 Residents
COVID-19 death data: New York Times Coronavirus (COVID-19) Data in the United States9
County population data: US Census Bureau’s 2014–2018 ACS 5-year estimates18