Using the Methods of the Public Health Disparities Geocoding Project to Monitor COVID-19 Inequities and Guide Action for Health Justice
Introduction
The COVID-19 pandemic is once again pointing to the need for systematic monitoring and analysis of health inequities – especially in a context of health data lacking social and economic information – to guide both understanding and action. In our latest publications, we have been using the methods of the Public Health Disparities Geocoding Project to document inequities in the population distribution of COVID-19 cases, hospitalizations, and deaths in the United States. In this update to our website, we provide the following resources, to assist others in carrying out this vital work – to clarify who, in what communities, are being hit hardest by COVID-19, and hence where:
(a) resources for testing, screening, and prevention (including adequate provision of personal protective equipment, especially for essential workers at their jobs and for use in transportation to & from these jobs) are urgently needed;
(b) locales to assist self-isolation of people who are positive should be based (e.g., if it is not possible for people to self-isolate at home, given household crowding); and
(c) support is needed to assist people with COVID-19 & their families, especially if they are in communities and social groups already burdened inequitably by premature morbidity and mortality from chronic diseases which exacerbate the severity of COVID-19.
We provide below our relevant conceptual and empirical publications.
We also provide an ACS/ABSM variable table that lists the relevant area-based socioeconomic measures we constructed using 5-year (2014-2018) US Census American Community Survey data which we supply here at the county, ZCTA (ZIPcode tabulation area), and census tract levels (for the entire United States). We request that if you use these data, please cite this webpage.
Lastly, we provide code in R to:
- extract ABSMs from the US Census American Community Survey
- replicate the analyses we conducted in our empirical papers using these variables
- replicate excess mortality analyses by ZIPcode social metrics
— Prepared by Nancy Krieger, Jarvis T. Chen, Pamela D. Waterman (May 15, 2020)
Definitions and Source Variables from the American Community Survey
| Total Population | B01003_001E |
| White Non-Hispanic Population | B01001H_001E |
| % of persons below poverty* | B17001_002E / B17001_001E |
| Index of Concentration at the Extremes (high income white households versus low income black households)* | ((B19001A_014E + B19001A_015E + B19001A_016E + B19001A_017E) – (B19001B_002E + B19001B_003E + B19001B_004E + B19001B_005E)) / B19001_001E |
| Index of Concentration at the Extremes (high income white non-Hispanic households versus low income people of color households)** | (B19001H_014E + B19001H_015E + B19001H_016E + B19001H_017E) – [(B19001_002E + B19001_003E + B19001_004E + B19001_005E) – (B19001H_002E + B19001H_003E + B19001H_004E + B19001H_005E)]/ B19001_001E |
| % crowding (>1 person per room) | (B25014_005E + B25014_006E + B25014_007E + B25014_011E + B25014_012E + B25014_013E) / B25014_001E |
| % population of color (not White Non-Hispanic) | B01003_001E -B01001H_001E) / B01003_001E |
* To see more about US census tract poverty data and the cut-point for poverty areas defined by the US census as >=20% below poverty, see the US Census Bureau Changes in Poverty Rates and Poverty Areas Over Time: 2005-2019.
**High-income refers to the top quintile for US household income and low-income refers to the bottom quintile for US household income, during the years specified.
Publications
Conceptual:
- Krieger N, Gonsalves G, Bassett MT, Hanage W, Krumholz HM. The fierce urgency of now: closing glaring gaps in US surveillance data on COVID-19. Health Affairs Blog, April 14, 2020.
- Krieger N. COVID-19, data, and health justice. To the Point(blog), Commonwealth Fund, Apr. 16, 2020.
- Chotiner I. The interwoven threads of inequality and health. The coronavirus crisis is revealing the inequities inherent in public health due to societal factors, Nancy Krieger, a professor of social epidemiology, says. (Interview with Nancy Krieger). The New Yorker, April 14, 2020.
- Krieger N. From structural injustice to embodied harm: measuring racism, sexism, heterosexism, and gender binarism for health equity studies. Ann Rev Public Health 2020 April 2); 41:37-62. doi:10.1146/annurev-publhealth-040119-094017 [epub 2019 Nov 25]
- Krieger N. Living and dying at the crossroads: racism, embodiment, and why theory is essential for a public health of consequence. Am J Public Health 2016; 106:832-833.
Empirical:
COVID-19 Publications
- Chen JT, Krieger N. Revealing the unequal burden of COVID-19 by income, race/ethnicity, and household crowding: US county vs ZIP code analyses. Harvard Center for Population and Development Studies Working Paper Series, Volume 19, Number 1. April 21, 2020.
- Chen JT, Waterman PD, Krieger N. COVID-19 and the unequal surge in mortality rates in Massachusetts, by city/town and ZIP Code measures of poverty, household crowding, race/ethnicity, and racialized economic segregation. Harvard Center for Population and Development Studies Working Paper Series, Volume 19, Number 2. May 9, 2020.
- with data used in: Ryan A, Lazar K. Disparities push coronavirus death rates higher. Harvard analysis finds mortality surged higher in communities with more poverty, people of color, and crowded housing. Boston Globe, May 9, 2020.
- Chin T, Kahn R, Li R, Chen JT, Krieger N, Buckee CO, Balsari S, Kiang SV. U.S. county-level factors relevant to COVID-19 burden and response. MedRxiv, posted April 11, 2020.
Publications re: Use of Index of Concentration at the Extremes (ICE) Measures:
- Krieger N, Waterman PD, Spasojevic J, Li W, Maduro G, Van Wye G. Public Health monitoring of privilege and deprivation using the Index of Concentration at the Extremes (ICE). Am J Public Health 2016; 106: 256-253. doi: 10.2105/AJPH.2015/302955.
- Krieger N, Kim R, Feldman J, Waterman PD. Using the Index of Concentration at the Extremes at multiple geographic levels to monitor health inequities in an era of growing spatial social polarization: Massachusetts, USA (2010-2014). Int J Epidemiol 2018; 47:788-819.
- Krieger N, Waterman PD, Gryparis A, Coull BA. Black carbon exposure, socioeconomic and racial/ethnic spatial polarization, and the Index of Concentration at the Extremes (ICE). Health & Place 2015; 34:215-228.
- Krieger N, Feldman JM, Waterman PD, Chen JT, Coull BA, Hemenway D. Local residential segregation matters: stronger association of census tract compared to conventional city-level measures with fatal and non-fatal assaults (total and firearm related), using the Index of Concentration at the Extremes (ICE) for racial, economic, and racialized economic segregation, Massachusetts (US), 1995-2010. J Urban Health 2017; 94:244-258.
Please cite as:
Krieger N, Chen JT, Waterman PD. Using the methods of the Public Health Disparities Geocoding Project to monitor COVID-19 inequities and guide action for social justice. Available as of May 15, 2020 at: https://www.hsph.harvard.edu/thegeocodingproject/covid-19-resources/
