COVID Local Risk Index
Why do we measure COVID Local Risk?
The SARS-CoV-2 coronavirus – which causes COVID-19 – is known to have infected over 100 million people in the U.S., resulting in over 1 million deaths and growing.(1) An increasing body of scientific and non-peer-reviewed literature has found associations between population characteristics and COVID-19 transmission, severity (including hospitalizations and ICU stays), and mortality. This rapidly emerging knowledge highlights the important roles of social and economic factors in driving the risk of infection and the roles of age, race/ethnicity, and comorbidities in determining severity of outcomes. (2-12)
The Congressional District Health Dashboard has created the COVID Local Risk Index to help policymakers and public health officials identify districts that are at elevated risk for COVID impact. The index combines the population’s risk of becoming infected with the SARS-CoV-2 coronavirus with the population’s risk of more severe COVID outcomes if infected. The index is derived using census tract-level data and is designed to be comparable across congressional districts, to help guide resource allocation to the communities needing the most help.
It is important to note that population-level risk is different from individual-level risk. Many factors not captured in this index can contribute to COVID risk in both individuals and populations, including, for example, the extent of direct exposure to other people infected with COVID, vaccination rates, mask policies and adherence, and others. Because of this, the index cannot predict exactly where case counts will be highest or where cases will be most severe. Instead, it highlights the risk of a population in a given congressional district for COVID infection and COVID-related negative health outcomes relative to congressional districts.
How do we measure COVID Local Risk?
The COVID Local Risk Index estimates congressional district-level risk of COVID infection and illness severity based on social and economic factors and the distribution of age, race/ethnicity, and underlying health outcomes in the community. It then compares a congressional district relative to all other congressional districts. The index incorporates data from multiple sources and includes components in three “themes”:
Social vulnerability: drawn from the CDC’s Social Vulnerability Index (SVI)
COVID-relevant chronic health conditions: obesity, coronary heart disease, chronic obstructive pulmonary disease, chronic kidney disease, and diabetes
COVID-relevant demographics: age and racial status
The index values are presented in categories ranging from 1 (lowest risk) to 10 (highest risk). For more information on calculation of the index, please see below and refer to the Technical Document.
Strengths and Limitations
Strengths of Metric
Limitations of Metric
Variables are weighted according to the magnitude of their importance in recent literature regarding risk for COVID infection and severity.
The analysis combines variables from CDC’s SVI, developed and used widely to measure vulnerability to natural disasters and disease, with data on a number of COVID-relevant chronic health conditions and demographic variables.
The index uses census tract-level data to derive estimates, which can generate more nuanced index values compared to indices using county- or state-level inputs.
The Dashboard was only able to validate this index against a small sample of COVID-19 case and death rates for 35 United States cities.(13) This index was reconstructed for congressional districts using the same inputs.
Indices may sometimes be less actionable than single-metric measures, due to the combined contributions of multiple components.
Data for emerging factors related to COVID risk (such as vaccination rates, masking etc.) are not yet available for congressional districts, and therefore are not included in the index.
Calculation of the COVID Local Risk Index adapts the analytic strategy proposed by the CDC’s SVI, with the addition of weights and additional themes.(14-16) Our methodology was partially informed by other indices.(14,17,18) This metric was calculated by aggregating estimates from smaller geographies to the congressional district level.
The COVID Local Risk Index is calculated by the following formula:
n = the number of congressional districts
See below for more information on component weights. For more information, please refer to the Congressional District Health Dashboard Technical Document.
Estimates for this metric were calculated using source data as follows:(14,19,20)
Centers for Disease Control and Prevention. COVID Data Tracker: Daily Updates for the United States. 2022; https://covid.cdc.gov/covid-data-tracker/#datatracker-home. Accessed May 2, 2022.
Rozenfeld Y, Beam J, Maier H, et al. A model of disparities: risk factors associated with COVID-19 infection. Int J Equity Health. 2020.
Ebinger JE, Achamallah N, Ji H, et al. Pre-existing traits associated with Covid-19 illness severity. PLOS ONE. 2020;15(7):e0236240.
Gottlieb M, Sansom S, Frankenberger C, Ward E, Hota B. Clinical Course and Factors Associated with Hospitalization and Critical Illness Among COVID-19 Patients in Chicago, Illinois. Acad Emerg Med. 2020.
Azar KMJ, Shen Z, Romanelli RJ, et al. Disparities In Outcomes Among COVID-19 Patients In A Large Health Care System In California. Health Aff (Millwood). 2020.
Petrilli CM, Jones SA, Yang J, et al. Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study. BJM. 2020;369:m1966.
Hirsch JS, Ng JH, Ross DW, et al. Acute kidney injury in patients hospitalized with COVID-19. Kidney International. 2020;98(1):209-218.
Kim L, Garg S, O’Halloran A, et al. Risk Factors for Intensive Care Unit Admission and In-hospital Mortality Among Hospitalized Adults Identified through the US Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET). Clinical Infectious Diseases. 2020.
Cummings MJ, Baldwin MR, Abrams D, et al. Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: a prospective cohort study. Lancet. 2020.
Gupta S, Hayek SS, Wang W, et al. Factors Associated With Death in Critically Ill Patients With Coronavirus Disease 2019 in the US. JAMA Intern Med. 2020.
van Gerwen M, Alsen M, Little C, et al. Risk factors and outcomes of COVID-19 in New York City; a retrospective cohort study. J Med Virol. 2020.
Williamson EJ, Walker AJ, Bhaskaran K, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020;584(7821):430-436.
Spoer BR, McCulley E, Lampe TM, et al. Validation of a neighborhood-level COVID Local Risk Index in 47 large U.S. cities. Health & place. 2022;76:102814-102814.
Centers for Disease Control and Prevention/ Agency for Toxic Substances and Disease Registry/ Geospatial Research Analysis a, Services Program,. Social Vulnerability Index 2018 Database US. 2018; https://svi.cdc.gov/data-and-tools-download.html. Accessed May 25, 2020.
Centers for Disease Control and Prevention/ Agency for Toxic Substances and Disease Registry/ Geospatial Research Analysis and Services Program. CDC SVI 2018 Documentation - 1/31/2020. 2020; https://svi.cdc.gov/Documents/Data/2018_SVI_Data/SVI2018Documentation.pdf. Accessed May 25, 2020.
Flanagan BE, Gregory EW, Hallisey EJ, Heitgerd JL, Lewis B. A social vulnerability index for disaster management. Journal of homeland security emergency management. 2011;8(1).
Social Progress Imperative. US Cities Covid-19 Vulnerability Index Methodology. 2020; https://socialprogressdotblog.files.wordpress.com/2020/04/methodology-for-us-cities-covid-19-vulnerability-index-2.pdf. Accessed May 29, 2020.
Surgo Foundation. The COVID-19 Community Vulnerability Index (CCVI). 2020; https://precisionforcovid.org/ccvi. Accessed June 4, 2020.
PLACES: Local Data for Better Health. Local Data for Better Health, 2020 Tract Release. 2020; https://chronicdata.cdc.gov/500-Cities-Places/PLACES-Census-Tract-Data-GIS-Friendly-Format-2020-/yjkw-uj5s. Accessed December 8, 2020.
US Census Bureau. Technical Documentation: Table & Geography Changes: 2014-2018 ACS 5-year Estimates. 2019; https://www.census.gov/programs-surveys/acs/technical-documentation/table-and-geography-changes/2018/5-year.html. Accessed March 3, 2020.
Last updated: January 24, 2023