Protocol - Residential Concentrations of Income

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The protocol is based on extracting data from the U.S. Census Bureau on a set of variables related to concentrations of wealth and deprivation. ICE is designed to reveal the extent to which an area’s residents are concentrated into groups at the extremes of deprivation and privilege: a value of -1 means that 100% of the population is concentrated in the most deprived group, and a value of 1 means that 100% of the population is concentrated into the most privileged group. Once the data are extracted, the ICE can be calculated at the census tract or census block group level.

Specific Instructions

If a current address has been collected for a study respondent, it is possible to use geocoding to link the address of a study participant to his or her local neighborhood (or other large geographical unit).

It is necessary to extract data for smaller units (e.g., census tracts) to calculate the ICE for each larger unit. To aid comparability between studies, the Social Determinants of Health-X Working Group recommends that researchers set the smaller area to the census tract and the larger area to the metropolitan statistical area.

Additionally, researchers can use the census variables, such as the entropy index, to calculate more basic diversity scores at the census-tract level.

ICE reveals the extent to which an area’s residents are concentrated into groups at extremes of poverty and privilege, a value of -1 (concentrated in most deprived). In contrast, the Index of Dissimilarity is more complex to determine. It describes the number or percentage of people in two different groups that would need to be reallocated to achieve equity. ICE is based on all residents in an area, and it is employed over the Index of Dissimilarity when the D-Index fails to be informative at the neighborhood level because of spatial social polarization.




The Index of Concentration at the Extremes is based on U.S. Census Bureau data. This protocol describes how to make calculations using 5-year American Community Survey (ACS) estimates.

The ACS data used in this protocol can be accessed by using Excel to read the Summary Files at the U.S. Census Bureau’s data.census.gov website (https://data.census.gov) or by using SAS programs to read the files. Users can find additional information on these tools at the following locations:

Using Excel to Access Summary Files: https://www2.census.gov/programs-surveys/acs/summary_file/2020/documentation/tech_docs/ACS_SF_Excel_Import_Tool.pdf

Using SAS to Access Summary Files: https://www.census.gov/content/dam/Census/library/publications/2019/acs/acs_summary-file_handbook_2019_ch04.pdf

The technical documentation for the American Community Survey (ACS) summary files is available online at https://www.census.gov/programs-surveys/acs/data/summary-file.html. Select “What ACS Summary File Data Users Need to Know” for an overview of the ACS Summary file and how it can be used to access data. Users not familiar with Census data should consult the technical materials.

To compute ICE, the following formula is used:

ICEi = (Ai - Pi)/Ti


Ai is equal to the number of affluent persons in neighborhood i (e.g., in the 80th income percentile)

Pi is equal to the number of poor persons in neighborhood i (e.g., in the 20th income percentile)

Ti is equal to the total population with known income level in neighborhood i

ICE is a single metric that simultaneously quantifies concentrated extremes of both privilege and deprivation, whereby a value of 1 connotes that all residents are in the privileged group and a value of –1 denotes that all residents are in the most deprived group.

ICE can meaningfully be computed for both smaller and larger geographic units (e.g., block group, census tract (CT), community district, city, county).

It is suggested to use 5-year annual average values for each variable because there are no public-use single-year CT-level ACS estimates available, and such estimates can both be imprecise and vary widely across years (because of both changing sampling frames and sample sizes).

Personnel and Training Required

Knowledge of Census data products and websites, such as Explore Census Data and/or publicly available data portals (e.g., https://data.census.gov/cedsci/), and/or commercial geospatial data products, such as that provided by vendors like GeoLyticsor Social Explorer.

The extracted data need to be manipulated, and the ICE needs to be calculated.

Equipment Needs

Access to a desktop/laptop computer with Internet access to download raw data from the U.S. Census Bureau’s data.census.gov website, and statistical packages (e.g., SPSS, SAS) for data manipulation.

Requirement CategoryRequired
Major equipment No
Specialized training No
Specialized requirements for biospecimen collection No
Average time of greater than 15 minutes in an unaffected individual No
Mode of Administration

Secondary Data Analysis


Infant, Toddler, Child, Adolescent, Adult, Senior, Pregnancy


Not applicable; derived from publicly available data

Selection Rationale

ICE provides an objective measure of residential segregation using U.S. Census Bureau data. It is well-established and used in a range of health studies. Its less complex to determine than other measures for residential segregation.



Derived Variables


Process and Review

Not Applicable

Protocol Name from Source

Index of Concentration at the Extremes (ICE)


U.S. Census Bureau ACS data products (5-year estimates). https://data.census.gov/cedsci/

Data.census.gov website. https://data.census.gov/

Massey, D. S. (2001). The prodigal paradigm returns: ecology comes back to sociology. In: A. Booth & A. Crouter (Eds.), Does it take a village? community effects on children, adolescents, and families (pp. 41-48). Lawrence Erlbaum Associates.

General References

Chambers, B. D., Baer, R. J., McLemore, M. R., & Jelliffe-Pawlowski, L. L. (2019). Using Index of Concentration at the Extremes as indicators of structural racism to evaluate the association with preterm birth and infant mortality-California, 2011-2012. Journal of Urban Health, 96(2), 159-170. https://doi.org/10.1007/s11524-018-0272-4

Feldman, J. M., Waterman, P. D., Coull, B. A., & Krieger, N. (2015). Spatial social polarization: using the Index of Concentration at the Extremes jointly for income and race/ethnicity to analyse risk of hypertension. Journal of Epidemiology and Community Health, 69(12), 1199-1207. https://doi.org/10.1136/jech-2015-205728

Krieger, N., Waterman, P. D., Gryparis, A., & Coull, B. A. (2015). Black carbon exposure, socioeconomic and racial/ethnic spatial polarization, and the Index of Concentration at the Extremes (ICE). Health & Place, 34, 215-228. https://doi.org/10.1016/j.healthplace.2015.05.008

Krieger, N., Kim, R., Feldman, J., & Waterman, P. D. (2018). Using the Index of Concentration at the Extremes at multiple geographical levels to monitor health inequities in an era of growing spatial social polarization: Massachusetts, USA (2010-14). International Journal of Epidemiology, 47(3), 788–819. https://doi.org/10.1093/ije/dyy004

Krieger, N., Waterman, P. D., Spasojevic, J., Li, W., Maduro, G., & Van Wye, G. (2016). Public health monitoring of privilege and deprivation with the Index of Concentration at the Extremes. American Journal of Public Health, 106(2), 256-263. https://doi.org/10.2105/AJPH.2015.302955 

Protocol ID


Export Variables
Variable Name Variable IDVariable DescriptiondbGaP Mapping
PX290801060000 Computed ICE value N/A
PX290801010000 Method used to access summary files N/A
PX290801020000 Neighborhood N/A
PX290801030000 Number of affluent persons in the more
neighborhood (e.g., in the 80th income percentile) show less
PX290801040000 Number of poor persons in neighborhood i more
(e.g., in the 20th income percentile) show less
PX290801050000 Total population with known income level in more
the neighborhood show less
Structural Social Determinants of Health
Measure Name

Residential Concentrations of Income

Release Date

December 14, 2022


The extent to which residents are concentrated into groups at extremes of poverty (e.g., in the 20th income percentile) and privilege (80th income percentile).


Measuring societal distributions of concentrations of privilege and poverty provides important data regarding inequity across race and income dimensions (and structural racism) that accounts for spatial polarization. It measures both economic and racial/ethnic segregation and may be used as a proxy or indicator of structural racism.


ACS, American Community Survey, neighborhood, societal distributions, Index of Concentration at the Extremes, Social Determinants of Health, U.S. Census, SES Measures (income, education, occupation)

Measure Protocols
Protocol ID Protocol Name
290801 Residential Concentrations of Income

There are no publications listed for this protocol.