Protocol - Neighborhood Concentrated Disadvantage

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The protocol is based on extracting data from the U.S. Census Bureau on a set of variables related to the concept of "concentrated disadvantage" (Sampson, et al.,1997). All the relevant variables are available from the long form of the 1990 and 2000 SF3, and from the 5-year ACS estimates. ACS estimates are annually updated; as of May 2022, 5-year data sets range from 2011-2015 to 2016-2020. Once the data are extracted, an index score of concentrated disadvantage can be calculated at the neighborhood level of interest; this is usually based on census tract or census block group data.

Specific Instructions

Assuming that information on current address (see PhenX Demographics domain, Current Address measure) has been collected for a study respondent, then it is possible to use geocoding to link the address of a study participant to his or her local neighborhood (a geographic area). The link is typically by a Census-defined area, such as a census block group or a census tract or by Zone Improvement Plan (ZIP) code area (captured by the U.S. Census Bureau as a ZIP Code Tabulation Area [ZCTA]).

When comparing the 2011-2015 ACS 5-year estimates with the 2016-2020 ACS 5-year estimates, there are several points to consider. For more information on the 2016-2020 ACS changes, please visit the American Community Survey Guidance for Data Users website: https://www.census.gov/programs-surveys/acs/guidance/comparing-acs-data/2020/5-year-comparison.html

Table B02001: Race. The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website.

The original paper by Sampson et al. (1997) was based on the use of variables from the 1990 Decennial Census and applied to a neighborhood definition based on aggregates of Census tracts, called neighborhood clusters.

The Social Environments Working Group (WG) recommends that researchers follow Sampson et al. (1997) and conduct a factor analysis (e.g., a principal components analysis using varimax rotation methods or alpha-scoring factor analysis). The extracted variables are typically very highly correlated, undermining any investigation of unique effects. Sampson et al. (1997, p. 920) find that, consistent with urban theory, these six, poverty-related variables are highly associated and load on the same factor; their work was based on 1990 Census data for Chicago. Other studies in other settings confirm that these variables (poverty, percentage of single-parent families, percentage of family members on welfare and unemployed, and a measure of racial segregation) load on a single factor with individual factor loadings typically exceeding 0.8.

The Social Environments WG recommends that investigators record and report the factor loading scores for each variable used in the factor analysis. These scores would vary across studies, but knowing how they vary (i.e., what other studies found) would allow for comparison between studies. Depending on the purpose of the study, investigators may want to remove the measure of Percent Black from the scale if the unique effects of racial concentration are a key research emphasis.

The calculation of concentrated disadvantage based on factor analysis generates a measure that is sample dependent (i.e., study specific). However, it is important to note that this is a well-established, robust, and highly cited measure across the social sciences and public health. The social science literature has long argued that neighborhood disadvantage is not a single-item construct captured by, for example, a measure of poverty (e.g., percentage of individuals below the poverty level) or measures such as the Index of Concentration at the Extremes (Massey, 2001).




Accessing and Understanding the American Community Survey (ACS) Data

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 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/programs-surveys/acs/library/handbooks/summary-file.html

The technical documentation for the American Community Survey (ACS) summary files is available online at http://www.census.gov/programs-surveys/acs/technical-documentation.html. Select the “Summary File Documentation” link, and then select the data set of interest. Users not familiar with Census data should consult the technical materials.

If the user is interested in additional variables beyond those included in the neighborhood concentrated disadvantage protocol, they should be aware that not all ACS estimates are available for all geographies. These missing estimates are due to data suppression techniques by which the U.S. Census Bureau limits disclosure of individual data and does not release estimates with poor statistical reliability. Additional information about data suppression and the specific estimates it impacts can be found at http://www.census.gov/programs-surveys/acs/technical-documentation/data-suppression.html.

Although block group data have long been available from the Census File Transfer Protocol site, not all tables have block groups available for download at data.census.gov. Information about the types of geographies that are available are in the Appendix Tables as detailed in the technical documentation at https://www.census.gov/programs-surveys/acs/library/handbooks/summary-file.html.

Calculating Neighborhood Concentrated Disadvantage

Concentrated disadvantage is derived from six Census variables:

1. Percent of Individuals Below the Poverty Line (derived from ACS Table C17002)

2. Percent of Households Receiving Public Assistance (derived from ACS Table B19057)

3. Percent Female-Headed Families (derived from ACS Table B11001)

4. Percent Unemployed (derived from ACS Table B23025)

5. Percent Less Than Age 18 (derived from ACS Table B01001)

6. Percent Black (derived from ACS Table B02001)

Concentrated disadvantage is calculated for all subareas within a study area.

While some commercial data products may include the derivation of some of these variables, the detailed material below is based on the assumption that the user will go to the U.S. Census Bureau (original source) for all the raw data counts needed to calculate the individual variables that create the measure Concentrated Disadvantage. The protocol text uses the unique ID of individual variables. These descriptions can be found in the “Table Shells” download on the Summary File Technical Documentation (available here https://www.census.gov/programs-surveys/acs/technical-documentation/table-shells.html). Note: users may download tables as Excel files from https://data.census.gov. The tables do not use the unique ID of the variables presented in the summary files but do contain header data that describe the variable.

1: "Percent of Individuals Below the Poverty Line" is derived from data in ACS 5-Year “Table C17002: Ratio of Income to Poverty Level in the Past 12 Months.”

Table C17002: Ratio of Income to Poverty Level in the Past 12 Months

Universe: Population for whom poverty status is determined.

There are eight variables included in table C17002 (see line 14188 of the ACS2020_Table_Shells.xlsx file available in the Technical Documentation).

Table C17002: Ratio of Income to Poverty Level in the Past 12 Months is reproduced below:

Variable Code

Variable Name




Under .50


.50 to .99


1.00 to 1.24


1.25 to 1.49


1.50 to 1.84


1.85 to 1.99


2.00 and over

The percent of individuals below the poverty line=[(C17002002 + C17002003) / C17002001] * 100.

2: "Percent of Households Receiving Public Assistance" is derived from ACS "TableB19057: Public Assistance Income in the Past 12 Months for Households.”

Table B19057: Public Assistance Income in the Past 12 Months for Households

Universe: Households.

There are three variables included in Table B19015. Table B19015 is reproduced below:

Variable Code

Variable Name




With public assistance income


No public assistance income

The “percent of households on public assistance”=(B19057002/B19057001) * 100.

From the ACS Summary File Subject Definitions, public assistance income “includes general assistance and Temporary Assistance to Needy Families (TANF). Separate payments received for hospital or other medical care (vendor payments) are excluded. This does not include Supplemental Security Income (SSI) or noncash benefits such as Food Stamps” (p. 87 of 2020 Subject Definitions document).

3: "Percent Female-Headed Families" is derived from ACS “Table B11001: Household Type (Including Living Alone).”

There are nine cells in Table B1101. The table is reproduced below:

Variable Code

Variable Name




Family households:


  Married-couple family


  Other family:


    Male householder, no wife present


    Female householder, no husband present


Nonfamily households:


  Householder living alone


  Householder not living alone

The “percent of female-headed families”=(B11001006/B11001001) * 100.

4: "Percent Unemployed" is derived from ACS "Table B23025: Employment Status for the Population 16 Years and Over."

Table B23025: Employment Status for the Population 16 Years and Over

Universe: Population 16 years and over.

From the 2020 Subject Definitions document (p. 68), the U.S. Census Bureau definition of being unemployed is the following:

"All civilians 16 years old and over are classified as unemployed if they (1) were neither &rsquot;at work&rsquot; nor &rsquot;with a job but not at work&rsquot; during the reference week,and (2) were actively looking for work during the last 4 weeks,and (3) were available to start a job. Also included as unemployed are civilians who did not work at all during the reference week, were waiting to be called back to a job from which they had been laid off, and were available for work except temporary illness. Examples of job-seeking activities are: registering at a public or private employment office; meeting with prospective employers; investigating possibilities for starting a professional practice or opening a business; placing or answering advertisements; writing letters of application; being on a union or professional register"

Table B23025 contains seven cells. The table is reproduced below.

Variable Code

Variable Name




  In labor force:


    Civilian labor force:






    Armed Forces


  Not in labor force

The "percent unemployed"=([B23025005 + B23025003] / B23025001) * 100.

5: "Percent Less Than Age 18" is derived from ACS "Table B01001: Sex by Age."

Table B01001: Sex by Age

Universe: Total Population.

There are 49 cells in ACS Table B01001 (ACS2020_Table_Shells.xlsx).

Users need to combine the counts for both males and females. Thus, the sum of males under age 18 years old (from under 5 years old to 15-17 years old) equals the sum of all cells B01001003 through B01001006 and for females the sum of all cells B01001027 through B01001030.

The "percent less than age 18"=

([(B01001003: B01001006) + (B01001027: B01001030)] / B01001001) * 100

6: "Percent Black" is derived from ACS "Table B02001: Race."

Table B02001: Race

Universe: Total population.

There are 10 cells in Table B02001 (reproduced below):

Variable Code

Variable Name




  White alone


  Black or African American alone


  American Indian and Alaska Native alone


  Asian alone


  Native Hawaiian and Other Pacific Islander alone


  Some other race alone


  Two or more races:


    Two races including Some other race


    Two races excluding Some other race, and three or more races

The "Percent Black"=(B02001003/B02001001) * 100

Personnel and Training Required

Knowledge of Census data products and websites, such as data.census.gov, and/or publicly available data portals (e.g., National Historical Geographic Information System), and/or commercial geospatial data products, such as that provided by vendors like GeoLytics or Social Explorer.

After extracting the necessary data, statistical methods are used (e.g., principal component analysis and factor analysis).

Equipment Needs

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

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 secondary data.

Selection Rationale

The Social Environments Working Group preferred an objective measure using U.S. Census Bureau data over a questionnaire that would rely on subjective judgment based on retrospective ascertainment, which is likely to be unreliable. Additionally, the measure of "concentrated disadvantage" is derived from the work of Sampson and colleagues (1997) on the Project on Human Development in Chicago Neighborhoods (PHDCN), which is a well-known, large-scale study.

The measure has been used in numerous papers including, the highly cited (3,000+ citations) paper by Sampson et al. (1997).



Logical Observation Identifiers Names and Codes (LOINC) Neighborhood disadvantage proto 63036-8 LOINC
caDSR Form PhenX PX211302 - Neighborhood Concentrated Disadvantage 6872836 caDSR Form
Derived Variables


Process and Review

The SDOH-X WG reviewed the measures in the Social Environments domain in May 2022.

Guidance from the SDOH-X WG includes:

• Updated protocol

Back-compatible: there are changes to the Data Dictionary, previous version of the Data Dictionary and Variable mapping in Toolkit archive (link)

The Expert Review Panel #2 (ERP 2) reviewed the measures in the Demographics, Social Environments and Environmental Exposures domains.

Guidance from ERP 2 includes:

• Replaced protocol

• New Data Dictionary

Back-compatible: there are changes to the Data Dictionary, previous version of the Data Dictionary and Variable mapping in Toolkit archive (link)

Protocol Name from Source

U.S. Census Bureau, 1990 and 2000 Decennial Censuses (SF3); and American Community Survey (ACS), 5-year estimates, 2011-2015 to 2016-2020.


Recommended data sources include the following:

The U.S. Census Bureau decennial Census (1990, 2000).

Data.census.gov, https://data.census.gov/cedsci/

American Community Survey (ACS) products (specifically, the 5-year estimates), http://www.census.gov/programs-surveys/acs.

General References

Kawachi, I., & Berkman, L. (2003). Neighborhoods and health. New York: Oxford University Press.

Massey, D. S. (2001). The prodigal paradigm returns: ecology comes back to sociology. In: Booth A, Crouter A, eds. Does It Take a Village? Community Effects on Children, Adolescents, and Families. Mahwah, NJ: Lawrence Erlbaum Associates; 41-48.

Massey, D. S., & Denton, N. (1993). American apartheid: Segregation and the making of the underclass. Cambridge, MA: Harvard University Press.

Sampson, R. J., Morenoff, J., & Gannon-Rowley, T. (2002). Assessing neighborhood effects: Social processes and new directions in research. Annual Review of Sociology, 28, 443-478.

Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277(5238), 918-924.

Wilson, W. J. (1987). The truly disadvantaged: The inner city, the underclass, and public policy. Chicago: University of Chicago Press.

Protocol ID


Export Variables
Variable Name Variable IDVariable DescriptiondbGaP Mapping
PX211302010000 Percent Of Individuals Below The Poverty more
Line (derived from ACS Table C17002) show less
Variable Mapping
PX211302060000 Percent Black (derived from ACS Table B02001) N/A
PX211302030000 Percent Female-Headed Families (derived from more
ACS Table B11001) show less
PX211302020000 Percent of Households Receiving Public more
Assistance (derived from ACS Table B19057) show less
Variable Mapping
PX211302040000 Percent Unemployed (derived from ACS Table B23025) N/A
PX211302050000 Percent Less Than Age 18 (derived from ACS more
Table B01001) show less
Social Environments
Measure Name

Neighborhood Concentrated Disadvantage

Release Date

May 31, 2016


This measure uses readily available secondary data from the U.S. Census Bureau.


This measure examines various population characteristics at the neighborhood level to determine the concentration of poverty. In the social science and public health literatures, one of the most important indicators for a host of individual outcome measures that are incorporated at the neighborhood level is Neighborhood Concentrated Disadvantage.


Social environments, American Community Survey, ACS, neighborhood poverty, public assistance, U.S. Census, SES Measures (income, education, occupation), environmental health disparities, neighborhood built environment

Measure Protocols
Protocol ID Protocol Name
211302 Neighborhood Concentrated Disadvantage

Denstel, K. D., et al. (2023) An examination of the relationships between the neighborhood social environment, adiposity, and cardiometabolic disease risk in adolescence: a cross-sectional study. BMC Public Health. 2023 September; 23(1): 1692. doi: 10.1186/s12889-023-16580-0

Wen, H. C., et al. (2019) Racial and Ethnic Differences in Obesity in People With Spinal Cord Injury: The Effects of Disadvantaged Neighborhood. Archives of Physical Medicine and Rehabilitation. 2019 September; 100(9): 1599-1606. doi: 10.1016/j.apmr.2019.02.008

Iwuchukwu, I. O., et al. (2019) Neighboorhood disadvantage is associated with hemorrhagic stroke in young adults. Stroke. 2019 February; 50(Supplement 1): Abstract TP216.

Sullivan, S. M., et al. (2018) Neighborhood Environment Measurements and Anthropometric Indicators of Obesity: Results From the Women and Their Children's Health (WaTCH) Study. Environment and Behavior. 2018 November; 50(9): 1032-1055. doi: 10.1177/0013916517726827

Forray, A., et al. (2017) Progesterone for smoking relapse prevention following delivery: A pilot, randomized, double-blind study. Psychoneuroendocrinology. 2017 December; 86: 96-103. doi: 10.1016/j.psyneuen.2017.09.012

Scribner, R. A., et al. (2017) The Social Determinants of Health Core: Taking a Place-Based Approach. Am J Prev Med. 2017 January; 52(1S1): S13-S19. doi: 10.1016/j.amepre.2016.09.025

Kepper, M., et al. (2016) Prepubertal children exposed to concentrated disadvantage: An exploratory analysis of inflammation and metabolic dysfunction. Obesity (Silver Spring). 2016 May; 24(5): 1148-53. doi: 10.1002/oby.21462