About the Neighborhood Atlas ^®

Living in a disadvantaged neighborhood has been linked to a number of healthcare outcomes, including higher rates of diabetes and cardiovascular disease, increased utilization of health services, and earlier death ^1-5 . Health interventions and policies that don't account for neighborhood disadvantage may be ineffective ¹ . The Neighborhood Atlas website was created in order to freely share measures of neighborhood disadvantage with the public, including educational institutions, health systems, not-for-profit organizations, and government agencies, in order to make these metrics available for use in research, program planning, and policy development. The site was launched May 1, 2018.

Data Availability

The 2015, 2020, and 2022 ADI are available through the Download section

1. Link BG, Phelan J. Social conditions as fundamental causes of disease. J Health Soc Behav 1995; Spec No: 80-94.

2. Ludwig J, Sanbonmatsu L, Gennetian L, et al. Neighborhoods, obesity, and diabetes– a randomized social experiment. N Engl J Med 2011; 365: 1509-19.

3. Kind AJ, Jencks S, Brock J, et al. Neighborhood socioeconomic disadvantage and 30-day rehospitalization: a retrospective cohort study. Ann Intern Med 2014; 161: 765-74.

4. Lantos PM, Hoffman K, Permar SR, et al. Neighborhood disadvantage is associated with high cytomegalovirus seroprevalence in pregnancy. J Racial Ethn Health Disparities 2018; 5(4): 782-786

5. Hu J, Kind AJH, Nerenz D. Area Deprivation Index predicts readmission risk at an urban teaching hospital. Am J Med Qual 2018; (33):493-501.

About the Area Deprivation Index (ADI)

The Area Deprivation Index (ADI) is based on a measure created by the Health Resources & Services Administration (HRSA) over three decades ago, and has since been refined, adapted, and validated to the Census block group neighborhood level by Amy Kind, MD, PhD and her research team at the University of Wisconsin-Madison. It allows for rankings of neighborhoods by socioeconomic disadvantage in a region of interest (e.g., at the state or national level). It includes factors for the theoretical domains of income, education, employment, and housing quality. It can be used to inform health delivery and policy, especially for the most disadvantaged neighborhood groups. "Neighborhood" is defined as a Census block group .

Limitations

The ADI is limited insofar as it uses American Community Survey (ACS) 5-Year Data for its construction. For example, the 2018 ADI uses the ACS Data for 2018, which represent a five-year "period" from 2014-2018. All limitations of the source data will persist throughout the ADI - results are subject to the accuracy and errors contained within the American Community Survey data release. The choice of geographic units will also influence the ADI value. In the case of the ADI the Census block group is the geographic unit of construction, as the Census block group is considered the closest approximation to a "neighborhood". As such, we can only recommend linking the ADI to census block groups as other geographic units (including 5-digit ZIP Codes, ZCTA, and others) will not be valid. Please see the FAQ below for additional download and linkage guidance.

Changes from Previous Versions

For methodological changes to the ADI vintages available on this site, please see the ADI changelog .

How to Use This Site

This site offers several different ways to use the Area Deprivation Index (ADI).

The Mapping function allows you to view a state or the entire country mapped by 2022 ADI. This will show areas of relatively high disadvantage as well as areas of moderate to less disadvantage. Neighborhoods may be ranked relative to the full nation or relative to other neighborhoods within just that one state. You may also use the Mapping function to select a state, then enter an address to view the ADI ranking for the Census block group that contains that address.

In addition to exploring the ADI through the Mapping function, you may download a PDF map for the nation or by state.

The Download function allows you to download ADI rankings by different geographic regions. Prior to downloading any ADI dataset, please read the download instructions.

For additional information about the ADI, please read our FAQ .

Funding

This project was supported by the National Institute on Aging of the National Institutes of Health under Award Number RF1AG057784 (PI: Kind), the National Institute On Minority Health And Health Disparities of the National Institutes of Health under Award Number R01MD010243 (PI: Kind), the National Institute on Aging of the National Institutes of Health under Award Number R01AG070883 (PI: Kind, Bendlin), and the University of Wisconsin School of Medicine and Public Health Department of Medicine. The content of this website is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or of the University of Wisconsin.

Citation

Kind AJH, Buckingham W. Making Neighborhood Disadvantage Metrics Accessible: The Neighborhood Atlas . New England Journal of Medicine , 2018. 378: 2456-2458. DOI: 10.1056/NEJMp1802313. PMCID: PMC6051533. University of Wisconsin School of Medicine and Public Health. {specify year} Area Deprivation Index {specify version} . Downloaded from https://www.neighborhoodatlas.medicine.wisc.edu/ {date} Example: Kind AJH, Buckingham W. Making Neighborhood Disadvantage Metrics Accessible: The Neighborhood Atlas . New England Journal of Medicine , 2018. 378: 2456-2458. DOI: 10.1056/NEJMp1802313. PMCID: PMC6051533. AND University of Wisconsin School of Medicine Public Health. 2015 Area Deprivation Index v2.0. Downloaded from https://www.neighborhoodatlas.medicine.wisc.edu/ May 23, 2019. Hu J, Bartels CM (co-first author), Rovin RA, Lamb LE, Kind AJH, Nerenz DR. Race, Ethnicity, Neighborhood Characteristics, and In-Hospital Coronavirus Disease- 2019 Mortality . Med Care . 2021 Oct 1;59(10):888-892. doi: 10.1097/MLR.0000000000001624. PMCID: PMC8446301. Javier Mora, Ashley N. Krepline, Mohammed Aldakkak, Kathleen K. Christians, Ben George, William A. Hall, Beth A. Erickson, Naveen Kulkarni, Douglas B. Evans, Susan Tsai, Adjuvant therapy rates and overall survival in patients with localized pancreatic cancer from high Area Deprivation Index neighborhoods , The American Journal of Surgery , 2020. Sheehy AM, Powell WR, Kaiksow FA, Buckingham WR, Bartels CM, Birstler J, Yu M, Bykovskyi AG, Shi F, Kind AJH. Thirty-Day Re-observation, Chronic Re-observation, and Neighborhood Disadvantage. Mayo Clin Proc . 2020 Dec;95(12):2644-2654. DOI: 10.1016/j.mayocp.2020.06.059 PMID: 33276837; PMCID: PMC7720926. Rahman M, Meyers DJ, Wright B. Unintended Consequences of Observation Stay Use May Disproportionately Burden Medicare Beneficiaries in Disadvantaged Neighborhoods. Mayo Clin Proc . 2020 Dec;95(12):2589-2591. PMID:33276830. Powell WR, Buckingham WR, Larson JL, Vilen L, Yu M, Salamat MS, Bendlin BB, Rissman RA, Kind AJH. Association of Neighborhood-Level Disadvantage With Alzheimer Disease Neuropathology. JAMA Netw Open . 2020;3(6):e207559. doi:10.1001/jamanetworkopen.2020.7559 Jencks SF, Schuster A, Dougherty GB, Gerovich S, Brock JE, Kind AJH. Safety-net Hospitals, Neighborhood Disadvantage, and Readmissions: An Observational Study under Maryland's All-Payer Program. Annals of Internal Medicine , 2019. 171(2):91-98. PMCID: PMC6736732. Kind AJH, Buckingham W. Making Neighborhood Disadvantage Metrics Accessible: The Neighborhood Atlas. New England Journal of Medicine , 2018. 378: 2456-2458. DOI: 10.1056/NEJMp1802313. PMCID: PMC6051533. Hu J, Kind AJH, Nerenz D. Area deprivation index predicts readmission risk at an urban teaching hospital. Am J Med Qual , 2018. 33(5): 493-501. PMCID: PMC6027592. Cited in Medicare Payment Advisory Commission (MedPAC). Report to the Congress: Medicare and the Healthcare Delivery System. 2018 (June). Kind AJH, Jencks S, Brock J, Yu M, Bartels C, Ehlenbach W, Greenberg C, Smith M. Neighborhood Socioeconomic Disadvantage and 30 Day Rehospitalization: A Retrospective Cohort Study. Annals of Internal Medicine , 2014. 161(11):765-774. PMCID: PMC4251560. For more abstracts and publications using the ADI, please see a list of additional citations. May 2023: The Neighborhood Atlas and ADI was chosen by Milwaukee Water Works as a key component of their water equity plan for the City of Milwaukee. November 2020: The Neighborhood Atlas was featured in a recent Wall Street Journal article "Alzheimer's Research Looks at Hot Spots Across the U.S." , which summarized several recent studies examining the link between geography and the development of Alzheimer's Disease. October 2020: A National Geographic article describes the University of Pittsburgh Medical Center's (UPMC) use of the Area Deprivation Index (ADI) as a decision-making tool in COVID-19 treatment. The National Academy of Medicine also cited the UPMC model policy for allocating scarce COVID-19 medications as a model for how to mitigate structural inequities during the pandemic in their recommendations on how to allocate scarce vaccines. June 2020: Articles in the New York Times and Wisconsin State Journal describe recent work at the University of Wisconsin linking neighborhood disadvantage with the presence of Alzheimer's disease-related brain changes at autopsy. April 15, 2020: An editorial in the New York Times by Harald Schmidt, Assistant Professor of Medical Ethics at the University of Pennsylvania, recommended the use of the Area Deprivation Index (ADI) as a potential tool in managing certain elements of the COVID-19 response. Related: The Commonwealth of Pennsylvania is using the ADI in its official approach to fairly ration scarce COVID-19 medications as a means to proactively mitigate health disparities in COVID-19 outcomes. See "Ethical Allocation Framework for Emerging Treatments of COVID-19" for additional information. What do the ADI values mean? The ADIs on this website are provided in national percentile rankings at the block group level from 1 to 100. The percentiles are constructed by ranking the ADI from low to high for the nation and grouping the block groups/neighborhoods into bins corresponding to each 1% range of the ADI. Group 1 is the lowest ADI and group 100 is the highest ADI. A block group with a ranking of 1 indicates the lowest level of "disadvantage" within the nation and an ADI with a ranking of 100 indicates the highest level of "disadvantage". Similarly, ADIs are also available in deciles from 1 to 10 for each individual state. The state deciles are constructed by ranking the ADI from low to high for each state alone without consideration of national ADIs. Again, group 1 is the lowest ADI (least disadvantaged) and 10 is the highest ADI (most disadvantaged).

What methodology was used to create these ADI datasets? The following two articles explain the methodology that was used to create these ADI datasets:

Singh GK. Area deprivation and widening inequalities in US mortality, 1969-1998 . Am J Public Health 2003;93(7):1137-43.

Kind AJH, Jencks S, Brock J, et al. Neighborhood socioeconomic disadvantage and 30-day rehospitalizations: an analysis of Medicare data . Ann Intern Med 2014;161(11):765-74.

What is new in Version 4 of the ADI? Construction of the version 4 ADI has minor standard shrinkage statistical updates included to mitigate the effect of year-to-year sampling variations in block group level component estimates within American Community Survey (ACS) data. This results in very little actual change in ADI ranking but buffers from known and future expected variation in ACS source data. Earlier changes include suppression of block groups containing any of the following: less than 100 people, less than 30 housing units or more than 33% of the population living in group quarters and Census data labeled as N/A or missing in the core component variables ⁶ . Geographic geographic imputation methods have continued to be applied to address missing data in the key component areas. This nested geographic imputation uses Census tract and county level data to supplement for missing values (N/A) at the block group in these variables. Methodological changes are also recorded in the ADI changelog .

6. Diez Roux AV, Kiefe CI, Jacobs DR, Haan M, Jackson SA, Nieto FJ, Paton CC, Schulz. Area Characteristics and Individual-Level Socioeconomic Position Indicators in Three Population-Based Epidemiological Studies. Annals of Epidemiology, 2001,11(6):395-405

Why are some block groups missing ADI ranks? When a Census block group falls into one or more of the suppression criteria mentioned above the ADI rank is replaced with a code describing the suppression reason. Three possible codes will appear in the ADI field: "PH" for suppression due to low population and/or housing, "GQ" for suppression due to a high group quarters population, and "PH-GQ" for suppression due to both types of suppression criteria. A code of "QDI" designates block groups without an ADI due to Questionable Data Integrity, stemming from missing data in the source ACS data.

How can I use the ADI? The ADI can be used for several different purposes. Health systems and health care providers can use the ADI to target program delivery by geographic location based on the area of greatest disadvantage. For example, the Centers for Medicare and Medicaid (CMS) is currently using the ADI to target program delivery of the Everyone with Diabetes Counts program .

The ADI can also be used for research purposes. For example, using the ADI based on 2000 Census data, Kind et al (2014) found that the risk of living in a disadvantaged neighborhood is similar to that of having a chronic lung disease, like emphysema, and worse than that of health conditions such as diabetes when it comes to readmission risk. Using the ADI based on 2013 ACS data, Joynt Maddox et al (2019) added social risk factors including neighborhood disadvantage to models used to calculate penalties under the CMS's Hospital Readmission Reduction Program. The authors found that accounting for these factors had a major impact on safety-net hospitals that serve patients from the most disadvantaged neighborhoods; over half would have seen a decline in their readmission penalty if such an adjustment had been applied. What is the difference between a percentile and a decile? A percentile splits the ADI scores into 100 equal sections, categorizing the individual block group/neighborhood, with those in the first percentile being the least disadvantaged, and those in the hundredth being the most. A decile groups the ADI scores into 10 equal sections. Percentiles are created using the ADI scores for the entire nation, and deciles are created for each state individually. What is the difference between a raw score and a ranking? A raw score is the actual score a neighborhood receives based on the theoretical domains that the ADI measures, while the rankings sort the scores by disadvantage at either the state or national level, allowing for easier comparison between neighborhoods. Because of the way that it is statistically constructed, the ADI should always only be used in a rank-type format. When we released our first public use ADI data set (2000 ADI), we released these as raw scores. However, to appropriately employ such scores, the raw scores must be converted to ranks. This extra step was a burden to some of our users. As such, to ensure statistically appropriate interpretation of the metric, we decided that for newer ADI releases using the Neighborhood Atlas, all would be converted to ranks to ensure ease of use. We do not currently have plans to release the raw scores for the ADIs available through the Neighborhood Atlas. Do you have a 5-digit ZIP Code dataset available, or a ZCTA-level dataset? No. In recent validation work that used 2009-2013 American Community Survey data, it became clear that the ADI should not be used at any levels other than those core geographic units defined by the Census (see diagram of Census levels ). Those with interest in using a ZIP-based methodology may still employ the 9-digit ZIP Code crosswalk, which was built to correspond directly to Census block groups and accompanies the Census block group level ADI. Why are some ZIP Codes missing ADI ranks? When ADI values are not represented in the nine digit ZIP Code file, it is due to one of three conditions. First, a "P" indicates that the ZIP Code is a post office box and not geographically representative nor included within ACS metrics. Second, a "U" indicates a unique ZIP Code, often these are assigned to businesses or large footprint entities who have large volume of mail delivery and would also be omitted from the ACS. Lastly, a blank ADI value indicates that the conversion of the block group ADI score to ZIP+4 did not produce a match. These are most common in coastal areas where a generalized ZIP+4 may be outside of a block group or offshore. "Employment of ZIP Code Tabulation Areas to link geographic data is a convenient but, ultimately, inferior method for this sort of assessment ⁷ . It results in relatively large geographic zones with linkages that can lead to less precise estimates, especially in areas in which concentrated poverty abuts more wealthy regions." – Excerpt drawn directly from Kind et al., Health Affairs , Sept 15, 2016.

7. Grubesic TH, Matisziw TC. On the use of ZIP Codes and ZIP Code tabulation areas (ZCTAs) for the spatial analysis of epidemiological data . Int J Health Geog 2006;5:58.

What versions of the ADI are available? The 2022 vintage ADI, constructed exclusively using the v4 methodology, the 2020 vintage ADI updated with the v4 methodologies, and the 2015 vintage ADI updated with the v3.1 methodologies are the only current versions of the ADI available. Can I request a different version of the ADI? If you have a research question in need of a different version of the ADI please fill out this form . Who do I contact if I need help? Please contact the Neighborhood Atlas team using our contact form . I am having trouble creating an account or logging in. The form to create an account is accessible below. When you first create your account, you should receive a confirmation email. You will need to validate your email. If you cannot find the validation email, please check your spam folder. Otherwise, please request a new confirmation email . If you have validated your email, using a different browser may be helpful. We have found that Chrome and Firefox work best; sometimes switching between the two also solves the problem. If you have any problems, please contact the Neighborhood Atlas team . Neighborhood Atlas is a registered trademark. Website produced by the Applied Population Lab , UW-Madison Privacy Statement • Terms of Use • Contact Us