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The Neighborhood Atlas—Free Social Determinants of Health Data for All!

Dr. Robin Barr
Robin BARR,
Director, DEA,
Division of Extramural Activities (DEA)
.

At NIA, we know that achieving and maintaining good health is about more than biology. The neighborhoods where we live, work, play, worship and grow older play significant roles1: Income levels, education, housing quality, and employment, or lack thereof, are all factors.

Residential segregation by race in the U.S. is still high, but has been falling since 1960. Yet segregation by income continues to rise. Certainly, studying these neighborhoods and their residents is a key step to advancing real-world health equities. The catch has been that gathering socioeconomic metrics about a particular neighborhood, and sharing and accessing any data collected, has proved difficult for researchers across many fields, including NIH-supported scientists who study aging. Until now!

Recognizing the accessibility problem, NIA, in partnership with the National Institute on Minority Health and Health Disparities (NIMHD) at the NIH, has funded a new publicly available tool that makes such metrics more accessible to all: The Neighborhood Atlas.

Easy-to-use, essential data

Developed by Amy Kind, M.D., Ph.D., and her team at the University of Wisconsin School of Medicine and Public Health, the Neighborhood Atlas2 is a user-friendly, online tool that enables customized ranking and mapping of neighborhoods according to socioeconomic disadvantage across the full U.S., including Puerto Rico. Anyone can use the Neighborhood Atlas, not just researchers: If you can use a smartphone mapping app, you can use the Atlas — no fancy degree required! Additionally, the Atlas includes free downloads of neighborhood disadvantage data, which you can easily link to other research resources in a number of ways, including by cross-linkages to more than 69 million nine-digit zip codes.

Atlas data have already been employed by U.S. state and federal agencies, community and other not-for-profit organizations, health systems and industry. Researchers are actively using these data across NIH, but we would like to see more use of it in research on aging.

You can use Atlas data to:

  • Characterize neighborhood disadvantage exposure across your study cohort, clinical trial or biomarker repository;
  • Target research outreach, recruitment and retention efforts for key populations;
  • Provide a novel lens by which to view your study outcomes; and
  • Precisely target new community partners in intervention development, testing and design.

All these uses and more, and it’s completely free! Hooray for open data!

NIA does support a number of resources that allow parsing of geographic data in multiple ways. These resources are largely linked to the Health and Retirement Study (HRS) and require a data-use agreement to access the HRS data along with the geographic coding. We will describe these resources in an upcoming post. The Neighborhood Atlas is remarkable for its openness and accessibility. That openness allows multiple uses.

We hope you will check it out. To learn more, see the citation linked below and other information contained on the Neighborhood Atlas home page. Please give it a try and let us know your thoughts below.

References:

  1. Office of Disease Prevention and Health Promotion. Healthy People 2020: Social Determinants of Health. https://www.healthypeople.gov/2020/topics-objectives/topic/social-determinants-of-health
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  2. Kind AJH, Buckingham W. Making Neighborhood Disadvantage Metrics Accessible: The Neighborhood Atlas. New England Journal of Medicine, 2018. 378(26):2456-2458. PMCID: PMC6051533
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Comments

Submitted by Sanjay Asthana, MD on November 21, 2019

Dear Robin,

Many thanks for bringing attention to the remarkable Neighborhood Atlas developed by Amy Kind! I am hoping your blog will entice some of the ADRCs to use the atlas in their research studies focused on social determinants of AD risk and progression. We certainly plan to do so at the Wisconsin ADRC.

On Wisconsin!

Best, Sanjay

Submitted by Phil Goetz on November 23, 2019

A nice dataset! Please forgive me for a digression, but I think it is important: Whether racial segregation is falling or rising depends on how you define "race", which races you measure the segregation of, how you measure it, and what boundaries you use for reporting. Causally untangling ethnic from income segregation further depends on the methods used for aggregating area incomes and models of income distribution. Measured by personal interactions, segregation has probably increased since 1960 in northern and western states.

Until 1950, people of minority ethnicities in northern and western cities lived in many widely-dispersed, highly-segregated communities, which were, however, small enough that people in them still had regular contact with people of other ethnicities. This was because these small communities arose wherever some individual landlord was willing to rent to ethnic minorities.

Since 1950, majority-white neighborhoods have been becoming more diverse, but at the same time, majority-minority areas with hardly any whites, too large to be called "neighborhoods", have been forming and growing less diverse, like the area from Randallstown to Baltimore Street in Baltimore, or northwest Prince George's County near DC. See for instance "Residential Segregation and Neighborhood Conditions in U.S. Metropolitan Areas", Douglas S. Massey (2001), http://www.asu.edu/courses/aph294/total-readings/massey%20--%20resident…, or race maps such as https://bestneighborhood.com/race-in-baltimore-md/ .

This appears to be driven by strong racial self-segregation among low-income groups, as areas of homogeneous low income further segregate into racial groups, while areas of homogeneous high income do not appear to (though this could be an artifact of insufficient resolution). By now, some cities have completely segregated into racial blocs (e.g., Milwaukee).

The extent to which segregation has increased is masked in statistical reports by the fact that the boundaries of regions for which demographics are reported don't correspond to the new ethnic boundaries, and are sometimes redrawn for political purposes to disperse minorities among different districts.

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