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U.S. Small-Area Life Expectancy Estimates Project (USALEEP)

Description

USALEEP (U.S. Small-Area Life Expectancy Estimates Project) provides census-tract–level estimates of life expectancy at birth for nearly all neighborhoods in the United States. Developed by CDC’s National Center for Health Statistics with partners, it uses death records and population data to show how long people are expected to live, on average, in small geographic areas. These data help researchers, health departments, and communities identify and address stark local differences in longevity linked to social, economic, and environmental conditions.

Questions this resource can help answer

  1. How does life expectancy vary between neighborhoods (census tracts) within my city or county?
  2. Which neighborhoods have the shortest and longest life expectancy, and how big is the gap (in years)?
  3. How does the average life expectancy in my city or county compare to my state or to the U.S. overall?
  4. Are shorter life expectancies clustered in areas with higher poverty, segregation, or environmental burdens?
  5. How do life expectancy patterns differ by urban vs. rural areas within my region?
  6. In which neighborhoods should we prioritize health, housing, or social investments to address longevity gaps?
  7. Have areas with major initiatives (e.g., health programs, redevelopment) historically had lower life expectancy than other parts of the city?

How do I use this resource?

To use USALEEP, start on the project page and download the census‑tract life expectancy dataset (and documentation). Open the file in a spreadsheet viewer or GIS program and filter it to your state, county, or city using FIPS codes or place names. Use the tract‑level life expectancy values to create maps, charts, or summary tables that show how longevity varies across neighborhoods, and compare high‑ and low‑life‑expectancy tracts to each other and to state or national averages.

Pro tips

Pair these estimates with other tract‑level data (e.g., income, race/ethnicity, housing, environmental exposures) to explore patterns and identify communities with shorter life expectancy where targeted policies, programs, or investments may be most needed.

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