# Estimation of Mortality via the Neighborhood Atlas and Reproducible Area Deprivation Indices

## TL;DR

This study shows that the corrected ReADI index better estimates area deprivation and mortality than the flawed NA-ADI, making it a more reliable tool for health policy.

## Contribution

The study introduces the Reproducible ADI (ReADI), a corrected and transparent version of the NA-ADI, improving accuracy in health equity research.

## Key findings

- ReADI aligns more closely with other deprivation indices than NA-ADI (R2 range 0.609 to 0.932 vs. 0.331 to 0.710).
- ReADI better reflects component weights with higher R2 and lower RMSE compared to NA-ADI.
- ReADI explains more variance in life expectancy, especially in underresourced urban areas (R2 difference of 0.064).

## Abstract

How do calculation errors, such as failing to standardize variables, in the Neighborhood Atlas Area Deprivation Index (NA-ADI) lead to differences relative to the corrected Reproducible ADI (ReADI), and to what extent are these errors associated with mortality?

In this cross-sectional study, the ReADI provided a corrected, multidimensional, and reliable measure of area deprivation, aligning better with other indices and improving estimates of association with mortality compared with the NA-ADI.

These findings suggest that the ReADI is an accurate, transparent, and policy-relevant measure that is more suitable than the NA-ADI for guiding health equity research and resource allocation.

This cross-sectional study evaluates the Reproducible Area Deprivation Index (ReADI) in comparison with the Neighborhood Atlas ADI other established deprivation indices for estimating mortality.

The Neighborhood Atlas Area Deprivation Index (NA-ADI) has been widely used in health policy research and incorporated into Medicare payment models such as the Accountable Care Organization Realizing Equity, Access, and Community Health model. However, calculation errors have been independently identified by multiple groups, showing that the NA-ADI contains distorted deprivation estimates that pose risks to equitable funding allocation and outcome adjustment.

To develop the Reproducible ADI (ReADI) as a corrected, transparent replacement aligned with the original ADI methodology and to compare its agreement with established deprivation indices, including the Social Vulnerability Index (SVI), Social Deprivation Index, French Deprivation Index, and Neighborhood Stress Score, and its performance for estimating mortality.

This cross-sectional study used 2011-2015 and 2018-2022 data from the American Community Survey (ACS), a population-based US sample across census block groups, tracts, and counties. US geographic levels with sufficient data included 235 952 block groups, 83 722 census tracts, and 3214 counties. Census tract–level 2011-2015 mortality data were obtained from the US Small-Area Life Expectancy Estimates Project. Data from the 2015 NA-ADI were accessed March 7, 2024, and data from the 2022 NA-ADI, December 1, 2024.

The ReADI was constructed using corrected methods described in the original work developing the ADI. The NA-ADI was downloaded and aggregated to tract and county levels. The SVI was from the Centers for Disease Control and Prevention. Other indices were independently constructed from the ACS.

The primary outcomes were ReADI and NA-ADI comparisons against each other, other deprivation indices, and mortality.

The ReADI more closely aligned with other deprivation indices (R2 range, 0.609 [95% CI, 0.586-0.630] to 0.932 [95% CI, 0.931-0.933]) compared with the NA-ADI (R2 range, 0.331 [95% CI, 0.300-0.362] to 0.710 [95% CI, 0.692-0.727]). ReADI scores also better reflected component weights (R2 ≥ 0.999 [95% CI, 0.996-1.000]; RMSE ≤ 0.042 [95% CI, 0.029-0.053]) compared with the NA-ADI (R2 range, 0.832 [95% CI, 0.574-0.932] to 0.844 [95% CI, 0.601-0.937]; RMSE range, 0.346 [95% CI, 0.279-0.414] to 0.405 [95% CI, 0.334-0.470]), consistent with correct computation. In 3332 high-discrepancy census tracts representing approximately 13 million individuals, the ReADI explained more variance in life expectancy (R2 difference, 0.064; 95% CI, 0.039-0.090), particularly in underresourced urban areas.

In this cross-sectional study, the ReADI was correctly calculated and should replace the flawed NA-ADI for future health policy applications. Its open-source methods support transparency, adaptability, and further development.