Variation in the Area Deprivation Index by Rurality and Region

the federal, state, and local levels.Recent evidence suggests there are potential limitations to the precision of the ADI, particularly when applied to urban areas and related to measures of housing quality, yet how the ADI relates to rurality, other demographic and geographic variations across the country, and implications for use in policy in rural areas are unknown.15,16 The findings presented in this brief related to the ADI and perspectives from experts may be informative for what measure(s) to use in the future, or how to structure their use.This study describes the implications of ADI applications specifically for rural communities.First, we describe variation in the ADI and ADI missingness by rurality and region.Using state and national ADI rankings from the Neighborhood Atlas and other publicly available data, we provide descriptive statistics and bivariate associations between the ADI and rurality and region.Second, we use a qualitative approach by conducting semi-structured interviews with experts about the potential benefits and limitations to applications of the ADI in policy and reimbursement, with a specific focus on rural communities.The findings of this study will help researchers, policymakers, and healthcare organizations incorporate the ADI and other area-level risk measures into policies and programs, ultimately improving patient outcomes and enabling more efficient use of resources. MethodsThis study uses a convergent, parallel mixed methods design. Quantitative DataThis study uses multiple, publicly available secondary data sources.We use both the national and state versions of the block group level 2019 ADI (v3.1) from the Neighborhood Atlas.Each version of the ADI represents the 5-year ACS data leading up to the ADI year (i.e., 2019 ADI uses ACS 2015-2019 5-year data).We supplemented these data sources with data from the American Community Survey (ACS, 2015-2019 5-year estimates), United States Department of Agriculture (USDA) 2013 Rural-Urban Continuum Codes (RUCCs), USDA 2010 Rural-Urban Commuting Area (RUCA) codes, USDA 2010 Frontier and Remote Area (FAR) codes, Appalachian Regional Commission counties, HHS regions by state, U.S./Mexico Border Health Commission counties, and Delta Regional Authority counties.The ADI was originally developed by HRSA and has since been validated, modified, and made publicly available by Kind, et al. at the Center for Health Disparities Research at the University of Wisconsin School of Medicine and Public Health.4 ADI scores are calculated using 17 measures across the domains of income, education, employment, and housing from 5-year ACS data.4 From raw scores, ADI ranks are generated in several formats, including national block group level percentile rankings from 1-100 and state block group decile rankings from 1-10.The rankings for the ADI are from low to high, with 1 being assigned to the least disadvantaged communities and 100 being the most disadvantaged communities.The ADI was constructed and is validated on the block group level.A separate, 9-digit zip code version of the ADI data is also available, but developers advise against using this version when it is possible to use the block group level instead.There are instances when block groups are missing ADI ranks.4 Reasons for missingness include low population and/or housing, high group living quarters, both, or missing data from ACS. 4 Table 2. Block Group-Level 2019 ADI Mean and Missingness by Region Mean 2019 National ADI 95% CI 2019