Purpose: Safety net health services, such as federally funded health clinics, are interventions that aim to mitigate inequality in resource distribution, thus primarily clustered in poor areas with lack of access to health care. However, not all neighborhoods with the most needs benefit from safety net health services. In this article, we explore the distribution of a federally funded health service intervention designed to serve impoverished areas, the medically underserved areas (MUAs), and the relationship between MUA designation and neighborhood sociodemographic characteristics. Methods: We explore the spatial distribution of MUAs. The 2010 U.S. census data including 868 census tracts in Chicago were used for the analysis. We then examined the likelihood of being designated as an MUA using census tract level neighborhood demographic variables. Results: We found that the likelihood of obtaining MUA designation increases for neighborhoods with higher levels of poverty, the likelihood of being designated as an MUA begins to decline beyond the tipping point, whereas the proportion of black residents continues to increase. In census tracts that were eligible but not designated, there was a greater proportion of black residents compared with white residents (p<0.01). The census tracks also had higher mean disadvantage scores (p<0.01) and lower social capital (p<0.01). Furthermore, MUA eligible areas that were not designated as MUAs were predominantly black neighborhoods in poverty. Conclusion: Studies have documented that receiving MUA designation substantially reduces disparities in access to health care, and yet, our study finding indicates that the most racially segregated poor neighborhoods are excluded from the benefits of having such federal health safety net program. Seemingly race-neutral safety net health services may still be distributed in a way that perpetuates racial inequality in health.
Keywords: Federally Qualified Health Centers; medically underserved areas; racial disparity; safety net services.
© Sage J. Kim et al., 2020; Published by Mary Ann Liebert, Inc.