• William H Gansa, Hannah Kleijwegt, Melissa Aldridge, and Claire Ankuda.
• Department of Medical Education, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
• J Palliat Med. 2024 Feb 1; 27 (2): 160167160-167.

AbstractBackground: End-of-life (EoL) care provided to Americans in urban and rural settings is distinct in terms of both available and delivered services. However, much less is known about which geographic, demographic, and health indicators are associated with disparities in EoL care and how individual versus regional characteristics influence quality of care (QoC). Objective: This study aimed to assess how regionality, rurality, and individual socioeconomic factors are associated with QoC in the last month of life (LML). Design: Nationally representative cross-sectional study using the proxy-completed LML questionnaire as part of the National Health and Aging Trends Study (NHATS). The data were linked at the zip code level to geographic and economic indicators. Settings/Subjects: A total of 2778 NHATS enrollees who died from 2012 to 2020. Measurements: Measurements included population density, socioeconomic indicators, health factors, and health outcomes. The primary independent variable was proxy-reported QoC during the LML (excellent vs. not excellent). Results: In our sample, 52.1% (n = 1447) reported not excellent care and 47.9% (n = 1331) reported excellent care. These populations varied in their demographic and socioeconomic characteristics. After accounting for survey weighting and design, decedents in the top (odds ratio [OR]: 1.58; 95% confidence interval [CI]: 1.08-2.32) income quartile had significantly greater odds of receiving excellent care than decedents in the bottom quartile. Decedents in zip codes with top quartile health outcome metrics had significantly greater odds of receiving excellent care (OR: 1.64; 95% CI: 1.17-2.29) than decedents in zip codes with bottom quartile health outcomes. County rurality index and county health factors were not correlated with QoC in the LML. Conclusions: High QoC at the EoL may be more associated with individual socioeconomic factors than regional indicators, including degrees of rurality. Clinicians should strive to recognize the interplay of individual characteristics and regional indicators to provide more personalized care.

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