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Where you live and where you receive care: Using cross-classified multilevel modeling to examine hospital and neighborhood variation in in-hospital mortality and mortality disparities
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Where you live and where you receive care: Using cross-classified multilevel modeling to examine hospital and neighborhood variation in in-hospital mortality and mortality disparities

Alina Schnake-Mahl, Ana V. Diez Roux, Bian Liu, Louisa W. Holaday, Albert Siu, Edwin McCulley, Usama Bilal and Katherine A. Ornstein
Annals of epidemiology, v 110, pp 16-22
01 Oct 2025
DOI: https://doi.org/10.1016/j.annepidem.2025.07.021
PMID: 40706885
Featured in Collection :   Research Supported by Drexel Libraries' OA Programs
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https://doi.org/10.1016/j.annepidem.2025.07.021View
Published, Version of Record (VoR) Open Access via Drexel Libraries Read and Publish Program 2025 Restricted CC BY-NC-ND V4.0

Abstract

Context Cross classified multilevel modeling Hospitals Neighborhoods Social determinants of health
Both hospitals and neighborhoods likely play important roles in driving health outcomes and inequities, but there has been limited prior research examining both contexts simultaneously. In this analysis we examine the contributions of these two critical contexts, neighborhoods and hospitals, to variation in in-hospital mortality and mortality disparities. We used cross-classified multi-level models, a statistical technique that can incorporate data from multiple non-nested levels, to examine the variation in contribution of neighborhoods and hospitals to in-hospital mortality. Our study focuses on COVID-19 in hospital mortality from New York State in 2020, as a methodological case study of cross classified multilevel modeling, given the well documented variation in COVID-19 in-hospital mortality across contexts. We found that nearly one in five patients hospitalized for COVID-19 died, and there was substantial variation in risk of in-hospital mortality by neighborhoods and hospitals, with more variation across hospitals (τ00:0.29) than across neighborhoods (τ00:0.02). Neighborhoods did not explain hospital variability and vice versa: both contexts appeared to contribute independently to in-hospital mortality rates. We also found several hospital, neighborhood, and individual factors were associated with in hospital mortality disparities in fully adjusted models: lower hospital quality and safety-net hospitals, social vulnerability, older age, not having private insurance, and being Hispanic or non-Hispanic other. Our findings suggest the importance of simultaneously considering hospital and neighborhood contexts to understand in-hospital outcome disparities. Understanding the contribution of these critical contexts has important implications for targeting interventions to ensure equitable hospital outcomes despite inequities in neighborhood and hospital contexts.

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being
#10 Reduced Inequalities

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Collaboration types
Domestic collaboration
Web of Science research areas
Public, Environmental & Occupational Health
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