Purpose: To describe three statistical approaches that help gain a comprehensive understanding of mechanisms underlying health inequities: univariate regression analysis, effect modification analysis, and mediation analysis.
Methods: We described how univariate regression analysis, effect modification analysis, and mediation analysis can be used to gain insight into mechanisms underlying health inequities. We demonstrated the application of these approaches using a motivating example from the Health and Retirement Study in which we studied the role of education in ethnic disparities in episodic memory.
Results: Univariate regression analysis showed that Hispanic individuals on average had lower episodic memory scores compared to non-Hispanic individuals. Effect modification analysis showed that the beneficial effect of education on episodic memory was less strong in Hispanic individuals compared to non-Hispanic individuals. Mediation analysis showed that the ethnic disparity in episodic memory was not only driven by effect modification, but also by differences in the distribution of education years across ethnic groups.
Conclusion: The combined study of effect modification and mediation provides a comprehensive understanding of the mechanisms that cause and sustain health inequities. Insight into these mechanisms is crucial to determine targets for interventions and policies aimed at eliminating health inequities.
Purpose: 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.
Methods: 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.
Results: 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.
Conclusions: 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.
Purpose: To add to existing knowledge on relationships between Conventionally-identified Adverse Childhood Experiences (ACEs) and adolescent reproductive health (ARH) outcomes, we identified contributions of Expanded (community-level) ACEs, integrating measures of ACE co-occurrence and burden.
Methods: Secondary analysis of 2012-2013 Philadelphia ACEs data from a population-based adult sample. Weighted regressions, adjusted for age, sex, race/ethnicity, and socioeconomic status, tested associations between Conventional and Expanded ACEs (separately and co-occurring) and ACE burden (lowest to highest exposure) with: early sexarche (<15 years), adolescent pregnancy (<19 years), and unintended adolescent pregnancy.
Results: Conventional ACEs showed strong dose-response relationships with all outcomes (aOR range: 2.04-4.96, p < 0.05). Expanded ACEs were associated with early sexarche (aOR=2.50; 95 % CI: 1.27, 4.94), adolescent pregnancy (aOR=1.69; 95 % CI: 1.16, 2.46), and unintended adolescent pregnancy (aOR=1.54; 95 % CI: 1.04, 2.29); dose-response patterns were inconsistent. Co-occurring Conventional and Expanded ACEs produced the greatest odds for all outcomes except early sexarche (aOR range: 3.20-14.97, p < 0.05).
Conclusions: Conventional and Expanded ACEs are important independently and jointly. ARH outcomes peaked when Conventional and Expanded ACEs co-occurred and both exposures were high. Results suggest that Conventional ACEs may be overestimated when assessed in isolation, highlighting the importance of considering Expanded ACEs to minimize bias and target appropriate interventions.

