Development of a hierarchical model to predict mortality outcomes based on healthcare barriers in the United States

Certain areas in the United States face significant barriers to accessing adequate healthcare services, leading to disparities in health outcomes. The current study aims to validate a hierarchical model that integrates several of these domains to better predict mortality. This ecological study utilized cross-sectional data from Calendar Year 2023 aggregated at the state level to examine the relationship between factors associated with barriers to healthcare and mortality outcomes. Standardized predictor variables were grouped into four domains: lack of facilities, insufficient staffing, lack of system access, and disproportionate costs. Hierarchical regression models predicted preventable death, all-cause mortality, and infant mortality. Hierarchical models accounted for 83% of the variance in preventable death, 71% in all-cause mortality, and 51% in infant mortality. System access and disproportionate costs significantly improved the models beyond facility and staffing metrics. Key predictors included lack of broadband access and medical debt, both of which were associated with higher preventable death and all-cause mortality. The percentage of Health Provider Shortage Areas was a predictor of preventable death and infant mortality. Findings provide initial validation of the predictive utility of a hierarchical model in integrating barriers to barriers to healthcare access across domains. Results further challenge previous models that focused primarily on facility- and staffing-based metrics, emphasizing the importance of systemic factors such as affordability and accessibility. By prioritizing interventions that address access and cost barriers, policy makers could more effectively reduce disparities in mortality outcomes.