A Nonseparable Multivariate Space-Time Model for Analyzing County-Level Heart Disease Death Rates by Race and Gender

TL;DR

This paper introduces a nonseparable multivariate space-time Bayesian model to analyze county-level heart disease death rates in the US, capturing complex correlations across race, gender, and geography.

Contribution

The paper develops a novel hierarchical Bayesian model with group-specific temporal correlations and evolving covariance structures for detailed disparity analysis.

Findings

Model outperforms existing approaches in fit quality.

Reveals detailed racial, gender, and geographic disparities.

Provides insights into temporal trends in heart disease mortality.

Abstract

While death rates due to diseases of the heart have experienced a sharp decline over the past 50 years, these diseases continue to be the leading cause of death in the United States, and the rate of decline varies by geographic location, race, and gender. We look to harness the power of hierarchical Bayesian methods to obtain a clearer picture of the declines from county-level, temporally varying heart disease death rates for men and women of different races in the US. Specifically, we propose a nonseparable multivariate spatio-temporal Bayesian model which allows for group-specific temporal correlations and temporally-evolving covariance structures in the multivariate spatio-temporal component of the model. After verifying the effectiveness of our model via simulation, we apply our model to a dataset of over 200,000 county-level heart disease death rates. In addition to yielding a superior fit than other common approaches for handling such data, the richness of our model provides insight into racial, gender, and geographic disparities underlying heart disease death rates in the US which are not permitted by more restrictive models.