07 - The Spatial Clustering of Cardiovascular Mortality and Associated Risk Factors.

Background: Cardiovascular diseases are the leading cause of death both globally and in the US. Yet, the prevalence of heart diseases varied among regions and racial groups, prompting questions about which populations and regions are at higher risk. Therefore, this study explored the racial and regional distribution of heart diseases and associated risk factors, using new prototypes.
Methods: We created new prototype using U.S. County data to show that Rurality and Plurality (Major racial group) matters. For location (rurality) data, the U.S. Department of Agriculture Rural–Urban Commuting Areas (RUCCA) was used. RUCCA data was regrouped into (1) metropolitan area; (2) non-metropolitan, adjacent to a metropolitan region; (3) non-metropolitan, non-adjacent to a metropolitan region. For Plurality US census data was used. For heart diseases data and other health risk factors, the 2021 county health ranking data was used. Univariate statistics were created to show the disparities in the prevalence of heart diseases and associated risk factors across rurality and plurality.
Results: Health disparities exist along racial and ethnic lines, as well as locations. Non-metropolitan, adjacent to a metropolitan region and non-metropolitan, non-adjacent to metropolitan regions disproportionately show higher heart disease prevalence. Also, counties with majority American Indians showed highest prevalence of smoking, obesity, uninsured population, and other disadvantage socioeconomic factors as compared to other racial groups.
Conclusion: This work provides new prototypes showing that location and plurality matters and continues to highlight variations in heart disease and associated risk factors among racial groups in the U.S. Health care leaders and policy makers should be proactive to develop prevention strategies and response plans to manage and control worse health outcomes in vulnerable rural populations

Heart diseaes

health access

racial disparities

Rurality

spatial analysis

clustering 

Mid-Level

Knowledge

Women in Statistics and Data Science 2022