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Spatial and Statistical Analyses of the Mortality Rates of Heart Disease in 2020 in Illinois, USA

Shunfu Hu

State public health agencies have collected massive mortality data at the county level over time. Those data sets are often stored in text-numeric formats, such as Comma-Separated Values (CSV).

Therefore, it is very difficult for the public and even health professionals to understand the spatial patterns of mortality across a state. On the other hand, health disparity has recently become a major topic in public health research, including health disparities in urban and rural areas, males and females, socioeconomic status, and race and ethnic groups. The objectives of this research are: 1) To determine if there are statistically significant spatial clusters of the mortality rates of heart disease at the county level in Illinois in 2020; and 2) To assess whether there is a significant difference the death rates between metropolitan counties and non-metropolitan counties. Spatial autocorrelation analysis (Global Moran’s I), cluster and outlier analysis (Local Moran’s I), and bivariate mapping method from ArcGIS Pro are performed to identify any significant spatial clusters/outliers of the mortality rates. Linear regression analysis between the mortality rates of heart disease in 2020 and the United States Center for Disease Control and Prevention’s Social Vulnerability Index (SVI) in 2020 is carried out to further analyze the spatial correlation between them. The results show that there exist spatial clusters/outliers of the mortality rates of heart disease across the state of Illinois; however, there is no statistically significant correlation between the mortality rates of heart disease and the SVI. By utilizing spatial and statistical analysis to explore the mortality rates of heart disease in metropolitan counties and non-metropolitan counties, this research aims to provide valuable insights for public health professionals and policymakers.