Analyzing spatial variations of heart disease and type-2 diabetes: A multi-scale geographically weighted regression approach

Abstract

Abstract Heart disease is the leading cause of death in the United States. A person who has type-2 diabetes is twice as likely to have heart disease than someone who doesn’t have diabetes. Therefore, analyzing factors associated with both diseases and their interrelationships is essential for cardiovascular disease control and public health. In this article, we propose a Multi-scale Geographically Weighted Regression (MGWR) approach to observe spatial variations of environmental and demographic risk factors such as alcohol consumption behavior, lack of physical activity, obesity rate, urbanization rate, and income from 2005 to 2015 in the United States. The MGWR model has applied to eight census divisions of the United States at the county level: New England, Middle Atlantic, East North Central, West North Central, South Atlantic, East South Central, West South Central, and Mountain. Results illustrate that there are notable differences in the spatial variation of the risk factors behind these two diseases. In particular, obesity has been a leading factor that associate with diabetes in the east, south-central, and south Atlantic regions of the U.S. On the other hand, smoking and alcohol consumption was the primary concern in the northern part of the U.S., in 2005. In 2015, alcohol consumption levels decreased, but the smoking level remained the same in those regions, which showed a significant impact on diabetes in the neighboring regions. Between 2005 and 2015, lack of physical exercise has become a significant risk factor associated with diabetes in the Northeast and West parts of the U.S. The proposed MGWR produced high goodness to fit (R2) for most of the areas in the United States.

Keywords

• Multi-scale Geographically Weighted Regression
• Public health
• Heart disease
• Diabetes
• Geographic Information Systems