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

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.

Geographical and temporal weighted regression: examining spatial variations of COVID-19 mortality pattern using mobility and multi-source data

“…Geographic Weighted Regression (GWR) is a statistical tool used to explore the spatial variation in relationships between variables (Cui et al, 2022). They are widely used in various research fields, including geography, environmental sciences, and urban planning.…”

“…GWR explores spatial object changes and related driving factors locally by establishing the local regression equation at each point in the spatial range and can predict future results (Fotheringham et al 2002). Many studies can be found to apply the GWR model to COVID-19 studies (Liu et al 2020;Jiao et al 2021;Wu and Zhang 2021;Cui et al 2022), which already proven the spatial heterogeneity issue in COVID-19 related studies. However, while GWR effectively captures spatial heterogeneity, it does not inherently account for temporal dynamics, which are crucial in understanding the evolution and progression of phenomena like the COVID-19 pandemic.…”

“…This study integrates various socioeconomic factors, mobility variables, and health conditions to understand their impact on COVID-19 mortality. This holistic approach allows for a more detailed and nuanced analysis than previous studies, which often considered these factors in isolation (Iyanda et al 2020;Liu et al 2020;Jiao et al 2021;Wu and Zhang 2021;Cui et al 2022). It aimed to quantitatively examine the non-stationary spatial and temporal associations between mortality and several socioeconomic factors such as mobility, health conditions, poverty level, and insurance coverage to assist policymakers in making better risk-informed decisions for public health management.…”