Assessing the Impact of Socio-Economic Determinants on Diabetes in Colorado

Figure 1: Short video visualizing rates of diabetes over time in counties throughout Colorado

Figure 2: Regions of Colorado situated into a chord diagram along with the risk factors of Diabetes to identify which areas had the strongest relationships with each risk factor

 

 

 

 

 

 

 

  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 3: Link to interactive map https://mpylate.github.io/group-project/co_income_map.html

 

 

 

 

 

Figure 4: Link to interactive map https://mpylate.github.io/group-project/co_map.html

Figure 5: SVI (Social Vulnerability Index) is defined by the CDC as a community’s ability to prevent human suffering and financial loss in a disaster. Social vulnerability indicators such as income level, education, race/ethnicity, and housing are strongly linked to health management. Communities with higher social vulnerability often face compound challenges, including limited access to nutritious food, preventive healthcare, and educational resources necessary for diabetes awareness and self-management

Figure 6: This stacked map visualizes the relationship between food access, food insecurity, diabetes rates, and median income, offering insights into areas affected by poverty. As shown, grocery store and supermarket density is highest in urban areas, where diabetes rates are lower, and median income is higher. Conversely, rural areas with limited food access and greater food insecurity tend to experience higher rates of diabetes and lower median incomes, highlighting a clear correlation between these factors. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 7: This visualization illustrations a positive correlation between diabetes and physicians, signifying a linear relationship between the two variables. This suggests that areas with higher diabetes prevalence also tend to have greater demand for healthcare services, reflected in the positive correlation in the figure. These findings stress the interconnectedness of food availability, healthcare access, and diabetes outcome, pointing for the need for holistic interventions that address both preventative measures and healthcare infrastructure in vulnerable communities.

Figure 8: This visualization reveals a clear trend:  where there is lower food availability there tends to be significantly higher rates of diabetes-related hospitalizations as well as overall higher diabetes prevalence. The data highlights the critical role that food availability plays not only in preventing diabetes but also in managing the condition, as limited access to nutritious food exacerbates health challenges and places additional strain on healthcare systems.

 

 

 Completed: November 2024

Project Description: This project was a group effort. RStudio was used, specifically circlize to create circular plots, dplyr to manipulate data, sf (simple features) to encode spatial vector data, tidycensus to access data from the U.S. Census Bureau, tmap to create maps, ggplot2 to develop plots, patchwork to combine ggplot2 plots into one graphic, spatstat to analyze spatial patterns, webr to allow code to run through a web browser, chorddiag to create a chord diagram, classInt to establish interval classes, corrplot to create a Pearson's correlation matrix, GGally to create scatterplot, gganimate to create gif, and ggspatial to create maps with spatial data. 

        This research conveys the significant socio-economic determinants contributing to the prevalence of diabetes in Colorado, such as food insecurity, poverty, and healthcare access. The findings reveal a complex relationship between these factors and the spatial distribution of diabetes.
        One of the key insights from the data and analysis is the disproportionate impact of diabetes on rural communities, where food insecurity, low median income, and limited access to healthy food options are ubiquitous. As seen in the Figure 2, food insecurity and poor food access correlate stronger with higher diabetes rates in these rural areas. These regions often have low access to grocery stores and supermarkets, which are crucial for providing fresh and nutritious food. Instead, residents in these areas are more likely to rely on convenience stores, which typically offer less nutritious, higher-calorie food options. This lack of food variety and access exacerbates the risk of obesity and diabetes.
        Additionally, the analysis of socio-economic factors such as median income and social vulnerability further illustrates the disparities between urban and rural areas. As shown in Figures 3 and 4, counties with lower median incomes, such as Pueblo County, Costilla County, and Kit Carson County, are more likely to experience higher diabetes rates. Another key finding from this study is the importance of healthcare access in managing and preventing diabetes prevalence. Figure 8 illuminates the trend that lower food availability and higher diabetes rates correlates with higher hospitalization rates for diabetes-related complications. This implies that limited access to nutritious food not only contributes to the onset of diabetes but also leads to more severe health outcomes that require costly medical intervention.             Furthermore, the correlation between diabetes rates and access to physicians (Figure 7) reinforces the idea that healthcare infrastructure plays a critical role in diabetes prevention and management. Areas with higher diabetes prevalence tend to have greater demand for healthcare services, which may strain local healthcare systems, particularly in rural regions.
        This research features the need for interventions that address the interconnected factors of food insecurity, healthcare access, and socio-economic inequality. Improving access to healthy food, increasing healthcare availability, and addressing the root causes of poverty could significantly reduce the burden of diabetes, particularly in rural and vulnerable communities. Furthermore, policymakers and healthcare providers must work collaboratively to create sustainable solutions that promote long-term health improvements, reduce health disparities, and ensure equitable access to care.
        In conclusion, the findings of this study demonstrate the critical role that socio-economic determinants, such as food insecurity, poverty, and access to healthcare, play in shaping the diabetes landscape in Colorado. The strong spatial and correlation patterns uncovered in this research provide valuable insights for policymakers and public health officials seeking to reduce diabetes prevalence and improve health outcomes for underserved populations. To combat the growing diabetes epidemic, it is essential to implement comprehensive strategies that tackle both the social determinants of health and the healthcare infrastructure that disproportionately affect vulnerable communities.