Executive Summary
Diabetes is not merely impacted by genetics and lifestyle. Factors of the built environment have a significant role in the self- management of diabetes as well. The availability of, and access to, resources ranging from transit options to nutritious food within one’s geographic location can determine outcomes when it comes to diabetes prevention, incidence, and management.
For example, the physical landscape of a neighborhood can dictate whether its residents cycle or walk to work, in tandem with public transportation, or whether they rely more heavily on personal private automobiles, be they cars, trucks, or vans, for transit and commuting. Similarly, food deserts, regions with limited access to affordable, healthy food, can arise from a lack of access to large affordable grocery stores due to distance, poor public transit, and socioeconomic status.
Vita Valens investigated the geographic distribution and incidence of a subset of the adult diabetic population of New York City, with the aim of developing a new diabetes-focused educational program. This paper investigates the use of incorporating factors of the built environment in the design and development of our educational program on diabetes self-management. Public data on correlated factors were leveraged to help understand the population in consideration, inform the development of materials, and shape the topics under discussion. By understanding the environmental factors that may contribute to diabetes incidence and outcomes, we developed an educational self-management program that uniquely addresses the challenges faced by the population we aimed to serve. We hope this model of data-driven preliminary research can serve as a model for other organizations interested in conducting self-management education efforts for chronic conditions like diabetes.
Background
Methodology
Vita Valens identified geographic regions within the outer boroughs with high diabetes incidence based on the sample population to plan diabetes intervention programming. A data driven approach was employed to the planning and implementing of this intervention. Specifically, a geographic cluster analysis was conducted to identify areas with the highest concentrations of individuals living with diabetes. Based on our data, which is a subset of the individuals with diabetes in the NY metropolitan area, diabetes prevalence across the target population was analyzed.
To provide targeted programming that addresses built-environment factors, especially as it pertains to transit deserts and the reliance of private automobiles for transportation and commuting, Vita Valens cross-examined the diabetes incidence clusters obtained from a density-based clustering algorithm against the transit and commute data, as well as healthy food access data from NYC’s Environment and Health Data Portal.
Discussion
Our original dataset showed the highest incidence of diabetes in clusters 17 and 19, in South Brooklyn. To understand the population in these regions better, as well as the unique challenges of built environment that they face, we looked at NYC Environment and Health data from 2017-2021.
When looking at data on commuting modes, we identified a relatively higher percentage of adults commuting by car, truck, or van in the regions of clusters 17 and 19 in Brooklyn than other regions in Brooklyn and more broadly in the city. This kind of reliance on private automobiles for transportation can belie a lack of exercise outside of structured and intentional exercise, and can be an effective point of programming to convey the importance of incorporating physical activity into one’s diabetes self-management plan, especially when physical activity is not prompted by necessity to commute around the city.
Clusters 17 and 19 also have a high incidence of unhealthy food access, quantified by NYC Environment and Health data as the ratio of bodegas to supermarkets extant within the geographic area. Supermarkets tend to stock higher quality nutritious food at a lower price point than bodegas, which tend to privilege more processed and unhealthy foods at a higher price point. When an area has a greater volume of bodegas than supermarkets, easy local access to quality food is lower than neighborhoods that have a greater number of supermarkets in them. Given the higher incidence of bodegas over supermarkets in these neighborhoods, we tailored our educational content towards strategies for maximizing the amount of nutritious food one can find in the area. This was achieved by providing maps of local supermarkets and food pantries, as well as transit options to reach them, to support patients and enable them to opt in for more nutritious food sources to the extent possible. This was also achieved by educating patients on food groups and ingredients in general, so that they can maximize nutrition even when accessed through a bodega.
This study focuses on leveraging data about the built environment of neighborhoods to tailor interventions on specific chronic conditions to address the real landscape of these patients’ everyday lives. Educating on the importance of cycling or walking as a means of transportation, as well as educating on nutrition and how to best access it within your local community, can help individuals with diabetes manage their conditions within the context of their home environments and provide more effective tools to mitigate adverse effects. Additional research focusing on more than just two high-density neighborhoods could provide greater insight into the efficacy of such data-driven tailoring of self-management education content. This study is a first step in this direction to providing more customized intervention contents for the patient populations one is trying to serve.
Conclusion
This study focuses on leveraging data about the built environment of neighborhoods to tailor interventions on specific chronic conditions to address the real landscape of these patients’ everyday lives. Educating on the importance of cycling or walking as a means of transportation, as well as educating on nutrition and how to best access it within your local community, can help individuals with diabetes manage their conditions within the context of their home environments and provide more effective tools to mitigate adverse effects. Additional research focusing on more than just two high-density neighborhoods could provide greater insight into the efficacy of such data-driven tailoring of self-management education content. This study is a first step in this direction to providing more customized intervention contents for the patient populations one is trying to serve.
Citations
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