This study used GIS to analyze disparities in outcomes related to Type 2 diabetes and hyperlipidemia. The researchers were able to use Electronic Medical Records to identify patients with Type 2 diabetes from an area of 13 primary care clinics in California. In addition, other socioeconomic and demographic data were used to compare with the A1c levels.The result of this study was that the researchers found that there was an association between the socioeconomic status of patients and their A1c levels, but not with low-density lipoprotein control. The results were used to guide diseases management programs to improve the health of the community.
This study is relevant to our project because it focuses on the disproportionate affect of outcomes related to diabetes, Diabetes is one of the leading chronic conditions that our project will analyze to determine potential contributing factors that lead to disparities seen in prevalence and outcomes related to diabetes. Also, this project also examines some of the same contributing factors that we will be considering with the socioeconomic status and how the socioeconomic status impact control of chronic conditions. Our project will examine additional chronic conditions and contributing factors, but this research study has a good foundation for a similar need that our project will focus on, which is a useful tool to help guide the development of disease management programs and health education to improve the health of the community.
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This paper discusses the additional difficulties faced by low income individuals with chronic conditions face with access to healthcare. GIS was used in this study in order to identify where patients were coming from during the move of the clinic to a more central location. Low income patients face difficulties with difficulty with transportation, so an accessible location is extremely important. This clinic targets low income blacks and Hispanics, and prior to an unexpected move, their audience was mapped using GIS. Following the move of the clinic, the audience of the clinic was again mapped. A different population was found to utilize this clinic with a move of only 6 miles.
The results of this study were used to explain the importance of location to a specific target audience. It also explains how one should advocate for a central location in order to better serve the chronically ill. This is related to our study as we can map where the most chronic illness occurs and our data can be used to reach those populations. In South Carolina, a move of 6 miles may still be in the same zip code, but could drastically alter the audience served. Our study will seek how moving around to different counties can affect one’s risk for chronic illnesses, and see if there is any correlation between locations and demographics and risk for chronic illness.
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Using GIS and Secondary Data to Target Diabetes-Related Public Health Efforts
This study used GIS to analyze counties in Michigan that had a high prevalence of diabetes in conjunction with few community resources and low to no medical resources. The objective was to use the chronic condition data, population demographics, and resource information to better target communities for prevention and management of diabetes. The study relied on secondary data collection to map the prevalence of diabetes, demographic information, medical resource information, community resources, population estimates, etc. to highlight areas with higher and lower rates of disease and higher and lower rates of resources. As a result of the study, regional trends were discovered in resource distribution in Michigan. The counties of the Upper Peninsula were found to have disparities in medical resources, but the Upper Peninsula was also found to have a higher number of community resources when compared to the Lower Peninsula. There were some areas that were identified as a having a high prevalence of diabetes but few resources. However, there was only minimal association found between the prevalence of diabetes and the resources for diabetes.
This study is relevant to our project because it not only focuses on diabetes, which is one of the leading chronic conditions analyzed by our project, but also because the objectives are similar. The study in Michigan placed an emphasis using the information from mapping diabetes and the other factors to be able to better target populations with a high risk for diabetes but few resources. Similarly, one of the goals of the project is to be able to take the information about diabetes, heart disease, and hypertension and identify major contributing factors to be able to better target high risk populations across North Carolina, South Carolina, and Georgia.