Using GIS and Secondary Data to Target Diabetes-Related Public Health Efforts

Objectives

To efficiently help communities prevent and manage diabetes, health departments need to be able to target populations with high risk but low resources. To aid in this process, we mapped county-level diabetes-related rates and resources/use using publicly available secondary data to identify Michigan counties with high diabetes prevalence and low or no medical and/or community resources.

Methods

We collected county-level diabetes-related rates and resources from Web-based sources and mapped them using geographic information systems (GIS) software. Data included age-adjusted county diabetes rates, diabetes-related medical resource and resource use (i.e., the number of endocrinologists and percentage of Medicare patients with diabetes who received hemoglobin A1c testing in the past year), community resources (i.e., the number of certified diabetes self-management education and diabetes support groups), as well as population estimates and demographics (e.g., rural residence, education, poverty, and race/ethnicity). We created GIS maps highlighting areas that had higher-than-median rates of disease and lower-than-median resources. We also conducted linear, logistic, and Poisson regression analyses to confirm GIS findings.

Results

There were clear regional trends in resource distribution across Michigan. The 15 counties in the Upper Peninsula were lacking in medical resources but higher in community resources compared with the 68 counties in the Lower Peninsula. There was little apparent association between need (diabetes prevalence) and diabetes-related resources/use. Specific counties with high diabetes prevalence and low resources were easily identified using GIS mapping.

Conclusion

Using public data and mapping tools identified diabetes health-service shortage areas for targeted public health programming.Diabetes is endemic in the United States and its prevalence is increasing. From 2004 to 2010, the age-adjusted rate of diabetes among adults rose from 7.3% to 8.4%, and annual incidence rates continue to rise.^1,2 Type 2 diabetes accounts for approximately 95% of diabetes cases,³ and this increased prevalence has been attributed primarily to lifestyle changes and the increasing rates of obesity in the U.S.^4–6Diabetes is a chronic condition that involves a considerable amount of medical care as well as careful disease self-management.⁷ In addition to regularly scheduled primary care visits and endocrinology visits for complicated cases,⁸ those with diabetes must adhere to appropriate self-management practices, including glucose monitoring; foot self-examinations; and regimens for nutrition, exercise, and prescribed medications.^9,10 These lifestyle changes can be confusing, especially regarding nutrition, and adherence is difficult.^11,12 However, public health strategies, such as individual and group health education within clinic and community settings, have proven effective in facilitating these changes.^13–16 As a result, diabetes self-management education (DSME) programs are recommended by the American Diabetes Association (ADA) for all people diagnosed with diabetes.^17,18 Diabetes support groups have also emerged as a popular community resource for diabetes management.¹⁹Unfortunately, demand for these services is rising at a time that public health budgets are shrinking.^20,21 In the face of limited public health funds, developing strategies to accurately target services is crucial. Historically, public health data have played a key role, frequently through large-scale surveillance efforts and longitudinal survey studies.^22,23 However, the expense of collecting these data places them outside the reach of most state and local public health efforts. The increased availability of electronically, publicly accessible health-related information^24,25 can allow state and local systems to more efficiently target diabetes programming.²⁶ This study describes one such method that was conducted using county-level Michigan data coupled with geographic information systems (GIS), a powerful tool for identifying and communicating problem areas. The purpose of this study was to demonstrate the utility of secondary data analysis and GIS in determining if diabetes rates were associated with diabetes-related resources and resource use, as well as to identify diabetes-related high need/low resource counties within Michigan.