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Statistics Colloquium
November 11 (Fri) 11 a.m.
Alavi Commons Room, 6625 Everett Tower

Mapping diabetes-related rates and resources using geographic information systems and secondary data

Amy B. Curtis
Interdisciplinary Health Sciences
Western Michigan University

Catherine L. Kothari
Kalamazoo Center for Medical Studies
Michigan State University

Background: In order to efficiently help communities prevent and manage diabetes, health departments need to be able to target high risk populations with low resources related to diabetes. To aid in this process, we mapped county-level diabetes-related rates and resources using secondary data sources to determine Michigan (MI) counties with favorable and very unfavorable profiles.

Methods: We collected diabetes-related rates and resources from web-based sources and mapped these using ArcGIS 10.2. Web sources included age-adjusted county diabetes rates (Centers for Disease Control and Prevention), percent of Medicare patient who received A1C testing in the past year (Dartmouth Atlas of Healthcare), number of endocrinologists (UCompare website), number of certified diabetes self-management education (DSME) programs (American Diabetes Association (ADA), diabetes support groups (MI Diabetes Outreach Networks), as well as population estimates and household incomes (U.S. Census). A1C testing and endocrinologist information was considered medical resources, while DSME and support groups were considered as community resources. Unfavorable profiles were indicated by above average rates of disease and below average resources.

Results: Of the 83 MI counties, 8 had unfavorable medical and higher than average diabetes rates, while 16 had unfavorable community profiles/high diabetes rates. Mapping allowed us to easily see the 3 counties (all in southern MI) with higher than average diabetes rates and low in all resources. In addition, the upper peninsula was lacking in medical resources, but higher in community resources compared to the lower peninsula.

Conclusions: Use of public data and mapping tools can help to identify counties with low and high diabetes-related needs and resources.

All statistics students are expected to attend.

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