Using Geographic Information Systems (GIS) to assess outcome disparities in patients with type 2 diabetes and hyperlipidemia.
OBJECTIVE
Geographic information systems (GIS) tools can help expand our understanding of disparities in health outcomes within a community. The purpose of this project was (1) to demonstrate the methods to link a disease management registry with a GIS mapping and analysis program, (2) to address the challenges that occur when performing this link, and (3) to analyze the outcome disparities resulting from this assessment tool in a population of patients with type 2 diabetes mellitus.


METHODS
We used registry data derived from the University of California Davis Health System's electronic medical record system to identify patients with diabetes mellitus from a network of 13 primary care clinics in the greater Sacramento area. This information was converted to a database file for use in the GIS software. Geocoding was performed and after excluding those who had unknown home addresses we matched 8528 unique patient records with their respective home addresses. Socioeconomic and demographic data were obtained from the Geolytics, Inc. (East Brunswick, NJ), a provider of US Census Bureau data, with 2008 estimates and projections. Patient, socioeconomic, and demographic data were then joined to a single database. We conducted regression analysis assessing A1c level based on each patient's demographic and laboratory characteristics and their neighborhood characteristics (socioeconomic status [SES] quintile). Similar analysis was done for low-density lipoprotein cholesterol.


RESULTS
After excluding ineligible patients, the data from 7288 patients were analyzed. The most notable findings were as follows: There was, there was found an association between neighborhood SES and A1c. SES was not associated with low-density lipoprotein control.


CONCLUSIONS
GIS methodology can assist primary care physicians and provide guidance for disease management programs. It can also help health systems in their mission to improve the health of a community. Our analysis found that neighborhood SES was a barrier to optimal glucose control but not to lipid control. This research provides an example of a useful application of GIS analyses applied to large data sets now available in electronic medical records.
Geraghty
Estella M
EM
Department of Internal Medicine, the University of California Davis School of Medicine, Sacramento, USA.
Balsbaugh
Thomas
T
Nuovo
Jim
J
Tandon
Sanjeev
S
D000368:Aged; D002140:California; D008078:Cholesterol, LDL / Q000097:blood; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000453:epidemiology* / Q000628:therapy*; D019468:Disease Management; D005260:Female; D040362:Geographic Information Systems; D006442:Glycated Hemoglobin / Q000378:metabolism; D054625:Healthcare Disparities / Q000706:statistics & numerical data*; D006801:Humans; D006937:Hypercholesterolemia / Q000097:blood / Q000453:epidemiology* / Q000628:therapy*; D016015:Logistic Models; D008297:Male; D016347:Medical Records Systems, Computerized / Q000706:statistics & numerical data; D008875:Middle Aged; D020380:Needs Assessment / Q000706:statistics & numerical data; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data*; D012042:Registries; D012959:Socioeconomic Factors
D008078:Cholesterol, LDL; D006442:Glycated Hemoglobin; C517652:hemoglobin A1c protein, human