Department of Internal Medicine, the University of California Davis School of Medicine, Sacramento, USA.
J Am Board Fam Med. 2010 Jan-Feb;23(1):88-96. doi: 10.3122/jabfm.2010.01.090149.
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.
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.
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.
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.
地理信息系统（GIS）工具可以帮助我们扩大对社区内健康结果差异的理解。本项目的目的是：（1）展示将疾病管理登记系统与 GIS 制图和分析程序相链接的方法；（2）解决在进行此链接时出现的挑战；（3）分析该评估工具在 2 型糖尿病患者人群中产生的结果差异。
我们使用来自加利福尼亚大学戴维斯健康系统电子病历系统的登记数据，从萨克拉门托大都市区的 13 个初级保健诊所网络中确定患有糖尿病的患者。将此信息转换为数据库文件，以供 GIS 软件使用。进行地理编码后，排除那些住址未知的患者，我们将 8528 条独特的患者记录与其各自的住址相匹配。从 Geolytics，Inc.（新泽西州东不伦瑞克）获取社会经济和人口统计学数据，这是美国人口普查局数据的提供商，使用的是 2008 年的估计值和预测值。然后将患者、社会经济和人口统计学数据合并到一个数据库中。我们进行了回归分析，根据每位患者的人口统计学和实验室特征及其居住环境特征（社会经济地位[SES]五分位数）评估 A1c 水平。对于低密度脂蛋白胆固醇也进行了类似的分析。
排除不合格患者后，分析了 7288 名患者的数据。最显著的发现如下：发现居住环境 SES 与 A1c 之间存在关联。SES 与低密度脂蛋白控制无关。
GIS 方法学可以帮助初级保健医生并为疾病管理计划提供指导。它还可以帮助医疗系统实现改善社区健康的使命。我们的分析发现，居住环境 SES 是实现最佳血糖控制的障碍，但不是控制血脂的障碍。这项研究提供了 GIS 分析在电子病历中现有大量数据集的有用应用的一个示例。
