Roth Caryn, Shivade Chaitanya P, Foraker Randi E, Embi Peter J
Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus OH, USA.
Stud Health Technol Inform. 2013;192:1100.
We combined patient-level clinical data derived from the Electronic Health Record (EHR) with area-level environmental and socioeconomic data to study factors independently associated with overweight and obesity. Our multinomial logistic regression model showed that area-level factors such as farmers' markets, grocery stores and percent college-educated at the zip code level were significantly associated with the outcomes. However, mismatch in the granularity of community and clinical data limited us in creating a discriminatory model. While these results are promising, they reveal challenges that must be overcome in order to maximize secondary use of EHR data to further explore population health status.
我们将源自电子健康记录（EHR）的患者层面临床数据与区域层面的环境和社会经济数据相结合，以研究与超重和肥胖独立相关的因素。我们的多项逻辑回归模型显示，区域层面的因素，如农贸市场、杂货店以及邮政编码区域内受过大学教育的人口百分比，与研究结果显著相关。然而，社区数据和临床数据在粒度上的不匹配限制了我们构建一个具有区分能力的模型。虽然这些结果很有前景，但它们也揭示了为了最大限度地二次利用电子健康记录数据以进一步探索人群健康状况而必须克服的挑战。
