Carrie D. Tomasallo is and Lawrence P. Hanrahan was with the Division of Public Health, Wisconsin Department of Health Services, Madison. Aman Tandias is with the Department of Family Medicine, School of Medicine and Public Health, University of Wisconsin, Madison. Timothy S. Chang is with Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin, Madison. Kelly J. Cowan and Theresa W. Guilbert were with the Department of Pediatrics Medicine, School of Medicine and Public Health, University of Wisconsin, Madison.
Am J Public Health. 2014 Jan;104(1):e65-73. doi: 10.2105/AJPH.2013.301396. Epub 2013 Nov 14.
We compared a statewide telephone health survey with electronic health record (EHR) data from a large Wisconsin health system to estimate asthma prevalence in Wisconsin.
We developed frequency tables and logistic regression models using Wisconsin Behavioral Risk Factor Surveillance System and University of Wisconsin primary care clinic data. We compared adjusted odds ratios (AORs) from each model.
Between 2007 and 2009, the EHR database contained 376,000 patients (30,000 with asthma), and 23,000 (1850 with asthma) responded to the Behavioral Risk Factor Surveillance System telephone survey. AORs for asthma were similar in magnitude and direction for the majority of covariates, including gender, age, and race/ethnicity, between survey and EHR models. The EHR data had greater statistical power to detect associations than did survey data, especially in pediatric and ethnic populations, because of larger sample sizes.
EHRs can be used to estimate asthma prevalence in Wisconsin adults and children. EHR data may improve public health chronic disease surveillance using high-quality data at the local level to better identify areas of disparity and risk factors and guide education and health care interventions.
我们将威斯康星州全州范围内的电话健康调查与大型威斯康星州卫生系统的电子健康记录 (EHR) 数据进行了比较,以估计威斯康星州的哮喘患病率。
我们使用威斯康星州行为风险因素监测系统和威斯康星大学初级保健诊所的数据开发了频率表和逻辑回归模型。我们比较了每个模型的调整后比值比 (AOR)。
在 2007 年至 2009 年期间,EHR 数据库包含 376,000 名患者(30,000 名哮喘患者),其中 23,000 名(1850 名哮喘患者)回应了行为风险因素监测系统电话调查。在调查和 EHR 模型中,大多数协变量(包括性别、年龄和种族/民族)的哮喘 AOR 大小和方向相似。由于样本量较大,EHR 数据比调查数据具有更大的统计能力来检测关联,尤其是在儿科和族裔人群中。
EHR 可用于估计威斯康星州成人和儿童的哮喘患病率。EHR 数据可以通过在本地使用高质量数据改善公共卫生慢性病监测,更好地识别差异和风险因素领域,并指导教育和医疗干预措施。