Lipscombe Lorraine L, Hwee Jeremiah, Webster Lauren, Shah Baiju R, Booth Gillian L, Tu Karen
Women's College Research Institute, Women's College Hospital, 76 Grenville Street, Toronto, ON, M5S 1B1, Canada.
Department of Medicine, University of Toronto, Suite RFE 3-805, 200 Elizabeth Street, Toronto, ON, M5G 2C4, Canada.
BMC Health Serv Res. 2018 May 2;18(1):316. doi: 10.1186/s12913-018-3148-0.
Health care data allow for the study and surveillance of chronic diseases such as diabetes. The objective of this study was to identify and validate optimal algorithms for diabetes cases within health care administrative databases for different research purposes, populations, and data sources.
We linked health care administrative databases from Ontario, Canada to a reference standard of primary care electronic medical records (EMRs). We then identified and calculated the performance characteristics of multiple adult diabetes case definitions, using combinations of data sources and time windows.
The best algorithm to identify diabetes cases was the presence at any time of one hospitalization or physician claim for diabetes AND either one prescription for an anti-diabetic medication or one physician claim with a diabetes-specific fee code [sensitivity 84.2%, specificity 99.2%, positive predictive value (PPV) 92.5%]. Use of physician claims alone performed almost as well: three physician claims for diabetes within one year was highly specific (sensitivity 79.9%, specificity 99.1%, PPV 91.4%) and one physician claim at any time was highly sensitive (sensitivity 93.6%, specificity 91.9%, PPV 58.5%).
This study identifies validated algorithms to capture diabetes cases within health care administrative databases for a range of purposes, populations and data availability. These findings are useful to study trends and outcomes of diabetes using routinely-collected health care data.
医疗保健数据有助于对糖尿病等慢性病进行研究和监测。本研究的目的是针对不同的研究目的、人群和数据源，在医疗保健管理数据库中识别并验证用于确定糖尿病病例的最佳算法。
我们将加拿大安大略省的医疗保健管理数据库与初级保健电子病历（EMR）的参考标准相链接。然后，我们利用数据源和时间窗口的组合，识别并计算了多种成人糖尿病病例定义的性能特征。
识别糖尿病病例的最佳算法是：在任何时间有一次糖尿病住院记录或医生诊断记录，并且有一次抗糖尿病药物处方或一次带有糖尿病特定收费代码的医生诊断记录[灵敏度84.2%，特异度99.2%，阳性预测值（PPV）92.5%]。仅使用医生诊断记录的效果也几乎相同：一年内有三次医生诊断记录为糖尿病的特异度很高（灵敏度79.9%，特异度99.1%，PPV 91.4%），在任何时间有一次医生诊断记录的灵敏度很高（灵敏度93.6%，特异度91.9%，PPV 58.5%）。
本研究识别出了经过验证的算法，可用于在医疗保健管理数据库中针对一系列目的、人群和数据可用性捕获糖尿病病例。这些发现对于利用常规收集的医疗保健数据研究糖尿病的趋势和结果很有用。
