Department of Community Health Sciences & Institute for Public Health, University of Calgary, 3280 Hospital Drive NW, Calgary T2N4Z6, Alberta, Canada.
BMC Psychiatry. 2014 Oct 17;14:289. doi: 10.1186/s12888-014-0289-5.
Administrative data are increasingly used to conduct research on depression and inform health services and health policy. Depression surveillance using administrative data is an alternative to surveys, which can be more resource-intensive. The objectives of this study were to: (1) systematically review the literature on validated case definitions to identify depression using International Classification of Disease and Related Health Problems (ICD) codes in administrative data and (2) identify individuals with and without depression in administrative data and develop an enhanced case definition to identify persons with depression in ICD-coded hospital data.
(1) Systematic review: We identified validation studies using ICD codes to indicate depression in administrative data up to January 2013. (2) VALIDATION: All depression case definitions from the literature and an additional three ICD-9-CM and three ICD-10 enhanced definitions were tested in an inpatient database. The diagnostic accuracy of all case definitions was calculated [sensitivity (Se), specificity (Sp), positive predictive value (PPV) and negative predictive value (NPV)].
(1) Systematic review: Of 2,014 abstracts identified, 36 underwent full-text review and three met eligibility criteria. These depression studies used ICD-9 and ICD-10 case definitions. (2) VALIDATION: 4,008 randomly selected medical charts were reviewed to assess the performance of new and previously published depression-related ICD case definitions. All newly tested case definitions resulted in Sp >99%, PPV >89% and NPV >91%. Sensitivities were low (28-35%), but higher than for case definitions identified in the literature (1.1-29.6%).
Validating ICD-coded data for depression is important due to variation in coding practices across jurisdictions. The most suitable case definitions for detecting depression in administrative data vary depending on the context. For surveillance purposes, the most inclusive ICD-9 & ICD-10 case definitions resulted in PPVs of 89.7% and 89.5%, respectively. In cases where diagnostic certainty is required, the least inclusive ICD-9 and -10 case definitions are recommended, resulting in PPVs of 92.0% and 91.1%. All proposed case definitions resulted in suboptimal levels of sensitivity (ranging from 28.9%-35.6%). The addition of outpatient data (such as pharmacy records) for depression surveillance is recommended and should result in improved measures of validity.
行政数据越来越多地被用于进行抑郁研究，并为卫生服务和卫生政策提供信息。使用行政数据进行抑郁监测是对调查的一种替代，调查可能需要更多的资源。本研究的目的是：（1）系统回顾文献中使用国际疾病分类和相关健康问题（ICD）代码在行政数据中识别抑郁的验证病例定义，（2）在行政数据中识别有和没有抑郁的个体，并开发一个增强的病例定义来识别 ICD 编码医院数据中的抑郁患者。
（1）系统回顾：我们确定了截至 2013 年 1 月使用 ICD 代码来表示行政数据中抑郁的验证研究。（2）验证：所有文献中的抑郁病例定义以及另外三个 ICD-9-CM 和三个 ICD-10 增强定义都在住院患者数据库中进行了测试。所有病例定义的诊断准确性都进行了计算[敏感度（Se）、特异性（Sp）、阳性预测值（PPV）和阴性预测值（NPV）]。
（1）系统回顾：在 2014 篇摘要中，有 36 篇进行了全文审查，有 3 篇符合入选标准。这些抑郁研究使用了 ICD-9 和 ICD-10 病例定义。（2）验证：随机选择了 4008 份医疗记录进行评估，以评估新的和以前发表的与抑郁相关的 ICD 病例定义的性能。所有新测试的病例定义都导致 Sp>99%，PPV>89%和 NPV>91%。敏感性较低（28-35%），但高于文献中确定的病例定义（1.1-29.6%）。
由于司法管辖区之间的编码实践存在差异，因此对 ICD 编码数据进行验证以识别抑郁非常重要。在行政数据中检测抑郁的最合适病例定义因情况而异。对于监测目的，最具包容性的 ICD-9 和 ICD-10 病例定义分别导致 89.7%和 89.5%的 PPV。在需要明确诊断的情况下，建议使用最不具包容性的 ICD-9 和-10 病例定义，其 PPV 分别为 92.0%和 91.1%。所有提出的病例定义的敏感性都不理想（范围为 28.9%-35.6%）。建议在抑郁监测中增加门诊数据（如药房记录），这应导致有效性测量得到改善。
