Frayne Susan M, Miller Donald R, Sharkansky Erica J, Jackson Valerie W, Wang Fei, Halanych Jewell H, Berlowitz Dan R, Kader Boris, Rosen Craig S, Keane Terence M
Center for Health Care Evaluation, VA Palo Alto Health Care System, 795 Willow Road, Menlo Park, CA 94025, USA.
Am J Med Qual. 2010 Jan-Feb;25(1):42-50. doi: 10.1177/1062860609346347. Epub 2009 Oct 23.
The authors estimated the validity of algorithms for identification of mental health conditions (MHCs) in administrative data for the 133 068 diabetic patients who used Veterans Health Administration (VHA) nationally in 1998 and responded to the 1999 Large Health Survey of Veteran Enrollees. They compared various algorithms for identification of MHCs from International Classification of Diseases, 9th Revision (ICD-9) codes with self-reported depression, posttraumatic stress disorder, or schizophrenia from the survey. Positive predictive value (PPV) and negative predictive value (NPV) for identification of MHC varied by algorithm (0.65-0.86, 0.68-0.77, respectively). PPV was optimized by requiring > or =2 instances of MHC ICD-9 codes or by only accepting codes from mental health visits. NPV was optimized by supplementing VHA data with Medicare data. Findings inform efforts to identify MHC in quality improvement programs that assess health care disparities. When using administrative data in mental health studies, researchers should consider the nature of their research question in choosing algorithms for MHC identification.
作者评估了1998年在全国范围内使用退伍军人健康管理局（VHA）且回复了1999年退伍军人参保者大型健康调查的133068名糖尿病患者行政数据中用于识别心理健康状况（MHC）算法的有效性。他们将根据国际疾病分类第九版（ICD - 9）编码识别MHC的各种算法与调查中自我报告的抑郁症、创伤后应激障碍或精神分裂症进行了比较。识别MHC的阳性预测值（PPV）和阴性预测值（NPV）因算法而异（分别为0.65 - 0.86和0.68 - 0.77）。通过要求有≥2个MHC ICD - 9编码实例或仅接受来自心理健康就诊的编码，PPV得到了优化。通过用医疗保险数据补充VHA数据，NPV得到了优化。这些发现为在评估医疗保健差异的质量改进项目中识别MHC的努力提供了信息。在心理健康研究中使用行政数据时，研究人员在选择MHC识别算法时应考虑其研究问题的性质。
