Division of General Internal Medicine, SE610 GH, University of Iowa Carver College of Medicine, 200 Hawkins Drive, Iowa City, IA 52242, USA.
Health Serv Res. 2010 Feb;45(1):79-97. doi: 10.1111/j.1475-6773.2009.01061.x. Epub 2009 Oct 29.
To assess the impact of facility case mix on cross-sectional variations and short-term stability of the "Nursing Home Compare" incontinence quality measure (QM) and to determine whether multivariate risk adjustment can minimize such impacts.
Retrospective analyses of the 2005 national minimum data set (MDS) that included approximately 600,000 long-term care residents in over 10,000 facilities in each quarterly sample. Mixed logistic regression was used to construct the risk-adjusted QM (nonshrinkage estimator). Facility-level ordinary least-squares models and adjusted R(2) were used to estimate the impact of case mix on cross-sectional and short-term longitudinal variations of currently published and risk-adjusted QMs.
At least 50 percent of the cross-sectional variation and 25 percent of the short-term longitudinal variation of the published QM are explained by facility case mix. In contrast, the cross-sectional and short-term longitudinal variations of the risk-adjusted QM are much less susceptible to case-mix variations (adjusted R(2)<0.10), even for facilities with more extreme or more unstable outcome.
Current "Nursing Home Compare" incontinence QM reflects considerable case-mix variations across facilities and over time, and therefore it may be biased. This issue can be largely addressed by multivariate risk adjustment using risk factors available in the MDS.
评估设施病例组合对“长期护理机构比较”（Nursing Home Compare）尿失禁质量指标（QM）的横断面变异和短期稳定性的影响，并确定多变量风险调整是否可以最大程度地减少此类影响。
对 2005 年全国最低数据集（MDS）进行回顾性分析，每个季度样本中包含约 60 万名来自 10000 多家机构的长期护理居民。混合逻辑回归用于构建风险调整的 QM（非收缩估计器）。使用设施层面的普通最小二乘法模型和调整后的 R² 来估计病例组合对当前发表的和风险调整后的 QM 的横断面和短期纵向变化的影响。
发表的 QM 的横断面变异的至少 50%和短期纵向变异的 25%是由设施病例组合解释的。相比之下，风险调整的 QM 的横断面和短期纵向变化受病例组合变化的影响较小（调整后的 R²<0.10），即使对于结果更为极端或更不稳定的机构也是如此。
当前的“长期护理机构比较”尿失禁 QM 反映了设施之间和随时间的大量病例组合变化，因此可能存在偏差。使用 MDS 中可用的风险因素进行多变量风险调整可以在很大程度上解决这个问题。
