Hamburg Center for Health Economics, University of Hamburg, Esplanade 36, 20354, Hamburg, Germany.
Schumpeter School of Business and Economics, University of Wuppertal, Rainer-Gruenter-Str. 21, 42119, Wuppertal, Germany.
Eur J Health Econ. 2021 Jul;22(5):833-846. doi: 10.1007/s10198-021-01292-2. Epub 2021 Apr 19.
The goal of this study is to provide empirical evidence of the impact of nurse staffing levels on seven nursing-sensitive patient outcomes (NSPOs) at the hospital unit level. Combining a very large set of claims data from a German health insurer with mandatory quality reports published by every hospital in Germany, our data set comprises approximately 3.2 million hospital stays in more than 900 hospitals over a period of 5 years. Accounting for the grouping structure of our data (i.e., patients grouped in unit types), we estimate cross-sectional, two-level generalized linear mixed models (GLMMs) with inpatient cases at level 1 and units types (e.g., internal medicine, geriatrics) at level 2. Our regressions yield 32 significant results in the expected direction. We find that differentiating between unit types using a multilevel regression approach and including postdischarge NSPOs adds important insights to our understanding of the relationship between nurse staffing levels and NSPOs. Extending our main model by categorizing inpatient cases according to their clinical complexity, we are able to rule out hidden effects beyond the level of unit types.
本研究旨在提供实证证据，说明医院科室层面的护士配备水平对 7 项护理敏感患者结局（NSPO）的影响。我们将一家德国医保机构的大量索赔数据与德国每家医院发布的强制性质量报告相结合，我们的数据集中包含了大约 320 万例在 900 多家医院进行的住院治疗，时间跨度为 5 年。考虑到我们数据的分组结构（即按科室类型对患者进行分组），我们使用住院病例（第 1 层）和科室类型（如内科、老年科）（第 2 层）的横截面、两级广义线性混合模型（GLMM）进行估计。我们的回归得出了 32 个符合预期方向的显著结果。我们发现，使用多层次回归方法区分科室类型，并纳入出院后 NSPO，可以深入了解护士配备水平与 NSPO 之间的关系。通过根据患者的临床复杂性对住院病例进行分类，我们扩展了我们的主要模型，从而排除了科室类型水平之外的潜在影响。
