The impact of nurse staffing levels on nursing-sensitive patient outcomes: a multilevel regression approach.
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.
['Dietermann|Karina|K|', 'Winter|Vera|V|', 'Schneider|Udo|U|', 'Schreyögg|Jonas|J|http://orcid.org/0000-0001-8030-2161']
D000359:Aftercare D003430:Cross-Sectional Studies D005858:Germany D006801:Humans D009741:Nursing Staff, Hospital D010351:Patient Discharge D010561:Personnel Staffing and Scheduling D000078329:Workforce
Nurse staffing Nursing-sensitive patient outcomes Acute care Multilevel models Quality of care