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hierarchical logistic models, mortality, nurse staffing, risk adjustment



  1. He, Jianghua
  2. Almenoff, Peter L.
  3. Keighley, John
  4. Li, Yu-Fang


Background: Studies about nurse staffing and patient outcomes often lack adequate risk adjustment because of limited access to patient information.


Objective: The aim of this study was to examine the impact of patient-level risk adjustment on the associations of unit-level nurse staffing and 30-day inpatient mortality.


Methods: This retrospective cross-sectional study included 284,097 patients discharged during 2007-2008 from 446 acute care nursing units at 128 Veterans Affairs medical centers. The association of nurse staffing with 30-day mortality was assessed using hierarchical logistic models under three levels of risk-adjustment conditions: using no patient information (low), using patient demographics and diagnoses (moderate), or using patient demographics and diagnoses plus physiological measures (high).


Results: Discriminability of the models improved as the level of risk adjustment increased. The c-statistics for models of low, moderate, and high risk adjustment were 0.64, 0.74, and 0.88 for non-ICU patients and 0.66, 0.76, and 0.88 for ICU patients. For non-ICU patients, higher RN skill mix was associated with lower 30-day mortality across all three levels of risk adjustment. For ICU patients, higher total nursing hours per patient day was strongly associated with higher mortality with moderate risk adjustment (p = .0002), but this counterintuitive association was not significant with low or high risk adjustment.


Discussion: Inadequate risk adjustment may lead to biased estimates about nurse staffing and patient outcomes. Combining physiological measures with commonly used administrative data is a promising risk-adjustment approach to reduce potential biases.