Concurrent and lagged effects of registered nurse turnover and staffing on unit-acquired pressure ulcers

Health Serv Res. 2014 Aug;49(4):1205-25. doi: 10.1111/1475-6773.12158. Epub 2014 Jan 30.

Abstract

Objective: We examined the concurrent and lagged effects of registered nurse (RN) turnover on unit-acquired pressure ulcer rates and whether RN staffing mediated the effects.

Data sources/setting: Quarterly unit-level data were obtained from the National Database of Nursing Quality Indicators for 2008 to 2010. A total of 10,935 unit-quarter observations (2,294 units, 465 hospitals) were analyzed.

Methods: This longitudinal study used multilevel regressions and tested time-lagged effects of study variables on outcomes.

Findings: The lagged effect of RN turnover on unit-acquired pressure ulcers was significant, while there was no concurrent effect. For every 10 percentage-point increase in RN turnover in a quarter, the odds of a patient having a pressure ulcer increased by 4 percent in the next quarter. Higher RN turnover in a quarter was associated with lower RN staffing in the current and subsequent quarters. Higher RN staffing was associated with lower pressure ulcer rates, but it did not mediate the relationship between turnover and pressure ulcers.

Conclusions: We suggest that RN turnover is an important factor that affects pressure ulcer rates and RN staffing needed for high-quality patient care. Given the high RN turnover rates, hospital and nursing administrators should prepare for its negative effect on patient outcomes.

Keywords: Registered nurse turnover; inpatient outcomes; nurse staffing; pressure ulcers.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Confidence Intervals
  • Databases, Factual
  • Hospitalization
  • Humans
  • Longitudinal Studies
  • Nursing Staff, Hospital / supply & distribution*
  • Odds Ratio
  • Outcome Assessment, Health Care
  • Personnel Turnover*
  • Pressure Ulcer / epidemiology*
  • Pressure Ulcer / etiology
  • Quality Indicators, Health Care
  • Regression Analysis
  • United States / epidemiology