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Van T, Annis AM, Yosef M, Robinson CH, Duffy SA, Li YF, Taylor BA, Krein S, Sullivan SC, Sales A. Nurse staffing and healthcare-associated infections in a national healthcare system that implemented a nurse staffing directive: Multi-level interrupted time series analyses. International journal of nursing studies. 2020 Apr 1; 104:103531.
BACKGROUND: In 2010, the Veterans Health Administration Office of Nursing Services (VHA ONS) issued a Staffing Methodology (SM) Directive, standardizing the method of determining appropriate nurse staffing for VHA facilities. OBJECTIVES: To assess associations between the Directive, nurse staffing trends, and healthcare-associated infections. RESEARCH DESIGN: We conducted multi-level interrupted time series analyses of nurse staffing trends and the rates of two healthcare-associated infections before and after implementation of the Directive, October 1, 2008 - June 30, 2014. SUBJECTS: Acute care, critical care, mental health acute care, and longterm care nursing units (called Community Living Centers, CLC in VHA) among 285 VHA facilities were included in nurse staffing trends analyses, while acute and critical care units in 123 facilities were used in the analysis of infection rates. MEASURES: Monthly rates were calculated at the facility unit level and included nursing hours per patient day (NHPPD) for all nursing personnel and number of catheter-associated urinary tract infections (CAUTI) and central line-associated bloodstream infections (CLABSI) per 1000 device days. RESULTS: Nursing hours per patient day increased in both time periods. However, the differential change in rate of nursing hours per patient day following implementation of the Directive was not statistically significant. On average, we found a statistically significant decrease of 0.05 unit in the post-Directive central line-associated bloodstream infection rates associated with a unit increase in nursing hours per patient day. CONCLUSIONS: System-wide implementation of Staffing Methodology may be one contributing factor impacting patient outcomes.