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PMCID: PMC5064797 DOI: 10.1186/s12912-016-0181-3


BACKGROUND: Time trends and seasonal patterns have been observed in nurse staffing and nursing-sensitive patient outcomes in recent years. It is unknown whether these changes were associated.

METHODS: Quarterly unit-level nursing data in 2004-2012 were extracted from the National Database of Nursing Quality Indicators® (NDNQI®). Units were divided into groups based on patterns of missing data. All variables were aggregated across units within these groups and analyses were conducted at the group level. Patient outcomes included rates of inpatient falls and hospital-acquired pressure ulcers. Staffing variables included total nursing hours per patient days (HPPD) and percent of nursing hours provided by registered nurses (RN skill-mix). Weighted linear mixed models were used to examine the associations between nurse staffing and patient outcomes at trend and seasonal levels.

RESULTS: At trend level, both staffing variables were inversely associated with all outcomes (p < 0.001); at seasonal level, total HPPD was inversely associated (higher staffing related to lower event rate) with all outcomes (p < 0.001) while RN skill-mix was positively associated (higher staffing related to higher event rate) with fall rate (p < 0.001) and pressure ulcer rate (p = 0.03). It was found that total HPPD tended to be lower and RN skill-mix tended to be higher in Quarter 1 (January-March) when falls and pressure ulcers were more likely to happen.

CONCLUSIONS: By aggregating data across units we were able to detect associations between nurse staffing and patient outcomes at both trend and seasonal levels. More rigorous research is needed to study the underlying mechanism of these associations.

Journal Title

BMC nursing [electronic resource]



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MeSH Keywords

Patient Safety; Pressure Ulcer; Accidental Falls; Nursing Service, Hospital; Personnel Staffing and Scheduling; Periodicity


Data aggregation; Hospital-acquired pressure ulcers; Inpatient falls; Patient safety; Within-unit variability