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Objective This study modeled the predictive power of unit/patient characteristics, nurse workload, nurse expertise, and hospital-acquired pressure ulcer (HAPU) preventive clinical processes of care on unit-level prevalence of HAPUs. Data Sources Seven hundred and eighty-nine medical-surgical units (215 hospitals) in 2009. Study Design Using unit-level data, HAPUs were modeled with Poisson regression with zero-inflation (due to low prevalence of HAPUs) with significant covariates as predictors. Data Collection/Extraction Methods Hospitals submitted data on NQF endorsed ongoing performance measures to CALNOC registry. Principal Findings Fewer HAPUs were predicted by a combination of unit/patient characteristics (shorter length of stay, fewer patients at-risk, fewer male patients), RN workload (more hours of care, greater patient [bed] turnover), RN expertise (more years of experience, fewer contract staff hours), and processes of care (more risk assessment completed). Conclusions Unit/patient characteristics were potent HAPU predictors yet generally are not modifiable. RN workload, nurse expertise, and processes of care (risk assessment/interventions) are significant predictors that can be addressed to reduce HAPU. Support strategies may be needed for units where experienced full-time nurses are not available for HAPU prevention. Further research is warranted to test these finding in the context of higher HAPU prevalence.

Numerous research reports have examined patterns of nurse staffing (Aiken, Sochalski, and Anderson 1996; Unruh, Fottler, and Talbott 2003; Aydin et al. 2004) and the relationship between nurse staffing and patient outcomes (Aiken et al. 2002; Needleman et al. 2002; Lang et al. 2004; Donaldson et al. 2005). These studies often reach different conclusions about historical changes in nurse staffing and the relationship of nurse staffing to patient outcomes. For example, two publications in the 1990s offered opposing findings about whether nurse staffing had declined in California, using different data sources (Anderson and Kohn 1996;Spetz 1998). Discrepancies also are found in the literature on the relationship between nurse staffing and patient outcomes. While many studies that use hospital-level data have found that higher levels of nurse staffing are associated with improved patient outcomes and lower mortality rates (Aiken et al. 2002; Needleman et al. 2002), most studies that examine data at the level of the hospital unit have found a weaker relationship or no relationship at all (e.g., Donaldson et al. 2005). It is unknown whether these different findings are the result of the use of varying measures of nurse staffing, differences in the importance of overall hospital staffing versus unit-level staffing, measurement error, or some other factor. Why do these studies obtain such different results? One possibility lies in different methods of measuring nurse staffing. Datasets used to measure nurse staffing can provide data by hospital, type of hospital unit, or specific unit. Nurse staffing can be measured as full-time equivalent employment (FTEE), nursing hours per patient day, share of registered nurses in total nursing staff, nurse-to-patient ratios, or other metrics. This paper compares nurse staffing measurements in two commonly used datasets—the American Hospital Association (AHA) Annual Survey of Hospitals and California's Office of Statewide Health Planning and Development (OSHPD) Hospital Annual Disclosure Report—as well as unit-level data collected by the California Nursing Outcomes Coalition (CalNOC) and the California Workforce Initiative (CWI). In 2006, Jiang, Stocks, and Wong analyzed disparities between AHA and OSHPD data, concluding that the OSHPD data on nurse staffing appeared more complete and, thus, more closely associated with patient outcomes. In this report, we examine principal differences between the AHA and OSHPD datasets as well as unit-level CalNOC and CWI data, compare the levels of nurse staffing measured by each, and discuss the ramifications of using alternative data sources and measures for nurse staffing and outcomes research.