A longitudinal examination of hospital registered nurse staffing and quality of care

by Barbara A. Mark, David W. Harless, Michael McCue, Yihua Xu

The relationship between hospital nurse staffing and quality of care continues to be a significant concern for health services researchers, health care executives, policymakers, and consumers. Several early studies that included nurse staffing as a hospital characteristic found that higher levels of nurse staffing were associated with reduced mortality (Scott, Forrest, and Brown 1976; Hartz et al. 1989; Kuhn et al. 1991; Manheim et al. 1992). At least two studies, however, found no significant relationship between nurse staffing and adverse events (Wan and Shukla 1987) or mortality (Al-Haider and Wan 1991). Other studies have reported mixed results, depending on the quality measure. For example, Silber et al. (1995) found that hospitals with high nurse-to-bed ratios had higher than expected complications rates, but lower than expected mortality rates. In another study, Silber, Rosenbaum, and Ross (1995) found that a high ratio of registered nurses (RNs) to beds was associated with lower mortality and failure to rescue (death following a complication), but more complications than expected.

Recent studies have been designed specifically to examine the relationship between nurse staffing and quality of care (American Nurses Association 1997, 2000; Kovner and Gergen 1998; Lichtig, Knauf, and Milholland 1999; Kovner et al. 2002; Needleman et al. 2002). Reflecting the lack of an agreed upon standard approach to these studies, there are inconsistencies among the studies in terms of measurement of nurse staffing, data sources, risk-adjustment methodologies, quality measures, or statistical approaches to data analysis. For example, the three studies sponsored by the American Nurses Association (ANA) used two separate definitions of nurse staffing: amount of nursing care, calculated as the number of licensed nursing hours per nursing intensity weight (which reflect the relative amount of nursing services required for patients in each DRG [diagnosis related group]) (Ballard et al. 1993); and skill mix, calculated as registered nurse hours as a proportion of total licensed nursing hours (American Nurses Association 1997, 2000; Lichtig, Knauf, and Millholand 1999). Kovner and Gergen's (1998) study measured staffing as the number of full-time equivalent RNs, while a more recent study converted FTE (full-time equivalent) RNs to hours using 2,040 hours per year worked (Kovner et al. 2002). Needleman et al. (2002) likewise calculated the number of hours of nursing care from FTEs, but used 2,080 hours per year worked.

In addition, risk-adjustment methodologies differ among studies. Studies by the ANA and Needleman et al. (2002) were based on New York's nursing intensity weights, while both of Kovner's studies used the Medicare case-mix index. Analytic approaches ranged from simple correlation and ordinary least squares (OLS) regression (American Nurses Association 1997, 2000; Lichtig, Knauf, and Milholland 1999; Kovner and Gergen 1998) to the general estimating equation (Kovner et al. 2002) and negative binomial regression (Needleman et al. 2002).

Finally, all of these studies used cross-sectional data or cross-sectional statistical methods. The conclusions derived from such studies may be biased if there are unobserved, time-invariant factors that affect hospital quality, and these factors are correlated with the explanatory variables of the model. We examined both the more commonly applied static, within-group (fixed effects) model to control for hospital heterogeneity and a dynamic panel model that addresses hospital fixed effects and controls for the influence of past circumstances through inclusion of the lagged value of the dependent variable.

Therefore, the primary purpose of out study was to evaluate previous research findings of the relationship between nurse staffing and quality of care by using panel data to examine the effects of change in nurse staffing on change in quality of care (in-hospital mortality and the nurse-sensitive outcome measures pneumonia, urinary tract infections, and decubitus ulcers) during the years 1990-1995. During that time period, hospitals also experienced increasing financial pressures brought about by increasing managed care penetration, market response to industry overcapacity, more stringent Medicare reimbursement policy, shorter lengths of stay, and an increase in patient acuity requiring the provision of more intensive nursing care. We therefore included a measure of hospital financial performance--operating margin--as a regressor in our model.

METHODS

Sample

Our sample was the 422 hospitals in the 1990-1995 longitudinal cohort of the Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS). These 422 hospitals, 49 percent of the HCUP base year sample, are located in 11 states (Arizona, Colorado, Florida, Illinois, Iowa, Massachusetts, New Jersey, Oregon, Pennsylvania, Washington, and Wisconsin). Due to inability to match hospitals across all datasets, we eliminated 6 hospitals; 2 more hospitals were eliminated because data were for a system rather than an individual hospital, and 2 others were dropped because revenue information was missing from all CMS files. Hospitals with staffing outliers (1) or fewer than 15 expected mortalities or complications were excluded.

Measures and Sources of Data

We measured five sets of variables: hospital characteristics (American Hospital Association Annual Survey, CMS case mix index file, CMS cost and capital file), market characteristics (Area Resources File, American Hospital Association Annual Survey, InterStudy data), financial performance (CMS cost and capital files; Solucient data), staffing (American Hospital Association Annual Survey, Online Survey Certification and Reporting System [OSCAR]) and quality of care (Healthcare Cost and Utilization Project data). Variable definitions and sources of data are displayed in Table 1. In general, measurement of these variables was straightforward. However, our approach to several of these variables requires additional explanation.

High Technology Services. We measured high technology services using a "Saidin index" (Spetz and Baker 1999), which is the weighted sum of the number of technologies and services available in a hospital, with the weights being the percentage of hospitals in the country that do not possess the technology or service. Thus, the index increases more with the addition of technologies that are relatively rare than with the addition of technologies that are more common.

Definition of the Relevant Market. We used the health service areas (HSAs) approach developed by Makuc et al. (1991) in which counties are aggregated into geographic regions based on flows of inpatient hospital admissions.

Calendar Year Adjustment. In the CMS files, most hospitals had reporting periods different than calendar years and some hospitals had reporting periods covering a period less than 365 days. To appropriately match data from CMS reports and calendar year data on quality of care, staffing, and other variables, we converted CMS data to calendar year equivalent data using weighted averages. The weights depended on the number of days falling in a particular reporting period and the number of days covered by the report (Needleman, Buerhaus, and Mattke 2001).

Calculation of Hospital RN Staffing. Prior to 1993, the AHA annual survey required hospitals to report staffing separately by hospital unit and nursing home/long-term care unit. After 1993, the reporting was done only for the total facility. Nursing homes, however, are required by CMS to comply with the Online Survey Certification and Reporting system (OSCAR). For 1994 and 1995, we obtained data on hospitals with nursing homes from the OSCAR system, which allowed us to subtract nursing home staffing from total facility staffing to arrive at hospital staffing. The AHA survey does not distinguish nurse staffing for inpatient and outpatient services; without an appropriate allocation method, estimates relating nurse staffing to quality of care would be biased. We followed Kovner and Gergen (1998) and Kovner et al. (2002) in allocating staffing to the inpatient facility based on the ratio of inpatient to outpatient gross revenues. (2)