The effect of a hospital nurse staffing mandate on patient health outcomes: Evidence from California's minimum staffing regulation

Abstract

We evaluate the impact of California Assembly Bill 394, which mandated maximum levels of patients per nurse in the hospital setting. When the law was passed, some hospitals already met the requirements, while others did not. Thus changes in staffing ratios from the pre- to post-mandate periods are driven in part by the legislation. We find persuasive evidence that AB394 had the intended effect of decreasing patient/nurse ratios in hospitals that previously did not meet mandated standards. However, these improvements in staffing ratios do not appear to be associated with relative improvements in measured patient safety in affected hospitals.

Introduction

Hospitals are currently under pressure to control the cost of medical care, while at the same time improving patient health outcomes, especially through the reduction of medical errors (Kohn et al., 1999). These twin concerns are at play in an important and contentious decision facing hospitals—choosing appropriate nurse staffing levels.

Intuitively, one would expect relatively high nurse staffing ratios to be associated with improved patient outcomes, and if this intuition is correct, these patient benefits should be a key consideration in the determination of nurse staffing levels. Ideally, hospitals’ decisions about nurse staffing should be guided by clear empirical evidence on this matter, and indeed a number of recent studies have examined this issue. The best known of these papers are the seminal contributions of Aiken et al. (2002) and Needleman et al. (2002) (see also the review by Kane et al., 2007). Using data from 168 hospitals in Pennsylvania covering a 20-month span, Aiken et al. (2002) demonstrate that cross-sectional variation in nurse staffing levels is negatively correlated with patient mortality, measured as risk-adjusted 30-day mortality and “failure to rescue rates” (i.e., rates of death from complications which, under normal circumstances, might have been prevented). The Needleman et al. (2002) analysis of administrative data from 799 hospitals in 11 states over a 1-year span also finds higher levels of nurse staffing to be associated with lower failure to rescue rates, and they also report improved patient outcomes along a variety of other specific dimensions, e.g., rates of urinary tract infection, upper gastrointestinal bleeding, pneumonia, and shock or cardiac arrest.¹

The regression analyses of Aiken et al. (2002) and Needleman et al. (2002) provide important evidence about cross-sectional correlations, but concerns remain about causal relationships. In this regard, there are two important potential problems.

The first problem is a particular form of omitted variable bias. There exists considerable variation across hospitals in the level of resources devoted to patient care. This variation exists in nurse staffing practices, of course, but also along many other dimensions—the quantity and quality of medical equipment, the adoption of educational efforts to keep medical staff current on best practices, the efficacy of management practices, etc. (e.g., McClellan and Staiger, 2000, Bloom et al., 2009, Propper and Van Reenen, 2010). In cross-sectional regression analyses, researchers often are careful to control for such factors. See, e.g., Aiken et al., 2002, Mark et al., 2003, Needleman et al., 2002, and Sochalski et al., 2008. Still, such work is limited by the extent to which all relevant factors can be measured and made available in data sets. If, as one might suspect, hospitals that have relatively high nurse staffing levels also have above-average levels of other (unobserved) factors that affect patient care, cross-sectional regression analysis will tend to overstate the impact of a high nurse/patient ratio on patient health outcomes.

The second problem has to do with endogenous sorting. In general we would expect that medical providers will devote relatively high resources to patients for whom these resources are likely to have the highest impact—often to those patients who are at greatest risk of adverse outcomes. For example, we expect high mortality rates on medical units with high nurse/patient ratios. Again, a researcher can attempt to control for the severity of patients’ medical conditions, but it is hard to know how effective observable data measures are at controlling for underlying patient severity. In this case, researchers will tend to underestimate the beneficial impact of high nurse-to-patient ratios on patient outcomes.

Similar concerns pertain to evaluations based on hospital-level panel data (e.g., Mark et al., 2003, Sochalski et al., 2008). Thus, hospitals that experience improved nurse staffing levels might well be increasing resources along other (unobserved) dimensions. Conversely, hospitals that increase their nurse staffing levels might well be doing so in response to increases in general acuity levels of their patients.

A sensible response to these concerns is for the researcher to search for exogenous shifts to nurse staffing, and then use that variation to explore the impact on patient outcomes. Although truly exogenous variation (e.g., randomized assignment) is unavailable for this purpose, there are some attempts to find “natural experiments” for generating plausibly exogenous changes in nurse-per-patient ratios. A good example of this approach is the innovative work of Evans and Kim (2006). Their identification strategy is to exploit natural variation that occurs in hospital admissions, which in turn creates variation in patient loads. Using this approach, Evans and Kim find that patients admitted when the patient loads are high tend to have higher mortality, but effects are estimated to be quite small and are not statistically significant in several of their specifications. As the authors acknowledge, interpretation is difficult because they “have no independent data about how hospitals deal with a sudden influx of patients”. Thus, if hospitals respond by offering overtime shifts to nurses, in fact the nurse-to-patient ratios might not be changing much when there is a surge in hospital admissions. This could lead the authors to underestimate the impact of patient loads on patient outcomes.²

Our paper contributes by providing a new analysis that exploits an arguably exogenous shock to nurse staffing levels for the purpose of studying the relationship between nurse staffing levels and patient outcomes. Specifically, we look at the impact of California Assembly Bill 394, which mandated maximum levels of patients per nurse in the hospital setting. When the law was passed, some hospitals already had acceptable staffing levels, while others had nurse staffing ratios that did not meet mandated standards. Thus changes in hospital-level staffing ratios from the pre- to post-mandate periods are driven in part by the legislation. Our goal is to look at the impact on key patient health outcomes.