a descriptive overview of nurses and nursing homes in California. The results are presented in Section V, and the fi nal Section concludes.
II. Background
Despite the interest in this topic, few studies have employed research designs that can credibly estimate the effect of a permanent staffi ng increase on patient
health outcomes. Aiken et al. 2002 uses cross- sectional data and a survey generated measure of patient loads for RNs and fi nds that each additional patient per registered
nurse is associated with 7 percent increase in the risk- adjusted likelihood of dying within 30 days of admission and a 7 percent increase in odds of failure- to- rescue in
hospitals. Subsequent studies have found similar results for other settings and out- comes and in the nursing home as well as hospital sectors Harrington et al. 2000;
Kane, Shamliyan, Mueller, Duval, and Wilt 2007; Mark, Harless, McCue, and Xu 2004; Needleman, Buerhaus, Pankratz, Leibson, Stevens, and Harris 2011; Needleman
et al. 2002; Sochalski 2004. As documented elsewhere Dobkin 2003; Evans and Kim 2006, however, there are strong reasons to doubt that these correlations refl ect causal
effects. Because hospitals with higher nurse staffi ng also likely have higher levels of other inputs doctors, support staff, or advanced equipment and attract more sched-
uled visits, the apparent effects of more nurses may be due partially to these other omitted inputs or favorable patient selection.
Recent studies by Dobkin 2003 and Evans and Kim 2006 better isolate exog- enous variation in nurse staffi ng levels and fi nd little evidence of a causal effect of
nurse staffi ng on patient outcomes. Dobkin 2003 focuses on reductions in staffi ng and services offered by hospitals over the weekend, controlling for differences in un-
observed illness severity among patients admitted on different days of the week. He shows that all the apparent excess mortality over the weekend found in an earlier
study Bell and Redelmeier 2001 was driven by nonrandom patient selection—even conditional on extensive initial diagnoses codes—rather than low staffi ng levels.
2
Ev- ans and Kim 2006 analyze differences in patient outcomes due to changes in nurse
to patient ratios that are caused by “surges” in admissions. They isolate all patients admitted to California hospitals on a Thursday over the years 1996 to 2000, and ex-
amine how their outcomes vary with the number of patients admitted the following Friday and Saturday. Because staffi ng levels are predetermined, surges in admission
reduce the effective staffi ng ratios for patients already admitted. Despite deviations in admissions from predicted values of 40 percent or more, Evans and Kim conclude that
patient mortality and other outcomes are largely unaffected by changes in effective staffi ng ratios.
While these studies go a long way to solving the internal validity issues of cross- sectional studies, their estimates may not be relevant for inferring the likely effects
2. Dobkin 2003 documents that the level of nurse staffi ng doesn’t change much over the weekend, though weekend nurses have lower seniority and are more likely to be sourced by temporary agencies. As Dobkin
is careful to emphasize, the effects he measures refl ect the combined effects of reduced numbers of doctors, support services, and this compositional change amongst the nurses.
of legislating higher nurse- patient ratios. One important reason is that permanent in- creases in nurse staffi ng levels that would be effected through minimum staffi ng legis-
lation may have quite different effects than these transitory fl uctuations. Hospital stays average about fi ve days in length, and changing the amount of nursing care over all
fi ve of these days is likely to impact care more than for just the fi rst one or two days.
3
More generally, the care received by any patient may react more to permanent rather than temporary staffi ng changes for a number of reasons. The effort levels of nurses
may respond inversely to temporary surges in admissions, or hospitals may increase triage efforts and encourage less acutely ill patients to return for care at a later date
when staffi ng ratios have normalized. Both mechanisms might lead to muted apparent effects of staffi ng levels on outcomes.
Despite the interest in mandated increases in nurse staffi ng ratios as a means to improve the quality of healthcare, few studies have effectively addressed this question.
Zhang and Grabowski 2004, Park and Stearns 2009, and Tong 2010 all study the effects of legislation aimed at increasing nurse staffi ng but the current study improves
methodologically on each in several ways.
4
For example, Zhang and Grabowski re- gress changes in quality of care measures on changes in staffi ng levels among nursing
homes surrounding passage of the national Nursing Home Reform Act NHRA in 1987, and fi nd no signifi cant effects. Unfortunately, for these relationships to refl ect
the causal effect of staffi ng it must be the case that all of the time- series variation in staffi ng levels is exogenous, an assumption that is untested and hard to accept as
plausible, especially given the other components of the NHRA for example, a man- dated reduction in the use of unnecessary restraints or drugs, etc.. Tong 2010 uses
a research design similar to that employed in this paper, but focuses on mortality as the quality of care measure for nursing homes. Mortality is an awkward quality of
care measure in this context since acutely ill patients are often transferred to a hospital so may not die in the nursing home. Moreover, in a recent national survey some 56
percent of nursing home residents had do- not- resuscitate advanced directives Center for Disease Control 2009, and so allowing a patient to die may not necessarily refl ect
poor care in this setting.
III. California Minimum Nurse Staffi ng Regulations and Research Design
