RESEARCH ARTICLE

Implications of the California Nurse
Staffing Mandate for Other States
Linda H. Aiken, Douglas M. Sloane, Jeannie P. Cimiotti,
Sean P. Clarke, Linda Flynn, Jean Ann Seago, Joanne Spetz,
and Herbert L. Smith
Objectives.
To determine whether nurse staffing in California hospitals, where
state-mandated minimum nurse-to-patient ratios are in effect, differs from two states
without legislation and whether those differences are associated with nurse and patient
outcomes.
Data Sources.
Primary survey data from 22,336 hospital staff nurses in California,
Pennsylvania, and New Jersey in 2006 and state hospital discharge databases.
Study Design.
Nurse workloads are compared across the three states and we examine
how nurse and patient outcomes, including patient mortality and failure-to-rescue, are
affected by the differences in nurse workloads across the hospitals in these states.
Principal Findings.
California hospital nurses cared for one less patient on average
than nurses in the other states and two fewer patients on medical and surgical units.
Lower ratios are associated with significantly lower mortality. When nurses’ workloads
were in line with California-mandated ratios in all three states, nurses’ burnout and job
dissatisfaction were lower, and nurses reported consistently better quality of care.
Conclusions.
Hospital nurse staffing ratios mandated in California are associated with
lower mortality and nurse outcomes predictive of better nurse retention in California
and in other states where they occur.
Key Words.
Nurse staffing, California nurse ratios
In 2004, California became the first state to implement minimum nurse-
to-patient staffing requirements in acute care hospitals (Coffman, Seago,
and Spetz 2002; Spetz 2004).
As of September 2009, 14 states and the District of Columbia had enac-
ted nurse staffing legislation and/or adopted regulations addressing nurse
staffing and another 17 states had introduced legislation (American Nurses
Association 2009). California remainstheonlystateto have enacted minimum
nurse staffing requirements, and as the amount of legislative and regulatory
r
Health Research and Educational Trust
DOI: 10.1111/j.1475-6773.2010.01114.x
1
Health Services Research
activity suggests, there is widespread interest in what can be learned from
California’s example.
For two decades, nurses have reported that there are not enough nurses
in hospitals to provide high-quality care (Aiken and Mullinix 1987; Aiken,
Sochalski, and Anderson 1996; Aiken et al. 2001). In response to these con-
cerns, Congress, in 1993, requested an Institute of Medicine (IOM) study of
the adequacy of nurse staffing in hospitals and nursing homes. The IOM
report concluded that there was insufficient evidence to support specific nurse
staffing ratios in hospitals and called for additional research (Wunderlich,
Sloan, and Davis 1996). Since then, the evidence supporting an association
between nurse staffing and better patient outcomes has grown. We reported in
2002 that each patient added to nurses’ workloads was associated with a 7
percent increase in mortality following common surgeries, and that nurse
burnout and job dissatisfaction, precursors of voluntary turnover, also in-
creased significantly as nurses’ workloads increased (Aiken et al. 2002). Rep-
lications in Canada, England, and Belgium produced similar findings as did
other studies in the United States (Aiken, Clarke, and Sloane 2002; Needle-
man et al. 2002; Estabrooks et al. 2005; Rafferty et al. 2007; Tourangeau et al.
2007; van den Heede et al. 2009). A meta-analysis of 90 studies commissioned
by the Agency for Healthcare Research and Quality (AHRQ) subsequently
concluded that there is an evident association between nurse staffing and
patient outcomes (Kane et al. 2007).
Registered nurse (RN) staffing in California hospitals increased sub-
stantially following the passage of the legislation and implementation of the
regulations (Donaldson et al. 2005; Bolton et al. 2007; Spetz et al. 2009).
Whether the increase in nurses is associated with improved outcomes has
been more difficult to determine. Researchers were unable to detect an impact
Address correspondence to Linda H. Aiken, R.N., Ph.D., F.A.A.N., Center for Health Outcomes
and Policy Research, University of Pennsylvania, 418 Curie Blvd, Philadelphia, PA 19104-4217;
e-mail: [email protected] Douglas M. Sloane, Ph.D., is with the Center for Health Out-
comes and Policy Research, University of Pennsylvania, School of Nursing, Philadelphia, PA.
Jeannie P. Cimiotti, R.N., D.N.Sc., is with the Center for Health Outcomes and Policy Research,
University of Pennsylvania, Philadelphia, PA. Sean P. Clarke, R.N., Ph.D., F.A.A.N., is with the
Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, University Health Network,
Toronto, ON, Canada. Linda Flynn, Ph.D., R.N., is with the School of Nursing, University of
Maryland, Baltimore, MD. Jean Ann Seago, Ph.D., R.N., F.A.A.N., is with the School of Nursing,
University of California, San Francisco, 2 Koret Way, San Francisco, CA. Joanne Spetz, Ph.D., is
with the School of Nursing, University of California, San Francisco, UCSF Laurel Heights, CA.
Herbert L. Smith, Ph.D., is with the Department of Sociology, Population Studies Center, Center
for Health Outcomes and Policy Research, University of Pennsylvania, Philadelphia, PA.
2 HSR: Health Services Research
xx:xx
of improved nurse staffing on falls or hospital-acquired pressure ulcers (Don-
aldson et al. 2005; Bolton et al. 2007), but research findings in general on the
association between hospital nurse staffing and falls and pressure ulcers have
been inconsistent in the literature (Lake and Cheung 2006). While not related
to the impact of the legislation on patient outcomes, Mark, Harless, and Spetz
(2009) found that wage growth for RNs in California after implementation of
mandated minimum nurse staffing increased more than RN wage growth in
other states; the researchers could not rule out alternative explanations for the
wage increases including the impact of the nurse shortage.
Research by Sochalski et al. (2008) using data before implementation of
mandated minimum ratios offers a glimpse of the possible impact on patient
outcomes. During the study period between 1993 and 2001, when RN levels
rose by roughly 1.2 percent per year, they found that more RN hours per
patient day were associated with lower mortality for patients with acute myo-
cardial infarction. They also found, as would be expected, that mortality re-
ductions associated with increased nurse staffing were greatest for hospitals
that began with the worst staffing ratios. If this result can be replicated when
hospital outcome data become available for the years following implemen-
tation, many would conclude the legislation produced a desirable outcome.
The California Department of Health undertook a multiyear process to
determine the minimum ratios to be mandated based upon research and other
factors. The California mandates can be viewed as a benchmark against which
to compare hospitals within California and between California and other
states. We compare patient-to-nurse ratios in California hospitals with similar
ratios in New Jersey and Pennsylvania hospitals, states without nurse staffing
legislation at the time of the study, and compare associated outcomes. We
report findings from California nurses about the impact of the legislation on
factors affecting the quality of hospital care. We examine potential unintended
consequences of the legislation: whether RNs in California perceive that
nursing skill mix in hospitals has been negatively affected by increased em-
ployment of licensed vocational nurses (LVNs), and whether non-nurse an-
cillary support services have been reduced——two issues that were concerns at
the outset of the legislation (Coffman, Seago, and Spetz 2002). We compare
the outcomes for nurses and indicators of quality of care across hospitals in all
three states according to the proportion of nurses with workloads consistent
with the benchmarks derived from the California-mandated ratios. Finally, we
compare patient outcomes——30-day inpatient mortality and failure-to-rescue
(FTR)——across hospitals in which nurses care for fewer and more patients
each. This provides an estimate of the possible impact on nurse retention,
Implications of the California Nurse Staffing 3
quality of care, and patient mortality in other states if nurse staffing ratios were
to improve to the levels mandated in California.
D
ATA AND
M
ETHODS
Our primary data are from surveys completed in 2006, 2 years after the start of
the mandatory ratios, by nearly 80,000 RNs in California, New Jersey, and
Pennsylvania. New Jersey and Pennsylvania were chosen to compare with
California not only because of survey funding availability but also because
neither state had enacted nurse staffing legislation at the time; they are ad-
mittedly a convenience sample of states. The hospitals, nurses, and patients in
the three states combined provide broad, diverse, and reasonably represen-
tative samplesof hospitals, nurses, and patients in theUnited Statesas a whole.
Large random samples of RNs were obtained from licensure lists in California
(40 percent), Pennsylvania (40 percent), and New Jersey (50 percent). Licen-
sure lists have no information about employment, so respondents include
nurses in all employment settings and those not in the workforce but main-
taining an active license. Our target population in this analysis was hospital
staff nurses. We asked nurses to provide the name of their employing hospital,
information on their work environments including their patient workloads,
andthenumbers ofnursesandpatients on their unit on theirlast shift.We then
aggregateresponsesbyhospitalthus creatinghospitalleveland withinhospital
specialty-related empirical measures of patient-to-nurse workloads and other
nurse-assessed outcomes related to quality of care for the majority of hospitals
in the three states of over 100 beds. This method of obtaining information
about hospitals practically eliminates response bias at the hospital level, which
is the greatest potential threat to validity in studies of hospital performance
involving primary data collection.
A modified Dillman approach using two survey mailings and a reminder
postcard yielded a response rate of 35.4 percent (Dillman 1978). This cannot
necessarily be interpreted as the response rate of the target population of hos-
pital staff nurses because the sample included all nurses holding active licenses
even though a large proportion were not working. To determine the extent to
whichpossibleresponsebiasexistedinthesample,arandomsampleofnon-
responders (
n
5
650 in Pennsylvania;
n
5
650 in California) was drawn. Nurses
in the second sample received a shortened survey, telephone reminders, and a
monetary incentive to encourage their responses. The second sample response
rate was 91 percent. Demographic differences in race/ethnicity, age, and
4 HSR: Health Services Research
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experience were found between the nurses who responded to the initial survey
(responders) and those who subsequently responded to the follow-up survey
(nonresponders); however, there were no differences between responders and
nonresponders on the workload measur
es and nurse-reported outcome mea-
sures used in these analyses (Smith 2008). Thedata presented here arerestricted
to the original sample of nurses, specifically to the 22,336 nurses who were
working in 604 adult nonfederal acute care hospitals in California (
N
5
9,257
RNs in 353 hospitals), New Jersey (
N
5
5,818 RNs in 73 hospitals), and
Pennsylvania (
N
5
7,261 RNs in 178 hospitals).
Nurse workloads were derived by asking each hospital RN how many
patients they were assigned on their last shift. Although the California leg-
islation allows the mandates to be met by either RNs or LVNs, we found that
most hospitals met the ratios withRNs and therefore restricted ourattention to
RNswhoprovideddirectbedsidecareinthesehospitals.Theirresponseswere
used to derive average (mean) nurse workloads for all staff nurses and average
workloads for nurses working on different types of units (e.g., medical–
surgical, pediatric) in each state. Our data pertain to unit type but not to
specific units. We first look at mean differences in nurse workloads across the
three states, overall, and by unit type. We consider whether differences in
nurse workloads across these states may result from differences in the acuity of
patients across states. Our previous research demonstrated the predictive va-
lidity of nurses’ reports of their patients’ needs for assistance with activities of
daily living (ADLs) and actual hospital mortality outcomes ( Justice et al.
2006). We then calculated the percentage of nurses in each hospital across all
threestatesthat reportthat theirworkloadon theirlastshiftwasat or below the
unit-type levels mandated by the California legislation. We use this hospital-
level measure in logistic regression models to determine whether the likeli-
hood of nurses reporting unfavorable outcomes for patients and nurses is
lower in hospitals that have higher percentages of nurses working within
staffing levels congruent with the benchmark established by the California-
mandated nurse ratios. In our final analyses, we use logistic regression models
to estimate the effects of nurse staffing on 30-day inpatient mortality and FTR,
or mortality for patients with complications using Silber’s method, in the
hospitals in each of the three states, taken one at a time (Aiken et al. 2002). The
effects ofnurseworkloadsonmortalityand FTRareestimated beforeand after
adjusting for differences in other hospital characteristics (size, technology, and
teaching status), and differences in patient characteristics.
Hospitals bed size categories include small (
o
100 beds), medium (101–
250 beds), and large (
4
251 beds). Teaching status is the ratio of residents and
Implications of the California Nurse Staffing 5
fellows to hospital beds and defined as follows: no postgraduate trainees
(nonteaching), 1:4 or smaller trainee:bed ratio (minor teaching) and those
higher than 1:4 (major teaching). High-technology hospitals are those per-
forming open heart surgery and/or organ transplants. These analyses are re-
stricted to surgical cases for which risk-adjustment models have been well
developed, and to patients in those hospitals (233 in California, 72 in New
Jersey, and 139 in Pennsylvania) with substantial numbers of nurses
(mean
5
47) to provide estimates of patient-to-nurse workloads. Data on pa-
tient characteristics, complications, mortality, and FTR for these analyses are
secondary data from state agencies. They were merged with data from our
surveyofnursesandwith AmericanHospitalAssociationdataonhospitalsize,
teaching status, and technology. The analyses follow a protocol similar to that
described in detail in prior work on a single state (Pennsylvania), which is
modified slightly here to allow us ultimately to estimate how many fewer
patients would have died in New Jersey and Pennsylvania hospitals had
the average nurse workload in those states been equivalent to the average
workload across California hospitals (Aiken et al. 2002). All analyses were
conducted with
STATA
version 10, using robust estimation procedures
to take account of the clustering of nurses and patients within hospitals
(StataCorp 2007).
We note that while nurse self-reports of workloads may be prone to the
types of biases associated with self-reports generally, our prior research with
theseself-reportedmethods(Aikenetal.2002,2008)haveshownthemtohave
considerable predictive validity, and better predictive validity than AHA
measures of nurse staffing. Our survey-based measures, unlike administrative
measures of staffing, allow us to focus explicitly on staffing at the patient
bedside. And finally, in our multivariate models, we rigorously control for
a substantial number of the characteristics of nurses that might affect their
reports such as education and experience, as well as the characteristics of
patients and hospitals that might affect our results.
R
ESULTS
Table 1 shows the average number of patients assigned per RN per shift,
overall, and by unit type, for the three states. Mean workloads for California
RNs are on average at or below the levels mandated by the California leg-
islation for all nurses except those working on intensive care units, where the
average patients assigned was 2.1, only very slightly higher than the mandated
6 HSR: Health Services Research
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2:1 ratio. Mean workloads in New Jersey and Pennsylvania on all unit types
are higher than in California and are generally above California-mandated
staffing levels.
Table 2 shows a substantial degree of compliance with the benchmark
staffing levels mandated by the California legislation for the nurses in that
state. By comparison, when these benchmark levels are applied to nurses in
the other states, smaller percentages of nurses were found to have workloads
that were at or below these benchmark levels. For example, while 88 percent
of the medical–surgical nurses in California cared for five patients or less on
their last shift, the same was true of only 19 and 33 percent of medical–surgical
nurses in New Jersey and Pennsylvania, respectively.
In addition to asking nurses about the number of patients assigned to
them on their last shift, our survey asked them about the number of patients
they cared for who required assistance with ADLs and the numbers of
patients who were high acuity and required intensive monitoring. Their
responses suggest that the better nurse staffing in California hospitals is not
Table1: Average Workloads Reported by Hospital Nurses, Overall, and by
Specialty, in California (CA), New Jersey (NJ), and Pennsylvania (PA)
Specialty
Patient/Nurse Workload Mandated
by California Legislation
Mean Patients per Shift
(Nurse Sample Size)
CA NJ PA
All staff nurses 4.1
a,b
5.4 5.4
(9,257) (5,818) (7,261)
Medical–surgical 5:1 4.8
a,b
6.8
b
6.5
(1,311) (802) (1,069)
Pediatric 4:1 3.6
a,b
4.6 4.4
(192) (129) (137)
Intensive care units 2:1 2.1
a,b
2.5
b
2.3
(2,011) (1,041) (1,272)
Telemetry 5:1 4.5
a,b
5.9
b
5.7
(515) (389) (483)
Oncology 5:1 4.6
a,b
6.3
b
5.7
(200) (121) (133)
Psychiatric 6:1 5.7
a,b
7.0
b
7.9
(122) (160) (215)
Labor/delivery 3:1 2.4
b
2.6 2.8
(674) (325) (290)
Notes
. Intensive care units include adult, neonatal, and pediatric intensive care units.
a
Significantly different from New Jersey at
p
o
.05.
b
Significantly different from Pennsylvania at
p
o
.05.
Implications of the California Nurse Staffing 7
explained by patients in that state requiring greater nursing care. Nurses in
California care for fewer patients who required assistance with all ADLs on
each shift than do nurses in New Jersey and Pennsylvania (2.1 per nurse per
shift versus 2.8 and 2.7, respectively), fewer high acuity patients (2.1 per nurse
per shift versus 2.5 and 2.4, respectively), and fewer patients who required
hourly or more frequent monitoring or treatments (2.2 per nurse per shift
versus 2.9 and 2.8, respectively).
Table 3 shows that the lower workloads for California nurses translate
into better evaluations of their work environment. Higher percentages of
hospital nurses in California than in New Jersey or Pennsylvania report that
their workloads were reasonable, that they received substantial support in
doing their jobs, that there were enough nurses to get their work done and
provide high-quality care, and that 30-min breaks were part of their typical
workday. A smaller percentage of nurses in California than in the other states
indicated that their workloads caused them to miss changes in patient
conditions.
The survey questionnaire sent to California nurses also included a series
of questions about the changes they had detected in their hospitals since the
staffing legislation was implemented. Table 4 shows that four times as many
nurses report decreases (relative to increases) in the number of patients
assigned to them since the legislation was implemented. Only 15 percent
Table2: Percentage of Nurses Reporting Patient Assignments at or below
California Benchmark Levels, by Specialty, in California (CA), New Jersey
(NJ), and Pennsylvania (PA)
Unit Type
Patient/Nurse Workload
Mandated by California Legislation
Mean Patients per Shift
(Nurse Sample Size)
CA (%) NJ (%) PA (%)
Medical–surgical 5:1 88
a,b
19
b
33
Pediatric 4:1 85
a,b
52 66
Intensive care units 2:1 85
a,b
63
b
71
Telemetry 5:1 93
a,b
35
b
52
Oncology 5:1 90
a,b
29
b
55
Psychiatric 6:1 81
a,b
56
b
42
Labor/delivery 3:1 94
a,b
88 89
Notes
. Sample sizes for each specialty in each state are given in Table 1.
Intensive care units include adult, neonatal, and pediatric intensive care units.
a
Significantly different from New Jersey at
p
o
.05.
b
Significantly different from Pennsylvania at
p
o
.05.
8 HSR: Health Services Research
xx:xx
reported an increase in use of LVNs, while 25 percent of the nurses reported
decreaseduse.One-thirdofnursesreportedadecreaseintheuseofunlicensed
personnel to provide direct patient care. Increases in float coverage by nurses
from other units and the use of supplemental or agency nurses were reported
Table3: Percentage of Hospital Nurses in California (CA), New Jersey (NJ),
and Pennsylvania (PA) Agreeing That Selected Practice Environment
Characteristics Were Present in Their Jobs
Practice Environment Characteristic
Percentage of Nurses Agreeing
Characteristic Is Present
CA NJ PA
A reasonable workload 73
a,b
59 61
Adequate support services allow me
to spend time with patients
66
a,b
53 55
Enough registered nurses on staff to
provide quality patient care
58
a,b
41 44
Enough staff to get work done 56
a,b
40 44
30-minute breaks during workday 74
a,b
51
b
45
Workload causes me to miss
changes in patient conditions
33
a,b
41
b
37
Notes
. All tests of significance adjust for clustering of nurses within hospitals.
Samples sizes as per ‘‘All Staff Nurses’’ in Table 1.
a
Significantly different from New Jersey at
p
o
.01.
b
Significantly different from Pennsylvania at
p
o
.01.
Table4: California Hospital Nurses’ Reports of Changes in Compliance
Strategies
Compliance Strategy
Percentage Indicating
Compliance Strategy
Increased Remained the Same Decreased
Patients assigned per nurse 10 49 42
Relief nurses to cover breaks 35 51 14
Nurses floating to cover other units 30 59 11
Use of supplemental/agency nurses 43 42 15
Use of licensed practical nurses (LVNs) 15 60 25
Use of unlicensed personnel 10 56 34
Non-nursing support services
(e.g., housekeeping, unit clerks)
76627
Note
. Samples sizes as per ‘‘All Staff Nurses’’ for California in Table 1.
Implications of the California Nurse Staffing 9
by 30 and 43 percent of nurses, respectively. Approximately half of California
hospital nurses reported that nurse-to-patient ratios had not changed in their
institutions since the legislation. This is consistent with reports that over half of
hospitals were already in compliance with the mandated ratios at the time of
the legislation (Coffman, Seago, and Spetz 2002).
In our sample, 94 percent of nurses are staff nurses, 6 percent are front-
line nurse managers or assistant nurse managers (or direct supervisors), and
1 percent are nursing administrators/supervisors (or mid- or executive-level
supervisors). Supervisory and line staff alike generally agreed that the legis-
lation produced itsintended effects regarding quality of care, nurse workloads,
nurse retention, and the relative attractiveness of employment in California
hospitals (not shown in tabular form). For example, 74 percent of staff nurses,
68 percent of front-line nurse managers or assistant nurse managers, and 62
percent of mid- or executive-level nursing administrators agreed that the
qualityofcare inCalifornia hospitalshas increased asa resultofthelegislation.
Likewise, two-thirds of staff nurses agreed that California nurses are more
likely to stay in their jobs as a result of the legislation, and 58 percent of front-
line managers and 49 percent of nurse executives agreed. We also find (not
shown in tabular form) that a significantly lower proportion of California
nursesexperiencehighburnout:29 percent,compared with 34 and36 percent
in New Jersey and Pennsylvania, respectively. Nurses in California are also
less likely to report being dissatisfied with their jobs (20 percent, compared
with 26 and 29 percent in New Jersey and Pennsylvania, respectively).
Table 5 provides evidence of the effects of better staffing on a variety of
practice outcomes. In these analyses, we pooled data across states to estimate,
using logistic regression models, how much these outcomes differed in hos-
pitals with higher versus lower percentages of nurses whose workloads were in
conformity with the California legislation. The odds ratios in the first two
columns of Table 4 are the estimated effects of a 10 percentage-point increase
in the number of nurses with workloads in compliance with a benchmark
established by the California legislation. Estimates in the first column are the
bivariate relationship between extent of compliance with the benchmark
staffing levels and these outcomes. Estimates in the second column include
controls for both characteristics of hospitals (location [state], size, technology,
and teaching status) and characteristics of nurses (age, sex, race, degree, ex-
perience, and type of unit assigned to). The fixed effects (dummy variables) for
state assure that the estimated odds ratios are not due to unspecified differ-
ences between states, either in the practice of nursing or the conduct of the
surveys. Adjustment for other hospital characteristics reduces the likelihood
10 HSR: Health Services Research
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that the effects we estimate do not pertain to nurse staffing levels, but to other
hospital characteristics with which these staffing levels are correlated. Adjust-
ment for nurse characteristics has a similar effect with respect to possible
correlations between the types of nurses in a hospital and the way in which
they evaluate their hospitals.
We find for every outcome that higher percentages of nurses in a hos-
pital reporting patient-to-nurse ratios in line with the benchmark set by the
California mandates are significantly associated with lower reports of unfa-
vorable outcomes. These results obtain——and often increase——with the
extensive controls for characteristics of both hospitals and nurses and for
state-level fixed effects. Many of these differences are sizable; in the third
column of the table, we show how much higher the odds on reporting these
Table5: Odds Ratios Indicating the Effects on Different Nurse-Reported
Outcomes of a 10–Percent Increase in Nurses with Workloads at or below the
California Benchmark Level
Nurse-Reported Outcome
Odds Ratios Associated
with Each Nurse-Reported Outcome
Implied Difference
between Hospitals at
25th and 75th Percentiles
No Controls
Controls for Hospital
and Nurse
Characteristics
Complaints from
patients or families
0.96
nn
0.95
nnn
1.2
Verbal abuse by patients 0.93
nn
0.94
nnn
1.3
Verbal abuse by staff 0.94
nn
0.96
nn
1.2
Burnout higher than
norm for all health care workers
0.92
nn
0.91
nnn
1.5
Dissatisfaction with current job 0.89
nn
0.90
nnn
1.5
Work environment poor or fair 0.89
nn
0.89
nnn
1.6
Quality of care poor or fair 0.92
nn
0.87
nnn
1.8
Not confident patients can manage
care after discharge
0.97
nn
0.94
nnn
1.3
Workload causes me to miss changes
in patient condition
0.93
nn
0.91
nnn
1.5
Workload cause me to look for
new position
0.91
nn
0.89
nnn
1.6
Notes
. Hospital characteristics include location (state), teaching status, technology status, and bed
size. Nurse characteristics include age, sex, race, degree, experience, and type of unit assigned to.
All tests of significance adjust for clustering of nurses within hospitals.
Samples sizes as per ‘‘All Staff Nurses’’ in Table 1.
nn
p
o
.01.
nnn
p
o
.001.
Implications of the California Nurse Staffing 11
outcomes are for nurses in hospitals at the 25th percentile of patient-to-nurse
compliance (in which roughly 50 percent of nurses have workloads lower than
the mandated levels) than for nurses in hospitals at the 75th percentile (in
which roughly 90 percent of nurses have patient assignments lower than the
mandated levels). Nurses in the former hospitals have significantly higher
oddsthannursesinthelatterhospitals ofreportingcomplaintsfrompatients or
families and verbal abuse by patients or staff, by factors ranging from roughly
1.2 to 1.3. They also have significantly higher odds on reporting high burnout,
job dissatisfaction, and poor or fair work environments and quality of care (as
opposed to good or excellent work environments and quality of care), by
factors ranging from 1.5 to 1.8. Additionally, nurses in the former (poorer
staffed) hospitals have significantly higher odds than nurses in the latter (better
staffed) hospitals of expressing little or no confidence that their patients can
manage their care after being discharged, and significantly higher odds on
reporting that their workloads cause them to miss changes in patient condi-
tions and to look for a new position, by factors ranging from 1.3 to 1.6.
Evidence ofthefavorable effects of betternurse staffing canbe found not
only in the comparison of nurse reports from better and poorer staffed hos-
pitals but also in differences between these hospitals in the severity-adjusted
likelihood that the patients being treated in these hospitals will be discharged
alive. The appendix table describes, separately for each state, the character-
istics of the 1,100,532 patients in the set of 444 larger hospitals with sizable
numbers of nurse respondents that were used in our analyses of mortality and
FTR. Overall mortality was just under 1 percent in each state. FTR was
roughly 3 percent in each state. The predominant comorbidities among the
surgical patients in all three samples of patients were hypertension, diabetes,
and cancer, and the most prevalent major diagnostic categories involved the
musculoskeletal, digestive, and hepatobiliary systems.
Table 6 shows odds ratios, which estimate the effects of hospital nurse
staffing (average patients per nurse) on 30-day inpatient mortality and FTR,
separately for each state. We show both unadjusted odds ratios, from bivariate
robust logistic regression models which look at the effect of nurse staffing
without taking account of patient characteristics or other hospital character-
istics, and adjusted odds ratios, which estimate the effect of nurse staffing in
multivariate models that include 130 patient-level controls, including age,
gender, admission type, comorbidities,and type of surgery, as wellas hospital-
level controls, for bed size, teaching status, and technology. The unadjusted
effects of staffing on mortality are significant in all three states, and while the
unadjusted effect of staffing on FTR is not significant in Pennsylvania, the
12 HSR: Health Services Research
xx:xx
adjusted effects of staffing on FTR, as well as on mortality more generally, are
significant in all three states. Even after these extensive adjustments for differ-
ences between, the effect of adding an additional patient to hospital nurse
workloads increases the odds on patients dying by a factor of 1.13 in Cal-
ifornia, 1.10 in New Jersey, and by a factor of 1.06in Pennsylvania. The effects
of increased workloads on FTR were substantially similar, with odds ratios of
1.15 in California, 1.10 in New Jersey, and 1.06 in Pennsylvania.
D
ISCUSSION
Nurse workloads in California hospitals in 2006, 2 years after the implemen-
tation of mandated nurse staffing ratios, were significantly lower than in New
Table6: Odds Ratios Indicating the Effect of Nurse Staffing on 30-Day
Inpatient Mortality and Failure to Rescue, in California, New Jersey, and
Pennsylvania
Hospital Sample Model Type
Odds Ratios Estimating the Effect
of Nurse Staffing on
30-Day
Inpatient Mortality Failure-to-Rescue
California Unadjusted 1.10
nn
1.15
nnn
(1.03–1.17) (1.08–1.23)
Adjusted 1.13
nnn
1.15
nnn
(1.07–1.20) (1.09–1.21)
New Jersey Unadjusted 1.12
nn
1.09
n
(1.03–1.22) (1.01–1.19)
Adjusted 1.10
n
1.10
n
(1.01–1.22) (1.01–1.21)
Pennsylvania Unadjusted 1.06
n
1.02
(1.00–1.12) (0.97–1.07)
Adjusted 1.06
n
1.06
n
(1.00–1.12) (1.00–1.12)
Notes
. The numbers of patients and hospitals used in the analyses in each state are shown in the
appendix.
Unadjusted odds ratios are from bivariate robust logistic regression models. Adjusted odds ratios
are from multivariate robust logistic regression models that controlled for 132 patient character-
istics, including age, gender, admission type, dummy variables for comorbidities and type of
surgery, and interaction terms, and three hospital characteristics——bed size, teaching status, and
technology.
n
,
nn
,
nnn
Odds ratios which are significant at the .05, .01, and .001 levels, respectively.
Implications of the California Nurse Staffing 13
Jersey and Pennsylvania hospitals. Nurses in California care for an average of
one fewer patient each, and these lower ratios have sizable effects on surgical
patient mortality. In medical and surgical units, where nurse recruitment and
retention has long been difficult nationally, nurses in California on average
care for over two fewer patients than nurses in New Jersey and 1.7 fewer
patients than nurses in Pennsylvania.
When we use the predicted probabilities of dying from our adjusted
models to estimate how many fewer deaths would have occurred in New
Jersey and Pennsylvania hospitals if the average patient-to-nurse ratios in
those hospitals had been equivalent to the average ratio across the California
hospitals, we get 13.9 percent (222/1,598) fewer surgical deaths in New Jersey
and 10.6 percent (264/2,479) fewer surgical deaths in Pennsylvania.
Other than reports of less support from unlicensed clinical and support
personnel, we find little evidence of unintended consequences of the Califor-
nia legislation that are likely to negatively affect the quality of the nurse work
environment or patient care, as have been anticipated (Buerhaus 1997; Coff-
man, Seago, and Spetz 2002; AONE Board of Directors 2003). Despite being
able to meet the mandated ratios with either RNs or LVNs, 85 percent of
nurses reported the same or decreased use of LVNs. A substantial share of
nurses report decreased use of unlicensed personnel (34 percent) and de-
creased availability of non-nursing support services such as housekeeping and
unit clerks (27 percent). However, there is little evidence in the research lit-
erature that having more unlicensed personnel in hospitals adversely affects
patient outcomes. The nursing skill mix in California hospitals appeared to
improve and there is much research evidence that more RNs relative to others
are associated with better patient outcomes. There is the possibility that re-
ductions in ancillary workers will increase nurses’ workloads, but we found no
evidence in our study to suggest that was the case. Over 40 percent of
California nurses report increased use of supplemental agency nurses. Our
previous research does not find supplemental nurses to be responsible for
adverse outcomes (Aiken et al. 2007), and a study of California hospitals finds
that the use of more supplemental nurses is associated with fewer falls with
injuries (Bolton et al. 2007).
Most California nurses, bedside nurses as well as managers, believe the
ratio legislation achieved its goals of reducing nurse workloads, improving
recruitment and retention of nurses, and having a favorable impact on quality
of care. Although our data are cross sectional and lack baseline measures, our
positive findings are bolstered by other research showing improved nurse
staffing in California hospitals between 2004 and 2006 (Bolton et al. 2007) and
14 HSR: Health Services Research
xx:xx
increases in satisfaction of California nurses between 2004 and 2006 (Spetz
2008).
Outcomes are better for nurses and patients in hospitals that meet a
benchmark based on California nurse staffing mandates whether the hospitals
are located in California. The higher the proportion of nurses in hospitals
whose patient assignment is in compliance with the benchmark set on Cal-
ifornia-mandated ratios, the lower the nurse burnout and job dissatisfaction,
thelesslikelynursesaretoreportthequalityoftheirworkenvironmentasonly
fair or poor, the less likely nurses are to report that their workload causes them
to miss changes in patients’ conditions, and the less likely nurses are to intend
to leave their jobs. Similarly, the higher the percentage compliance with
benchmark based on California ratios, regardless of the hospital state location,
the less likely nurses are to report complaints from patients or families, verbal
abuse of nurses by staff or patients, quality of care that is poor or only fair, and
lack of confidence that their patients can manage after discharge.
The use of the same nurses to assess the impact of the California leg-
islation and to report on quality of care and job satisfaction may be construed
as a study limitation. We have tried to minimizethissource of potentialbiasby
obtaining reports from nurses in states without legislation and by using in-
dependentpatientdatatovalidatethebetteroutcomes forCaliforniahospitals.
Our study is cross sectional and we cannot establish causality in the associ-
ations we observe.
From a policy perspective, our findings are revealing. The California
experience may inform other states that are currently debating nurse ratio
legislation including Massachusetts (Coalition to Protect Massachusetts
Patients 2008) and Minnesota (Ostberg 2008), or other strategies for improv-
ing nurse staffing, such as mandatory reporting of nurse staffing, as enacted in
New Jersey (New Jersey Revision of Statutes 2005; Rainer 2005) and Illinois
(Kevin and Stickler 2007), and mandating the process by which hospitals
determine staffing as in Oregon (Oregon Revision of Statutes 2005). There are
multiple strategies to improve hospital nurse staffing; state-mandated nurse
staffing ratio is one. Improved nurse staffing, however it is achieved, is
associated with better outcomes for nurses and patients.
A
CKNOWLEDGMENTS
Joint Acknowledgment/Disclosure Statement
: We thank Tim Cheney for assistance
with data analysis. This research was supported by the National Institute of
Implications of the California Nurse Staffing 15
Nursing Research, National Institutes of Health (R01NR04513), the Robert
Wood Johnson Foundation, and AMN Healthcare Inc.
Role of Sponsor
: The funding organizations had no role in the design and
conduct of the study, in the collection, management, analysis, and interpre-
tation of the data, or in the preparation, review, or approval of the manuscript.
Institutional Review Board approval
: This study (Outcomes of Nurse Practice
Environments, NIH/NINR, Linda H. A
iken, PI) has been approved by the In-
stitutionalReviewBoardoftheUniversityofPennsylvania(protocolno. 176400).
Disclosures
: None.
Disclaimers
: None.
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S
UPPORTING
I
NFORMATION
Additional supporting information may be found in the online version of this
article:
Appendix SA1: Author Matrix.
Appendix SA2: Patient Characteristics in the Study Hospitals in Cal-
ifornia, New Jersey, and Pennsylvania.
Please note: Wiley-Blackwell is not responsible for the content or func-
tionality of any supporting materials supplied by the authors. Any queries
(other than missing material) should be directed to the corresponding author
for the article.
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