Review

Nursing resources and patient outcomes in intensive care:

A systematic review of the literature

Elizabeth West a,*, Nicholas Mays b, Anne Marie Rafferty c,Kathy Rowan d, Colin Sanderson b

a School of Health and Social Care, University of Greenwich, Southwood Site, Avery Hill Road, Eltham, London SE9 2UG, UKb London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK

c Florence Nightingale School of Nursing and Midwifery, King’s College London, James Clerk Maxwell Building,

57 Waterloo Road, London SE1 8WA, UKd Intensive Care National Audit and Research Centre, Tavistock House, Tavistock Square, London WC1H 9HR, USA

Received 7 November 2006; received in revised form 25 May 2007; accepted 3 July 2007

Abstract

Objectives: To evaluate the empirical evidence linking nursing resources to patient outcomes in intensive care settings as a

framework for future research in this area.

Background: Concerns about patient safety and the quality of care are driving research on the clinical and cost-effectiveness of

health care interventions, including the deployment of human resources. This is particularly important in intensive care where a

large proportion of the health care budget is consumed and where nursing staff is the main item of expenditure. Recommenda-

tions about staffing levels have been made but may not be evidence based and may not always be achieved in practice.

Methods: We searched systematically for studies of the impact of nursing resources (e.g. nurse–patient ratios, nurses’ level of

education, training and experience) on patient outcomes, including mortality and adverse events, in adult intensive care.

Abstracts of articles were reviewed and retrieved if they investigated the relationship between nursing resources and patient

outcomes. Characteristics of the studies were tabulated and the quality of the studies assessed.

Results: Of the 15 studies included in this review, two reported a statistical relationship between nursing resources and both

mortality and adverse events, one reported an association to mortality only, seven studies reported that they could not reject the

null hypothesis of no relationship to mortality and 10 studies (out of 10 that tested the hypothesis) reported a relationship to

adverse events. The main explanatory mechanisms were the lack of time for nurses to perform preventative measures, or for

patient surveillance. The nurses’ role in pain control was noted by one author. Studies were mainly observational and

retrospective and varied in scope from 1 to 52 units. Recommendations for future research include developing the mechanisms

linking nursing resources to patient outcomes, and designing large multi-centre prospective studies that link patient’s exposure to

nursing care on a shift-by-shift basis over time.

# 2007 Elsevier Ltd. All rights reserved.

Keywords: Nursing; Outcomes assessment; Hospital mortality; Complications; Intensive care; Health services research

www.elsevier.com/ijns

Available online at www.sciencedirect.com

International Journal of Nursing Studies 46 (2009) 993–1011

�* Corresponding author. Tel.: +44 1865 512 938.

E-mail address: Lizwestbarron@googlemail.com (E. West).

0020-7489/$ – see front matter # 2007 Elsevier Ltd. All rights reserved.

doi:10.1016/j.ijnurstu.2007.07.011

What is already known about the topic?

A previous systematic review concluded that there is

currently insufficient evidence to reject the hypothesis

E. West et al. / International Journal of Nursing Studies 46 (2009) 993–1011994

of no association between nursing resources and mortality

in intensive care.

� S

everal non-systematic reviews suggest that there may be

a link between nurse staffing and the development of

adverse events among patients in intensive care.

� T

here are many methodological difficulties to be over-

come in conducting research in this area.

What this paper adds?

� This paper describes and critiques studies of the impact of

nursing resources on mortality and adverse events in one

systematic review.

� F

ocuses attention on the methods used and devises a way

of assessing the scientific rigour of observational studies

which are notoriously difficult to evaluate.

� F

inds that studies of adverse events in ICU were likely to

report significant relationships between staffing and out-

comes but that the number of positive associations was

small relative to the number of hypothesised relationships

tested.

� L

inks study findings to features of research design. The

three studies that found a relationship between nursing

and mortality were small prospective studies whereas the

seven studies that found no association were large multi-

unit studies based on administrative data.

� S

hows that several studies that failed to reject the hypoth-

esis of no association between staffing levels and out-

comes had little variation in staffing levels.

1. Introduction

Historians of critical care nursing trace its origins to

the increasing demand for health care in the 1950s and to

the invention of the ‘iron lung,’ a precursor of the modern

ventilator (Reis Miranda et al., 1998; Bennett and Bion,

1999; Fairman and Lynaugh, 1998). Intensive care units

(ICUs) were not designed simply to care for the most

seriously ill, but for those for whom survival was possible,

but not certain. Patients admitted to intensive care were to

be closely observed by skilled nurses capable of inter-

vening clinically and of mobilising the resources of the

hospital on their behalf. Although intensive care is often

associated with high technology, this can obscure the

importance of the two cardinal organisational features

of intensive care: triage and surveillance (Fairman and

Lynaugh, 1998; Sandelowski, 2000). Advances in tech-

nology are tools to support staff in monitoring and treating

patients who are critically ill, rather than a substitute for

skilled health care staff (Sandelowski, 2000; West et al.,

2004).

For many, intensive care is central to the activities of the

hospital because its function is so clearly aligned with the

main goal of saving lives. Clinicians and managers have

often maintained staffing levels in ICU by, for example,

closing beds in other parts of the hospital, transferring staff

from other parts of the hospital or employing agency nurses

(DoH, 2000a). Recommendations about staffing in ICUs

have been in place since the late 1960s in the UK and the

‘gold standard’ of one nurse to each ICU patient is widely

accepted (British Medical Association, 1967; Royal College

of Nursing, 2000, 2003). However, there is now a great deal

of uncertainty and debate about the levels of staffing and

skill mix that are required for patient safety.

Critical to Success, a report by the Audit Commission

(1999), found wide variations in the numbers and grades of

staff employed in ICUs in the UK and in the number of

nurses who were supernumerary (i.e. not engaged in direct

patient care). They also found that mortality was unexpect-

edly high in some UK ICUs, but they were unable to

establish whether or not this was linked to different staffing

levels. The report recommended a more flexible approach to

nurse staffing but this is difficult to implement in the absence

of good measures of staff resources, patient needs and

outcomes. There is clearly a need for sound empirical

evidence to guide decisions about the deployment of staff

to ensure patient safety and improve both the quality and

cost-effectiveness of care.

2. Purpose of the study

This paper reviews empirical evidence about the link

between nursing resources and patient outcomes in inten-

sive care, assesses its strengths and weaknesses, and

identifies where further research is required. The focus

is on whether and to what extent characteristics of the

nursing workforce, such as the number of nurses per

patient and the skill mix of the nursing staff, affect rates

of mortality and adverse events, such as post-operative

complications and hospital-acquired infections. Although

there is a burgeoning literature on organisational behaviour

in intensive care, including topics such as, communication

and collaboration within teams, we focus on variables

associated with the concept of human capital (Becker,

1964), in this case, the number of nurses and their levels

of education, training and experience. Limiting the scope

of the review in this way allows closer scrutiny of the

quality of papers included. This is important because many

of the studies in this area are observational rather than

experimental, and as such are notoriously difficult to

evaluate (Downs and Black, 1998).

Much is already known about the importance of medical

staff in intensive care. Pronovost et al. (2002) reviewed 26

observational studies of the impact of ICU physician staffing

patterns on patient outcomes and concluded that high inten-

sity physician staffing (a closed ICU or one where consulta-

tion with an intensivist is mandatory) was associated with

reduced hospital and ICU mortality and length of stay. The

research question addressed here is whether there is any

evidence that patterns of nurse staffing are similarly impli-

cated in patient outcomes.

E. West et al. / International Journal of Nursing Studies 46 (2009) 993–1011 995

3. Methods

3.1. Search strategy

Previous authors have drawn attention to the difficulties

of defining standard search terms in this area. Pronovost

et al. (2002), for example, reported that their comprehensive

search for empirical evidence on physician staffing patterns

did not uncover some of their own work. Carmel and Rowan

(2001) note that their review of the literature on organisa-

tional factors related to patient mortality was hampered by

the fact that electronic databases tend to adopt a biomedical-

intervention perspective. We therefore used many different

terms and strategies to try to ensure that the search for

relevant articles was as complete as possible:

Unit labels in

tensive care units; intensive care; surgical

intensive care; critical care; high dependency;

open, closed, critically ill

Outcomes o

utcome assessment, treatment outcomes,

mortality, morbidity, adverse events, infections,

length of stay, complications, error/s,

readmission/s, admission/s

Nursing n

ursing staff, hospital staffing

Workforce la

bour force, health care workforce, manpower,

workforce policy, training, education, nurse/s,

number, size, staffing levels, ratio/s, skill mix,

substitution, specialisation, training, education,

grade/s, staff development, human resources, HR

management, personnel staffing and scheduling

Workload v

olume of activity, workload

Methods m

ulti-level modelling, case mix adjustment, risk

adjustment, APACHE, SAPS, case-control study,

retrospective study

The ‘related articles’ feature in Pubmed was used in

locating studies, as was Google Scholar. The bibliographies

of articles retrieved at the beginning of the search were

scanned for new references.

3.2. Inclusion and exclusion criteria

Studies were included if they were conducted exclusively

in intensive or critical care settings and allowed data on one or

more of the nursing workforce variables to be related to data

on mortality or adverse events. Studies conducted in acute

medical and surgical units or in neonatal or paediatric inten-

sive care were excluded because these settings are so different

that they warrant separate reviews. Single-unit studies were

included. Only studies published in English in refereed jour-

nals between 1990 and 2006 were included. The only quality

criterion that was used to selected studies was that they had to

have employed some method of risk adjustment. In summary,

studies were included if they met the following criteria:

� C

onducted exclusively in one or more adult ICUs

� D

ependent variable was mortality or adverse events

� H

uman capital characteristics of the nursing workforce

formed at least one of the independent variables

� P

ublished in English between 1990 and 2006

� U

sed some form of risk adjustment

The first author read all the abstracts and retrieved all the

articles that in her judgement met these criteria.

3.3. Previous reviews

Several reviews have already been conducted in this area.

Carmel and Rowan (2001) found in 63 publications about 54

different studies of the organisation of intensive care, which

they grouped into eight categories: staffing, teamwork,

volume and pressure of work, protocols, admission to inten-

sive care, technology, structure and error. Articles on staffing

were the most common and fell into a number of different

strands of research on management and personnel, intensi-

vist-led units, medical and nursing intensity and nursing

autonomy. Five studies focussed on the impact of nursing

intensity (mainly nurse–patient ratios, but one study also

examined level of nurse qualifications) on mortality, but

none were able to reject the hypothesis of no association.

This was a useful source of information about studies

published prior to 2000.

Numata et al. (2006) reviewed the literature on nurse–

patient ratios and mortality in ICUs and identified nine

observational studies, five of which were included in a

meta-analysis. The unadjusted risk ratios of nurse staffing

(high versus low) on hospital mortality were combined to

give a pooled estimate of 0.65 (95% CI 0.47–0.91). How-

ever, after adjusting for covariates the association between

staffing and mortality became non-significant in all but one

study (Tarnow-Mordi et al., 2000). Numata et al. (2006)

acknowledged that their meta-analysis was based on a small

number of studies, four of which were conducted in the same

region of the United States. They concluded that there was

insufficient evidence to support an independent association

between nurses staffing levels and the mortality of critically

ill patients. They identified a range of methodological

challenges: lack of an agreed operational definition of nurse

staffing, lack of variation in staffing levels in some studies,

staffing levels which varied from shift-to-shift measured at

one point in time, confounding factors not included or

controlled in any way, failure to use statistical methods

appropriate for the research design, crude methods of risk

adjustment due to the use of administrative databases and the

tendency to analyse hospital mortality without considering

the care that the patient received outside the unit.

Both of the above reviews focussed on mortality as the

key variable to be explained, but there is a growing literature

on the impact of nursing on a much broader range of patient

outcomes, including safety, adverse events and complica-

tions, as well as patients’ and relatives’ satisfaction with care

and their subjective experiences of it. Williams et al. (2003)

conducted a rapid review of different aspects of workload in

E. West et al. / International Journal of Nursing Studies 46 (2009) 993–1011996

ICUs, including the policy context and secular trends. They

identified 12 studies of nurse staffing, and presented evi-

dence for a positive effect of increased nurse staffing on

patient outcomes. More recently, Carayon and Gurses (2005)

conducted a wide ranging review of the literature. Their

main goal was to devise a ‘human factors engineering’

framework to guide modification of the work setting to

improve patient safety and to improve the quality of nurses’

work lives in ICUs. They cite several studies that have shown

that medical errors are common in ICUs and may be

attributable to problems in communication between nurses

and physicians, to impaired access to information, and to

high intensity nursing workloads. They identified 22 studies

in all, but made no attempt to evaluate them or aggregate

their findings. They concluded that there were at least four

different kinds of workload associated with the unit, job,

patient and situation. Most studies to date have focussed on

workload associated with the first two: the unit or the patient.

Each of the above reviews makes a contribution to

knowledge about workforce, workload and outcomes in

intensive care. The emerging consensus seems to be that

while there is some evidence that the nursing workforce has

an impact on patients’ risks of adverse events, there is

insufficient evidence of a link with mortality. This presents

a puzzle because some of the adverse events studied have

known links to mortality. Why would nursing workforce

characteristics affect the more proximate events but not the

final outcome? The process of dying is likely to be one of an

accumulation of adverse events as the patient deteriorates

and clinicians take increasing risks in an attempt to save their

life. It seems important then to begin to integrate these two

separate strands of work into one coherent analysis.

This study uses systematic review methods to identify

and appraise empirical evidence about the impact of any

human capital characteristic of the nursing workforce (such

as skill mix, education or employments status, in addition to

nurse–patient ratios) on any patient outcomes, particularly

adverse events such as post-operative infections and mor-

tality. It adds to the literature a deeper description of the

methods used and attempts to evaluate the quality and

scientific rigour of the studies reviewed.

4. Results of the literature search

Fifteen studies were identified. Table 1 in the appendix to

this paper shows the key features of each study, including

location, research design, sample, sources of data, nursing

variables, outcome variables, factors adjusted for, findings

and comments.

4.1. Study quality

Research designs employed in this field divide into the

quasi-experimental such as case control or cohort studies

with an emphasis on direct comparison of results for

matched groups, and more ‘natural’ observational studies

which rely on multivariate analysis to adjust for the effects of

known confounders. Quasi-experimental studies (case con-

trol and cohort designs) try to come as close to an experiment

as possible with random assignment to ‘treatment’ and

‘control’ groups with different levels of exposure to an

intervention, e.g. patients exposed to high levels of staff

compared with patients exposed to low staffing levels. Non-

experimental research designs are more familiar in the social

sciences, where it has been argued that if a significant

relationship persists after partialing out the effects of con-

founding variables (robust dependence), then a causal rela-

tionship can be said to exist (Goldthorpe, 2000). In

observational studies of this type there are no ‘control’

group or ‘treatment’ groups and the effects of confounding

variables are controlled statistically. Although quasi-experi-

mental studies do also use multi-variate statistics they tend to

do so to compensate for failures in matching case and control

groups rather than their main strategy. For the purposes of

this paper therefore we make a very clear distinction

between these two types of studies because the differences

are relevant to the ways in which the studies should be

evaluated.

A great number of tools exist for evaluating randomised

trials (Moher et al., 1995). Downs and Black (1998) also

devised a quality checklist for non-randomised as well as

randomised studies but their work only extends as far as

quasi-experimental designs. To date, we are not aware of any

systematic attempt to develop measures of quality for studies

of this type and so we designed a rudimentary measure

focussing on the following areas as a preliminary step

towards more formal attempts to establish criteria for obser-

vational studies.

1. G

eographical scope (4): The numbers of patient and units

in the study. The concern here is with how far the results

can be generalised to other populations, and the geogra-

phical coverage of the study is salient. High scores would

be given to a census or random sample of units across a

nation or a large geographical area. Medium scores

would be given to a study of a smaller number of units

and a low score would be given to a single-unit study.

2. Q

uality of data (4): How and why were the data were

collected. High scores would be assigned to data that

were collected prospectively to answer the specific ques-

tions in the study. Low scores would be given to studies

that use data collected for other purposes, e.g. adminis-

trative data.

3. V

alidity of key independent variables (4): How sensitive

and specific are the key workforce measures as indicators

of the number and quality of nurses available to indivi-

dual patients? Nurse–patient ratios are often averaged

over long periods of time and may be simply dichot-

omised into ‘high’ or ‘low.’ High scores would be given if

the information on nurses was collected over time and

linked to individual patients. Medium scores would be

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Table 1

Studies of nursing resources and patient outcomes: Main characteristics and results

Reference N of units Nursing variables Risk adjustment,

other controls

Relationship to

mortality, size of

effect, significance

Relationship to

adverse events,

size of effect;

significance

Comments

Location N of patients

Period studied Data analysis

Design

Giraud et al.

(1993)

France

1989

Prospective

observational

study

� 2 ICUs

� 382 patients (400

consecutive

admissions)

� Monitored daily

by physicians

for iatrogenic

complications

(defined as

adverse events

that was

independent of the

patients disease)

� Non-parametric

Kruskal–Wallis test

� Multi-variate

logistic regression

� Cox survival analysis

� Nursing workload

subjectively

assessed by each

nurse on each

shift, with score

for each patient

in their care. Patient’s

total nursing workload

was sum of all shift

scores during their stay.

� OMEGA system

measures nursing

workload based

on 47 diagnostic

and therapeutic items.

� Age

� Organ System

Failure Score

� SAPS

� Prognosis

� For patients who

stayed >24 h,

mortality was

2� higher for

patients who

developed

complications after

adjusting for Organ

System Failure Score

and prognosis

RR=1.92 (1.28–2.56)

� Total number of

iatrogenic

complications was

316 which occurred

in 31% of patients

in sample, 107

complications were

defined as major

with 3 leading to

death.

� Increased risk

of major complications

when nursing

workload (measured

using Omega system

and subjective

assessment) was high

or excessive.

The total nursing

workload score in

patients who did not

go on to

develop complications

was 16.1�1.4. A score

of 52.8�5.0 was

associated with

patients who developed

moderate complications

and patients who

developed major

complications had a

score on the nursing

workload tool of

115�15 which was

significant at the

� Complications often

related to human errors

and nurses were

frequently involved

because so many errors

were due to deficiencies

in surveillance.

� Assumption that nurses

subjective scoring of

workload from 1 to 4

would take into account

factors that they did not

measure such as nurses

education is question

able and testable.

� Authors note that the

increased risk of death

observed after

occurrence of major

iatrogenic

complications might

reflect the intense

diagnostic and

therapeutic efforts

resorted to in the

most severely ill prior

to their death, i.e. the

causal ordering of

these variables is

uncertain.

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98Table 1 (Continued )

Reference N of units Nursing variables Risk adjustment,

other controls

Relationship to

mortality, size of

effect, significance

Relationship to

adverse events,

size of effect;

significance

Comments

Location N of patients

Period studied Data analysis

Design

p<.0001 on the

Kruskal–Wallis

test.

Shortell et al.

(1994)

USA

1988–1990

Prospective,

multi-centre

observational

study

� 42 ICUs

� 26 units in stratified

random sample;

14 volunteer units

‘largely representative

of national population.’

� 17,440 patients

used to calculate

unit SMRs using

logistic regression

� Organisational

assessment

questionnaire

� Patient data

on mainly

consecutive admissions

average study

period=10 m/unit

� OLS used to test

hypotheses with unit

SMR as dependent

variable

� Nurse/patient ratio

(data collected

on each shift during

the study period)

� Apache III

� Primary disease

category

� Duration of

hospitalisation

� Location prior to

admission

� Elective or

emergency surgery

� No association

between nurse patient

ratio and unit SMR.

(OLS estimated

coefficient .12 with

SE=.137, standardised

coefficient Beta=.14

in standard deviations

of both the dependent

and predictor variables.

Not tested � No association with

LOS

� Nurse/patient ratios did

not vary greatly

between units (range

.31–1.31)

� Interesting negative

correlation

(�.34, p<=.01)

between diagnostic

diversity and nurse/

patient ratio which may

suggest that specialist

units need fewer, but

more expert, staff

� Main level of analysis is

the unit

� Found that

technological

availability and

diagnostic diversity

were the strongest

correlates of

risk-adjusted

mortality.

� Quality of caregiver

interaction is the

strongest correlate of

unit efficiency,

evaluated technical

quality of care, ability

to meet family needs

and nursing turnover.

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Thorens et al.

(1995)

Switzerland

Descriptive and

case control

study

� 1 ICU

� 87 COPD patients:

15 in study year+

72 from earlier years

� 1 year prospective

data collection

compared to 5 years

of data already

collected.

� Each patient followed

individually by lead

author

� Head nurse kept data

on nurses

� Correlations

� 1-way ANOVA

� Index comparing

number of nurses

and their qualifications

with ideal for

dependency-adjusted

number of

patients

� Patient dependency

in calculation of

the nursing index

� Risk of developing

ventilator-assisted

pneumonia increases

linearly by �1%/day

of mechanical

ventilation with a

mortality rate of

>50% implies a

link to nurse staffing,

but not directly

tested in the study.

� Duration of weaning

off mechanical

ventilation for patients

with COPD

� Negative correlation

with nursing index

coefficient not

given, Spearman’s

rank correlation

p=.025

� In the first 5 years,

duration of mechanical

ventilation increased

from 7.3�8.0

to 38.2�25.8 (p=.006)

� In the 6th year, the

number of nurses

increased and the

duration of ventilation

to 9.9�13 days

(p<.001, year 5

vs year 6)

� In first 5 years unit

had 13 beds and a

shortage of nurses.

In year 6 bed numbers

increased to 18

and number of nurses

and doctors increased.

Could there have been

other significant

changes as well?

� Calculating the nursing

index over a year is

highly aggregated.

� Statistical tests of the

hypothesis do not allow

for control variables.

� Authors discuss the

possible mechanisms

that might link shortage

of nurses to increased

time to weaning of

patients with COPD,

including surveillance,

early detection of

disorders and increasing

patient comfort

decreasing the need for

analgaesics

Bastos et al.

(1996)

Brazil

1990–1991

Multi-centre,

prospective,

observational

study

� 10 ICUs

� 1734 consecutive

adult admissions

� Questionnaire about

hospital

� Unit data given to

ICU Director.

� Multi-variate

regression

� Nurse staffing ratios

across all shifts.

� Apache III

� May have controlled

for hospital factors

but not reported.

� No association

with

SMR (b=0.32,

p=0.12)

Not tested � Nursing not the main

focus.

� Little variation in

staffing levels across

units (1:1–1:2).

� Relationship found

between amount of

technology available

and SMR but no

association with

diagnostic diversity.

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0Table 1 (Continued )

Reference N of units Nursing variables Risk adjustment,

other controls

Relationship to

mortality, size of

effect, significance

Relationship to

adverse events,

size of effect;

significance

Comments

Location N of patients

Period studied Data analysis

Design

Fridkin et al

(1996)

VA, Tucson

USA

1992–3

Cohort study

� 1 centre

� All SICU patients

in period

� Correlations

� Logistic

regression.

� Average monthly

nurse–patient ratio

(registered nurses

only)

� Age

� Mortality

� Length of SICU

hospital stay.

� N of patients

with >14 days

assisted ventilation

or on total parenteral

nutrition

� Period of

hospitalisation

(outbreak vs

pre-outbreak)

Not tested � Patient to

nurse ratio

increased significantly

in the outbreak

period compared

with the pre-outbreak

period from 1.18

to 1.40 (p<.01).

� Correlation between

CVC-BSI and

nurse to patient

ratio; Spearman’s

Rank Correlation

Coefficient=0.49 p<1.

� Logistic regression

showed that the

occurrence of at least

one CVC-BSI was

strongly associated

with a higher nurse

to patient ratio

� Authors argue that high

nurse to patient ratios

mean nurses do not

have enough time

to care for CVCs.

� Case control elements

to this study as well—

not discussed here as

not related to testing

hypothesis about nurse-

patient ratios

� Short study period,

wide confidence

intervals of relative

risks

Reis-Miranda

et al. (1998)

12 European

countries

4 months

Prospective

observational

� 89 units

(non-random

selection)

� Daily data on

patients and nursing

workload (TISS)

� Questionnaires

and site visits.

� Fixed effects—DV

measure of

(perceived) ICU

performance

(measure developed

by Shortell).

� Multiple regression

� Nurses/bed

� N of nurses included

in a composite

independent variable

‘wealth of the unit’

� NB: these are

not the same—the

first refers only to

nurses and the

second includes

a number of

other variables

in one composite

score.

� SAPSII

� Country

� ICU

� Technology

� Centralisation

� Organisational

factors

� Nurses’ morale/stress

� Process vs results

culture

� Budgetary factors

Mortality decreased by

� Organisational

commitment

� Results culture

� Not significant

in final models.

Not tested � Nurses’ participation in

decision-making and

communication with

doctors were

inadequate, but there

were few shortages

of nurses in the units

in this study. In fact,

their analysis of

workload suggested

that there were more

than enough nurses

for the number of

patients.

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1� Random effects—dv

patient outcome.

� Logistic regression

� EOF

� Country

� Optimum number

of beds was by

their calculation 9

� Difficult to summarise

this study which was

complex and published

as a book.

Audit Commission

(1999)

England & Wales

Prospective

survey conducted

in 1998 and linked

to a benchmarking

database (ICNARC)

Observational study

� 227 (100%) Trusts

in England and Wales

and 243 (85%)

adult ICUs returned

surveys

� ICNARC supplied data

on 15,805 patients

in 79 ICUs. 52 units

agreed to allow the

use of their survival

data.

� Report does not state

exactly which statistical

tests were used.

� Nurse staffing ratios

and skill mix

� Apache II

� Not clear whether

any other controls

used.

� No association Not tested � This work is highly

relevant to the research

question in this study,

but primarily a ‘value

for money’ study rather

than a piece of

academic research.

Details about how the

research was conducted

are not as complete as

they would be in a

journal article. Results

are not presented in

tables.

� ICUS that supplied

survival data are not

randomly selected.

They are units that have

self-selected for

benchmarking their

practice, so may not be

representative of units

in the UK.

Vicca (1999)

England

19 months

Retrospective

(descriptive)

cohort study

� 1 ICU

� 50 patients with

MRSA

� Information on nurse

staffing for each 8 h

shift

� Correlations

� N of trained ITU

nurses per shift/

number of patients,

for each 8 h shift.

� Trained+extra

staff/number of

patients

� Staffing level:

total number nurses—

total dependency score

� Total number of

nurses/shift dependency

score.

� Patient

dependency

included in

calculations of

workload.

Not tested Weak but significant

(t-test) �ve

correlations between

MRSA and Mean

staff/patient ratio

r=�0.150 (�0.069

to �0.229);

p<0.001.Peak

staff/patient ratio

r=�0.145 (�0.064

to �0.224);

p<0.001.Mean

nurse/patient ratio

� Use of correlations

as method of analysis

limits the inferences

that can be drawn from

this study.

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2Table 1 (Continued )

Reference N of units Nursing variables Risk adjustment,

other controls

Relationship to

mortality, size of

effect, significance

Relationship to

adverse events,

size of effect;

significance

Comments

Location N of patients

Period studied Data analysis

Design

� Sum of daily staffing

levels on each

of three shifts

� Peak, trough and

mean scores in a day

r=�0.145 (�0.065 to

�0.225); p<0.001.Trough

staff/patient ratio

r=�0.137 (�0.055

to �0.216); p<0.001.

Number of MRSA

cases and daily

surplus or deficit;

peak staffing level

r=�0.147 (�0.066

to �0.226); p<0.001.

Trough staffing levels

r=�0.171 (�0.090 to

-0.249); p<0.001.

Daily total

r=�0.166 (�0.086 to

�0.245); p<0.001.Pronovost et al.

(1999)

Maryland, USA

1994–1996

Retrospective

and prospective

observational

study

� 39 ICUs (85%) units

invited to participate

returned their

questionnaires)

� 2606 patients with

abdominal aortic

surgery

� Patient data linked

to prospective

survey of ICU directors

� Multi-level multi-

variate regression

� Nurses/patient during

day & evening Fewer

(less than or equal

to 1:2) vs More (>1:2)

� Socio-demographic

variables

� Romano–Charlson

illness severity

� Hospital volume

� Surgeon volume

� No association

between nurse/patient

ratio and in-hospital

mortality in

multivariate

regression

� No association

between nurse/patient

ratios and

complications on

a list selected by

experts (full list not

given in the text)

� Association with

hospital LOS: fewer

nurses in evening

associated with

mean increase 20%

(7–33%)

� Association with

ICU LOS: fewer

nurses in day

associated with mean

increase 49%

(17–91%)

� Main focus of the

paper was on the

impact of daily

rounds by an ICU

physician who had a

big impact on mortality,

adverse events and

resource use

� Authors did not discuss

why nurse/patient ratios

should be related to

increased stay in ICU

and hospital, and not

related to complications

and mortality

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3Robert et al. (2000)

France? or US?

Large inner city

public hospital

1994–1995

Nested case-control

study

� 1 20-bed SICU

� 28 ‘cases’ with BSI

compared to 99

randomly selected

controls who stayed

in SICU at least

3 days

� Multi-variate logistic

regression

� Regular staff, pool

nurses and nurse-

patient ratio

� Periods 1 (8 months)

vs 2 (5 months)

� Regular nurses

hours per

patient=10.6 vs

9.1 and pool nurses

hours per patient=2.2

vs 4.4

� Total nurse hours

per patient=12.8

vs 13.5

� Apache score

� BSI and control

patients were

similar on many

dimensions but

did differ on a

number of

characteristics that

might be associated

with BSI (e.g.

longer stays in

SICU)

� BSI patients were

significantly more

likely to die

� BSI cases’ had signifi

cantly lower regular

nurse to patient ratios

and higher pool nurse

to patient ratios for

the 3 days before BSI.

� Increased risk of BSI

in period 2 (more

pool nurses) in

multivariate analysis:

OR 3.8 (1.2–8.0)

� Overall N–P ratios

not significant.

� No control for other

changes that might have

occurred between

periods and affected

BSI.

� Tarnow-Mordi

et al. (2000)

� Scotland

� 1992–1995

� Prospectively

collected data,

observational

cohort study

� 1 unit

� 1050 patients

(all admitted

that met Apache

II criteria)

� Data collected

each shift

� Multiple logistic

regression

� Occupancy per shift

� Total ICU nursing

requirement as

defined by UK

ICS for each shift

� Ratio of occupied

to appropriately

staffed beds per shift

� Patients grouped on

composite measure of

ICU workload based on

� Average nursing

requirement/occupied

bed/shift

� Peak occupancy in

any shift during stay

� Apache II

� Patients divided

into 4 categories

of admissions.

� Medical establishment

constant over the

period.

� Patients exposed

to high ICU

workload were more

likely to die than

those exposed to

lower workloads.

Measures of workload

most associated with

mortality were peak

occupancy, average

nursing

requirement/occupied

bed/shift and the ratio

of occupied to appro-

priately staffed beds.

� Adjusted odds ratio for

mortality with moderate

workload as the refer-

ence

category:

Low workload OR

2.0 (1.2–3.3)

Intermediate OR 1.9

(1.2–3.1)

High OR 3.1 (1.9–

5.0)

Not tested � Included time-varying

covariates but did not

really exploit them

� Did not exclude patients

own dependency scores

for the calculation of

unit level dependency

scores (as patients get

closer to death their

need for nursing

increases)

� 337 deaths in total, 49

more than would have

been predicted by

Apache II alone.

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4Table 1 (Continued )

Reference N of units Nursing variables Risk adjustment,

other controls

Relationship to

mortality, size of

effect, significance

Relationship to

adverse events,

size of effect;

significance

Comments

Location N of patients

Period studied Data analysis

Design

Pronovost

et al. (2001)

Maryland, USA

1994–1996

Retrospective and

prospective

observational

study

� 38 ICUs

� See above for

sample size and

data characteristics

� Bi-variate

analysis to assess

relationship with

each complication,

significant ones put

into multi-variate

multi-level models

Nurses/patient during

day

Fewer (1:3 or 4) vs

More (1:1 or 2)

� Socio-demographic

variables

� Romano–Charlson

illness severity

� Hospital factors

� Surgeon factors

� No association � 14 complications

identified by 4 ICU

physicians

� Fewer nurses

associated with:

Reintubation RR

1.6 (1.1–2.5)

Complications

RR 1.7 (1.3–2.4)

Any medical

complication RR

2.1 (1.5–2.9)

Pulmonary

insufficiency

after procedure

RR 4.5 (2.9–6.9)

� Authors argue that

impact of nurses on

pulmonary

complications has ‘face

validity’ because nurses

who care for 3 or more

patients will have less

time to devote for

prevention of

pulmonary

complications.

� Coding of

complications and

co-morbid conditions

may not be as accurate

a principal diagnosis.

� 7 hospitals with 478

patients had fewer

nurses and 31 hospitals

with 2128 patients had

more nurses in ICU

Amaravadi

et al. (2000)

Maryland, USA

1994–1998

Multi-centre,

cross-sectional

retrospective

observational

with elements

of a cohort

design

� 35 ICUs

� 366 patients with

oesophageal resection

� Hospital discharge

data

� Staffing survey data

for 1996, multi-level

logistic regression

Average night-time

nurse/patient ratios

Low <1:2 vs

High >1:2

� Age, sex, race

� Type of operation

� Type of admission.

� Hospital

� Surgeon volume

No association NNPR<1:2 associated

with:

Reintubation OR

2.6 (1.4–4.5); p=0.0001

Pneumonia OR 2.4

(1.2–4.7); p=0.012

Septicaemia OR 3.6

(1.1–12.5); p=0.04

� Authors identify 12

complications that

might be associated

with NNPR, but

findings reported for

only 9 of these

� Complications that

were not significantly

related to NNPR were:

aspiration, post-op

infection, myocardial

infarction, cardiac

arrest, surgical

complications& acute

renal failure. No results

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5are reported for

pulmonary

insufficiency, cardiac

complications&

re-operation for

bleeding

� Also more convincing

evidence that

NNPR<1:2 associated

with increased LOS

and costs.

Dimick et al.

(2001)

Maryland, USA

1996–8

Multi-centre

retrospective

cohort study

� 51 ICUs

� 556 patients with

hepatic surgery

� Hospital discharge

data

� Survey of

organisational factors

� Bi-variate analysis

for variable selection

� Logistic regression

for mortality

� Linear regression

for adverse events

Average night-time

nurse/patient ratios

Fewer (51:3) vs

More (41:2)

� Demographic

characteristics

� Romano–Charlson

co-morbidity index

� Ruptured vs

non-ruptured aorta

� Type of admission

� Type of operation

� Hospital volume

� Surgeon volume

� No association � Fewer vs more

nurses reintubation

OR 2.9 (1.0–8.1);

p=0.04

� No significant

relationship with

aspiration, pulmonary

insufficiency,

pneumonia,

septicaemia,

post-operative

infection,

cardiac complications,

cardiac arrest, acute

myocardial infarction

and acute renal failure.

� Overall rate of

complications was

28%.

� 240 patients in 25

hospitals had fewer

nurses and 316 in 8

hospitals had more

nurses.

� Reintubation was only

one of 10 possible

complications analysed.

� Authors suggest that

nursing is more

important at night when

there are fewer other

professionals around.

� Hospital costs of

patients cared for by

fewer nurses were 14%

higher

Dang et al.

(2002)

Maryland, USA

1994–6

Multi-centre

retrospective

observational

study

� 38 units

� 2606 with

abdominal aortic

surgery

� Hospital discharge

data on patients

� Survey of

organisational factors

� Multiple logistic

regression for

14 complications

Nurse/patient

ratio:

Low=1:3 day and

night

Medium=1:3 on

day or night

High 41:2 day and

night

� Socio-demographic

variables

� Romano–Charlson

� Ruptured vs non-

ruptured aorta

� Type of admission

� N of cases of

AA surgery

at hospital

each year

� N of ICU beds

Not tested � Respiratory

complications

(low vs high): OR

2.33 (1.50–3.60)

� Cardiac complications

(medium vs high)

OR 1.78 (1.16–2.72)

� Other complications

(medium vs high)

OR 1.74 (1.15 to 2.63)

� Extends Pronovost et al.

(1999) by examining

nurse staffing on all

shifts and controlling

for nursing unit

structure

� 40% of patients in the

sample developed a

complication.

� Authors suggest that

link between nursing

E. West et al. / International Journal of Nursing Studies 46 (2009) 993–10111006T

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given if the nursing variables were measured at one point

in time. Low scores would be given if there was little

variation in the variable because it cannot produce a

satisfactory test of the hypothesis.

4. C

ontrols (3): Are there adjustments for ‘supply-side’

confounders such as variations in medical staffing,

equipment, use of protocols, etc.? High scores would

be given when the authors include a range of variables

that might affect the dependent variable over and

above the nursing variables. Low scores are given

when there is little discussion of possible rival hypoth-

eses and no attempt to include control variables in the

models.

5. R

isk Adjustment (1): Are variations in the case mix of

patients adjusted for using an appropriate set of prog-

nostic variables or a validated prospective scoring system

such as APACHE or SAPS? Studies that did not use any

form of risk adjustment were excluded from the study.

High scores would be given to studies that use standard,

well-known forms of risk adjustment and low scores

would be given where the procedure by which risk

adjustment is achieved is by a process that is non-

standard.

6. S

tatistical analysis (3): Were the methods used appro-

priate to the data? For example, did the analysis exploit

the multi-level nature of the data if present? In time-series

data, was autocorrelation taken into account? Multi-level

modelling would obtain a high score, time-series analysis

a medium score and comparison of means would achieve

a low score.

7. R

eporting (2): Was the description of the study clear and

complete? Was enough information given in the paper to

replicate the study? High scores are given for clearly

written descriptions of the work and low scores follow

the conclusion that it would be difficult to replicate this

study.

8. In

terpretation (3): Was the interpretation of results objec-

tive and balanced? Where the conclusions supported by

the data? Is there any discussion of the possibility that

significant results could have occurred by chance? High

scores would be given to a study where the conclusions

are clearly supported by the analysis and there is a

balanced discussion of the strengths and weaknesses of

the study. Medium scores are given when the interpreta-

tion is not closely linked to the analysis and which fails to

mention some of the weaknesses of the study. Low scores

are given to studies where the interpretation seems biased

or subjective.

This scoring system was applied to eight studies1 and the

results are shown in Table 2.

Although the Audit Commission (1999) study met all the criteria

inclusion in this table the published report did not contain

ugh information about how the study was conducted to pursue

issue of quality.

E. West et al. / International Journal of Nursing Studies 46 (2009) 993–1011 1007

Table 2

Quality of non-experimental observational studies

Study Scope (4) Data (4) IV (4) Control (3) RA (1) Analysis (3) Clarity (2) Interp. (3). Total (24)

Shortell et al. (1994) 4 4 0 2 1 2 2 2 17

Bastos et al. (1996) 3 4 0 1 1 2 1 2 14

Reis-Miranda (1998) 4 4 1 3 1 3 1 2 19

Pronovost et al. (1999) 3 2 2 1 0 3 2 2 15

Tarnow-Mordi et al. (2000) 1 4 4 2 1 2 2 3 19

Dimick et al. (2001) 3 2 2 2 0 3 2 2 16

Pronovost et al. (2001) 3 2 2 1 0 3 2 3 16

Dang et al. (2002) 3 2 2 2 0 3 2 3 17

4.2. Studies of the impact of nursing on adverse events

Ten studies focussed on whether nursing resources affect

the risk of an adverse event. Five were large-scale observa-

tional studies, and five were smaller case-control and cohort

studies.

Giraud et al. (1993) followed 382 patients in two ICUs in

France. Nursing workload was subjectively assessed by each

nurse on each shift using carefully defined and operationa-

lised variables. Complications occurred in 31% of admis-

sions, with 13% being described as major (severe

hypotension, respiratory distress, pneumothorax and cardiac

arrest). There was an increased risk of major complications

when nursing workload was described as high or excessive.

The authors called for preventative measures to be targeted

at the elderly and the most severely ill patients as most

vulnerable to adverse events.

Robert et al. (2000) focussed on the influence of the

composition of the nursing staff on primary bloodstream

infection (BSI) rates in a surgical ICU. This was described as

a nested case-control study covering two periods that dif-

fered both in staff/patient ratio and in the proportion of pool

nurses (i.e. nurses who were members of the hospital pool

service or agency nurses, as opposed to nurses who were

permanently assigned to the SICU) on duty. Patients who

contracted BSI were more likely to have been in hospital

during the 5-month period when the unit was more depen-

dent on pool nurses than during an 8 month reference period.

Also ‘case’ patients had significantly lower regular nurse-to-

patient ratios and higher pool nurse-to-patient ratios for the 3

days before contracting BSI. In multi-variate analyses,

admission to hospital when the ratio of pool nurses was

high, total parenteral nutrition and CVC days were shown to

be significant independent risk factors for BSI. This study is

important because it focuses on differences in nursing

resources above and beyond simple nurse–patient ratios that

might affect patients, but the study design is vulnerable to

concurrent confounding; other possible factors that might

have changed between the two periods were not controlled

for.

Thorens et al. (1995) investigated whether nursing vari-

ables were related to the duration of weaning from mechan-

ical ventilation in 87 patients with chronic obstructive

pulmonary disease (COPD) on one ICU unit over 6 years.

A composite ‘index of nursing’ was constructed to compare

the effective workforce of the nurses (number and qualifica-

tions) with the ideal workforce required by the number and

condition of the patients in the unit at the time. They found

that below a threshold in the available workforce of ICU

nurses, the weaning time for patients with COPD increased

dramatically. As with the study by Robert et al. (2000), the

main threat to validity in this study comes from comparing

event rates for different periods in time; it seems quite likely

that there were confounders not accounted for in the model.

For example, at the same time as the number of nurses

increased, the number of doctors did too, and there is no way

of knowing whether the change in the duration of weaning is

due to the increase in nurses, increase in doctors, or to some

other differences between the two periods not included in the

model.

Another single centre study was conducted by Fridkin

et al (1996) who gathered data on all patients who developed

a central venous catheter (CVC) associated BSI during an

outbreak in 1992–1993. Controlling for a number of factors

that pre-dispose patients to contracting CVC BSI, such as

total parenteral nutrition, assisted ventilation and duration of

hospitalisation, they found that the nurse/patient ratio also

had a significant impact on the probability of infection.

Vicca (1999) found that the acquisition of methicillin resis-

tant staphylococcus aureus (MRSA) in the ICU of a tertiary

referral centre correlated with peaks of nursing staff work-

load and times of reduced nurse/patient ratios within the

unit. This study used correlation analysis which is less

powerful than the statistical analyses in some of the other

studies.

A team at Johns Hopkins has published 5 large scale

studies in this area. Pronovost et al. (1999), in a study

focussed on ICU physicians, showed a relationship between

nurse staffing and length of stay. Using a questionnaire

developed by Shortell et al. (1994), they gathered informa-

tion from medical directors about the organisation and

staffing of 46 ICUs in Maryland and linked this to outcome

data on patients undergoing abdominal aortic surgery. They

found that having fewer nurses on duty during the day

increased hospital length of stay and number of days spent

in the ICU.

E. West et al. / International Journal of Nursing Studies 46 (2009) 993–10111008

A later study using the same data investigated whether

the link between nursing resources and length of stay was

due to patients developing medical and surgical complica-

tions after abdominal aortic surgery (Pronovost et al., 2001).

Control variables in the models included hospital character-

istics, surgical volume and daily rounds by an ICU physi-

cian. The Romano–Charleson co-morbidity index was used

to adjust for case mix, and nurse-to-patient ratios on the day

shifts were dichotomised into ‘more ICU nurses’ (1:1 and

1:2) and ‘fewer ICU nurses’ (1:3 and 1:4). Patients in

hospitals with fewer ICU nurses were more likely to have

post-operative complications, particularly pulmonary insuf-

ficiency and reintubation. This was found in models that

included daily rounds by an ICU physician, suggesting that

medicine and nursing inputs had independent effects on

patient outcomes.

Pronovost et al. (1999, 2001) investigated the impact of

nurse to patient ratios on the day shift but two later studies

explored the theory that nursing takes on an increased

importance at night when fewer physicians and ancillary

staff are present. Amaravadi et al. (2000) investigated

whether the night time nurse to patient ratio, dichotomised

into one nurse caring for one or two patients (>1:2) versus

one nurse caring for three or more patients (<1:2) in the

ICU, affected patients undergoing oesophageal resection.

Multi-variate analysis was used to adjust for case mix, and

for hospital and surgeon volume. A ratio of nurses to patients

greater than 1:2 was associated with an increased the prob-

ability of complications such as pneumonia, reintubation,

and septicaemia, increased length of stay and increased

costs.

Dimick et al. (2001) asked whether nurse to patient

ratios at night had an effect on patients’ experiences after

another high-risk surgical procedure, hepatic surgery.

They found a significant increase in post-operative pulmon-

ary complications and use of resources for patients

receiving post-operative care in ICUs in which one nurse

provided care for three or more ICU patients at night.

However, only reintubation was significant in the multi-

variate analysis,

Building on these studies that focussed on nursing work-

force characteristics on either day or night shifts, Dang et al.

(2002) investigated the impact of nurses on all shifts on

cardiac, respiratory and other complication in patients who

had abdominal aortic surgery in 38 units in Maryland

between 1994 and 1996. They argued that nurse staffing

has an impact on outcomes because nurses are responsible

for monitoring patients, co-ordinating care and more spe-

cifically for post-operative pulmonary hygiene. Multiple

logistic regression and multi-level hierarchical modelling

showed that there was a statistically significant increase in

the likelihood of respiratory complications in patients cared

for in low nursing intensity versus high nursing intensity

conditions and that there was an increased likelihood of

cardiac and other complications in medium versus high

intensity nurse staffing.

Taken together, the five studies from Johns Hopkins are

very similar and it is difficult to give them as much analytical

weight as independent studies. Their strengths are that they

cover many sites, use high quality administrative and survey

data, articulate the mechanisms by which they expect nur-

sing to be implicated in patient outcomes and consult experts

about the most appropriate complications to be used as the

dependent variables. The fact that they focus on one surgical

procedure in one state of the US is a limitation, and the crude

measures of nurse staffing and risk adjustment procedures as

well as the cross-sectional retrospective research designs on

which they are based are also weaknesses. They tend not to

discuss the fact that sometimes only a minority of the

hypotheses they test are supported in any one study and

that some of the statistically significant relationships may

have occurred by chance.

4.3. Studies of the impact of nursing resources on

mortality

There were 10 studies of the effect of nursing resources

on mortality. These included studies of nurse staffing ratios

(Bastos et al., 1996; Dimick et al., 2001; Pronovost et al.,

1999, 2001; Reis Miranda et al., 1998; Shortell et al., 1994)

and skill mix (Audit Commission, 1999). Only three studies

detected a significant relationship.

Giraud et al. (1993), as well as finding an increased risk

of major complications when nursing workload was high,

found that the patients who developed complications were

twice as likely to die. This study is distinguished by the use

of survival analysis, and adjustment for age, organ system

failure, SAPS and disease prognosis. However, as the

authors note at the end of the paper, it is possible that when

death seems imminent staff may become more interven-

tionist and take more risky clinical decisions. This would

expose the patient to harm as well as benefit, and so the

complications might arise from an increase risk of dying,

rather than death being the result of the complications. They

concluded: ‘‘Major iatrogenic complications were frequent,

associated with increased morbidity and mortality rates,

related to high or excessive nursing workload and were

often secondary to human errors.’’

Robert et al. (2000) found significant relationships

between staffing characteristics, BSI and mortality. This

study was described in detail above and in Table 1.

One UK study also found a relationship with mortality.

Tarnow-Mordi et al. (2000) investigated whether hospital

mortality was independently related to nursing requirement

and other measures of workload in one Scottish hospital

between January 1992 and December 1995. They controlled

for patient characteristics using APACHE II scores. Using a

formula to calculate the number of ‘appropriately staffed

beds,’ they found that patients who were treated in times

when the ICU workload was high were more likely to die

than those who were there during periods of low workload.

In fact mortality was more than twice as high in patients

E. West et al. / International Journal of Nursing Studies 46 (2009) 993–1011 1009

treated when the unit workload was high. Three measures of

workload were particularly important: peak occupancy,

average nursing requirement per occupied bed per shift

and the ratio of occupied to appropriately staffed beds.

The main limitation of this study is that it was based on

data from only one unit. However the study is unique in that

levels of nurse staffing relate to individual patients, whereas

most studies use a measure of nurse staffing for a unit at one

point in time. The authors draw attention to the fact that they

did not exclude each patient’s own scores in calculating the

measure of unit level dependency which may introduce bias.

Two studies of technology in ICU included nurse staffing

as control variables; Bastos et al. (1996) studied 10 units in

Brazil and Shortell et al. (1994) studied 42 ICUs across the

USA. Although neither found a relationship between nursing

characteristics and mortality, both point out that there was

very little variation in nurse staffing across the units that they

studied, so neither can be considered a satisfactory test of the

hypothesis.

Lack of variation in staffing was not a problem in the

study conducted by the Audit Commission (1999) in the UK,

but they too failed to find an association between staffing

characteristics (nurse–patient ratios and skill mix) and

patient mortality. This study is difficult to evaluate because

very few details of the statistical analysis are included in the

published report. The investigation was designed primarily

to assess the extent to which units across the UK were

performing in terms of ‘value for money’ so the focus

was on comparing expenditure and resource use and only

secondarily on linking these to patient outcomes.

In spite of finding relationships between nursing

resources and other adverse outcomes neither Amaravadi

et al. (2000) nor Dimick et al. (2001) were able to detect a

significant effect on mortality.

5. Discussion

A systematic search of the literature identified 15 studies

of the link between nursing resources and patient outcomes

in ICUs. This review builds on previous work in the area by

bringing together studies of both mortality and adverse

events into one systematic review. It also devotes attention,

not just to the findings, but to the methods by which they

were obtained. We devised a rudimentary system for eval-

uating aspects of observational studies. This showed that

studies vary in quality and suggest that this may be relevant

to the interpretation of their findings.

All the included studies that examined relationships

between nursing resources and adverse events found a link

with at least one outcome. However, studies that investigated

the impact of workforce variables on a large number of

adverse events sometimes reported positive associations for

only a few of the relationships tested, without discussing the

possibility that some of these might have occurred by

chance. Five of the studies of adverse events emerged from

the same research team (Amaravadi et al., 2000; Dang et al.,

2002; Dimick et al., 2001; Pronovost et al., 1999, 2001) and

use different parts of the same data, and so may not be

regarded as independent sources of evidence. Five of the

other six (Fridkin et al., 1996; Giraud et al., 1993; Robert

et al. (2000); Thorens et al., 1995; Vicca, 1999) were

conducted in single units. Taken together these considera-

tions suggest that while there may be more evidence of a link

between ICU nursing resources and complications than there

is between ICU nursing and mortality, the evidence is not yet

convincing.

In only three of ten tests of the link between nursing

resources and mortality was the null hypothesis rejected.

The three studies concerned were based in one or two units,

whereas those that found little or no evidence for an associa-

tion were large multi-centre studies. The small-scale studies

were based on detailed descriptive information about the

links between nursing resources and patient outcomes, with

careful articulation of the mechanisms involved, attention to

the operationalisation of the variables, and prospectively

collected data. They tended to use quasi-experimental case

control and cohort designs. However these small studies

have limited generalisability. Some have design flaws and

some rely on descriptive statistics, such as correlations rather

than more powerful inferential statistics such as regression

analysis.

It seems premature to conclude that there is no associa-

tion between nursing resources and mortality. If the link is a

weak one it may not be detectable in large studies using

crude indicators and poor adjustment for confounding, while

small studies with good data may detect it unreliably, i.e.

with large confidence intervals.

Table 3 summarises the findings.

5.1. Future research

Mortality is the most important dependent variable in the

intensive care setting because about 30% of all patients

admitted to ICUs die, and mortality varies across units in

ways that are currently difficult to explain. However this

review suggests that the relationship between nursing

resources and mortality may be quite weak, at least above

some threshold level, and large studies using carefully

developed and prospectively collected measurements may

be needed to estimate the strength of this relationship with

precision. An alternative would be meta-analyses of small

studies, but this requires standardisation of the measure-

ments and classifications used, which may not be possible

retrospectively.

Studies of adverse events are also important because they

may tell us more about the processes by which patients

deteriorate towards death. Adverse events can be linked to

mortality through the concept of ‘failure-to-rescue,’ defined

as death after an adverse occurrence that could have been

amenable to medical intervention (Silber et al., 1992).

E. West et al. / International Journal of Nursing Studies 46 (2009) 993–10111010

Table 3

Main findings of 15 studies

First author Adverse

events

Mortality

Amaravadi et al. (2000) U

Audit Commission (1999) X

Bastos et al. (1996) X

Dang et al., 2002 U

Dimick et al. (2001) U X

Fridkin et al (1996) U

Giraud et al. (1993) U U

Pronovost et al. (1999) U X

Pronovost et al. (2001) U X

Reis Miranda et al. (1998) X

Robert et al. (2000) U U

Shortell et al. (1994) X

Tarnow-Mordi et al. (2000) U

Thorens et al. (1995) U

Vicca (1999) U

Proportion positive 10/10 3/10

A tick means that the study found an association; a cross means that

they were unable to reject the null of no association and a blank cell

means that the hypothesis was not tested.

The hypotheses in the studies reviewed above are derived

from the theory that overwork and staff shortages will

interfere with task performance, including surveillance,

monitoring, early detection of adverse events and preventa-

tive measures (e.g., hand washing, pulmonary hygiene, early

ambulation). One study also links nursing to patients’

experience of pain which may affect the development of

complications and progress through the unit. Experience or

at least familiarity with the unit and its routines and practices

was cited in one study, as was the nurses’ role in co-

ordinating care. Several authors argued that nurses are more

important at night when there are fewer doctors and ancillary

staff around. The conceptual frameworks for studies of this

type could be enhanced by greater attention to what nurses

actually do in intensive care and by interviewing experts

about those characteristics of the nursing staff that would be

most relevant to patient outcomes.

In most of the studies reviewed above there appears to be

an assumption that more nurses will always be better.

However, that may not always be the case. Theoretically

at least, one might imagine a situation where there might be

too many nurses, or perhaps more realistically where adding

more nurses brings little or no additional benefit to patients

in ICUs, at the expense of patients in other parts of the

hospital. Future research needs not only to further develop

the mechanisms linking nursing and outcomes, but to specify

more complex and interesting functional forms of that

relationship, including threshold effects.

The majority of large studies identified in the course of

this review were observational, which means that the impact

of confounding is controlled by including in the model all

variables that are thought to affect the dependent variable.

This is difficult to achieve using existing databases. Future

studies using this design need to devote much more attention

to specifying the full model and justifying the control

variables included in the model.

The majority of studies in this review were cross-sec-

tional and there are well known problems in inferring causal

relationships from studies at one point in time. If studies in

this tradition could also incorporate temporal information

into their analysis and use time series for multiple units,

survival analysis or event history methods they would be

able to make stronger causal claims. It is particularly

important that in future studies an attempt is made to

measure each patient’s exposure to nursing as a variable

that changes from shift to shift, rather than simply measuring

the number of nurses in a unit at one point in time.

To date, studies have focussed on providing empirical

evidence of a link between nursing resources and patient

outcomes. An important further step will be to work out the

clinical and cost implications of the results. In their study of

volume of patients, Iapichino et al. (2004) showed that

relative mortality decreased by 3.4% and 17.0% for every

five extra patients treated per bed per year in overall volume

and high-risk volume, respectively. They concluded that

while total volume was statistically significant, only high

risk volume was clinically significant. Pronovost et al.

(2001) also tried to work out the clinical implications of

their findings and more recently report results of financial

modelling of the Leapfrog Intensive Care Physician (ICP)

staffing standards (Pronovost et al., 2004).

6. Conclusions

ICUs consume a large amount of the health care budget and

nurses are the biggest single expense. However, the evidence

base for current staffing decisions is not well developed and is

the subject of considerable debate (Adomat and Hicks, 2003).

This review confirms that the implications for outcomes and

patient safety of possible changes in ICU staffing are still

uncertain. The relationship between nursing workforce char-

acteristics and patient outcomes should continue to be the

focus of audit and research at unit and national level.

Acknowledgements

The authors would like to thank D.N. Barron for his

critical reading of previous drafts and colleagues at LSHTM

for their support of the larger project of which this study is a

part. Funding for the project was provided by a post-doctoral

fellowship to the first author from the Health Foundation.

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