-1The Feedback Intervention Trial (FIT) - improving hand hygiene compliance in
UK healthcare workers (HCWs): a stepped wedge cluster randomised
controlled trial.
Chris Fuller, RGN, MSc ,
Royal Free Campus, University College London Medical School, University
College , London, England
Susan Michie, PhD ,
University College London, London, England
Joanne Savage, BSc, MRes ,
Royal Free Campus, University College London Medical School, University
College London, London, England
John McAteer, MSc ,PhD,
University College London, London, England
Sarah Besser, MSc ,
Royal Free Campus, University College London Medical School, University
College London, London, England1
Andre Charlett, PhD ,
Health Protection Agency, London, United Kingdom
Andrew Hayward, FRCP, FFPHM ,
Royal Free Campus, University College London Medical School, University
College London, London, England
Barry D. Cookson, FRCPath, FRCP , FRIPH,
Health Protection Agency, London, England
Ben S. Cooper, BSc, MSc, PhD ,
Health Protection Agency, London, England2
Georgia Duckworth, MSc, FRCPath, FRCP,
Health Protection Agency, London, England
Miranda Murray, PhD ,
Health Protection Agency, London, United Kingdom
Annette Jeanes, RGN, MSc,
University College London Hospitals, London, United Kingdom,
Jenny Roberts, PhD, London School of Hygiene and Tropical Medicine,
London, United Kingdom
Louise Teare, FRCPath , DipHIC,
1
-2Mid-Essex NHS Trust, Chelmsford, United Kingdom
Sheldon Stone, BSc, MD, FRCP ,
Royal Free Campus, University College London Medical School, University
College London London, England
Now at Kings College London, London, England
Now at Mahidol University Tropical Medicine Research Unit, Bangkok,
Thailand
1
2
Trial registration: National Research Register Archive
http://www.nihr.ac.uk Identifier N0649197172
2
-3-
ABSTRACT
Authors * Correspondence to: Dr Sheldon Stone, Academic Department of
Geriatric Medicine, Royal Free campus, University College London Medical
School, Rowland Hill Street, London NW3 2PF Tel: 0044 2074332789 Fax 0044
8302378; Email: [email protected]
ABSTRACT
Introduction: Sustained improvements in hand hygiene are key to the WHO
strategy to reduce healthcare associated infection, but are hard to achieve,
with no strong evidence for an effective intervention. Systematic reviews
suggest feedback may be the most effective intervention and call for large
well designed long term randomised controlled trials which apply behavioural
theory to intervention design to optimise effectiveness
Method: Stepped Wedge cluster RCT (1st Oct 2006-31st Dec 2009), with a null
hypothesis that a theoretically grounded feedback intervention did not
increase hand hygiene more than routine practice in 60 wards (16 intensive
therapy units (ITUs) & 44 acute care of the elderly (ACE) wards in 16
English/Welsh hospitals, already enrolled in the pragmatically designed
national cleanyourhands campaign.
Intervention: Based on Goal Setting and Control theories, it was delivered to
HCWs individually and in groups in 20 minute sessions weekly, documented on
forms, as part of an audit cycle repeated every four weeks. Observation and
feedback were coupled to personalised goal setting and action planning.
Computer-generated randomisation of hospitals into intervention at five
steps; hospitals aware only of own allocation.
Outcomes:-Primary-observed hand hygiene compliance (%) by blinded
observers. Secondary- monthly procurement soap & alcohol hand rub (AHR)
(L/bed day)
Analysis: Generalised linear mixed regression models, accounting for
confounders, temporal trends and fidelity to intervention (forms used/month);
all 60 wards randomised, followed up to end of trial; 33 wards (11 ITU, 22 ACE)
implementing intervention for a mean (SD) of 12 (7) months.
Findings : Intention to treat analysis: Estimated odds ratio (OR) for hand
hygiene compliance rose post-randomisation (1.44; 95% CI 1.18,1.76;p<0.001)
in ITUs but not ACE wards (1.06 [0.87,1.27];p=0.5), equating to 7%-9% absolute
increase in compliance on ITUs. Per protocol analysis: OR for compliance
3
-4rose for both ACE wards (1.67 [1.28-2.22];p<0.001) & ITUs (2.09 [1.552.81];p<0.001), equating to 10%-13% and 13%-18% absolute rises in
compliance respectively. Fidelity to intervention closely related to
compliance on ITUs (OR for compliance 1.12 [1.04,1.20];p=0.003 per
completed form). Secondary outcome-ITT analysis for 20 outcome: A 31% (1155%; p=0.003) relative increase in soap (but not AHR) procurement for ITUs
only, with an effect of fidelity to intervention of a 12% (4-20%; p=0.003) rise per
form.
Interpretation: A theoretically grounded feedback intervention, coupling
feedback to personalised goal setting and action planning, produced a
moderate but significant sustained
improvement in hand hygiene
compliance in wards where a national hand hygiene intervention was
already part of routine practice. The effect increased with fidelity to
intervention and was greater on ITU than ACE wards. Implementation was
difficult and a study to improve this is needed. Wards already engaged in
hand hygiene improvement programmes may wish to integrate this
intervention’s techniques into their standard audit and appraisal systems.
Funding: Patient Safety Research Programme, Trustees of the Royal Free and
GOJO industries, none of whom had any role in study design, data collection
and analysis, decision to publish, or preparation of the manuscript.
Ethics: Multicentre Research Ethics Comittee 05/MREC10/2
4
-5INTRODUCTION
Systematic(1,2) and critical reviews (3) have shown that in randomised and
other controlled trials in a wide variety of hospital and community settings,
over a wide range of background infection rates, hand hygiene significantly
reduces infection, with odds ratios of 0.4 for infection common. Despite this
evidence, hand hygiene compliance amongst healthcare workers (HCWs) is
poor, with levels of 25-40% being common (4-6).
Sustained improvements in hand hygiene are key to the WHO strategy to
reduce international rates of HCAI (7-9). To that end, national and regional
hand hygiene campaigns have been introduced in many countries (10, 11),
including the cleanyour hands campaign in England and Wales, which was
rolled out to all acute trusts in December 2004, and comprises bedside
placement of AHR, posters and patient empowerment materials encouraging
HCWs to clean their hands, plus audit and feedback of hand hygiene
compliance at least once every 6 months. However, hand hygiene
compliance is difficult to change and any change is difficult to sustain. The
first systematic review of educational and behavioural interventions (12) to
improve hand hygiene compliance identified feedback as the intervention
most likely to be successful, although the authors commented that its effect
was modest, short-lived and that it probably needed to be repeated regularly
as part of a multi-faceted intervention. The review called, as did a subsequent
systematic review (13), for well-designed and long-term controlled trials of
behavioural interventions to improve hand hygiene as previous studies were
short-term and used weak designs.
Systematic reviews of controlled trials of feedback in multiple fields of health
care generally report modest but significant effects in improving adherence
to evidence-based guidelines (14,15). However, effects may have been
limited by the absence of behavioural theory to inform intervention design
(15,16).
5
-6This paper reports the results of a three year stepped wedge cluster
randomised controlled trial of a feedback intervention, the Feedback
Intervention Trial (FIT), to improve hand hygiene compliance in HCWs on
wards already carrying out the pragmatically designed cleanyourhands
campaign. The intervention was designed using Goal-setting Theory (17) and
Control Theory (18). These conceptualise behaviour as goal driven and
feedback controlled, with goal-setting and action planning augmenting the
effect of feedback. Operant learning theory (19) was also used, providing a
reinforcement component to the intervention. This is based on the principle
that contingently associating target behaviours with reward, itself a goal,
increases the frequency of those behaviours.
HYPOTHESIS
The
null
hypothesis was
that the behaviourally designed feedback
intervention has no effect on hand hygiene compliance compared to
standard practice, which included routine use of the pragmatically designed
national cleanyourhands campaign.
METHODS
Ethics statement: ethical approval was received from the
Multi-centre
Research Ethics Committee (Scotland B) (05/MRE10/2). Ward managers,
infection control nurses and ward co-ordinators gave written consent. Each
trust, hospital, and ward was assigned a confidential ID code and all patient
data was collected in anonymised aggregate form.
Trial design: A stepped wedge cluster randomised controlled design (20) was
chosen following piloting to ensure equity, prevent contamination and
disappointment effects in wards that would have otherwise been unselected
and facilitate roll out of the intervention. This design also has the advantages
of increasing power, controlling for secular time trends and exploring the
relationship between the timing of interventions and their effectiveness (20).
6
-7Participants Sixty acute care of the elderly (ACE) or general medical wards
and intensive therapy units (ITUs) in 16 acute NHS trust hospitals across
England and Wales (14 general acute and 2 teaching hospitals) participated
in the trial (Table 1). These wards were chosen as they were likely to have high
levels of HCAI (21,22). In each hospital, one ITU and a maximum of 3 ACE
wards were recruited. Sites were recruited by requests posted on the
cleanyourhands campaign website and by contacting infection control
teams directly through the NOSEC study (www.idrn.org/nosec.php) which
was evaluating the cleanyourhands campaign. Sites were eligible if they still
wished to be involved after three or four site visits to gain the support of senior
infection control team and management, ward managers, senior nurses and
consultants, could offer the ITU and two or three ACE wards as the clinical
settings for the trial and were implementing the cleanyourhands campaign as
part of routine practice.
Intervention
A
theory-based,
sustainable
intervention
was
designed
by
Health
Psychologists (23,24) following the MRC framework for complex interventions
(25). The development phase involved identifying an appropriate theoretical
framework and associated techniques (16,25) to inform intervention design.
Goal-setting, control and operant learning theories (17-19) were identified,
and the individual and group level behaviour change techniques of
feedback, goals, action planning and contingent reward were selected. The
feasibility/piloting phase involved presentation of a prototype intervention to
focus groups with HCWs to identify possible problems of implementation.
Subsequent refinements to the intervention were made as a result.
Data
collection methods were developed for outcomes (26), fidelity to the
intervention protocol and for the assessment of potential confounders. The
refined intervention and data collection methods were then piloted on 7
wards in 3 hospitals (none of which were participants in the full trial). These
wards were sequentially introduced to the intervention over an eight month
period. The pilot study is described in full elsewhere (23) and its outcomes
were used to inform final refinements prior to the definitive RCT phase.
7
-8-
The intervention was carried out by an allocated “ward coordinator” who
was generally a junior ward sister or infection control link nurse, and involved a
repeating four-week cycle.
Week1: Hand hygiene observation of an individual Nurse/Health Care
Assistant for a 20-minute period. Immediate feedback was given after the
period of observation, and, for instances of non-compliance with hand
hygiene, the person observed was helped to formulate an action plan to
improve behaviour.
For example, when a HCW didn’t clean hands after
touching patient equipment but not the patient, the action was set as “X will
use AHR even if only touching patient equipment”.
Observation was discrete and carried out by staff who were routinely in the
ward, minimising the chance of awareness of observation and therefore
atypically compliant behaviour. Observations were carried out from a point
of the ward where patient care activities and at least one sink could be
observed; for reasons of patient privacy the researcher did not go behind
patient curtains to continue observation. If compliance was 100%, the staff
member was praised and given a certificate that was filed for use in annual
professional development appraisal. If there were two or more instances of
poor compliance during the observation period, the staff member was
observed at some point within the subsequent month.
The aim was that
every member of staff would be observed at least once a year.
Week 2: As for week one except that a “non-nurse” (doctor or other health
care professional) was observed.
Week 3: Hand hygiene observation of a ward area for 20 minutes, recording
the hand hygiene behaviour of all HCWs entering that area (group
compliance). Poor practice was documented but feedback was not given at
the time.
8
-9Week 4: The week 3 observations (group compliance) were fed back and
action plans formulated at a ward meeting. For example, when student nurse
practice was observed to be poor, the following action plan was set. “All
student nurse assessors to take student nurses through hand hygiene practice
on arrival on ward”.
Ward coordinators were provided with forms to record goals, observations,
feedback and action plans.
Training the implementers: Ward co-ordinators were trained in hand hygiene
observation (26) and how to provide feedback, help HCWs to set their own
hand hygiene goals and make action plans. Training comprised discussion of
the training materials and a series of structured exercises, delivered by study
personnel and usually completed in 1 to 1 ½ hours (www.idrn.org/nosec.php).
In total 62 visits to trusts were made to train ward staff and ICNs. These could
be difficult to organise and representatives from 11 wards (7 trusts) never
attended training. Initial visits were followed up 6-8 weeks later and further
training was given as requested (36 wards). Wards were offered no further
follow-up from the research team six months after starting the intervention.
Fidelity to intervention: Ward co-ordinators were asked to fill out a form
(www.idrn.org/nosec.php) each time that an observation and/or feedback
session took place and return them to the study team. The number of forms
returned to the study team each month was used as a proxy measure of
fidelity to the intervention.
Outcomes: Data were collected from 1st October 2006 to 31st December 2009.
Primary outcomes: Hand hygiene compliance was measured by covert direct
observation by an observer blinded as to ward allocation or randomisation to
the intervention. Observation periods were for one hour, every 6 weeks, using
a reliable measure, the Hand Hygiene Observation Tool (26) with proven
sensitivity to change. Compliance was expressed as a percentage of the
9
- 10 hand hygiene moments that were associated with observed hand hygiene
behaviour (use of alcohol hand rub [AHR] or soap).
Secondary outcomes: Monthly soap and AHR procurement data (litres per
bed day) were collected as a proxy measure of hand hygiene compliance
for each of the study wards, as this reflects 24-hour, seven days a week use; is
not subject either to observer bias or to reactive effects from direct
observation; and is less labour intensive to collect than directly observed
compliance. Data were collected either from hospital supplies departments
or directly from NHS Supply Chain. Fidelity to intervention was measured by
forms returned per month and confounders by noting staffing levels, skills mix
and agency rates.
Tertiary: a. HCAI data (cases per 10,000 bed days)- Hospitals’ own ward data
on hospital-acquired methicillin resistant and sensitive Staphylococcus aureus
bacteraemias (MR&MSSAB) and Clostridium difficile infection (CDI), already
routinely collected for national mandatory reporting (cases per 10,000 bed
days), plus their routine ward surveillance data on MRSA acquisition (MRSAA)
(numbers of patients found to have MRSA colonisation or infection after
admission who were not previously known to have MRSA) was collected.
Anonymised confidential MRSA prevalence swabs were to be collected
quarterly but, despite receiving ethical approval, only 12 wards (three trusts)
agreed to this and therefore it had to be abandoned.
b. Antibiotics prescribed for MRSA infections were recorded as a proxy
measure of all clinical MRSA infection (vancomycin, doxycycline and
teicoplanin). Data were supplied as units of issue and converted into defined
daily doses (DDDs) using WHO Collaborating Centre for Drug Statistics
Methodology classifications (27).
Denominator: Bed-days- ward bed-days per month were recorded to act as
a denominator for all outcome measures except compliance.
10
- 11 Potential confounding factors and other data:
a. Monthly antibiotic data – ward level defined daily doses of ciprofloxacin
and cephalosporins, as these might affect levels of MRSAB and CDI.
b. Staffing levels – numbers of registered nurses, healthcare assistants and
bank staff. These data were only collected for days on which hand hygiene
observations were undertaken by the study researchers as potential
confounders affecting hand hygiene compliance.
c. Data on average bed occupancy, average length of stay, number of
female admissions and source of admission (care home or nursing home)
were also collected where available.
Sample size:
A simulation approach (28) was used to provide an estimate of power for a
stepped wedge design of total duration 36 months, six weekly hand hygiene
observations of compliance in each ward, and one ITU and two ACE wards at
each hospital. The parameters for simulation were based on observations on
one ITU and three ACE wards in the pilot study.
A linear “mixed” model fitted to the simulated compliance data, with fixed
effects; intervention, ward, compliance measurement occasion, and baseline
compliance with hospital as a random effect. One thousand simulations were
performed for each combination of intervention effect from 0% to 12%
increase in steps of 1%, and number of participating trusts 10, 16, and 20. The
estimated power was defined as the proportion of simulations for which the
intervention effect was significant at the 5% level. This gave 79.2% power to
detect differences in hand hygiene compliance of 7% or greater for 16 trusts,
and 88.8% power to detect differences of 8% or more
Randomisation: After the initial baseline period trusts were randomised into
the intervention at two monthly intervals (Figure 1). Sequence generation:
11
- 12 Sites were allocated a number between 1 and 16 by the research team.
These numbers were then randomly sorted using the Research Randomiser
website. (http://www.randomizer.org/form.htm). Sites were then entered into
the intervention in this order in blocks of 2 to 4, at five predefined timepoints.
Allocation concealment mechanism: Infection control teams were informed
of their trust’s allocation in May 2007. Only the research team knew the
allocation of all trusts, the ward managers and infection control nurses being
aware only of their own trust’s allocation.
The first two hospitals were randomised to start in month 10 of the study (July
2007). The final two hospitals were randomised to start in month 18 of the
study (April 2008).
Blinding: The researcher observing hand hygiene compliance was blinded to
the intervention status of the wards. The adequacy of the blinding was tested
and found to be good, with no evidence that the observer underestimated
compliance in wards doing the intervention and over-estimated it on
implementing wards. This has been described elsewhere (29). All other
outcome measures were collected from routine data sources that were
unaware of ward status, or indeed of the very existence of a trial.
Statistical methods
A mixed effect logistic regression analysis (30) was conducted with the
number of compliant hand hygiene opportunities as the outcome variable,
and the total number of hand hygiene opportunities as the binomial
denominator. To allow for dependencies of observations, hospital and ward
were fitted as hierarchical random effects within this model both assumed to
follow a Gaussian distribution. To allow for the secular temporal trends in hand
hygiene compliance over the study, the consecutive month was fitted as a
categorical factor. For the ITT analysis a binary factor was used representing
the study months in each ward pre and post randomisation. To allow for the
delay in implementation in the per protocol analysis the intervention was
assessed using a three level factor “pre- randomisation”, “post-randomisation
but pre-implementation” and “post- implementation”.
12
For the wards that
- 13 failed to implement, an analysis was performed to assess whether there was
any change in hand hygiene compliance.
The number of registered nurses, healthcare assistants and bank staff, and the
ratio of actual to expected staff numbers have been fitted as covariates to
allow for potential confounding. The type of ward (ACE or ITU) was
considered to be an effect modifier and the interaction between this and the
intervention was included in the statistical models. The fidelity to intervention
proxy measurement was fitted as a covariate, and its interaction with type of
ward was also assessed.
Results were presented as estimated odds ratios (95% CI) for hand hygiene
compliance on each ward type, comparing post-randomisation and postimplementation with pre-randomisation (reference) compliance.
Similar regression analyses were performed with the secondary and tertiary
outcomes. For the monthly volume of AHR/liquid soap procured the data
were smoothed to allow for bulk orders. This was simply half of the previous
month’s added to half of the current month’s consumable orders. This was
divided by the number of bed days and a logarithmic transformation was
then applied. This was used as the outcome variable in a linear mixed model.
For the tertiary infections outcomes a mixed effects Poisson regression model
was used.
Protocol: there was a predefined protocol (www.idrn.org/nosec.php) which
was followed with the exception of three protocol violations. As described
above, it did not prove possible to perform the MRSA prevalence screening.
Secondly, so few ward staff filled out the organisational climate questionnaire,
intended to measure ward culture as a potential confounder, that this was
dropped from the protocol. Thirdly, delayed registration for Research and
Development at individual trusts, shortened the baseline pre-randomisation
13
- 14 phase from twelve months to nine in the first trusts randomised to the
intervention.
RESULTS
Participant flow: The trial start and finish dates were pre-specified as 1st
October 2006 - 30th September 2009. The flow diagram (Figure 1) shows there
were 60 wards randomised, of which 33 (22 ACE and 11 ITU) went on to
implement the intervention, with a mean (SD) delay in implementation of 5 (4)
months (Figure 2) and a mean (SD) duration of implementation of 12 (7)
months. Eight wards began implementation very late, and for these the end
of the trial was extended to December 31st 2009 in order to ensure that they
had a year of data collection post-implementation.
Numbers analysed: For the primary outcome, ITT analysis was conducted for
the 60 wards randomised into the intervention, and per-protocol analysis was
performed for the 33 implementing wards. The 27 non-implementing wards
had their pre- and post-randomisation compliance analysed.
For the secondary outcomes, adequate soap data were available for 28
wards, 16 of which implemented the intervention (4 ITUs and 12 ACE) and 12
of which did not (3 ITUs and 9 ACE). Adequate AHR data was available for 37
wards, 23 of which implemented the intervention (10 ITUs and 13 ACE) and 14
did not (14 ACE wards). ITT analysis was carried out for all 28 wards with
adequate soap data and for all 37 wards with adequate AHR data.
Per-
protocol analysis was carried out for all 16 implementing wards with
adequate soap data and for all 23 implementing wards with adequate AHR
data. Only 15 wards had adequate soap and AHR data together (2 ITUs and
13 ACE), of which only 9 wards (ACE) implemented the intervention.
For the tertiary outcomes, MRSAB data was available for all but 5 ACE wards,
MSSAB in all but 8 wards (1 ITU, 7 ACE), CDI in all but 5 ACE wards, MRSAA in
all but 6 wards ( 1 ITU, 5 ACE) and antibiotic proxy marker data in all but 15
wards (3 ITU, 12 ACE).
14
- 15 If fidelity to intervention had been 100%, this should have generated four
forms returned every four weeks from each ward i.e. a total of 4968. The total
number of forms returned was 974 (19.6%), range 0-69 per ward, representing
33.1% of the 2948 forms expected from the 33 implementing wards. Data
were available from all wards as to whether 0,1,2,3 or 4 forms had been
returned each month of the study.
Outcomes
(i)
-primary (hand hygiene compliance)
The initial intention to treat analysis from the mixed effects models showed no
effect of any of the potential confounders, which were then excluded from
the analysis. There was a highly significant effect of the intervention in ITUs but
not on ACE wards (Table 2).
Although hand hygiene compliance gradually fell during the trial, the
increased odds of hand hygiene compliance in ITUs equated to an increase
of 9% when the hand hygiene compliance without the intervention was 50%
and to an increase of 7% when hand hygiene compliance without the
intervention was 70% (Figure 3). In the ACE wards, where the intervention had
no significant effect, this equated to an increase in hand hygiene
compliance of only 1%. Fluctuations seen in the last three months in Figure 3
reflect the fact that these data points are based only on the 8 wards who
implemented very late and for whom the end of the trial was extended (see
above). Excluding these data points, it appears that the intervention
maintained compliance on ITUs at 61% by the end of the study wheareas
without the intervention it fell from 63% to 52%.
Per-protocol analysis in implementing wards (Table 3) showed a highly
significant increase in the estimated odds of hand hygiene compliance in
both types of ward equating to an increase in hand hygiene compliance of
15
- 16 13% in ACE wards when hand hygiene compliance without the intervention
was 50%, and of 10% when hand hygiene compliance without the
intervention was 70%. For ITUs there was an increase of 18% when hand
hygiene compliance without the intervention was 50% and of 13% when hand
hygiene compliance without the intervention was 70% (Figure 4). Fluctuations
seen in the last three months again reflect the fact that these data points
come from only eight wards. Excluding these data points it appears that
without the intervention compliance fell on ITUs from 61% to 43% by the end of
the study, whereas on the implementing wards compliance was maintained
at 61% by the end. For ACE wards, where compliance fell from 58% to 39% by
the end of the study on non implementing wards, the intervention appeared
to reduce this fall to 52% on implementing wards.
There was no significant difference in the odds of hand hygiene compliance
pre-randomisation between implementers and non-implementers for ITUs
(0.82 [0.61, 1.11]; p=0.2) or for ACE wards (1.12[0.93, 1.35]; p=0.2).
Table 4 shows a significant effect of fidelity to intervention on ITUs, with strong
evidence of an increase in hand hygiene compliance. The estimated odds
ratio for an increase in hand hygiene compliance for each returned form is
1.103 (95% CI 1.026 to 1.188, p=0.008). There was no such effect seen in
ACE/GM wards, with the estimated odds ratio for each returned form being
0.998 (95% CI 0.948 to 1.050, p=0.9).
(ii)
–secondary (consumables)
Table 5 summarises the results of the intention to treat analysis and shows that
liquid
soap
procurement
increased
significantly
by
over
30%
post-
randomisation in ITUs, with a non significant trend towards increasing
procurement in ACE wards of 13%. There was no evidence of a rise in AHR
procurement with the estimated relative change (95% CI) in AHR in the postcompared to pre-randomisation levels of 1.064 (0.933 to 1.214) p=0.4 in ITUs
and 1.027 (0.919-1.148); p= 0.6 in ACE wards.
16
- 17 -
The per-protocol analysis also showed a 30% rise in soap procurement in ITUs
(95% C.I.) 1.3 [1.03-1.63]), but not in ACE wards (1.02 [0.84-1.25]). However, this
result is based on only four implementing ITUs with adequate soap data. For
these wards there was an increase in liquid soap with increased numbers of
forms returned, the estimated relative change per form returned being 1.118
(95% CI 1.039 to 1.202, p=0.003) (Table 6). There was no such effect in the 12
implementing ACE wards with adequate soap data, the estimated relative
change per form returned being 0.973 (95% CI 0.937 to 1.010, p=0.16).
Per-protocol analysis showed no increase in AHR procurement for the wards
with the estimated relative change post implementation being 1.183 (0.989 to
1.416) for ACE wards and 1.098 (0.904 to 1.333) for ITUs. There was no
evidence of an effect of fidelity to intervention with the estimated relative
change per form returned being 1.01 (95% CI 0.98 to 1.05, p=0.5), and 1.02
(95% CI 0.96 to 1.07, p=0.5), in the ACE and ITU wards respectively.
17
- 18 (iii) Tertiary outcomes (table 7)
Table 6 shows that on intention to treat analyses the intervention had no
effect on MRSAB, MSSAB or CDI on either type of ward, but was associated
with a significant rise in MRSAA on both. There was some confounding by
antibiotic prescription. Ciprofloxacin use was associated with a rise in MRSAB
and a fall in CDI and MRSAA. Cephalosporin use was associated with a rise in
MSSAB. Per-protocol analyses (available from authors) were very similar, with
no evidence of an effect of fidelity to intervention. ITT analysis showed a
significant decrease in the proxy marker for MRSA infection on ACE wards, but
not ITU, with per-protocol analyses showing a significant decrease in both
types of ward, with an effect of fidelity to intervention on ACE wards
(estimated IRR per form returned 0.93 [95% CI 0.91-0.95]; p<0.0001).
Ciprofloxacin use acted as a confounder, significantly increasing the proxy
marker of infection to a similar degree in both ITT and per protocol analyses
DISCUSSION
The principal findings of this trial were that a feedback intervention, designed
using behavioural theory, produced a moderate but significant improvement
in hand hygiene compliance on both intention to treat and per-protocol
analyses on wards whose routine practice included implementation of the
pragmatically designed national cleanyourhands campaign. It therefore
disproved the original null hypothesis, despite difficulties in implementation
and a downwards temporal trend in hand hygiene compliance. The effect
differed by ward type, being stronger on ITUs, where it was easier to
implement and where its effectiveness increased in line with fidelity to
intervention, than on ACE wards. The effect of the intervention on
implementing, compared to non-implementing, wards equated to an
absolute difference in hand hygiene compliance of 10-13% on ACE wards,
and of 13-18% on ITUs, which remained relatively constant over time,
consistent with sustained effect of the intervention.
18
- 19 -
The principal strengths of this study are its size; its duration, which enabled
sustainability to be assessed; its use of behavioural theory to design the
intervention; its ability to measure its effect against a background of an
existing pragmatic national hand hygiene campaign; and its use of the MRC
framework for evaluating complex interventions (25). The trial was also
designed and reported in line with the CONSORT (31) and ORION (32)
guidelines
for
transparent
reporting
of
RCTs
and
infection
control
interventions. Its stepped wedge design facilitated retention of hospitals by
avoiding disappointment effects, increased power, incorporated and
adjusted for secular trends, and enabled examination of the relationship
between the timing of the intervention and its effectiveness. Through the
longer duration of stepped wedge trials it assessed sustainability (20,28). The
study therefore meets the requirements of reviews that call for large, welldesigned long term trials of behavioural hand hygiene interventions (12,13),
and explicit application of behavioural theory to intervention design in order
to optimise effectiveness (33,34).
The main limitation of the study was that it was difficult to implement, so that
delays and failures in implementation were common, despite the preparatory
modelling and piloting of the intervention. This finding is consistent with welldocumented difficulties of implementation in healthcare settings (35). It may
also reflect changes in the NHS and general context within which the study
was conducted, including having to compete for staff time with other quality
improvement initiatives. It may have been avoided had there been a larger
number of research staff to train, liaise with and encourage the ward staff.
Indeed,
data
from
a
cross-sectional
interview
study
to
investigate
implementation in the pilot study suggests that implementation might have
been more effective if it had been as an integral part of the ward’s audit
programme, had been carried out by infection control or ward staff who
already have responsibility for assessing and appraising staff, and if there had
19
- 20 been more than one implementer per ward with designated time for delivery
of the intervention (23).
A second limitation was that ward implementers, once trained, neither had
their training repeated nor their performance monitored. This might have
reduced the effect of the intervention, and been partly responsible for the
gradual decline in compliance seen over the lifetime of the study. This fall
might also reflect a general reduction in compliance over the fourth and final
year of the CYHC (January-December 2008), when its effect might have
started to wear off. It may also reflect generic changes in working practices
and pressures in the NHS. Although this fall suggests some caution should be
exercised in interpreting the effect of the intervention, the downward trend
was allowed for in the intention to treat, per-protocol and per-fidelity analyses
which showed the intervention raised the odds of compliance on
implementing wards. For ITUs the intervention appeared broadly to maintain
compliance levels and for ACE wards to reduce its fall over time. It is possible
that with better supervision of ward co-ordinators the effect might have been
greater and this hypothesis needs to be tested in further implementation
studies.
The study was further limited by incomplete data for secondary outcomes,
procurement of soap and AHR. This problem had not been encountered in
the pilot trial, and arose from lack of ward level requisition points or ward level
recording by individual trusts. The literature (5,36) differs widely regarding the
relative merits of using, and correlations between, directly observed
compliance and procurement data, a proxy marker of hand hygiene
compliance. The consensus view is that both should be measured (5, 37) and
this lack of ward level data needs addressing by the NHS for both clinical
audit and hand hygiene intervention studies. However, despite the relative
lack of data, the effect of the intervention on soap procurement mirrored
that on directly observed compliance for ITUs and provides further support for
its efficacy.
20
- 21 The study was also limited by the reluctance of ward staff to carry out
anonymised confidential screening for MRSA prevalence, which was the only
pathogen-related outcome
the
study was
powered
to detect. No
conclusions can therefore be drawn regarding the effect of the intervention
on infection. National changes in screening and other infection control
practice (38) may account for the observed increase in MRSAA and the
observed decrease in the surrogate marker of total MRSA infection,
interpretation of each of these being further hampered by lack of common
definition or validation respectively.
Comparison with the results of other RCTs of the effectiveness of audit and
feedback on healthcare worker adherence to best practice in general is
useful, even though none of these concentrated on hand hygiene.
Systematic review (14) of these trials report considerable heterogeneity in
effectiveness. For dichotomous outcomes, the effect varied from a 70%
increase to a 16% decrease in compliance, and for continuous outcomes,
from a 68% increase to a 10% decrease. Effectiveness increased however with
increasing intensity of feedback, and lower baseline compliance. Subsequent
analyses (15) combined continuous and dichotomous outcomes to report an
overall significant result for the effectiveness of audit and feedback in 61 trials
(adjusted odds ratio of compliance with desired practice 1.43 [1.28, 1.61]),
but the review was underpowered to demonstrate that interventions utilising
behavioural theory were more effective because few trials explicitly used
theory to design their intervention. However, the results of the current study
are consistent with the findings of both reviews with regard to the size of
effect, and the relationship of outcome to fidelity to intervention and baseline
compliance.
Comparison with other hand hygiene feedback intervention studies (39-47)
included in systematic reviews (12,13) is difficult because this is the only long
term randomised controlled trial and the only feedback study to couple
feedback to personalised goal setting and action planning. Although the size
of the effect after two years of the intervention was within the range (7-14%
21
- 22 increase in compliance) seen previously in studies that have examined
effects after a similar length of time (39,40,42), other studies compare their
intervention with a baseline that includes no specific hand hygiene
intervention. This is the only study where standard practice included
implementation of a national multimodal hand hygiene campaign. Despite
this the intervention was still effective possibly because the use of behavioural
theory in its design maximised its effectiveness (15,33).
This gives the trial’s findings extra relevance, as National Audit Office data
(48) show that audit and feedback, which formed part of this campaign,
occurs at least once a month on nearly all wards in over 80% of acute trusts in
England and Wales. Data was not routinely collected on implementation of
the cleanyourhands campaign from infection control and ward staff in case
this acted as a prompt to alter “routine practice”, although data on near
patient placement of AHR, routinely collected in the hand hygiene
observation tool (26) , showed near universal implementation of this, the key
component of the CYHC, on all wards throughout the study. The WHO’s SAVE
LIVES initiative encourages the widespread use of multimodal hand hygiene
interventions similar to the CYHC (7,8). Our study suggests that the FIT
intervention may improve hand hygiene in such settings and could be the
next step in hand hygiene improvement after a hospital has adopted the
WHO intervention.
It would, clearly, be premature to recommend routine clinical use of the FIT
intervention. At the moment the results would generalise only to ITUs and ACE
wards. It is hard to comment on how results might generalise to other settings
or to health services or countries that had not been subject to a sustained,
centrally co-ordinated and funded national campaign to improve hand
hygiene compliance. Further research is being undertaken to identify the
barriers to and the facilitators of implementation and to identify the extent to
which each component and process of the intervention contributes towards
its effect. The post-randomisation pre-implementation rise in the odds of
compliance on implementing wards may indicate that there were
22
- 23 characteristics of those wards that eventually facilitated implementation, but
those characteristics do not appear to include better baseline hand hygiene
compliance as there was no difference in the odds of pre-randomisation
hand hygiene compliance in implementing and non-implementing wards.
Possible reasons for the greater implementation in ITUs include a larger staffing
pool from which to recruit ward coordinators, the high degree of
specialisation and training in this setting, or even an increased perception of
the need to reduce HCAI in this especially vulnerable population, but this is
entirely speculative. A further implementation study in a variety of settings
informed by the findings of the above research, with attention paid to
monitoring the performance of ward implementers is required, as well as the
development of models to assess cost-effectiveness, before the intervention
can be offered routinely in acute hospital settings. However, hospitals keen to
improve their hand hygiene compliance may wish to integrate some of the
additional techniques used in this trial into their standard infection control,
audit and appraisal schemes.
In conclusion, the current study has shown, on the basis of intention to treat
and per-protocol analyses, that a feedback intervention informed by
behavioural science results in moderate but significant and sustained
increases in hand hygiene compliance and soap procurement on both ITUs
and ACE wards already implementing a national hand hygiene campaign as
part of routine practice. The effect increases with fidelity to intervention. The
intervention proved harder to implement than anticipated. Study of the
predictors of implementation and fidelity to intervention is required to improve
implementation, followed by a further implementation study. Nonetheless,
consideration could be given by infection control staff to employing this
intervention with the same cycle and behavioural principles of feedback
delivery to supplement their current audit and appraisal systems. Although
audit and feedback is often suggested as a useful tool for hand hygiene
improvement (5, 37, 39), this study puts its use on a firmer footing than
previous non-randomised studies, providing the strongest evidence yet that
23
- 24 this is an effective technique, when coupled with a repeating cycle of
personalised goal-setting and action planning.
--------------------------------------------------------------------------------------------------------------
Conflict of interests
Barry Cookson has received consultancy fees from Wyeth (vaccines),
Carefusion (chlorhexidine preparations), Baxter (intravenous lines) and 3M,
Rubbermaid, and Spire (infection control) and lecture fees from Wyeth.
Sheldon Stone received grant funding from GOJO industries who, as with
other funders, had no role in the trial’s design, conduct and analysis, decision
to publish, or preparation of the manuscript. No other conflict of interests
declared.
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48. Report by the comptroller and auditor general HC 560 session 2008-9
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(last accessed 21st September 2009)
29
- 30 -
Figure 1. Flowchart showing study recruitment and attrition
Recruited wards.
n=62 (16 ITUs, 46 ACE/Med, 16 Trusts)
Closed before randomisation
n=2 (2 ACE/Med, 1 Trust)
Randomised wards
n=60 (44 ACE/Med, 16 ITU, 16 Trusts
Wards not starting intervention
5 ITU, 22 ACE/Med, 11 trusts
Wards starting the intervention
11 ITU, 22 ACE/Med, 13 Trusts
Wards dropping
out before
study end.
1 ITU,
3 ACE/Med
1 Trust
Wards closing
before study end
3 ACE/Med, 2
Trusts
Wards closing
before study end
1 ACE/Med, 1 Trust
30
Wards continuing
beyond study end
2 ITUs, 6 ACE/Med,
2 trusts
- 31 FIGURE 2: TIMELINE FOR RANDOMISATION AND IMPLEMENTATION
Ward
Months in the study
0
10
20
30
40
Month
= Baseline and pre-implementation periods
= Implementation periods
= Randomisation
31
- 32 FIGURE 3 –HAND HYGIENE COMPLIANCE IN ITUs (upper panel) and ACE wards
(lower panel)-Intention to treat analysis (black = pre- and blue = postrandomisation)
32
- 33 FIGURE 4 –HAND HYGIENE COMPLIANCE IN ITUs (upper panel) and ACE wards
(lower panel)-Per protocol analysis (black = pre- and green = postimplementation)
33
- 34 Table 1
Characteristics of Intensive Therapy Units (ITU) and Acute Care of the
Elderly/General Medical Wards (ACE)
All wards
Wards
Implementing FIT
Wards
Not Implementing FIT
16
11
5
Mean (sd)
number of
beds
10.9 (3.0)
10.6 (2.6)
12.2 (3.1)
Mean (sd)
number of staff
(WTE)
7.5 (2.1)
7.4 (2.5)
44
7.6 (1.9)
22
Mean (sd)
number of
beds
28.2 (5.6)
28.0 (5.8)
28.8 (8.9)
Mean (sd)
number of staff
(WTE)
7.5 (2.0)
7.6 (2.1)
7.5 (2.1)
ITU
ACE/GM
22
Table 2:
Estimated odds ratios (95% CI) of hand hygiene compliance for the intervention
allowing for effect modification by type of ward
Factor
ACE
Before randomisation
After randomisation
ITU
Before randomisation
After randomisation
Estimated odds 95% CI
ratio
P value
Reference
1.06
0.87 to 1.27
0.5
Reference
1.44
1.18 to 1.76
<0.001
34
- 35 Table 3: Estimated odds ratios (95% CI) for the intervention allowing for effect
modification by type of ward in a model excluding the potential confounders
Factor
Estimated
odds ratio
ACE
Before randomisation
After randomisation before implementation
After implementation
ITU
Before randomisation
After randomisation before implementation
After implementation
95% CI
P value
Reference
1.39
1.67
1.08 to 1.80
1.26 to 2.22
0.01
<0.001
Reference
1.70
2.09
1.26 to 2.30
1.55 to 2.81
<0.001
<0.001
Table 4: Estimated odds ratios (95% CI) for hand hygiene compliance on ITUs
for 0, 1, 2, 3, or 4 forms returned in any one month compared to the
compliance prior to randomisation
Factor
ITU
After implementation no forms returned
After implementation one form returned
After implementation two forms returned
After implementation three forms returned
After implementation >=four forms returned
Estimated
odds ratio
95% CI
P value
1.83
2.02
2.23
2.46
2.71
1.33 to 2.50
1.50 to 2.72
1.65 to 3.02
1.78 to 3.40
1.90 to 3.88
<0.001
<0.001
<0.001
<0.001
<0.001
Table 5:
Estimated relative change (95% CI) in liquid soap procurement by type of
ward
Ward
ACE
ITU
Estimated relative change (95% CI)
1.133 (0.987 to 1.300) p= 0.08
1.314 (1.114 to 1.548) p=0.003
35
- 36 TABLE 6 Estimated relative change in soap procurement on ITUs for 0, 1, 2, 3,
or 4 forms returned in any one month compared to the compliance prior to
randomisation
ITU
After implementation no forms returned
After implementation one form returned
After implementation two forms returned
After implementation three forms returned
After implementation >=four forms returned
1.10
1.22
1.37
1.53
1.71
0.85 to 1.41
0.98 to 1.54
1.09 to 1.72
1.19 to 1.96
1.28 to 2.28
0.5
0.08
0.007
0.001
<0.001
Table 7 Estimated IRR (95% CI) for the intervention allowing for effect
modification by type of ward and adjusting for potential confounders (ITT
analysis)
INFECTION
ITU
ACE
MRSAB1
2.07 (0.81, 5.3)
1.59 (0.63, 3.99)
MSSAB 2
1.16 (0.56, 2.43)
1.43 [0.74, 2.78)
CDI 3
1.42 (0.86-2.32)
0.98 (0.71-1.35)
MRSAA4
1.64 (1.18-2.27)*
1.51 (1.14-1.99)**
Proxy marker5
1.02 [0.96, 1.08)
0.83 (0.80, 0.87) #
*
p=0.003
**p=0.004 #p<0.0001
1 IRR
for each DDD of ciprofloxacin used: 1.014 (95% CI 1.00-1.03) p=0.03
2 IRR
for each DDD of cephalosporin used: 1.007 (95% CI 1.003-1.012; p<0.002
3IRR
for each DDD of ciprofloxacin 0.995 (95% CI 0.99-0.998; p=0.004).
4IRR
for each DDD of ciprofloxacin used: 0.996 (0.993-.0999) p=0.045
5IRR
for each DDD of ciprofloxacin used: 1.0023 (95% CI 1.0019-1.0028); p <0.0001)
36