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Antimicrobial stewardship in companion animal practice:
an implementation trial in 135 general practice veterinary clinics
L. Y. Hardefeldt
1,2
*, B. Hur
1,3
, S. Richards
1,2
, R. Scarborough
1,2
, G. F. Browning
1,2
, H. Billman-Jacobe
1,2
,
J. R. Gilkerson
1,2
, J. Ierardo
4
, M. Awad
4
, R. Chay
4
and K. E. Bailey
1,2
1
Asia-Pacific Centre for Animal Health, Melbourne Veterinary School, Faculty of Veterinary and Agricultural Sciences, University of
Melbourne, Parkville, Victoria, Australia;
2
National Centre for Antimicrobial Stewardship, Peter Doherty Institute, Grattan St, Carlton,
Victoria, Australia;
3
School of Computing and Information Systems, University of Melbourne, Parkville, Victoria, Australia;
4
Greencross
Vets Pty Ltd, Brisbane, Queensland, Australia
*Corresponding author. E-mail: Laura.Hardefeldt@unimelb.edu.au
Received 19 September 2021; accepted 2 February 2022
Background: Antimicrobial stewardship programmes (ASPs) have been widely implemented in medical practice
to improve antimicrobial prescribing and reduce selection for multidrug-resistant pathogens.
Objectives: To implement different antimicrobial stewardship intervention packages in 135 veterinary practices
and assess their impact on antimicrobial prescribing.
Methods: In October 2018, general veterinary clinics were assigned to one of three levels of ASP, education only
(CON), intermediate (AMS1) or intensive (AMS2). De-identified prescribing data (1 October 2016 to 31 October
2020), sourced from VetCompass Australia, were analysed and a Poisson regression model fitted to identify
the effect of the interventions on the incidence rates of antimicrobial prescribing.
Results: The overall incidence rate (IR) of antimicrobial prescribing for dogs and cats prior to the intervention was
3.7/100 consultations, which declined by 36% (2.4/100) in the implementation period, and by 50% (1.9/100)
during the post-implementation period. Compared with CON, in AMS2 there was a 4% and 6% reduction in
the overall IR of antimicrobial prescribing, and a 24% and 24% reduction in IR of high importance antimicrobial
prescribing, attributable to the intervention in the implementation and post-implementation periods, respec-
tively. A greater mean difference in the IR of antimicrobial prescribing was seen in high-prescribing clinics.
Conclusions: These AMS interventions had a positive impact in a large group of general veterinary practices, re-
sulting in a decline in overall antimicrobial use and a shift towards use of antimicrobials rated as low importance,
with the greatest impact in high-prescribing clinics.
Introduction
Antimicrobial resistance (AMR) is a major threat to public health
globally. In 2016, a review on AMR, commissioned by the UK gov-
ernment, found that approximately 700 000 people die each year
because of MDR infections. This review predicted that by 2050 the
global mortality rate could exceed 10 million people each year.
1
Direct, or indirect, contact with animals can result in human ac-
quisition of MDR pathogens of animal origin.
2–5
Antimicrobials
are an essential part of current and future veterinary medicine,
and their use is justified to ensure optimal animal welfare and
to ensure supplies of safe food for the community, but the misuse
of antimicrobials in human or animal health cannot be justified.
Comprehensive antimicrobial stewardship programmes (ASPs)
have been widely implemented in medical practice over the past
decade to improve antimicrobial prescribing and reduce the selec-
tion pressure for the development of multidrug-resistant (MDR)
pathogens. Most global
6,7
and national action plans
8,9
have called
for implementation of ASPs in veterinary practices. Despite these
calls, implementation of comprehensive ASPs in veterinary prac-
tices is uncommon. Medical ASPs have used restrictive interven-
tions, which reduce the freedom of prescribers to select some
antimicrobials, and persuasive interventions aimed at behaviour
change. Persuasive interventions are focused on addressing pre-
disposing factors (practitioner education), reinforcing factors
(audit and feedback) and enabling factors (decision support).
© The Author(s) 2022. Published by Oxford University Press on behalf of British Society for Antimicrobial Chemotherapy.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://
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JAC Antimicrob Resist
https://doi.org/10.1093/jacamr/dlac015
JAC-
Antimicrobial
Resistance
Many ASPs have elements of both restriction and persuasion, but
neither of these has been found to be more successful than the
other over the long term.
10
Recently there have been reports of im-
plementation of hospital-style ASPs in veterinary practice on a
small scale,
11
but large-scale implementation, and assessment
of that implementation, have not been reported. Implementing re-
striction in veterinary medicine would require new legislation,
which is not currently being considered in Australia. Therefore, per-
suasive interventions will have to form the basis of veterinary anti-
microbial stewardship (AMS) in the medium term. In addition, we
believe that a comprehensive programme integrating many ele-
ments is likely to be more successful than restriction alone.
Studies of antimicrobial use patterns by Australian veterinar-
ians have found that they predominantly use antimicrobials
rated as medium importance
12–15
by the Australian Scientific
and Technical Advisory Group on AMR (ASTAG).
16
In addition, in-
appropriate use of antimicrobials was common.
13,14
The enablers
of and barriers to AMS in veterinary practices have also been in-
vestigated recently in this population. Veterinarians were gener-
ally supportive of additional AMS measures and recognized the
threat that AMR posed both to patients and to the wider commu-
nity.
17
The components that veterinarians believed were required
for successful ASP implementation were determined to be: train-
ing in AMS and infection prevention; guidelines for antimicrobial
use; resources, including client educational material; and access
to cost-effective culture and susceptibility testing.
17
There is
strong evidence in the literature that planned interventions can
change prescribing practices and control infection outcomes in
human hospitals,
18–21
and, while the challenges in general veter-
inary practice have been shown to be different to those in human
medicine, we predict that the outcomes can be similar.
The aim of this project was to assess the effectiveness of im-
plementation of a comprehensive antimicrobial stewardship
package, compared with education alone, in a large number of
veterinary clinics. This was measured by the changes in clinic
rates of total antimicrobial prescribing and high-importance anti-
microbial prescribing. A secondary aim was to evaluate strategies
for increasing the uptake of the stewardship package, to maxi-
mize implementation.
Methods
Veterinary clinics
A corporate group of veterinary clinics was enrolled in the ASP trial in
2018. The corporate group included specialist, emergency and general
practice veterinary clinics, but only general practice clinics were consid-
ered for the study (n=152) because of the relatively small number of spe-
cialist and emergency clinics in the group and the potential for different
antimicrobial use patterns in these practices to bias the findings. Clinics
were excluded if they joined the corporate group after the initial pro-
gramme materials were distributed in October 2018 (n=5), if they
were staffed only by casual/locum veterinarians (n=10), or if the clinic
was closed for a period of time greater than 4 months during the trial
(n=2) (Figure 1). There were three levels of veterinary leadership in the
corporation. Firstly, a veterinary director was present in each clinic. One
of six regional clinical directors oversaw a cohort of clinics and these re-
gional directors reported to a single chief veterinary officer for the corpor-
ate group.
Study period
Clinical records of consultations from all participating clinics were exam-
ined and all consultations with antimicrobial prescribing were identi-
fied.
22
The clinics were enrolled in October 2018. Clinical records from
the 2 year period prior to the trial were also examined. Antimicrobial pre-
scribing in the pre-trial period (1 October 2016 to 30 September 2018)
was compared with prescribing during the implementation period (1
October 2018 to 31 July 2019) and the post-implementation period (1
August 2019 to 31 October 2020).
Intervention
The interventions were co-designed by the research team, the chief veter-
inary officer and the regional clinical directors responsible for the ASP.
Three arms of the trial were designed: a control group that received edu-
cation only (CON), an intermediate ASP (AMS1) and an intensive ASP
(AMS2) (Figure 2). At the beginning of the implementation period
(October 2018) a prescribing guideline poster
23
was sent to all clinics in
the corporate group, with space for annotation of clinic-specific prescrib-
ing policies incorporated into the poster. The education programme con-
sisted of eight webinars, presented by national and international
specialists recruited by the research team, on antimicrobial therapy in
veterinary medicine (gastrointestinal disease, dental disease, peri-operative
Figure 1. Flow diagram of clinic recruitment and allocation. CON, education only intervention; AMS1, intermediate intervention; AMS2, intensive
intervention.
Hardefeldt et al.
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management, urinary tract syndromes, and skin disease), antimicrobial
stewardship, infection control and delayed prescribing. Webinars were
delivered online during the implementation period (1 October 2018 to
31 July 2019), through the corporate practice education platform, and
were available for viewing online following the presentation.
AMS1 and AMS2 clinics were asked to appoint a stewardship cham-
pion to promote and lead implementation of the ASP. The champion
co-ordinated implementation of a traffic light system for categorizing
all the antimicrobials used in the clinic (Figure 3), based on the ASTAG
16
classification of antimicrobial importance for animal and human health.
This required clinics in AMS1 and AMS2 to colour-code antimicrobials
according to their importance and, for AMS2 only, to restrict use of anti-
microbials with a high importance rating. Diagnostic testing guidelines
were provided for dermatological and urinary tract disorders, and stew-
ardship champions were encouraged to develop other guidelines with
their colleagues. Stewardship champions were also expected to conduct
audit and feedback (AMS2), especially for new and recent graduates and
new staff, and to track AMR isolates cultured in the clinic. A delayed pre-
scription system was co-designed with the chief veterinary officer and a
regional clinical director and implemented in AMS2. Resources for all in-
terventions are freely available (vetantibiotics.fvas.unimelb.edu.au).
Clinics were mailed and e-mailed copies of the AMS programme to which
Figure 2. Interventions included in each arm of the antimicrobial stewardship trial in general practice veterinary clinics. CON, education only intervention;
AMS1, intermediate intervention; AMS2, intensive intervention. The CON programme (n=44) included only the first level of interventions (a), the AMS1
programme (n=47) included all interventions from the first two levels (a and b) and the AMS2 programme (n=44) included all the interventions (a,
b and c).
Figure 3. Categorization of antimicrobials for the traffic light colour-coding system implemented in AMS1 and AMS2.
Antimicrobial stewardship in veterinary practice
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they were allocated at the beginning of the implementation period
(October 2018). The lead regional clinical director promoted the pro-
grammes at team leader meetings inMay 2019. Regional clinical directors
were then expected to support programme implementation within their
cohort of clinics. During the implementation (1 October 2018 to 31 July
2019) and post-implementation periods (1 August 2019 to 31 October
2020), the research team was available to support the regional clinical di-
rectors and also answer questions directly from the individual clinics.
Members of the research team attended the various state team leader
meetings held throughout September 2019 to support the leadership
teams in the implementation of the programme. Two members of the re-
search team also attended the corporate group’s annual conference in
October 2019 to answer the questions of individual veterinarians, nurses
and practice managers and promote the programme to the attendees.
Allocation
Initially, clinics were allocated to one of the three arms of the intervention
trial using a random number generator in Microsoft Excel. Clinic allocation
was then reviewed by the regional clinical directors. Allocation was al-
tered based on clinic human resources, the existing level of interest in
the clinic and other factors (staff turnover, number of casuals/locums,
clinic management structures) that may have affected the ability of
the clinic to implement the allocated programme (Figure 1).
Evaluation
For all participating clinics, de-identified data (1 January 2016 to 31
October 2020) were sourced from VetCompass Australia (Version 0.5).
24
Antimicrobials were categorized based on the antimicrobial importance
ratings of the ASTAG, which classifies the antimicrobials as low, medium
or high importance (Figure 3).
16
Inventory items, which map to all pre-
scriptions and consultation texts, were extracted from the records. A pre-
viously described method was used to label antimicrobials within the
inventory items and map them to their active ingredients and ASTAG im-
portance rating.
15
As many consultation records were blank, with no in-
ventory items associated with them, the consultation table was
inner-joined to the table with the inventory items associated with
them. At least one inventory item and one clinical note combined for a
single consultation was required for inclusion in the study. All topical anti-
microbials and other routes of antimicrobial therapy (e.g. intrasynovial,
intraperitoneal) were excluded from analysis.
Data analysis
Descriptive statistics were computed, with percentages reported as a pro-
portion of consultations in the data set that received antimicrobial ther-
apy or as a proportion of total consultations. High-prescribing clinics
were defined as those in the top 25% of antimicrobial prescribers in the
pre-trial period.
A multilevel Poisson regression model was used to identify factors
that were associated with the incidence rate (IR) of antimicrobial therapy
(exposure to antimicrobials) in the study population. The fixed effect ex-
planatory variables assessed in the model included the AMS programme
level, the trial period, the species of animal treated, the season, the
12 month period prior to the implementation period, and the impact of
the COVID-19 pandemic. The random effect variables were regional clin-
ical director and clinic. Exposure variables were total consultations.
Unconditional associations between each of the hypothesized explana-
tory variables and the outcome of interest were computed using an inci-
dence rate ratio (IRR). Explanatory variables with unconditional
associations significant at P,0.20 (two-sided) were selected for multi-
variable modelling. For the multivariable model, the outcome of interest
was parameterized as a function of the explanatory variables with uncon-
ditional associations significant at P,0.20, as described above.
Explanatory variables that were not significant were then removed
from the model one at a time, beginning with the least significant, until
the estimated regression coefficients for all explanatory variables re-
tained were significant at an alpha level of ,0.05. Explanatory variables
that were excluded at the initial screening stage were tested for inclusion
in the final model and were retained in the model if their inclusion chan-
ged any of the estimated regression coefficients by more than 20%.
Plausible two-way interactions were tested, and significance was set at
an alpha level of 0.05. Two-way ANOVA was performed to evaluate the
effect of the intervention on prescribing rates in high-prescribing clinics
compared with low-prescribing clinics. Data were tested for normality
using the Shapiro–Wilks test. Homogeneity of variances across the six
groups was tested using Levene’s test. The impact of the COVID-19 pan-
demic was evaluated as a confounding factor in the Poisson model by
marking prescribing events during the pandemic in clinics involved in
the trial (between 15 March 2020 to 31 October 2020). Data analysis
was performed using functions within Stata v14.
Ethics
This research was approved by the University of Melbourne Faculty of
Veterinary and Agricultural Sciences Human Ethics Advisory Group under
Approval No. 1851029.1.
Results
Veterinary clinics (n=135) were enrolled in the study in October
2018. The number of veterinarians in each clinic varied between
clinics and within clinics over the trial periods, with most having
between one and three full-time equivalent veterinarians (range
1–9). Over the total study period (1 October 2016 to 31 October
2020), there were 8 394568 consultations and 240 700 systemic
antimicrobial treatments dispensed across the 135 participating
clinics (Table 1). Eight antimicrobials accounted for 94% of all
antimicrobials dispensed: amoxicillin/clavulanate (n=95625,
40%), cefalexin (n=32734, 14%), metronidazole (n=25261,
10%), cefovecin (n=24461, 10%), doxycycline (n=21290, 8.8%),
enrofloxacin (n=12 190, 5.1%), cefazolin (n=8251, 3.4%) and
amoxicillin (n=5971, 2.5%).
In the pre-trial period (1 October 2016 to 30 September 2018),
the overall IR of antimicrobial prescribing to dogs and cats was
3.7 per 100 consultations. AMS1 and AMS2 clinics had a lower
rate of prescribing compared with CON (Table 2). The IR of anti-
microbial prescribing for cats was 9% lower than that for dogs
(IRR 0.91, 95% CI 0.90–0.92, P,0.001). The IR of prescribing de-
creased by 14% in the period from 1 October 2016 to 30
September 2018 compared to the period from 1 October 2016
to 30 September 2017 (IRR 0.86, 95% CI 0.86–0.87, P,0.001)
(Figure 4). The IR of antimicrobial prescribing varied considerably
between clinics, from 1.29 to 8.9 per 100 consultations (mean of
3.6 per 100 consultations, SD 1.22), but 89% of clinics (n=125)
prescribed an antimicrobial in fewer than 5 per 100 consultations.
The overall IR of antimicrobial prescribing decreased during
the implementation and post-implementation periods. The IR
of antimicrobial prescribing during the implementation phase
was 2.4 per 100 consultations, a decrease of 36% from the pre-
trial period. This reduced further in the post-implementation
phase to 1.9 per 100 consultations, a decrease of 50% from the
pre-trial period (Table 1). During both the implementation and
post-implementation phases of the trial, and after adjusting for
the effect of the COVID-19 pandemic, species, season, and
Hardefeldt et al.
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random effects of regional clinical director and clinic, the change
in antimicrobial prescribing attributable to the AMS2 intervention
was a 4% and 6% reduction compared with CON (AMS2 # imple-
mentation IRR 0.96, 95% CI 0.94–0.99, P=0.011; AMS2 # post-
implementation IRR 0.94, 95% CI 0.91–0.96, P,0.001)
(Table S1, available as Supplementary data at JAC-AMR Online).
The adjusted rate of antimicrobial prescribing in clinics enrolled
in AMS1 did not differ from that of clinics enrolled in CON in either
the implementation or post-implementation phases (Table S1).
The proportion of prescriptions for medium-importance anti-
microbials (out of total antimicrobial prescriptions) decreased
during the implementation and post-implementation periods in
all groups, with a corresponding increase in the prescribing of
low-importance antimicrobials (Table 3). After adjusting for fixed
effects of COVID-19 impact, species and season and random ef-
fects of regional clinical director and clinic, the effect of the inter-
vention (intervention # trial period) differed between AMS1 and
AMS2 (Table 3, Tables S2 to S4). In AMS1 the change attributable
to the intervention in the implementation period was a 17% in-
crease in the IR of prescribing of low importance antimicrobials,
with no change in prescribing of medium- and high-importance
antimicrobials, whereas in AMS2 there was no change to prescrib-
ing of low- and medium-importance antimicrobials, but a 24%
reduction in prescribing of high-importance antimicrobials attrib-
utable to the intervention. Similarly, in the post-implementation
period, in AMS1 there was a 10% increase in the IR of prescribing
of low-importance antimicrobials and a 5% increase in prescrib-
ing of medium-importance antimicrobials, but no change in pre-
scribing of high-importance antimicrobials attributable to the
intervention. In AMS2, in the post-implementation period, the
change in IR attributable to the intervention in low-importance
antimicrobial prescribing was an increase of 12%, and for
medium- and high-importance antimicrobials there was a de-
crease of 6% and 24%, respectively (Table 3, Tables S2 to S4).
The effects of the AMS interventions on the pre-existing pre-
scribing practices of clinics were examined to determine the ef-
fects in high-prescribing clinics. High-prescribing clinics were
defined as those clinics in the top 25% of antimicrobial prescri-
bers overall in the pre-trial period. Tests for normality and homo-
geneity of variances showed that the assumptions of the
two-way ANOVA were met. There was a significant interaction
between the level of intervention and the change in IR of pre-
scribing of high-prescribing clinics compared with all other clinics
[F (2127) =4.01, P=0.02]. Irrespective of the level of intervention
there was a significant effect in reducing the IR of antimicrobial
prescribing in high-prescribing clinics compared with all other
clinics (Table 4).
Discussion
This quantitative evaluation of a large-scale implementation trial in
veterinary practice demonstrated that a bundle of AMS
Table 1. Overall antimicrobial prescribing
Consultations
Period
a
Antimicrobial All IR
b
Adjusted IRR
c
(95% CI) Pvalue
Pre-trial 152877 4179535 3.7 Ref –
Implementation 40424 1714557 2.4 0.64 (0.63–0.66) ,0.001
Post-implementation 47399 2500476 1.9 0.50 (0.49–0.51) ,0.001
Total 240700 8394568 2.9 NA NA
IR, incidence rate; IRR, incidence rate ratio.
a
Pre-trial period: 1 October 2016 to 30 September 2018; Implementation period: 1 October 2018 to 31 July 2019; Post-implementation: 1 August 2019
to 31 October 2020.
b
Per 100 consultations.
c
Adjusted for fixed effects (intervention level, COVID-19 pandemic, species, season) and random effects (regional clinical director, clinic).
Table 2. Antimicrobial prescribing in the pre-trial period
Consultations
Intervention No. clinics Antimicrobial All IRR
a
95% CI Pvalue
CON 44 48640 1345572 Ref ––
AMS1 47 51768 1351551 0.80 0.70–0.93 0.003
AMS2 44 44393 1315806 0.78 0.67–0.90 0.001
CON, education-only intervention; AMS1, intermediate intervention; AMS2, intensive intervention; IRR, incidence rate ratio.
a
Adjusted for fixed effects (COVID-19 pandemic, species, season) and random effects (regional clinical director, clinic). Full models are provided in the
Supplementary data (Table S1).
Antimicrobial stewardship in veterinary practice
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Figure 4. Unadjusted incidence rate (per 100 consultations) of antimicrobial prescribing in CON (yellow), AMS1 (purple) and AMS2 (blue) for (a) low-,
(b) medium- and (c) high-rated antimicrobials according to the Australian Scientific and Technical Advisory Group on AMR in the pre-trial and trial
periods (1/1/2016 to 31/10/2020). CON, education only intervention; AMS1, intermediate intervention; AMS2, intensive intervention.
Hardefeldt et al.
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interventions in antimicrobial prescribing can have an impact in
general veterinary practice. Compared with the pre-trial period
(3.7 per 100 consultations), the use of antimicrobials declined in
the implementation period (to 2.4 per 100 consultations) and post-
implementation period(to 1.9 per 100 consultations)and the clinics
in AMS2 had a greater reduction in overall antimicrobial use com-
pared with clinics in CON. Although antimicrobial use had declined
during the 2 year pre-trial period, (13% between the two 12 month
periods), this was accelerated and sustained during the implemen-
tation (36%) and post-implementation (50%) periods. There was a
high level of commitment to the project from corporate leadership,
and planning discussions with regional clinical directors (starting in
July2018)mayhaveresultedinearlypromotionofAMSandim-
proved antimicrobial use, even before the implementation phase
started.
A previous study of AMS in companion animal practice in The
Netherlands also resulted in an overall reduction in antimicrobial
use,
11
similar in magnitude to that seen in our study. In that
study, as in the trial described here, there were absolute
decreases in both first- and second-choice antimicrobial use
overall, but the impact attributable to our trial was an increase
in the use of low-importance antimicrobials in AMS1 in both im-
plementation and post-implementation periods and in AMS2 in
the post-implementation period. Although prescribing rates of
antimicrobials with a high importance rating also decreased dur-
ing the implementation and post-implementation periods in our
study, the proportion of consultations in which high-importance
antimicrobials were prescribed was higher in all arms of the trial,
indicating that prescribers decreased use of antimicrobials with
low- and medium-importance ratings more than they decreased
use of those with a high-importance rating. A change in the rate
of prescribing of high-importance antimicrobials that was attrib-
utable to the intervention was only seen in AMS2, in which there
was a 24% reduction compared with CON in both implementa-
tion period and post-implementation periods (Table 3). The inter-
ventions had an impact on the use of high-importance
antimicrobials in AMS2 by reducing the adjusted rate of prescrib-
ing and maintaining the overall proportion of consultations in
Table 3. Prescribing of antimicrobials according to their importance as defined by the Australian Scientific and Technical Advisory Group on
Antimicrobial Resistance (ASTAG)
ASTAG importance/intervention
Pre-trial Implementation Post-implementation
IR
a
(% AM) IRR
b
(95% CI), Pvalue (IR
a
[%AM]) IRR
b
(95% CI), Pvalue (IR
a
[%AM])
Low
CON (Ref) 0.422 (12) (0.354 [15]) (0.391 [20])
AMS1 0.424 (11) 1.17 (1.09–1.27), ,0.001 (0.378 [15]) 1.10 (1.03–1.17), 0.002 (0.396 [19])
AMS2 0.415 (12) 1.03 (0.96–1.11), 0.428 (0.341 [15]) 1.12 (1.05–1.19), ,0.001 (0.406 [25])
Medium
CON (Ref) 2.63 (73) (1.51 [64]) (1.13 [59])
AMS1 2.93 (76) 1.00 (0.97–1.04), 0.832 (1.77 [70]) 1.05 (1.02–1.09), 0.001 (1.38 [65])
AMS2 2.47 (73) 1.01 (0.97–1.05), 0.611 (1.42. [68]) 0.94 (0.91–0.97), ,0.001 (0.981 [59])
High
CON (Ref) 0.600 (15) (0.480 [20]) (0.397 [21])
AMS1 0.478 (12) 0.98 (0.92–1.05), 0.603 (0.383 [15]) 1.01 (0.95–1.07), 0.816 (0.344 [16])
AMS2 0.492 (15) 0.76 (0.71–0.82), ,0.001 (0.323 [16]) 0.76 (0.71–0.81), ,0.001 (0.267 [16])
IR, incidence rate; %AM, proportion of antimicrobial prescriptions out of total antimicrobial prescriptions; IRR, incidence rate ratio.
a
Unadjusted rate per 100 consultations.
b
Change attributable to intervention (intervention # trial period). Interaction term adjusted for the fixed effects of species, season, intervention group
and time and the random effects of regional clinical director and clinic, the full models available in the Supplementary data (Tables S2 to S4).
Table 4. Effect of intervention on antimicrobial prescribing in high prescribing clinics (top 25% of IR of antimicrobial prescriptions)
High prescribing clinics IR
a
All other clinics IR
a
Intervention Pre-trial Trial
b
Mean difference 95% CI Pre-trial Trial
b
Mean difference 95% CI Pvalue
CON 6.5 2.3 4.2 2.4–6.1 4.1 1.8 2.3 0.7–3.9 ,0.001
AMS1 7.3 2.5 4.8 1.7–7.8 3.7 1.7 2.0 0.1–3.9 ,0.001
AMS2 5.9 2.4 3.5 2.0–5.0 3.7 1.6 2.1 0.3–3.9 ,0.001
a
Per 100 consultations
b
Trial incorporates implementation and post-implementation periods.
Antimicrobial stewardship in veterinary practice
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which antimicrobials were prescribed in comparison to the in-
crease seen in CON. In AMS1, the change attributable to the inter-
vention was an increase in the rate of prescribing of
low-importance antimicrobials with no change in the rate of
high-importance prescribing (Table 3). This contrasts with the trial
in The Netherlands, in which the adjusted proportion of third-line
antimicrobial use reduced (although not significantly). The inter-
ventions also differed markedly, being less labour-intensive in our
study and therefore able to be implemented in a much larger
number of clinics. However, the overall impact was similar, sug-
gesting that different interventions can have similar outcomes
in different populations.
A trial in the UK focusing on the highest priority critically im-
portant antimicrobial use was successful in reducing the use of
these antimicrobials when this was the sole target of the inter-
vention.
25
Identifying the interventions that are likely to be ef-
fective in changing behaviour in a population is key to
successful AMS interventions, and co-design with participants is
encouraged to allow for adaptation to the social, economic, edu-
cational and cultural backgrounds where they will be applied.
26,27
Co-design has been used extensively in AMS interventions in hu-
man medicine,
28–31
but only in a smaller number of studies in ve-
terinary medicine.
32,33
Our interventions focused on promoting
guidelines, implementing a traffic-light system and delivery
of educational webinars, while the trial in The Netherlands
discussed above focused on small group education and self-
reflection. A wide range of planned interventions have also
reduced inappropriate prescribing in human hospitals.
18–21
Qualitative investigation of the implementability of interventions
targeting antimicrobials with a high-importance rating is needed
in this population because the proportional use of these drugs did
not reduce. It is possible that the behavioural drivers for using
these drugs are not sufficiently understood to design effective in-
terventions. Further research is needed to explore these behav-
ioural drivers in more detail.
In this intervention trial, the greatest impact was seen in
clinics that were in the top 25% of prescribers in the pre-trial per-
iod. This was an interesting finding, as in this trial high prescribers
were not targeted with social norm feedback, an intervention
that has been effective in the medical sector.
34,35
There was
also a greater difference in clinics in the CON intervention
(IR mean difference of 2.3) than in the AMS1 (IR mean difference
of 2.0) and AMS2 (IR mean difference of 2.1) interventions. It is
possible that regional clinical directors or practice managers
identified high-prescribing clinics and put more effort into imple-
menting ASPs in these practices, and this had an impact on the
implementation of the interventions. Alternatively, high-
prescribing practices may be able to achieve greater reductions
in use more easily than practices with low prescribing rates.
This should be further investigated with future surveys and inter-
views of participants.
The challenge in implementing interventions aimed at behav-
ioural change is maintaining that change over time. There is con-
cerning evidence from some studies that change does not last.
Even intervention teams that have produced positive clinical out-
comes have warned that a single intervention is not likely to re-
sult in sustained change.
36
However, well-designed intervention
bundles can have a sustained impact.
37
The barriers to AMS in ve-
terinary practice have been evaluated previously and the
commitment of practice leaders has been identified as a critical
factor.
17
The involvement of all levels of management within
this corporate group is likely to have been critical in the successful
implementation of the bundled interventions and their short-
and medium-term sustainability. This interest of management
in AMS may also explain the reduction in antimicrobial use in
the pre-trial period. This should be further investigated using qua-
litative methods.
This trial was designed to investigate the most effective way
to reduce antimicrobial use in a sustained manner. An implemen-
tation trial model was adopted, as we recognized that steward-
ship interventions are rarely ‘one-size-fits-all’and that
interventions need to be adapted based on cultural, socioeco-
nomic and personnel factors. The ‘buy-in’to the strategy by prac-
titioners was as important as any of the interventions we
implemented. For this reason, we identified that some practices
needed to start at a lower level of intervention in the first phase
of this trial. This approach is in contrast to a randomized con-
trolled trial and may have had an impact on the external validity
of the results in this study. Appropriate antimicrobial use is mea-
sured in medical AMS,
38–40
but the method for measuring this
outcome is yet to be developed in veterinary medicine. While ap-
propriate antimicrobial use could not be directly evaluated, the
reduction in overall antimicrobial use and the shift towards pre-
scribing antimicrobials with a low-importance rating, from those
with a medium-importance rating, was used as a proxy for this
outcome. Another limitation of this study is that we do not
know what interventions were fully implemented in each veterin-
ary clinic and cannot evaluate the effectiveness of each of the in-
dividual interventions within the bundles. However, as discussed
previously, a bundle of interventions has been shown to be more
sustainable than individual interventions.
37
Hence, a study of
bundled interventions is more useful in achieving the goal of im-
proving antimicrobial use over the long-term, and minimizing the
likelihood of selection of antimicrobial resistance. An evaluation
survey of participants and participant interviews will help eluci-
date which intervention elements were most useful in veterinary
clinics and, in combination with the results presented here, will
guide further implementation of AMS interventions. The effect
of delayed prescribing was not evaluated in this study as it was
inconsistently recorded, but this is unlikely to have had a major
impact on the outcomes of the study.
This corporate group of clinics may not be an ideal indicator of
the likelihood of implementation success in independent general
veterinary practices. The distribution of antimicrobial use across
these clinics was similar to that seen in previous studies of veter-
inary antimicrobial use in Australia,
12,15
but the trial clinics were
predominantly located in metropolitan regions and only at-
tended to companion animal patients, so results may vary in a
population of practices that treat horses and food animals,
and/or practices in rural and regional areas. The AMS interven-
tions trialled may also have different effects in emergency and
referral clinics, which were excluded from this study.
In conclusion, we have shown a positive impact of three differ-
ent AMS programmes in a large group of general veterinary prac-
tices, resulting in both a decline in overall antimicrobial use and a
shift in use towards prescribing of low-importance antimicrobials,
with the greatest impact seen in high-prescribing clinics. Further
research is needed to evaluate the experience of veterinarians
Hardefeldt et al.
8of10
involved in the trial and to investigate the impact that these inter-
ventions may have in practices that see horses and/or food pro-
duction animals in addition to dogs and cats.
Acknowledgements
We thank all the Greencross Vets staff who participated in the trial. We
thank Tracey Fisher for assistance with data acquisition, and Jessica
Ierardo, Lindsay Evans, Adam Jeffrey, Adam Sternberg, Fiona Ludbrooke
and Veronica Monaghan for promoting the trial.
Funding
This research was undertaken with the assistance of information and
other resources from the VetCompass Australia consortium under the pro-
ject ‘VetCompass Australia: Big Data and Real-time Surveillance for
Veterinary Science’, which was supported by the Australian Government
through the Australian Research Council through the Linkage
Infrastructure, Equipment and Facilities scheme (LE160100026). This
work was supported by the National Health and Medical Research
Council through the Centres of Research Excellence programme (grant
no. 1079625). B.H., S.R. and R.S. are recipients of Australian
Postgraduate Award scholarships. L.Y.H. is funded by the Australian
Research Council through the Discovery Early Career Research Fellowship
program (grant no. DE200100030).
Transparency declarations
None to declare.
Supplementary data
Tables S1 to S4 are available as Supplementary data at JAC-AMR Online.
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