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Identification of Behaviour Change Techniques in Deprescribing Interventions: A Systematic Review and Meta-Analysis


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Aims: Deprescribing interventions safely and effectively optimise medication use in older people. However, questions remain about which componentsof interventions are key to effectively reduce inappropriate medication use. This systematic review examines the behaviour change techniques (BCTs) of deprescribing interventions and summarises intervention effectiveness on medication use and inappropriate prescribing. Methods: MEDLINE, EMBASE, Web of Science and Academic Search Complete and grey literature were searched for relevant literature. Randomised controlled trials (RCTs) were included if they reported on interventions in people aged ≥65 years. The BCT taxonomy was used to identify BCTs frequently observed in deprescribing interventions. Effectiveness of interventions on inappropriate medication use was summarised in meta-analyses. Medication appropriateness was assessed in according to STOPP criteria, Beers' criteria and national or local guidelines Between study heterogeneity was evaluated by I-squared and Chi-squared statistics. Risk of bias was assessed using the Cochrane Collaboration Tool for randomised controlled studies. Results: Of the 1561 records identified, 25 studies were included in the review. Deprescribing interventions were effective in reducing number of drugs and inappropriate prescribing, but a large heterogeneity in effects was observed. BCT clusters including goals and planning; social support; shaping knowledge; natural consequences; comparison of behaviour; comparison of outcomes; regulation; antecedents; and identity had a positive effect on the effectiveness of interventions. Conclusions: In general, deprescribing interventions effectively reduce medication use and inappropriate prescribing in older people. Successful deprescribing is facilitated by the combination of BCTs involving a range of intervention components.
Content may be subject to copyright.
Identication of behaviour change techniques
in deprescribing interventions: a systematic
review and meta-analysis
Correspondence Christina R. Hansen, Pharmaceutical Care Research Group, School of Pharmacy, Cavanagh Pharmacy Building,
University College Cork (UCC), College Road, Cork, Ireland. Tel.: +353 21 490 1690; E-mail:
Received 1 February 2018; Revised 31 July 2018; Accepted 12 August 2018
Christina R. Hansen
, Patricia M. Kearney
, Laura J. Sahm
, Shane Cullinan
C.J.A. Huibers
, Stefanie Thevelin
, Anne W.S. Rutjes
, Wilma Knol
, Sven Streit
and Stephen Byrne
Pharmaceutical Care Research Group, School of Pharmacy, Cavanagh Pharmacy Building, University College Cork, Cork, Ireland,
Section for Social
and Clinical Pharmacy, Department of Pharmacy, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen Ø, Denmark,
Department of Medicine, University College Cork, Cork, Ireland,
Department of Geriatric Medicine, Cork University Hospital, Cork, Ireland,
Department of Epidemiology & Public Health, UCC, Cork, Ireland,
Pharmacy Department, Mercy University Hospital, Cork, Ireland,
School of
Department of Geriatric Medicine and Expertise Centre Pharmacotherapy in Old
Persons, University Medical Centre Utrecht, Utrecht, The Netherlands,
Clinical Pharmacy Research Group, Louvain Drug Research Institute,
Université Catholique de Louvain, Brussels, Belgium,
Institute of Social and Preventive Medicine, University of Bern, Switzerland & Institute of
Primary Health Care (BIHAM), University of Bern, Switzerland, and
Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
Keywords behaviour change techniques, deprescribing, meta-analysis, systematic review
Deprescribing interventions safely and effectively optimize medication use in older people. However, questions remain about
which components of interventions are key to effectively reduce inappropriate medication use. This systematic review examines
the behaviour change techniques (BCTs) of deprescribing interventions and summarizes intervention effectiveness on medication
use and inappropriate prescribing.
MEDLINE, EMBASE, Web of Science and Academic Search Complete and grey literature were searched for relevant literature.
Randomized controlled trials (RCTs) were included if they reported on interventions in people aged 65 years. The BCT taxonomy
was used to identify BCTs frequently observed in deprescribing interventions. Effectiveness of interventions on inappropriate
medication use was summarized in meta-analyses. Medication appropriateness was assessed in accordance with STOPP criteria,
Beerscriteria and national or local guidelines. Between-study heterogeneity was evaluated by I-squared and Chi-squared statis-
tics. Risk of bias was assessed using the Cochrane Collaboration Tool for randomized controlled studies.
Of the 1561 records identied, 25 studies were included in the review. Deprescribing interventions were effective in reducing
number of drugs and inappropriate prescribing, but a large heterogeneity in effects was observed. BCT clusters including goals
and planning;social support;shaping knowledge;natural consequences;comparison of behaviour;comparison of outcomes;regulation;
antecedents;andidentity had a positive effect on the effectiveness of interventions.
British Journal of Clinical
Br J Clin Pharmacol (2018) •• ••–•• 1
© 2018 The Authors. British Journal of Clinical Pharmacology
published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproductioninany
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In general, deprescribing interventions effectively reduce medication use and inappropriate prescribing in older people.
Successful deprescribing is facilitated by the combination of BCTs involving a range of intervention components.
Older people (aged 65years)aremorevulnerableto
medication-related harm and inappropriate prescribing
than younger chronically medicated people [1, 2]. Age-
related physiological changes contribute to iatrogenic
vulnerability in older people, but it is equally a conse-
quence of their multimorbidity and frequent use of multi-
ple medications [1, 37]. Vulnerability, polypharmacy and
multimorbidity represent complex challenges in the care
of older people and often exclude them from clinical trials
[6, 810]. Therefore, some prescriptions in multimorbid
older people are without clear-cut evidence to support
them and inappropriate prescribing is highly prevalent
[1113]. Excessive inappropriate prescribing in older people
has turned the focus of current research towards
deprescribing the systematic process of identifying and
discontinuing drugs in patients for which existing and
potential harms outweigh the benets [14]. Making
informed decisions to deprescribe with the goal of reducing
inappropriate prescribing and improving patient outcome
is hampered by a lack of evidence of withdrawal effects in
older people and is further challenged by prescriber- and
patient-related factors [15, 16].
Research has demonstrated safety and effectiveness of
deprescribing in older people (aged 65 years) [17] whilst
reluctance of prescribers to deprescribe a medication
commenced by another prescriber is described as well [18].
Although evidence suggests that pharmacist involvement
and patient-centred interventions are effective, the best ways
to engage and support prescribers in deprescribing remain
unclear [16, 1923]. Previous reviews examining the effects
of deprescribing interventions on clinical outcomes call for
a better understanding of successful implementation of
deprescribing [6, 1719].
Within the clinical context of patient care, there is a need
to ensure that behaviour change is a part of any intervention
design in order to maximize the chance that prescribers are
enacting on recommendations [24, 25]. Recent advances in
behavioural science provide insight into the components of
complex interventions aiming at behaviour change. The
Behaviour Change Techniques (BCTs) taxonomy version 1
(BCTTv1) [26] is designed to assist in the identication of
BCTs of interventions. A BCT is dened as an observable,
replicable, and irreducible component of an intervention
designed to alter or redirect causal processes that regulate
behaviour[27]. A clear description of BCTs will clarify the
essential content of these complex interventions in a
consistent way to assist in future replication of effective
interventions [28]. The application of the BCT taxonomy to
deprescribing is novel. This review was designed to comple-
ment previous reviews [6, 17, 19] on deprescribing by offering
a broader analysis of behaviour change techniques in
deprescribing interventions.
The aims of this review are (i) to identify behaviour
change techniques used more frequently in interventions
effective in reducing number of drugs and inappropriate pre-
scribing, (ii) to describe other characteristics of deprescribing
interventions and (iii) to determine intervention effective-
ness on drug use, prescribing appropriateness and Medica-
tion Appropriateness Index (MAI) score in meta-analyses.
A systematic search of the primary, secondary and grey
literature to identify randomized controlled trials (RCTs)
on deprescribing was undertaken on December 14, 2016.
This systematic review was reported according to the
PRISMA guidelines for systematic reviews and meta-
analyses [29], and was registered in Prospero (record no.
Search strategy
The search strategy was designed in conjunction with an
experienced medical librarian (JM) who was trained in
systematic review methodology. A combination of text words
and subject headings (such as MeSH terms) related to the
intervention was used, without restricting publication date
or language (Table S1).
The following electronic bibliographic databases were
searched: MEDLINE, EMBASE, The Cochrane Central Register
of Controlled Trials, Web of Science and Academic Search
Complete. Grey literature was searched via the Google
search engine and from screening reference lists of
included studies as well as relevant systematic reviews.
Additional searches were done in the System for Information
on Grey Literature in Europe (OpenSIGLE) and the clinical
trial registries, namely, International Stan-
dard Registered Clinical/soCial sTudy Number (ISRCTN),
WHO International Clinical Trials Registry Platform (ICTRP)
and the Australian New Zealand Clinical Trials Register
Study selection
One reviewer (C.H.) screened titles of all retrieved citations.
Two reviewers (C.H. and S.C.) independently screened
abstracts and full-texts for eligibility according to protocol-
dened inclusion and exclusion criteria. Any disagreements
between reviewers were resolved by consensus and both
reviewers agreed on the nal inclusion of studies.
Inclusion and exclusion criteria
Inclusion was restricted to randomized controlled study
design, including randomized controlled trials (RCTs) and
cluster RCTs. The control group could involve either active
interventions or inactivity, e.g. sham or no intervention. This
C. R. Hansen et al.
2 Br J Clin Pharmacol (2018) •• ••–••
study design was chosen to allow for between-study
comparison of intervention effectiveness in meta-analyses.
Studies were included if they reported on interventions
encouraging the deprescribing of existing drugs or the
reduction of existing inappropriate prescribing. Only those
interventions involving older patients (aged 65 years) or a
healthcare professional with prescribing, dispensing or
administration authority were included. No restrictions were
applied to language, clinical setting of the intervention,
sample size, blinding procedures or other design characteris-
tics. We excluded interventions specically focusing on the
clinical effects of drug withdrawal processes, e.g. opioid
withdrawal effects.
Risk of bias assessment
Risk of bias was assessed separately by two reviewers (C.H.
and A.R.) using the Cochrane Collaboration Tool for random-
ized controlled studies [30] with a descriptive purpose of
summarizing the quality of the studies that met inclusion
criteria. Studies were not excluded from data analysis because
of methodological aws if they otherwise met inclusion
criteria. Incomplete outcome data was in general rated as
high risk of bias if the loss of patients to follow-up was 20%
or higher and rated as low risk of bias if the loss was 10% or
less. Imbalance in the numbers lost to follow-up between in-
tervention and control groups was also considered to intro-
duce bias. The risk of bias assessment is described in detail
in Table S2.
Data extraction strategy
Data were collected using a pre-agreed data extraction form
(see Table S3). Two reviewers (C.H. and L.S.) independently
pilot tested the form on two randomly chosen studies both
included in the review. Thereafter data extraction on all
studies was completed independently by L.S. and C.H.
Disagreements on study inclusion/exclusion were resolved
by discussion leading to consensus; where consensus could
not be achieved, the study was excluded. Primary outcomes
were: (i) number of total and inappropriate prescriptions
and/or drugs as dened in the individual studies according
to prescribing appropriateness criteria, e.g. STOPP criteria,
Beerscriteria and local or national prescribing guidelines;
(ii) proportion of participants with a reduction in number of
total and inappropriate prescriptions and/or drugs; and (iii)
implementation of recommendations. Secondary outcome
was change in MAI score.
Behaviour change techniques coding
Coding of BCTs was performed independently by two
reviewers (C.H. for all interventions and C.J.A., S.T. and L.S.
for a subset of interventions each) by identifying BCTs for
each intervention using the BCTTv1 [26]. C.H. had
completed online training in BCTTv1. A coding manual and
instructions made by C.H. were given to the other reviewers
and, exercises from the online training were made available
to them. Any questions about the coding were solved by
discussion and consensus between the reviewers. The target
behaviour was the decision making to discontinue a drug or
an inappropriate prescription. Findings were tabulated across
studies by computing frequencies. The information was used
to determine the BCTs used more frequently in studies that
reported effectiveness of interventions to reduce number of
drugs and/or improve prescribing appropriateness.
Statistical analysis
We calculated odds ratios (OR) with standard deviations (SD)
for each of the reported outcomes and used RevMan v5.3 to
statistically combine the outcome data [31]. Continuous out-
comes were expressed as difference in means between groups
with a 95% condence interval (95% CI). The level of
between-study heterogeneity was evaluated by calculation
of the I
and Chi-squared statistics. Where possible, stratied
random effects meta-analyses was used to identify factors
affecting intervention effectiveness. Subgroup analyses were
performed by risk of bias assessment, intervention setting
and intervention target. If the level of reporting did not allow
for inclusion of a study in one or more meta-analyses,
additional information was sought from the study authors.
If the information was not made available, the study was
excluded from the meta-analysis.
Literature search and review process
The database search identied 1444 records, and grey liter-
ature yielded 117 records. After removal of duplicates and
title screening, 178 abstracts were screened for eligibility
and 58 of these met the inclusion criteria. Assessment of
full texts resulted in 25 studies included in this review
[3256]. Study selection and reasons for exclusion are illus-
trated in Figure 1.
Study characteristics
Included studies were RCTs (n= 22) [3241, 4345, 47,
4956] and cluster RCTs (n= 3) [42, 46, 48] with a
follow-up period from 6 weeks [45] to 13 months [42]. A
total of 20 812 patients were enrolled in the studies
ranging from 95 [41] to 1188 per study [55]. Detailed study
characteristics are provided in Table 1. Three studies aimed
primarily to reduce the number of drugs taken by patients
[41, 44, 46]. Other objectives included reduced prevalence
of inappropriate medications [32, 33, 38, 39, 42, 49],
improved prescribing appropriateness [3436, 47, 5055],
or better patient health outcomes and medicines manage-
ment [35, 40, 48, 56]. Ten out of the 25 studies included
in this review showed evidence to support intervention
effectiveness [34, 35, 37, 40, 41, 45, 46, 50, 53, 56]. Most
of the studies reporting intervention effectiveness of the
key outcomes of this review delivered recommendations
or feedback to the prescriber orally, often face-to-face, and
many of them followed up on the recommendations/
feedback given. Recommendations and feedback were
given immediately after identication of a problem or at
the time of prescribing using an on-demand service. For
studies reporting no intervention effectiveness on the key
outcomes, some delivered recommendations using written
communication and many of the interventions did not fol-
low up on the recommendations with the prescriber. None
Behaviour change techniques in deprescribing interventions
Br J Clin Pharmacol (2018) •• ••–•• 3
of the included studies reported the use of explicit theories
of behaviour change as part of the interventions and no
study reported the use of a systematic and theoretical ap-
proach, such as the UK Medical Research Councilscom-
plex intervention framework [57], in the intervention
design. Reported educational interventions were based on
the principles of constructive learning theory in one study
[39] and social constructivist learning and self-efcacy the-
ory in another study [46].
Risk of bias
Risk of bias assessment is illustrated in Figure 2. Risk of bias
not pertaining to any of the dened categories were catego-
rized as othersand these are described in Table S2.
Behaviour change techniques
All but one study [48] reported the behaviour change compo-
nents underpinning the intervention. The BCT coding is
Figure 1
PRISMA ow chart of study selection
C. R. Hansen et al.
4 Br J Clin Pharmacol (2018) •• ••–••
Table 1
Characteristics of included studies (n=25)
(year) Country Setting
No. of patients
Mean age of
(±SD), years
Intervention (I)
Delivered by (D)
Target behaviour
Target person(s) (P)
Allard et al.
(2001) [32]
80.6 (4.5) (I) Medication review and
suggestions made and
mailed to GPs
(D) Multidisciplinary team
of physicians, pharmacists
and nurses
Reducing the number
of potentially inappropriate
prescriptions given.
(P) GPs.
Bregnhøj et al.
(2009) [47]
Primary care
physician practice
76.5 (7.2) (I) Interactive educational
meeting (single intervention)
and combined with individualized
feedback on prescribed medication
(combined intervention)
(D) Clinical pharmacologist
and pharmacists
Improving prescribing
(P) GPs.
Crotty et al.
(2004) [48]
Nursing home
84.5 (5.0) (I) Medication review and case
(D) Multidisciplinary team of
geriatrician, pharmacist,
representative of the Alzheimers
Association of South Australia
Improving medication
(P) Residential care
staff and residentsGPs.
Dalleur et al.
(2014) [33]
Teaching hospital
85.0 (5.2) (I) Medication review and
recommendations provided
to discontinue medications
based on the STOPP criteria
(D) Multidisciplinary team of
nurses, geriatricians, dietician,
occupational therapist,
physiotherapist, speech
therapist and psychologist
Disconti nuation of PIMs
(P) Hospital physicians
Fick et al.
(2004) [49]
Primary care physician
Not specied Not specied (I) Decision support service
comprising educational
brochure, list of suggested
inappropriate medications
based on the STOPP criteria,
and list of patients with
STOPP criteria identied
(D) Research team and
expert panel of physicians
and pharmacists
Changing prescribing
behaviour and decreasing
PIM use.
(P) GPs
Frankenthal et al.
(2014) [56]
IsraelChronic care
geriatric facility
82.7 (8.7) (I) Medication review and
recommendations provided
based on the STOPP/
START criteria
(D) Study pharmacist
Improving clinical and
economic outcomes by
recommendations. (P)
Chief physicians.
Gallagher et al.
(2011) [34]
Teaching hospital
75.6 (7.3) (I) Medication review and
recommendations provided
to change medications based
on the STOPP/START criteria
(D) Research physician
Improving prescribing
(P) Hospital physician
and medical care team
et al. (2014) [35]
Nursing home
84.4 (12.7) (I) Educational workshops,
material and on-demand
advice on prescriptions
(D) Nursing home physician
with geriatric expertise
Improving the quality
of prescriptions
(P) Nursing home physicians
Hanlon et al. (1996)
Ambulatory clinic
69.8 (3.8) (I) Medication review
and prescribing
Improving prescribing
(P) GPs and patients
Behaviour change techniques in deprescribing interventions
Br J Clin Pharmacol (2018) •• ••–•• 5
Table 1
(year) Country Setting
No. of patients
Mean age of
(±SD), years
Intervention (I)
Delivered by (D)
Target behaviour
Target person(s) (P)
(D) Pharmacists
Lenaghan et al.
(2007) [37]
Primary care physician
(I) Medication review and
development of action
plan of agreed amendments
(D) Pharmacists
Reducing hospital
admissions and number
of drug items prescribed
(P) GPs and patients
Meredith et al.
(2002) [50]
Home health setting
80.0 (8.0) (I) Medication review and
development of action plan
to address identied problem
(D) Multidisciplinary team of
physicians, nurses and pharmacists
Improving medication use
(P) Nurses and patients
Milos et al.
(2013) [38]
Nursing home and
87.4 (5.7) (I) Medication review and
feedback given to physician
on drug-related problems
(D) Pharmacists
Reducing the number of
patients using PIMs
(P) GPs
Pitkälä et al.
(2014) [39]
Nursing home
83.0 (7.2) (I) Staff training and list of
harmful medications provided
to encourage nurses to bring
this to the physiciansattention
(D) Research team
Improving the use of
potentially harmful
(P) Nurses
Pope et al.
(2011) [40]
(I) Clinical assessment by a
senior doctor and multidisciplinary
medication review using Beers
criteria. Recommendations
given to GP
specialist registrar and a
multidisciplinary panel of
consultant geriatricians,
specialist registrars, hospital
pharmacists and senior
nurse practitioners
Reducing the number
of drugs prescribed
(P) GPs
Potter et al.
(2016) [41]
Nursing home
84.0 (7.0) (I) Medication review and
cessation plan of non-
benecial medications
(D) Research team of GP
and geriatrician
Reducing the total
number of medicines
(P) GPs and patients
Richmond et al.
(2010) [51]
Primary care trusts
80.4 (4.1) (I) Pharmaceutical care
including medication reviews
(D) Research team
Improving prescribing
(P) GPs
Saltvedt et al.
(2005) [52]
Teaching hospital
82.1 (5.0) (I) Comprehensive geriatric
assessment and treatment
of all illnesses
(D) Multidisciplinary team
of geriatrician, nurses,
residents, occupational
therapists and physiotherapists
Increasing the number
of drugs withdrawn
(P) Medical care team
Schmader et al.
(2004) [53]
46% aged
54% aged
74 years
(I) Treatment in a geriatric
evaluation and management
unit (GEMU) in either
inpatient or outpatient
care or both
(D) Pharmacists and a
multi-disciplinary team
of geriatrician, social
worker and nurse
Improving prescribing
(P) Medical care team
C. R. Hansen et al.
6 Br J Clin Pharmacol (2018) •• ••–••
presented in Table S4. Based on the reported results, 10 of
the 25 studies showed an effect on the key outcomes (i)
or (ii) of this review when comparing the intervention
group to the control group [34, 35, 37, 40, 41, 45, 46, 50,
53, 56]. No direct pattern was seen between the number
of individual BCTs used and reported intervention effec-
tiveness. The median number of BCTs used were similar
for studies reporting effective and non-effective interven-
tions (6 BCTs, IQR 38and5BCTs,IQR47, respectively).
BCT clusters coded more frequently in studies reporting
effectiveness [34, 35, 37, 40, 41, 45, 46, 50, 53, 56] com-
pared to studies reporting no effectiveness were: goals and
planning;social support;shaping knowledge;natural conse-
quences;comparison of behaviour;comparison of outcomes;reg -
ulation;antecedents;andidentity (see Figure 3).
Intervention effectiveness
(a) Drug use
Overall, the mean number of drugs post-intervention was sig-
nicantly lower among intervention participants compared
to the control participants in the presence of moderate
between-study heterogeneity (mean difference 0.96, 95%
Table 1
(year) Country Setting
No. of patients
Mean age of
(±SD), years
Intervention (I)
Delivered by (D)
Target behaviour
Target person(s) (P)
Spinewine et al.
(2007) [54]
82.2 (6.6) (I) Pharmaceutical care
including medication
review and development
of a therapeutic care plan
with prescribing
(D) Pharmacists
Improving prescribing
(P) Medical care team
and patients
Tamblyn et al.
(2003) [42]
Primary care physician
12 560
75.4 (6.3) (I) Electronic alerts instituted
in the electronic patient
prescription record to
identify prescribing problems
(D) Research team
Reducing inappropriate
(P) GPs
Tannenbaum et al.
(2014) [46]
Community pharmacy
75.0 (6.3) (I) Educational booklet
to empower and encourage
patients to discontinue
(D) Research team
Disconti nuation of
(P) Patients
Vinks et al.
(2009) [43]
The Netherlands
Community pharmacy
76.6 (6.5) (I) Medication review
and prescribing
(D) Pharmacists
Reducing the number
of potential DRPs and the
number of drugs prescribed
(P) GPs
Weber et al.
(2008) [44]
Ambulatory clinic
(I) Electronic messages
sent to physician via
electronic medication
record to give
(D) Pharmacist
and geriatrician
Reducing medication
(P) GPs
Williams et al.
(2004) [45]
Ambulatory clinic
73.7 (5.9) (I) Medication review
based on MAI and
prescribing recommendations
provided and action plan made
(D) Pharmacists
Simplifying medication
(P) Patients
Zermansky et al.
(2001) [55]
Primary care physician
73.5 (6.5) (I) Prescription review
and treatment recommendations
given to patients
(D) Pharmacist and physician
Making changes to repeat
prescriptions and reducing
the number of medicines
(P) Patients
The low percentages of females reported was explained by the nature of male patients in Veterans Affairs (VA) clinics
The SDs were not reported and could not be retrieved from the authors
Behaviour change techniques in deprescribing interventions
Br J Clin Pharmacol (2018) •• ••–•• 7
CI 1.53, 0.38, heterogeneity I
= 70% and P= 0.002,
Figure S1). Regarding the difference in change in the number
of drugs taken per patient, deprescribing interventions
lowered the number (0.74, 95% CI 1.26, 0.22), but
effects varied greatly across studies (I
(Figure 4). Stratied analyses by: (i) whether the intervention
was patient-centred or targeting solely healthcare profes-
sionals (Figure S2), (ii) intervention setting (Figure 4) and
(iii) study quality (Figure S3) showed no effect of these
factors on summary estimates. In addition, the unexplained
variation within subgroups remained large.
(b) Prescribing appropriateness
Deprescribing interventions demonstrated a relatively
small effect and a high level of heterogeneity on the number
Figure 3
Frequency of behaviour change techniques (BCTs) coded for studies reporting intervention effectiveness on the key outcomes of this review
compared to studies reporting no effectiveness of interventions. The frequencies are weighed values based on the number of studies in each
group, i.e. effectiveness versus no effectiveness
Figure 2
Results of risk of bias assessment
C. R. Hansen et al.
8 Br J Clin Pharmacol (2018) •• ••–••
of inappropriate drugs per participant comparing interven-
tion and control groups post-intervention (0.19, 95% CI
0.40, 0.02, heterogeneity I
= 90% and P=0.07,FigureS4).
The proportion of participants with at least one inappropriate
drug, as dened in the individual studies, were reduced when
a deprescribing intervention was applied, but condence in-
tervals were wide, and a high level of heterogeneity was
present (Figure 5).
(c) Implementation of recommendations
Only four studies reported implementation rates of rec-
ommendations to discontinue a medication or change a med-
ication [36, 38, 43, 49]. Action was taken in 55.1% of
recommendations given by a pharmacist compared to only
19.8% of the nurse recommendations as part of usual phar-
maceutical care [36]. In the study by Vinks et al.[43],27.7%
of pharmacistsrecommendations were implemented, and
action was taken in 56% of drug-related problems identied
by a pharmacist in Milos et al. [38]. A lower recommendation
implementation rate of 15.4% was shown in Fick et al.[49].
This result was based on self-reported action taken by the
physicians; only 71% of physicians reported this, which
may explain the lower frequency of action observed.
(d) MAI score
Seven studies reported changes in MAI scores for participants
pre- and post-interventions [34, 36, 47, 48, 51, 53, 54]. Across
studies, deprescribing interventions demonstrated a signicant
effect on reducing the MAI score comparing intervention and
control groups post-intervention (5.04, 95% CI 7.40,
2.68, heterogeneity I
= 88% and P<0.0001, Figure S5).
Effectiveness of deprescribing interventions is determined by
a combination of factors. Consistent with the ndings of re-
cent reviews [6, 17], our meta-analysis showed that
deprescribing interventions are effective in reducing the
number of drugs and inappropriate prescribing (reduced
MAI scores) in older people, although the evidence is
Based on the ndings of the BCT coding exercise, effec-
tive deprescribing interventions included: (i) a goal and an
action plan to solve prescribing problems, (ii) monitoring
of behaviour, (iii) social support and the use of a credible
source, and (iv) clear instructions and guidance on imple-
mentation to the prescriber and information about health
consequences of doing/not doing the behaviour. Support
from colleagues and information about potential risks and
benets to the patients in the presence/absence of a
behaviour change may also be effective techniques of
Differences in the delivery of prescribing recommenda-
tions were seen in the studies reporting intervention
effectiveness compared to studies reporting no effect on
keyoutcomesofthisreview.Studies reporting effectiveness
[34, 35, 37, 40, 41, 45, 46, 50, 53, 56] used oral and face-
to-face communication to discuss and implement
deprescribing recommendations consistent with the princi-
ples of educational outreach to inform clinical decision mak-
ing as described by Soumerai and Avorn [58]. Investigation
of the delivery of recommendations to deprescribe may pro-
vide useful information on the delivery of a successful
deprescribing intervention in addition to the use of BCTs.
Figure 4
Mean difference in the change in number of drugs comparing experimental (intervention) group and control group. Subgroup analysis on inter-
vention setting (outpatient setting versus hospital setting)
Behaviour change techniques in deprescribing interventions
Br J Clin Pharmacol (2018) •• ••–•• 9
Pharmacist recommendations to reduce drug intake and
inappropriate prescribing were frequently enacted on in
some studies [36, 38], consistent with previous literature
reporting benets of pharmacist-led interventions to
optimize medication use in older people [21, 59]. Other
studies [43, 49] reported a lower acceptance rate of phar-
macist recommendations, between 15% and 28% of recom-
mendations enacted on. Recent research has demonstrated
a high level of agreement between prescribers and pharma-
cists in the assessment of potential target medications for
deprescribing [60, 61]. In contrast, other research studies
indicate that acceptance rates for recommendations made
by pharmacists are lower than those made by their physi-
cian colleagues [62]. The lower uptake of pharmacist
recommendations despite a high level of agreement
about deprescribing is noteworthy. It may indicate that
challenges to deprescribing are in fact dependent on the
particular ways deprescribing interventions are delivered,
particularly when there is a question of behaviour change.
Based on the ndings of this review, we suggest that future
research should investigate the behaviours associated with
theacceptanceandrejectionof deprescribing recommenda-
tions to gain a better understanding of a successful delivery
of deprescribing interventions.
This is the rst review to identify BCTs in deprescri-
bing interventions necessary to achieve a change in
behaviours towards deprescribing. Our ndings comple-
ment previous reviews on deprescribing [17, 19] by offer-
ing a broader analysis of BCTs that are effective for
Limitations and strengths
The review ndings are based on a comprehensive search
of the literature. The novel aspect of this review is in the
use of a validated taxonomy to describe intervention con-
tent that facilitates behaviour change. Limitations of this
review reside mostly in the limited data available. RCTs to
date are of a relatively small size (often 100 participants)
and usually with short follow-up periods. Other limitations
relate to the high-risk blinding procedures; these were
needed because the interventions in question required
blinding of the personnel whose behaviour was targeted,
and this was logistically difcult. Absence of blinding
procedures for outcome assessors were not considered to
introduce important bias because the study outcomes,
e.g. number of drugs taken, was not a particularly subjec-
tive measure. Random sequence generation and allocation
concealment were considered high importance biases in
this review because participant characteristics such as
multimorbidity, age and polypharmacy could have an
impact on the number of drugs taken and risk of inappro-
priate prescribing [1, 58].
The meta-analysis was reliant on published or reported
data and, while some reported outcomes were adjusted for
baseline patient characteristics, others were not, which
makes the direct comparison of intervention effect on
specic outcomes open to question. Similarly, and as
described in a previous review [27], the BCT coding was lim-
ited to the intervention descriptions reported in the studies.
Limited reporting on interventions used to encourage
deprescribing could have resulted in BCTs being undercoded
Figure 5
Number of participants with inappropriate drugs comparing experimental (intervention) group and control group. Subgroup analysis on risk of
bias assessment (allocation concealment)
C. R. Hansen et al.
10 Br J Clin Pharmacol (2018) •• ••–••
and others overcoded due to assumptions made about the
strategies used based on the information available. For
example, we assumed that the reporting of prescribing
recommendations given to the prescriber would involve
BCT codes: instructions on how to perform a behaviour and
feedback on behaviour. Prescribing recommendations were
a commonly used intervention in the studies and this
may have resulted in these two BCTs being overcoded.
One study was also excluded from the BCT coding due
to lack of information which could have potentially
impacted the true ndings of this review. Furthermore,
we were unable to code BCTs in the control groups due
to limited reporting of the control conditions. The con-
trol conditions such as usual care in hospital settings or
in outpatient settings could include BCTs with potential
implications on the interpretation of the review ndings.
Reporting of future behaviour change interventions and
control conditions will benet from the use of compre-
hensive checklists, such as the TIDieR [63], and give
reviewers the ability to adequately code BCTs and exten-
sively appraise the reporting quality of such interven-
tions. This will improve the identication of
relationships between BCTs used and intervention
The main limitation of our pooled estimates is the
presence of typically large between-study variation and,
for some of the analyses, the wide condence intervals
including trivial effects. Some may argue that a meta-
analysis should not be done in the presence of impor-
tant heterogeneity. Meta-analytical methods, however,
allow for the exploration of sources of heterogeneity
and we fully acknowledge that the magnitude of the
summary estimates should be interpreted with care. To
minimize the level of heterogeneity due to different
study designs, we also decided to limit the inclusion
criteria to randomized controlled studies and cluster ran-
domized controlled studies only. Although the direction
of effect was favouring deprescribing, the magnitude of
effect was very variable. This inconsistency, together
with the imprecision and risk of bias issues lower our
condence in the estimates of effect so that the magni-
tude of effect is very low.
Deprescribing interventions are effective in reducing the
number of drugs taken by patients and improving pre-
scribing inappropriateness. Their success may be
explained by a combination of BCTs spanning a range
of different intervention functions, although we could
not empirically show this. The use of BCTs and delivery
of such behaviour change interventions should be consid-
ered of importance to facilitate successful implementation
of deprescribing. This review contributes to the existing
evidence by critically analysing the content of depres-
cribing interventions in terms of behaviour change,
clearly demonstrating that the current evidence base is
too small to derive strong conclusions on determinants
of success.
C.H., S.C., L.S. and S.B. conducted the study selection for this
review, performed data extraction and evaluated study
quality. A.R. veried quality assessments. C.H., P.K., L.S.,
S.T. and C.J.A. performed the quantitative meta-analyses
and the behaviour change analysis. C.H. drafted the
manuscript with contributions from D.O.M., P.K., S.B., L.S.,
S.C., W.K., S.T., C.J.A. and S.S. A.R. helped in the
interpretation of results. All authors read and approved the
nal manuscript. S.B. was the senior author.
Competing Interests
In cases where a co-author of this review was also a co-author
of an included study, the author in question was not involved
in the study selection, quality evaluation or data analysis.
The authors wish to thank Prof. Anne Spinewine and Prof.
Nicolas Rondondi for their expert opinions and advice on the
protocol and the manuscript. The authors would also like to thank
expertise and help with developing the literature search strategy.
This work is part of the project OPERAM: OPtimising thERapy
to prevent Avoidable hospital admissions in the Multimorbid
elderlysupported by the European Commission (EC) HORIZON
2020, grant agreement number 634238, and by the Swiss State
Secretariat for Education, Research and Innovation (SERI) under
contract number 15.0137. The opinions expressed, and arguments
employed herein are those of the authors and do not necessarily
reflect the official views of the EC and the Swiss government.
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Supporting Information
Additional supporting information may be found online in
the Supporting Information section at the end of the article.
http:/ /onlineli brar y.w /10.1111/bcp .13742/suppinfo
Table S1 Search strategy
Table S2 Risk of bias assessment
Table S3 Data extraction form
Table S4 Behaviour change techniques taxonomy version 1
(BCTTv1) applied to the included studies and the prevalence
of each BCT and BCT cluster
Figure S1 Mean number of drugs per patient post-interven-
tion comparing experimental (intervention) group and con-
trol group
Figure S2 Subgroup analysis on target person (patient or
healthcare professional) for mean difference in the change
in number of drugs per patient
Figure S3 Subgroup analysis on risk of bias assessment (ran-
dom sequence generation) for mean difference in the change
in number of drugs per patient
Figure S4 Mean difference in the number of inappropriate
drugs per participant comparing experimental (intervention)
group and control group
Figure S5 Mean difference in the change in MAI score per
participant comparing experimental (intervention) group
and control group
Behaviour change techniques in deprescribing interventions
Br J Clin Pharmacol (2018) •• ••–•• 13
... This consumer leaflet was developed as a patient decision aid to assist patients with making the choice to deprescribe their opioids. A systematic review showed that effective deprescribing interventions include an action plan to solve prescribing problems, monitoring of behaviour, the use of a credible source, information about the health consequences of not changing the targeted behaviour, and the benefits of behavioural change [38]. It also showed that studies reporting effectiveness incorporated oral and in-person discussions regarding the implementation of deprescribing recommendations [38]. ...
... A systematic review showed that effective deprescribing interventions include an action plan to solve prescribing problems, monitoring of behaviour, the use of a credible source, information about the health consequences of not changing the targeted behaviour, and the benefits of behavioural change [38]. It also showed that studies reporting effectiveness incorporated oral and in-person discussions regarding the implementation of deprescribing recommendations [38]. The consumer leaflets developed in this study incorporate the above elements and may be useful to support prescribers, such as general practitioners, in aiding patients through the deprescribing process [39]. ...
Full-text available
Introduction: Globally, the rate of opioid prescription is high for chronic musculoskeletal conditions despite guidelines recommending against their use as their adverse effects outweigh their modest benefit. Deprescribing opioids is a complex process that can be hindered by multiple prescriber- and patient-related barriers. These include fear of the process of, or outcomes from, weaning medications, or a lack of ongoing support. Thus, involving patients, their carers, and healthcare professionals (HCPs) in the development of consumer materials that can educate and provide support for patients and HCPs over the deprescribing process is critical to ensure that the resources have high readability, usability, and acceptability to the population of interest. Objective: This study aimed to (1) develop two educational consumer leaflets to support opioid tapering in older people with low back pain (LBP) and hip or knee osteoarthritis (HoKOA), and (2) evaluate the perceived usability, acceptability, and credibility of the consumer leaflets from the perspective of consumers and HCPs. Design: This was an observational survey involving a consumer review panel and an HCP review panel. Participants: 30 consumers (and/or their carers) and 20 HCPs were included in the study. Consumers were people older than 65 years of age who were currently experiencing LBP or HoKOA, and with no HCP background. Carers were people who provided unpaid care, support, or assistance to an individual meeting the inclusion criteria for consumers. HCPs included physiotherapists (n = 9), pharmacists (n = 7), an orthopaedic surgeon (n = 1), a rheumatologist (n = 1), nurse practitioner (n = 1) and a general practitioner (n = 1), all with at least three years of clinical experience and who reported working closely with this target patient population within the last 12 months. Methods: Prototypes of two educational consumer leaflets (a brochure and a personal plan) were developed by a team of LBP, OA, and geriatric pharmacotherapy researchers and clinicians. The leaflet prototypes were evaluated by two separate chronological review panels involving (1) consumers and/or their carers, and (2) HCPs. Data collection for both panels occurred via an online survey. Outcomes were the perceived usability, acceptability, and credibility of the consumer leaflets. Feedback received from the consumer panel was used to refine the leaflets, before circulating the leaflets for further review by the HCP panel. Additional feedback from the HCP review panel was then used to refine the final versions of the consumer leaflets. Results: Both consumers and HCPs perceived the leaflets and personal plan to be usable, acceptable, and credible. Consumers rated the brochure against several categories, which scored between 53 and 97% positive responses. Similarly, the overall feedback provided by HCPs was 85-100% positive. The modified System Usability Scale scores obtained from HCPs was 55-95% positive, indicating excellent usability. Feedback for the personal plan from both HCPs and consumers was largely positive, with consumers providing the highest positive ratings (80-93%). While feedback for HCPs was also high, we did identify that prescribers were hesitant to provide the plan to patients frequently (no positive responses). Conclusions: This study led to the development of a leaflet and personal plan to support the reduction of opioid use in older people with LBP or HoKOA. The development of the consumer leaflets incorporated feedback provided by HCPs and consumers to maximise clinical effectiveness and future intervention implementation.
... The eight systematic reviews included a total of 122 unique trials (range of 5-41 trials per SR; 23 trials (18,9%) were included in more than one SR). [12][13][14][15][16][17][18][19] Table 1 and Figure 2 ...
Deprescribing aims to address the problem of medication overuse in older adults. There has been an increasing number of systematic reviews of 'deprescribing'. We aimed to describe the categories of trials included in recent systematic reviews, and to make recommendations for future research. We categorised 122 trials included in eight recent deprescribing systematic reviews into : discontinuation, deprescribing implementation, medication optimisation (including medication initiation) and non-initiation trials. We identified heterogeneity and inconsistency in the categories of trials included in deprescribing systematic reviews. For example, 39 trials (32.0%) involved medication initiation in addition to the deprescribing component. It is now time for international researchers to develop and validate terminology used for trials involving discontinuation/deprescribing of medications, and to provide recommendations for evidence synthesis that will better inform future research, and translation into practice and policy.
... The most commonly evaluated deprescribing interventions have been multifaceted interventions involving combinations of different strategies such as tools, medication reviews (pharmacist-led, physician-led, or collaborative), and education (Isenor et al. 2021a, b;Hansen et al. 2018a, b, c, Rankin et al. 2018Dills et al. 2018). These deprescribing interventions are feasible, safe, and might reduce the number of medications people are taking as well as various measures of inappropriate prescribing. ...
... These reviews were important because they looked at enablers, barriers, challenges and behaviour change techniques in various clinical settings. [28][29][30] This current review adds to the knowledge by mapping intervention design components of randomisedcontrolled trials using the Consolidated Framework for implementation Research (CFIR) construct. [31] This assessment allows the reader to explore possible relationships between intervention components and outcomes. ...
Full-text available
Background: Mixed findings about deprescribing impact have emerged from varied study designs, interventions, outcome measures and targeting sub-categories of medications or morbidities. This systematic review controls for study design by reviewing randomised-controlled trials (RCTs) of deprescribing interventions using comprehensive medication profiles. The goal is to provide a synthesis of interventions and patient outcomes to inform healthcare providers and policy makers about deprescribing effectiveness. Objectives: This systematic review aims to (1) review RCT deprescribing studies focusing on complete medication reviews of older adults with polypharmacy across all health settings, (2) map patients' clinical and economic outcomes against intervention and implementation strategies and (3) inform research agendas based on observed benefits and best practices. Methods: The PRISMA framework for systematic reviews was followed. Databases used were EBSCO Medline, PubMed, Cochrane Library, Scopus and Web of Science. Risk of bias was assessed using the Cochrane Risk of Bias tool for randomised trials. Results: Fourteen articles were included. Interventions varied in setting, preparation, use of interdisciplinary teams, validated guidelines and tools, patient-centredness and implementation strategy. Thirteen studies (92.9%) found deprescribing interventions reduced the number of drugs and/or doses taken. No studies found threats to patient safety in terms of primary outcomes including morbidity, hospitalisations, emergency room use and falls. Four of five studies identifying health quality of life as a primary outcome found significant effects associated with deprescribing. Both studies with cost as their primary outcome found significant effects as did two with cost as a secondary outcome. Studies did not systematically study how intervention components influenced deprescribing impact. To explore this gap, this review mapped studies' primary outcomes to deprescribing intervention components using the Consolidated Framework for Implementation Research. Five studies had significant, positive primary outcomes related to health-related quality of life (HRQOL), cost and/or hospitalisation, with four reporting patient-centred elements in their intervention. Conclusions: RCT primary outcomes found deprescribing is safe and reduces drug number or dose. Five RCTs found a significant deprescribing impact on HRQOL, cost or hospitalisation. Important future research agendas include analysing (1) understudied outcomes like cost, and (2) intervention and implementation components that enhance effectiveness, such as patient-centred elements.
... This makes it difficult to improve an intervention or disentangle why an intervention was unsuccessful [34]. For interventions to be effective, they depend on the coordinated efforts of the health professionals, the patient, and relatives [19,35,36]. Additionally, they often require several interacting educational and behavioral components in order to change the clinical behavior of the health professionals (e.g. ...
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Introduction First-line treatment for behavioral and psychiatric symptoms of dementia is non-pharmacological. Still, psychotropic medication is widely used, despite its limited effect and harmful side-effects. More than half of all nursing home residents with dementia receive antidepressants, even though deprescribing is safe and feasible. Interventions to promote deprescribing of antidepressants in nursing homes are few and complex. To optimize the deprescribing process through an intervention, transparency for the development of the intervention is needed. We aim to describe the steps in the development and tailoring of an intervention targeting GPs, nursing home staff, and relatives to enhance collaboration on reducing the use of antidepressants in institutionalized older persons with dementia in Denmark. Method A step-wise process guided by the core elements in the Medical Research Council constituted the tailoring process. Five steps were included; 1) a literature search, 2) interviews with stakeholders, 3) drafting the intervention prototype, 4) professionals’ assessment of the intervention, and 5) refinement of the intervention. The steps were conducted from June 2020 to June 2022. Results Based on the literature search, interviews with stakeholders, and professionals’ assessment of the intervention, four main themes were identified; 1) focusing on antidepressants, 2) importance of professional qualifications, 3) collaboration and communication, and 4) patient and relative involvement. They guided intervention development and refinement of the final intervention, which included 1) a case-based training course and 2) a dialog tool including a symptom assessment scale to be used in a structured consultation at the nursing home. Conclusion This study presents a detailed account of the tailoring process for a complex intervention to optimize deprescribing of antidepressants for older persons with dementia at nursing homes. By presenting a thorough development process, we expect to achieve increased adherence to the intervention which is currently being tested in an ongoing cluster randomized controlled trial. The transparency of the process will also increase the future development of other similar complex interventions.
Objectives: Deprescribing reduces polypharmacy in older adults. A thorough study of the effect of deprescribing interventions on clinical outcomes in older adults is presently lacking. As a result, we evaluated the impact of deprescribing on clinical outcomes in older patients. Design: Meta-analysis and systematic review of randomized controlled trials (RCTs). PubMed, EMBASE, and Cochrane Library were searched from the time of creation to March 2023. Setting and participants: Randomized controlled trial with participants at least 60 years old. Measures: Mortality, falls (number of fallers), hospitalization rates, emergency department visits, medication adherence, HRQoL (health-regulated quality of life), incidence of ADR (adverse drug reactions), PIM (potentially inappropriate medication), and PPO (potentially prescription omission) were evaluated in the meta-analysis. Results: A total of 32 RCTs (18,670 patients) were included. Deprescribing interventions significantly reduced proportions of older adults with PIM, PPO, and the incidence of ADRs. The interventions group also improved medication compliance. Conclusions and implications: Compared to routine care, deprescribing interventions significantly improve clinical outcome indicators for older adults.
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Importance: For older adults with frailty syndrome, reducing polypharmacy may have utility as a safety-promoting treatment option. Objective: To investigate the effects of family conferences on medication and clinical outcomes in community-dwelling older adults with frailty receiving polypharmacy. Design, setting, and participants: This cluster randomized clinical trial was conducted from April 30, 2019, to June 30, 221, at 110 primary care practices in Germany. The study included community-dwelling adults aged 70 years or older with frailty syndrome, daily use of at least 5 different medications, a life expectancy of at least 6 months, and no moderate or severe dementia. Interventions: General practitioners (GPs) in the intervention group received 3 training sessions on family conferences, a deprescribing guideline, and a toolkit with relevant nonpharmacologic interventions. Three GP-led family conferences for shared decision-making involving the participants and family caregivers and/or nursing services were subsequently held per patient at home over a period of 9 months. Patients in the control group received care as usual. Main outcomes and measures: The primary outcome was the number of hospitalizations within 12 months, as assessed by nurses during home visits or telephone interviews. Secondary outcomes included the number of medications, the number of European Union list of the number of potentially inappropriate medication (EU[7]-PIM) for older people, and geriatric assessment parameters. Both per-protocol and intention-to-treat analyses were conducted. Results: The baseline assessment included 521 individuals (356 women [68.3%]; mean [SD] age, 83.5 [6.17] years). The intention-to-treat analysis with 510 patients showed no significant difference in the adjusted mean (SD) number of hospitalizations between the intervention group (0.98 [1.72]) and the control group (0.99 [1.53]). In the per-protocol analysis including 385 individuals, the mean (SD) number of medications decreased from 8.98 (3.56) to 8.11 (3.21) at 6 months and to 8.49 (3.63) at 12 months in the intervention group and from 9.24 (3.44) to 9.32 (3.59) at 6 months and to 9.16 (3.42) at 12 months in the control group, with a statistically significant difference at 6 months in the mixed-effect Poisson regression model (P = .001). After 6 months, the mean (SD) number of EU(7)-PIMs was significantly lower in the intervention group (1.30 [1.05]) than in the control group (1.71 [1.25]; P = .04). There was no significant difference in the mean number of EU(7)-PIMs after 12 months. Conclusions and relevance: In this cluster randomized clinical trial with older adults taking 5 or more medications, the intervention consisting of GP-led family conferences did not achieve sustainable effects in reducing the number of hospitalizations or the number of medications and EU(7)-PIMs after 12 months. Trial registration: German Clinical Trials Register: DRKS00015055.
Over half of older adults experience polypharmacy, including medications that may be inappropriate or unnecessary. Deprescribing, which is the process of discontinuing or reducing inappropriate and/or unnecessary medications, is an effective way to reduce polypharmacy. This review summarizes: 1) the process of deprescribing and conceptual models and tools that have been developed to facilitate deprescribing; 2) barriers, enablers and factors associated with deprescribing; and 3) characteristics of deprescribing interventions in completed trials, as well as 4) implementation considerations for deprescribing in routine practice. In conceptual models of deprescribing, multi-level factors of the patient, clinician, and health care system are all related to the efficacy of deprescribing. Numerous tools have been developed for clinicians to facilitate deprescribing, yet most require substantial time and, thus, may be difficult to implement during routine health care encounters. Multiple deprescribing interventions have been evaluated, which mostly include one or more of the following components: patient education, medication review, identification of deprescribing targets, and patient and/or provider communication about high-risk medications. Yet, there has been limited consideration of implementation factors in prior deprescribing interventions, especially with regard to the personnel and resources in existing health care systems and the feasibility of incorporating components of deprescribing interventions into the routine care processes of clinicians. Future trials require a more balanced consideration of both effectiveness and implementation when designing deprescribing interventions. This article is protected by copyright. All rights reserved Graphical Abstract
Les personnes âgées sont deux fois plus exposées au risque iatrogène que la population générale, pouvant conduire dans 10 à 20 % des cas à une hospitalisation. Avec le vieillissement de la population, l’optimisation des prescriptions des personnes âgées demeure ainsi une priorité de santé publique constante. L’objectif de ce travail était l’étude des prescriptions potentiellement inappropriées (PPI) au niveau populationnel et la mise à disposition d’outils pour améliorer le bon usage des médicaments dans la population âgée. Une mise à jour de la liste française Laroche (2007) a été effectuée par consensus Delphi pour aboutir à un nouvel outil de détection des PPI (REvision des prescriptions MEDIcamenteuses potentiellement inapproprié[e]s chez les Seniors ou REMEDI[e]S). Une utilisation de cet outil a montré que la prévalence des PPI était élevée en France (56,7 %) et associée à des coûts importants. Une étude Québécoise a révélé des résultats similaires (prévalence à 48,3 %) et a permis de démontrer que l’incidence était non négligeable (7,8 %) et que les PPI étaient persistantes au cours du temps pour près d’un quart des personnes âgées. Outre la détection des PPI, la déprescription est une approche complémentaire pour réduire la fréquence des PPI. Le questionnaire revised Patients’ Attitudes Towards Deprescribing a alors été traduit et validé en français parmi quatre pays francophones ; ce questionnaire a révélé qu’une majorité de personnes âgées et d’aidants seraient d’accord pour arrêter un médicament. Ces résultats mettent en évidence la nécessité de sensibiliser constamment les professionnels de santé et les autorités sanitaires sur la problématique des PPI. La déprescription semblant être bien acceptée, présente de nouvelles opportunités pour optimiser davantage les prescriptions du sujet âgé.
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IntroductionMultimorbidity and polypharmacy are risk factors for drug-related hospital admissions (DRAs) in the ageing population. DRAs caused by medication errors (MEs) are considered potentially preventable. The STOPP/START criteria were developed to detect potential MEs in older people.Objective The aim of this study was to assess the detectability of MEs with a STOPP/START-based in-hospital medication review in older people with polypharmacy and multimorbidity prior to a potentially preventable DRA.Methods Hospitalised older patients (n = 963) with polypharmacy and multimorbidity from the intervention arm of the OPERAM trial received a STOPP/START-based in-hospital medication review by a pharmacotherapy team. Readmissions within 1 year after the in-hospital medication review were adjudicated for drug-relatedness. A retrospective assessment was performed to determine whether MEs identified at the first DRA were detectable during the in-hospital medication review.ResultsIn total, 84 of 963 OPERAM intervention patients (8.7%) were readmitted with a potentially preventable DRA, of which 72 patients (n = 77 MEs) were eligible for analysis. About half (48%, n = 37/77) of the MEs were not present during the in-hospital medication review and therefore were not detectable at that time. The pharmacotherapy team recommended a change in medication regimen in 50% (n = 20/40) of present MEs, which corresponds to 26% (n = 20/77) of the total identified MEs at readmission. However, these recommendations were not implemented.ConclusionMEs identified at readmission were not addressed by a prior single in-hospital medication review because either these MEs occurred after the medication review (~50%), or no recommendation was given during the medication review (~25%), or the recommendation was not implemented (~25%). Future research should focus on optimisation of the timing and frequency of medication review and the implementation of proposed medication identifier: NCT02986425. December 8, 2016.FundingEuropean Union HORIZON 2020, Swiss State Secretariat for Education, Research and Innovation (SERI), Swiss National Science Foundation (SNSF)Graphical abstract
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Purpose: It is contentious whether potentially inappropriate prescribing (PIP) is predominantly a phenomenon of late life or whether it has its origins in early old age. This study examined the pattern of PIP in an early old-aged population over 5 years. Methods: Secondary data analysis of a population-based primary care cohort, of patients aged 60-74 years. Medication data were extracted from electronic patient records in addition to information on comorbidities and demographics. Explicit START criteria (PPOs) and STOPP criteria (PIMs) were used to identify PIP. Generalised estimating equations were used to describe trends in PIP over time and adjusted for age, gender and number of medicines. Results: A total of 978 participants (47.8%) aged 60-74 years were included from the cohort. At baseline, PPOs were detected in 31.2% of patients and PIMs were identified in 35.6% at baseline. Prevalence of PPOs and PIMs increased significantly over time (OR 1.08, 95% CI 1.07; 1.09 and OR 1.04, 95% CI 1.0; 1.06, respectively). A higher number of medicines and new diagnoses were associated with the increasing trend in both PPO and PIM prevalence observed over time, independent of PPOs and PIMs triggered by drug combinations. Conclusions: Potentially inappropriate prescribing is highly prevalent among early old-aged people in primary care and increases as they progress to more advanced old age, suggesting that routine application of STOPP/START criteria in this population would significantly improve medication appropriateness.
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Potentially inappropriate prescribing (PIP) in the multi-morbid elderly is a major healthcare problem. Explicit criteria such as Screening Tool of Older Persons’ Prescriptions (STOPP) and Screening Tool to Alert to Right Treatment (START) are well recognized for identifying PIP instances, and the application of STOPP/START criteria has been shown to reduce adverse drug reactions (ADRs) in older people. Two randomized controlled trials were conducted in the same hospital whereby a pharmacist and physician individually applied the STOPP/START criteria to older patients’ medication lists at hospital admission and made recommendations to the attending teams. All of the physician’s recommendations were delivered in both oral and written forms. All of the pharmacist’s recommendations were delivered in written form, and approximately one third communicated orally. Attending teams accepted 37.8% of the pharmacist’s STOPP/START recommendations compared to 83.4% of the physician’s STOPP/START recommendations. Whilst the physician’s intervention focused solely on STOPP/START recommendations, the pharmacist’s intervention was multifaceted - other than the pharmacist’s STOPP/START recommendations, the remainder addressed medicines reconciliation, renal dose adjustment, and other prescribing criteria issues. With the same control cohort (n=372), the physician’s intervention resulted in a significantly greater absolute risk reduction in ADRs than the pharmacist’s (9.3% vs 6.8%) in comparable intervention cohorts (360 patients vs 361 patients). The greater acceptance rate for the physician’s recommendations was attributed to having a narrower intervention focus, communicating the recommendations in both oral and written form, and the physician having an already recognized prescribing role within the hospital.
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It is not known how clinicians assess polypharmacy or the medication-related characteristics that influence their assessment. The aim of this study was to examine the level of agreement between clinicians when assessing polypharmacy and to identify medication-related characteristics that influence their assessment. Twenty cases of patients with varying levels of comorbidity and polypharmacy were used to examine clinician assessment of polypharmacy. Medicine-related factors within the cases included Beers and STOPP Criteria medicines, falls-risk medicines, drug burden index (DBI) medicines, medicines causing postural hypotension, and pharmacokinetic drug–drug interactions. Clinicians were asked to rate cases on the degree of polypharmacy, likelihood of harm, and potential for the medication list to be simplified. Inter-rater reliability analysis, correlations , and multivariate logistic regression analyses were conducted to identify medicine factors associated with clinicians' assessment. Eighteen expert clinicians were recruited (69.2% response rate). Strong agreement was observed in clini-cians' assessment of polypharmacy (intraclass correlation coefficients [ICC] = 0.94), likelihood to cause harm (ICC = 0.89), and ability to simplify medication list (ICC = 0.90). Multivariate analyses demonstrated number of medicines (P < 0.0001) and DBI scores (P = 0.047) were significantly associated with assessment of polypharmacy. Medicines associated with harm were significantly associated with the number of medicines (P = 0.01) and Beers criteria medicines (P = 0.003). Ability to simplify the medication regimen was significantly associated with number of medicines (P = 0.03) and medicines from the STOPP criteria (P = 0.018). Among clinicians, strong consensus exists with regard to assessment of polypharmacy, medication harm, and ability to simplify medications. Definitions of polypharmacy need to take into account not only the numbers of medicines but also potential for medicines to cause harm or be inappropriate, and validate them against clinical outcomes. Abbreviations ICC, intraclass correlation coefficients; DBI, drug burden index; MACS, multidisciplinary ambulatory consultation service.
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Objective: To evaluate studies of pharmacist-led interventions on potentially inappropriate prescribing among community-dwelling older adults receiving primary care to identify the components of a successful intervention. Data sources: An electronic search of the literature was conducted using the following databases from inception to December 2015: PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, MEDLINE (through Ovid), Trip, Centre for Reviews and Dissemination databases, Cochrane Database of Systematic Reviews, ISI Web of Science, ScienceDirect,, metaRegister of Controlled Trials, ProQuest Dissertations & Theses Database (Theses in Great Britain, Ireland and North America). Review methods: Studies were included if they were randomised controlled trials or quasi-randomised studies involving a pharmacist-led intervention compared to usual/routine care which aimed to reduce potentially inappropriate prescribing in older adults in primary care. Methodological quality of the included studies was independently assessed. Results: A comprehensive literature search was conducted which identified 2193 studies following removal of duplicates. Five studies met the inclusion criteria. Four studies involved a pharmacist conducting a medication review and providing feedback to patients or their family physician. One randomised controlled trial evaluated the effect of a computerised tool that alerted pharmacists when elderly patients were newly prescribed potentially inappropriate medications. Four studies were associated with an improvement in prescribing appropriateness. Conclusion: Overall, this review demonstrates that pharmacist-led interventions may improve prescribing appropriateness in community-dwelling older adults. However, the quality of evidence is low. The role of a pharmacist working as part of a multidisciplinary primary care team requires further investigation to optimise prescribing in this group of patients.
Without a complete published description of interventions, clinicians and patients cannot reliably implement interventions that are shown to be useful, and other researchers cannot replicate or build on research findings. The quality of description of interventions in publications, however, is remarkably poor. To improve the completeness of reporting, and ultimately the replicability, of interventions, an international group of experts and stakeholders developed the Template for Intervention Description and Replication (TIDieR) checklist and guide. The process involved a literature review for relevant checklists and research, a Delphi survey of an international panel of experts to guide item selection, and a face-to-face panel meeting. The resultant 12-item TIDieR checklist (brief name, why, what (materials), what (procedure), who intervened, how, where, when and how much, tailoring, modifications, how well (planned), how well (actually carried out)) is an extension of the CONSORT 2010 statement (item 5) and the SPIRIT 2013 statement (item 11). While the emphasis of the checklist is on trials, the guidance is intended to apply across all evaluative study designs. This paper presents the TIDieR checklist and guide, with a detailed explanation of each item, and examples of good reporting. The TIDieR checklist and guide should improve the reporting of interventions and make it easier for authors to structure the accounts of their interventions, reviewers and editors to assess the descriptions, and readers to use the information.
Older people with chronic disease have great potential to benefit from their medications but are also at high risk of harm from their medications. The use of medications is particularly important for symptom control and disease progression in older people. Under-treatment means older people can miss out on the potential benefits of useful medications, while over-treatment (polypharmacy) puts them at increased risk of harm. Deprescribing attempts to balance the potential for benefit and harm by systematically withdrawing inappropriate medications with the goal of managing polypharmacy and improving outcomes. The evidence base for deprescribing in older people is growing. Studies to reduce polypharmacy have used a range of methods. Most evidence for deprescribing relates to the withdrawal of specific medications, and evidence supports attempts to deprescribe potentially inappropriate medicines (such as long-term benzodiazepines). There is also evidence that polypharmacy can be reduced by withdrawing specific medications using individualised interventions. More work is needed to identify the sub-groups of older people who may most benefit from deprescribing and the best approaches to undertaking the deprescribing interventions.
Jansen and colleagues explore the role of shared decision making in tackling inappropriate polypharmacy in older adults Too much medicine is an increasingly recognised problem,1 2 and one manifestation is inappropriate polypharmacy in older people. Polypharmacy is usually defined as taking more than five regular prescribed medicines.3 It can be appropriate (when potential benefits outweigh potential harms)4 but increases the risk of older people experiencing adverse drug reactions, impaired physical and cognitive function, and hospital admission.5 6 7 There is limited evidence to inform polypharmacy in older people, especially those with multimorbidity, cognitive impairment, or frailty.8 Systematic reviews of medication withdrawal trials (deprescribing) show that reducing specific classes of medicines may decrease adverse events and improve quality of life.9 10 11 Two recent reviews of the literature on deprescribing stressed the importance of patient involvement and shared decision making.12 13 Patients and clinicians typically overestimate the benefits of treatments and underestimate their harms.14 When they engage in shared decision making they become better informed about potential outcomes and as a result patients tend to choose more conservative options (eg, fewer medicines), facilitating deprescribing.15 However, shared decision making in this context is not easy, and there is little guidance on how to do it.16 We draw together evidence from the psychology, communication, and decision making literature (see appendix on For each step of the shared decision making process we describe the unique tasks required for deprescribing decisions; identify challenges for older adults, their companions, and clinicians (figure); give practical advice on how challenges may be overcome; highlight where more work is needed; and identify priorities for future research (table).17 18 Schematic representation of challenges for clinicians and older patients associated with each step of the process of shared decision …
Aims: This study aims to determine if potentially inappropriate prescribing (PIP) is associated with increased healthcare utilisation, functional decline and reduced quality of life (QoL) in a community-dwelling older cohort. Method: This prospective cohort study included participants aged ≥65 years from The Irish Longitudinal Study on Ageing (TILDA) with linked administrative pharmacy claims data who were followed up after two years. PIP was defined by the Screening Tool for Older Persons Prescriptions (STOPP) and Screening Tool to Alert doctors to Right Treatment (START). The association with number of emergency department (ED) visits and GP visits reported over 12 months was analysed using multivariate negative binomial regression adjusting for confounders. Marginal structural models investigated the presence of time-dependent confounding. Results: Of participants followed up (n = 1,753), PIP was detected in 57% by STOPP and 41.8% by START, 21.7% reported an ED visit and 96.1% visited a GP (median 4, IQR 2.5-6). Those with any STOPP criterion had higher rates of ED visits (adjusted incident rate ratio (IRR) 1.30, 95% confidence interval (CI) 1.02-1.66) and GP visits (IRR 1.15, 95%CI 1.06-1.24). Patients with two or more START criteria had significantly more ED visits (IRR 1.45, 95%CI 1.03-2.04) and GP visits (IRR 1.13, 95%CI 1.01-1.27) than people with no criteria. Adjusting for time-dependent confounding did not affect the findings. Conclusions: Both STOPP and START were independently associated with increased healthcare utilisation and START was also related to functional decline and QoL. Optimising prescribing to reduce PIP may provide an improvement in patient outcomes. This article is protected by copyright. All rights reserved.