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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
... 71 This combination re-enforces information and desired practice, which have been reported to be effective in changing healthcare staff behaviours in de-prescribing interventions. 72 A recent systemic review of medication-related interventions delivered in hospital and following discharge 71 reported that the size of the treatment effect increased with the intensity (factoring number and repetition of components) of medicationrelated intervention delivered. However, such intervention intensity needs to be balanced with routine deliverability. ...
Full-text available
Background Patients recovering from an episode in an intensive care unit (ICU) frequently experience medication errors on transition to the hospital ward. Structured handover recommendations often underestimate the challenges and complexity of ICU patient transitions. For adult ICU patients transitioning to a hospital ward, it is currently unclear what interventions reduce the risks of medication errors. The aims were to examine the impact of medication-related interventions on medication and patient outcomes on transition from adult ICU settings and identify barriers and facilitators to implementation. Methods The systematic review protocol was preregistered on PROSPERO. Six electronic databases were searched until October 2020 for controlled and uncontrolled study designs that reported medication-related (ie, de-prescribing; medication errors) or patient-related outcomes (ie, mortality; length of stay). Risk of bias (RoB) assessment used V.2.0 and ROBINS-I Cochrane tools. Where feasible, random-effects meta-analysis was used for pooling the OR across studies. The quality of evidence was assessed by Grading of Recommendations, Assessment, Development and Evaluations. Results Seventeen studies were eligible, 15 (88%) were uncontrolled before-after studies. The intervention components included education of staff (n=8 studies), medication review (n=7), guidelines (n=6), electronic transfer/handover tool or letter (n=4) and medicines reconciliation (n=4). Overall, pooled analysis of all interventions reduced risk of inappropriate medication continuation at ICU discharge (OR=0.45 (95% CI 0.31 to 0.63), I ² =55%, n=9) and hospital discharge (OR=0.39 (95% CI 0.2 to 0.76), I ² =75%, n=9). Multicomponent interventions, based on education of staff and guidelines, demonstrated no significant difference in inappropriate medication continuation at the ICU discharge point (OR 0.5 (95% CI 0.22 to 1.11), I ² =62%, n=4), but were very effective in increasing de-prescribing outcomes on hospital discharge (OR 0.26 (95% CI 0.13 to 0.55), I ² =67%, n=6)). Facilitators to intervention delivery included ICU clinical pharmacist availability and participation in multiprofessional ward rounds, while barriers included increased workload associated with the discharge intervention process. Conclusions Multicomponent interventions based on education of staff and guidelines were effective at achieving almost four times more de-prescribing of inappropriate medication by the time of patient hospital discharge. Based on the findings, practice and policy recommendations are made and guidance is provided on the need for, and design of theory informed interventions in this area, including the requirement for process and economic evaluations.
... Research to date has noted that there exists no clear methodology to engaging in deprescribing, and that the process is complex. 28 In addition to ceasing or reducing medications, deprescribing also encompasses using non-pharmacological approaches to care. 29 This study did not explore how patients would be included in the approach to their care, or the behaviour changes associated with clinicians incorporating this into clinical practice as part of personalised care. ...
Clinicians are responsible for both commencing and stopping medications. This study evaluates the attitudes of older acute medical inpatients about deprescribing. Overall, patients are positive toward stopping medications, want to be involved and do not feel a clinician is giving up on them if a medication is stopped. Patients on fewer medications counterintuitively feel a greater medication burden, are more interested in being involved in decision making and consider deprescribing appropriate to a greater degree than patients who are taking more medications. Conversely, they also reported greater concerns about stopping medications. We discuss these findings in the context of the positive and negative effects of deprescribing, in the context of patient engagement and shared decision making, and how clinicians can work with inpatients to reduce potentially inappropriate medications.
... However, psychological and pharmacological dependency on BSHs means that complete discontinuation is particularly challenging. Knowledge of the patient's bedtime and sleep patterns, medications taken for sleep disorders and behavioral information can also facilitate discontinuation [24]. However, there are very few literature data on the patient's bedtime and sleep patterns in older patients. ...
Full-text available
Many older adults take benzodiazepines and sedative-hypnotics for the treatment of sleep disorders. With a view to considering the possible discontinuation of hypnotics, the objectives of the present study were to describe bedtime habits and sleep patterns in older adults and to identify the sleep medications taken. An expert group developed a structured interview guide for assessing the patients’ bedtime habits, sleep patterns, and medications. During an internship in a community pharmacy, 103 sixth-year pharmacy students conducted around 10 interviews each with older adults (aged 65 or over) complaining of sleep disorders and taking at least one of the following medications: benzodiazepines, benzodiazepine derivatives (“Z-drugs”), antihistamines, and melatonin. A prospective, observational study was carried out from 4 January to 30 June 2016. The pharmacy students performed 960 interviews (with 330 men and 630 women; mean ± standard deviation age: 75.1 ± 8.8). The most commonly taken hypnotics were the Z-drugs zolpidem (n = 465, 48%) and zopiclone (n = 259, 27%). The vast majority of patients (n = 768, 80%) had only ever taken a single hypnotic medication. The median [interquartile range] prescription duration was 120 (48–180) months. About 75% (n = 696) of the patients had at least 1 poor sleep habit, and over 41% (n = 374) had 2 or more poor sleep habits. A total of 742 of the patients (77%) reported getting up at night—mainly due to nycturia (n = 481, 51%). Further, 330 of the patients (35%) stated that they were keen to discontinue their medication, of which 96 (29%) authorized the pharmacist to contact their family physician and discuss discontinuation. In France, pharmacy students and supervising community pharmacists can identify problems related to sleep disorders by asking simple questions about the patient’s sleep patterns. Together with family physicians, community pharmacists can encourage patients to discuss their hypnotic medications.
... Generally, these reviews found deprescribing interventions to be safe and feasible, but they concluded that better evidence on their effects on clinical outcomes was needed; a medication review directed at deprescribing in nursing home residents showed that deprescribing significantly reduced mortality by 26% and the number of fallers by 24%, in a subgroup meta-analysis [35]. Similarly, Hansen et al [36] conducted a systematic review on behavior change techniques in deprescribing and found a combination of such techniques involving a range of interventions to be successful. A 2019 review by Ulley et al [37] found insufficient evidence to confirm that deprescribing improves medication adherence. ...
Full-text available
Background: Deprescribing, a relatively recent concept, has been proposed as a promising solution to the growing issues of polypharmacy and use of medications of questionable benefit among older adults. However, little is known about the health outcomes of deprescribing interventions. Objective: This paper presents the protocol of a study that aims to contribute to the knowledge on deprescribing by addressing two specific objectives: (1) describe the impact of deprescribing in adults ≥60 years on health outcomes or quality of life; and (2) determine the characteristics of effective interventions in deprescribing. Methods: Primary studies targeting three concepts (older adults, deprescribing, and health or quality of life outcomes) will be included in the review. The search will be performed using key international databases (MEDLINE, EMBASE, CINAHL, Ageline, PsycInfo), and a special effort will be made to identify gray literature. Two reviewers will independently screen the articles, extract the information, and evaluate the quality of the selected studies. If methodologically feasible, meta-analyses will be performed for groups of intervention studies reporting on deprescribing interventions for similar medications, used for similar or identical indications, and reporting on similar outcomes (eg, benzodiazepines used against insomnia and studies reporting on quality of sleep or quality of life). Alternatively, the results will be presented in bottom-line statements (objective 1) and a matrix outlining effective interventions (objective 2). Results: The knowledge synthesis may be limited by the availability of high-quality clinical trials on deprescribing and their outcomes in older adults. Additionally, analyses will likely be affected by studies on the deprescribing of different types of molecules within the same indication (eg, different pharmacological classes and medications to treat hypertension) and different measures of health and quality of life outcomes for the same indication. Nevertheless, we expect the review to identify which deprescribing interventions lead to improved health outcomes among seniors and which of their characteristics contribute to these outcomes. Conclusions: This systematic review will contribute to a better understanding of the health outcomes of deprescribing interventions among seniors. Trial registration: PROSPERO International Prospective Register of Systematic Reviews CRD42015020866; International registered report identifier (irrid): PRR1-10.2196/25200.
Successful implementation of deprescribing requires exploring healthcare professionals’ opinions, preferences, and attitudes towards deprescribing. The aim of this study was to develop and validate the questionnaire exploring healthcare providers’ opinions preferences and attitudes towards deprescribing (CHOPPED questionnaire). This was a cross-sectional on-line survey. A comprehensive 58-item questionnaire, in two versions (for pharmacists and physicians), was developed through an extensive literature review and interviews with experts. The questionnaire was validated, and its reliability was assessed through data collected from 356 pharmacists and 109 physicians. Exploratory factor analysis was performed, and 37- and 35-item questionnaires were developed. Ten factors were identified: knowledge, awareness, patient barriers and facilitators, competencies barriers and facilitators, collaboration barriers and facilitators, and healthcare system barriers and facilitators. The CHOPPED tool has satisfactory face, content (CVR > 0.62) (content validity ratio), construct, and criterion validity. The reliability statistics of all factors in both versions was acceptable with Cronbach’s alpha > 0.6. Test–retest reliability analysis showed that gamma rank correlations of total factor scores were strong and very strong (between 0.519 and 0.938). The CHOPPED tool can be used as a valid and reliable tool to explore healthcare providers’ opinions and attitudes toward discontinuing medications in the primary care setting in Croatia.
Importance: Self-management is a critical component of stroke rehabilitation. A better understanding of the use of theory and behavior change techniques (BCTs) informs the development of more effective stroke self-management interventions. Objective: To examine what theories and BCTs have been applied in stroke self-management interventions; investigate the extent to which these interventions encourage implementation of behavior changes; and appraise their effectiveness to enhance self-efficacy, quality of life, and functional independence. Data Sources: Ovid MEDLINE, Embase, Scopus, CINAHL, Cochrane Library, and were searched from inception to May 26, 2020. Study Selection and Data Collection: Randomized controlled trials (RCTs) in six databases were reviewed for inclusion and analysis. We included trials that involved community-dwelling adult stroke survivors, assessed the effectiveness of self-management interventions, and explicitly mentioned the use of theory in the development of the intervention. We assessed use of theory and BCTs using the Theory Coding Scheme and BCT taxonomy v1, respectively. Findings: A total of 3,049 studies were screened, and 13 RCTs were included. The predominant theory and BCT categories were Social Cognitive Theory (7 studies) and goals and planning (12 studies), respectively. Significant and small effect sizes were found for self-efficacy (0.27) and functional independence (0.19). Conclusions and Relevance: Theory-based self-management interventions have the potential to enhance stroke outcomes. Systematic reporting on the use of theory and BCTs is recommended to enhance clarity and facilitate evaluations of future interventions. What This Article Adds: This review supports and guides occupational therapy practitioners to use theory-based self-management intervention as a routine part of stroke rehabilitation to improve stroke survivors’ experience in the community.
Interpreting results from deprescribing interventions to generate actionable evidence is challenging owing to inconsistent and heterogeneous outcome definitions between studies. We sought to characterize deprescribing intervention outcomes and recommend approaches to measure outcomes for future studies. A scoping literature review focused on deprescribing interventions for polypharmacy and informed a series of expert panel discussions and recommendations. Twelve experts in deprescribing research, policy, and clinical practice interventions participating in the Measures Workgroup of the US Deprescribing Research Network sought to characterize deprescribing outcomes and recommend approaches to measure outcomes for future studies. The scoping review identified 125 papers reflecting 107 deprescribing studies. Common outcomes included medication discontinuation, medication appropriateness, and a broad range of clinical outcomes potentially resulting from medication reduction. Panel recommendations included clearly defining clinically meaningful medication outcomes (e.g., number of chronic medications, dose reductions), ensuring adequate sample size and follow‐up time to capture clinical outcomes resulting from medication discontinuation (e.g., quality of life [QOL]), and selecting appropriate and feasible data sources. A new conceptual model illustrates how downstream clinical outcomes (e.g., reduction in falls) should be interpreted in the context of initial changes in medication measures (e.g., reduction in mean total medications). Areas needing further development include implementation outcomes specific to deprescribing interventions and measures of adverse drug withdrawal events. Generating evidence to guide deprescribing is essential to address patient, caregiver, and clinician concerns about the benefits and harms of medication discontinuation. This article provides recommendations and an initial conceptual framework for selecting and applying appropriate intervention outcomes to support deprescribing research.
Purpose Adverse drug reactions (ADRs) account for 10% of acute hospital admissions in older people, often under-recognised by physicians. The Dutch geriatric guideline recommends screening all acutely admitted older patients with polypharmacy with an ADR trigger tool comprising ten triggers and associated drugs frequently causing ADRs. This study investigated the performance of this tool and the recognition by usual care of ADRs detected with the tool. Methods A cross-sectional study was performed in patients ≥ 70 years with polypharmacy acutely admitted to the geriatric ward of the University Medical Centre Utrecht. Electronic health records (EHRs) were screened for trigger–drug combinations listed in the ADR trigger tool. Two independent appraisers assessed causal probability with the WHO-UMC algorithm and screened EHRs for recognition of ADRs by attending physicians. Performance of the tool was defined as the positive predictive value (PPV) for ADRs with a possible, probable or certain causal relation. Results In total, 941 trigger–drug combinations were present in 73% ( n = 253/345) of the patients. The triggers fall, delirium, renal insufficiency and hyponatraemia covered 86% ( n = 810/941) of all trigger–drug combinations. The overall PPV was 41.8% ( n = 393/941), but the PPV for individual triggers was highly variable ranging from 0 to 100%. Usual care recognised the majority of ADRs (83.5%), increasing to 97.1% when restricted to possible and certain ADRs. Conclusion The ADR trigger tool has predictive value; however, its implementation is unlikely to improve the detection of unrecognised ADRs in older patients acutely admitted to our geriatric ward. Future research is needed to investigate the tool’s clinical value when applied to older patients acutely admitted to non-geriatric wards.
Background: Individuals with dementia or mild cognitive impairment frequently have multiple chronic conditions (defined as ≥2 chronic medical conditions) and take multiple medications, increasing their risk for adverse outcomes. Deprescribing (reducing or stopping medications for which potential harms outweigh potential benefits) may decrease their risk of adverse outcomes. Objective: To examine the effectiveness of increasing patient and clinician awareness about the potential to deprescribe unnecessary or risky medications among patients with dementia or mild cognitive impairment. Design, setting, and participants: This pragmatic, patient-centered, 12-month cluster randomized clinical trial was conducted from April 1, 2019, to March 31, 2020, at 18 primary care clinics in a not-for-profit integrated health care delivery system. The study included 3012 adults aged 65 years or older with dementia or mild cognitive impairment who had 1 or more additional chronic medical conditions and were taking 5 or more long-term medications. Interventions: An educational brochure and a questionnaire on attitudes toward deprescribing were mailed to patients prior to a primary care visit, clinicians were notified about the mailing, and deprescribing tip sheets were distributed to clinicians at monthly clinic meetings. Main outcomes and measures: The number of prescribed long-term medications and the percentage of individuals prescribed 1 or more potentially inappropriate medications (PIMs). Analysis was performed on an intention-to-treat basis. Results: This study comprised 1433 individuals (806 women [56.2%]; mean [SD] age, 80.1 [7.2] years) in 9 intervention clinics and 1579 individuals (874 women [55.4%]; mean [SD] age, 79.9 [7.5] years) in 9 control clinics who met the eligibility criteria. At baseline, both groups were prescribed a similar mean (SD) number of long-term medications (7.0 [2.1] in the intervention group and 7.0 [2.2] in the control group), and a similar proportion of individuals in both groups were taking 1 or more PIMs (437 of 1433 individuals [30.5%] in the intervention group and 467 of 1579 individuals [29.6%] in the control group). At 6 months, the adjusted mean number of long-term medications was similar in the intervention and control groups (6.4 [95% CI, 6.3-6.5] vs 6.5 [95% CI, 6.4-6.6]; P = .14). The estimated percentages of patients in the intervention and control groups taking 1 or more PIMs were similar (17.8% [95% CI, 15.4%-20.5%] vs 20.9% [95% CI, 18.4%-23.6%]; P = .08). In preplanned subgroup analyses, adjusted differences between the intervention and control groups were -0.16 (95% CI, -0.34 to 0.01) for individuals prescribed 7 or more long-term medications at baseline (n = 1434) and -0.03 (95% CI, -0.20 to 0.13) for those prescribed 5 to 6 medications (n = 1578) (P = .28 for interaction; P = .19 for subgroup interaction for PIMs). Conclusions and relevance: This large-scale educational deprescribing intervention for older adults with cognitive impairment taking 5 or more long-term medications and their primary care clinicians demonstrated small effect sizes and did not significantly reduce the number of long-term medications and PIMs. Such interventions should target older adults taking relatively more medications. Trial registration: Identifier: NCT03984396.
Background: Deprescribing can reduce the use of inappropriate or unnecessary medication; however, the economic value of such interventions is uncertain. Objective: This study seeks to identify and synthesise the economic evidence of deprescribing interventions among community-dwelling older adults. Methods: Full economic evaluation studies of deprescribing interventions, conducted in the community or primary care settings, in community-dwelling adults aged ≥ 65 years were systematically reviewed. MEDLINE, EconLit, Scopus, Web of Science, CEA-TUFTS, CRD York and Google Scholar databases were searched from inception to February 2021. Two researchers independently screened all retrieved articles according to inclusion and exclusion criteria. The main outcome was the economic impact of the intervention from any perspective, converted into 2019 US Dollars. The World Health Organization threshold of 1 gross domestic product per capita was used to define cost effectiveness. Studies were appraised for methodological quality using the extended Consensus on Health Economics Criteria checklist. Results: Of 6154 articles identified by the search strategy, 14 papers assessing 13 different interventions were included. Most deprescribing interventions included some type of medication review with or without a supportive educational component (n = 11, 85%), and in general were delivered within a pharmacist-physician care collaboration. Settings included community pharmacies, primary care/outpatient clinics and patients’ homes. All economic evaluations were conducted within a time horizon varying from 2 to 12 months with outcomes in most of the studies derived from a single clinical trial. Main health outcomes were reported in terms of quality-adjusted life-years, prevented number of falls and the medication appropriateness index. Cost effectiveness ranged from dominant to an incremental cost-effectiveness ratio of $112,932 per quality-adjusted life-year, a value above the country’s World Health Organization threshold. Overall, 85% of the interventions were cost saving, dominated usual care or were cost effective considering 1 gross domestic product per capita. Nine studies scored > 80% (good) and two scored ≤ 50% (low) on critical quality appraisal. Conclusions:There is a growing interest in economic evaluations of deprescribing interventions focused on community-dwelling older adults. Although results varied across setting, time horizon and intervention, most were cost effective according to the World Health Organization threshold. Deprescribing interventions are promising from an economic viewpoint, but more studies are needed.
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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.