A systematic overview of systematic reviews evaluating interventions addressing polypharmacy


  • Members of the PHARM-DC group : Carmel M Hughes ,  Cynthia A Jackevicius ,  Denis O'Mahony


  • 1 Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA.
  • 2 Department of Medicine, Brigham and Women's Hospital, Boston, MA.
  • 3 Department of Pharmacy, Cedars-Sinai Medical Center, Los Angeles, CA.
  • 4 Division of Geriatrics, David Geffen School of Medicine at UCLA, Los Angeles, CA.
  • 5 David Geffen School of Medicine at UCLA, Los Angeles, CA.
  • PMID: 31612924
  • PMCID: PMC7170727
  • DOI: 10.1093/ajhp/zxz196

Purpose: To systematically evaluate and summarize evidence across multiple systematic reviews (SRs) examining interventions addressing polypharmacy.

Summary: MEDLINE, the Cochrane Database of Systematic Reviews, and the Database of Abstracts of Reviews of Effects (DARE) were searched for SRs evaluating interventions addressing polypharmacy in adults published from January 2004 to February 2017. Two authors independently screened, appraised, and extracted information. SRs with Assessment of Multiple Systematic Reviews (AMSTAR) scores below 8 were excluded. After extraction of relevant conclusions from each SR, evidence was summarized and conclusions compared. Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology was used to assess evidence quality. Six SRs met the inclusion criteria, 4 of which used meta-analytic pooling. Five SRs focused on older adults. Four were not restricted to any specific disease type, whereas 1 focused on proton pump inhibitors and another focused on patients with severe dementia. Care settings and measured outcomes varied widely. SRs examining the impact on patient-centered outcomes, including morbidity, mortality, patient satisfaction, and utilization, found inconsistent evidence regarding the benefit of polypharmacy interventions, but most concluded that interventions had either null or uncertain impact. Two SRs assessing medication appropriateness found very low-quality evidence of modest improvements with polypharmacy interventions.

Conclusion: An overview of SRs of interventions to address polypharmacy found 6 recent and high-quality SRs, mostly focused on older adults, in which both process and outcome measures were used to evaluate interventions. Despite the low quality of evidence in the underlying primary studies, both SRs that assessed medication appropriateness found evidence that polypharmacy interventions improved it. However, there was no consistent evidence of any impact on downstream patient-centered outcomes such as healthcare utilization, morbidity, or mortality.

Keywords: aged; deprescriptions; polypharmacy; review; systematic review.

© American Society of Health-System Pharmacists 2019. All rights reserved. For permissions, please e-mail: [email protected].

Publication types

  • Research Support, N.I.H., Extramural
  • Systematic Review
  • Clinical Trials as Topic*
  • Inappropriate Prescribing / prevention & control*
  • Medication Therapy Management / organization & administration*
  • Patient Acceptance of Health Care / statistics & numerical data
  • Patient Discharge
  • Patient Transfer / organization & administration
  • Polypharmacy*
  • Systematic Reviews as Topic
  • Treatment Outcome

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  • K23 AG049181/AG/NIA NIH HHS/United States
  • L30 AG048588/AG/NIA NIH HHS/United States
  • R01 AG058911/AG/NIA NIH HHS/United States

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  • Volume 5, Issue 12
  • Interventions to improve the appropriate use of polypharmacy in older people: a Cochrane systematic review
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  • Janine A Cooper 1 ,
  • Cathal A Cadogan 1 ,
  • Susan M Patterson 2 ,
  • http://orcid.org/0000-0002-5992-3681 Ngaire Kerse 3 ,
  • Marie C Bradley 1 ,
  • Cristín Ryan 1 ,
  • Carmel M Hughes 1
  • 1 School of Pharmacy, Queen's University Belfast , Belfast , UK
  • 2 Belfast , UK
  • 3 Department of General Practice and Primary Health Care , University of Auckland , Auckland , New Zealand
  • Correspondence to Professor Carmel M Hughes; c.hughes{at}qub.ac.uk

Objective To summarise the findings of an updated Cochrane review of interventions aimed at improving the appropriate use of polypharmacy in older people.

Design Cochrane systematic review. Multiple electronic databases were searched including MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials (from inception to November 2013). Hand searching of references was also performed. Randomised controlled trials (RCTs), controlled clinical trials, controlled before-and-after studies and interrupted time series analyses reporting on interventions targeting appropriate polypharmacy in older people in any healthcare setting were included if they used a validated measure of prescribing appropriateness. Evidence quality was assessed using the Cochrane risk of bias tool and GRADE (Grades of Recommendation, Assessment, Development and Evaluation).

Setting All healthcare settings.

Participants Older people (≥65 years) with ≥1 long-term condition who were receiving polypharmacy (≥4 regular medicines).

Primary and secondary outcome measures Primary outcomes were the change in prevalence of appropriate polypharmacy and hospital admissions. Medication-related problems (eg, adverse drug reactions), medication adherence and quality of life were included as secondary outcomes.

Results 12 studies were included: 8 RCTs, 2 cluster RCTs and 2 controlled before-and-after studies. 1 study involved computerised decision support and 11 comprised pharmaceutical care approaches across various settings. Appropriateness was measured using validated tools, including the Medication Appropriateness Index, Beers’ criteria and Screening Tool of Older Person's Prescriptions (STOPP)/ Screening Tool to Alert doctors to Right Treatment (START). The interventions demonstrated a reduction in inappropriate prescribing. Evidence of effect on hospital admissions and medication-related problems was conflicting. No differences in health-related quality of life were reported.

Conclusions The included interventions demonstrated improvements in appropriate polypharmacy based on reductions in inappropriate prescribing. However, it remains unclear if interventions resulted in clinically significant improvements (eg, in terms of hospital admissions). Future intervention studies would benefit from available guidance on intervention development, evaluation and reporting to facilitate replication in clinical practice.

  • polypharmacy
  • systematic review
  • interventions

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/


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Strengths and limitations of this study

The updated Cochrane review that is summarised in this paper used systematic and rigorous methods to identify, appraise and synthesise available evidence for the effectiveness of interventions aimed at improving appropriate polypharmacy for older patients.

No language restrictions were placed on the search strategy and no apparent publication bias was detected.

The included studies were limited by their small sample sizes and poor quality owing to risks of bias, with little opportunity to pool data.

Despite improvements in appropriate prescribing, it must be noted that assessments were based on surrogate markers of appropriate polypharmacy and the clinical significance of these improvements in terms of other relevant outcomes, for example, hospital admissions, is unclear.

Several studies focused on reducing the number of medications, rather than improving the overall appropriateness of prescribing, including underprescribing.


The WHO has predicted that the number of older people (conventionally defined as ≥65 years) worldwide will reach 1.5 billion by 2050. 1 , 2 This population growth poses significant challenges for healthcare systems, as older people use a disproportionate amount of healthcare resources (eg, medications). 3 , 4

Although there is no single agreed definition of the term ‘polypharmacy’, 5 , 6 this has been described as the use of four or more medications. 7 The potential for negative outcomes with the use of multiple medications in older people is well documented (eg, adverse drug events (ADEs), non-adherence, drug interactions). 8 , 9 A critical objective that poses considerable challenges for healthcare professionals (HCPs) is to obtain a balance between aggressively treating diseases and avoiding medication-related harm. 10

Polypharmacy has been identified as the principal determinant of potentially inappropriate prescribing (PIP) in older people. 11 The term PIP encompasses overprescribing, misprescribing and underprescribing. 12 Underprescribing is an important clinical issue because patients with polypharmacy have an increased likelihood of not receiving potentially beneficial, clinically indicated medications compared with patients receiving fewer medications. 13 Accordingly, a range of assessment tools have been developed to identify PIP in older people and to optimise prescribing. 14

Despite the potential for negative consequences in older patients receiving polypharmacy, there is increasing acceptance that the prescribing of multiple medications can be appropriate, and under certain circumstances, should be encouraged. 15 , 16 Thus, polypharmacy can refer to the prescribing of many drugs (appropriately) or too many drugs (inappropriately). 16 Achieving appropriate polypharmacy involves prescribing the correct drugs under the appropriate circumstances to treat the right diseases. Ensuring appropriate polypharmacy is of considerable importance because PIP is highly prevalent in older people and has considerable cost implications for healthcare systems. 11 , 17

The updated Cochrane review that is summarised in this paper 18 sought to determine the effectiveness of interventions aimed at improving appropriate polypharmacy in older people. A recent Cochrane publication, which consisted of an overview of systematic reviews, highlighted that few reviews have considered the implications of polypharmacy on interventions seeking to improve safe and effective medicine use by consumers, including patients and their carers. 19

This systematic review followed the Cochrane Collaboration methodology, and is available from the Cochrane Library. 18

Inclusion criteria

This review looked at interventions in any setting that targeted older people (≥65 years) who had more than one long-term medical condition and were receiving polypharmacy (≥4 regular medications).

Randomised controlled trials (RCTs), including cluster RCTs (cRCTs), non-randomised controlled clinical trials, controlled before-and-after studies (CBAs) and interrupted time series (ITS) studies meeting the Effective Practice and Organisation of Care (EPOC) specification 20 were eligible for inclusion. Any type of intervention that aimed to improve appropriate polypharmacy in any healthcare setting was eligible for inclusion. With the exception of ITS design, studies had to compare the intervention against usual care as defined by the study. Interventions studies that focused on people with single long-term conditions or who were receiving short-term polypharmacy, for example, chemotherapy, were excluded. No language restrictions were applied.

Outcome measures

Primary outcomes were the change in the prevalence of appropriate polypharmacy and the number of hospital admissions. As there is no universally applicable tool to assess polypharmacy appropriateness in older people, validated measures of inappropriate prescribing (eg, Beers’ criteria 21 and the Medication Appropriateness Index (MAI) 22 ) were used as surrogate markers. Studies using expert opinion alone to determine medication appropriateness were excluded.

The following secondary outcomes were included: medication-related problems (eg, adverse drug reactions, medication errors); medication adherence; health-related quality of life (assessed by a validated method).

Search methods for identification of studies

Search strategies (see full review 18 ) comprised keywords and controlled vocabulary such as MeSH (medical subject headings). The following electronic databases were searched for primary studies (all records through to November 2013): Evidence-Based Medicine Reviews, Cochrane Central Register of Controlled Trials, Ovid SP, Health Technology Assessment, National Health Service Economic Evaluation Database, Cochrane Methodology Register, American College of Physicians Journal Club, the Joanna Briggs Institute, MEDLINE, EMBASE, CINAHL, EBSCO Host, PsycINFO.

Related systematic reviews were identified through the Cochrane Database of Systematic Reviews and Database of Abstracts of Reviews of Effects. Authors were contacted for further information where necessary.

Data screening and extraction

The retrieved titles and abstracts were screened independently by two authors against inclusion criteria. Where uncertainty occurred, full-text articles were retrieved and assessed. Any remaining uncertainty or disagreement was resolved by consensus through discussion with another author. Data were extracted independently by two authors.

Assessment of risk of bias

Two authors independently assessed risk of bias using the Cochrane Collaboration's assessment tool 23 and used GRADE (Grades of Recommendation, Assessment, Development and Evaluation) to assess the quality of the evidence for each primary outcome for which data were pooled. 24

Data analysis

Intervention effect was measured using validated assessment tools of prescribing appropriateness (eg, summated MAI, Beers’ criteria). The mean and SD were calculated for summated MAI and number of Beers’ drugs postintervention in each study's intervention and control groups. Where available, the mean change (and SD) from pre to post was determined in the intervention and control group. Based on these numbers, the mean differences were calculated and results presented with 95% CIs. Estimates for dichotomous outcomes from individual studies are presented as risk ratios with 95% CIs.

If at least two studies were homogeneous in terms of participants, interventions and outcomes, the results were pooled in a meta-analysis. In the presence of statistical heterogeneity (I 2 statistic >50%), a random-effects model was applied for meta-analysis. In the absence of statistical heterogeneity, a fixed-effects model was used.

Sensitivity analyses were conducted for studies with a high risk of bias or a unit of analysis error. Where outcome data could not be combined, a narrative summary was reported. Reporting bias was examined using risk of bias tables and funnel plots corresponding to meta-analysis of the primary outcome to assess potential publication bias. Data analysis was conducted using RevMan V.5.2.

Results of the search

Figure 1 provides an overview of the search. In this update, two studies were identified and added, 25 , 26 bringing the total number of included studies to 12. It was not possible to include data from these two studies in any meta-analysis because data were skewed or participants were not considered to be homogeneous with other study populations.

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PRISMA flow chart: risk of bias in included studies (n=12).

The included studies consisted of eight RCTs, 25–32 two cRCTs 33 , 34 and two CBAs. 35 , 36 In total, 22 438 older patients were involved, the majority of whom were female (65.6%). On average, patients were 76 years old (based on 12 studies) and receiving nine medicines at baseline (based on 11 studies).

The studies were conducted in three types of settings ( table 1 ): hospital (outpatient clinics); 27 , 29 , 30 hospital/care home interface; 28 inpatient setting; 25 , 26 , 31 primary care; 32 , 34 nursing homes. 33 , 35 , 36 The studies were carried out in five countries: Australia (two studies), Belgium (two studies), Canada (two studies), Ireland (one study) and the USA (five studies).

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Characteristics of included studies

Description of interventions

All interventions were classified as organisational according to EPOC definitions.

Eleven studies examined complex, multifaceted, pharmaceutical care-based interventions in various settings, using validated assessment criteria to give recommendations on improving the appropriateness of prescribing. In all settings, pharmaceutical care (ie, responsible provision of drug therapy to achieve definitive outcomes that improve patients’ quality of life 37 ) was commonly provided by pharmacists working closely with other HCPs.

The models of pharmaceutical care provided were complex and variable. For example, pharmacists conducted independent medication reviews either using patient notes 28 , 33 or with patients during a face-to-face encounter. 27 , 29–32 , 34 In other cases, recommendations from medication reviews were followed up with prescribers and other HCPs. 27–29 , 31 , 33

Patient education was provided as part of the intervention in four studies involving face-to-face interventions. Patients were given information about their prescribed medications (eg, administration) and specialised medication scheduling tools (eg, monitored dosage systems) to encourage adherence. 27 , 29 , 31 , 32

Education was also provided to prescribers and other HCPs involved in the multidisciplinary team as part of the intervention in five studies. 27–29 , 31 , 33

The only unifaceted study 34 examined computerised decision support (CDS) provided to general practitioners in their own practices.

The timing of intervention provision was variable. A number of interventions were delivered at specific time points, for example, hospital admission, attendance at outpatient clinics, 27 , 29 , 30 , 32 nursing home visits, 33 , 35 , 36 hospital discharge to a nursing home. 28 In other cases, interventions were delivered over a period of time, such as during hospital inpatient stay and at discharge. 30 , 31

Risk of bias in included studies

The included studies showed evidence of potential bias ( figure 2 ). Only three studies showed evidence of allocation concealment 25 , 28 , 33 and only one study demonstrated protection against contamination. 33

Risk of bias in included studies (n=12).

Funnel plots of postintervention estimates of the change in MAI and summated MAI showed little evidence of publication bias. 18

GRADE approach to quality assessment

Based on GRADE, 24 the overall quality of evidence for each primary outcome for which data were included in a meta-analysis was rated as ‘low’ or ‘very low’ ( table 2 ). Although all studies included in the meta-analyses involved randomisation, and, where assessed, no evidence of publication bias was found, 18 the quality of evidence was downgraded for each outcome based on other GRADE considerations (ie, study limitations, consistency of effect, imprecision, indirectness).

Summary of findings table

Prevalence of appropriate use of polypharmacy

The primary outcome of interest was the change in the prevalence of appropriate polypharmacy. Seven validated measures of prescribing appropriateness were used in the included studies, either alone or in combination.

Medication Appropriateness Index

The MAI was used in three ways to assess the appropriateness of polypharmacy. First, data from four studies (210 intervention participants, 214 control participants) were pooled in a meta-analysis using the change in summated MAI score from baseline to follow-up. 27 , 28 , 31 , 33 There was a greater overall reduction in the mean change in summated MAI score in the intervention group compared with the control (mean difference −6.78, 95% CI −12.34 to −1.22; table 2 ). There was marked heterogeneity between the studies (I 2 =96%, p<0.0001). Sensitivity analyses in which one study with a unit of analysis error (nursing homes were the unit of randomisation but the analysis was conducted at patient level) 33 and another study with a large effect size and high risks of bias 31 were removed from analysis showed consistent changes in summated MAI with variable effects on heterogeneity ( table 2 ).

Second, postintervention pooled data from five studies 27–31 (488 intervention participants, 477 control participants) showed a lower summated MAI score (mean difference −3.88, 95% CI −5.40 to −2.35) in the intervention group compared with the control group ( table 2 ). There was little evidence of heterogeneity between these estimates (I 2 =0%). This was consistent with the findings of Gallagher et al , 25 which were not included in the meta-analysis because the data were skewed.

Third, one study 32 expressed the MAI score as the number of inappropriate prescriptions. The percentage of inappropriate prescriptions decreased in all MAI domains (n=10) in the intervention group and increased in five domains in the control group. These data could not be included in a meta-analysis.

Beers’ criteria

Pooled data from two studies 30 , 31 (298 intervention participants, 288 control participants) showed that intervention group participants were prescribed fewer Beers’ drugs than control group participants postintervention (mean difference −0.1, 95% CI −0.28 to 0.09; I 2 =89%; table 2 ).

Spinewine et al 31 also reported the proportion of patients taking one or more Beers’ drugs preintervention and postintervention. Similar improvements were reported in the proportion of intervention and control group patients receiving one or more Beers’ drugs between hospital admission and discharge (OR 0.6, 95% CI 0.3 to 1.1). As this was the only study to report the results in this format, meta-analysis was not possible.

McLeod criteria

One study used the McLeod criteria 38 to identify the initiation and discontinuation rates of 159 prescription-related problems. 34 The reported relative rate of initiation of inappropriate prescriptions for the intervention group was 0.82 (95% CI 0.69 to 0.98). However, the intervention did not appear to have an effect on the relative rate of discontinuation of pre-existing prescription-related problems (1.06, 95% CI 0.89 to 1.26). Meta-analysis was not possible as these criteria were not used in other studies.

STOPP and START criteria

Two studies 25 , 26 used the Screening Tool of Older Person's Prescriptions (STOPP) criteria to screen for PIP in older patients admitted to hospital. Gallagher et al 25 reported lower (p<0.001) proportions of patients in the intervention group compared with the control group with one or more STOPP criteria medications for each of the postintervention assessments (discharge, 2, 4 and 6 months postdischarge). Dalleur et al 26 reported no difference in the proportion of patients with one or more STOPP criteria medications from hospital admission to discharge between the intervention and control groups (OR 1.5, 95% CI 0.49 to 4.89, p=0.454). However, at group level, the discontinuation rate of potentially inappropriate medications as identified using STOPP criteria was higher in the intervention group compared with the control group (OR 2.75, 95% CI 1.22 to 6.24, p=0.013). Data from these studies were not pooled because participants were not homogeneous.

In the Gallagher et al 25 study, the Screening Tool to Alert doctors to Right Treatment (START) criteria were also used. For each of the postintervention assessments (discharge, 2, 4 and 6 months postdischarge), lower proportions of patients with one or more START criteria medications were reported in the intervention group compared with the control group (p<0.001). This was the only study that used these criteria; therefore, meta-analysis was not possible.

Assessment of Underutilisation of Medication

Two studies assessed under-use of medication using the Assessment of Underutilisation of Medication (AUM) index. 25 , 30 Gallagher et al 25 reported a greater reduction in the proportion of intervention group patients with prescribing omissions postintervention (by the AUM index) compared with the control group (absolute risk reduction 21.2%, 95% CI 13.3% to 29.1%). Schmader et al 30 reported a reduction in the number of conditions with omitted drugs postintervention in the intervention group relative to the control group; the difference in change in AUM score was −0.3 (p<0.0001). As each study assessed underprescribing on two different levels (ie, patient, medical condition), meta-analysis was not possible.

Spinewine et al 31 reported that the magnitude of the reduction in Assessing Care of Vulnerable Elderly (ACOVE) scores was greater in the intervention group (baseline score: 50.0, postintervention score: 14.6, p<0.001) compared with the control group (baseline score: 58.9, postintervention score: 44.4, p=0.02). Intervention patients were six times more likely than control patients to have at least one prescribing improvement based on these criteria (OR 6.1, 95% CI 2.2 to 17.0). Meta-analysis was not possible; no other studies used this outcome measure.

Hospital admissions

Five studies measured hospital admissions. 25 , 28 , 31 , 32 , 35 Two studies 25 , 31 reported no difference in hospitalisations between intervention and control groups at follow-up and the remaining studies reported some overall reductions in hospital admissions between the two groups. The statistical significance of these reductions varied based on the methods of assessment employed in the individual studies. Owing to differences in the measurement of hospital admissions and the expression of results, meta-analysis was not possible.

Secondary outcomes

Meta-analysis of secondary outcome assessments was not possible due to differences across studies in design and reporting. Evidence of the effect of the interventions on medication-related problems (six studies) 28–30 , 32 , 35 , 36 was conflicting. One study reported improved adherence scores in intervention patients. 32 No differences in HRQoL were reported between intervention and control groups at baseline or follow-up (two studies). 29 , 32

Given the association between polypharmacy and PIP in older people, 11 , 17 interventions to improve appropriate polypharmacy in this cohort are of considerable importance. Only two studies were added to the original review, bringing the total number of studies included in the updated review to 12. These two additional studies did not change the conclusions of the original review and serve to highlight the lack of intervention studies aimed at improving appropriate polypharmacy in older people that have been conducted to date. Coupled with the findings of Ryan et al , 19 it is evident that interventions targeting polypharmacy are under-researched at both the level of healthcare provider and recipient.

The included studies aimed to ensure the prescribing of appropriate medications to older people that enhanced their quality of life. However, several studies focused on reducing the number of prescribed medications without assessing underprescribing and, therefore, did not consider the overall appropriateness of prescribing. This needs to be addressed as underprescribing is common in older populations with variable prevalence rates depending on medication classes and care settings. 39 Nevertheless, the interventions reduced inappropriate prescribing with resultant improvements in the appropriateness of polypharmacy in older patients. For example, pooled data showed a significant reduction in intervention group patients’ mean MAI score compared with control group patients ( table 2 ). Assessments involving other validated tools also showed improvements in the appropriateness of prescribing. Although these results are promising and indicate that the interventions described in this review were successful in improving appropriate polypharmacy, the clinical impact is not known. For example, it is unclear to what extent a reduction in the magnitude of 3.88 in summated MAI score (a weighted average rating based on 10 assessment criteria) represents a clinically significant reduction in the risk of harm ( table 2 ). This is because the predictive validity of many tools that are currently used to evaluate prescribing appropriateness has not been established. 40 Therefore, the impact of improvements on the overall appropriateness of prescribing on clinical outcomes is unclear.

The findings from our review are consistent with other reviews for a number of outcomes. For example, a related Cochrane review of interventions to optimise prescribing for older people in care homes 41 found no evidence of an intervention effect on ADEs and hospital admissions. Other studies of interventions conducted across various settings have also been unable to detect the effect of pharmaceutical care on these outcomes. 42 , 43

Despite the uncertainty as to the effect of the identified interventions to improve appropriate polypharmacy on a number of outcome measures, this review provides useful guidance for the direction of future research.

Strengths and weakness of this review

The updated Cochrane systematic review that is summarised in this paper represents the most comprehensive overview, using a rigorous methodology, of the existing body of evidence of the effectiveness of interventions aimed at improving appropriate polypharmacy in older patients. Previous reviews have assessed interventions targeting medication use in older people, but have not focused on polypharmacy or exclusively used validated assessment tools. 7 , 44 No language restrictions were placed on the search strategy and all of the studies were published in English, including those studies that were conducted in countries where English is not the first language. Despite the small number of included studies, no apparent publication bias was detected.

Overall, the included studies were limited by their small sample sizes and poor quality, with little opportunity to pool data. There was evidence of potential biases ( figure 2 ) in the studies which may have influenced the reported effect estimates. Although improvements in appropriate polypharmacy were noted, the findings of meta-analyses relating to MAI scores should be treated cautiously, as the intervention did not seem to work consistently across all studies.

It must also be noted that assessments were based on surrogate markers and the clinical significance of these improvements in terms of clinically relevant outcomes, for example, hospital admissions, is unclear as meta-analysis was not possible. Several studies focused on reducing the number of medications, rather than improving the overall appropriateness of prescribing, including underprescribing.

Implications for clinical practice and future research

Inappropriate prescribing is highly prevalent and commonly associated with polypharmacy in older populations. 11 , 17 However, rigorous evaluations of interventions seeking to address this are lacking. The findings of this review indicate that pharmaceutical care-based interventions appear to improve appropriate polypharmacy in older people based on observed reductions in inappropriate prescribing, especially when the provision of care involves a multidisciplinary element. 25 , 27–33 CDS showed potential as an intervention, although this was evaluated in only one study. 34

Surrogate markers of appropriate polypharmacy were used as there is no universally applicable tool to assess the appropriateness of polypharmacy. Despite observed improvements in prescribing appropriateness, it is unclear if the identified interventions resulted in clinically significant improvements, for example, reduction in medication-related problems. In addition to the above noted issues with the predictive validity of existing tools for assessing appropriate prescribing, many studies did not assess outcomes such as adherence, hospitalisations and quality of life, which are arguably the critical outcomes for patients and some studies may have lacked sufficient follow-up periods to detect any significant changes. Future studies should focus on these types of clinical outcomes.

Overall, the quality and reporting of included studies was poor. Future research should pay greater attention to available guidance on intervention development and evaluations 45 to ensure rigour in study design. Methods of specifying and reporting complex interventions, 46 as well as their implementation strategies, are necessary to strengthen the evidence base required for interventions to be more effective, implementable and replicable across different settings. 47 , 48

Future studies should use clearer definitions of appropriate polypharmacy because the term ‘polypharmacy’ can be both negative and positive, and this duality of meaning makes objective research difficult. 49 A recent report by the King's Fund in the UK 6 raised the need to reconsider current definitions of polypharmacy due to the increasing numbers of medications being prescribed to patients. The publication of this report 6 coincided with the abstract screening process in the update of this review. Therefore, for the purpose of this update, the definition of polypharmacy was not changed from the original review. However, future updates may need to reconsider the criteria used to define polypharmacy.

Development of new, universal, easily applied, valid and reliable outcome measures to evaluate effectiveness of interventions should be a priority for future research. Ideally the measure should be globally applicable across various healthcare and cultural settings; for example, STOPP/START are validated instruments that could help to fulfil this need. 50 In contrast to other tools, such as the Beers’ criteria, STOPP/START have been specifically developed for use in European countries. Although STOPP/START-related research is still at a relatively early stage, the criteria are endorsed by the European Union Geriatric Medicine Society and set for wider application in future research. 51 The use of START offers a promising strategy to decrease underprescribing 39 and could serve to improve appropriate polypharmacy when combined with STOPP.


The findings of an updated Cochrane review that are summarised in this paper highlight the lack of existing intervention studies of suitable quality aimed at improving the appropriate use of polypharmacy in older patients. Overall, the interventions included in this review demonstrated benefits in this respect based on observed reductions in inappropriate prescribing. However, it remains unclear if interventions resulted in clinically significant improvements in terms of hospital admissions, medication-related problems and patients’ overall quality of life. Future studies would benefit from guidance relating to intervention development, evaluation and reporting. In addition, more detailed and systematic reporting of interventions in published papers could facilitate replication of effective interventions and uptake into clinical practice.


The authors would like to acknowledge the valuable input of Alexandra McIlroy (Queen’s University Belfast) and Michelle Fiander (EPOC Group) in development of the search strategy. They would also like to thank all members of the EPOC Group at Newcastle University, UK, led by Professor Martin Eccles, for their kind assistance with preparation of the protocol. They would also like to thank Julia Worswick (EPOC Group) for her assistance and Mike Steinman for his helpful comments. The authors would like to acknowledge the valuable input of Dr Chris Cardwell (Centre for Public Health, Queen's University Belfast) into data analysis.

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JAC and CAC are joint first authors.

Contributors JAC and CAC drafted the summary review. All authors contributed to, and agreed on, the final submission. SMP prepared the original review protocol under the direction of CMH, NK and CR Cardwell (CRC). CAC and CR were involved in updating the review. SMP undertook the database searches and reviewed the literature identified in the original review. CMH and CAC undertook the second review update including data extraction, risk of bias assessment and writing of the review update. MCB, NK and CR acted as independent co-review authors.

Funding This work was supported by The Dunhill Medical Trust (grant number: R298/0513) and the Research and Development Office, Northern Ireland, UK.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

Data sharing statement No additional data are available.

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  • Research article
  • Open access
  • Published: 17 April 2021

A systematic review of the evidence for deprescribing interventions among older people living with frailty

  • Kinda Ibrahim   ORCID: orcid.org/0000-0001-5709-3867 1 , 2 ,
  • Natalie J. Cox 1 , 3 ,
  • Jennifer M. Stevenson 4 , 5 ,
  • Stephen Lim 1 , 2 ,
  • Simon D. S. Fraser 2 , 6 &
  • Helen C. Roberts 1 , 2 , 3  

BMC Geriatrics volume  21 , Article number:  258 ( 2021 ) Cite this article

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Older people living with frailty are often exposed to polypharmacy and potential harm from medications. Targeted deprescribing in this population represents an important component of optimizing medication. This systematic review aims to summarise the current evidence for deprescribing among older people living with frailty.

The literature was searched using Medline, Embase, CINAHL, PsycInfo, Web of Science, and the Cochrane library up to May 2020. Interventional studies with any design or setting were included if they reported deprescribing interventions among people aged 65+ who live with frailty identified using reliable measures. The primary outcome was safety of deprescribing; whereas secondary outcomes included clinical outcomes, medication-related outcomes, feasibility, acceptability and cost-related outcomes. Narrative synthesis was used to summarise findings and study quality was assessed using Joanna Briggs Institute checklists.

Two thousand three hundred twenty-two articles were identified and six (two randomised controlled trials) were included with 657 participants in total (mean age range 79–87 years). Studies were heterogeneous in their designs, settings and outcomes. Deprescribing interventions were pharmacist-led ( n  = 3) or multidisciplinary team-led ( n  = 3). Frailty was identified using several measures and deprescribing was implemented using either explicit or implicit tools or both. Three studies reported safety outcomes and showed no significant changes in adverse events, hospitalisation or mortality rates. Three studies reported positive impact on clinical outcomes including depression, mental health status, function and frailty; with mixed findings on falls and cognition; and no significant impact on quality of life. All studies described medication-related outcomes and reported a reduction in potentially inappropriate medications and total number of medications per-patient. Feasibility of deprescribing was reported in four studies which showed that 72–91% of recommendations made were implemented. Two studies evaluated and reported the acceptability of their interventions and further two described cost saving.

There is a paucity of research about the impact of deprescribing in older people living with frailty. However, included studies suggest that deprescribing could be safe, feasible, well tolerated and can lead to important benefits. Research should now focus on understanding the impact of deprescribing on frailty status in high risk populations.

Trial registration

The review was registered on the international prospective register of systematic reviews (PROSPERO) ID number: CRD42019153367 .

Peer Review reports

One-third of people aged over 65 years live with multimorbidity and take five or more regular medicines (polypharmacy), increasing to 50% in over 85 year olds [ 1 , 2 ]. Polypharmacy in older people is associated with increased risk of serious adverse events, falls, cognitive impairment, functional decline, hospitalisation, length of stay and death, [ 3 , 4 , 5 ]. Such harms are amplified in older people living with frailty, a complex geriatric syndrome resulting in decreased physiological reserve [ 6 ]. In frail older people the harms might outweigh benefits for some medications e.g. intensive blood glucose control in type 2 diabetes, or the known time to benefit exceeds projected life expectancy e.g. statins [ 7 , 8 ]. Additionally, the goals of drug treatment in older people living with frailty may change compared with older people in general, shifting the focus from reducing the risk of disease and prolonging life to reducing the burden of treatment and maintaining quality of life [ 9 ]. The bi-directional relationship between polypharmacy and frailty has been reported. Drugs and frailty might interact through network of connections, including physiological changes, multiple pathologies and chronic diseases, life expectancy and functional or cognitive status [ 10 , 11 , 12 , 13 , 14 ]. Frailty may influence factors such as drug pharmacokinetics and pharmacodynamics, toxicity, and therapeutic efficacy. In turn, these factors may be involved in the development of frailty [ 15 ].

The cure for polypharmacy appears simple and involves deprescribing - the process of tapering /dose reduction, stopping, or switching drugs, with the goal of managing polypharmacy and improving outcomes [ 16 ]. There has been considerable research conducted on deprescribing since the term was first used in 2003 [ 17 ], and more recently there has been a focus on deprescribing for those living with frailty. Several tools have been developed to assist physicians with deprescribing decisions such as STOPPFrail [ 18 , 19 ]. However, investigation of the impact of deprescribing on those living with frailty has been limited to date.

Several systematic reviews have synthesised the evidence on outcomes of deprescribing interventions among older people in general [ 20 , 21 ], or defined by setting including care homes [ 22 , 23 ], primary care and community [ 24 , 25 ] and hospitals [ 26 ]. These reviews reported that deprescribing is feasible, well tolerated, safe, and generally effective in reducing the number of inappropriate prescriptions. However, these reviews either did not include frail older people or frailty was poorly defined in their included studies, for example based on age or setting such as being in a care home with definition subject to international variability [ 27 ]. There is an increasing awareness that identifying frail older people or those at risk of frailty using reliable tools should be part of routine clinical practice, to guide appropriate interventions to improve clinical outcomes [ 28 ]. The dynamic nature of frailty highlights a potential for preventive and restorative interventions to maintain the capacity for self-care and to prevent disabilities, falls, functional decline, institutionalisation, hospitalisation and death [ 29 ]. For example, it could be crucial to identify older people living with frailty and polypharmacy as priority patients for a medication review and deprescribing intervention, which could potentially reduce medication-related harm and improve patients’ outcomes. Using objective, reliable measures to assess frailty in the context of research studies on deprescribing is also important to assess whether study results can be extrapolated to patients with similar scores, or to measure whether frailty status affects response to deprescribing interventions and vice versa.

Therefore, the aim of this systematic review was to explore the safety and impact of deprescribing among older people living with frailty identified by reliable measures.

Data sources and searches

The search strategy was developed with a senior librarian and used the following databases: Medline, Embase, CINAHL, PsycInfo, Web of science, and the Cochrane library from database conception until January 2020. Keywords such as deprescribing, deprescribe*, polypharmacy, inappropriate prescribing were used (see Appendix ). Reference lists of retrieved articles were searched for additional relevant studies. The search was re-run in May 2020 but no further eligible papers were retrieved. The review was carried out using the methods recommended by the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement [ 30 ] and was registered on the international prospective register of systematic reviews (PROSPERO) ID number: CRD42019153367.

Study inclusion

Type of studies.

We anticipated a small number of studies to explore deprescribing in frail older people. Only interventional studies with any design, setting or language were included (Table  1 ).

Type of participants

We included interventions that targeted an older population with a median age of 65 years and over, who are identified to be frail using reliable measures including but not limited to Fried Frailty Phenotype, FRAIL scale, PRISMA-7, electronic-Frailty Index, Edmonton Frail Scale Gérontopôle Frailty Screening Tool and Clinical Frailty Scale. To be included, studies had to have at least 50% of their study population identified as frail.

Type of interventions

Studies involving deprescribing as the only intervention or as part of medication review intervention where deprescribing accounts for at least 50% of the total recommendations were included. Studies where deprescribing formed part of a multi-dimensional intervention (such as in combination with nutritional and physical activity components) were excluded as it is difficult to ascertain which component of the intervention was responsible for the reported outcomes.

Type of outcomes

The primary outcome we chose was safety of deprescribing. We defined safety in terms of reported adverse events, hospital admission and/or all-cause mortality.

Secondary outcomes included clinical outcomes (such as frailty status, function, falls, cognition, depression, quality of life), medication-related outcomes (such as changes in number of medications and Potentially Inappropriate Medications PIMs), feasibility of deprescribing (defined by the number of patients/proportion who successfully stopped medications), its acceptability by patients or healthcare practitioners, and cost-related outcomes.

Study selection

Two authors (KI & NC) independently screened the title and abstracts of identified articles using the Rayyan electronic platform to identify studies that met the inclusion criteria [ 31 ]. Following each stage, any disagreement was resolved by discussion.

Quality assessment

Study quality was assessed separately by two authors (SL & SF) using the standardized Joanna Briggs Institute checklists for each study type, with total scores of 13 for randomized controlled trials (RCTs) and 9 for non-randomised experimental studies. Final scoring was agreed by discussion. A score ≥ 7/13 for RCTs and 5/9 for non-randomised experimental trials were considered to represent good quality.

Data abstraction and synthesis

Due to heterogeneity of study designs and outcome measures, quantitative synthesis (meta-analysis) was not possible and narrative synthesis of the findings was conducted following the Synthesis Without Meta-analysis (SWiM) guideline [ 32 ]. Data from included studies were extracted independently by two authors (KI & JS) into a pre-defined template for conceptualisation and construction of the literature review (Table  2 ). Data abstracted included: year of publication, country, setting, number and age of participants, description of the deprescribing intervention and any comparator, types and classes of medications most frequently deprescribed, frailty measures used, deprescribing tools and outcomes of deprescribing. Studies were grouped according to intervention type (pharmacist-led or multidisciplinary team-led) due to the heterogeneity of study designs and outcomes. Outcome data were summarised for each study and compared.

Two thousand three hundred twenty-two articles were identified, and 57 articles were selected for full text assessment from which six journal articles were included in this review (Fig.  1 ). Six conference abstracts were excluded due to limited information available and poor quality of reporting. No articles in any language other than English were identified. The quality of the full text articles included was good: ≥6/9 for the four non-randomised experimental studies and 10/13 for the two randomised-controlled trials RCTs.

figure 1

PRISMA Flow Diagram of identification of articles

Participants characteristics

The total number of participants in all included studies was 657, while individual sample sizes ranging from 46 to 177 and mean participant age range from 79 to 85 years. The Canadian Clinical Frailty Score (CFS) was the most commonly used frailty measure ( n  = 2) [ 35 , 36 ], with others including: the Edmonton Frailty Scale [ 34 ], Identification of Seniors At Risk (ISAR) [ 38 ], the Electronic Frailty Index (e-FI) [ 33 ], and Fried Frailty Phenotype [ 37 ].

Study characteristics and deprescribing interventions

This review includes two RCTs, two pre- and post- comparison studies and two prospective interventional cohort studies (Table 2 ). Studies were conducted in Ireland, Belgium, New Zealand, Canada, and Israel. Study settings included: hospital (3), care home (1), primary care (1), community (1). All included articles were published between 2014 and 2019.

Several tools and algorithms were used to guide deprescribing. Two studies used explicit criteria (lists of drug names targeted); one used the STOPP criteria [ 38 ], and one used the STOPPFrail tool [ 36 ]. Two studies utilised implicit criteria (lists of evaluative questions or a process) developed by the research teams themselves including the Garfinkel algorithm for concurrent deprescribing of multiple medications [ 37 ], and guidelines for targeted deprescribing of anticholinergic and sedative medicines [ 34 ]. The remaining two studies used a combination of both explicit and implicit criteria; one used STOPP and Beer’s alongside pharmacist’s judgment [ 33 ], and the second used an algorithm based on STOPP guidelines, Beers criteria, Choose Wisely and Choose Wisely Canada [ 35 ]. The medications most frequently deprescribed across the studies regardless of the setting were: benzodiazepines, antidepressants, neuroleptics, opiates, lipid-lowering agents (statins), vitamin and nutritional supplements, proton pump inhibitors, and cardiovascular drugs (aspirin, antiplatelets, b-blockers, digoxin).

Due to heterogeneity of the outcome measures and study designs, studies were grouped according to interventions: pharmacist-led deprescribing interventions ( n  = 3) and multidisciplinary team-led intervention ( n  = 3).

Pharmacist-led deprescribing

Three of the six studies described pharmacist-led deprescribing interventions: one across three care home facilities [ 34 ], one in primary care (across six practices) [ 33 ] and one in hospital [ 35 ]. The three studies were non-randomised experimental studies with a good quality score, > 6/9. Follow up periods were 3 months (after discharge from hospital), 6 months (in primary care) and twelve months (in care home).

The care home and primary care studies used a person-centred, collaborative, pharmacist-led deprescribing medication review with the General Practitioner (GP) [ 33 , 34 ]. Both interventions involved a detailed medication review process that engaged patients and their relatives in decisions about medication discontinuation; both drafted and shared a medication plan and followed patients for close monitoring. While the hospital study was a prospective interventional cohort study that employed medication reviews by a Medication Rationalization (MERA) team, involving physicians and led by pharmacists, for 53 frail inpatients (CFS ≥4). These were compared with 51 patients in the control arm who did not receive the MERA review [ 35 ].

In the care home study, the pharmacist used implicit guidelines of sedatives and anticholinergic medicines developed by the research team among 46 residents identified to be frail using Edmonton Frailty Scale [ 34 ]. Whereas in the two studies in primary and secondary care, pharmacists used the STOPP and Beers criteria to identify prescribing problems among 54 community dwelling older people with some degree of frailty using the electronic frailty index (e-FI) [ 33 ] and 53 frail inpatients using the Clinical Frailty Score (CFS ≥4) [ 35 ].

Multidisciplinary team (MDT)-led deprescribing

Three studies implemented multidisciplinary team-led deprescribing focused medication review: two were RCTs in hospital settings (good quality score of 10/13) and one was a longitudinal prospective interventional study in community (good quality, 6/9). Follow up periods were three and twelve months in hospital studies and 3 years in the community study.

One RCT of 146 patients (74 in the intervention group vs 72 in the control group) living with frailty (ISAR ≥2/6) implemented medication review using STOPP criteria on admission by an inpatient geriatric team consisting of nurses, geriatricians, a dietician, an occupational therapist, a physiotherapist, a speech therapist, and a psychologist [ 38 ]. Another RCT of 130 inpatients (65 in the intervention group vs 65 in the control group) living with frailty (CFS score ≥ 7) used a STOPPFrail-guided deprescribing intervention by a research physician pre-discharge to a care home [ 36 ]. A longitudinal prospective nonrandomized study among 177 community dwelling older people living with frailty (median Fried Frailty Phenotype = 3) employed concurrent deprescribing of multiple medications based on the Garfinkel algorithm by a geriatric consultant in collaboration with GPs [ 37 ]. The intervention group included patients who had 3 or more medications deprescribed (Poly-De-Prescribing PDP group n  = 122), while the control group included participants who agreed to stop only 2 medications or less ( n  = 55).

Outcomes of deprescribing

The outcomes reported in the included studies are presented in Table 2 ; safety of deprescribing in three studies; clinical outcomes ( n  = 3); medication-related outcomes ( n  = 6); feasibility of deprescribing ( n  = 4); acceptability ( n  = 2) and cost-related outcomes ( n  = 2).

Primary outcome

Safety of deprescribing.

Three studies reported the safety of deprescribing and its impact on adverse events [ 34 , 36 , 37 ]. Potential adverse drug reactions using the UKU-SERS score, in a pharmacist-led deprescribing intervention among 46 care home residents, decreased by a mean difference of 2.8 (95% CI; p  < 0.05) after 3 months and 4.2 (95% CI; p  < 0.05) after 6 months of deprescribing of sedative and antipsychotic medications [ 34 ]. In addition, adverse effects of psychotropic medications decreased significantly by a mean difference of 1.8 (95%, CI; p  < 0.05) 3 months after deprescribing, and by a mean difference of 2.24 (95%, CI; p  < 0.05) after 6 months of deprescribing. One RCT in hospital reported that 88% of deprescribing recommendations based on STOPPFrail were accepted and implemented and no adverse events were reported of MDT-led deprescribing during 3 months follow-up [ 36 ].

Two MDT-led deprescribing studies in hospital and community showed no significant differences in unplanned hospitalisation and mortality [ 36 , 37 ]. The RCT in hospital showed no statistically significant differences between the intervention and control groups for 3 months unscheduled hospital presentations (0.14 intervention vs 0.08 control, 95% CI, P  = 0.27) and mortality (0.18 vs 0.28, 95% CI; P  = 0.22) [ 36 ] . Similarly a longitudinal cohort study in community reported that the incidence of hospitalisations per patient per year (0.39 intervention vs 1.02 comparator, p  = 0.1006) and survival (77% intervention vs 67% comparator group, p  = 0.026) was comparable between the groups after 3 years [ 37 ]. This study also reported that the incidence of significant complications per patient/year was significantly reduced in the PDP group [0.22 intervention vs 1.72 comparator group, p  = 0.0047] [ 37 ].

Secondary outcomes

Clinical outcomes.

Frailty and function: Two studies reported the outcomes of deprescribing on frailty and function [ 34 , 37 ]. Pharmacist-led deprescribing sedatives and anticholinergic medicines among 46 care home residents showed a significant decrease in frailty scoring (mean difference of 1.35, 95% CI, P  < 0.05) using the Edmonton Frailty scale, after 6 months of deprescribing. Another pharmacist-led deprescribing study did not report whether deprescribing led to changes in frailty status but reported a positive and statistically significant correlation between number of PIMs (using STOPP and Beers criteria) and frailty using e-FI (r = 0.280, P  = .040) [ 33 ]. The impact of deprescribing on functional status defined using a 5 point scale (1 = independent, 2 = frail, 3 = mild disability, 4 = disability, 5 = severe disability) was examined in another MDT-led deprescribing study [ 37 ]. Patients in the poly-deprescribing group had less functional deterioration compared to the comparator group [ 37 ] (69.1%) vs 42(34.4%), P  < 0.001).

Falls: The impact on falls was mixed on reports from two studies [ 34 , 36 ]. Significant decreases in falls rate, defined as the number of falls in the past 90 days, was reported after pharmacist-led deprescribing psychotropic medicines among care home residents [ 34 ]. But on the other hand, falls risk (determined using an in-house falls risk assessment tool utilised by most residential care facilities in New Zealand) remained the same 6 months after deprescribing in the same study. Another MDT-led deprescribing RCT study using STOPPFrail at hospital discharge reported no significant difference in incidence of falls (0.27 vs 0.30, 95% CI, P  = 0.75) and non-vertebral fractures (0.02 vs 0.09, 95%, P  = 0.18), among patients who moved to nursing home after 3 months of deprescribing [ 36 ].

Cognition, depression and mental health status: two studies reported outcomes on cognition, depression and mental status [ 34 , 37 ]. No change in cognition using the interRAI cognitive performance scale was observed after 3 and 6 months of pharmacist-led deprescribing of antipsychotic medications among care home residents (mean difference of 0, p  = 0.26). However, significant improvement of depression scores using the geriatric depression scale (GDS) (mean difference of − 2, p  < 0.05) were seen after 6 months [ 34 ]. Another MDT-led deprescribing study reported that patients in the poly-deprescribing (PDP) group had improvement in mental status using the Mini Mental State Examination (MMSE) test (3 comparator vs 63 intervention, p  < 0.0001) and cognitive status (0 comparator vs 7 intervention, P  = 0.0004) [ 37 ]. These improvements occurred within 3 months after deprescribing in 83% and persisted for ⩾ 2 years in 68%.

Quality of life (QoL): Two studies assessed QoL of participants and showed no significant differences after implementing deprescribing interventions [ 34 , 36 ]. QoL among care home residents was assessed using EQ-5D-3L and showed no significant difference pre and 6 months post deprescribing [ 34 ]. QoL in one RCT in hospital using QUALIDEM or ICECAP-O scores showed deterioration in both the intervention and the control group from baseline to 3 months follow-up, but no statistically significant differences were found in the mean changes between groups [ 36 ].

Medication-related outcomes

All six studies reported medication-related outcomes [ 33 , 34 , 35 , 36 , 37 , 38 ]. Four studies reported a significant reduction in the number of medications taken by patients living with frailty after implementing deprescribing, ranging from a mean of 2–3 medicines stopped per patient [ 34 , 35 , 36 ] across the different settings to unsurprisingly 7 medications per patient when poly-deprescribing of three or more drugs was implemented in people’s home [ 37 ]. Two studies also reported significant decreases of potentially inappropriate medications associated with deprescribing interventions [ 33 , 38 ]; for example the deprescribing interventions in two hospital studies reported a mean decrease in number of PIMs of 2.2 ( p  < 0.01) in a pharmacist-led study and reduction in PIMs was twice as high for the intervention group compared to the control group in a MDT-led deprescribing intervention. One care home study reported a significant decrease in the drug burden index (by 0.34) 6 months after deprescribing in care home residents with a pharmacist-led deprescribing intervention [ 34 ].

Feasibility of deprescribing

Four studies reported the feasibility of deprescribing among older people with frailty [ 34 , 35 , 36 , 37 ]. They displayed that 72–91% of the suggestions to deprescribe medications made by either pharmacists or the MDT were accepted and implemented across the different settings [ 34 , 35 , 36 , 37 ]. For example, in care homes, forty-five PIMs were identified and suggested to be stopped by pharmacists, of which 82% were agreed upon by the residents’ GP and 96% were agreed upon by the resident or their relatives/family resulting in the implementation of 72% of the recommendations [ 34 ]. Similarly in two hospital studies, 72 and 81% of the recommendations made were accepted and implemented by the admitting physician and then patients [ 35 , 36 ]. In one community study, 91% of the recommendations made by a geriatrician were accepted by GPs [ 37 ].

Deprescribing was also reported to be well tolerated as most medications stopped were not restarted. For example, in care homes, medicines were re-prescribed by the GP in only five instances (15%); stopping medication was not completed in 13 residents (28%) due to mood changes, increased pain levels or overall health deterioration [ 34 ]. Similarly in hospital, of the 162 medications that were stopped only 40 (25%) were restarted during hospital admission or at time of discharge and 81% of medications stopped during hospitalisation remained discontinued after 3 months [ 35 ]. Another RCT study among hospitalised older patients discharged to care homes showed that only three medications stopped at discharge by the MDT were restarted [ 36 ].

Acceptability of deprescribing

Two studies evaluated the acceptability of their deprescribing interventions and showed that patients and healthcare professionals were happy to stop unnecessary medication [ 35 , 37 ]. For example, following a pharmacist-led intervention 87% participants felt comfortable stopping medications as recommended by the team and only a small number found the experience stressful or confusing (5 and 11% respectively) [ 35 ]. In the poly-deprescribing intervention in community, the overall satisfaction of patient/family from the changes was defined as high/very high in 89% [ 37 ].

Cost-related outcomes

Two studies reported the cost implications of deprescribing [ 35 , 36 ]. A pharmacist-led intervention reported a total saving of $1508.47 or $94.28 per 100 patient-days when STOPP criteria were implemented in hospital [ 35 ]. Use of STOPPFrail by an MDT at discharge from hospital also led to a mean change in monthly medication cost of –$74.97 compared to –$13.22 in the control group (mean difference $61.74; 95% CI; P  = .02) [ 36 ].

This review expands on prior literature reviews by synthesising studies on medication deprescribing that specifically addressed older people living with frailty, as they are more vulnerable to the adverse effects of medicines compared to older people in general. Only six studies (two were RCTs) with overall good quality that reported the outcomes of deprescribing interventions among older people, with reliably identified frailty, were found. The outcomes of deprescribing in older people living with frailty were similar to those reported in older people in general in terms of feasibility, acceptability and safety, as mortality and hospitalisation rates did not increase after stopping medications. Deprescribing interventions led to a significant reduction in the number of medications and PIMs with potential cost saving. Included studies also suggest some evidence of potential improvements in function, frailty status, mental health and depression scores. Outcomes did not differ when the intervention was led by a pharmacist or MDT including mainly medical practitioners and whether explicit or implicit criteria were used. But the heterogeneous study designs limit our ability to make firm conclusions regarding this matter.

Deprescribing medications has raised some ethical dilemmas and fear of negative outcomes has been reported by prescribers as a barrier to deprescribing [ 39 ]. Among older people with identified frailty, there is some evidence from the included studies in this review that deprescribing is safe, as it did not adversely change hospitalisation and mortality rates. A number of systematic reviews have investigated the impact of deprescribing on mortality among general population of older people; one reported that deprescribing reduced mortality in non-randomized studies but no changes were observed in RCTs [ 40 ]; other reviews suggested a reduction in all-cause mortality with deprescribing interventions in nursing home residents [ 22 , 23 ]. We reported some evidence that deprescribing is feasible and well tolerated by older people living with frailty and is acceptable by healthcare professionals and patients, which is in agreement with existing studies in older people in general [ 41 , 42 ]. In our review we identified that 72–91% of recommendations made were implemented and very few patients (25%) restarted their medications. A recent review of 26 papers reported the proportion of patients who successfully stopped their medication varied from 20 to 100% and in 19 studies the proportion was > 50% [ 24 ]. The feasibility and safety of deprescribing should encourage clinicians to regularly discuss the decision to continue or deprescribe chronic medications with their patients living with frailty, following a patient-centred, structured deprescribing process with planning, tapering and close monitoring during, and after medication withdrawal.

Few studies in the review reported clinical outcomes such as frailty, falls, cognition and depression; with more focus placed on the success of the interventions in reducing number of medications and especially inappropriate ones. This focus on process and lack of clinical outcome data with inconsistency in outcome measurement have also been highlighted as limitations in deprescribing studies to date. A 2017 review of deprescribing interventional studies among older people in general reported the outcome measures most commonly used were number of medications or PIMs stopped, healthcare use, and adverse events [ 43 ]. Patient-reported outcomes, geriatric syndromes (e.g. falls, fractures, gait speed, depression and delirium) or cost evaluations were infrequently reported, and frailty was not used as either inclusion criteria or an outcome measure. There is no consensus among researchers and clinicians on appropriate outcomes of deprescribing and more research is needed in this area. Frailty should be considered as an outcome in deprescribing interventions in older people and the focus should be placed on understanding the impact of deprescribing on frailty trajectory.

The strong relationship between polypharmacy and frailty and the potential to reverse frailty status [ 44 , 45 ], makes it important to understand the impact of deprescribing on frailty. Only one study included in our review examined the impact of deprescribing on frailty status among 46 care home residents using the Edmonton frailty tool and reported positive results [ 34 ]. The Edmonton frailty tool consists of 9 domains including number of medications [ 46 ]. It is unclear from the study which domains were influenced by the deprescribing intervention or to what extent the improvement could simply reflect a decrease in the number of medications used. Another included study reported that frailty and PIMs were significantly correlated but did not report the impact of deprescribing on frailty status. There is a lack of research on the impact of stopping medications on frailty status but some current registered clinical trials propose to measure this relationship [ 47 , 48 ]. It is also important to understand the mechanism by which deprescribing might influence frailty via functional or cognitive changes or through other possible mechanisms.

No effect of deprescribing on the quality of life among older people with frailty was reported in our review. These findings are consistent with literature published in older people in general [ 20 , 21 , 49 , 50 ]. Possible explanations for this might be that the impact of deprescribing on QoL may depend on the specific combination of medication(s), patient population and patients’ preferences, clinical setting, timing of QoL measurement or the QoL measurement tools used. We found a positive impact of deprescribing sedative and psychotic medications using a specific algorithm on rate of falls among older care home residents living with frailty, but no similar impact was obtained when the STOPPFrail tool was used among hospitalised patients discharged to care homes. This might be explained by the fact that the deprescribing algorithm focused on sedative and psychotic medications resulting in a higher proportion of anticholinergic medications being stopped compared to a tool with a broader remit like STOPPFrail. The inconsistency in reported findings regarding the relationship between falls and deprescribing is clear in the literature. For example, a recent review published in 2017 reported that falls-risk drug withdrawal strategies did not significantly change the rate of falls, number of people who fell or rate of fall-related injuries over a 6 to 12 months follow-up period in five included papers [ 51 ]. However, another review suggested that deprescribing interventions could significantly reduce the number of people who fall in care homes by 24% [ 22 ]. They related this to the significant reduction in number of residents on PIMs by 60% such as anticholinergics, which have been consistently associated with cognitive impairment and falling in older people. As we mentioned above, the impact of deprescribing on falls could be mediated by the deprescribing tools used and further research should explore this relationship.

The intervention process, who led deprescribing or the deprescribing tools used, appeared to have no differing effects in reducing unnecessary medications in our review. But the heterogeneity in study designs and the small number of included studies limit our ability to conclude whether one approach is more or less effective than another. Other reviews suggested that pharmacist-led deprescribing intervention in older people in general were more effective in reducing unnecessary medications compared to interdisciplinary team interventions [ 52 , 53 ]. The concurrent use of both explicit lists of potentially inappropriate medications and systematic appraisal of every medication taken was suggested to help improve complex regimes [ 54 ]. Deprescribing techniques may be guided by the clinical situation. Stopping medicines one at a time might be most appropriate for managing people whose health status is stable in out-patient settings, whereas ‘concurrent deprescribing’ of multiple medications may be more appropriate for inpatients where it is easier to monitor for withdrawal effects [ 54 ]. It is also recommended to use deprescribing as a ‘drug holiday trial’ as sometimes drugs will need to be restarted when symptoms recur or withdrawal effects are experienced, which necessitates monitoring and follow up [ 54 ].

This review is the first to summarise the evidence and impact of deprescribing among older people identified as living with frailty. Most published reviews focused on the general population of older people or in a specific setting. With the increasing awareness of the importance of identifying frailty using reliable measures to allow implementation of effective interventions, this review expands our knowledge of the evidence of deprescribing among this population who are more vulnerable to harm from medications. However, there were several limitations in our review. The inclusion criteria required a reliable and valid measure of frailty. This is important to allow extrapolation of the study results to patients with similar scores, or to measure whether frailty status affects response to deprescribing interventions. However, we may have excluded articles assessing frail older people but which utilised less specific methods of assessing frailty or those that assumed frailty depending on age or setting such as studies in care homes. Multicomponent interventions including deprescribing or medication review where deprescribing accounted for less than half of the recommendations were excluded, as our aim was to understand the evidence and impact of deprescribing among those living with frailty. We did not search the grey literature and may have missed some additional resources. Although we followed SWiM criteria, our synthesis of the studies should be treated with caution because of the limited number of included studies and their heterogeneity. We were also unable to perform a meta-analysis because of the heterogeneity of outcomes within the included studies.

This review highlights the paucity of published literature on deprescribing among older people living with frailty. The included studies used objective frailty measures and thus may not capture all studies that included frail older people. Studies were heterogenous in their settings, designs and outcomes reported making it difficult to make definite conclusions. However, we suggest that deprescribing could be safe, feasible, well tolerated and can lead to important benefits on geriatric conditions such as depression, function and frailty. Deprescribing interventions in this review appear to be effective whether led by pharmacists or multidisciplinary teams using explicit or implicit tools. This has implications for clinical practice as deprescribing could be effectively led by pharmacists in liaison with GPs in community settings, whereas multidisciplinary teams (with or without access to pharmacists) could play a key role in deprescribing in acute settings. However, more research is needed in the area of deprescribing and frailty and future studies should include those living with frailty in their samples. Moreover, in order to address the gap in our understanding of the effectiveness of deprescribing interventions on reducing and reversing frailty, or stopping its progression, adequately powered randomised controlled trials that include reliable measures of frailty should be conducted.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.


The Preferred Reporting Items for Systematic reviews and Meta-Analyses

The international prospective register of systematic reviews

Potentially Inappropriate Medications

Randomized controlled trials

The Synthesis Without Meta-analysis

The Canadian Clinical Frailty Score

Identification of Seniors At Risk

The Electronic Frailty Index

General Practitioner

Medication Rationalization

Multidisciplinary team


The geriatric depression scale

Mini Mental State Examination

Quality of life

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We thank Paula Sands, Health Services librarian for her support with developing the search strategies.

Authors did not receive funding to complete this review. However, authors receive support from the National Institute of Health Research (NIHR). H.C.R, SF, SL and K. I receive support from the NIHR Applied Research Collaboration (ARC) Wessex. KI, N.J.C and H.C.R receive support from the NIHR Southampton Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health.

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KI, NC, JMS, SL, SF and HCR contributed to the conception and design of the review. KI & NC completed literature search and screening, KI & JMS extracted data from included studies and SL & SF assessed the quality of studies. KI drafted the manuscript and HCR contributed editing. All authors read and approved the final manuscript.

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Ibrahim, K., Cox, N.J., Stevenson, J.M. et al. A systematic review of the evidence for deprescribing interventions among older people living with frailty. BMC Geriatr 21 , 258 (2021). https://doi.org/10.1186/s12877-021-02208-8

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a systematic overview of systematic reviews evaluating interventions addressing polypharmacy

The Relationship of Continuity of Care, Polypharmacy and Medication Appropriateness: A Systematic Review of Observational Studies

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Worldwide, polypharmacy and medication appropriateness-related outcomes (MARO) are growing public health concerns associated with potentially inappropriate prescribing, adverse health effects, and avoidable costs to health systems. Continuity of care (COC) is a cornerstone of high-quality care that has been shown to improve patient-relevant outcomes. However, the relationship between COC and polypharmacy/MARO has not been systematically explored.

The aim of this systematic review was to investigate the operationalization of COC, polypharmacy, and MARO as well as the relationship between COC and polypharmacy/MARO.

We performed a systematic literature search in PubMed, Embase, and CINAHL. Quantitative observational studies investigating the associations between COC and polypharmacy and/or COC and MARO by applying multivariate regression analysis techniques were eligible. Qualitative or experimental studies were not included. Information on the definition and operationalization of COC, polypharmacy, and MARO and reported associations was extracted. COC measures were assigned to the relational, informational, or management dimension of COC and further classified as objective standard, objective non-standard, or subjective. Risk of bias was assessed by using the NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies.

Twenty-seven studies were included. Overall, substantial differences existed in terms of the COC dimensions and related COC measures. Relational COC was investigated in each study, while informational and management COC were only covered among three studies. The most frequent type of COC measure was objective non-standard ( n  = 16), followed by objective standard ( n  = 11) and subjective measures ( n  = 3). The majority of studies indicated that COC is strongly associated with both polypharmacy and MARO, such as potentially inappropriate medication (PIM), potentially inappropriate drug combination (PIDC), drug–drug interaction (DDI), adverse drug events (ADE), unnecessary drug use, duplicated medication, and overdose. More than half of the included studies ( n  = 15) had a low risk of bias, while five studies had an intermediate and seven studies a high risk of bias.


Differences regarding the methodological quality of included studies as well as the heterogeneity in terms of the operationalization and measurement of COC, polypharmacy, and MARO need to be considered when interpreting the results. Yet, our findings suggest that optimizing COC may be helpful in reducing polypharmacy and MARO. Therefore, COC should be acknowledged as an important risk factor for polypharmacy and MARO, and the importance of COC should be considered when designing future interventions targeting these outcomes.

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1 Background

Due to aging populations and multimorbidity, polypharmacy (taking multiple drugs simultaneously) is an increasing public health problem worldwide [ 1 , 2 , 3 , 4 , 5 ]. Across Europe, approximately one-third of people aged > 65 years are affected by polypharmacy [ 6 ]. Because of the heterogeneity of definitions [ 7 ] and due to different settings and populations studied, the worldwide prevalence of polypharmacy varies widely between 10 and 90% [ 8 ]. Studies have shown that polypharmacy is associated with potentially inappropriate prescribing [ 9 ] and several adverse health events [ 10 , 11 , 12 ]. Accordingly, polypharmacy directly and indirectly affects health care spending and causes avoidable costs [ 13 , 14 ]. Several interventions have been developed to tackle the growing problem of polypharmacy and associated adverse events; these interventions appear beneficial in terms of improving medication appropriateness-related outcomes (MARO), such as potentially inappropriate prescribing as measured by the Medication Appropriateness Index, Beers’ criteria, and the STOPP/START criteria. Yet, evidence of improvements in clinical outcomes (e.g., reduction of hospital admissions), including patient-reported outcomes, remains inconclusive [ 15 , 16 , 17 , 18 , 19 ].

Suboptimal care transitions and a lack of collaboration between health care providers (e.g., physicians) have been identified as major problems impeding optimal medication management processes and patient safety [ 20 , 21 , 22 , 23 ]. In this regard, continuity of care (COC), widely acknowledged as a cornerstone of high-quality care, is highly relevant [ 24 ]. According to Haggerty et al. [ 25 ], COC comprises three dimensions: relational continuity, representing an ongoing therapeutic relationship between a patient and one or more providers, informational continuity , representing the use of information on past events and personal circumstances to make current care appropriate for each individual, and management continuity , representing a consistent and coherent approach to the management of a health condition that is responsive to a patient’s changing needs. Furthermore, COC can be assessed using three types of measure: ‘objective standard measures’ (e.g., continuity indices), ‘objective non-standard measures’ (e.g., all other quantitative indices of patient–provider contact), and ‘subjective measures’ (patient-reported assessments of continuity) [ 26 ].

Evidence suggests that improving COC leads to improved patient-reported outcome measures (e.g., patient satisfaction [ 26 ] and quality of life [ 27 ]), reduced mortality [ 28 , 29 ], fewer emergency hospital admissions [ 30 ], fewer hospitalizations [ 31 , 32 ], and decreased healthcare costs [ 33 ]. Furthermore, a recent systematic review investigating relational COC in community pharmacies and its effect on patient outcomes found positive effects of higher COC on medication adherence, inappropriate drug use, and the use of other costly services (e.g., visits to the emergency department) [ 34 ]. However, there is limited evidence regarding the association of COC with polypharmacy and MARO [ 24 , 26 ]. Therefore, this study aims (i) to give an overview of how observational studies examining the relationship between COC and polypharmacy on the one hand and COC and MARO on the other operationalize these concepts and (ii) to perform a narrative synthesis of the results of these studies. The former is necessary since COC [ 25 , 35 , 36 , 37 , 38 , 39 ], polypharmacy [ 7 ], and MARO [ 40 ] are defined and measured in various ways, hampering the comparability of results.

This systematic review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) statement [ 41 ] (Electronic Supplementary Material [ESM] Tables S4 and S5).

2.1 Search Strategy

We performed a systematic literature search from inception to 06 February 2023 using the databases MEDLINE via PubMed, Embase, and CINAHL via EBSCOhost Web. The search strategy included terms related to COC, polypharmacy, MARO, and relevant MeSH terms. For Embase and CINAHL, the same search terms were used (see supplement 1 in the ESM). Additionally, reference lists of relevant studies were searched manually for further relevant publications. Databases were chosen due to their relevance and the search strategy was developed in accordance with published COC- and MARO-related systematic reviews [ 16 , 19 , 27 , 34 ].

2.2 Study Selection

Studies were included if they investigated the relationship between COC and polypharmacy and/or MARO. We included only studies focusing on the continuity of physician care, rather than COC with respect to nurses, pharmacies, or other care providers. Any operationalization of COC, polypharmacy, and MARO was eligible. Only quantitative observational studies (including those using written questionnaires and quantitative interviews) applying multivariate regression analysis techniques were included to ensure that included studies properly controlled for confounding factors. Any experimental and qualitative studies (or reviews of such), editorials, commentaries, conference abstracts, or study protocols were excluded. Experimental studies were excluded as clarifying the operationalization of COC, polypharmacy, and MARO and their relationship in observational studies is a necessary step before interventions targeting COC to improve polypharmacy and MARO can be properly evaluated. The selection was limited to articles published in English and German (see supplement 2 in the ESM). Two investigators (DL and JG) independently screened search results and assessed the eligibility of potentially relevant studies. Discrepancies were resolved by consensus. Another investigator (DG/JW) was involved if consensus could not be reached.

2.3 Data Extraction, Categorization, and Analysis

The following data were extracted from the included studies: information related to study design/analysis, data source (register, claims, administrative and pharmacy data summarized as ‘register/claims data’), country, setting (of exposure), and population. Regarding analyses and outcomes, information on how COC was operationalized was extracted and categorized according to the three dimensions ( relational continuity, informational continuity, and management continuity ) proposed by Haggerty et al. [ 25 ]. Additionally, studies were categorized by their type of COC measure into objective standard measures, objective non-standard measures, and subjective measures according to van Walraven et al. [ 26 ]. Key findings of the studies and reported effect sizes, that is, odds ratios (OR), risk ratios (RR), incidence rate ratios (IRR) resulting from regression models, were also extracted (Table 1 ). Finally, information related to the operationalization of polypharmacy and MARO was extracted (Table 2 ; Tables S1 and S2 in the ESM). One investigator (DL) performed the data extraction, which was verified by a second investigator (JG). Disagreements were resolved by consensus after discussion.

The results of the included studies were synthesized narratively, since the variety of COC, polypharmacy, and MARO measures as well as differences in reported outcomes and study designs did not allow a quantitative synthesis. For those studies reporting OR, RR, and IRR, we visualized point estimates of the effect sizes as well as reported confidence intervals with forest plots. These plots were grouped by type of COC measure and type of outcome.

2.4 Quality Appraisal

Risk of bias was assessed using the NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies, which comprises 14 criteria and rating guidance [ 42 ]. This tool classifies the risk of bias of studies as good (low risk of bias), fair (intermediate risk of bias), or poor (high risk of bias). Two reviewers made independent judgments on each of the items (DL, JG). Disagreements between the two reviewers were resolved by consensus after discussion.

3.1 Study Selection

The literature search identified 1984 articles, resulting in 1758 articles after duplicates were removed. After screening titles and abstracts according to the eligibility criteria, we selected 175 articles for full-text review. Full-text articles ( n  = 160) were excluded with the following reasons: (i) no quantitative association of COC and either polypharmacy or MARO investigated ( n  = 117), (ii) experimental design or review of interventional studies ( n  = 39), (iii) conference abstract (no full-text available) ( n  = 3), (iv) language other than English or German ( n  = 1). Finally, 27 studies that met the inclusion criteria were included in the narrative synthesis, including 12 studies that were found by searching the reference lists manually (Fig. 1 ).

figure 1

PRISMA 2020 flow diagram . Reason 1: No quantitative association of COC and either polypharmacy or MARO investigated, reason 2: experimental design or review of interventional studies, reason 3: conference abstract (no full-text available), reason 4: language other than English or German. COC continuity of care, MARO medication appropriateness-related outcomes

3.2 Study Characteristics and Methodological Findings

Table 1 summarizes the included studies’ main study characteristics and results. The majority of studies ( n  = 16) investigated the relationship between COC and MARO [ 43 , 44 , 45 , 46 , 47 , 51 , 56 , 57 , 58 , 60 , 61 , 63 , 64 , 66 , 68 , 69 ]. Seven studies focused on the relationship between COC and polypharmacy [ 48 , 53 , 54 , 62 , 65 , 67 ], and four studies investigated both the relationship between COC and MARO and between COC and polypharmacy [ 49 , 50 , 52 , 59 ].

The included studies were from North America ( n  = 12), Europe ( n  = 6), and Asia ( n  = 9). Most of the studies ( n  = 9) were from the US [ 46 , 48 , 51 , 57 , 60 , 63 , 66 , 67 , 69 ] and Taiwan ( n  = 6) [ 43 , 44 , 45 , 47 , 50 , 62 ]. The population of interest was mostly at least 60 years old. Only five studies included younger patients [ 45 , 48 , 51 , 57 , 58 ]. Nine studies focused on patients with specific diseases or risks, such as patients with a mental and/or behavioral disorder or dementia [ 47 , 48 , 51 , 52 , 53 , 54 , 62 , 67 , 68 ]. All studies included outpatient data, while only two studies [ 51 , 69 ] included inpatient data. Sample sizes varied substantially between 384 [ 60 ] and 2,318,766 participants [ 50 ]. Cross-sectional analyses were performed in 20 [ 46 , 48 , 49 , 52 , 53 , 54 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 ] and longitudinal analyses in eight studies [ 43 , 44 , 45 , 47 , 50 , 51 , 55 , 69 ]. One study performed both cross-sectional and longitudinal analyses [ 69 ]. Most studies ( n  = 19) performed their analyses based on register/claims data [ 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 59 , 61 , 64 , 68 ]. Five studies used questionnaires/interviews [ 63 , 65 , 66 , 67 , 69 ]. One study based its analyses on medical records [ 60 ]. A combination of multiple data sources was used by two studies [ 58 , 62 ]. The main setting (of exposure) was primary care/outpatient. Only two studies included providers from the primary care/outpatient and secondary care/inpatient setting [ 51 , 69 ]. The following subsections describe methodological characteristics of the included studies, including the measures used to capture COC, polypharmacy, and MARO. Table 2 gives an overview of the frequency of these measures overall and for studies investigating polypharmacy and MARO, respectively.

3.2.1 Operationalization of Continuity of Care (COC)

The most frequent COC dimension investigated was relational continuity , which was considered in every study. Only three studies [ 48 , 57 , 66 ] additionally considered informational continuity and management continuity (Table 1 ). Regarding the operationalization of COC, substantial differences were observed.

Objective standard COC measures were used by 11 studies. Among those, different COC indices were used to measure relational continuity , such as the Continuity of Care Index (COCI), the Usual Provider of Care (UPC) index, and the Sequential Continuity of Care Index (SECON). The COCI was analyzed in six studies [ 43 , 44 , 45 , 47 , 50 , 52 ]. The studies differed in terms of their aggregation level. For example, two studies analyzed COCI at the site level in addition to the physician level [ 43 , 45 ]. Moreover, the variables’ scale of measurement was variously defined as continuous [ 45 , 47 ], ordinal [ 44 , 45 , 50 , 52 ], or binary (e.g., low vs high COCI) [ 43 , 50 ]. The UPC index was also calculated in six studies [ 43 , 44 , 52 , 53 , 54 , 64 ]. Two of these studies used the UPC index to conduct supplementary sensitivity analyses beyond their primary COCI-based analyses [ 43 , 44 ]. Differences in the aggregational level (physician level vs site level) and the variables’ scale of measurement also existed among those studies. Two studies [ 48 , 57 ] operationalized COC via care density, a proxy measure that may reflect how frequently a patient’s doctors collaborate/share patients. Thus, care density corresponds to better communication and information sharing between the patient’s care team, forming a social network of providers [ 70 ]. This was the only COC measure identified that represents informational and management COC. The SECON was only used by one study that also calculated the COCI and the UPC index [ 52 ]. Multiple objective standard measures of COC were used by three studies [ 43 , 44 , 52 ] (Table 2 ; Tables S1 and S2 in the ESM).

Among studies using objective non-standard measures of COC ( n  = 16), the majority ( n  = 11) used the number of prescribers [ 46 , 48 , 51 , 55 , 56 , 59 , 60 , 61 , 63 , 68 , 69 ] to measure COC, with a high number of prescribers indicating low COC. Further measures were the number of treating physicians [ 49 , 61 ], the number of providers [ 57 , 67 ], the number of specialties [ 58 ], the tendency to visit multiple providers [ 62 ], and having a single primary care physician [ 61 ]. Exposure variables were treated as binary, ordinal, or continuous (Table 2 ; Tables S1 and S2 in the ESM).

Subjective measures of COC were used by three studies [ 65 , 66 , 69 ] (Table 2 ). In particular, patients were asked if they have a regular physician [ 65 ], whether they usually see the same physician [ 69 ], or whether they experienced a gap in care coordination [ 66 ]. These COC measures were treated as binary variables (yes vs no) (Tables S1 and S2, see ESM). Overall, a combination of the different types of COC measures was used by three studies [ 48 , 57 , 69 ].

3.2.2 Operationalization of Polypharmacy

Polypharmacy was mostly defined as having five or more medications prescribed (binary variable) [ 49 , 50 , 52 , 55 , 59 , 62 , 65 , 67 ]. Some studies (additionally) included extreme/excessive polypharmacy (≥10 medications prescribed) [ 50 , 52 , 53 , 54 , 55 , 62 ]. One study operationalized multiclass psychotropic polypharmacy as taking two or more psychotropic medications from different drug classes for 60 days or more [ 48 ]. Observational periods varied from 2 weeks to 1 year; two studies also considered persistent (>181 days) polypharmacy [ 50 , 62 ] (Table 2 ; Table S1, see ESM).

3.2.3 Operationalization of Medication Appropriateness-Related Outcomes (MARO)

Overall, seven categories of MARO were investigated: Potentially inappropriate medication (PIM) [ 44 , 46 , 47 , 49 , 52 , 56 , 59 , 64 , 69 ], drug–drug interaction (DDI) [ 45 , 50 , 57 , 64 , 66 , 68 ], adverse drug events (ADE) [ 58 , 63 ], duplicated medication [ 43 , 44 ], unnecessary drug use [ 60 ], overdose [ 51 ], and potential inappropriate drug combination (PIDC) [ 61 ] (Table 2 ).

Regarding the operationalization of PIM, different versions of the Beers criteria [ 71 ] were applied [ 46 , 47 , 64 ]. Other instruments were used, such as the Japanese STOPP-J list [ 59 ], the Norwegian General Practice (NORGEP) criteria, which are based on the Beers criteria [ 56 ], the German PRISCUS list [ 49 ], and the STOPP/START criteria [ 52 ]. PIM was always analyzed by using a binary (yes vs no) variable. Concerning DDI, the outcome variable was dichotomized (yes vs no) in all but one included study, which treated DDI as a continuous variable [ 45 ]. PIDC, as used by Tamblyn et al. [ 61 ], is a combination of PIM and DDI, identified by an expert review. Duplicated medications were used as outcomes by Cheng and Chen [ 43 ] and Chu et al. [ 44 ]. ADE were defined as either the presence of an ADE-specific code [ 58 ] or as a binary (yes vs no) outcome self-reported by the study participants [ 63 ]. One study [ 60 ] measured unnecessary drug use based on the Medication Appropriateness Index [ 72 ]. Finally, overdose as an outcome was defined as the occurrence of one or more medical claims containing a diagnosis code for opioid or benzodiazepine poisoning on a person-day of opioid-benzodiazepine overlap [ 51 ] (Table S2, see ESM).

3.3 Association Between COC and Polypharmacy

Studies using objective standard measures of COC [ 48 , 50 , 52 , 53 , 54 ] found mixed effects concerning the association between COC and polypharmacy (Table 1 ). For example, higher COC (highest quartile, ref.: lowest quartile) was not associated with polypharmacy but with a reduced risk of extreme polypharmacy [ 52 ]. Two studies by Guilcher et al. [ 53 , 54 ] also showed a significant negative association between COC and polypharmacy. Furthermore, COC (care density) was associated with the likelihood of receiving psychotropic polypharmacy. However, this relationship between COC (care density) and psychotropic polypharmacy varied depending on the type of physicians involved in the care team, and a significant negative relationship between COC (care density) and psychotropic polypharmacy was only observed among patients with only PCPs involved in their care teams, while a significant positive relationship was observed among patients who had both PCPs and specialists involved in their care team [ 48 ]. Weng et al. [ 50 ] showed that the proportion of patients with polypharmacy was significantly lower in a high COC group (87.80%) compared with a low COC group (94.29%) and that higher COC was related to fewer DDI events. This latter effect was partially mediated by polypharmacy. Fig. 2 shows the associations between COC and polypharmacy in studies using objective standard COC measures.

figure 2

Association between COC and polypharmacy for objective standard COC measures. Solid green line indicates significant negative association between COC and polypharmacy; dashed red line indicates non-significant negative association; blue dotted line indicates significant positive association; [ 50 ] was not visualized, as results were not reported as OR, RR, or IRR; [ 54 ] uses low COC as the reference category. Therefore, an RR of 1.07 indicates a negative relationship between high COC and polypharmacy. *OR for care teams of PCPs only; **OR for care teams of specialists only; ***OR for care teams with both PCPs and specialists. COC continuity of care, COCI Continuity of Care Index (physician level), IRR incidence rate ratio, MARO medication appropriateness-related outcomes, OR odds ratio, PCP primary care physician/practitioner, RR risk ratio, SECON sequential continuity of care, UPC usual provider of care

All studies using objective non-standard COC measures (e.g., number of prescribers/providers/treating physicians) [ 48 , 49 , 55 , 59 , 62 , 67 ] demonstrated associations between COC and (different levels of) polypharmacy. Regarding polypharmacy (≥ 5 medications) [ 49 , 55 , 59 , 62 , 67 ], studies consistently showed a significant association with COC. For instance, one study demonstrated that higher COC (lower number of treating physicians) is a predictor of polypharmacy (independent of multimorbidity) in men and women above the age of 60 years [ 49 ]. Regarding subjective COC measures, one study showed that patients reporting low COC (not having a regular physician) are more than twice as likely to be taking five or more prescribed drugs than those patients with high COC (having a regular physician) [ 65 ]. Figure 3 shows the associations between COC and polypharmacy in studies using objective non-standard or subjective COC measures.

figure 3

Association between COC and polypharmacy for objective non-standard and subjective COC measures. For [ 49 ], only the OR for 2 vs 1 physician among women was visualized. The association between the number of treating physicians and polypharmacy was significantly positive in all other subgroups. Solid green line indicates significant negative association between COC and polypharmacy. COC continuity of care, OR odds ratio

3.4 Association Between COC and MARO

Studies using objective standard measures of COC [ 43 , 44 , 45 , 47 , 50 , 57 , 64 ] to investigate the association of COC with PIM [ 44 , 47 , 52 , 64 ], DDI [ 45 , 50 , 57 , 64 ], and medication duplication [ 43 , 44 ] demonstrated negative relationships (Table 1 ). In terms of PIM, however, one study [ 52 ] showed mixed results (significant or non-significant negative associations) depending on the type of analysis. Figure 4 shows the associations between COC and MARO in studies using objective standard COC measures.

figure 4

Association between COC and MARO for objective standard COC measures. Solid green line indicates significant negative association between COC and MARO; dashed red line indicates non-significant negative association. [ 43 ] was not visualized, as results were not reported as OR, RR, or IRR. COC continuity of care, COCI Continuity of Care Index (physician level), DDI drug–drug interaction, HF heart failure, IRR incidence rate ratio, MARO medication appropriateness-related outcomes, OR odds ratio, PIM potentially inappropriate medication, RR risk ratio, SECON sequential continuity of care, UPC usual provider of care

Objective non-standard COC measures were used by 12 studies [ 46 , 49 , 51 , 56 , 57 , 58 , 59 , 60 , 61 , 63 , 68 , 69 ]. Regarding PIM, most studies revealed negative associations with COC [ 46 , 49 , 56 , 59 ]. However, one study showed that having low COC (high number of prescribers) was not significantly associated with PIM [ 69 ]. DDI [ 57 , 68 ], ADE [ 58 , 63 ], unnecessary drug use [ 60 ], overdose [ 51 ], and PIDC [ 61 ] were found to be negatively associated with COC. Thus, the more prescribers/providers involved in the care process (representing lower COC), the higher the likelihood of inappropriate prescribing. Subjective COC measures were used by two studies [ 66 , 69 ]. These studies also identified negative associations of COC (gap in care coordination) and DDI [ 66 ] and COC (usually seeing the same physician) and PIM [ 69 ]. Figure 5 shows the associations between COC and MARO in studies using objective non-standard or subjective COC measures.

figure 5

Association between COC and MARO for objective non-standard and subjective COC measures. Solid green line significant negative association between COC and MARO; red dashed line non-significant negative association. For [ 46 ], the OR for having 2 vs 1 prescriber was visualized (a significant negative association between COC and PIM was also found for 3 and 4+ vs 1 prescriber). For [ 49 ], only the OR for 2 vs 1 physician among women was visualized. The association between the number of treating physicians and polypharmacy was significantly positive in all other subgroups. For [ 56 ], the OR for 3–4 vs 1–2 prescribers was visualized (a significant negative association between COC and PIM was also found for 5 + vs 1–2 prescribers). For [ 69 ], only the longitudinal model was visualized. ADE adverse drug event, COC continuity of care, COCI Continuity of Care Index, DDI drug–drug interaction, MARO medication appropriateness-related outcomes, OR odds ratio, PIDC potential inappropriate drug combination, PIM potentially inappropriate medication

3.5 Risk of Bias Assessment

Overall, 15 studies [ 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 ] had a low risk of bias, five studies [ 58 , 59 , 60 , 61 , 62 ] had an intermediate risk of bias, and seven studies [ 63 , 64 , 65 , 66 , 67 , 68 , 69 ] were deemed to have a high risk of bias (Table S3, see ESM). Among studies at high risk of bias, common problems were that the exposure measures (criteria 9) and outcome measures (criteria 11) were not clearly defined, valid, reliable, and implemented consistently across all study participants, that the exposure was not measured before the outcome(s) (criteria 6), or that the exposure was not assessed more than once over time (criteria 10). Loss to follow-up (criteria 13) was deemed not applicable (N/A) to the included studies, as all studies were retrospective.

4 Discussion

This systematic review had two aims. First, we aimed to explore how COC, polypharmacy and MARO are defined, operationalized, and measured in the included studies. Second, we aimed to investigate the relationship between COC and polypharmacy and MARO.

4.1 Methodological Findings: Measuring COC

COC is a multi-faceted concept with various measures available to researchers [ 38 , 73 ]. However, different COC measures reflect the dimensions of relational, informational, and management continuity to various degrees. We found that the majority of included studies used objective non-standard measures, mainly the number of prescribers, while a significant minority used objective standard measures, with only three studies using subjective measures.

Objective non-standard measures are typically simple to compute but are only partially adequate for measuring COC. First, these measures quantify the amount of patient-provider interaction (e.g., the number of particular patient-doctor encounters [ 26 ]) without considering the distribution of these interactions. However, this distribution is important to relational continuity. Second, objective non-standard measures do not adequately capture informational or management continuity. For instance, it is not plausible to consider a patient with a moderate number of prescribers who do not effectively communicate and share information about the patient to have a higher COC than a patient with a somewhat larger but well-connected group of prescribers who adhere to joint treatment plans.

Similarly, objective standard measures were also mainly used to measure relational continuity. However, these measures may be better suited to measuring all COC dimensions than objective non-standard measures. First, they can capture aspects of relational continuity beyond the mere number of providers or prescribers, such as the distribution of visits to different providers. Second, while they were not commonly used, objective standard measures that are capable of measuring informational and management COC do exist (e.g., care density). When measuring relational continuity, included studies used a variety of COC indices and different cut-off values for high and low COC even when the same index was used. In fact, for the COCI, there is no agreed-upon cut-off value for high and low continuity [ 74 ]. This makes it more difficult to compare results between studies.

Few studies used subjective COC measures [ 53 , 55 , 68 ]. While these patient-reported measures are more susceptible to bias than objective COC measures, subjective measures are a valuable supplement to objective measures relying on claims data. Overall, our findings on utilized measures of COC are consistent with other studies showing that objective COC measures referring to relational continuity are most commonly used [ 24 , 26 , 33 ].

The results indicate that there is no agreed-upon approach for measuring COC as a multi-faceted concept in the context of polypharmacy and medication appropriateness. Future research in this area should aim to measure all three dimensions of COC and use multiple COC measures to make comparing measures and their results easier. This means researchers should ensure that the measures used cover all COC dimensions. For example, studies may use a relational COC measure in concert with an informational and management COC measure such as care density or an appropriate subjective measure. However, it remains unclear to what extent these measures appropriately capture informational and management continuity. For instance, care density is only a surrogate measure for care communication and collaboration, based on the premise that certain aspects of coordination may be reflected and/or facilitated by patients seeing physicians whose patient panels significantly overlap [ 75 ]. It should also be taken into account that a high number of patients shared between physicians does not indicate that these physicians necessarily exchange (sufficient) information about their patients [ 75 ]. Therefore, care density is only able to examine conditions that are more or less favorable toward management and informational continuity (care coordination) [ 75 ]. This highlights the need for developing, validating, and using new COC measures referring to the management and information dimension of COC. In addition, researchers should use a combination of different types of COC measures, even within COC dimensions. This means using and comparing multiple COC indices, including those with different methodological approaches (e.g., dispersion, density, or sequence of doctor visits [ 33 , 38 ]) when utilizing objective standard measures. This was only done in three studies [ 43 , 44 , 52 ]. Furthermore, many objective standard measures of relational continuity are calculated from values commonly used as objective non-standard measures. In such cases, researchers should report and compare the values and associations with polypharmacy/MARO for both the objective standard and objective non-standard measures. This was only done in two studies [ 48 , 57 ]. Finally, sensitivity analyses may be appropriate when using COC indices to acknowledge methodological differences in operationalization.

Similar conclusions can be drawn from an analysis of the data types used by the included studies. A large majority of studies used claims data or similar data types to measure continuity, allowing researchers to reach very large sample sizes and compute objective standard and objective non-standard measures. However, COC indices based on claims data cannot fully capture the multiple dimensions of COC [ 33 , 76 ]. A small number of studies used survey data to measure continuity. While survey data alone is also inadequate to capture all three dimensions of continuity [ 77 ], future studies should use appropriate survey-based measures to complement claims-based measures to capture COC in all its facets [ 76 ]. This is particularly important when investigating the association between COC and polypharmacy or MARO, as research has shown discrepancies between COC measured through survey data and claims data [ 78 ].

4.2 Methodological Findings: Measuring Polypharmacy and MARO

Substantial differences existed concerning how polypharmacy and MARO were operationalized and analyzed. While most of the studies defined polypharmacy using a numerical threshold of more than five drugs, which is commonly used in the literature [ 7 ], studies differed concerning the timeframe in which the numerical threshold could be reached. For instance, some studies analyzed the number of drugs within a 1-year period, while others focused on the day of maximum prescriptions. This finding aligns with current research showing that polypharmacy continues to lack a universally accepted definition [ 7 , 79 ]. However, operationalizations based solely on numerical data do not adequately capture the complexity of the problem and make it difficult to assess the safety and appropriateness of drug therapy in clinical practice. For instance, using multiple medications is not necessarily harmful and associated with adverse health effects but may even be entirely reasonable and appropriate for some (multimorbid) patients. Thus, the use of strict numerical cut-offs to measure and operationalize polypharmacy has been criticized. Accordingly, some authors propose distinguishing between appropriate and inappropriate polypharmacy and placing more emphasis on qualifying the term polypharmacy rather than quantifying it [ 80 , 81 ]. However, there is little evidence on how to distinguish between appropriate and inappropriate polypharmacy [ 82 ]. In the absence of a uniform definition, studies should continue to use the five-drug threshold to ensure comparability across studies. However, researchers should perform sensitivity analyses with higher or lower thresholds to test the robustness of their results. Finally, future research should work toward developing a useable definition of ‘inappropriate polypharmacy’, moving away from strictly quantitative definitions. Regarding the operationalization of MARO, future research should aim to use agreed-upon definitions and operationalizations (particularly concerning DDI) to ensure the comparability of results.

4.3 Methodological Findings: Risk of Bias

Most studies had a low risk of bias ( n  = 15/24). These studies examined exposure and outcome based on register/claims data. Studies using questionnaire/interview-based data had a higher risk of bias, indicating that large claims databases can be useful for analyzing COC. However, this is due to the subjective measures used in the included questionnaire/interview-based studies and does not show that subjective measures are generally inappropriate for measuring COC. Instead, these results again highlight the importance of developing suitable and agreed-upon subjective measures for COC, especially the informational and management dimensions.

Additionally, several studies had a higher risk of bias because they failed to address time-dependent bias, a common methodological flaw in COC research [ 26 , 83 ]. Appropriate accounting for the relative timing of COC and outcomes was ensured by only 11 studies [ 43 , 44 , 45 , 47 , 50 , 51 , 52 , 55 , 60 , 65 , 69 ]. In terms of the study design, longitudinal studies had rather good quality compared with cross-sectional studies. However, well conducted, cross-sectional studies did exist [ 46 , 48 , 49 , 52 , 53 , 54 , 56 , 57 ]. Overall, differences regarding the methodological quality of included studies need to be considered when interpreting the results. Future studies should ensure that COC is measured before outcomes, or at least address the issue of relative timing with appropriate methods; aim to have a longitudinal design to investigate the long-term effects of COC on outcomes; and use register/claims data to reduce potential recall bias and to expand the study period at comparatively low cost.

4.4 Empirical Findings

Yet, despite the conceptual variety and differences in quality between studies, our findings suggest a strong association between COC and polypharmacy and between COC and MARO. These results yield that (i) lower COC increases the chance of polypharmacy and (ii) lower COC increases the chance of MARO such as PIM, PIDC, DDI, ADE, unnecessary drug use, medication duplication, and overdose. As shown by Weng et al. [ 50 ], the relationship between COC and inappropriate prescribing (DDI) is mediated by polypharmacy, indicating that polypharmacy itself is an important risk factor for several drug-related adverse events [ 84 ].

Our results contribute to the findings of Choi and Lee [ 34 ], who investigated the relationship of relational COC between patients and community pharmacy (CP) pharmacists. They showed that a high degree of relational COC between patients and CP pharmacists was associated with improved medication adherence. Patients who had visited a single pharmacy were more adherent to their medication regimen compared with those visiting multiple pharmacies. Moreover, a high level of relational continuity could lower inappropriate drug use and emergency department visits caused by adverse drug reactions [ 34 ]. Other studies also showed the importance of doctor–patient COC for safer medication management, demonstrating that higher COC was associated with higher medication adherence and compliance [ 85 , 86 , 87 ].

4.5 Implications for Research and Practice

Our findings have significant implications for health care research and practice. Concerning the operationalization and measurement of COC, our methodological findings highlight that researchers should (i) ensure that all three dimensions of COC (relational, informational, and management continuity) are covered by the COC measures used, (ii) use and compare different COC measures of the same type, (iii) use a combination of subjective and objective COC measures, and (iv) draw from a combination of claims data and patient-reported survey data when doing so. These steps will help researchers better understand and use the various tools available for measuring COC. In particular, future research should aim to identify or develop an appropriate and agreed-upon operationalization of COC, polypharmacy, and MARO to ensure the comparability of results. Researchers investigating the link between COC and outcomes such as polypharmacy or MARO should use longitudinal study designs where possible and give particular regard to the relative timing of exposures and outcomes.

Following these recommendations may also allow future research to improve health care practice regarding COC. Our findings indicate that low COC is a significant risk factor for polypharmacy and MARO, highlighting the need for appropriate interventions to improve COC. However, designing and targeting these interventions will require a more detailed understanding of the underlying causal links between the three dimensions of continuity and outcomes, such as polypharmacy or MARO. Overall, health care providers and researchers involved in intervention planning should acknowledge low COC as an important risk factor for polypharmacy/MARO and consider all three dimensions of COC when designing interventions. This contributes to the findings of Facchinetti et al. on the importance of developing interventions that address all continuity dimensions simultaneously [ 88 ].

4.6 Limitations

While several COC-related systematic reviews have been published, including various health-related outcomes [ 26 , 28 , 29 , 32 , 89 , 90 ], this review is the first to explore doctor–patient COC in polypharmacy and medication management. Nevertheless, some limitations of this review need to be considered. First, there was substantial heterogeneity between studies regarding the measurement and operationalization of exposure and outcomes variables. This allowed us to analyze the methodological approaches to measuring COC, polypharmacy, and MARO used by included studies, but complicated the comparison of empirical findings between different studies. Second, some studies had strong methodological flaws, such as the relative timing of the measurement of exposure and outcomes. Third, in terms of the generalizability of the results, population and health system-related differences need to be considered. However, despite different populations and health care systems studied, the empirical findings of the included studies were quite consistent. Fourth, the literature search was restricted to articles published in English and German. As a significant minority of included studies were from non-English and non-German speaking countries, it is likely that there are further relevant studies that we did not include. Fifth, we included only quantitative studies. Therefore, qualitative approaches to exploring the relationship between COC and polypharmacy/MARO could not be considered. Sixth, due to different operationalizations of MARO, the search strategy may not have been sufficient to identify all relevant studies on this topic. Seventh, another limitation is that this review and its methods were not registered in a review study registry (e.g., PROSPERO) before it was conducted. However, the methodological aspects were pre-specified in the work process and described transparently in this article. Finally, a meta-analysis of effect sizes across studies could not be conducted, given the heterogeneity of study characteristics.

5 Conclusion

This systematic review summarized evidence supporting the negative associations between COC and polypharmacy and between COC and MARO. Despite differences in the operationalization of COC, polypharmacy, and MARO, our findings suggest that improving COC is a promising approach to managing polypharmacy and preventing inappropriate prescribing. However, further research is necessary to develop agreed-upon definitions and operationalizations of the concepts involved, including operationalization of COC that covers all continuity dimensions and an appropriate definition of inappropriate polypharmacy. This will allow researchers and practitioners to design interventions targeting the specific causal links between different continuity dimensions and outcomes, such as inappropriate polypharmacy or MARO.

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Lampe, D., Grosser, J., Gensorowsky, D. et al. The Relationship of Continuity of Care, Polypharmacy and Medication Appropriateness: A Systematic Review of Observational Studies. Drugs Aging 40 , 473–497 (2023). https://doi.org/10.1007/s40266-023-01022-8

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Systematic review article, polypharmacy management in the older adults: a scoping review of available interventions.


  • 1 Department of Family Medicine, Medical University of Lodz, Lodz, Poland
  • 2 Department of Clinical Nursing, Faculty of Health Science, Wroclaw Medical University, Wroclaw, Poland
  • 3 IT Department, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy
  • 4 Psychology Unit, Istituti Clinici Scientifici Maugeri IRCCS, Montescano Institute, Pavia, Italy
  • 5 UCIBIO/REQUIMTE, Faculty of Pharmacy and Porto4Ageing, University of Porto, Porto, Portugal

Background: Polypharmacy paves the way for non-adherence, adverse drug reactions, negative health outcomes, increased use of healthcare services and rising costs. Since it is most prevalent in the older adults, there is an urgent need for introducing effective strategies to prevent and manage the problem in this age group.

Purpose: To perform a scoping review critically analysing the available literature referring to the issue of polypharmacy management in the older adults and provide narrative summary.

Data sources: Articles published between January 2010–March 2018 indexed in CINHAL, EMBASE and PubMed addressing polypharmacy management in the older adults.

Results: Our search identified 49 papers. Among the identified interventions, the most often recommended ones involved various types of drug reviews based on either implicit or explicit criteria. Implicit criteria-based approaches are used infrequently due to their subjectivity, and limited implementability. Most of the publications advocate the use of explicit criteria, such as e.g. STOPP/START, Beers and Medication Appropriateness Index (MAI). However, their applicability is also limited due to long lists of potentially inappropriate medications covered. To overcome this obstacle, such instruments are often embedded in computerised clinical decision support systems.

Conclusion: Multiple approaches towards polypharmacy management are advised in current literature. They vary in terms of their complexity, applicability and usability, and no “gold standard” is identifiable. For practical reasons, explicit criteria-based drug reviews seem to be advisable. Having in mind that in general, polypharmacy management in the older adults is underused, both individual stakeholders, as well as policymakers should strengthen their efforts to promote these activities more strongly.


Recently, polypharmacy (also called polytherapy or polypragmasy) became an important public health problem due to its far-reaching consequences, such as possible negative effects on individual health, as well as increased use of healthcare services and costs ( Fried et al., 2014 ). In particular, polypharmacy is known to cause a higher risk of adverse drug events and drug-drug interactions. It also often leads to medication non-adherence. All these provide negative health outcomes as well as increased risk of geriatric syndromes (e.g., cognitive impairment, or falls). This, in turn, leads to increased risk of hospitalization and institutionalization, as well as much greater health care expenditures ( Maher et al., 2014 ). Therefore, polypharmacy is considered to be “one of the greatest prescribing challenges” ( Payne and Avery, 2011 ).

Obviously, polypharmacy is not limited to older adults. Nevertheless, the highest prevalence of this scenario comes with older age. A nationwide cohort study in Sweden among individuals aged ≥65 has found prevalence of polypharmacy reaching 44%, and prevalence of extreme polypharmacy (defined as taking ten drugs or more) of 11.7% ( Morin et al., 2018 ). Data from the United Kingdom highlight that 20.8% of individuals with two clinical conditions have been prescribed four to nine medicines, whereas 10.1% of them—ten or more medicines. In patients with six or more comorbidities, relevant values were 47.7 and 41.7%, respectively, and these figures increased with age ( Barnett et al., 2012 ). In Poland, polypharmacy has been observed among 55.0% of the citizens aged 80+ ( Kardas et al., 2021 ). Scottish data show that around 35% of those aged 85 years and above receive more than ten medicines ( Stewart et al., 2017a ). A recent analysis of a large European cohort has found polypharmacy (defined as concurrent use of five or more medications) to be present in 32.1% of citizens aged 65 years or above, ranging from 26.3 to 39.9% across the studied countries ( Midão et al., 2018 ). High prevalence of polypharmacy in the older adults has also been observed outside Europe, e.g., in countries such as Brazil ( Pereira et al., 2017 ) and United States ( Quinn and Shah, 2017 ).

Thus, the burden of polypharmacy is a direct consequence of demographic challenge which, though observed worldwide, is particularly pronounced in Europe. According to Eurostat data, currently those aged 65 years or above, account for 19.2% of the European Union’s population, and this proportion is expected to rise up to 29.1% by 2080, whereas percentage of those aged over 80 years, is expected to increase even more dramatically—from the present 5.4–12.7% ( Eurostat (2015). People i, 2015 ).

The longer citizens live, the higher are the chances of multimorbidity which is defined by the World Health Organization as “the co-occurrence of two or more chronic medical conditions in one person” ( World Health Organization, 2008 ). Prolonged life expectancy, the privilege of people living in the 21st century, means much longer years lived with chronic conditions the number of which grows even more with age. Current statistics estimate that over 70% of people aged over 65 years are affected by multimorbidity ( National Guideline Centre, 2016 ). It has a major impact on healthcare systems, e.g., primary care physicians in England care for patients with multimorbidity in 78% of their consultations ( Salisbury et al., 2011 ), whilst in several other settings, e.g., geriatrics, this percentage may reach 100%.

Ageing and multimorbidity, i.e., two interlinked factors mentioned above, are to a large extent responsible for the observed rapid rise in global prevalence of polypharmacy ( Guthrie et al., 2015 ). However, the current paradigm of healthcare seriously increases the chances of polypharmacy in the older adults as well. Undoubtedly, it is a consequence of single-disease oriented guidelines promoting pharmacotherapy as a routine solution. This approach leads to undesirable effects, such as difficulties in integrating care in multimorbidity cases, poor communication between patients, carers and their multiple care providers, and a lack of patient-focused (rather than condition-focused) care plans ( Boyd et al., 2005 ; May et al., 2009 ). Unfortunately, the guidelines only seldom tend to address the complex nature of multimorbidity trying to address it from the patient’s perspective in order to prioritize certain conditions or treatments over the other ones, thus reducing the burden of prescribed drugs ( Montori et al., 2013 ; Farmer et al., 2016 ). Similarly, “defensive medicine” makes the initiation of therapy easy and always correct, contrary to a more conservative approach which accepts that not every condition is automatically the reason for taking a medication, thus giving both the prescriber and the patient more freedom in making their choices based on accepted priorities ( Austad et al., 2016 ).

Despite the significance of the problems created by polypharmacy in the older adults, this subject is only seldom tackled in European countries in a systematic way. An extensive search for polypharmacy guidance documents (both published in peer-reviewed journals and made available as grey literature) performed recently across Europe has identified only five European countries that actually have such documents targeting older patients ( Stewart et al., 2017a ).

There is a variety of tools aimed at reduction of inappropriate polypharmacy using either implicit (judgement-based) or explicit (item list-based) criteria ( Kaufmann et al., 2014 ). Unfortunately, their practical implementation in older adults care is very limited. Recent research shows that healthcare professionals (HCPs) are often either unaware of such tools or disregard them as not being user-friendly ( Mc Namara et al., 2017 ). For example, the use of various forms of drug reviews has been reported in half of 32 studied European countries only ( Bulajeva et al., 2014 ).

Under such circumstances, healthcare professionals should be supported and motivated to implement polypharmacy targeting interventions. Therefore, the overall aim of this paper was to summarize available information on the methods to prevent and manage polypharmacy in the older adults. Accepting the perspective of practical approach and pragmatic guidance to polypharmacy management, the objective of this scoping review was to map available interventions and more complex strategies, and discuss their implementability. The rationale behind the approach was a common belief that there is no “one-size-fits-all” solution for polypharmacy management in the older adults. Therefore, in order to help HCPs select an approach that would satisfy their requirements best and increase overall application of polypharmacy management, the literature search strategy was designed to identify the scientific publications detailing a broad spectrum of interventions available for polypharmacy management in the older adults. In order to reflect the state-of-the-art findings, the literature search was limited to items published from 2010 onward.

Materials and Methods

Search strategy.

In this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed ( Moher et al., 2009 ). The electronic databases, i.e., CINHAL, EMBASE and PubMed, were systematically searched in accordance with the predefined literature search strategy based on a various combination of keywords including “polypharmacy” and its equivalents, terms corresponding to a systematic approach to polypharmacy management, such as “intervention” etc., and various identifiers of older age. The Supplementary Online Material S1 provides the combination of search terms that were used to identify relevant publications.

Inclusion Criteria

Publications were included if: (A) they outlined interventions addressing polypharmacy (however, not implementation of guidelines) in the older adults in any of the following settings: 1) clinical practice, 2) health care systems, 3) scientific research; and (B) they were published in the years between 2010 and 2018. What is noteworthy is that the definition of an “intervention” was not explicit in order to allow for a broad spectrum of search results that could be of potential interest to the readers. Similarly, we accepted various definitions of the “elderly” used by the authors, not limited to the traditional convention defining the “elderly” as those aged 65 years or above. ( Orimo, 2006 ).

Exclusion Criteria

Articles were excluded if they: 1) were not peer-reviewed; 2) were written in a language other than English; 3) were not devoted to interventions addressing polypharmacy; or 4); did not present intervention descriptions in full details (e.g., letters, comments, conference proceedings, editorials, erratum, etc., as opposed to original articles, reviews, systematic reviews, randomized controlled trials and guidelines).

Study Selection

Studies meeting the inclusion criteria were initially selected, based on screening the titles and abstracts by one researcher (PL). Copies of full-text papers considered as potentially relevant after the first screening were then fully analysed independently by two researchers (out of the three: BJ-P, MK-M, and PL). In the case of different opinions on possible inclusion of an article into the study, the third author (PK) was consulted to reach a consensus.

Data Extraction Process and Analysis

The data was extracted from each eligible paper according to the predefined framework which included the source, year of publication, country of origin, type of the publication, definitions of polypharmacy used by the authors, target for intervention (i.e., multimorbidity or individual disease typical for elderly people), characteristics of intervention, settings, healthcare professionals involved in/suggested to deliver the intervention, and results of intervention implementation (for publications assessing implementation of interventions only). The extracted data are presented in the Supplementary Online Material S2 . Further elaboration of the extracted data involved grouping according to the predefined criteria and a statistical analysis with descriptive statistics. The final analysis of the extracted data took the form of a narrative, descriptive summary and synthesis.

Characteristics of Selected Studies

The literature search included 244 publications. Subsequently, 127 duplicates were removed, and the titles and abstracts of the remaining 117 articles were reviewed, which resulted in elimination of 67 papers that did not meet the inclusion criteria. A further detailed review of the full-text articles led to elimination of another paper. A final set of 49 articles that met the inclusion criteria was accepted for synthesis. For details of article screening and the exclusion process, see the PRISMA flow chart in Figure 1 . The identified publications originated from a variety of European as well as non-European countries and included original articles, reviews, systematic reviews, randomized controlled trials and guidelines. A few papers were focused on one specific disease characteristic for older people [e.g., diabetes ( Dunning, 2017 ), hip fracture ( Komagamine and Hagane, 2017 ), etc.], whereas a majority of the publications did not define the type of disease. One study was focused on the patients with multimorbidity ( Bokhof and Junius-Walker, 2016 ). All the reviewed studies were focused on elderly patients.


FIGURE 1 . PRISMA flow chart of the literature search and study selection. Note: * Excluded due to not detailing interventions to manage polypharmacy (56 items) or not meeting other eligibility criteria (e.g., not providing the details of the intervention, 11 items in total); # excluded for not meeting eligibility criteria (non-English-language publication).

Aims of Identified Interventions

Across the reviewed literature, some attention is paid to prevention of polypharmacy. Optimal or appropriate prescribing was advised as a general method of polypharmacy prevention ( Kaufman, 2011 ; Nobili et al., 2011 ; Cadogan et al., 2015 ; Cadogan et al., 2016 ; Cadogan et al., 2017 ). This recommendation, however, was not necessarily followed by detailed practical guidance. Only one publication provides recommendations on how to prevent polypharmacy in very specific patients, i.e., critically ill older adults who, when staying at an intensive care unit, are at risk of developing delirium ( Garpestad and Devlin, 2017 ). In fact, strategies of polypharmacy management identified in our search predominantly target correction of polypharmacy. Specific aims of relevant interventions include one or several out of the below-listed ones:

1. Reduction of polypharmacy (lowering the number of drugs prescribed and/or used)

2. Increasing the use of a recommended medication

3. Lowering the costs (drug costs, and/or overall healthcare system expenditures)

4. Enhancing patient adherence to medication

5. Increasing effectiveness of drug therapy (e.g., avoidance of hospitalisations, etc.)

6. Securing patient safety (e.g., avoidance of adverse drug reactions)

Targets of Identified Interventions

Although the role of patients is emphasized, and relevant recommendations include better patients’ health literacy and awareness of their complex multiple medication regimens ( Bokhof and Junius-Walker, 2016 ), patients are not perceived as those who actively initiate any formalised action against polypharmacy. In fact, it is also suggested that general practitioners (GPs) might support patients by “inviting” their contribution to polypharmacy and medication safety, as their awareness of the significance of their active role in addressing polypharmacy is currently very low ( Schöpf et al., 2017 ).

Thus, the reviewed literature supports the use of interventions targeting polypharmacy which are initiated by healthcare professionals. A suggested trigger to employ such an intervention is just presence of polypharmacy in an older adult. This advice, however, is not easy to implement due to current lack of common standard definition of polypharmacy. In fact, the authors adopted various existing definitions, as illustrated in Table 1 . Among them, the most commonly used definition of polypharmacy was taking concurrently five or more medications. However, in some publications other threshold values were also used, ranging from 1 to >9. Moreover, in several papers a qualitative approach to polypharmacy definition was preferred and the most common one was the imprecise definition describing it as “the use of a number of different medicines possibly prescribed by different doctors and often filled in different pharmacies, by a patient who may have one or several health problems” ( Kaufman, 2011 ; Nobili et al., 2011 ; Clyne et al., 2016 ; Dunning, 2017 ; Lin et al., 2018 ). Finally, in nine papers the operational definition of polypharmacy was not precisely detailed ( Planton and Edlund, 2010 ; Sergi et al., 2011 ; Mansur et al., 2012 ; Van der Linden et al., 2014 ; Yamanouchi et al., 2014 ; Cadogan et al., 2016 ; Garpestad and Devlin, 2017 ; Heaton et al., 2017 ; McNicholl et al., 2017 ) leaving it open to individual interpretation.


TABLE 1 . Definition of polypharmacy used in reviewed publications.

Who Should Provide a Polypharmacy Management Intervention

The reviewed literature pointed to a range of healthcare professionals who may or should provide polypharmacy addressing intervention. The most common setting in which polypharmacy management interventions were most successful was primary care and they were implemented either by GPs, or by primary healthcare team ( Kaufman, 2011 ; Nobili et al., 2011 ; Sabzwari et al., 2013 ; Bergert et al., 2014 ; Kann et al., 2015 ; Bokhof and Junius-Walker, 2016 ; Cadogan et al., 2016 ; Clyne et al., 2016 ; Sinnige et al., 2016 ; Cadogan et al., 2017 ; Franco et al., 2017 ; Schöpf et al., 2017 ; Tommelein et al., 2017 ). However, some interventions were provided at community or hospital pharmacies, by pharmacists alone, in the form of pharmaceutical care, or in cooperation with a physician, e.g., under an umbrella of collaborative physician-pharmacist medication therapy management (MTM) program ( Mansur et al., 2012 ; Patterson et al., 2012 ; Doan et al., 2013 ; Cooper et al., 2015 ; Jódar-Sánchez et al., 2015 ; Wilson et al., 2015 ; Cadogan et al., 2016 ; Chau et al., 2016 ; Jokanovic et al., 2017 ; Komagamine and Hagane, 2017 ; Malet-Larrea et al., 2017 ; McNicholl et al., 2017 ; McNicholl et al., 2017 ; Tommelein et al., 2017 ; Lin et al., 2018 ). Specialists who are perfectly prepared to take care of polypharmacy in the older adults are geriatricians, thus relevant interventions could be included in the geriatric consultation ( Eyigor and Kutsal, 2012 ; Kojima et al., 2014 ; Van der Linden et al., 2014 ). Finally, other settings also allow for polypharmacy interventions which have been successfully provided in various hospital settings such as teaching hospitals ( Harugeri et al., 2010 ; Urfer et al., 2016 ; Lin et al., 2018 ), acute care hospitals ( Komagamine and Hagane, 2017 ), acute geriatric wards ( Mansur et al., 2012 ; Van der Linden et al., 2014 ). It is worth emphasizing that such interventions are also advisable in the case of residential aged care facilities ( Kojima et al., 2014 ; Jokanovic et al., 2017 ). Some studies highlight the need for an interdisciplinary approach, e.g., in order to execute Comprehensive Geriatric Assessment (CGA), the authors suggest an interdisciplinary team comprising nurses, occupational and physical therapists, social workers, general practitioners and geriatricians ( Sergi et al., 2011 ).

How Often Should an Intervention Be Provided

The available literature does not pay much attention to the question of how often interventions targeting polypharmacy should be repeated. According to one publication included in our review, GPs should scrutinize senior people’s medications on each consultation whenever a patient meets the criteria of polypharmacy ( Dunning, 2017 ). The recently published WHO report on medication safety in polypharmacy generally recommends that “appropriate polypharmacy should be considered at every point of initiation of a new treatment for the patient, and when the patient moves across different health care settings.” ( World Health Organization, 2019 ) As for residents of care homes, the NICE guidelines advise that an interval in medication reviews “should be no more than 1 year” and that many residents may require reviews more often. ( National Institute for Health and Clinical Excellence, 2015 ) Obviously, practical implementation of relevant interventions is limited by many factors, such as the availability of qualified staff, a paradigm of the local healthcare system, reimbursement of the intervention, etc.

Details of Identified Interventions

Full list of all types of interventions identified in the reviewed studies is presented in Table 2 .


TABLE 2 . Polypharmacy interventions identified in reviewed publications.

For obvious reasons, effective management of polypharmacy should start with its prevention. Appropriate prescribing is the method that undoubtedly satisfies this expectation. Thus, a thorough risk–benefit analysis of each medicine should be made whenever any drug is prescribed ( Kaufman, 2011 ; Nobili et al., 2011 ; Bokhof and Junius-Walker, 2016 ; Cadogan et al., 2016 ; Cadogan et al., 2017 ) . If, however, polypharmacy is already in place, deprescribing is another logical step to be taken, as suggested by several publications ( Bokhof and Junius-Walker, 2016 ; Sharma et al., 2016 ; Urfer et al., 2016 ; Jokanovic et al., 2017 ; Kaufman, 2017 ; Komagamine and Hagane, 2017 ; Schöpf et al., 2017 ). Although not limited to, the concept, aims, and practice of deprescribing overlap much with polypharmacy management. One of its definitions describes it as “the process of withdrawal of an inappropriate medication, supervised by a health professional with the goal of managing polypharmacy and improving outcomes” ( Reeve et al., 2015 ). This broad concept has been supported by specific guidance, e.g., patient-centred deprescribing strategy, proposed in one of the publications ( Kaufman, 2017 ). The strategy includes five steps: 1. comprehensive medication history; 2. identification of potentially inappropriate medications; 3. determination if medication can be ceased and prioritisation; 4. planning and executing withdrawal; and finally, 5. monitoring, support and documentation.

A practical implementation of the deprescribing process in older adults may be guided by four crucial questions as proposed by Page et al. (2016) , i.e.:

1. Is it an inappropriate prescription (e.g., a case without clear indication, obvious contraindications, or a consequence of “prescribing cascade”)?

2. Does the drug lead to adverse effects or interactions that outweigh symptomatic effects or potential future benefits?

3. Are drugs taken for symptom relief but the symptoms are stable?

4. Is drug intended to prevent serious future events but the potential benefit is unlikely to be realised due to limited life expectancy?

If the answer to any of these questions is positive, then the medication should be considered for deprescribing.

No matter whether deprescribing comes under its own name, or not, it is the major aim of corrective polypharmacy addressing interventions. Perhaps, the most well-known and crucial part of this process is a drug review.

Indeed, various forms of drug reviews and identification of potentially inappropriate medications were the most often suggested procedures according to our literature review (see Table 2 ). Drug reviews might be stand-alone procedures. However, they might be also embedded in more complex programs, being the core item of e.g., Comprehensive Geriatric Assessment ( Sergi et al., 2011 ; Eyigor and Kutsal, 2012 ; Sharma et al., 2016 ; Pazan and Wehling, 2017 ), pharmaceutical care ( Patterson et al., 2012 ; Cooper et al., 2015 ; Tommelein et al., 2017 ), and collaborative physician—pharmacist medication therapy management ( Lin et al., 2018 ).

Effective polypharmacy management with drugs reviews may require that several additional factors are taken into consideration, such as:

• Settings: hospital vs. outpatient, in the latter case: primary care vs. specialised care (e.g., outpatient geriatric clinic).

• A healthcare professional to perform drug review (e.g., a physician, pharmacist, nurse, other)

• The purpose and related scope of the drug review

• Criteria to guide drug review (implicit vs. explicit)

• A tool to base drug review on (comprehensive vs. limited in scope; validated vs. non-validated)

• A method used for drug review (manual vs. supported by a computerised clinical decision system)

Depending on their purpose, drug reviews may have a different scope. Therefore, current literature distinguishes three types of such reviews ( Shaw and Seal, 2015 ; Clyne et al., 2008 ):

• Type 1—Prescription review, performed often without the patient, addressing technical issues relating to the prescription (e.g., duplications, possible drug-drug interactions etc.)

• Type 2—Concordance and compliance review, performed most often in the patient’s presence, addressing issues relating to their medicine-taking behaviour

• Type 3—Clinical medication review, requiring the patient’s presence, addressing issues relating to their use of medicines in the context of their clinical conditions

Drug reviews are advised to be undertaken by all physicians and particularly frequently by GPs ( Kaufman, 2011 ). Pharmacists seem to be competent to carry out drug reviews as well. The medication review with follow-up (MRF) performed by pharmacists in community pharmacies provided a decreased number of prescribed medicines, reduction of emergency department visits and hospitalizations, improvement of quality of life of patients, and it also lowered the mean daily cost of prescribed medication ( Jódar-Sánchez et al., 2015 ; Malet-Larrea et al., 2017 ). In Spanish study, the cost analysis showed that MRF saved the national health system € 97 per patient in 6 months. It was calculated that for every 1 euro invested in MRF a service returned a benefit of € 3.3 to € 6.2 ( Malet-Larrea et al., 2017 ) .

In practical terms, drug reviews are usually formalised, and driven by either implicit (judgement-based), or explicit criteria. Due to their usefulness, explicit criteria-based screening tools are used most often to help systematic assessment of drug safety and appropriateness. In publications covered by this review, the tools most often recommended for use in clinical practice were the ones based on such criteria, i.e., STOPP/START criteria, Beers Criteria and MAI index. A short overview of these three instruments is presented below.

Beers Criteria

In 1991, a geriatrician Mark H. Beers published criteria on potentially inappropriate use of medication in the older adults agreed by experts ( Beers, 1997 ). After a few updates, the last version in 2019 (stewarded by the American Geriatrics Society) included not only evidence-based recommendations on drugs to be avoided, but also guidance on which medication should be used with caution, expected to cause significant drug-drug interactions or be reduced depending on the kidney function in seniors. ( By the 2019 American Geri, 2019 ) These are the longest running explicit criteria for potentially inappropriate medication for older patients with five updates since the first publication. They are useful as a clinical, educational and public health tool developed to be used in conjunction with healthcare providers. However, the main disadvantage of Beers criteria is the fact that two large European studies have shown a lack of their association with adverse drug reactions ( Onder et al., 2005 ; Laroche et al., 2007 ). Due to a large number of presented drugs, it is a challenge to create a simple checklist using these criteria. Also, additional software is required to take full advantage of its potential ( Levy, 2017 ). It should be emphasized that being of American origin, Beers criteria may include or miss medications used or not in Europe ( O’Mahony, 2019 ).


Proposed for the first time in 2008 by an Irish geriatrician Denis O’Mahony and his colleagues, it is a list of potential prescribing omissions (underprescribed drugs) and potentially inappropriate medications for seniors. In its second version published in 2015, the list included revised criteria included in the first version divided into groups depending on the body systems approved by 19 experts from 13 European countries . ( O’Mahony et al., 2015 ). Its definite advantage is the evidence for correlation with reduction of adverse drug events . ( Hamilton et al., 2011 ). They are endorsed and used by several European societies including the National Institute for Clinical Excellence (NICE) and the United Kingdom Royal College of General Practitioners ( O’Mahony, 2019 ). However, these criteria (currently planned for 5-year periods) ( O’Mahony, 2019 ) need updating, and just like other explicit criteria (e.g., Beers) they cannot evaluate drug therapy omission, adherence, life expectancy, issues related to comorbidities or patient preferences. Some studies show that they ignore a majority of drug-related problems in seniors ( Verdoorn et al., 2015 ).

Medication Appropriateness Index

In 1992, a clinical pharmacist Joseph Hanlon and a geriatrician Kenneth Schmader proposed criteria in a form of ten questions enabling assessment of drugs taken by a patient. ( Hanlon et al., 1992 ) By providing an answer to each question based on a three-point scale (“A” being appropriate, “B” being marginally appropriate, and “C” being inappropriate), appropriateness index can be calculated for each drug. A weighting system for each MAI question has also been developed. In order to obtain a total MAI score per person, the scores for individual drugs were summed up ( Hanlon et al., 1992 ). This method was quite easy to perform; therefore, it was employed in multiple studies. It also considered drug-drug or drug-disease interactions. However, its main disadvantage was the time needed for answering the questions. It took 10 minutes per drug, which ( Hanlon et al., 1992 ) made it impossible to use MAI in a busy outpatient clinic without application of computer software. Moreover, patient medication adherence was not included. The MAI score did not help the clinician to prioritize which drugs should be changed, neither did it provide assistance in how to modify drug regimens to avoid adverse drug withdrawal events that could occur in older adults. ( Hanlon and Schmader, 2013 ).

Along with the validated reliable instruments, we have identified three studies based on the development and/or testing of new screening tools ( Doan et al., 2013 ; Van der Linden et al., 2014 ; Tommelein et al., 2017 ). One of them was focused on development and validation of RASP checklist to systematically identify Potentially Inappropriate Medications (PIMs) in the older adults ( Van der Linden et al., 2014 ). The second study used GheOP³S tool for identification of potentially inappropriate prescribing (PIP) in community-dwelling older people on polypharmacy ( Tommelein et al., 2017 ). The third one analysed CYP-mediated patients’ drug-drug interactions ( Doan et al., 2013 ). Detailed characteristics of these studies are provided in the Supplementary Online Material S3 .

Implicit criteria-based approaches are usually employed by more complex strategies, such as comprehensive geriatric assessment (CGA). Typically, CGA includes a drug review, performed with the involvement of interdisciplinary team comprising nurses, occupational and physical therapists, social workers, general practitioners and geriatricians ( Sergi et al., 2011 ). With the use of several evaluation tools exploring cognitive, clinical, nutritional, functional and social parameters, the team conducts a global assessment of an older adult with the primary aim of drug therapy optimisation and correction of medications used for untreated or under-treated conditions ( Sergi et al., 2011 ).

It is noteworthy that some publications advised concurrent use of more than one screening tool. For example, one review ( Planton and Edlund, 2010 ) suggested the use of both ARMOR (Assess, Review, Minimize, Optimize, Reassess) and Beers criteria, along with the recommendation to avoid drugs covering side effects of other drugs (i.e., the so-called “prescribing cascade”), whereas another one suggested the use of two explicit-based approaches, i.e., Beers and STOPP criteria ( Levy, 2017 ).

Drug reviews can be further facilitated by implementing specific computerised decision support systems and mobile applications which most often use one or many validated screening tools, at first those based on explicit criteria. Such an approach proved to be an effective element of primary care and pharmaceutical care, leading to reductions in inappropriate prescribing ( Patterson et al., 2012 ; Cooper et al., 2015 ). Multidimensional geriatric assessment could be also improved by dedicated IT solutions providing on-line access to information on patients, alerts indicating inappropriate drugs prescribed, assessment of the effects of accompanying diseases, reviewing potential drug-drug interactions, etc. ( Eyigor and Kutsal, 2012 ).

Comprehensive Strategies

Our search revealed comprehensive strategies described in dedicated guidelines. One of these, focused on geriatric patients on multimedication ( Bergert et al., 2014 ), was designed especially for GPs. They identified eight key steps as components of appropriate prescription process:

Step 1. Patient evaluation and collecting information

Step 2. Medication review

Step 3. Agreeing with patients on treatment objectives

Step 4. Prescription decision

Step 5. Communication and obtaining patient agreement

Step 6. Drug dispensing

Step 7. Medication usage

Step 8. Monitoring and assessment

As for medication review in Step 2, these guidelines suggest the use of several instruments, including MAI, STOPP/START and PRISCUS. It is noteworthy that, in Step 3, after agreeing overall objectives of the treatment with the patient, along with their expectations for a pharmaceutical treatment, a GP is supposed to prescribe a drug (Step 4), communicate this to the patient, and obtain their agreement (Step 5).

Being one of only very few well-organized polypharmacy management programs in Europe ( Stewart et al., 2017a ), the NHS Scotland Polypharmacy Guidance ( Wilson et al., 2015 ) offers probably the most complete guidance to polypharmacy management, as evaluated by our search. This guidance accepts a patient-centred approach to ensuring safe and appropriate use of medicines in polypharmacy. Therefore, it advocates a drug review process that should be focused on the patient as a whole rather than a jigsaw of conditions. The updated third edition of the guidance, published in 2018, provides a holistic model of care based on a comprehensive approach to medication review and provides healthcare professionals with practical tips to improve prescribing in polypharmacy and make it less problematic ( Scottish Government Polyp, 2018 ). This approach may be easily adopted to the need of polypharmacy management in the older adults ( Wilson et al., 2015 ). It recommends that clinicians step back from the usual process of chronic condition management to specifically consider the challenges of multimorbidity. They should realize that patients need a “multimorbidity focus” and initiate a process that enables patients to prioritize their own care needs.

In practical terms, the guidance is composed of seven steps to follow (see Table 3 ). It starts with establishing treatment objectives in cooperation with the patient (Step 1), and it is followed by identification of essential (Step 2) and unnecessary drugs (Step 3). Then, it is checked whether therapeutic objectives have been achieved (Step 4), which is followed by identification of potential or actual adverse drug reactions (Step 5). At the end of the process it is verified whether therapy costs can be minimized (Step 6) and checked if the patient is willing and able to receive drug therapy as planned (Step 7). This model provides a cohesive structure for a polypharmacy management process that is holistic, patient-centred and applicable to older adults across a range of health care settings. It should be emphasized that this model is not based on any specific explicit criteria-based tools. Instead, it uses its own set of potentially unnecessary drugs.


TABLE 3 . An overview of key considerations of 7 Steps of NHS Scotland Polypharmacy Guidance, 3rd edition [from ( Wilson et al., 2015 ), with modifications].

This approach is well-designed and based on strong evidence, however, it is also time—consuming. List of medications that should be considered by healthcare professionals following Steps from 2 to 7 includes almost 100 drugs, groups of drugs and scenarios. This might be a serious disadvantage, especially in primary care settings. Busy practitioners may not necessarily be able to manage that big load of data. To overcome this limitation, in Scotland, since 2013 pharmacists have been funded to work in general practice and support appropriate polypharmacy management ( Mair et al., 2019 ). Recently, an application has also been made available for clinicians to help practical realization of this process, along with a toolkit for patients taking multiple medicines, as well as their carers to support self-management and shared decision-making during consultation and medicine reviews ( The Scottish Government Polypharmacy, 2018 ).

It is noteworthy that from the interventions described above, several ones were analysed and checked in order to confirm their effectiveness in clinical outcomes in randomized controlled trials, interventional or prospective studies. They included several interventions, e.g., assessment of appropriateness of polypharmacy ( Komagamine and Hagane, 2017 ; Lin et al., 2018 ), drug reviews ( Jódar-Sánchez et al., 2015 ; Malet-Larrea et al., 2017 ; McNicholl et al., 2017 ) or checklists improving quality of drug prescription ( Urfer et al., 2016 ). A complex intervention to be used in a nursing home (covering a drug list review, identification of potentially inappropriate medications using the Beers criteria, potential drug-drug interactions and contraindicated medications using the Epocrates online drug-drug interaction program) has been assessed in a prospective study which demonstrated a decrease in potentially inappropriate medications, contraindicated drugs, and medication costs. ( Kojima et al., 2014 ) Characteristics of the studies providing evidence of effectiveness for selected interventions that have been identified in our search are presented in the Supplementary Online Material S4 .

Our review clearly shows that current scientific literature devotes a lot of attention to polypharmacy, not only in its general aspect, but particularly focusing on older adults. Consequently, various potentially useful approaches to polypharmacy management have been described, ranging from narrow-focused screening tools up to comprehensive programs and complex strategies. This large variety of solutions enables healthcare professionals to adopt polypharmacy-addressing interventions that suit their needs and preferences, taking into account specificity of the clinical scenario. On the other hand, it may lead to obvious confusion in less experienced medical staff who, in their busy daily practice, may not find enough time or motivation to learn and implement a new service which might be certainly time-consuming. Indeed, there is evidence that the uptake of available strategies is more than limited ( Mc Namara et al., 2017 ).

Theoretically, the most effective polypharmacy strategy could be appropriate prescribing. If each and every drug initiated in a patient satisfied the criteria of appropriate prescribing, the multidrug therapies could be avoided, and the prevalence of polypharmacy would reduce. Unfortunately, the current fragmented architecture of the healthcare systems, and single disease-oriented clinical guidelines do not help practical implementation of this concept ( Farmer et al., 2016 ). Instruments designed to promote appropriate prescribing are mostly based on implicit criteria and thus not easy to implement, particularly in the digital version.

A very interesting finding of our review was that current literature does not perceive the patients as those who take care of their therapies in terms of initiating activities aimed at reduction of inappropriate polypharmacy. Apart from the NHS Scotland Polypharmacy Guidance, which takes the patient’s perspective into account along the whole cycle of polypharmacy management, most of other publications reserve a much less important role for the patient making them an object rather than a subject of relevant interventions. In the light of current limited use of available tools by healthcare professionals, this paradigm perhaps needs to be changed. Being provided with necessary knowledge, even an older adult may be an important ally for HCPs in adoption of polypharmacy management interventions.

In absence of patients’ pressure to get involved in polypharmacy issues, healthcare professionals are expected to self-initiate relevant activities. Here again, available literature does not help much, not providing a clear message on when to consider such an activity, and how often to include it in routine care. Perhaps, the most frustrating problem is current lack of uniform definition of polypharmacy, which not only hinders implementation of available interventions, but also makes their benchmarking much more elusive ( Masnoon et al., 2017 ).

The most common operational definitions of polypharmacy, applied in the reviewed publications, were based on the number of concurrently prescribed and/or used drugs, with five and more being the most frequent option. This, however, deserves a comment. Although polypharmacy has numerous negative consequences, in some cases is desirable. Perhaps, for every patient there is an optimal number of drugs to be used (e.g., for hypertension to be controlled according to certain recommended levels, often two or more medications are required). It results from a rational compromise between the benefits of providing evidence-based therapies for particular conditions, and the negative consequences of using too many drugs at the same time. Thus, “appropriate polypharmacy” or “optimal polypharmacy” should be distinguished from inappropriate one ( Rankin et al., 2018 ). Unfortunately, this distinction is subject to case-by-case approach. Therefore, it may cause confusion, as it cannot be concluded with a simple uniform threshold that would be suitable for everyone, which dichotomizes the number of drugs used concurrently to be either acceptable or too high ( Masnoon et al., 2017 ).

Our findings undoubtedly show that available interventions might be successfully implemented by a range of healthcare professionals, first of all GPs, pharmacists, and geriatricians. Some tools are dedicated or are most suitable for each out of these groups [e.g., recommendations for treating adult and geriatric patients on multimedication designed by and for GPs ( Bergert et al., 2014 )], whereas others are much more generic, and might be implemented across different settings [e.g. STOPP/START ( O’Mahony et al., 2015 )].

Our results show that various forms of drug reviews are particularly often used for polypharmacy management in the older adults. However, despite an obvious value of drug reviews, they are not necessarily employed routinely in clinical practice. On the contrary, in Europe, various forms of these reviews were reported in only 16 out of 32 studied countries ( Bulajeva et al., 2014 ). Most often, medication reviews were reported to be carried out in hospital settings (14 countries), followed by 13 countries reporting implementation of such a procedure in community settings, and only six in nursing homes. In community settings, those were mostly reviews targeting prescription and verifications of patients’ medicine-taking behaviours (reported in nine and 11 countries, respectively), and much less often, medication reviews in the context of patients’ clinical conditions (reported in six countries only). Another important question is which approach to choose to guide the drug review. A systematic review of tools to assess potentially inappropriate prescribing found that out of 46 different instruments identified, 39 did not have any validation in clinical settings ( Kaufmann et al., 2014 ).

From a practical point of view, the core assumption on a strategy used for drug review is very important. According to the applied criteria, approaches may be divided into two different categories, i.e., those based on implicit and explicit criteria. Strategies based on implicit criteria involve highly individualized clinicians’ assessments relying mostly on their experience. These strategies are designed usually as protocols, algorithms or concepts examples of which are ARMOR ( Haque, 2009 ) or the Prescribing Optimization Method ( Drenth-van Maanen et al., 2009 ). Implicit criteria are usually short and concise. However, since they depend on clinicians’ knowledge and experience, they are highly subjective and thus, of limited applicability across patient populations, or in benchmarking ( Levy, 2017 ). Last but not least, implementation of these strategies is very limited by the fact that they are extremely time-consuming. For example, comprehensive geriatric assessment has proved effective in reducing the number of prescriptions and daily drug doses ( Sergi et al., 2011 ). On the other hand, it takes a lot of time, particularly when performed face-to-face with the patient ( Martin-Khan et al., 2016 ). For all these reasons, this approach is not often used in clinical practice.

The other type of strategies aimed at reducing polypharmacy is based on explicit criteria. These are much easier to use, straightforward criteria which allow for objective elimination of inappropriate drugs, consisting mostly of lists of medications to be excluded from a patient’s treatment regimen. Most well-known examples of such an approach illustrated by our review are Beers ( By the 2019 American Geri, 2019 ) and STOPP/START criteria ( O’Mahony et al., 2015 ). It is noteworthy that explicit criteria are those which can be particularly well embedded in computer decision support systems and relevant applications. Interestingly, our findings show that explicit STOPP/START and Beers criteria are the validated tools most often used in polypharmacy management in the older adults. However, even these criteria are not generally accepted as a “golden standard”. On the contrary, they are criticized for not listing a relevant number of drug-related problems ( Verdoorn et al., 2015 ) and a limited clinical value ( Parekh et al., 2019 ). Some authors suggest that they should be used in a complementary fashion to improve detection of adverse drug reactions ( Brown et al., 2016 ). Actually, some decision support systems use both these sets of criteria in parallel ( Monteiro et al., 20192019 ). Moreover, practical use of these criteria might be difficult. A recent systematic review on identifying potentially inappropriate prescribing in older people with dementia found that out of 15 studies using the Beers criteria, as many as 13 did not use the full tool ( Hukins et al., 2019 ). Due to the large number of potentially contraindicated medications listed (114 recommendations in the START/STOPP and 90 in the Beers), the use of these criteria is particularly limited in primary care ( Croke, 2020 ).

Complex and time-consuming nature of polypharmacy management encourages the use of various decision-support systems. Indeed, a rising number of computer decision-support systems and dedicated applications is available to help clinicians manage polytherapy in real life conditions of busy practice ( Eyigor and Kutsal, 2012 ; Patterson et al., 2012 ; Patterson et al., 2014 ; Cooper et al., 2015 ; Bokhof and Junius-Walker, 2016 ; Sinnige et al., 2016 ). Of course, such solutions possess some disadvantages also: they produce dozens of alerts, of which some are of low clinical usefulness, and therefore, subject to overriding ( Knight et al., 2019 ).

Unfortunately, even the availability of such enablers does not guarantee frequent implementation of polypharmacy management mechanisms. A good illustration of the problem is the case of the German FORTA (“Fit fOR The Aged”) guidelines. Originally released in Germany in 2008 as a tool for aiding physicians in screening for unnecessary, inappropriate or harmful medications and drug omissions in older patients in an everyday clinical setting ( Wehling, 2009 ), it was validated in a clinical trial ( Wehling et al., 2016 ), and turned into the application ( Pazan and Wehling, 2017 ). However, a study conducted in 2018 in general practitioners in Baden-Württemberg, Germany revealed that out of 872 surveyed GPs, 39 knew the FORTA list, and 15 declared to use the FORTA App only ( Meyer and Wehling, 2020 ).

This scoping review possesses several limitations. First of all, it was limited to English language publications, and thus, articles published in other languages were excluded. Moreover, among a number of approaches available for polypharmacy management, we were not able to prioritise one over the other, due to the lack of objective benchmarking criteria. Nevertheless, we believe that comprehensive review of available methods provided in this paper will help interested stakeholders make their own choices, and thus, meet the aim of this exercise.

This scoping review showed a variety of approaches being suggested for and/or employed for the management of polypharmacy in the older adults. These approaches vary in their replicability, complexity, and applicability. The most often recommended ones were various types of drug reviews, guided by either implicit or explicit criteria. Of these, implicit criteria based approaches are used infrequently due to their subjectivity, and limited practical implementability. To the contrary, most of the reviewed publications advocated the use of explicit criteria-based approaches. However, their practical applicability is somehow limited due to very long lists of potentially inappropriate medications covered. To overcome this, that sort of criteria are often embedded in clinical decision support systems.

Our results show that currently, no gold standard exists for polypharmacy management in older adults, and various approaches are used in parallel. Depending on the purpose of drug review, its settings, and available time, the users are free to employ one of existing interventions and/or tools. For practical purposes, employing a drug review based on one of the available explicit criteria seem to be the best choice. Having in mind that in general, polypharmacy management in the older adults is underused, both individual stakeholders, as well as policymakers should strengthen their efforts to promote these activities more strongly.

Data Availability Statement

The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding author.

Author Contributions

Contribution of each author is equal regarding preparing the manuscript. BJ-P, PK, MK-M and PL performed the search of the literature.

This work was partially funded by the Erasmus + Programme of the European Union, under the Skills4Adherence project (Grant number 2017-1-PL01-KA202-038672). KP received speaker’s honoraria from Aflofarm, Fresenius, Polpharma and Sandoz, outside this work. KP, M-KM, LP got funding from a grant from The European Commission ERASMUS + Project Skills4Adherence (Grant Agreement Number: 2017-1-PL01-KA202-038672). Several authors got funding from a grant from European Union’s Horizon 2020 for GATEKEEPER project (grant agreement No 857223) (KP and LP), and European Union’s Health Programme for SIMPATHY project (663082) (KP and LP), outside this work.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2021.734045/full#supplementary-material

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Keywords: polypharmacy, elderly, older adults, adverse drug event, adverse drug reaction, explicit criteria, inappropriate prescribing, multimorbidity

Citation: Kurczewska-Michalak M, Lewek P, Jankowska-Polańska B, Giardini A, Granata N, Maffoni M, Costa E, Midão L and Kardas P (2021) Polypharmacy Management in the Older Adults: A Scoping Review of Available Interventions. Front. Pharmacol. 12:734045. doi: 10.3389/fphar.2021.734045

Received: 30 June 2021; Accepted: 26 October 2021; Published: 26 November 2021.

Reviewed by:

Copyright © 2021 Kurczewska-Michalak, Lewek, Jankowska-Polańska, Giardini, Granata, Maffoni, Costa, Midão and Kardas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: M. Kurczewska-Michalak, [email protected]

This article is part of the Research Topic

Evidence for Assessing Drug Safety and Drug Use in Older People

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Am Fam Physician. 2019;100(1):32-38

Related editorial: Deprescribing Is an Essential Part of Good Prescribing .

Author disclosure: No relevant financial affiliations.

Polypharmacy, defined as regular use of at least five medications, is common in older adults and younger at-risk populations and increases the risk of adverse medical outcomes. There are several risk factors that can lead to polypharmacy. Patient-related factors include having multiple medical conditions managed by multiple subspecialist physicians, having chronic mental health conditions, and residing in a long-term care facility. Systems-level factors include poorly updated medical records, automated refill services, and prescribing to meet disease-specific quality metrics. Tools that help identify potentially inappropriate medication use include the Beers, STOPP (screening tool of older people's prescriptions), and START (screening tool to alert to right treatment) criteria, and the Medication Appropriateness Index. No one tool or strategy has been shown to be superior in improving patient-related outcomes and decreasing polypharmacy risks. Monitoring patients' active medication lists and deprescribing any unnecessary medications are recommended to reduce pill burden, the risks of adverse drug events, and financial hardship. Physicians should view deprescribing as a therapeutic intervention similar to initiating clinically appropriate therapy. When deprescribing, physicians should consider patient/caregiver perspectives on goals of therapy, including views on medications and chronic conditions and preferences and priorities regarding prescribing to slow disease progression, prevent health decline, and address symptoms. Point-of-care tools can aid physicians in deprescribing and help patients understand the need to decrease medication burden to reduce the risks of polypharmacy.

As the size of the older adult population (those older than 62) and the number of younger people with complex health conditions have increased in the United States, polypharmacy has become a growing problem. 1 Polypharmacy has negative consequences for patients and the health care system ( Table 1 ) . 2 – 17 For example, patients taking more than four medications have an increased risk of injurious falls, and the risk of falls increases significantly with each additional medication, regardless of medication type. 18

Although there is no standard definition for polypharmacy, the definition widely used in the literature is the regular use of at least five medications. 19 This threshold does not account for whether the medications are appropriate per the Beers criteria. 20 Multiple medications may be indicated in patients with certain complex medical conditions, such as heart disease or diabetes mellitus. In these cases, polypharmacy may be appropriate. However, prescribing to meet disease-specific quality metrics could increase polypharmacy for patients with multiple medical conditions, which increases the risk of adverse consequences and increases the patient's financial burden. 21

Risk Factors for Polypharmacy

Polypharmacy is most recognized in older adults, because patients with one or more chronic conditions have longer medication lists. 21 , 22 Older adults with multiple subspecialist physicians and no primary care physician are particularly vulnerable to polypharmacy. Adults residing in long-term care facilities are also at risk, because they are more frail than community-dwelling populations and have multiple medical issues and cognitive impairment that often warrant pharmacologic treatment. Up to 91% of patients in long-term care take at least five medications daily. 23

Younger adults with chronic pain, such as fibromyalgia, or with developmental disabilities, especially those with additional chronic medical conditions, may experience polypharmacy because of multiple treatments and modalities. 22 Other conditions associated with polypharmacy in younger patients include diabetes, heart disease, stroke, and cancer. 24

A population often overlooked with regard to polypharmacy is patients with mental health conditions. 22 , 25 These patients are often prescribed psychotropic medications with adverse effects, and more medications may be added to mitigate side effect profiles. Although patients with chronic mental health and medical issues may require multiple medications to achieve maximal functioning and prevent disease sequelae, this appropriate polypharmacy can still increase the risk of adverse drug events.

Polypharmacy risk factors can occur at the patient level and at the health care system level ( Table 2 ) . 21 – 26 For example, poor medical record keeping can lead to polypharmacy if discontinued medications are not removed from the record and are refilled automatically or if a physician receives an automated refill request for a discontinued medication.

Assessment Tools for Polypharmacy

No validated tool or strategy has been proven superior in improving polypharmacy-related patient outcomes. Also, no one validated tool assesses all aspects of potentially inappropriate medication use or polypharmacy. 19 Based on a 2018 Cochrane review, it is unclear if interventions to reduce inappropriate polypharmacy improve patient-oriented outcomes. 19 Although interventions may reduce potential prescribing omissions, this needs to be validated in future trials. 19

Assessment tools may be explicit, implicit, or mixed. 27 Explicit tools, such as the Beers, STOPP (screening tool of older people's prescriptions), and START (screening tool to alert to right treatment) criteria, have rigid standards, and clear criteria assist with quick and easy decision-making. However, patient complexity is not considered in the decision-making process. 20 , 28 These tools allow for comparison of a patient's medication list to a set of potentially inappropriate medications and to check for medication duplication; medication and disease interactions; and medication adjustments required for certain disease states, such as renal impairment. The recently updated Beers criteria list potentially inappropriate medications by drug class and disease state. 20 The STOPP and START criteria are used together to identify medications that may be inappropriate (STOPP) and alternative medications that can be started to safely treat a disease (START). 28

Implicit tools, such as the Medication Appropriateness Index, are more time-consuming, because they are based on physician judgment rather than set criteria, but they are more patient-centered and consider patient complexity. Implicit tools are inherently limited by the physician's knowledge, experiences, and attitudes and are less reliable than explicit tools in clinical studies. 27 , 29 The Medication Appropriateness Index ( https://globalrph.com/medcalcs/medication-appropriateness-index-calculator/ ) includes 10 questions that address medication need; optimal therapy for diseases and conditions; medication duplications; appropriateness of dosage, formulation, and duration of treatment; medication and disease interactions; and directions for use. 30 Although the questions are clear and straightforward, it can take considerable time to apply the Medication Appropriateness Index to each medication prescribed.

Approach to Deprescribing

Physicians should identify and prioritize medications to discontinue and discuss potential deprescribing with the patient. 31 – 34 There are many definitions for deprescribing. One definition is: a systematic process to identify and discontinue medications in instances in which existing or potential harms outweigh potential benefits within the context of an individual patient's care goals, current level of functioning, life expectancy, values, and preferences. 31 Deprescribing discontinues medications, decreases medication dosages, and changes medications to optimize clinical outcomes. 31 , 35 – 38 Evidence is lacking that a structured approach to decreasing the absolute number of medications, as opposed to discontinuing potentially inappropriate medications, improves patient outcomes. 19 , 20 , 31 , 35 , 39 This is likely because deprescribing efforts are focused, patient-specific interventions with considerable variability in patient characteristics and medications used. Guidelines often discuss how to initiate therapies but rarely discuss when and how to discontinue them. Judicious prescribing is as important as judicious deprescribing ( Table 3 ) . 13 , 26 , 32 , 40

Physicians should view deprescribing as initiating a “therapeutic intervention” similar to initiating clinically appropriate therapy. 31 , 33 , 41 When deprescribing, it is imperative to consider patient/caregiver perspectives on goals of therapy, including views on medications and chronic conditions and preferences and priorities regarding prescribing to slow disease progression, prevent health decline, and address symptoms. 38 Only one-third of older adults specifically discuss health care decision-making priorities with their primary care physicians. 39 However, patients are more likely to consider deprescribing if the physician recommends it. 42 – 44 Physicians should also examine specific therapy goals at every patient visit—disease control (primary/secondary prevention, symptom control/management) vs. acute symptom management. 38 A specific follow-up plan for deprescribing should be developed with the patient. 31 , 33 , 38 , 41 Furthermore, practices and health systems should adopt streamlined approaches to medication reconciliation and tracking, because up-to-date medication lists might help identify potential medications for deprescribing and reduce physician, staff, and patient burden. Figure 1 presents an approach to deprescribing. 10 , 13 , 31 , 32 , 34 , 35 , 38 , 40

Once a medication reconciliation and deprescribing plan has been put into place, it should be considered at each visit as time allows and comprehensively reviewed at health maintenance visits. 19 , 20 , 29 , 32 There are several resources available that physicians can use at the point of care to help identify problems with medication prescribing ( Table 4 ) .

Challenges to Deprescribing

The benefits of deprescribing and shortened medication lists are recognized at the patient, physician, and system levels. However, time constraints, patient resistance, and lack of systematic support hinder acceptance of deprescribing as routine medical care. Patients may be reluctant to discontinue medications, even when presented with evidence that the medications are not beneficial and may cause physiologic harm and financial distress. Patients taking chronic medications may worry about conditions worsening and resist discontinuing medications despite new guidelines (e.g., concurrent use of opioids and benzodiazepines is now discouraged). 45 Patients taking medications prescribed by previous physicians may fear contradicting the original care plan by stopping their medications. 33 Automated refills of discontinued medications may confuse patients and delay deprescribing because of unclear communication.

Primary care physicians may feel pressured to address multiple issues per visit and may not have adequate time to counsel patients on polypharmacy or to deprescribe through shared decision-making. Additionally, pressure from patients who desire medications with unclear benefit may cause physicians to prescribe medications to minimize interruption in clinic workflow.

Patients with multiple prescribers may be reluctant for one physician to stop medications prescribed by another. Such deprescribing must occur with clear interphysician communication to formulate a comprehensive patient care plan.

Three professional organizations in the American Board of Internal Medicine Foundation's Choosing Wisely campaign (American Geriatrics Society, American Society of Health-System Pharmacists, and American Psychiatric Association) specifically mention polypharmacy and the need to review medications regularly, question the utility of adding new medications, and deprescribe when appropriate. 32 Such recommendations can persuade physicians to consider deprescribing and can reassure patients that deprescribing medications is evidence based and beneficial.

Data Sources: A PubMed search was completed using the key terms polypharmacy, multiple medications, risks, potentially inappropriate medications, and deprescribing. The search included randomized controlled trials, clinical trials, reviews, and meta-analyses, as well as case reports and evidence-based guidelines. Searches were also performed in the Cochrane Database of Systematic Reviews, UpToDate, the Canadian Task Force on Preventive Health Care, the ABIM Foundation's Choosing Wisely website, the Centers for Disease Control and Prevention guideline on prescribing opioids for chronic pain, and the U.S. Preventive Services Task Force recommendations. Search dates: July and August 2018, and February 2019.

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