Interventions to enhance in-home taking medication among older adults. A systematic review

Interventions to enhance in-home taking medication among older adults. 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A systematic review María Durán-Luque, María Rocío Robles-Muñoz, María Eugenia Velasco García, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6976707/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Global population ageing is linked to increasing multimorbidity and polypharmacy. This shift places pressure on caregivers, who often lack training and face challenges like medication mismanagement. Our objective was to collate scientific evidence on interventions to enhance medication management among multimorbid older adults living in the community. Methods: We conducted a systematic review and meta-analysis (PROSPERO: CRD42024513056) following PRISMA guidelines. PubMed, Web of Science, and ClinicalTrials.gov were searched up to July 9, 2024. Eligible studies were RCTs or quasi-experimental designs involving home-dwelling adults aged ≥60 years with ≥2 chronic conditions or ≥5 medications, assessing adherence or health outcomes, and ≥30 days of follow-up. Screening, data extraction, and quality assessment were performed in duplicate. Random-effects meta-analyses were conducted using R. ORs (95% CI) were calculated for binary outcomes and SMDs (95% CI) for continuous variables. Results: Of 7,648 citations, 43 studies met criteria. The Morisky Scale showed a favourable but non-significant effect (OR=1.45; 95% CI 0.86–2.44), and continuous measures showed no effect (SMD=0.00; 95% CI=-0.09–0.10; I²=9%). Readmissions showed a protective effect at medium-term (OR=0.41; 95% CI 0.25–0.69). ED visits were inconclusive due to heterogeneity. Primary care contacts showed a weak, non-significant effect. No effect was found for quality of life or mortality. DRPs and costs lacked conclusive evidence. Risk of bias was high in most studies. Conclusions: No intervention consistently improved adherence or outcomes. This review highlights evidence gaps, including standardised outcomes, long-term effects, economic evaluation, and methodological quality, to optimise future interventions and support evidence-based policymaking. Registration: This systematic review was prospectively registered in PROSPERO with the registration number CRD42024513056. aged frail elderly home environment multimorbidity polypharmacy drug therapy medication therapy management meta-analysis systematic review Figures Figure 1 Figure 2 Figure 3 Contributions to the literature Polypharmacy in older adults is associated with poor health outcomes and increased healthcare utilisation. The effectiveness of multidisciplinary interventions remains unclear due to low-quality evidence and a lack of robust trials. This study highlights the need for tailored, multifactorial approaches that involve caregivers and address the complexities of home care. The findings underscore the importance of standardised outcomes, subgroup analyses, and economic and long-term evaluations to support sustainable strategies and inform policy-making. Introduction Population ageing presents a significant health challenge in the 21st century (1,2). The United Nations projects a doubling of older adults (≥65 years) by 2050, comprising 16% of the global population (3). This demographic shift will lead to an increase in chronic diseases, multimorbidity, and polypharmacy (4). Multimorbidity, defined as the coexistence of ≥2 chronic conditions (5), affects over 50% of older adults worldwide, representing a public health concern (6) due to its association with reduced quality of life, high mortality, functional decline, and increased healthcare utilisation (7,8). These issues are compounded by polypharmacy, defined as the concurrent use of ≥5 medications (9), which affects 32.1% of Europeans aged ≥65 years (10) and increases the risk of adverse drug events, medication non-adherence, and ineffective treatment regimens (11,12). Healthcare systems, designed primarily for acute illnesses, struggle to meet the complex needs of patients with multimorbidity (13,14). Consequently, 80-90% of care required by older adults is provided at home by informal caregivers (15), often family members, predominantly women (15–17). Shifting family structures and increased female participation in the workforce have strained families' care capacity (15,18–20), leading to reliance on migrant women, particularly in high-income countries (15,20). These caregivers often lack formal training or adequate support, heightening risks of medication errors and adverse health outcomes (21). Medication safety is a global priority, and strategies to improve adherence have involved various healthcare professionals. However, most studies have focused on isolated aspects of adherence, and robust evidence remains limited, particularly for home-dwelling older adults (23–25). The Cochrane systematic review by Cross et al. highlighted this gap, reporting insufficient evidence on the effectiveness of interventions in this population, primarily due to limited trial quality (26). This review aims to collate updated evidence on interventions to improve in-home medication management among multimorbid older adults living in the community (independently or with caregivers), and to assess the quality and the strength of the resulting recommendations. Methods This systematic followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines (27) (Supplementary Table 1) and was prospectively registered in PROSPERO with the registration number CRD42024513056. Search methods A preliminary search was conducted in November and December 2023 using PubMed and Web of Science. The final search was updated on 9 th July 2024, applying the following strategy: (ELDERLY OR OLDER ADULTS OR DWELLING ADULTS OR CARER OR CAREGIVER) AND (POLYPHARMACY OR MULTIPLE MEDICATIONS) AND (MEDICATION MANAGEMENT OR MEDICATION ADHERENCE OR SAFE* MEDICATION) AND (HOME CARE OR TRANSITIONAL CARE OR PHARMACEUTICAL CARE) NOT CHILD*. References from included studies and prior systematic reviews were manually searched. ClinicalTrials.gov was searched using the keywords "Home-dwelling", "elderly", "polypharmacy" and "intervention”, without additional results. Studies published from 2000 onward were included without language restrictions. Inclusion and exclusion criteria Study design Eligible studies included Randomized Controlled Trials (RCTs) , cluster RCTs , quasi-experimental studies , interrupted time series (ITS) analyses, and pre/post studies. Pilot RCTs, observational studies, or qualitative research, and those with <30 days follow-up were excluded. Population The review focused on older adults at high risk of Drug-Related Problems (DRPs), living independently in the community or with their caregivers, including those recently discharged from hospitals or nursing facilities to home-based care. Participants had a mean age of ≥ 60 years, with ≥ 2 chronic conditions or using ≥5 long-term medications. Those institutionalised or whose medication was managed by home nurses were excluded. Community or transitional care settings were included; residential or nursing homes, or hospitals without post-discharge follow-up were excluded. Intervention Interventions aimed to enhance medication adherence or health outcomes; palliative care was excluded. Comparator Standard care practices were used as the comparator. Outcomes Studies that measured at least one of the following main outcomes were considered for inclusion: Adherence, measured by indirect methods, validated questionnaires (such as the Morisky Scale), pill count, electronic databases, Medication Events Monitoring System (MEMS) and medication ratios such as the Proportion of Days Covered (PDC) and Medication Possession Ratio (MPR). Other self-report adherence scales and indirect measures were also included. Adverse health outcomes: hospital (re)admissions, emergency department (ED) visits, length of stay, drug-related problems (DRPs), adverse drug events (ADEs), and drug withdrawal symptoms (ADWES) were considered. Although not in the PROSPERO protocol, healthcare encounters (e.g., outpatient visits, specialist consultations, and primary care contacts) were also included as adverse health outcomes because they may indicate health complications, medication management challenges, or overuse of healthcare services. Health-related quality of life (measured using the EuroQol-5D ), as well as elders' knowledge, attitudes, and behaviours related to medication; impact on functioning, cognition, urinary incontinence, falls, fractures, sleep quality, appetite, mortality, and costs related to pharmaceuticals and healthcare were considered as secondary outcomes. Studies reporting only secondary outcomes were excluded. Study selection Two authors (M.D-L., M.R.R-M.) independently screened studies by title/abstract using the Rayyan web tool for systematic reviews, applying eligibility criteria. Relevant full-text articles were retrieved and assessed for eligibility, with the authors blinded to each other's decisions. Disagreements were resolved through discussion, and a third author (C.G-P, A.B-C.) was consulted if necessary. Excluded studies were listed in an Excel spreadsheet with reasons (Supplementary table 2). Selected studies were imported into Mendeley Reference Manager. Data extraction Data extraction was performed independently by two review authors (M.D-L., M.R.R-M.). Discrepancies were resolved through discussion or consultation with a third author (C.G-P., A.B-C.) if necessary. All data were tabulated using a pre-designed Excel spreadsheet. Quality assessment Risk of bias was independently evaluated by two authors (M.D-L., M.R.R-M.). We used the updated Cochrane Risk of Bias 2 (ROB 2) (28) for randomised studies and the Risk Of Bias In Non-randomised Studies of Interventions (ROBINS-I) (29) tool for non-randomised studies. To visually summarise the risk of bias, the robvis tool was employed. Studies were rated as high, low, or some concerns. Quantitative analysis Meta-analysis was conducted using a random-effects model due to anticipated heterogeneity across studies. The analysis was performed in R software version 4.4.1, specifically using the metafor package version 4.6.0. Effect sizes were expressed as standardised mean differences (SMD) for continuous outcomes, and odds ratios (ORs) for dichotomous outcomes. Pooled estimates were calculated assigning study weights based on the inverse of the variance of the study effect size, which includes the intra- and inter-study variability. Adherence continuous outcomes were reported on different scales, SMD was used as a unitless effect size, calculated by dividing the mean difference between the intervention and control groups by the standard deviation of post-intervention values. Heterogeneity across studies was assessed using I² statistics. Results A total of 7,648 records were identified through database searches and 14 through reverse searching. After removing duplicates and screening by title and abstract, 54 reports were assessed for eligibility. Following full-text review, 44 articles met the eligibility criteria, with two independent publications reporting different outcomes from the same RCT (30,31). The screening process is summarised in Figure 1. Studies Characteristics of the included studies, 35 RCTs and 8 quasi-experimental, are summarised in table 1 and detailed in Supplementary Tables 3a and 3b. Published between 2001 (29) and 2023 (30–32), most were conducted in the United States (9/43, 20.9%), United Kingdom (6/43, 13.9%), and Spain (5/43, 11.6%). One involved several countries (29). Setting The most frequent setting was primary care (16/43, 37.2%), followed by the community (11/43, 25.6%), hospital discharge (4/43, 9.3%) and mixed settings (4/43, 9.3%). Participants There was a wide range of sample sizes from 59 to 4,960 participants. A total of 26,720 participants were included; follow-up periods varied from 1 to 24 months. In 37 studies (86.0%), the mean age ranged from 60.1 ± 11.7 to 85.5 ± 4.0 years. Five studies (11.6%) reported age as median [IQR], and one study (2.3%) provided age as percentage distributions. Gender was reported in 42 studies (97.7%), with the proportion of females ranging from 22.5% to 78.0%. Comorbidities were quantitatively reported (mean or median) in 12 studies (27.9%) and 3 studies (6.9%) used comorbidity scores, such as ISAR, Charlson or CIRS (30–32). One study (2.3%) reported comorbidities categorically, with most participants having one or two (32). Seven studies (16.3%) provided qualitative data on the distribution of chronic conditions, mainly cardiovascular or respiratory diseases, and hypertension, while 20 (46.5%) targeted multimorbid patients without providing details. The mean number of medications was reported in 36 studies (83.7%), with a range of 3.7 ± 2.34 (68) to 17.55 ± 4.10 (39). Three studies (33,45,63) reported median values, ranging from 7 [5–8] (63) to 8.3 [7.4–9.3] (45). Seven studies (16.3%) did not provide data on medication numbers. Other variables, such as marital status, education level, dependency, and caregiver involvement, were also reported where available (Supplementary Tables 3a and 3b). Interventions Most studies did not specify whether interventions targeted patients or caregivers. Some focused exclusively on patients (5 studies, 11.6%), who were cognitively intact (32) or self-managed their medication (34–36), while others involved caregivers in supervision (33), medication management (37), care participation (29,45), or consent processes (38,46,61,69). Details on interventions are in Supplementary table 4. They were categorised as: medication management strategies, educational interventions, and digital/technology tools. Adherence support tools were considered as complementary strategies. Medication management strategies were featured in 31 of the 43 included studies with medication review being the most common (27–29,31–33,35,38–61). Educational interventions were central to 32 studies (27–29,33–39,41,43,45,48–50,52–55,57,59–70). The use of technology and digital tools was less prevalent, appearing in five studies (32,42,47,63,67). Adherence support tools were employed in 15 studies (27,32,35,38,41,42,44,45,47,53,56,57,59,60,67). Pharmacists were the main providers, leading 26 studies (60.5%) (Supplementary Tables 3a and 3b). Quality appraisal Of 35 RCTs, 60.0% had a high risk of bias, 31.4% had some concerns, and 8.6% had a low risk of bias. Risk of bias related to randomisation was the lowest, while the risk due to missing outcome data was the highest (Supplementary Table 5a, Figure 2a). For quasi-experimental studies, 87.5% showed a high risk of bias: 62.5% had a serious risk of bias, while 25% were rated as critical risk of bias. Only 12.5% had a moderate risk of bias. (Supplementary Table 5b, Figure 2b). The highest risk domains related to confounding factors and missing data, whereas the lowest risk concerned outcome measurement and selective reporting of results. Outcomes Primary outcomes: The findings regarding primary outcomes are summarised in Table 2 and detailed in Supplementary Table 6. Adherence measurement: Morisky Medication Adherence Scale: For the MMAS-4, four studies (33–36) were meta-analysed, producing an OR of 1.45 (95% CI 0.86-2.44), with moderate heterogeneity (I² = 55%) and a p-value of 0.08. This suggests a favourable non-significant effect (Figure 3a). Objective Methods: For objective methods, most studies did not provide an effect size. Methods like pill count or Medication Possession Ratios (MPR) showed comparable adherence between groups. One study (37) reported a near-significant effect at 6 months regarding the Drug Score (OR = 0.7; 95% CI = 0.5-1.0), which disappeared at 9 months. Due to the variability in methods, these results were unsuitable for meta-analysis. Other self-report tools and scales: Among the 10 studies employing other self-report tools to measure medication adherence (38–47), six demonstrated a beneficial effect (38–40,43,45,47). A meta-analysis was conducted focusing on continuous measures, including only 6 articles that provided sufficient data (37,41,42,46,48,49). The results, displayed in Figure 3b (SMD= 0.00; 95% CI= -0.09-0.10), with low heterogeneity (I² = 9%) and p = 0.36, suggested no effect. Adverse health outcomes measurement: Of the 34 studies reporting one or more adverse health outcomes, 30 addressed healthcare utilisation. Specialised care: Of the four studies examining patients with one or more hospitalisations (33,38,50,51), none found statistically significant differences, though two (50,51) noted trends suggesting increased hospitalisations in the intervention group. Among the 10 studies assessing total number of hospitalisations (32,34,37,44,46,52–56), two (44,55) reported statistically significant reductions in the intervention groups. For readmissions, stratified analysis by follow-up duration (Figure 3c) showed no significant differences for short-term follow-up (OR=0.75; 95% CI=0.47-1.19). A protective and clearly significant effect was observed for studies with a follow-up period of 2-3 months (OR=0.41; 95% CI=0.25-0.69), while the effect reversed and lost significance for studies with a follow-up of 6 months (OR=1.22; 95% CI=0.76-1.96). Regarding ED visits, only five studies could be included in the meta-analysis, but moderate to strong heterogeneity prevented conclusive results (Figure 3d). Nine studies compared length of stay (37,44,46,47,53,57–60), mostly showing no significant differences between groups. Some suggested trends of longer stays in the intervention group (47,58–60) and one found that the intervention group had a significantly longer length of stay compared to the control group (47). Contacts with Primary Care: Under this category we grouped: Primary Care visits (33,36,46,53,61), contacts with the General Practitioner (GP) (38), GP Home visits (31), Primary Care Nurse consultations (34) and telephone consultations (59). Six studies provided data which could be synthesise (31,44,46,53). The results (Figure 3e) showed a weak but significant effect in favour of an increased number of contacts in the intervention group (SMD = 0.06; 95% CI= -0.04- 0.16), with moderate heterogeneity (I² = 42%), p= 0.10. DRPs (30,44,48,53,62–64), ADEs (34,48), and ADWEs (50,59) were also assessed. However, comparability of the results was limited due to differences in strategies used to define, measure, and report these problems. Other adverse health outcomes, summarised in Supplementary Table 6, could not be meta-analysed or did not provide results of interest (Supplementary Figures 1-4). Secondary outcomes (Supplementary table 7): Regarding quality of life, six out of 10 studies were meta-analysed, revealing no significant effect of the interventions (SMD = -0.03; 95% CI = -0.14 to 0.09), with moderate heterogeneity (I²=41%), p=0.13 (Supplementary Figure 5). Five studies evaluated patients’ beliefs and attitudes towards medication (37,41,44,46,47), all using the Beliefs about Medicines Questionnaire (BMQ). Most found no significant differences between groups regarding patients’ beliefs and attitudes towards medication, although one (47), reported a significant positive effect on reducing concerns and improving the necessity-concerns balance. Three studies assessed patients’ knowledge of medicines through various methods (38,42,46) finding no significant differences. Five assessed mobility or functional status (32,37,49,63,65), and three analysed falls (41,49,66), with only one (66) observed a positive impact. Seven studies addressed healthcare costs (31,38,44,46,47,55,64), but conceptual differences prevented results synthesis. Finally, ten studies reported mortality (33,36,47,49,51,59,60,67–69). Results from the meta-analysis, including nine studies, showed no significant effect (Supplementary Figure 6). Supplementary Table 8 summarises the tools, methods, and metrics used for outcome assessment. Discussion This review aimed to collate evidence on interventions to improve medication management for multimorbid older adults living in the community. Optimising medication management in this population is a public health priority, given the challenges of ageing societies and polypharmacy. No single intervention consistently improved adherence or patient outcomes. A medium-term reduction in readmissions was observed, but there was no significant effect on adherence, quality of life, or mortality. Heterogeneity in ED visits prevented conclusive results, and primary care contacts showed a weak, non-significant increase. Inconsistent outcome measurement limited conclusions on DRPs and healthcare costs. Overall, methodological quality was low to moderate. Strengths include broad outcome assessment and systematic quality evaluation using validated tools (28,29). Diverse intervention types provided insights across various healthcare settings. No language restrictions reduced language bias. Although the search was limited to PubMed and Web of Science, these databases cover varied populations and inclusion of trial registries mitigated publication bias. Meta-analyses identified trends where possible. Limitations include heterogeneity and a lack of standardised classifications for interventions and outcomes, complicating overall conclusions and assessment of multifaceted interventions. Variations in settings and resources, small sample sizes, short follow-up, and methodological differences limited comparability. Lack of stratification by gender, socioeconomic status, or caregiver type may have masked subgroup effects. Random-effects models provided conservative estimates. Given these limitations, findings should be interpreted with caution, and future research must aim for greater methodological rigour and standardisation. Findings on adherence align with Burgos-Alonso et al. (70) and Cross et al. (26), both reporting inconclusive results, likely due to similar inclusion criteria. In contrast, Roncal-Belzunce et al. (71) reported benefits, especially with collaborative nursing approaches, possibly due to a combined analysis of objective and subjective measures. For ED visits, we found no significant reduction and high heterogeneity, unlike Roncal-Belzunce et al. (71) likely reflecting different measurement approaches. Cross et al. (26) suggested reductions in ED visits with mixed interventions, combining ED and hospital admissions data. Our results for mortality and quality of life are consistent with Roncal-Belzunce et al. (71) who found no significant effects. While they reported DRP improvements, we could not meta-analyse due to inconsistent reporting. From an economic perspective, our findings align with Laberge et al. (72), who found inconclusive evidence on nursing intervention costs. Roncal-Belzunce et al (71). also noted potential reductions in medication-related costs, but not total care costs. Implications for Clinical Practice and Research Improving adherence and outcomes requires addressing patient- and system-level factors. Health status, beliefs, behaviours, and motivations shape adherence (73,74) and require tailored strategies. System factors, including the healthcare environment, care accessibility, and provider support, are equally important (74) . At the core lies the patient-caregiver dyad, bridging the patient-system gap. Informal caregivers, central to home medication management, often lack training and support (75). Effective interventions should be collaborative, tailored to socio-health needs, and reflect varying health literacy (76, 77,78) ensuring active involvement of both patients and caregivers in decision-making (79). Social, demographic, and cultural factors, especially gender, as women often assume unsupported caregiving roles must also be considered (80). Caregivers must be integrated into healthcare strategies (81) through training and tailored tools (75). Overcoming structural barriers, improving provider training and ensuring transparent, well-coordinated transitional care are crucial (82) particularly in home settings where oversight is limited and caregiver dependence is high (83). Despite their importance, intervention cost-effectiveness remains underexplored (72). This review highlights persistent gaps in the evidence and offers insights to guide future research and clinical practice. Future research should standardise outcomes, evaluate subgroup and caregiver programme impacts, and conduct economic evaluations to guide policy. Conclusion No single intervention consistently improved adherence or patient outcomes. Tailored, multifactorial strategies, integrating caregivers and addressing home care complexities, are essential. The findings highlight the lack of harmonisation in study designs and variables, along with suboptimal quality. Robust trials, standardised outcomes, subgroup evaluations, long-term follow-up, and economic evaluation are needed to optimise home-based medication management interventions for multimorbid older adults and inform policy-making. Declarations Ethics approval and consent to participate Ethical review and approval were not required for this study because it is based solely on the retrieval and synthesis of data from previously published randomized controlled trials (RCTs). Consent for publication Not applicable. Availability of data and materials Not applicable to this article. Competing interests The authors declare that they have no competing interests. Funding This research received no funding. Authors' contributions AB-C: Conceptualization, Methodology, Supervision, Writing –Reviewing and Editing. CG-P: Conceptualization, Methodology, Supervision, Writing –Reviewing and Editing. AC-V: Conceptualization, Methodology, Supervision, Writing–Reviewing and Editing. MN-N: Conceptualization, Methodology, Supervision, Writing–Reviewing and Editing. MD-L: Data Curation, Investigation, Writing– Original Draft Preparation. MRR-M: Data Curation, Investigation, Writing –Original Draft Preparation. MEV-G: Formal Analysis, Visualization, Writing – Original Draft Preparation. All authors read and approved the final manuscript. Acknowledgements MN-N is supported by a research contract from the Carlos III Research Institute (Juan Rodés JR23/00025). Use of Large Language Models (LLMs) The authors used ChatGPT to support language editing and improve clarity of the English in the manuscript. No content was generated by AI related to the design, analysis, interpretation, or scientific conclusions of the study. Therefore, ChatGPT is not included as an author. References Divo MJ, Martinez CH, Mannino DM. Ageing and the epidemiology of multimorbidity. European Respiratory Journal. 2014 Oct 1;44(4):1055–68. Jia Hao L, Omar MS, Tohit N. Polypharmacy and Willingness to Deprescribe Among Elderly with Chronic Diseases. Int J Gerontol. 2018 Dec 1;12(4):340–3. UN. Ageing [Internet]. 2023 [cited 2024 Dec 3]. Available from: https://www.un.org/en/global-issues/ageing Plácido AI, Herdeiro MT, Morgado M, Figueiras A, Roque F. Drug-related Problems in Home-dwelling Older Adults: A Systematic Review. Vol. 42, Clinical Therapeutics. Excerpta Medica Inc.; 2020. p. 559-572.e14. Reason B, Terner M, Moses McKeag A, Tipper B, Webster G. The impact of polypharmacy on the health of Canadian seniors. Fam Pract. 2012 Aug;29(4):427–32. Xu X, Mishra GD, Jones M. Evidence on multimorbidity from definition to intervention: An overview of systematic reviews. Vol. 37, Ageing Research Reviews. Elsevier Ireland Ltd; 2017. p. 53–68. McPhail SM. Multimorbidity in chronic disease: Impact on health care resources and costs. Vol. 9, Risk Management and Healthcare Policy. Dove Medical Press Ltd; 2016. p. 143–56. Makovski TT, Schmitz S, Zeegers MP, Stranges S, van den Akker M. Multimorbidity and quality of life: Systematic literature review and meta-analysis. Vol. 53, Ageing Research Reviews. Elsevier Ireland Ltd; 2019. Masnoon N, Shakib S, Kalisch-Ellett L, Caughey GE. What is polypharmacy? A systematic review of definitions. Vol. 17, BMC Geriatrics. BioMed Central Ltd.; 2017. Midão L, Giardini A, Menditto E, Kardas P, Costa E. Polypharmacy prevalence among older adults based on the survey of health, ageing and retirement in Europe. Arch Gerontol Geriatr. 2018 Sep 1;78:213–20. Pasina L, Brucato AL, Falcone C, Cucchi E, Bresciani A, Sottocorno M, et al. Medication non-adherence among elderly patients newly discharged and receiving polypharmacy. Drugs Aging. 2014 Apr 1;31(4):283–9. Mohammed MA, Moles RJ, Chen TF. Medication-related burden and patients’ lived experience with medicine: a systematic review and metasynthesis of qualitative studies. Available from: http://bmjopen.bmj.com/ Ploeg J, Matthew-Maich N, Fraser K, Dufour S, McAiney C, Kaasalainen S, et al. Managing multiple chronic conditions in the community: a Canadian qualitative study of the experiences of older adults, family caregivers and healthcare providers. BMC Geriatr. 2017 Jan 31;17(1):1–15. van der Aa MJ, van den Broeke JR, Stronks K, Plochg T. Patients with Multimorbidity and Their Experiences with the Healthcare Process: A Scoping Review. J Comorb. 2017 Jan;7(1):11–21. Commission Economic Policy Committee Economic E, Affairs European Economy Institutional Papers F. The 2018 Ageing Report: Economic and Budgetary Projections for the EU Member States (2016-2070). In. Available from: https://ec.europa.eu/info/publications/economic-and-financial-affairs-publications_en. García-Calvente M del M, Mateo-Rodríguez I, Maroto-Navarro G. El impacto de cuidar en la salud y la calidad de vida de las mujeres. Gac Sanit. 2004;18:83–92. OECD. Health at a Glance 2023 [Internet]. OECD; 2023. (Health at a Glance). Available from: https://www.oecd.org/en/publications/health-at-a-glance-2023_7a7afb35-en.html Abajo M, Rodríguez-Sanz M, Malmusi D, Salvador M, Borrell C. Gender and socio-economic inequalities in health and living conditions among co-resident informal caregivers: a nationwide survey in Spain. J Adv Nurs. 2017 Mar 1;73(3):700–15. Cascella Carbó GF, García-Orellán R. Burden and Gender inequalities around Informal Care. Vol. 38, Investigacion y Educacion en Enfermeria. Facultad de Enfermeria de la Universidad de Antioquia; 2020. Rodríguez-Lobato P, Millán-Franco M. PAULA RODRÍGUEZ-LOBATO, MARIO MILLÁN-FRANCO Género, Trabajo Social y fenómeno migratorio: las cuidadoras internas latinoamericanas en España Gender, Social Work and migration phenomenon: Latin American live-in caregivers in Spain. Parand A, Garfield S, Vincent C, Franklin BD. Carers’ medication administration errors in the domiciliary setting: A systematic review. PLoS One. 2016 Dec 1;11(12). WHO. Medication Without Harm [Internet]. [cited 2024 Dec 3]. Available from: https://www.who.int/initiatives/medication-without-harm Pratiwi H, Kristina SA, Widayanti AW, Prabandari YS, Kusuma IY. A Systematic Review of Compensation and Technology-Mediated Strategies to Maintain Older Adults’ Medication Adherence. Vol. 20, International Journal of Environmental Research and Public Health. MDPI; 2023. Bloomfield HE, Greer N, Linsky AM, Bolduc J, Naidl T, Vardeny O, et al. Deprescribing for Community-Dwelling Older Adults: a Systematic Review and Meta-analysis. Vol. 35, Journal of General Internal Medicine. Springer; 2020. p. 3323–32. Dietrich FM, Hersberger KE, Arnet I. Benefits of medication charts provided at transitions of care: A narrative systematic review. BMJ Open. 2020 Oct 22;10(10). Cross AJ, Elliott RA, Petrie K, Kuruvilla L, George J. Interventions for improving medication-taking ability and adherence in older adults prescribed multiple medications. Vol. 2020, Cochrane Database of Systematic Reviews. John Wiley and Sons Ltd; 2020. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Vol. 372, The BMJ. BMJ Publishing Group; 2021. Sterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: A revised tool for assessing risk of bias in randomised trials. The BMJ. 2019;366. Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, et al. ROBINS-I: A tool for assessing risk of bias in non-randomised studies of interventions. BMJ (Online). 2016;355. Ahmad A, Nijpels G, Dekker JM, Kostense PJ, Hugtenburg JG. Effect of a pharmacist medication review in elderly patients discharged from the hospital. Arch Intern Med. 2012 Sep 24;172(17):1346–7. van der Heijden AAWA, de Bruijne MC, Nijpels G, Hugtenburg JG. Cost-effectiveness of a clinical medication review in vulnerable older patients at hospital discharge, a randomized controlled trial. Int J Clin Pharm. 2019 Aug 1;41(4):963–71. Jerant A, Moore-Hill M, Franks P. Home-based, peer-led chronic illness self-management training: Findings from a 1-year randomized controlled trial. Ann Fam Med. 2009;7(4):319–27. Campins L, Serra-Prat M, Gózalo I, López D, Palomera E, Agustí C, et al. Randomized controlled trial of an intervention to improve drug appropriateness in communitydwelling polymedicated elderly people. Fam Pract. 2017 Feb 1;34(1):36–42. Del Cura-González I, López-Rodríguez JA, Leiva-Fernández F, Gimeno-Miguel A, Poblador-Plou B, López-Verde F, et al. How to Improve Healthcare for Patients with Multimorbidity and Polypharmacy in Primary Care: A Pragmatic Cluster-Randomized Clinical Trial of the MULTIPAP Intervention. J Pers Med. 2022 May 1;12(5). Suárez-Varela MTM. Estudio sobre la utilidad del pastillero para mejorar el cumplimiento terapéutico. Aten Primaria. 2009 Apr;41(4):185–91. Sáez de la Fuente J, Granja Berná V, Lechuga Vázquez P, Otero Perpiña B, Herreros de Tejada López-Coterilla A, Medina Asensio J. Efficiency of the Information Given at Discharge and Adherence of Polymedicated Patients. Farmacia Hospitalaria (English Edition). 2011 May;35(3):128–34. Muth C, Uhlmann L, Haefeli WE, Rochon J, Van Den Akker M, Perera R, et al. Effectiveness of a complex intervention on Prioritising Multimedication in Multimorbidity (PRIMUM) in primary care: Results of a pragmatic cluster randomised controlled trial. BMJ Open. 2018 Feb 1;8(2). Bernsten C, Björkman I, Caramona M, Crealey G, Frøkjaer B, Grundberger E, et al. Improving the Well-Being of Elderly Patients via Community Pharmacy-Based Provision of Pharmaceutical Care A Multicentre Study in Seven European Countries. Vol. 18, Drugs & Aging. 2001. Biswas A, Sinha N, Ray K, Tripathi SK. A study on drug use and medication management perspectives among elderly and the impact of professional oversight. Journal of Clinical and Diagnostic Research. 2018 May 1;12(5):FC01–7. Martínez-Mardones F, Benrimoj SI, Ahumada-Canale A, Plaza-Plaza JC, Garcia-Cardenas V. BC Clinical impact of medication reviews with follow-up in cardiovascular older patients in primary care: A cluster-randomized controlled trial. Br J Clin Pharmacol. 2023 Jul 1;89(7):2131–43. Messerli M, Blozik E, Vriends N, Hersberger KE. Impact of a community pharmacist-led medication review on medicines use in patients on polypharmacy--a prospective randomised controlled trial. BMC Health Serv Res. 2016 Apr 23;16:145. Nazareth I, Burton A, Shulman S, Smith P, Haines A, Timberall H. A pharmacy discharge plan for hospitalized elderly patientsÐa randomized controlled trial. Shim YW, Chua SS, Wong HC, Alwi S. Collaborative intervention between pharmacists and physicians on elderly patients: A randomized controlled trial. Ther Clin Risk Manag. 2018 Jun 15;14:1115–25. Syafhan NF, Al Azzam S, Williams SD, Wilson W, Brady J, Lawrence P, et al. General practitioner practice-based pharmacist input to medicines optimisation in the UK: pragmatic, multicenter, randomised, controlled trial. J Pharm Policy Pract. 2021 Dec 1;14(1). Wu JYF, Leung WYS, Chang S, Lee B, Zee B, Tong PCY, et al. Effectiveness of telephone counselling by a pharmacist in reducing mortality in patients receiving polypharmacy: Randomised controlled trial. Br Med J. 2006 Sep 9;333(7567):522–5. Yang C, Lee DTF, Wang X, Chair SY. Effects of a nurse-led medication self-management intervention on medication adherence and health outcomes in older people with multimorbidity: A randomised controlled trial. Int J Nurs Stud. 2022 Oct 1;134. Odeh M, Scullin C, Fleming G, Scott MG, Horne R, McElnay JC. Ensuring continuity of patient care across the healthcare interface: Telephone follow-up post-hospitalization. Br J Clin Pharmacol. 2019 Mar 1;85(3):616–25. Chrischilles EA, Hourcade JP, Doucette W, Eichmann D, Gryzlak B, Lorentzen R, et al. Personal health records: A randomized trial of effects on elder medication safety. Journal of the American Medical Informatics Association. 2014;21(4):679–86. Kouladjian O’Donnell L, Gnjidic D, Sawan M, Reeve E, Kelly PJ, Chen TF, et al. Impact of the Goal-directed Medication Review Electronic Decision Support System on Drug Burden Index: A cluster-randomised clinical trial in primary care. Br J Clin Pharmacol. 2021 Mar 1;87(3):1499–511. Herrinton LJ, Lo K, Alavi M, Alexeeff SE, Butler KM, Chang C, et al. Effectiveness of Bundled Hyperpolypharmacy Deprescribing Compared With Usual Care Among Older Adults: A Randomized Clinical Trial. JAMA Netw Open. 2023 Jul 3;6(7):e2322505. Olesen C, Harbig P, Buus KM, Barat I, Damsgaard EM. Impact of pharmaceutical care on adherence, hospitalisations and mortality in elderly patients. Int J Clin Pharm. 2014;36(1):163–71. Lenaghan E, Holland R, Brooks A. Home-based medication review in a high risk elderly population in primary care - The POLYMED randomised controlled trial. Vol. 36, Age and Ageing. 2007. p. 292–7. Lenander C, Elfsson B, Danielsson B, Midlöv P, Hasselström J. Effects of a pharmacist-led structured medication review in primary care on drug-related problems and hospital admission rates: A randomized controlled trial. Scand J Prim Health Care. 2014 Dec 1;32(4):180–6. Leendertse AJ, De Koning GHP, Goudswaard AN, Belitser S V., Verhoef M, De Gier HJ, et al. Preventing hospital admissions by reviewing medication (PHARM) in primary care: An open controlled study in an elderly population. J Clin Pharm Ther. 2013 Oct;38(5):379–87. Matzke GR, Moczygemba LR, Williams KJ, Czar MJ, Lee WT. Impact of a pharmacist-physician collaborative care model on patient outcomes and health services utilization. American Journal of Health-System Pharmacy. 2018 Jul 15;75(14):1039–47. Moreno G, Fu JY, Chon JS, Bell DS, Grotts J, Tseng CH, et al. Reducing Emergency Department Visits among Patients with Diabetes by Embedding Clinical Pharmacists in the Primary Care Teams. Med Care. 2021 Apr 1;59(4):348–53. Bolas H, Brookes K, Scott M. Evaluation of a hospital-based community liaison pharmacy service in Northern Ireland. Vol. 26, Pharm World Sci. 2004. Briggs S, Pearce R, Dilworth S, Higgins I, Hullick C, Attia J. Clinical pharmacist review: A randomised controlled trial. EMA - Emergency Medicine Australasia. 2015 Oct 1;27(5):419–26. McCarthy C, Clyne B, Boland F, Moriarty F, Flood M, Wallace E, et al. GP-delivered medication review of polypharmacy, deprescribing, and patient priorities in older people with multimorbidity in Irish primary care (SPPiRE Study): A cluster randomised controlled trial. PLoS Med. 2022 Jan 1;19(1). Lembeck MA, Thygesen LC, Sørensen BD, Rasmussen LL, Holm EA. Effect of single follow-up home visit on readmission in a group of frail elderly patients - A Danish randomized clinical trial. BMC Health Serv Res. 2019 Oct 25;19(1). Al-Rashed SA, Wright DJ, Roebuck N, Sunter W, Chrystyn H. Blackwell Science, Ltd Oxford, UK BCPBritish Journal of Clinical Pharmacology 0306-5251Blackwell Publishing 2002 54Original Article Pre-discharge pharmaceutical counsellingS. Vol. 54, J Clin Pharmacol. 2002. Geurts MME, Stewart RE, Brouwers JRBJ, de Graeff PA, de Gier JJ. Implications of a clinical medication review and a pharmaceutical care plan of polypharmacy patients with a cardiovascular disorder. Int J Clin Pharm. 2016 Aug 1;38(4):808–15. Köberlein-Neu J, Mennemann H, Hamacher S, Waltering I, Jaehde U, Schaffert C, et al. Interprofessional medication management in patients with multiple morbidities - A cluster-randomized trial (the WestGem study). Dtsch Arztebl Int. 2016 Nov 4;113(44):731–40. Moczygemba LR, Barner JC, Lawson KA, Brown CM, Gabrillo ER, Godley P, et al. Impact of telephone medication therapy management on medication and health-related problems, medication adherence, and medicare part D drug costs: A 6-month follow up. American Journal Geriatric Pharmacotherapy. 2011 Oct;9(5):328–38. Insel KC, Einstein GO, Morrow DG, Koerner KM, Hepworth JT. Multifaceted Prospective Memory Intervention to Improve Medication Adherence. J Am Geriatr Soc. 2016 Mar 1;64(3):561–8. Poorcheraghi H, Negarandeh R, Pashaeypoor S, Jorian J. Effect of using a mobile drug management application on medication adherence and hospital readmission among elderly patients with polypharmacy: a randomized controlled trial. BMC Health Serv Res. 2023 Dec 1;23(1). Holland R, Lenaghan E, Harvey I, Smith R, Shepstone L, Lipp A, et al. Does home based medication review keep older people out of hospital? The HOMER randomised controlled trial. Vol. 330, British Medical Journal. 2005. p. 293–5. Hugtenburg JG, Borgsteede SD, Beckeringh JJ. Medication review and patient counselling at discharge from the hospital by community pharmacists. Pharmacy World and Science. 2009 Dec;31(6):630–7. Westberg SM, Swanoski MT, Renier CM, Gessert CE. Evaluation of the Impact of Comprehensive Medication Management Services Delivered Posthospitalization on Readmissions and Emergency Department Visits [Internet]. Vol. 20, Journal of Managed Care & Specialty Pharmacy JMCP September. 2014. Available from: www.amcp.org Burgos-Alonso N, Torrecilla M, Mendiguren A, Pérez-Gómez Moreta M, Bruzos-Cidón C. Strategies to Improve Therapeutic Adherence in Polymedicated Patients over 65 Years: A Systematic Review and Meta-Analysis. Pharmacy. 2024 Feb 17;12(1):35. Roncal-Belzunce V, Gutiérrez-Valencia M, Leache L, Saiz LC, Bell JS, Erviti J, et al. Systematic review and meta-analysis on the effectiveness of multidisciplinary interventions to address polypharmacy in community-dwelling older adults. Vol. 98, Ageing Research Reviews. Elsevier Ireland Ltd; 2024. Laberge M, Sirois C, Lunghi C, Gaudreault M, Nakamura Y, Bolduc C, et al. Economic evaluations of interventions to optimize medication use in older adults with polypharmacy and multimorbidity: A systematic review. Vol. 16, Clinical Interventions in Aging. Dove Medical Press Ltd; 2021. p. 767–79. Devine F, Edwards T, Feldman SR. Barriers to treatment: Describing them from a different perspective. Vol. 12, Patient Preference and Adherence. Dove Medical Press Ltd.; 2018. p. 129–33. Kardas P, Lewek P, Matyjaszczyk M. Determinants of patient adherence: A review of systematic reviews. Front Pharmacol. 2013;4 JUL. Akgun-Citak E, Attepe-Ozden S, Vaskelyte A, van Bruchem-Visser RL, Pompili S, Kav S, et al. Challenges and needs of informal caregivers in elderly care: Qualitative research in four European countries, the TRACE project. Arch Gerontol Geriatr. 2020 Mar 1;87. Xu HY, Yu YJ, Zhang QH, Hu HY, Li M. Tailored Interventions to Improve Medication Adherence for Cardiovascular Diseases. Vol. 11, Frontiers in Pharmacology. Frontiers Media S.A.; 2020. Kreuter MW, Thompson T, McQueen A, Garg R. Addressing Social Needs in Health Care Settings: Evidence, Challenges, and Opportunities for Public Health. In: Annual Review of Public Health. Annual Reviews Inc.; 2020. p. 329–44. Martins NFF, Abreu DPG, Silva BT da, Semedo DSDRC, Pelzer MT, Ienczak FS. Functional health literacy and adherence to the medication in older adults: integrative review. Vol. 70, Revista brasileira de enfermagem. 2017. p. 868–74. Carman KL, Dardess P, Maurer M, Sofaer S, Adams K, Bechtel C, et al. Patient and family engagement: A framework for understanding the elements and developing interventions and policies. Health Aff. 2013;32(2):223–31. Simons M, Goossensen A, Nies H. Interventions fostering interdisciplinary and inter-organizational collaboration in health and social care; an integrative literature review. Vol. 28, Journal of Interprofessional Education and Practice. Elsevier Inc.; 2022. Khatri R, Endalamaw A, Erku D, Wolka E, Nigatu F, Zewdie A, et al. Continuity and care coordination of primary health care: a scoping review. BMC Health Serv Res. 2023 Dec 1;23(1). Ljungholm L, Edin-Liljegren A, Ekstedt M, Klinga C. What is needed for continuity of care and how can we achieve it? – Perceptions among multiprofessionals on the chronic care trajectory. BMC Health Serv Res. 2022 Dec 1;22(1). LaValley S, Wahler R, Singh R, Monte S, Brady L. CAREGIVERS’ ROLES IN MEDICATION MANAGEMENT FOR OLDER FAMILY MEMBERS. Innov Aging. 2018 Nov 1;2(suppl_1):290–290. Tables Table 1. Overview of the characteristics of the included studies Characteristics of the included studies Variable Categories Studies n (%) Variable Categories Studies n (%) Country USA 9 (20.9%) Setting Primary Care 16 (37.2%) United Kingdom 6 (13.9%) Community 11 (25.6%) Spain 5 (11.6%) Hospital Discharge 4 (9.3%) Netherlands 4 (9.3%) Mixed: Hospital-Community 4 (9.3%) Ireland 3 (6.9%) Outpatient Care 2 (4.6%) Australia 2 (4.6%) Secondary Care 2 (4.6%) China 2 (4.6%) Emergency Department 1 (2.3%) Denmark 2 (4.6%) Geriatric Hospital 1 (2.3%) Germany 2 (4.6%) Hospital 1 (2.3%) Multicountry ¹ 1 (2.3%) Military Medical Centre 1 (2.3%) Other Countries² 7 (16.3%) Sample size (n) ≤100 5 (11.6%) 101 - 300 13 (30.2%) Intervention provider Pharmacist 26 (60.5%) 301 - 500 10 (23.2%) Pharmacist- Physician 7 (16.3%) >500 15 (34.9%) Nurse 5 (11.6%) Follow-up period (months) ≤ 3 9 (20.9%) General Practitioner 2 (4.6%) 3-6 15 (34.9%) Electronic tool 2 (4.6%) 6-12 16 (37.2%) Physician-Nurse 1 (2.3%) > 12 3 (6.9%) Patient Characteristics Variable Categories Studies n (%) Variable Categories Studies n/N (%) Age 60 - 65 6 (13.9%) Comorbidities (Mean/median) Total: 12/43 (27.9%) 65 - 75 18 (41.9%) 75 - 85 18 (41.9%) ≤ 4 4/12 (33.3%) > 85 1 (2.3%) 4 - 6 3/12 (25.0%) Female gender ≤ 25% 1 (2.3%) >6 5/12 (41.6%) 26 - 50% 10 (23.2%) 51 - 75% 28 (65.1%) Comorbidities- Qualitative reporting Total: 7/43 (16.3 %) > 75% 3 (6.9%) Cardiovascular disease 6/7 (85.7%) Not provided 1 (2.3%) Respiratory disease 5/7 (71.4%) Number of medications 3 - 6 4 (9.3%) Hypertension 5/7 (71.4%) 6 - 10 23 (53.5%) >10 9 (20.9%) Comorbidities Not provided 20/43 (46.5%) 1. Denmark, Germany, Netherlands, Northern Ireland, Republic of Ireland, Portugal, Sweden 2. Brazil, Chile, India, Iran, Malaysia, Sweden, Switzerland Table 2. Summary of findings Main Outcomes Tool Studies reporting outcome (n ₀/N₀) Studies reporting significant Impact (n₁/N₁) Summary of effects Adherence Total 28/43 17/28 Mixed, mostly beneficial MMAS-4 8/28 3/8 beneficial effect 1/8 negative effect Mixed MMAS-8 3/28 2/3 Mostly beneficial Objective Methods 10/28 5/10 Mixed, beneficial effect Other self-report scales 8/28 5/8 Mixed, mostly Beneficial effect Mixed Methods (Subjective-Objective) ¹ 1/28 1/1 Beneficial effect Hospitalisations (Number) Total 10/43 2/10 Mixed, mostly non-effect Hospital Episode Statistics/ Records of County Council 3/10 None Non-effect Electronic record system/ Medical History 4/10 2/4 Beneficial Self-report 1/10 None Non-effect Unspecified tool 2/10 None Non-effect Hospitalisations (patients with event) Total 4/43 None Non-effect Electronic Health Tools 3/4 None Non-effect Self-report 1/4 None Non-effect Number of Readmissions Total 3/43 1/3 Mixed effects Hospital Episode Statistics 1/3 1/1 Negative effect Patient Interview/ Medical History 1/3 None Non-effect Unspecified tool 1/3 None Non-effect Readmissions (patients with event) Total 9/43 5/9 Mixed effects, mostly beneficial Electronic Health record 2/9 None Non-effect National Health Service Register/ PAS²/ Hospital’s computer records 4/9 2/4 Mixed Self-report 1/9 1/1 Beneficial Self-report/ Medical records 1/9 1/1 Beneficial Unspecified tool 1/9 1/1 Beneficial ED visits Total 9/43 1/9 Mixed, mostly non-effect Electronic Health record / Medical History 6/9 1/6 Beneficial Self-report 1/9 None Non-effect Patient Interview/ Medical History 1/9 None Non-effect Unspecified tool 1/9 None Non-effect Specialist Physician Visits Total 3/43 None Non-effect Main Outcomes Tool Studies reporting outcome (n ₀/N₀) Studies reporting significant Impact (n₁/N₁) Summary of effects Outpatient visits Total 8/43 None Non-effect Length of stay Total 9/43 1/9 Mixed, negative effect Electronic Medical Record 2/9 None Mixed, negative effect Self-report 1/9 None Non-effect Unspecified tool 3/9 None Non-effect Records of County Council/ National Health Service Register/ Hospital’s computer records 3/9 1/3 Mixed, negative effect Contacts with Primary Care Total 10/43 1/10 Mixed, negative effect Electronic Medical Record 3/10 1/3 Mixed, negative effect Self-report 3/10 None Non-effect Patient Interview/ Medical History 1/10 None Non-effect Unspecified tool 1/10 None Non-effect Records of County Council/ PAS ² 2/10 None Non-effect DRPs Total 7/43 4/7 Mixed, beneficial effect Medication analysis/ Patient Interview 1/7 1/1 Beneficial effect Self-report 2/7 None Non-effect Unspecified tool 1/7 None Non-effect Electronic Medical Record/ Electronic Medical Chart/Patient file 3/7 3/3 Beneficial effect n ₀/N₀: number of studies reporting a specific outcome out of the total number of studies included. n₁/N₁: number of studies using a particular tool that show a significant impact out of the total number of studies that used that tool. 1. Self-report /Pill count/ Computerised information 2. PAS: Patient Administration System Note: The totals do not sum to the overall number of studies as one study may report multiple outcomes or use different tools to measure adherence. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFiles.zip Graphicalabstract.tif Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6976707","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":484696771,"identity":"27427c38-b97a-453f-a7f4-2eac00e48c90","order_by":0,"name":"María Durán-Luque","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYJCCAwxsIIq5gYGhAkSDGMRpYQSqPAPSwkhYCwNcC2MbTC8eoNt+9uGBH2WH8/lnN7ZJvJ1XG83fDtTyo2IbTi1mZ9INDvacO2w5487BNsm5247nzjjM2MDYc+Y2bi0H0hgO8LYdNmC4kdgmzbvtWG4DUAszYxseLeefMRz8C9QiD9Yy51jufIJabqQxHAbZYgDW0lCTu4GwlmcMh2XOpRsY3khstpxz7EDuRqCWg3j9cj6N+eObMmsDuRvJB2+8qanLnXf+8MEHPypwa0EFPAyHwfQBItWDtdQRr3gUjIJRMApGDAAA/ehioKhY/AcAAAAASUVORK5CYII=","orcid":"","institution":"University of Granada","correspondingAuthor":true,"prefix":"","firstName":"María","middleName":"","lastName":"Durán-Luque","suffix":""},{"id":484696772,"identity":"0e38b0bd-276d-4f29-8f5b-adbec3a83a36","order_by":1,"name":"María Rocío Robles-Muñoz","email":"","orcid":"","institution":"Clínico San Cecilio University Hospital","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"Rocío","lastName":"Robles-Muñoz","suffix":""},{"id":484696773,"identity":"9875447a-f04a-44d0-8477-0625179fdec2","order_by":2,"name":"María Eugenia Velasco García","email":"","orcid":"","institution":"University of Granada","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"Eugenia Velasco","lastName":"García","suffix":""},{"id":484696774,"identity":"893c036b-aea6-40f0-84ac-1f68da0da35c","order_by":3,"name":"Celia Gómez-Peña","email":"","orcid":"","institution":"Clínico San Cecilio University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Celia","middleName":"","lastName":"Gómez-Peña","suffix":""},{"id":484696775,"identity":"1d63b04d-1e29-4e19-83be-fe28cbd21b34","order_by":4,"name":"Ángel Cobos-Vargas","email":"","orcid":"","institution":"Clínico San Cecilio University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ángel","middleName":"","lastName":"Cobos-Vargas","suffix":""},{"id":484696776,"identity":"f9d37c00-4a90-48c6-92db-344ef9b0b8f5","order_by":5,"name":"María Núñez-Núñez","email":"","orcid":"","institution":"Clínico San Cecilio University Hospital","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"","lastName":"Núñez-Núñez","suffix":""},{"id":484696777,"identity":"63b54041-e20f-478d-92b4-f365c4ab89ab","order_by":6,"name":"Aurora Bueno-Cavanillas","email":"","orcid":"","institution":"University of Granada","correspondingAuthor":false,"prefix":"","firstName":"Aurora","middleName":"","lastName":"Bueno-Cavanillas","suffix":""}],"badges":[],"createdAt":"2025-06-25 16:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6976707/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6976707/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86764044,"identity":"b0ac6f78-2601-4716-b263-82fa98e74aad","added_by":"auto","created_at":"2025-07-15 10:51:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":863623,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRISMA flow diagram of study selection.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFlow of records through identification, screening, eligibility, and inclusion phases according to PRISMA 2020 guidelines.\u003c/p\u003e","description":"","filename":"Figure1Flowdiagram.png","url":"https://assets-eu.researchsquare.com/files/rs-6976707/v1/53d7b3ec25960531167bfeac.png"},{"id":86764042,"identity":"ff5cc67e-1a6c-46a2-b748-c533fa001d9e","added_by":"auto","created_at":"2025-07-15 10:51:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2196209,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea. Risk of bias summary for randomised controlled trials (RoB 2 tool).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVisual representation of the risk of bias domains across included RCTs, assessed using the RoB 2 tool.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb. Risk of bias summary for quasi-experimental studies (ROBINS-I tool).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVisual summary of risk of bias assessments across non-randomised studies, evaluated with the ROBINS-I tool.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6976707/v1/847543063f439f4e96fe7cea.png"},{"id":86764047,"identity":"4d6f8028-3bbc-4f7b-ad43-c2e574f4ec6a","added_by":"auto","created_at":"2025-07-15 10:51:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":57806549,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea. Forest plot of the effect of interventions on medication adherence (MMAS-4 scale).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePooled odds ratios with 95% confidence intervals from included studies using the MMAS-4 adherence measure. Random-effects model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb. Forest plot of continuous adherence outcomes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStandardised mean differences (SMDs) with 95% confidence intervals from studies reporting adherence as a continuous variable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec. Forest plot of hospital readmissions stratified by follow-up duration.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOdds ratios for readmissions at short-, medium-, and long-term follow-up intervals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed. Forest plot of emergency department (ED) visits.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePooled effect of interventions on the frequency of ED visits. High heterogeneity prevented firm conclusions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee. Forest plot of primary care contacts.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEffect of interventions on the use of primary care services based on pooled data from included studies\u003c/p\u003e","description":"","filename":"Figure3aeForestPlots.png","url":"https://assets-eu.researchsquare.com/files/rs-6976707/v1/7155e6b6162aad0790f96a8f.png"},{"id":86766411,"identity":"34a092ad-a586-40e2-a86e-eac410465549","added_by":"auto","created_at":"2025-07-15 11:08:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":88634095,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6976707/v1/7fa06455-5025-4a0c-885e-a74d60bea17c.pdf"},{"id":86764045,"identity":"78bf5398-8c0b-4379-a77e-3f39f627a1ec","added_by":"auto","created_at":"2025-07-15 10:51:47","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3290914,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFiles.zip","url":"https://assets-eu.researchsquare.com/files/rs-6976707/v1/f8d9d98c9386ebe493dc99fa.zip"},{"id":86764043,"identity":"bb0d59ca-a607-49fc-83c1-c3f73f7ce407","added_by":"auto","created_at":"2025-07-15 10:51:47","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":82600,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract.tif","url":"https://assets-eu.researchsquare.com/files/rs-6976707/v1/bfb5d6875767bdc6ee493b97.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Interventions to enhance in-home taking medication among older adults. A systematic review","fulltext":[{"header":"Contributions to the literature","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003ePolypharmacy in older adults is associated with poor health outcomes and increased healthcare utilisation. The effectiveness of multidisciplinary interventions remains unclear due to low-quality evidence and a lack of robust trials.\u003c/li\u003e\n \u003cli\u003eThis study highlights the need for tailored, multifactorial approaches that involve caregivers and address the complexities of home care.\u003c/li\u003e\n \u003cli\u003eThe findings underscore the importance of standardised outcomes, subgroup analyses, and economic and long-term evaluations to support sustainable strategies and inform policy-making.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003ePopulation ageing presents a significant health challenge in the 21st century\u0026nbsp;(1,2). The United Nations projects a doubling of older adults (≥65 years) by 2050, comprising 16% of the global population (3). This demographic shift will lead to an increase in chronic diseases, multimorbidity, and polypharmacy (4). Multimorbidity, defined as the coexistence of ≥2 chronic conditions (5), affects over 50% of older adults worldwide, representing a public health concern (6) due to its association with reduced quality of life, high mortality, functional decline, and increased healthcare utilisation (7,8). These issues are compounded by polypharmacy, defined as the concurrent use of ≥5 medications (9), which affects 32.1% of Europeans aged ≥65 years (10) and increases the risk of adverse drug events, medication non-adherence, and ineffective treatment regimens (11,12).\u003c/p\u003e\n\u003cp\u003eHealthcare systems, designed primarily for acute illnesses, struggle to meet the complex needs of patients with multimorbidity\u0026nbsp;\u0026nbsp;(13,14).\u0026nbsp;Consequently, 80-90% of care required by older adults is provided at home by informal caregivers (15), often family members, predominantly women (15–17).\u0026nbsp;Shifting family structures and increased female participation in the workforce have strained families' care capacity (15,18–20), leading to reliance on migrant women, particularly in high-income countries (15,20). These caregivers often lack formal training or adequate support, heightening risks of medication errors and adverse health outcomes (21).\u003c/p\u003e\n\u003cp\u003eMedication safety is a global priority, and strategies to improve adherence have involved various healthcare professionals. However, most studies have focused on isolated aspects of adherence, and robust evidence remains limited, particularly for home-dwelling older adults (23–25). The Cochrane systematic review by Cross et al. highlighted this gap, reporting insufficient evidence on the effectiveness of interventions in this population, primarily due to limited trial quality (26). This review aims to collate updated evidence on interventions to improve in-home medication management among multimorbid older adults living in the community (independently or with caregivers), and to assess the quality and the strength of the resulting recommendations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis systematic followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines (27) (Supplementary Table 1) and was prospectively registered in PROSPERO with the registration number CRD42024513056.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSearch methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA preliminary search was conducted in November and December 2023 using PubMed and Web of Science.\u0026nbsp;The final search was updated on 9\u003csup\u003eth\u003c/sup\u003e July 2024, applying the following strategy: (ELDERLY OR OLDER ADULTS OR DWELLING ADULTS OR CARER OR CAREGIVER) AND (POLYPHARMACY OR MULTIPLE MEDICATIONS) AND (MEDICATION MANAGEMENT OR MEDICATION ADHERENCE OR SAFE* MEDICATION) AND (HOME CARE OR TRANSITIONAL CARE OR PHARMACEUTICAL CARE) NOT CHILD*.\u003c/p\u003e\n\u003cp\u003eReferences from included studies and prior systematic reviews were manually searched. ClinicalTrials.gov was searched using the keywords \u0026quot;Home-dwelling\u0026quot;, \u0026quot;elderly\u0026quot;, \u0026quot;polypharmacy\u0026quot; and \u0026quot;intervention\u0026rdquo;, without additional results. Studies published from 2000 onward were included without language restrictions.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eInclusion and exclusion criteria\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEligible studies included\u0026nbsp;\u003cstrong\u003eRandomized Controlled Trials (RCTs)\u003c/strong\u003e, \u003cstrong\u003ecluster RCTs\u003c/strong\u003e, \u003cstrong\u003equasi-experimental studies\u003c/strong\u003e, \u003cstrong\u003einterrupted time series (ITS)\u003c/strong\u003e analyses, and\u0026nbsp;\u003cstrong\u003epre/post studies.\u0026nbsp;\u003c/strong\u003ePilot RCTs, observational studies, or qualitative research, and those with \u0026lt;30 days follow-up were excluded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe review focused on older adults at high risk of Drug-Related Problems (DRPs), living independently in the community or with their caregivers, including those recently discharged from hospitals or nursing facilities to home-based care. Participants had a mean age of \u0026ge; 60 years, with \u0026ge; 2 chronic conditions or using \u0026ge;5 long-term medications. Those institutionalised or whose medication was managed by home nurses were excluded.\u003c/p\u003e\n\u003cp\u003eCommunity or transitional care settings were included; residential or nursing homes, or hospitals without post-discharge follow-up were excluded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntervention\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInterventions aimed to enhance medication adherence or health outcomes; palliative care was excluded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparator\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStandard care practices were used as the comparator.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudies that measured at least one of the following main outcomes were considered for inclusion:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAdherence, measured by indirect methods, validated questionnaires (such as the Morisky Scale), pill count, electronic databases, Medication Events Monitoring System (MEMS) and medication ratios such as the Proportion of Days Covered (PDC) and Medication Possession Ratio (MPR). Other self-report adherence scales and indirect measures were also included.\u003c/li\u003e\n \u003cli\u003eAdverse health outcomes: hospital (re)admissions, emergency department (ED) visits, length of stay, drug-related problems (DRPs), adverse drug events (ADEs), and drug withdrawal symptoms (ADWES) were considered. Although not in the PROSPERO protocol, healthcare encounters (e.g., outpatient visits, specialist consultations, and primary care contacts) were also included as adverse health outcomes because they may indicate health complications, medication management challenges, or overuse of healthcare services.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eHealth-related quality of life\u003c/strong\u003e (measured using the \u003cstrong\u003eEuroQol-5D\u003c/strong\u003e), as well as elders\u0026apos; knowledge, attitudes, and behaviours related to medication; impact on functioning, cognition, urinary incontinence, falls, fractures, sleep quality, appetite, mortality, and costs related to pharmaceuticals and healthcare were considered as secondary outcomes. Studies reporting only secondary outcomes were excluded.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003e\u003cstrong\u003eStudy selection\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTwo authors (M.D-L., M.R.R-M.) independently screened studies by title/abstract using the Rayyan web tool for systematic reviews, applying eligibility criteria. Relevant full-text articles were retrieved and assessed for eligibility, with the authors blinded to each other\u0026apos;s decisions. Disagreements were resolved through discussion, and a third author (C.G-P, A.B-C.) was consulted if necessary. Excluded studies were listed in an Excel spreadsheet with reasons (Supplementary table 2). Selected studies were imported into Mendeley Reference Manager.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n \u003cli\u003e\u003cstrong\u003eData extraction\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eData extraction was performed independently by two review authors (M.D-L., M.R.R-M.). Discrepancies were resolved through discussion or consultation with a third author (C.G-P., A.B-C.) if necessary. All data were tabulated using a pre-designed Excel spreadsheet.\u003c/p\u003e\n\u003col start=\"4\"\u003e\n \u003cli\u003e\u003cstrong\u003eQuality assessment\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eRisk of bias was independently evaluated by two authors (M.D-L., M.R.R-M.). We used the updated Cochrane Risk of Bias 2 (ROB 2) (28) for randomised studies and the Risk Of Bias In Non-randomised Studies of Interventions (ROBINS-I) (29)\u0026nbsp;tool for non-randomised studies. To visually summarise the risk of bias, the robvis tool was employed. Studies were rated as high, low, or some concerns.\u003c/p\u003e\n\u003col start=\"5\"\u003e\n \u003cli\u003e\u003cstrong\u003eQuantitative analysis\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eMeta-analysis was conducted using a random-effects model due to anticipated heterogeneity across studies. The analysis was performed in R software version 4.4.1, specifically using the metafor package version 4.6.0. Effect sizes were expressed as standardised mean differences (SMD) for continuous outcomes, and odds ratios (ORs) for dichotomous outcomes. Pooled estimates were calculated assigning study weights based on the inverse of the variance of the study effect size, which includes the intra- and inter-study variability.\u003c/p\u003e\n\u003cp\u003eAdherence continuous outcomes were reported on different scales, SMD was used as a unitless effect size, calculated by dividing the mean difference between the intervention and control groups by the standard deviation of post-intervention values. Heterogeneity across studies was assessed using I\u0026sup2; statistics.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 7,648 records were identified through database searches and 14 through reverse searching. After removing duplicates and screening by title and abstract, 54 reports were assessed for eligibility. Following full-text review, 44 articles met the eligibility criteria, with two independent publications reporting different outcomes from the same RCT (30,31). The screening process is summarised in Figure 1.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eStudies\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCharacteristics of the included studies, 35 RCTs and 8 quasi-experimental, are summarised in table 1 and detailed in Supplementary Tables 3a and 3b. Published between 2001 (29) and 2023 (30–32), most were conducted in the United States (9/43, 20.9%), United Kingdom (6/43, 13.9%), and Spain (5/43, 11.6%). One involved several countries\u0026nbsp;(29).\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eSetting\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe most frequent setting was primary care (16/43, 37.2%), followed by the community (11/43, 25.6%), hospital discharge (4/43, 9.3%) and mixed settings (4/43, 9.3%).\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThere was a wide range of sample sizes from 59 to 4,960 participants. A total of 26,720 participants were included; follow-up periods varied from 1 to 24 months. In 37 studies (86.0%), the mean age ranged from 60.1 ± 11.7 to 85.5 ± 4.0 years. Five studies (11.6%) reported age as median [IQR], and one study (2.3%) provided age as percentage distributions. Gender was reported in 42 studies (97.7%), with the proportion of females ranging from 22.5% to 78.0%.\u003c/p\u003e\n\u003cp\u003eComorbidities were quantitatively reported (mean or median) in 12 studies (27.9%) and 3 studies (6.9%) used comorbidity scores, such as ISAR, Charlson or CIRS\u0026nbsp;(30–32). One study (2.3%) reported comorbidities categorically, with most participants having one or two (32). Seven studies (16.3%) provided qualitative data on the distribution of chronic conditions, mainly cardiovascular or respiratory diseases, and hypertension, while 20 (46.5%) targeted multimorbid patients without providing details.\u003c/p\u003e\n\u003cp\u003eThe mean number of medications was reported in 36 studies (83.7%), with a range of 3.7 ± 2.34 (68) to 17.55 ± 4.10 (39). Three studies (33,45,63) reported median values, ranging from 7 [5–8] (63) to 8.3 [7.4–9.3] (45). Seven studies (16.3%) did not provide data on medication numbers. Other variables, such as marital status, education level, dependency, and caregiver involvement, were also reported where available (Supplementary Tables 3a and 3b).\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eInterventions\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eMost studies did not specify whether interventions targeted patients or caregivers. Some focused exclusively on patients (5 studies, 11.6%), who were cognitively intact\u0026nbsp;(32)\u0026nbsp;or self-managed their medication \u0026nbsp;(34–36), while others involved caregivers in supervision\u0026nbsp;(33), medication management\u0026nbsp;(37),\u0026nbsp;care participation\u0026nbsp;(29,45), or consent processes (38,46,61,69).\u003c/p\u003e\n\u003cp\u003eDetails on interventions are in Supplementary table 4. They were categorised as: medication management strategies, educational interventions, and digital/technology tools. Adherence support tools were considered as complementary strategies.\u003c/p\u003e\n\u003cp\u003eMedication management strategies were featured in 31 of the 43 included studies with medication review being the most common\u0026nbsp;(27–29,31–33,35,38–61). Educational interventions were central to 32 studies\u0026nbsp;(27–29,33–39,41,43,45,48–50,52–55,57,59–70). The use of technology and digital tools was less prevalent, appearing in five studies\u0026nbsp;(32,42,47,63,67). Adherence support tools were employed in 15 studies\u0026nbsp;(27,32,35,38,41,42,44,45,47,53,56,57,59,60,67).\u0026nbsp;Pharmacists were the main providers, leading 26 studies (60.5%) (Supplementary Tables 3a and 3b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuality appraisal\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf 35 RCTs, 60.0% had a high risk of bias, 31.4% had some concerns, and 8.6% had a low risk of bias. Risk of bias related to randomisation was the lowest, while the risk due to missing outcome data was the highest (Supplementary Table 5a, Figure 2a).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor quasi-experimental studies, 87.5% showed a high risk of bias: 62.5% had a serious risk of bias, while 25% were rated as critical risk of bias. Only 12.5% had a moderate risk of bias. (Supplementary Table 5b, Figure 2b).\u0026nbsp;The highest risk domains related to confounding factors and missing data, whereas the lowest risk concerned outcome measurement and selective reporting of results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcomes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary outcomes:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings regarding primary outcomes are summarised in Table 2 and detailed in Supplementary Table 6.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdherence measurement:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eMorisky Medication Adherence Scale:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the MMAS-4, four studies (33–36) were meta-analysed, producing an OR of 1.45 (95% CI 0.86-2.44), with moderate heterogeneity (I² = 55%) and a p-value of 0.08. This suggests a favourable non-significant effect (Figure 3a).\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eObjective Methods:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor objective methods, most studies did not provide an effect size. Methods like pill count or Medication Possession Ratios (MPR) showed comparable adherence between groups.\u003c/p\u003e\n\u003cp\u003eOne study (37) reported a near-significant effect at 6 months regarding the Drug Score (OR = 0.7; 95% CI = 0.5-1.0), which disappeared at 9 months. Due to the variability in methods, these results were unsuitable for meta-analysis.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eOther self-report tools and scales:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAmong the 10 studies employing other self-report tools to measure medication adherence\u0026nbsp;(38–47), six demonstrated a beneficial effect (38–40,43,45,47). A meta-analysis was conducted focusing on continuous measures, including only 6 articles that provided sufficient data (37,41,42,46,48,49). The results, displayed in Figure 3b (SMD= 0.00; 95% CI= -0.09-0.10), with low heterogeneity (I² = 9%) and p = 0.36, suggested no effect.\u003c/p\u003e\n\u003cp\u003eAdverse health outcomes measurement:\u003c/p\u003e\n\u003cp\u003eOf the 34 studies reporting one or more adverse health outcomes, 30 addressed healthcare utilisation.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eSpecialised care:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOf the four studies examining patients with one or more hospitalisations (33,38,50,51), none found statistically significant differences, though two (50,51) noted trends suggesting increased hospitalisations in the intervention group. Among the 10 studies assessing total number of hospitalisations (32,34,37,44,46,52–56), two (44,55) reported statistically significant reductions in the intervention groups.\u003c/p\u003e\n\u003cp\u003eFor readmissions,\u0026nbsp;stratified analysis by follow-up duration (Figure 3c) showed no significant differences for short-term follow-up (OR=0.75; 95% CI=0.47-1.19). A protective and clearly significant effect was observed for studies with a follow-up period of 2-3 months (OR=0.41; 95% CI=0.25-0.69), while the effect reversed and lost significance for studies with a follow-up of 6 months (OR=1.22; 95% CI=0.76-1.96).\u003c/p\u003e\n\u003cp\u003eRegarding ED visits, only five studies could be included in the meta-analysis, but moderate to strong heterogeneity prevented conclusive results (Figure 3d).\u003c/p\u003e\n\u003cp\u003eNine studies compared length of stay (37,44,46,47,53,57–60), mostly showing no significant differences between groups. Some suggested trends of longer stays in the intervention group (47,58–60) and one found that the intervention group had a significantly longer length of stay compared to the control group (47).\u003c/p\u003e\n\u003cp\u003eContacts with Primary Care:\u003c/p\u003e\n\u003cp\u003eUnder this category we grouped: Primary Care visits (33,36,46,53,61), contacts with the General Practitioner (GP) (38), GP Home visits (31), Primary Care Nurse consultations (34) and telephone consultations (59). Six studies provided data which could be synthesise (31,44,46,53). The results (Figure 3e) showed a weak but significant effect in favour of an increased number of contacts in the intervention group (SMD = 0.06; 95% CI= -0.04- 0.16), with moderate heterogeneity (I² = 42%), p= 0.10.\u003c/p\u003e\n\u003cp\u003eDRPs (30,44,48,53,62–64), ADEs (34,48), and ADWEs (50,59) were also assessed. However, comparability of the results was limited due to differences in strategies used to define, measure, and report these problems.\u003c/p\u003e\n\u003cp\u003eOther adverse health outcomes, summarised in Supplementary Table 6, could not be meta-analysed or did not provide results of interest (Supplementary Figures 1-4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSecondary outcomes\u003c/strong\u003e (Supplementary table 7):\u003c/p\u003e\n\u003cp\u003eRegarding quality of life, six out of 10 studies were meta-analysed, revealing no significant effect of the interventions (SMD = -0.03; 95% CI = -0.14 to 0.09), with moderate heterogeneity (I²=41%), p=0.13 (Supplementary Figure 5). Five studies evaluated patients’ beliefs and attitudes towards medication (37,41,44,46,47), all using the Beliefs about Medicines Questionnaire (BMQ). Most found no significant differences between groups regarding patients’ beliefs and attitudes towards medication, although one (47), reported a significant positive effect on reducing concerns and improving the necessity-concerns balance.\u003c/p\u003e\n\u003cp\u003eThree studies assessed patients’ knowledge of medicines through various methods (38,42,46) finding no significant differences. Five assessed mobility or functional status (32,37,49,63,65), and three analysed falls (41,49,66), with only one (66) observed a\u0026nbsp;positive impact. Seven studies addressed healthcare costs (31,38,44,46,47,55,64), but conceptual differences prevented results synthesis. Finally, ten studies reported mortality (33,36,47,49,51,59,60,67–69). Results from the meta-analysis, including nine studies, showed no significant effect (Supplementary Figure 6).\u003c/p\u003e\n\u003cp\u003eSupplementary Table 8 summarises the tools, methods, and metrics used for outcome assessment.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis review aimed to collate evidence on interventions to improve medication management for multimorbid older adults living in the community. Optimising medication management in this population is a public health priority, given the challenges of ageing societies and polypharmacy. \u0026nbsp;No single intervention consistently improved adherence or patient outcomes. A medium-term reduction in readmissions was observed, but there was no significant effect on adherence, quality of life, or mortality. Heterogeneity in ED visits prevented conclusive results, and primary care contacts showed a weak, non-significant increase. Inconsistent outcome measurement limited conclusions on DRPs and healthcare costs. Overall, methodological quality was low to moderate.\u003c/p\u003e\n\u003cp\u003eStrengths include broad outcome assessment\u0026nbsp;and systematic quality evaluation using validated tools (28,29). Diverse intervention types provided insights across various healthcare settings. No language restrictions reduced language bias. Although the search was limited to PubMed and Web of Science, these databases cover varied populations and inclusion of trial registries mitigated publication bias. Meta-analyses identified trends where possible.\u003c/p\u003e\n\u003cp\u003eLimitations include heterogeneity and a lack of standardised classifications for interventions and outcomes, complicating overall conclusions and assessment of multifaceted interventions. Variations in settings and resources, small sample sizes, short follow-up, and methodological differences limited comparability. Lack of stratification by gender, socioeconomic status, or caregiver type may have masked subgroup effects. Random-effects models provided conservative estimates.\u0026nbsp;Given these limitations, findings should be interpreted with caution, and future research must aim for greater methodological rigour and standardisation.\u003c/p\u003e\n\u003cp\u003eFindings on adherence align with Burgos-Alonso et al. (70) and Cross et al. (26), both reporting inconclusive results, likely due to similar inclusion criteria. In contrast, Roncal-Belzunce et al. (71) reported benefits, especially with collaborative nursing approaches, possibly due to a combined analysis of objective and subjective measures.\u003c/p\u003e\n\u003cp\u003eFor ED visits, we found no significant reduction and high heterogeneity, unlike Roncal-Belzunce et al. (71)\u0026nbsp;likely reflecting different measurement approaches.\u0026nbsp;\u0026nbsp;Cross et al. (26) suggested reductions in ED visits with mixed interventions,\u0026nbsp;combining ED and hospital admissions data.\u003c/p\u003e\n\u003cp\u003eOur results for mortality and quality of life are consistent with Roncal-Belzunce et al. (71)\u0026nbsp;who found no significant effects. While they reported DRP improvements, we could not meta-analyse due to inconsistent reporting.\u003c/p\u003e\n\u003cp\u003eFrom an economic perspective, our findings align with Laberge et al. (72), who found inconclusive evidence\u0026nbsp;on nursing intervention costs. Roncal-Belzunce et al (71). also noted potential reductions in medication-related costs, but not total care costs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications for Clinical Practice and Research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImproving adherence and outcomes requires addressing patient- and system-level factors. Health status, beliefs, behaviours, and motivations shape adherence\u0026nbsp;(73,74) and require tailored strategies. System factors, including the healthcare environment, care accessibility, and provider support, are equally important (74) .\u003c/p\u003e\n\u003cp\u003eAt the core lies the patient-caregiver dyad,\u0026nbsp;bridging the patient-system gap.\u0026nbsp;Informal caregivers, central to home medication management, often lack training and support\u0026nbsp;(75). Effective interventions should be collaborative, tailored to socio-health needs, and reflect varying health literacy (76, 77,78) ensuring active involvement of both patients and caregivers in decision-making (79).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSocial, demographic, and cultural factors, especially gender, as women often assume unsupported caregiving roles must also be considered (80).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCaregivers must be integrated into healthcare strategies (81) through training and tailored tools (75). Overcoming structural barriers, improving provider training and ensuring transparent, well-coordinated transitional care are crucial (82)\u0026nbsp;particularly in home settings where oversight is limited and caregiver dependence is high (83). Despite their importance, intervention cost-effectiveness remains underexplored (72).\u003c/p\u003e\n\u003cp\u003eThis review highlights persistent gaps in the evidence and offers insights to guide future research and clinical practice. Future research should standardise outcomes, evaluate subgroup and caregiver programme impacts, and conduct economic evaluations to guide policy.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eNo single intervention consistently improved adherence or patient outcomes. Tailored, multifactorial strategies, integrating caregivers and addressing home care complexities, are essential.\u003c/p\u003e\n\u003cp\u003eThe findings highlight the lack of harmonisation in study designs and variables, along with suboptimal quality. Robust trials, standardised outcomes, subgroup evaluations, long-term follow-up, and economic evaluation are needed to optimise home-based medication management interventions for multimorbid older adults and inform policy-making.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical review and approval were not required for this study because it is based solely on the retrieval and synthesis of data from previously published randomized controlled trials (RCTs).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable to this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAB-C: Conceptualization, Methodology, Supervision, Writing –Reviewing and Editing. CG-P: Conceptualization, Methodology, Supervision, Writing –Reviewing and Editing. AC-V: Conceptualization, Methodology, Supervision, Writing–Reviewing and Editing. MN-N: Conceptualization, Methodology, Supervision, Writing–Reviewing and Editing. MD-L: Data Curation, Investigation, Writing– Original Draft Preparation. MRR-M: Data Curation, Investigation, Writing –Original Draft Preparation. MEV-G: Formal Analysis, Visualization, Writing – Original Draft Preparation.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMN-N is supported by a research contract from the Carlos III Research Institute (Juan Rodés JR23/00025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUse of Large Language Models (LLMs)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors used ChatGPT to support language editing and improve clarity of the English in the manuscript. No content was generated by AI related to the design, analysis, interpretation, or scientific conclusions of the study. Therefore, ChatGPT is not included as an author.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDivo MJ, Martinez CH, Mannino DM. Ageing and the epidemiology of multimorbidity. European Respiratory Journal. 2014 Oct 1;44(4):1055\u0026ndash;68. \u003c/li\u003e\n\u003cli\u003eJia Hao L, Omar MS, Tohit N. Polypharmacy and Willingness to Deprescribe Among Elderly with Chronic Diseases. Int J Gerontol. 2018 Dec 1;12(4):340\u0026ndash;3. \u003c/li\u003e\n\u003cli\u003eUN. Ageing [Internet]. 2023 [cited 2024 Dec 3]. Available from: https://www.un.org/en/global-issues/ageing\u003c/li\u003e\n\u003cli\u003ePl\u0026aacute;cido AI, Herdeiro MT, Morgado M, Figueiras A, Roque F. Drug-related Problems in Home-dwelling Older Adults: A Systematic Review. Vol. 42, Clinical Therapeutics. Excerpta Medica Inc.; 2020. p. 559-572.e14. \u003c/li\u003e\n\u003cli\u003eReason B, Terner M, Moses McKeag A, Tipper B, Webster G. The impact of polypharmacy on the health of Canadian seniors. Fam Pract. 2012 Aug;29(4):427\u0026ndash;32. \u003c/li\u003e\n\u003cli\u003eXu X, Mishra GD, Jones M. Evidence on multimorbidity from definition to intervention: An overview of systematic reviews. Vol. 37, Ageing Research Reviews. Elsevier Ireland Ltd; 2017. p. 53\u0026ndash;68. \u003c/li\u003e\n\u003cli\u003eMcPhail SM. Multimorbidity in chronic disease: Impact on health care resources and costs. Vol. 9, Risk Management and Healthcare Policy. Dove Medical Press Ltd; 2016. p. 143\u0026ndash;56. \u003c/li\u003e\n\u003cli\u003eMakovski TT, Schmitz S, Zeegers MP, Stranges S, van den Akker M. Multimorbidity and quality of life: Systematic literature review and meta-analysis. Vol. 53, Ageing Research Reviews. Elsevier Ireland Ltd; 2019. \u003c/li\u003e\n\u003cli\u003eMasnoon N, Shakib S, Kalisch-Ellett L, Caughey GE. What is polypharmacy? A systematic review of definitions. Vol. 17, BMC Geriatrics. BioMed Central Ltd.; 2017. \u003c/li\u003e\n\u003cli\u003eMid\u0026atilde;o L, Giardini A, Menditto E, Kardas P, Costa E. Polypharmacy prevalence among older adults based on the survey of health, ageing and retirement in Europe. Arch Gerontol Geriatr. 2018 Sep 1;78:213\u0026ndash;20. \u003c/li\u003e\n\u003cli\u003ePasina L, Brucato AL, Falcone C, Cucchi E, Bresciani A, Sottocorno M, et al. Medication non-adherence among elderly patients newly discharged and receiving polypharmacy. Drugs Aging. 2014 Apr 1;31(4):283\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eMohammed MA, Moles RJ, Chen TF. Medication-related burden and patients\u0026rsquo; lived experience with medicine: a systematic review and metasynthesis of qualitative studies. Available from: http://bmjopen.bmj.com/\u003c/li\u003e\n\u003cli\u003ePloeg J, Matthew-Maich N, Fraser K, Dufour S, McAiney C, Kaasalainen S, et al. Managing multiple chronic conditions in the community: a Canadian qualitative study of the experiences of older adults, family caregivers and healthcare providers. BMC Geriatr. 2017 Jan 31;17(1):1\u0026ndash;15. \u003c/li\u003e\n\u003cli\u003evan der Aa MJ, van den Broeke JR, Stronks K, Plochg T. Patients with Multimorbidity and Their Experiences with the Healthcare Process: A Scoping Review. J Comorb. 2017 Jan;7(1):11\u0026ndash;21. \u003c/li\u003e\n\u003cli\u003eCommission Economic Policy Committee Economic E, Affairs European Economy Institutional Papers F. The 2018 Ageing Report: Economic and Budgetary Projections for the EU Member States (2016-2070). In. Available from: https://ec.europa.eu/info/publications/economic-and-financial-affairs-publications_en.\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a-Calvente M del M, Mateo-Rodr\u0026iacute;guez I, Maroto-Navarro G. El impacto de cuidar en la salud y la calidad de vida de las mujeres. Gac Sanit. 2004;18:83\u0026ndash;92. \u003c/li\u003e\n\u003cli\u003eOECD. Health at a Glance 2023 [Internet]. OECD; 2023. (Health at a Glance). Available from: https://www.oecd.org/en/publications/health-at-a-glance-2023_7a7afb35-en.html\u003c/li\u003e\n\u003cli\u003eAbajo M, Rodr\u0026iacute;guez-Sanz M, Malmusi D, Salvador M, Borrell C. Gender and socio-economic inequalities in health and living conditions among co-resident informal caregivers: a nationwide survey in Spain. J Adv Nurs. 2017 Mar 1;73(3):700\u0026ndash;15. \u003c/li\u003e\n\u003cli\u003eCascella Carb\u0026oacute; GF, Garc\u0026iacute;a-Orell\u0026aacute;n R. Burden and Gender inequalities around Informal Care. Vol. 38, Investigacion y Educacion en Enfermeria. Facultad de Enfermeria de la Universidad de Antioquia; 2020. \u003c/li\u003e\n\u003cli\u003eRodr\u0026iacute;guez-Lobato P, Mill\u0026aacute;n-Franco M. PAULA RODR\u0026Iacute;GUEZ-LOBATO, MARIO MILL\u0026Aacute;N-FRANCO G\u0026eacute;nero, Trabajo Social y fen\u0026oacute;meno migratorio: las cuidadoras internas latinoamericanas en Espa\u0026ntilde;a Gender, Social Work and migration phenomenon: Latin American live-in caregivers in Spain. \u003c/li\u003e\n\u003cli\u003eParand A, Garfield S, Vincent C, Franklin BD. Carers\u0026rsquo; medication administration errors in the domiciliary setting: A systematic review. PLoS One. 2016 Dec 1;11(12). \u003c/li\u003e\n\u003cli\u003eWHO. Medication Without Harm [Internet]. [cited 2024 Dec 3]. Available from: https://www.who.int/initiatives/medication-without-harm\u003c/li\u003e\n\u003cli\u003ePratiwi H, Kristina SA, Widayanti AW, Prabandari YS, Kusuma IY. A Systematic Review of Compensation and Technology-Mediated Strategies to Maintain Older Adults\u0026rsquo; Medication Adherence. Vol. 20, International Journal of Environmental Research and Public Health. MDPI; 2023. \u003c/li\u003e\n\u003cli\u003eBloomfield HE, Greer N, Linsky AM, Bolduc J, Naidl T, Vardeny O, et al. Deprescribing for Community-Dwelling Older Adults: a Systematic Review and Meta-analysis. Vol. 35, Journal of General Internal Medicine. Springer; 2020. p. 3323\u0026ndash;32. \u003c/li\u003e\n\u003cli\u003eDietrich FM, Hersberger KE, Arnet I. Benefits of medication charts provided at transitions of care: A narrative systematic review. BMJ Open. 2020 Oct 22;10(10). \u003c/li\u003e\n\u003cli\u003eCross AJ, Elliott RA, Petrie K, Kuruvilla L, George J. Interventions for improving medication-taking ability and adherence in older adults prescribed multiple medications. Vol. 2020, Cochrane Database of Systematic Reviews. John Wiley and Sons Ltd; 2020. \u003c/li\u003e\n\u003cli\u003ePage MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Vol. 372, The BMJ. BMJ Publishing Group; 2021. \u003c/li\u003e\n\u003cli\u003eSterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: A revised tool for assessing risk of bias in randomised trials. The BMJ. 2019;366. \u003c/li\u003e\n\u003cli\u003eSterne JA, Hern\u0026aacute;n MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, et al. ROBINS-I: A tool for assessing risk of bias in non-randomised studies of interventions. BMJ (Online). 2016;355. \u003c/li\u003e\n\u003cli\u003eAhmad A, Nijpels G, Dekker JM, Kostense PJ, Hugtenburg JG. Effect of a pharmacist medication review in elderly patients discharged from the hospital. Arch Intern Med. 2012 Sep 24;172(17):1346\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003evan der Heijden AAWA, de Bruijne MC, Nijpels G, Hugtenburg JG. Cost-effectiveness of a clinical medication review in vulnerable older patients at hospital discharge, a randomized controlled trial. Int J Clin Pharm. 2019 Aug 1;41(4):963\u0026ndash;71. \u003c/li\u003e\n\u003cli\u003eJerant A, Moore-Hill M, Franks P. Home-based, peer-led chronic illness self-management training: Findings from a 1-year randomized controlled trial. Ann Fam Med. 2009;7(4):319\u0026ndash;27. \u003c/li\u003e\n\u003cli\u003eCampins L, Serra-Prat M, G\u0026oacute;zalo I, L\u0026oacute;pez D, Palomera E, Agust\u0026iacute; C, et al. Randomized controlled trial of an intervention to improve drug appropriateness in communitydwelling polymedicated elderly people. Fam Pract. 2017 Feb 1;34(1):36\u0026ndash;42. \u003c/li\u003e\n\u003cli\u003eDel Cura-Gonz\u0026aacute;lez I, L\u0026oacute;pez-Rodr\u0026iacute;guez JA, Leiva-Fern\u0026aacute;ndez F, Gimeno-Miguel A, Poblador-Plou B, L\u0026oacute;pez-Verde F, et al. How to Improve Healthcare for Patients with Multimorbidity and Polypharmacy in Primary Care: A Pragmatic Cluster-Randomized Clinical Trial of the MULTIPAP Intervention. J Pers Med. 2022 May 1;12(5). \u003c/li\u003e\n\u003cli\u003eSu\u0026aacute;rez-Varela MTM. Estudio sobre la utilidad del pastillero para mejorar el cumplimiento terap\u0026eacute;utico. Aten Primaria. 2009 Apr;41(4):185\u0026ndash;91. \u003c/li\u003e\n\u003cli\u003eS\u0026aacute;ez de la Fuente J, Granja Bern\u0026aacute; V, Lechuga V\u0026aacute;zquez P, Otero Perpi\u0026ntilde;a B, Herreros de Tejada L\u0026oacute;pez-Coterilla A, Medina Asensio J. Efficiency of the Information Given at Discharge and Adherence of Polymedicated Patients. Farmacia Hospitalaria (English Edition). 2011 May;35(3):128\u0026ndash;34. \u003c/li\u003e\n\u003cli\u003eMuth C, Uhlmann L, Haefeli WE, Rochon J, Van Den Akker M, Perera R, et al. Effectiveness of a complex intervention on Prioritising Multimedication in Multimorbidity (PRIMUM) in primary care: Results of a pragmatic cluster randomised controlled trial. BMJ Open. 2018 Feb 1;8(2). \u003c/li\u003e\n\u003cli\u003eBernsten C, Bj\u0026ouml;rkman I, Caramona M, Crealey G, Fr\u0026oslash;kjaer B, Grundberger E, et al. Improving the Well-Being of Elderly Patients via Community Pharmacy-Based Provision of Pharmaceutical Care A Multicentre Study in Seven European Countries. Vol. 18, Drugs \u0026amp; Aging. 2001. \u003c/li\u003e\n\u003cli\u003eBiswas A, Sinha N, Ray K, Tripathi SK. A study on drug use and medication management perspectives among elderly and the impact of professional oversight. Journal of Clinical and Diagnostic Research. 2018 May 1;12(5):FC01\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;nez-Mardones F, Benrimoj SI, Ahumada-Canale A, Plaza-Plaza JC, Garcia-Cardenas V. BC Clinical impact of medication reviews with follow-up in cardiovascular older patients in primary care: A cluster-randomized controlled trial. Br J Clin Pharmacol. 2023 Jul 1;89(7):2131\u0026ndash;43. \u003c/li\u003e\n\u003cli\u003eMesserli M, Blozik E, Vriends N, Hersberger KE. Impact of a community pharmacist-led medication review on medicines use in patients on polypharmacy--a prospective randomised controlled trial. BMC Health Serv Res. 2016 Apr 23;16:145. \u003c/li\u003e\n\u003cli\u003eNazareth I, Burton A, Shulman S, Smith P, Haines A, Timberall H. A pharmacy discharge plan for hospitalized elderly patients\u0026ETH;a randomized controlled trial. \u003c/li\u003e\n\u003cli\u003eShim YW, Chua SS, Wong HC, Alwi S. Collaborative intervention between pharmacists and physicians on elderly patients: A randomized controlled trial. Ther Clin Risk Manag. 2018 Jun 15;14:1115\u0026ndash;25. \u003c/li\u003e\n\u003cli\u003eSyafhan NF, Al Azzam S, Williams SD, Wilson W, Brady J, Lawrence P, et al. General practitioner practice-based pharmacist input to medicines optimisation in the UK: pragmatic, multicenter, randomised, controlled trial. J Pharm Policy Pract. 2021 Dec 1;14(1). \u003c/li\u003e\n\u003cli\u003eWu JYF, Leung WYS, Chang S, Lee B, Zee B, Tong PCY, et al. Effectiveness of telephone counselling by a pharmacist in reducing mortality in patients receiving polypharmacy: Randomised controlled trial. Br Med J. 2006 Sep 9;333(7567):522\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eYang C, Lee DTF, Wang X, Chair SY. Effects of a nurse-led medication self-management intervention on medication adherence and health outcomes in older people with multimorbidity: A randomised controlled trial. Int J Nurs Stud. 2022 Oct 1;134. \u003c/li\u003e\n\u003cli\u003eOdeh M, Scullin C, Fleming G, Scott MG, Horne R, McElnay JC. Ensuring continuity of patient care across the healthcare interface: Telephone follow-up post-hospitalization. Br J Clin Pharmacol. 2019 Mar 1;85(3):616\u0026ndash;25. \u003c/li\u003e\n\u003cli\u003eChrischilles EA, Hourcade JP, Doucette W, Eichmann D, Gryzlak B, Lorentzen R, et al. Personal health records: A randomized trial of effects on elder medication safety. Journal of the American Medical Informatics Association. 2014;21(4):679\u0026ndash;86. \u003c/li\u003e\n\u003cli\u003eKouladjian O\u0026rsquo;Donnell L, Gnjidic D, Sawan M, Reeve E, Kelly PJ, Chen TF, et al. Impact of the Goal-directed Medication Review Electronic Decision Support System on Drug Burden Index: A cluster-randomised clinical trial in primary care. Br J Clin Pharmacol. 2021 Mar 1;87(3):1499\u0026ndash;511. \u003c/li\u003e\n\u003cli\u003eHerrinton LJ, Lo K, Alavi M, Alexeeff SE, Butler KM, Chang C, et al. Effectiveness of Bundled Hyperpolypharmacy Deprescribing Compared With Usual Care Among Older Adults: A Randomized Clinical Trial. JAMA Netw Open. 2023 Jul 3;6(7):e2322505. \u003c/li\u003e\n\u003cli\u003eOlesen C, Harbig P, Buus KM, Barat I, Damsgaard EM. Impact of pharmaceutical care on adherence, hospitalisations and mortality in elderly patients. Int J Clin Pharm. 2014;36(1):163\u0026ndash;71. \u003c/li\u003e\n\u003cli\u003eLenaghan E, Holland R, Brooks A. Home-based medication review in a high risk elderly population in primary care - The POLYMED randomised controlled trial. Vol. 36, Age and Ageing. 2007. p. 292\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eLenander C, Elfsson B, Danielsson B, Midl\u0026ouml;v P, Hasselstr\u0026ouml;m J. Effects of a pharmacist-led structured medication review in primary care on drug-related problems and hospital admission rates: A randomized controlled trial. Scand J Prim Health Care. 2014 Dec 1;32(4):180\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eLeendertse AJ, De Koning GHP, Goudswaard AN, Belitser S V., Verhoef M, De Gier HJ, et al. Preventing hospital admissions by reviewing medication (PHARM) in primary care: An open controlled study in an elderly population. J Clin Pharm Ther. 2013 Oct;38(5):379\u0026ndash;87. \u003c/li\u003e\n\u003cli\u003eMatzke GR, Moczygemba LR, Williams KJ, Czar MJ, Lee WT. Impact of a pharmacist-physician collaborative care model on patient outcomes and health services utilization. American Journal of Health-System Pharmacy. 2018 Jul 15;75(14):1039\u0026ndash;47. \u003c/li\u003e\n\u003cli\u003eMoreno G, Fu JY, Chon JS, Bell DS, Grotts J, Tseng CH, et al. Reducing Emergency Department Visits among Patients with Diabetes by Embedding Clinical Pharmacists in the Primary Care Teams. Med Care. 2021 Apr 1;59(4):348\u0026ndash;53. \u003c/li\u003e\n\u003cli\u003eBolas H, Brookes K, Scott M. Evaluation of a hospital-based community liaison pharmacy service in Northern Ireland. Vol. 26, Pharm World Sci. 2004. \u003c/li\u003e\n\u003cli\u003eBriggs S, Pearce R, Dilworth S, Higgins I, Hullick C, Attia J. Clinical pharmacist review: A randomised controlled trial. EMA - Emergency Medicine Australasia. 2015 Oct 1;27(5):419\u0026ndash;26. \u003c/li\u003e\n\u003cli\u003eMcCarthy C, Clyne B, Boland F, Moriarty F, Flood M, Wallace E, et al. GP-delivered medication review of polypharmacy, deprescribing, and patient priorities in older people with multimorbidity in Irish primary care (SPPiRE Study): A cluster randomised controlled trial. PLoS Med. 2022 Jan 1;19(1). \u003c/li\u003e\n\u003cli\u003eLembeck MA, Thygesen LC, S\u0026oslash;rensen BD, Rasmussen LL, Holm EA. Effect of single follow-up home visit on readmission in a group of frail elderly patients - A Danish randomized clinical trial. BMC Health Serv Res. 2019 Oct 25;19(1). \u003c/li\u003e\n\u003cli\u003eAl-Rashed SA, Wright DJ, Roebuck N, Sunter W, Chrystyn H. Blackwell Science, Ltd Oxford, UK BCPBritish Journal of Clinical Pharmacology 0306-5251Blackwell Publishing 2002 54Original Article Pre-discharge pharmaceutical counsellingS. Vol. 54, J Clin Pharmacol. 2002. \u003c/li\u003e\n\u003cli\u003eGeurts MME, Stewart RE, Brouwers JRBJ, de Graeff PA, de Gier JJ. Implications of a clinical medication review and a pharmaceutical care plan of polypharmacy patients with a cardiovascular disorder. Int J Clin Pharm. 2016 Aug 1;38(4):808\u0026ndash;15. \u003c/li\u003e\n\u003cli\u003eK\u0026ouml;berlein-Neu J, Mennemann H, Hamacher S, Waltering I, Jaehde U, Schaffert C, et al. Interprofessional medication management in patients with multiple morbidities - A cluster-randomized trial (the WestGem study). Dtsch Arztebl Int. 2016 Nov 4;113(44):731\u0026ndash;40. \u003c/li\u003e\n\u003cli\u003eMoczygemba LR, Barner JC, Lawson KA, Brown CM, Gabrillo ER, Godley P, et al. Impact of telephone medication therapy management on medication and health-related problems, medication adherence, and medicare part D drug costs: A 6-month follow up. American Journal Geriatric Pharmacotherapy. 2011 Oct;9(5):328\u0026ndash;38. \u003c/li\u003e\n\u003cli\u003eInsel KC, Einstein GO, Morrow DG, Koerner KM, Hepworth JT. Multifaceted Prospective Memory Intervention to Improve Medication Adherence. J Am Geriatr Soc. 2016 Mar 1;64(3):561\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003ePoorcheraghi H, Negarandeh R, Pashaeypoor S, Jorian J. Effect of using a mobile drug management application on medication adherence and hospital readmission among elderly patients with polypharmacy: a randomized controlled trial. BMC Health Serv Res. 2023 Dec 1;23(1). \u003c/li\u003e\n\u003cli\u003eHolland R, Lenaghan E, Harvey I, Smith R, Shepstone L, Lipp A, et al. Does home based medication review keep older people out of hospital? The HOMER randomised controlled trial. Vol. 330, British Medical Journal. 2005. p. 293\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eHugtenburg JG, Borgsteede SD, Beckeringh JJ. Medication review and patient counselling at discharge from the hospital by community pharmacists. Pharmacy World and Science. 2009 Dec;31(6):630\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eWestberg SM, Swanoski MT, Renier CM, Gessert CE. Evaluation of the Impact of Comprehensive Medication Management Services Delivered Posthospitalization on Readmissions and Emergency Department Visits [Internet]. Vol. 20, Journal of Managed Care \u0026amp; Specialty Pharmacy JMCP September. 2014. Available from: www.amcp.org\u003c/li\u003e\n\u003cli\u003eBurgos-Alonso N, Torrecilla M, Mendiguren A, P\u0026eacute;rez-G\u0026oacute;mez Moreta M, Bruzos-Cid\u0026oacute;n C. Strategies to Improve Therapeutic Adherence in Polymedicated Patients over 65 Years: A Systematic Review and Meta-Analysis. Pharmacy. 2024 Feb 17;12(1):35. \u003c/li\u003e\n\u003cli\u003eRoncal-Belzunce V, Guti\u0026eacute;rrez-Valencia M, Leache L, Saiz LC, Bell JS, Erviti J, et al. Systematic review and meta-analysis on the effectiveness of multidisciplinary interventions to address polypharmacy in community-dwelling older adults. Vol. 98, Ageing Research Reviews. Elsevier Ireland Ltd; 2024. \u003c/li\u003e\n\u003cli\u003eLaberge M, Sirois C, Lunghi C, Gaudreault M, Nakamura Y, Bolduc C, et al. Economic evaluations of interventions to optimize medication use in older adults with polypharmacy and multimorbidity: A systematic review. Vol. 16, Clinical Interventions in Aging. Dove Medical Press Ltd; 2021. p. 767\u0026ndash;79. \u003c/li\u003e\n\u003cli\u003eDevine F, Edwards T, Feldman SR. Barriers to treatment: Describing them from a different perspective. Vol. 12, Patient Preference and Adherence. Dove Medical Press Ltd.; 2018. p. 129\u0026ndash;33. \u003c/li\u003e\n\u003cli\u003eKardas P, Lewek P, Matyjaszczyk M. Determinants of patient adherence: A review of systematic reviews. Front Pharmacol. 2013;4 JUL. \u003c/li\u003e\n\u003cli\u003eAkgun-Citak E, Attepe-Ozden S, Vaskelyte A, van Bruchem-Visser RL, Pompili S, Kav S, et al. Challenges and needs of informal caregivers in elderly care: Qualitative research in four European countries, the TRACE project. Arch Gerontol Geriatr. 2020 Mar 1;87. \u003c/li\u003e\n\u003cli\u003eXu HY, Yu YJ, Zhang QH, Hu HY, Li M. Tailored Interventions to Improve Medication Adherence for Cardiovascular Diseases. Vol. 11, Frontiers in Pharmacology. Frontiers Media S.A.; 2020. \u003c/li\u003e\n\u003cli\u003eKreuter MW, Thompson T, McQueen A, Garg R. Addressing Social Needs in Health Care Settings: Evidence, Challenges, and Opportunities for Public Health. In: Annual Review of Public Health. Annual Reviews Inc.; 2020. p. 329\u0026ndash;44. \u003c/li\u003e\n\u003cli\u003eMartins NFF, Abreu DPG, Silva BT da, Semedo DSDRC, Pelzer MT, Ienczak FS. Functional health literacy and adherence to the medication in older adults: integrative review. Vol. 70, Revista brasileira de enfermagem. 2017. p. 868\u0026ndash;74. \u003c/li\u003e\n\u003cli\u003eCarman KL, Dardess P, Maurer M, Sofaer S, Adams K, Bechtel C, et al. Patient and family engagement: A framework for understanding the elements and developing interventions and policies. Health Aff. 2013;32(2):223\u0026ndash;31. \u003c/li\u003e\n\u003cli\u003eSimons M, Goossensen A, Nies H. Interventions fostering interdisciplinary and inter-organizational collaboration in health and social care; an integrative literature review. Vol. 28, Journal of Interprofessional Education and Practice. Elsevier Inc.; 2022. \u003c/li\u003e\n\u003cli\u003eKhatri R, Endalamaw A, Erku D, Wolka E, Nigatu F, Zewdie A, et al. Continuity and care coordination of primary health care: a scoping review. BMC Health Serv Res. 2023 Dec 1;23(1). \u003c/li\u003e\n\u003cli\u003eLjungholm L, Edin-Liljegren A, Ekstedt M, Klinga C. What is needed for continuity of care and how can we achieve it? \u0026ndash; Perceptions among multiprofessionals on the chronic care trajectory. BMC Health Serv Res. 2022 Dec 1;22(1). \u003c/li\u003e\n\u003cli\u003eLaValley S, Wahler R, Singh R, Monte S, Brady L. CAREGIVERS\u0026rsquo; ROLES IN MEDICATION MANAGEMENT FOR OLDER FAMILY MEMBERS. Innov Aging. 2018 Nov 1;2(suppl_1):290\u0026ndash;290. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Overview of the characteristics of the included studies\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"566\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics of the included studies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudies n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudies n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"12\" valign=\"top\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"10\" valign=\"top\"\u003e\n \u003cp\u003eSetting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrimary Care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (37.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnited Kingdom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (13.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCommunity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (25.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHospital Discharge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNetherlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMixed: Hospital-Community\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIreland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOutpatient Care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSecondary Care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEmergency Department\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDenmark\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGeriatric Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMulticountry \u0026sup1;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMilitary Medical Centre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eOther Countries\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e7 (16.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eSample size (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026le;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e101 - 300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (30.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003eIntervention provider\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePharmacist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26 (60.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e301 - 500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (23.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePharmacist- Physician\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (16.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (34.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNurse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eFollow-up period\u003cbr\u003e\u0026nbsp;(months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026le; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGeneral Practitioner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (34.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eElectronic tool\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (37.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePhysician-Nurse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt; 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient Characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudies n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudies n/N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60 - 65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (13.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eComorbidities (Mean/median)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e12/43 (27.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65 - 75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (41.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75 - 85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (41.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026le; 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4/12 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt; 85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 - 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3/12 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eFemale gender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026le; 25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5/12 (41.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26 - 50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (23.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51 - 75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28 (65.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eComorbidities- Qualitative reporting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e7/43 (16.3 %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt; 75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCardiovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6/7 (85.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot provided\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRespiratory disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5/7 (71.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eNumber of medications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 - 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5/7 (71.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 - 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23 (53.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eComorbidities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot provided\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20/43 (46.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e1. Denmark, Germany, Netherlands, Northern Ireland, Republic of Ireland, Portugal, Sweden\u003c/p\u003e\n\u003cp\u003e2. Brazil, Chile, India, Iran, Malaysia, Sweden, Switzerland\u003c/p\u003e\n\u003cp\u003eTable 2. Summary of findings\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"699\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMain Outcomes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTool\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudies reporting outcome (n ₀/N₀)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudies reporting significant Impact (n₁/N₁)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSummary of effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdherence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28/43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17/28\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMixed, mostly beneficial\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMMAS-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8/28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3/8 beneficial effect\u003cbr\u003e\u0026nbsp;1/8 negative effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMMAS-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3/28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2/3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMostly beneficial\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eObjective Methods\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10/28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5/10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMixed, beneficial effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOther self-report scales\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8/28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5/8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMixed, mostly Beneficial effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMixed Methods (Subjective-Objective) \u0026sup1;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1/28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1/1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBeneficial effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHospitalisations (Number)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10/43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMixed, mostly non-effect\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHospital Episode Statistics/ Records of County Council\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eElectronic record system/ Medical History\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2/4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBeneficial\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSelf-report\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnspecified tool\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHospitalisations (patients with event)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4/43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eElectronic Health Tools\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3/4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSelf-report\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1/4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Readmissions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3/43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1/3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMixed effects\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHospital Episode Statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1/3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1/1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNegative effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePatient Interview/ Medical History\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1/3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnspecified tool\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1/3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReadmissions (patients with event)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9/43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMixed effects, mostly beneficial\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eElectronic Health record\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNational Health Service Register/ PAS\u0026sup2;/ Hospital\u0026rsquo;s computer records\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2/4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMixed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSelf-report\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1/1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBeneficial\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSelf-report/ Medical records\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1/1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBeneficial\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnspecified tool\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1/1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBeneficial\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eED visits\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9/43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMixed, mostly non-effect\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eElectronic Health record / Medical History\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1/6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBeneficial\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSelf-report\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePatient Interview/ Medical History\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnspecified tool\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecialist Physician Visits\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3/43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"699\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMain Outcomes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTool\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudies reporting outcome (n ₀/N₀)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudies reporting significant Impact (n₁/N₁)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSummary of effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutpatient visits\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e8/43\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eNon-effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLength of stay\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e9/43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eMixed, negative effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eElectronic Medical Record\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e2/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eMixed, negative effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eSelf-report\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eNon-effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eUnspecified tool\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e3/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eNon-effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eRecords of County Council/ National Health Service Register/ Hospital\u0026rsquo;s computer records\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e3/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1/3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eMixed, negative effect\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContacts with Primary Care \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e10/43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eMixed, negative effect\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eElectronic Medical Record\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e3/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1/3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eMixed, negative effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eSelf-report\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e3/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eNon-effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003ePatient Interview/ Medical History\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eNon-effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eUnspecified tool\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eNon-effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eRecords of County Council/ PAS \u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e2/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eNon-effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDRPs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e7/43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e4/7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eMixed, beneficial effect\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eMedication analysis/ Patient Interview\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1/7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1/1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eBeneficial effect\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eSelf-report\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e2/7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eNon-effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eUnspecified tool\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1/7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eNon-effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eElectronic Medical Record/ Electronic Medical Chart/Patient file\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e3/7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e3/3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eBeneficial effect\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cp\u003e\u003cstrong\u003en ₀/N₀:\u0026nbsp;\u003c/strong\u003enumber of studies reporting a specific outcome out of the total number of studies included.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en₁/N₁:\u003c/strong\u003e number of studies using a particular tool that show a significant impact out of the total number of studies that used that tool.\u003c/p\u003e\n \u003cp\u003e1. Self-report /Pill count/ Computerised information\u003c/p\u003e\n \u003cp\u003e2. PAS: Patient Administration System\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e The totals do not sum to the overall number of studies as one study may report multiple outcomes or use different tools to measure adherence.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"aged, frail elderly, home environment, multimorbidity, polypharmacy, drug therapy, medication therapy management, meta-analysis, systematic review","lastPublishedDoi":"10.21203/rs.3.rs-6976707/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6976707/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Global population ageing is linked to increasing multimorbidity and polypharmacy. This shift places pressure on caregivers, who often lack training and face challenges like medication mismanagement. Our objective was to collate scientific evidence on interventions to enhance medication management among multimorbid older adults living in the community.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We conducted a systematic review and meta-analysis (PROSPERO: CRD42024513056) following PRISMA guidelines. PubMed, Web of Science, and ClinicalTrials.gov were searched up to July 9, 2024. Eligible studies were RCTs or quasi-experimental designs involving home-dwelling adults aged ≥60 years with ≥2 chronic conditions or ≥5 medications, assessing adherence or health outcomes, and ≥30 days of follow-up. Screening, data extraction, and quality assessment were performed in duplicate. Random-effects meta-analyses were conducted using R. ORs (95% CI) were calculated for binary outcomes and SMDs (95% CI) for continuous variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Of 7,648 citations, 43 studies met criteria. The Morisky Scale showed a favourable but non-significant effect (OR=1.45; 95% CI 0.86–2.44), and continuous measures showed no effect (SMD=0.00; 95% CI=-0.09–0.10; I²=9%). Readmissions showed a protective effect at medium-term (OR=0.41; 95% CI 0.25–0.69). ED visits were inconclusive due to heterogeneity. Primary care contacts showed a weak, non-significant effect. No effect was found for quality of life or mortality. DRPs and costs lacked conclusive evidence. Risk of bias was high in most studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e No intervention consistently improved adherence or outcomes. This review highlights evidence gaps, including standardised outcomes, long-term effects, economic evaluation, and methodological quality, to optimise future interventions and support evidence-based policymaking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegistration: \u003c/strong\u003eThis systematic review was prospectively registered in PROSPERO with the registration number CRD42024513056.\u003c/p\u003e","manuscriptTitle":"Interventions to enhance in-home taking medication among older adults. A systematic review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-15 10:51:42","doi":"10.21203/rs.3.rs-6976707/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0ab35023-1621-4f9e-86d6-c986e5e5217e","owner":[],"postedDate":"July 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-15T10:51:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-15 10:51:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6976707","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6976707","identity":"rs-6976707","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}