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Introduction: Multimorbidity is a major concern in primary care. Nevertheless, evidence of prevalence and patterns of multimorbidity, and their determinants, are scarce. The aim of this study is to systematically review studies of the prevalence, patterns and determinants of multimorbidity in primary care.
Methods: Systematic review of literature published between 1961 and 2013 and indexed in Ovid (CINAHL, PsychINFO, Medline and Embase) and Web of Knowledge. Studies were selected according to eligibility criteria of addressing prevalence, determinants, and patterns of multimorbidity and using a pretested proforma in primary care. The quality and risk of bias were assessed using STROBE criteria. Two researchers assessed the eligibility of studies for inclusion (Kappa = 0.86).
Results: We identified 39 eligible publications describing studies that included a total of 70,057,611 patients in 12 countries. The number of health conditions analysed per study ranged from 5 to 335, with multimorbidity prevalence ranging from 12.9% to 95.1%. All studies observed a significant positive association between multimorbidity and age (odds ratio [OR], 1.26 to 227.46), and lower socioeconomic status (OR, 1.20 to 1.91). Positive associations with female gender and mental disorders were also observed. The most frequent patterns of multimorbidity included osteoarthritis together with cardiovascular and/or metabolic conditions.
Conclusions: Well-established determinants of multimorbidity include age, lower socioeconomic status and gender. The most prevalent conditions shape the patterns of multimorbidity. However, the limitations of the current evidence base means that further and better designed studies are needed to inform policy, research and clinical practice, with the goal of improving health-related quality of life for patients with multimorbidity. Standardization of the definition and assessment of multimorbidity is essential in order to better understand this phenomenon, and is a necessary immediate step.
Background: Although polypharmacy can cause adverse health outcomes, patients often know little about their medication. A regularly conducted medication review (MR) can help provide an overview of a patient’s medication, and benefit patients by enhancing their knowledge of their drugs. As little is known about patient attitudes towards MRs in primary care, the objective of this study was to gain insight into patient-perceived barriers and facilitators to the implementation of an MR.
Methods: We conducted a qualitative study with a convenience sample of 31 patients (age ≥ 60 years, ≥3 chronic diseases, taking ≥5 drugs/d); in Hesse, Germany, in February 2016. We conducted two focus groups and, in order to ensure the participation of elderly patients with reduced mobility, 16 telephone interviews. Both relied on a semi-structured interview guide dealing with the following subjects: patients’ experience of polypharmacy, general design of MRs, potential barriers and facilitators to implementation etc. Interviews were audio-recorded, transcribed verbatim, and analysed by two researchers using thematic analysis.
Results: Patients’ average age was 74 years (range 62–88 years). We identified barriers and facilitators for four main topics regarding the implementation of MRs in primary care: patient participation, GP-led MRs, pharmacist-led MRs, and the involvement of healthcare assistants in MRs. Barriers to patient participation concerned patient autonomy, while facilitators involved patient awareness of medication-related problems. Barriers to GP-led MRs concerned GP’s lack of resources while facilitators related to the trusting relationship between patient and GP. Pharmacist-led MRs might be hindered by a lack of patients’ confidence in pharmacists’ expertise, but facilitated by pharmacies’ digital records of the patients’ medications. Regarding the involvement of healthcare assistants in MRs, a potential barrier was patients’ uncertainty regarding the extent of their training. Patients could, however, imagine GPs delegating some aspects of MRs to them.
Conclusions: Our study suggests that patients regard MRs as beneficial and expect indications for their medicines to be checked, and possible interactions to be identified. To foster the implementation of MRs in primary care, it is important to consider barriers and facilitators to the four identified topics.
Background: In oldest-old patients (>80), few trials showed efficacy of treating hypertension and they included mostly the healthiest elderly. The resulting lack of knowledge has led to inconsistent guidelines, mainly based on systolic blood pressure (SBP), cardiovascular disease (CVD) but not on frailty despite the high prevalence in oldest-old. This may lead to variation how General Practitioners (GPs) treat hypertension. Our aim was to investigate treatment variation of GPs in oldest-olds across countries and to identify the role of frailty in that decision.
Methods: Using a survey, we compared treatment decisions in cases of oldest-old varying in SBP, CVD, and frailty. GPs were asked if they would start antihypertensive treatment in each case. In 2016, we invited GPs in Europe, Brazil, Israel, and New Zealand. We compared the percentage of cases that would be treated per countries. A logistic mixed-effects model was used to derive odds ratio (OR) for frailty with 95% confidence intervals (CI), adjusted for SBP, CVD, and GP characteristics (sex, location and prevalence of oldest-old per GP office, and years of experience). The mixed-effects model was used to account for the multiple assessments per GP.
Results: The 29 countries yielded 2543 participating GPs: 52% were female, 51% located in a city, 71% reported a high prevalence of oldest-old in their offices, 38% and had >20 years of experience. Across countries, considerable variation was found in the decision to start antihypertensive treatment in the oldest-old ranging from 34 to 88%. In 24/29 (83%) countries, frailty was associated with GPs’ decision not to start treatment even after adjustment for SBP, CVD, and GP characteristics (OR 0.53, 95%CI 0.48–0.59; ORs per country 0.11–1.78).
Conclusions: Across countries, we found considerable variation in starting antihypertensive medication in oldest-old. The frail oldest-old had an odds ratio of 0.53 of receiving antihypertensive treatment. Future hypertension trials should also include frail patients to acquire evidence on the efficacy of antihypertensive treatment in oldest-old patients with frailty, with the aim to get evidence-based data for clinical decision-making.
Multimorbilidad en medicina de familia y los principios Ariadne : un enfoque centrado en la persona
(2017)
La multimorbilidad, definida como la presencia de dos o más enfermedades crónicas en un mismo individuo, conlleva consecuencias negativas para la persona e importantes retos para los sistemas sanitarios. En atención primaria, donde recae esencialmente la atención de este grupo de pacientes, la consulta es más compleja que la de un paciente con una única enfermedad debido, entre otros, al hecho de tener que manejar mayor cantidad de información clínica, disponer de poca evidencia científica para abordar la multimorbilidad, y tener que coordinar la labor de múltiples profesionales para garantizar la continuidad asistencial. Además, para poder implementar correctamente los planes de tratamiento en estos pacientes es necesario un proceso de toma de decisiones compartida médico-paciente. Entre las distintas herramientas disponibles para apoyar dicho proceso, recientemente se ha desarrollado una dirigida específicamente a pacientes con multimorbilidad en atención primaria y que se describe en el presente artículo: los principios Ariadne.
Background: Multimorbidity is associated with negative effects both on people’s health and on healthcare systems. A key problem linked to multimorbidity is polypharmacy, which in turn is associated with increased risk of partly preventable adverse effects, including mortality. The Ariadne principles describe a model of care based on a thorough assessment of diseases, treatments (and potential interactions), clinical status, context and preferences of patients with multimorbidity, with the aim of prioritizing and sharing realistic treatment goals that guide an individualized management. The aim of this study is to evaluate the effectiveness of a complex intervention that implements the Ariadne principles in a population of young-old patients with multimorbidity and polypharmacy. The intervention seeks to improve the appropriateness of prescribing in primary care (PC), as measured by the medication appropriateness index (MAI) score at 6 and 12 months, as compared with usual care.
Methods/Design: Design: pragmatic cluster randomized clinical trial. Unit of randomization: family physician (FP). Unit of analysis: patient. Scope: PC health centres in three autonomous communities: Aragon, Madrid, and Andalusia (Spain). Population: patients aged 65–74 years with multimorbidity (≥3 chronic diseases) and polypharmacy (≥5 drugs prescribed in ≥3 months). Sample size: n = 400 (200 per study arm). Intervention: complex intervention based on the implementation of the Ariadne principles with two components: (1) FP training and (2) FP-patient interview. Outcomes: MAI score, health services use, quality of life (Euroqol 5D-5L), pharmacotherapy and adherence to treatment (Morisky-Green, Haynes-Sackett), and clinical and socio-demographic variables. Statistical analysis: primary outcome is the difference in MAI score between T0 and T1 and corresponding 95% confidence interval. Adjustment for confounding factors will be performed by multilevel analysis. All analyses will be carried out in accordance with the intention-to-treat principle.
Discussion: It is essential to provide evidence concerning interventions on PC patients with polypharmacy and multimorbidity, conducted in the context of routine clinical practice, and involving young-old patients with significant potential for preventing negative health outcomes.
Trial registration: Clinicaltrials.gov, NCT02866799
Background Polypharmacy interventions are resource-intensive and should be targeted to those at risk of negative health outcomes. Our aim was to develop and internally validate prognostic models to predict health-related quality of life (HRQoL) and the combined outcome of falls, hospitalisation, institutionalisation and nursing care needs, in older patients with multimorbidity and polypharmacy in general practices.
Methods Design: two independent data sets, one comprising health insurance claims data (n=592 456), the other data from the PRIoritising MUltimedication in Multimorbidity (PRIMUM) cluster randomised controlled trial (n=502). Population: ≥60 years, ≥5 drugs, ≥3 chronic diseases, excluding dementia. Outcomes: combined outcome of falls, hospitalisation, institutionalisation and nursing care needs (after 6, 9 and 24 months) (claims data); and HRQoL (after 6 and 9 months) (trial data). Predictor variables in both data sets: age, sex, morbidity-related variables (disease count), medication-related variables (European Union-Potentially Inappropriate Medication list (EU-PIM list)) and health service utilisation. Predictor variables exclusively in trial data: additional socio-demographics, morbidity-related variables (Cumulative Illness Rating Scale, depression), Medication Appropriateness Index (MAI), lifestyle, functional status and HRQoL (EuroQol EQ-5D-3L). Analysis: mixed regression models, combined with stepwise variable selection, 10-fold cross validation and sensitivity analyses.
Results Most important predictors of EQ-5D-3L at 6 months in best model (Nagelkerke’s R² 0.507) were depressive symptoms (−2.73 (95% CI: −3.56 to −1.91)), MAI (−0.39 (95% CI: −0.7 to −0.08)), baseline EQ-5D-3L (0.55 (95% CI: 0.47 to 0.64)). Models based on claims data and those predicting long-term outcomes based on both data sets produced low R² values. In claims data-based model with highest explanatory power (R²=0.16), previous falls/fall-related injuries, previous hospitalisations, age, number of involved physicians and disease count were most important predictor variables.
Conclusions Best trial data-based model predicted HRQoL after 6 months well and included parameters of well-being not found in claims. Performance of claims data-based models and models predicting long-term outcomes was relatively weak. For generalisability, future studies should refit models by considering parameters representing well-being and functional status.
Introduction: Clinically complex patients often require multiple medications. Polypharmacy is associated with inappropriate prescriptions, which may lead to negative outcomes. Few effective tools are available to help physicians optimise patient medication. This study assesses whether an electronic medication management support system (eMMa) reduces hospitalisation and mortality and improves prescription quality/safety in patients with polypharmacy. Methods and analysis: Planned design: pragmatic, parallel cluster-randomised controlled trial; general practices as randomisation unit; patients as analysis unit. As practice recruitment was poor, we included additional data to our primary endpoint analysis for practices and quarters from October 2017 to March 2021. Since randomisation was performed in waves, final study design corresponds to a stepped-wedge design with open cohort and step-length of one quarter. Scope: general practices, Westphalia-Lippe (Germany), caring for BARMER health fund-covered patients. Population: patients (≥18 years) with polypharmacy (≥5 prescriptions). Sample size: initially, 32 patients from each of 539 practices were required for each study arm (17 200 patients/arm), but only 688 practices were randomised after 2 years of recruitment. Design change ensures that 80% power is nonetheless achieved. Intervention: complex intervention eMMa. Follow-up: at least five quarters/cluster (practice). recruitment: practices recruited/randomised at different times; after follow-up, control group practices may access eMMa. Outcomes: primary endpoint is all-cause mortality and hospitalisation; secondary endpoints are number of potentially inappropriate medications, cause-specific hospitalisation preceded by high-risk prescribing and medication underuse. Statistical analysis: primary and secondary outcomes are measured quarterly at patient level. A generalised linear mixed-effect model and repeated patient measurements are used to consider patient clusters within practices. Time and intervention group are considered fixed factors; variation between practices and patients is fitted as random effects. Intention-to-treat principle is used to analyse primary and key secondary endpoints.
Objectives: Investigate the effectiveness of a complex intervention aimed at improving the appropriateness of medication in older patients with multimorbidity in general practice.
Design: Pragmatic, cluster randomised controlled trial with general practice as unit of randomisation.
Setting: 72 general practices in Hesse, Germany.
Participants: 505 randomly sampled, cognitively intact patients (≥60 years, ≥3 chronic conditions under pharmacological treatment, ≥5 long-term drug prescriptions with systemic effects); 465 patients and 71 practices completed the study.
Interventions: Intervention group (IG): The healthcare assistant conducted a checklist-based interview with patients on medication-related problems and reconciled their medications. Assisted by a computerised decision support system, the general practitioner optimised medication, discussed it with patients and adjusted it accordingly. The control group (CG) continued with usual care.
Outcome measures: The primary outcome was a modified Medication Appropriateness Index (MAI, excluding item 10 on cost-effectiveness), assessed in blinded medication reviews and calculated as the difference between baseline and after 6 months; secondary outcomes after 6 and 9 months’ follow-up: quality of life, functioning, medication adherence, and so on.
Results: At baseline, a high proportion of patients had appropriate to mildly inappropriate prescriptions (MAI 0–5 points: n=350 patients). Randomisation revealed balanced groups (IG: 36 practices/252 patients; CG: 36/253). Intervention had no significant effect on primary outcome: mean MAI sum scores decreased by 0.3 points in IG and 0.8 points in CG, resulting in a non-significant adjusted mean difference of 0.7 (95% CI −0.2 to 1.6) points in favour of CG. Secondary outcomes showed non-significant changes (quality of life slightly improved in IG but continued to decline in CG) or remained stable (functioning, medication adherence).
Conclusions: The intervention had no significant effects. Many patients already received appropriate prescriptions and enjoyed good quality of life and functional status. We can therefore conclude that in our study, there was not enough scope for improvement.
Trial registration number: ISRCTN99526053. NCT01171339; Results.
Meeting Abstract : 10. Deutscher Kongress für Versorgungsforschung, 18. GAA-Jahrestagung. Deutsches Netzwerk Versorgungsforschung e. V. ; Gesellschaft für Arzneimittelanwendungsforschung und Arzneimittelepidemiologie e. V. 20.-22.10.2011, Köln
Hintergrund: Multimedikation als Folge von Multimorbidität ist ein zentrales Problem der Hausarztpraxis und erhöht das Risiko für unangemessene Arzneimittel-Verordnungen (VO). Um die Medikation bei älteren, multimorbiden Patienten zu optimieren und zu priorisieren, wurde eine computergestützte, durch Medizinische Fachangestellte (MFA) assistierte, komplexe Intervention (checklistengestütztes Vorbereitungsgespräch sowie Überprüfung eingenommener Medikamente durch MFA, Einsatz des web-basierten ArzneimittelinformationsDienstes AiD, spezifisches Arzt-Patienten-Gespräch) entwickelt und in einer 12-monatigen Pilotstudie auf Machbarkeit getestet. Ein auf 9 Items reduzierter MAI [1] wurde eingesetzt, um dessen Eignung als potentielles primäres Outcome der Hauptstudie zu prüfen.
Material und Methoden: In die Pilotstudie in 20 Hausarztpraxen mit Cluster-Randomisation auf Praxisebene in Kontrollgruppe (Regelversorgung b. empfohlenem Standard) vs. Interventionsgruppe (komplexe Intervention b. empfohlenem Standard) wurden 5 Pat./Praxis eingeschlossen (≥65 Jahre, ≥3 chron. Erkrankungen, ≥5 Dauermedikamente, MMSE ≥26, Lebenserwartung ≥6 Monate). Zur Bewertung des MAI wurden an Baseline (T0), 6 Wo. (T1) & 3 Mon. (T2) nach Intervention erhoben: VO, Diagnosen, Natrium, Kalium & Kreatinin i.S., Größe, Gewicht, Geschlecht, Cumulative Illness Rating Scale (CIRS) [2] durch die Hausarztpraxis; Symptome für unerwünschte Arzneimittelwirkungen im Patienten-Telefoninterview.
Für den MAI wurde die Angemessenheit jeder VO in den 9 Kategorien Indikation, Effektivität, Dosierung, korrekter & praktikabler Applikationsweg, Arzneimittelwechselwirkung, Drug-disease-Interaktion, Doppelverordnung, Anwendungsdauer 3-stufig bewertet (1 = korrekt - 3 = unkorrekt) und für die Auswertung auf Patientenebene summiert. Die Bewertung erfolgte ohne Kenntnis der Gruppenzugehörigkeit. Deskriptive Statistiken und Reliabilitätsanalysen, ungewichtete Auswertung und Gewichtung n. Bregnhoj [3].
Ergebnisse: Es wurden N=100 Patienten in die Studie eingeschlossen, im Mittel 76 Jahre (Standardabweichung, SD 6; Range, R: 64-93) , 52% Frauen, durchschnittlich 9 VO/Pat. (SD 2; R 4-16), mittlerer CIRS-Score 10 (SD 4; R 0-23). Basierend auf N=851 VO (100 Pat.) zu T0 betrug der Reliabilitätskoeffizient (RK, Cronbachs Alpha) der ungewichteten 9 Items 0,70. Items 1-5 wiesen akzeptable Trennschärfen auf (0,52-0,64), die der Items 6, 7 & 9 fielen mit 0,21-0,29 niedriger aus, die des Item 8 betrug 0,06. Auf der Basis der 9 gewichteten Items fiel die interne Konsistenz des MAI erwartet höher aus (0,75). Die Reliabilitätsanalysen auf VO-Ebene zeigten einen RK von 0,67 (ungewichtet) vs. 0,75 (gewichtet), die Trennschärfen waren vergleichbar. Zur Zwischenauswertung betrug der MAI (T1-T0) in der Interventionsgruppe (5 Praxen, 24 Pat.) -0,9 (SD 5,6), in der Kontrollgruppe (7 Praxen, 35 Pat.) -0,5 (SD 4,9); die Differenz zwischen beiden Gruppen Mi–Mk -0,4 [95% Konfidenzintervall: -3,4;2,6].
Schlussfolgerung: Der MAI ist als potentielles primäres Outcome in der Hauptstudie geeignet: wenige fehlende Werte, Darstellung von Unterschieden prä-post und zwischen den Gruppen, akzeptable interne Konsistenz. Der niedrige Trennschärfekoeffizient des Items 8 weist darauf hin, dass dieses Item nicht mit dem Gesamt-Skalenwert korreliert, auch die Items 6, 7 und 9 korrelieren wesentlich schwächer mit dem Gesamt-Skalenwert als die Items 1 bis 5. Eine Wichtung z.B. der Items 2, 5, 6 und 9 könnte erwogen werden, um den Fokus der Intervention in der Hauptzielgröße angemessen abzubilden.
Multimorbidity is a health issue mostly dealt with in primary care practice. As a result of their generalist and patient-centered approach, long-lasting relationships with patients, and responsibility for continuity and coordination of care, family physicians are particularly well placed to manage patients with multimorbidity. However, conflicts arising from the application of multiple disease oriented guidelines and the burden of diseases and treatments often make consultations challenging. To provide orientation in decision making in multimorbidity during primary care consultations, we developed guiding principles and named them after the Greek mythological figure Ariadne. For this purpose, we convened a two-day expert workshop accompanied by an international symposium in October 2012 in Frankfurt, Germany. Against the background of the current state of knowledge presented and discussed at the symposium, 19 experts from North America, Europe, and Australia identified the key issues of concern in the management of multimorbidity in primary care in panel and small group sessions and agreed upon making use of formal and informal consensus methods. The proposed preliminary principles were refined during a multistage feedback process and discussed using a case example. The sharing of realistic treatment goals by physicians and patients is at the core of the Ariadne principles. These result from i) a thorough interaction assessment of the patient’s conditions, treatments, constitution, and context; ii) the prioritization of health problems that take into account the patient's preferences – his or her most and least desired outcomes; and iii) individualized management realizes the best options of care in diagnostics, treatment, and prevention to achieve the goals. Goal attainment is followed-up in accordance with a re-assessment in planned visits. The occurrence of new or changed conditions, such as an increase in severity, or a changed context may trigger the (re-)start of the process. Further work is needed on the implementation of the formulated principles, but they were recognized and appreciated as important by family physicians and primary care researchers.