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Objective: Patients with type 2 diabetes require patient-centred care as guided by the Chronic Care Model (CCM). Many diabetes patients in Singapore are managed by the Primary Care Networks (PCNs) which organised healthcare professionals (HCPs) comprising general practitioners, nurses and care coordinators into teams to provide diabetes care. Little is known about how the PCNs deliver care to people with type 2 diabetes. This study evaluated the consistency of diabetes care delivery in the PCNs with the CCM., Design: This was a mixed-method study. The Assessment of Chronic Illness Care (ACIC version 3.5) survey was self-administered by the HCPs in the quantitative study (ACIC scores range 0-11, the latter indicating care delivery most consistent with CCM). Descriptive statistics were obtained, and linear mixed-effects regression model was used to test for association between independent variables and ACIC total scores. The qualitative study comprised semi-structured focus group discussions and used thematic analysis., Setting: The study was conducted on virtual platforms involving the PCNs., Participants: 179 HCPs for quantitative study and 65 HCPs for qualitative study., Results: Integrated analysis of quantitative and qualitative results found that there was support for diabetes care consistent with the CCM in the PCNs. The mean ACIC total score was 5.62 (SD 1.93). The mean element scores ranged from 6.69 (SD 2.18) (Health System Organisation) to 4.91 (SD 2.37) (Community Linkages). The qualitative themes described how the PCNs provided much needed diabetes services, their characteristics such as continuity of care, patient-centred care; collaborating with community partners, financial aspects of care, enablers for and challenges in performing care, and areas for enhancement., Conclusion: This mixed-methods study informs that diabetes care delivery in the Singapore PCNs is consistent with the CCM. Future research should consider using independent observers in the quantitative study and collecting objective data such as patient outcomes., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Introduction: In view of growing populations with chronic conditions, many countries are redesigning their health systems. However, little information is available about how health systems perform from the perspective of people living with chronic conditions. The Organisation for Economic Co-operation and Development (OECD) Member States therefore mandated the OECD to initiate the International Survey of People Living with Chronic Conditions (PaRIS survey), which aims to provide insight in outcomes and experiences of care as reported by people living with chronic conditions. The PaRIS-SUR consortium has been tasked by the OECD to support the development and implementation of the survey., Methods and Analysis: As primary care services play a pivotal role in the management of chronic conditions, the PaRIS survey will be implemented in the primary care setting. Data will be collected with a survey among users of primary care services aged 45 years or older, of whom many have chronic conditions. An additional survey is conducted among their primary care providers. The nested study design will allow analysis of the patient-reported data in relation to characteristics of and care provided by primary care providers within and across countries. In 2022, the survey will be tested in a Field Trial in participating countries. Data for cross-country comparison will be collected by the Main Survey in 2023., Ethics and Dissemination: Informed consent will be obtained from primary care providers and service users. National Project Managers search ethical approval of the survey in their country, if required. Reporting by the OECD will focus on questions for international comparison. A secured information technology platform will be developed for participants and stakeholders in countries to receive feedback and answer their own questions. Findings will also be disseminated through an international OECD flagship report, conferences, scientific papers and policy briefs, to inform strategies to improve care for people living with chronic conditions throughout the world., Competing Interests: Competing interests: The authors’ institutes received funding from OECD Member States, other countries participating in the PaRIS survey and the European Commission to develop and implement the PaRIS survey. Authors LT, RW, OG and JB work for a private company., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: Over 160 000 participants per year complete the 12-item Oxford Hip and Knee Scores (OHS/OKS) as part of the NHS England Patient-Reported Outcome Measures (PROMs) programme. We used a modern computational approach, known as computerised adaptive testing (CAT), to simulate individually tailored OHS and OKS assessment, with the goal of reducing the number of questions a patient must complete without compromising measurement accuracy., Methods: We fit the 2018/2019 PROMs data to an item response theory (IRT) model. We assessed IRT model assumptions alongside reliability. We used parameters from the IRT model with data from 2017/2018 to simulate CAT assessments. Two simulations were run until a prespecified SE of measurement was met (SE=0.32 and SE=0.45). We compared the number of questions required to meet each cut-off and assessed the correlation between the full-length and CAT administration., Results: We conducted IRT analysis using 40 432 OHS and 44 714 OKS observations. The OHS and OKS were both unidimensional (root mean square error of approximation 0.08 and 0.07, respectively) and marginal reliability 0.91 and 0.90. The CAT, with a precision limit of SE=0.32 and SE=0.45, required a median of four items (IQR 1) and two items (IQR 1), respectively, for the OHS, and median of four items (IQR 2) and two items (IQR 0) for the OKS. This represents a potential 82% reduction in PROM length. In the context of 160 000 yearly assessments, these methodologies could result in the omission of some 1 280 000 redundant questions per year, which equates to 40 000 hours of patient time., Conclusion: The application of IRT to the OHS and OKS produces an efficient and substantially reduced CAT. We have demonstrated a path to reduce the burden and potentially increase the compliance for these ubiquitous outcome measures without compromising measurement accuracy., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

The median age for total hip arthroplasty (THA) is over 70 years with the corollary that many individuals have multiple multimorbidities. Despite the predicted improvement in quality of life, THA might be denied even to those with low levels of multimorbidity., Objective: To evaluate how pre-existing levels of multimorbidity influence the likelihood and timing of THA., Setting: Longitudinal record linkage study of a UK sample linking their primary care to their secondary care records., Participants: A total of 28 025 patients were included, based on the recording of the diagnosis of hip osteoarthritis in a national primary care register, Clinical Practice Research Datalink. Data were extracted from the database on background health and morbidity status using five different constructs: Charlson Comorbidity Index, Electronic Frailty Index and counts of chronic diseases (from list of 17), prescribed medications and number of primary care visits prior to recording of osteoarthritis., Outcome Measures: The record of having received a THA as recorded in the primary care record and the linked secondary care database: Hospital Episode Statistics., Results: 40% had THA: median follow 10 months (range 1-17 years). Increased multimorbidity was associated with a decreased likelihood of undergoing THA, irrespective of the method of assessing multimorbidity although the impact varied by approach., Conclusion: Markers of pre-existing ill health influence the decision for THA in the elderly with end-stage hip osteoarthritis, although these effects are modest for indices of multimorbidity other than eFI. There is evidence of this influence being present even in people with moderate decrements in their health, despite the balance of benefits to risk in these individuals being positive., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

Total hip arthroplasty (THA) surgery for elderly people with multimorbidity increases the risk of serious health hazards including mortality. Whether such background morbidity reduces the clinical benefit is less clear., Objective: To evaluate how pre-existing health status, using multiple approaches, influences risks of, and quality of life benefits from, THA., Setting: Longitudinal record linkage study of a UK sample linking their primary care to their secondary care records., Participants: A total of 6682 patients were included, based on the recording of the diagnosis of hip osteoarthritis in a national primary care register and the recording of the receipt of THA in a national secondary care register.Data were extracted from the primary care register on background health and morbidity status using five different constructs: Charlson Comorbidity Index, Electronic Frailty Index (eFI) and counts of comorbidity disorders (from list of 17), prescribed medications and number of primary care visits prior to recording of THA., Outcome Measures: (1) Postoperative complications and mortality; (2) postoperative hip pain and function using the Oxford Hip Score (OHS) and health-related quality of life using the EuroQoL (EQ)-5D score., Results: Perioperative complication rate was 3.2% and mortality was 0.9%, both increased with worse preoperative health status although this relationship varied depending on the morbidity construct: the eFI showing the strongest relationship but number of visits having no predictive value. By contrast, the benefits were not reduced in those with worse preoperative health, and improvement in both OHS and EQ-5D was observed in all the morbidity categories., Conclusions: Independent of preoperative morbidity, THA leads to similar substantial improvements in quality of life. These are offset by an increase in medical complications in some subgroups of patients with high morbidity, depending on the definition used. For most elderly people, their other health disorders should not be a barrier for THA., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

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., Competing Interests: Competing interests: FMG, BSM, MB and CM received grants from the German Statutory Healthcare Insurance Company Techniker Krankenkasse during the course of the study. CS has been employed by Boehringer Ingelheim GmbH & Co KG since October 2019., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Introduction: Despite the enormous potential for adverse events in primary healthcare (PHC), the knowledge about how to improve patient safety in this context is still sparse. We describe the methods for the development and evaluation of an intervention targeted at PHC professionals to improve patient safety in Spanish PHC centres., Methods and Analysis: The intervention will consist in using the patient reported experiences and outcomes of safety in primary care (PREOS-PC) survey to gather patient-reported experiences and outcomes concerning the safety of the healthcare patients receive in their PHC centres, and feed that information back to the PHC professionals to help them identify opportunities for safer healthcare provision. The study will involve three stages. Stage 1 (developing the intervention) will involve: (i) qualitative study with 40 PHC providers to optimise the acceptability and perceived utility of the proposed intervention; (ii) Spanish translation, cross-cultural adaptation and validation of the PREOS-PC survey; (iii) developing the intervention components; and (iv) developing an online tool to electronically administrate PREOS-PC and automatically generate feedback reports to PHC centres. Stage 2 (piloting the intervention) will involve a 3-month feasibility (one group pre-post) study in 10 PHC centres (500 patients, 260 providers). Stage 3 (evaluating the intervention) will involve: (i) a 12-month, two-arm, two-level cluster randomised controlled trial (1248 PHC professionals within 48 PHC centres; with randomisation at the centre level in a 1:1 ratio) to evaluate the impact of the intervention on patient safety culture (primary outcome), patient-reported safety experiences and outcomes (using the PREOS-PC survey), and avoidable hospitalisations; (ii) qualitative study with 20 PHC providers to evaluate the acceptability and perceived utility of the intervention and identify implementation barriers., Ethics and Dissemination: The study was approved by the Ethics Committee of the Balearic Islands (CEI IB: 3686/18) with the 1964 Helsinki Declaration and its later amendments. The results will be disseminated in peer-reviewed publications and national and international conferences., Trial Registration Number: NCT03837912; pre-results., Competing Interests: Competing interests: IR-C and JMV co-developed the PREOS-PC questionnaire, which is now being licensed by Oxford Innovation Ltd., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: The aim of this study was to identify, with soft clustering methods, multimorbidity patterns in the electronic health records of a population ≥65 years, and to analyse such patterns in accordance with the different prevalence cut-off points applied. Fuzzy cluster analysis allows individuals to be linked simultaneously to multiple clusters and is more consistent with clinical experience than other approaches frequently found in the literature., Design: A cross-sectional study was conducted based on data from electronic health records., Setting: 284 primary healthcare centres in Catalonia, Spain (2012)., Participants: 916 619 eligible individuals were included (women: 57.7%)., Primary and Secondary Outcome Measures: We extracted data on demographics, International Classification of Diseases version 10 chronic diagnoses, prescribed drugs and socioeconomic status for patients aged ≥65. Following principal component analysis of categorical and continuous variables for dimensionality reduction, machine learning techniques were applied for the identification of disease clusters in a fuzzy c-means analysis. Sensitivity analyses, with different prevalence cut-off points for chronic diseases, were also conducted. Solutions were evaluated from clinical consistency and significance criteria., Results: Multimorbidity was present in 93.1%. Eight clusters were identified with a varying number of disease values: nervous and digestive; respiratory, circulatory and nervous; circulatory and digestive; mental, nervous and digestive, female dominant; mental, digestive and blood, female oldest-old dominant; nervous, musculoskeletal and circulatory, female dominant; genitourinary, mental and musculoskeletal, male dominant ; and non-specified, youngest-old dominant . Nuclear diseases were identified for each cluster independently of the prevalence cut-off point considered., Conclusions: Multimorbidity patterns were obtained using fuzzy c-means cluster analysis. They are clinically meaningful clusters which support the development of tailored approaches to multimorbidity management and further research., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: The aim was to compare multimorbidity patterns identified with the two most commonly used methods: hierarchical cluster analysis (HCA) and exploratory factor analysis (EFA) in a large primary care database. Specific objectives were: (1) to determine whether choice of method affects the composition of these patterns and (2) to consider the potential application of each method in the clinical setting., Design: Cross-sectional study. Diagnoses were based on the 263 corresponding blocks of the International Classification of Diseases version 10. Multimorbidity patterns were identified using HCA and EFA. Analysis was stratified by sex, and results compared for each method., Setting and Participants: Electronic health records for 408 994 patients with multimorbidity aged 45-64 years in 274 primary health care teams from 2010 in Catalonia, Spain., Results: HCA identified 53 clusters for women, with just 12 clusters including at least 2 diagnoses, and 15 clusters for men, all of them including at least two diagnoses. EFA showed 9 factors for women and 10 factors for men. We observed differences by sex and method of analysis, although some patterns were consistent. Three combinations of diseases were observed consistently across sex groups and across both methods: hypertension and obesity, spondylopathies and deforming dorsopathies, and dermatitis eczema and mycosis., Conclusions: This study showed that multimorbidity patterns vary depending on the method of analysis used (HCA vs EFA) and provided new evidence about the known limitations of attempts to compare multimorbidity patterns in real-world data studies. We found that EFA was useful in describing comorbidity relationships and HCA could be useful for in-depth study of multimorbidity. Our results suggest possible applications for each of these methods in clinical and research settings, and add information about some aspects that must be considered in standardisation of future studies: spectrum of diseases, data usage and methods of analysis., Competing Interests: Competing interests: None declared., (© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.)

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., Competing Interests: Competing interests: CM, FMG, CG, MB, SH, WEH, JR and LU report receiving grants from the German Federal Ministry of Education and Research, BMBF, grant number 01GK0702, during the course of the study. WEH reports other grants from Dosing, Heidelberg, during the course of the study; he received personal fees, non-financial support and other from Aqua Institute Göttingen; personal fees, non-financial support and other from Aspen Europe; personal fees and other from Diaplan; grants, personal fees, non-financial support and other from Actelion; personal fees and other from GSK GER/UK/Slovakia/France; personal fees from Thieme Verlag; personal fees and other from Daiichi Sankyo; personal fees and other from Bristol-Myers Squibb; personal fees and other from MSD Sharp & Dohme; personal fees and other from AstraZenica; personal fees and other from Boehringer; personal fees and other from Grünenthal; personal fees and other from KWHC; personal fees and other from Novartis; personal fees and other from Berlin-Chemie; grants, personal fees and other from Landesapothekerkammer BW; grants and other from BMBF (DZIF, ESTHER); grants, personal fees, non-financial support and other from BayerPharma; grants from CHIESI; personal fees and other from Doctrina Med; personal fees and other from GSK France, UK, Germany, Slovakai; personal fees, non-financial support and other from Pfizer; grants from Smooth ClinicalTrials; grants from Sumaya Biotec; grants from Klaus Tschira Stiftung; other from University Frankfurt; grants from Vaximm, outside the submitted work. FO, MvdA, JMV, RP and JAK have nothing to disclose., (© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.)

Objective: To improve medication appropriateness and adherence in elderly patients with multimorbidity, we developed a complex intervention involving general practitioners (GPs) and their healthcare assistants (HCA). In accordance with the Medical Research Council guidance on developing and evaluating complex interventions, we prepared for the main study by testing the feasibility of the intervention and study design in a cluster randomised pilot study., Setting: 20 general practices in Hesse, Germany., Participants: 100 cognitively intact patients ≥65 years with ≥3 chronic conditions, ≥5 chronic prescriptions and capable of participating in telephone interviews; 94 patients completed the study., Intervention: The HCA conducted a checklist-based interview with patients on medication-related problems and reconciled their medications. Assisted by a computerised decision-support system (CDSS), the GPs discussed medication intake with patients and adjusted their medication regimens. The control group continued with usual care., Outcome Measures: Feasibility of the intervention and required time were assessed for GPs, HCAs and patients using mixed methods (questionnaires, interviews and case vignettes after completion of the study). The feasibility of the study was assessed concerning success of achieving recruitment targets, balancing cluster sizes and minimising drop-out rates. Exploratory outcomes included the medication appropriateness index (MAI), quality of life, functional status and adherence-related measures. MAI was evaluated blinded to group assignment, and intra-rater/inter-rater reliability was assessed for a subsample of prescriptions., Results: 10 practices were randomised and analysed per group. GPs/HCAs were satisfied with the interventions despite the time required (35/45 min/patient). In case vignettes, GPs/HCAs needed help using the CDSS. The study made no patients feel uneasy. Intra-rater/inter-rater reliability for MAI was excellent. Inclusion criteria were challenging and potentially inadequate, and should therefore be adjusted. Outcome measures on pain, functionality and self-reported adherence were unfeasible due to frequent missing values, an incorrect manual or potentially invalid results., Conclusions: Intervention and trial design were feasible. The pilot study revealed important limitations that influenced the design and conduct of the main study, thus highlighting the value of piloting complex interventions., Trial Registration Number: ISRCTN99691973; Results., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/)

Introduction: The feedback and public reporting of PROMs data aims to improve the quality of care provided to patients. Existing systematic reviews have found it difficult to draw overall conclusions about the effectiveness of PROMs feedback. We aim to execute a realist synthesis of the evidence to understand by what means and in what circumstances the feedback of PROMs data leads to the intended service improvements., Methods and Analysis: Realist synthesis involves (stage 1) identifying the ideas, assumptions or 'programme theories' which explain how PROMs feedback is supposed to work and in what circumstances and then (stage 2) reviewing the evidence to determine the extent to which these expectations are met in practice. For stage 1, six provisional 'functions' of PROMs feedback have been identified to structure our review (screening, monitoring, patient involvement, demand management, quality improvement and patient choice). For each function, we will identify the different programme theories that underlie these different goals and develop a logical map of the respective implementation processes. In stage 2, we will identify studies that will provide empirical tests of each component of the programme theories to evaluate the circumstances in which the potential obstacles can be overcome and whether and how the unintended consequences of PROMs feedback arise. We will synthesise this evidence to (1) identify the implementation processes which support or constrain the successful collation, interpretation and utilisation of PROMs data; (2) identify the implementation processes through which the unintended consequences of PROMs data arise and those where they can be avoided., Ethics and Dissemination: The study will not require NHS ethics approval. We have secured ethical approval for the study from the University of Leeds (LTSSP-019). We will disseminate the findings of the review through a briefing paper and dissemination event for National Health Service stakeholders, conferences and peer reviewed publications., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)