Andrew Clegg

Objective To develop the anticholinergic medication index (ACMI), a new anticholinergic burden measure derived from a clinical prediction model for falls and delirium, scoring 88 anticholinergic medicines prescribed in primary care.Design Development and external validation of a risk prediction model.Setting UK primary and secondary care data from Connected Bradford and the Secure Anonymised Information Linkage (SAIL) databank, 1 January 2019 to 31 December 2019.Participants For development cohort, 71 757 Bradford and Craven residents aged >65 years on 1 January 2019 who contributed data to Connected Bradford between 1 January 2019 and 31 December 2019. For external validation cohort, 678 134 Welsh residents aged >65 years on 1 January 2019 who contributed data to SAIL between 1 January 2019 and 31 December 2019.Main outcome measures Composite outcome of hospital admission for delirium or hospital admission for a fall or fracture (as an indicator of an injurious fall) during the study period.Results 71 757 patients (4147 (5.8%) with the composite outcome) comprised the development cohort and 678 134 patients (43 675 (6.4%) with the composite outcome) comprised the external validation cohort. Based on anticholinergic medications recorded in electronic health records as predictors, a prediction model for falls and delirium was developed and validated. Model coefficients for anticholinergic medications were transformed into ACMI scores. Of 88 anticholinergic medications, codeine, prednisolone, and furosemide were the most commonly prescribed (8.3%, 6.5%, and 6.0% of patients prescribed these anticholinergic drugs at least once in the development cohort, respectively). Model discrimination and calibration in the development cohort was good (C index=0.794, R 2=0.06, calibration slope=0.97). The model performed well in external validation with good discrimination (C index=0.721, 95% confidence interval (CI) 0.718 to 0.734, R2=0.04) but with some evidence of miscalibration (calibration slope=0.69). The ACMI medications were scored 0.1 (desloratadine) to 2.3 (amantadine).Conclusions The ACMI was designed to be implemented in primary care to automatically calculate anticholinergic burden. Combined with the model predicted risk of delirium or falls, the ACMI allows clinicians to prioritise patients for structured medication reviews to reduce overprescribing.

Context: There is limited evidence regarding the priorities of older people living in care homes to inform research activity. Objectives: This review aimed to identify research priorities from the perspective of residents, their relatives, community-dwelling older people, health/care professionals and care home staff. Methods: Included studies were from economically developed countries and designed to identify the research priorities of care home residents. Excluded studies described priorities relating to specific health conditions. Medline, Embase, PsycInfo, CINAHL were searched (January 2010–August 2024), alongside grey literature. Studies were assessed for quality but not excluded on this basis. A bespoke data extraction form was used, and content analysis undertaken to synthesise findings. Findings: Thirty-five reports were included. Study designs varied, including priority setting exercises, surveys, interviews, focus groups, literature reviews. Priorities – and unmet needs, where future research would be beneficial – were organised into themes: residents’ daily life; residents’ quality of life; health prevention, assessment and management; caring for residents; care home staff; health and care services. Limitations: Limiting the search to the English language might have excluded reports of relevance, but a large body of evidence from a wide range of countries was included. Priorities and un-met needs were included, as were reports that weren’t exclusive to care homes. This approach might have been insufficiently targeted; however, it increased resident representation in our findings. Implications: Review findings provide a valuable resource for researchers and health/care staff wishing to focus their research or service provision on areas of importance for care home residents.

BACKGROUND Digital literacy and internet access are increasingly recognised as “super social determinants of health” because of their influence on wider social determinants of health. Older people are more likely to experience digital exclusion, with research suggesting several factors influencing digital engagement. OBJECTIVE To explore the barriers and facilitators older adults reported regarding their digital engagement. METHODS A cross-sectional population-based questionnaire was administered to adults aged 65+ to explore digital engagement and identify characteristics associated with digital exclusion. Optional free-text responses were analysed using the capability, opportunity, motivation and behaviour (COM-B) model and Theoretical Domains Framework (TDF) to examine barriers and facilitators influencing digital engagement. Comments were coded using deductive framework analysis across the TDF domains. Coding was undertaken by multiple researchers to ensure consistency, with discrepancies resolved through discussion. Domain-specific comments were narratively summarised to describe recurring patterns, similarities and differences in participants’ experiences of digital engagement. RESULTS 1,114 survey participants provided free-text responses. Analysis identified barriers and facilitators across all TDF domains. The most prominent were Environmental context and resources, Beliefs about capabilities, Social influence, and Cognitive and interpersonal skills. Environmental context comments highlighted cost, poor connectivity, limited access to digital resources, and preferences for offline services as influencing digital engagement. Social support facilitated digital engagement but also highlighted concerns around dependency on others. Beliefs about capabilities focused on confidence in using technology and varied considerably. Perceived capability was influenced by familiarity, usability, and the ability to troubleshoot problems independently. Cognitive and interpersonal skills affected participants’ ability to learn and engage with digital technologies independently. CONCLUSIONS This study highlights the complexity of factors influencing older adults’ digital engagement. Framed around the COM-B model, findings indicated that opportunity and motivation were discussed more frequently than capability. While access to technology remains important, digital inclusion also depends on confidence, perceptions of technology, social influences, and equitable access. Findings suggest that interventions should extend beyond improving access alone and adopt a multi-level approach to facilitating digital engagement amongst older people involving service designers, policymakers, health and social care professionals, and community organisations.

BACKGROUND Digital exclusion is a super social determinant of health because it can affect many areas of life that influence our health and wellbeing. Older people are more likely to be digitally excluded, but existing estimations of the prevalence of digital exclusion amongst older people are thought to have several limitations. OBJECTIVE To estimate the prevalence of digital exclusion and to describe the internet use and online activities of those aged 65 and over. METHODS A paper-based questionnaire was posted to 5,982 people aged 65+, registered at two GP practices in Yorkshire, UK. Questions covered demographics, factors known or with potential to predict digital exclusion, internet usage, access to devices and connectivity. Participants returned completed questionnaires by post. Additional completions were achieved via follow up telephone calls to non-responders and community group visits. Participants were defined as digitally included if they had independently done any of 22 online tasks and classed as digitally excluded if they had not. Digitally included participants were further categorised as wide or narrow users. Descriptive statistics were produced to describe the digital activities of the sample population. Univariable odds ratios representing the odds of digital exclusion were estimated using logistic regression. RESULTS An excellent response resulted in a representative sample of 3,300 participants aged 65-100. The prevalence of digital exclusion was 16.9%, narrow digital use 29.2% and wide use 52.3%. Nearly 80% of participants had access to a smart device. Multiple characteristics, including older age, Asian ethnicity, living alone, living in a more deprived area, frailty and health problems were associated with increased odds of digital exclusion and narrow use. There was no evidence of an association between gender and digital exclusion. CONCLUSIONS Our findings suggest nearly half of people aged 65+ are digitally excluded or narrow users, who will miss out on some benefits of the digital world. Evidence from this survey can inform how digital engagement is discussed, assessed and supported. Understanding the breadth and detail of older people’s online activities- including those only possible with help from other people- could inform better design and delivery of healthcare and other services. CLINICALTRIAL ISRCTN https://www.isrctn.com/ISRCTN18306736 (registered 23/11/2023)

Background: Our aim was to develop and evaluate the electronic frailty index+, a prognostic tool, including four integrated prognostic-decision models, to stratify older people into subgroups for targeting key interventions. Methods: Design: Prognostic model development, internal validation and external validation using large data sets and longitudinal cohort study data, with decision curve and health economic analysis. Population: Patients aged 65+ years. Key outcomes: The 12-month outcomes for prognostic models: new home care package care home admission emergency department attendance/hospitalisation with fall/fracture all-cause mortality. Statistical methods: Prognostic modelling: We developed and internally validated models for our key outcomes in one large data set. We used internal–external cross-validation for the home care model and full external validation for the remaining three models in a second large data set. We used CARE75+ to investigate additional predictive value of clinical measures practical for primary care. Decision curve analysis: We translated the prognostic models into a framework to support clinical decision-making. Health economic evaluation: We integrated the falls prediction models with effect size estimates from network meta-analysis to examine potential cost savings. Results: We used data from 660,417 patients in SAIL, 88,947 in Connected Bradford and 252 CARE75+ participants. New home care package: Model performance was promising in internal–external cross-validation, with average calibration slope 1.00 (95% confidence interval 0.99 to 1.01), average calibration-in-the-large −0.01 (95% confidence interval −0.02 to 0.01), average observed/expected ratio 0.99 (95% confidence interval 0.98 to 1.01) and average C-statistic 0.81 (95% confidence interval 0.81 to 0.81). Emergency department attendance/hospitalisation with fall/fracture: Model performance was promising on internal and external validation, although with some evidence for overprediction of falls risk, with calibration slope 1.25 (95% confidence interval 1.24 to 1.27), calibration-in-the-large −0.931 (95% confidence interval −0.938 to −0.920), observed/expected ratio 0.43 (95% confidence interval 0.42 to 0.44), C-statistic 0.83 (0.82 to 0.83). Care home admission: Model performance was promising on internal validation, but it showed some miscalibration on external validation, with calibration slope 0.75 (95% CI 0.74 to 0.76), calibration-in-the-large −1.60 (−1.62 to −1.58) and observed/expected ratio 0.25 (95% CI 0.24 to 0.25), C-statistic of 0.86 (95% CI 0.86 to 0.86). All-cause mortality: The model showed excellent performance across the full range of predicted risks on external validation, with average calibration slope 1.00 (0.98 to 1.01), average calibration-in-the-large −0.23 (−0.27 to −0.19), average observed/expected ratio 0.77 (0.75 to 0.79) and average C-statistic 0.83 (0.82 to 0.83). Economic modelling: Modelling indicated that provision of multifactorial assessment and treatment for people with an annual falls risk of ≥ 40% has the largest cost reduction per targeted person (£1025). Discussion: All four prediction models have promising predictive performance, although some had evidence of overprediction of risk (miscalibration). Decision curve analysis indicates potential clinical utility, and economic modelling provides novel information for policy-makers and commissioners. Future work: Future research should include model impact studies to evaluate use of the models in routine care. Limitations: We were unable to complete external validation of the home care prediction model. Study registration: This study is registered as ClinicalTrials.gov ID NCT04113174. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 127905) and is published in full in Health Technology Assessment ; Vol. 30, No. 61. See the NIHR Funding and Awards website for further award information.

Background: HomeHealth is a home-based, voluntary sector service supporting older people with mild frailty to maintain independence through behaviour change. Support workers discuss the person’s priorities and enable setting/achieving goals around mobility, nutrition, socialising and/or psychological well-being. Aims: We tested clinical and cost-effectiveness of HomeHealth for maintaining independence in older people with mild frailty in a randomised controlled trial. Methods: Design: Single-blind, parallel randomised controlled trial open between 18 January 2021 and 4 July 2023, with mixed-methods process evaluation. Setting: Community-dwelling older people aged 65+ years with mild frailty from 27 general practices and community settings in London, Yorkshire and Hertfordshire. Randomisation: Participants were randomised 1 : 1 to receive HomeHealth or treatment as usual. Outcomes: Primary outcome was independence in activities of daily living (modified Barthel Index), analysed using linear mixed models. Secondary outcomes included frailty phenotype score, extended activities of daily living, well-being, psychological distress, loneliness, cognition, falls and mortality. Health economic outcomes included quality of life, capability and service use, including hospital admissions. Cost-effectiveness acceptability curves and cost-effectiveness planes were used to represent the probability of cost-effectiveness compared to treatment as usual. Process evaluation: We conducted semistructured interviews with participants receiving the intervention, HomeHealth workers and other stakeholders supporting service delivery. Interviews were thematically analysed. Fidelity of audio-recorded appointments was assessed by two independent raters. We evaluated potential mechanisms of impact using data from appointments attended, types of goals set and progress towards goals. Findings: We recruited 388 participants, mean age 81.4 years (standard deviation 6.5), 64% female and 94% White British/European. HomeHealth did not improve Barthel Index scores at 12 months (0.250, 95% confidence interval −0.932 to 1.432). At 6 months, we found small significant reductions in psychological distress (−1.237, 95% confidence interval −2.127 to −0.348), and frailty phenotype score (−0.252, 95% confidence interval −0.487 to −0.017). At 12 months, we found significant improvements in well-being (1.449, 95% confidence interval 0.124 to 2.775), reduced unplanned admissions (incidence rate ratio 0.65, 95% confidence interval 0.54 to 0.92) with lower associated costs (−£586/participant, 95% confidence interval −351 to −821). There were no differences in other outcomes. HomeHealth dominates treatment as usual with a negative point estimate for incremental costs (−796, 95% confidence interval −2016 to 424), positive point estimate for incremental quality-adjusted life-years (0.009, −0.021 to 0.039) and high probability of cost-effectiveness. Process evaluation: Sixty-four semistructured interviews were completed, including 49 participants and 15 HomeHealth workers/stakeholders. The service was acceptable and safe, with good fidelity of delivery. Participants made progress on personalised goals, most working on enhancing mobility. They found the service empowering, and received emotional/practical support. Engagement was more challenging when participants identified no need for change, had significant memory impairment or new/declining illness. Flexibility around varying symptoms and incorporating behaviour change into existing routines promoted engagement. Conclusion: HomeHealth did not improve independent functioning for older people with mild frailty. There were small significant improvements in frailty status, psychological distress and well-being and a 35% reduction in unplanned admissions, with high probability of cost-effectiveness. Limitations: We used a pragmatic design with intervention delivery in real-world settings during/after the COVID-19 pandemic, potentially with more variability in delivery. Our findings might not apply to other geographical settings/healthcare systems. Funding: This synopsis presents independent research funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme as award number NIHR128334.

Background: Structured medication reviews (SMRs) were introduced in 2020 to address polypharmacy in patients most at risk of medicines-related harm. Aim: To evaluate the impact of SMRs on prescribing in primary care. Design and setting: Retrospective observational cohort study of electronic health records from patients aged ≥65 years, prescribed ≥1 medications, and fulfilling the specific eligibility criteria for an SMR, registered at practices contributing data to the Oxford Clinical Informatics Digital Hub, between 1 April 2020 and 30 September 2022. Method: The association between SMRs and prescription changes was examined by matching individuals who received an SMR to individuals who did not receive an SMR, according to age, sex, and primary care practice, using cumulative density sampling. Analyses were undertaken using adjusted logistic regression. Results: Of 635 698 eligible patients, 82 285 (12.9%, 95% confidence interval [CI] = 12.9 to 13.0) received ≥1 SMR during the study observation period. In those prescribed potentially inappropriate drug combinations prior to an SMR, between 12.5% and 40.0% were corrected up to 3 months later. In matched analyses, SMRs were most strongly associated with an increase in new prescriptions of angiotensin-converting enzyme inhibitors (adjusted odds ratio [aOR] 1.56, 95% CI = 1.35 to 1.81), statins (aOR 1.78, 95% CI = 1.57 to 2.02), and antidepressants (aOR 1.45, 95% CI = 1.28 to 1.63). SMRs were also most strongly associated with stopping these drug classes in those previously prescribed treatment. Conclusion: SMRs were associated with starting new medications and stopping existing prescriptions compared with usual care. Further work is needed to understand if these changes improved patient outcomes.

Aim: To model projected trends in frailty prevalence, associated service use and costs in people aged 50 and over in England to 2040. Design: System dynamics simulation modelling. Setting: Adult population (aged 50 and over) of England. Participants: Routine data from primary care patients aged 50 and over (2.2 million individuals) from participating practices from the Royal College of General Practitioners Research Surveillance Centre (RCGP RSC) database between 2006 and 2017. Outcome measures: Projected frailty prevalence, primary, secondary and urgent care service use and costs in those aged 50 and over between 2025 and 2040. Results: The population of England aged 50 and over is projected to increase from 23.1 million in 2025 to 24 million in 2040. Frailty prevalence in this group will rise from 70.2% to 76.1%, with associated service use costs increasing by £10 billion. Measures to reduce frailty incidence or progression could reduce costs by £310 million/annum and £644 million/annum, respectively. Conclusions: Frailty prevalence and associated service use and costs will increase substantially in the ageing population. A shift in focus to prevention and slowing progression in middle age and the younger old would substantially reduce service use and costs by older people living with frailty.

Background: Structured Medication Reviews (SMRs) were introduced into primary care in England for patients living with multiple long-term conditions (MLTCs), polypharmacy, increased frailty, in care homes or at risk of medicines-related harm. SMRs aim to optimise the therapeutic potential of medication and reduce medicine-related harms through holistic reviews. Aim: To explore the day-to-day work being undertaken with, and by, clinical pharmacists to implement, embed and integrate SMRs into practice, and consider how to optimise SMRs. Design and setting: Qualitative one-to-one interviews with clinical pharmacists undertaking SMRs and SMR service leaders/managers (SMR leads) in England between February 2023 and November 2024. Method: Participants were recruited as part of a wider evaluation of the roll-out of SMRs in England. Interview topic guides and qualitative data analysis were informed by Normalization Process Theory (NPT). Results: Eighteen clinical pharmacists and five SMR leads participated. Participants reported often having to explain the purpose of SMRs and clinical pharmacists' roles to patients, partly due to patients not being informed about SMRs. Participants valued SMRs and expressed that trust-building and tailored consultations were important for optimising medications. Integration varied due to high workload, inconsistent leadership support, inadequate administrative/pharmacist technician resource and lack of training. However, participants described SMRs as valuable for identifying and addressing unmet needs and supporting holistic, person-centred care across MLTC pathways. Conclusion: The findings demonstrate the need for improved information on SMRs for patients and primary care teams, adequate and appropriate resource allocation, and enhanced support for consultation skills training to optimise medicines use.

Background/Objectives: Simple frailty assessments, such as the clinical frailty scale (CFS), are prognostic for worse outcomes in older adults with cancer and could support treatment decision-making. This interview study aims to explore clinicians’ experiences of using simple frailty assessments in oncology, including the impacts on patient care and barriers and facilitators to successful implementation. Methods: Semi-structured individual interviews were conducted with clinicians at three UK sites that had implemented CFS screening in lung cancer clinics as part of a national pilot, to explore how frailty assessments are applied and are impacting care. Purposive sampling targeted a range of professionals involved in assessing frailty and making treatment decisions. Recordings were transcribed verbatim and analysed thematically. Results: Ten clinicians participated, and four main themes were identified. ‘Assessing fitness and frailty’ explores the central role of performance status (PS), as well as its limitations, and what frailty assessments add. ‘Scoring and interpreting CFS’ describes the ease and relative yield of CFS use, particularly for patients with ‘borderline’ PS scores (e.g., PS 1–2 or 2–3), and the importance of contextual interpretation. ‘Role of frailty and impacts of assessment’ highlights how frailty assessments can enhance patient-centered care and support, and clinical and shared decision-making, with potential for streamlined care and system-level benefits. ‘Barriers and facilitators to implementation’ are described, including time, culture, guidance, and training, with recommendations provided. Conclusions: Assessing frailty has wide-ranging potential benefits for patients, oncology teams, and the wider system, but barriers must be overcome. Specific recommendations are provided to support the routine implementation of frailty assessments, which is a key step towards the benefits of frailty-informed care being realised at scale.