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Abstract Background Antidepressants have a pivotal role in the treatment of many psychiatric disorders, but there are concerns about long-term use and adverse effects. The objectives of this study were (1) to examine time trends in antidepressant use, (2) to estimate the prevalence of long-term and potential high-risk antidepressant use, and (3) to examine patient characteristics associated with potential deprescribing indications (PDIs) (i.e., simultaneous long-term and potential high-risk antidepressant use). Methods Repeated population-based cross-sectional study for all 609,299 people aged ≥ 18 years resident in the Tayside or Fife regions of Scotland. The prevalence of antidepressant use was examined on June 30th (index date) of each year from 2012 to 2019, while the prevalence of long-term and potential high-risk use as well as PDIs was assessed and compared on the same dates in 2012 and 2019. Binary logistic regression modeling was used to examine patient characteristics associated with PDIs. Results Antidepressant use increased by 27% from 12.0 to 15.3% among adult residents between 2012 and 2019. While the proportion of antidepressants users dispensed ≥ 1 antidepressant for > 2 years increased from 54.3 to 61.9% between 2012 and 2019, the proportion of antidepressant users triggering ≥ 1 indicator of potential high-risk use decreased slightly from 37.9 to 34.7%. In 2019, potential high-risk use most commonly related to indicators targeting fall risk (16.0%), cardiovascular risks (14.1%), insomnia (10.6%), and risk of orthostatic hypotension (8.6%). More than 1 in 4 (25.8%) antidepressant users had PDIs. The main risk factors associated with PDIs included increasing age (65–79, adjusted OR 14.12; 95% CI, 13.15–15.17), increasing number of drugs taken concomitantly (≥ 15 drugs, adjusted OR 7.37; 95% CI, 6.71–8.10), use of tricyclic antidepressants (≥ 50 mg) (adjusted OR 5.49; 95% CI, 5.02–6.01), and concomitant use of ≥ 2 antidepressants (adjusted OR 5.52; 95% CI, 5.20–5.85). Conclusions Long-term and potential high-risk use of antidepressants is widespread, and potential deprescribing indications (PDIs) are increasing, suggesting the need for a critical review of their ongoing use by clinicians. If deemed necessary, future deprescribing interventions may use the criteria applied here for identification of patients with PDIs and for evaluating intervention effectiveness.

Abstract Background Innovation for reforming health and social care is high on the policy agenda in the United Kingdom in response to the growing needs of an ageing population. However, information about new innovations of care being implemented is sparse. Methods We mapped innovations for people in later life in two regions, North East England and South East Scotland. Data collection included discussions with stakeholders (n = 51), semi-structured interviews (n = 14) and website searches that focused on technology, evaluation and health inequalities. We analysed qualitative data using framework and thematic analyses. Quantitative data were analysed descriptively. Results One hundred eleven innovations were identified across the two regions. Interviewees reported a wide range of technologies that had been rapidly introduced during the COVID-19 pandemic and many remained in use. Digital exclusion of certain groups of older people was an ongoing concern. Innovations fell into two groups; system-level ones that aimed to alleviate systems pressures such as preventing hospital (re)admissions, and patient-level ones which sought to enhance health and wellbeing directly. Interviewees were aware of the importance of health inequalities but lacked data to monitor the impact of innovations on these, and evaluation was challenging due to lack of time, training, and support. Quantitative findings revealed that two thirds of innovations (n = 74, 67%) primarily focused on the system level, whilst a third (n = 37, 33%) primarily focused on the patient-level. Overall, over half (n = 65, 59%) of innovations involved technologies although relatively few (n = 12, 11%) utilised advanced technologies. Very few (n = 16, 14%) focused on reducing health inequalities, and only a minority of innovations (n = 43, 39%) had undergone evaluation (most of which were conducted by the service providers themselves). Conclusions We found a wide range of innovative care services being developed for people in later life, yet alignment with key policy priorities, such as addressing health inequalities, was limited. There was a strong focus on technology, with little consideration for the potential to widen the health inequality gap. The absence of robust evaluation was also a concern as most innovations were implemented without support to monitor effectiveness and/or without plans for sustainability and spread.

Background: Expanding primary care multidisciplinary teams (MDTs) was a key component of the 2018 Scottish GP contract, with more than 4700 MDT staff appointed since then. Aim: To explore patients’ views on primary care MDT expansion in Scotland. Design & setting: A mixed-methods evaluation, which included a postal survey and semi-structured telephone interviews with patients in Scotland. Method: A survey was undertaken of patients who had recently consulted a GP in deprived urban, affluent urban, and remote and rural areas, assessing awareness of five MDT roles and attitudes towards receptionist signposting. In addition, 30 individual interviews were conducted, exploring patients' MDT-care experiences. Results: Of 1053 survey responders, most were unaware of the option of MDT rather than GP consultations for three out of five roles (69% unaware of link worker appointments; 69% mental health nurse; and 58% pharmacist). Reception signposting was less popular in deprived urban areas (34% unhappy versus 29% in remote and rural versus 21% affluent urban; P

Video-Reflexive Ethnography (VRE) is an innovative and participatory research and improvement methodology that involves videoing in-situ work practices and collaboratively analysing this footage with participants during reflexive sessions. This involves participants ‘slowing down’, engaging reflexively with their everyday working practices, and taking time out to discuss issues collectively. VRE has increasingly been used across a range of different healthcare settings. However, one setting that has received less attention is the Acute Medical Unit (AMU). AMUs are busy short-stay hospital departments with very high patient throughput and large multidisciplinary teams where patients receive initial assessment, diagnosis and treatment before being moved to other wards or settings. The aim of this study was to examine how VRE as a research and improvement methodology can be applied, in the busy and complex setting of an AMU. In this paper we outline some of the methodological challenges encountered in this setting and discuss how these were transformed into opportunities and solutions. Then, we evaluate our work by using the four guiding principles at the heart of VRE (care, collaboration, reflexivity and exnovation) to test if, and how, the methodology can be used in such a complex and busy setting without losing its methodological rigor and impact. We show how it is possible to initiate and achieve the core principles of VRE in the complex and busy AMU setting through careful planning, constant revision of data collection methods, remaining highly flexible and adaptable to the spatial and temporal rhythms of the ward and being sensitive to hierarchical inter- and intra-professional relationships and vulnerabilities. Finally, we share recommendations for using VRE in other busy and complex settings.

Objectives To describe the effect of multimorbidity on adverse patient centred outcomes in people attending emergency department.Design Population based cohort study.Setting Emergency departments in NHS Lothian in Scotland, from 1 January 2012 to 31 December 2019.Participants Adults (≥18 years) attending emergency departments.Data sources Linked data from emergency departments, hospital discharges, and cancer registries, and national mortality data.Main outcome measures Multimorbidity was defined as at least two conditions from the Elixhauser comorbidity index. Multivariable logistic or linear regression was used to assess associations of multimorbidity with 30 day mortality (primary outcome), hospital admission, reattendance at the emergency department within seven days, and time spent in emergency department (secondary outcomes). Primary analysis was stratified by age (

Objectives To assess whether age, sex, comorbidity count, and race and ethnic group are associated with the likelihood of trial participants not being enrolled in a trial for any reason (ie, screen failure).Design Bayesian meta-analysis of individual participant level data.Setting Industry funded phase 3/4 trials of chronic medical conditions.Participants Participants were identified using individual participant level data to be in either the enrolled group or screen failure group. Data were available for 52 trials involving 72 178 screened individuals of whom 24 733 (34%) were excluded from the trial at the screening stage.Main outcome measures For each trial, logistic regression models were constructed to assess likelihood of screen failure in people who had been invited to screening, and were regressed on age (per 10 year increment), sex (male v female), comorbidity count (per one additional comorbidity), and race or ethnic group. Trial level analyses were combined in Bayesian hierarchical models with pooling across condition.Results In age and sex adjusted models across all trials, neither age nor sex was associated with increased odds of screen failure, although weak associations were detected after additionally adjusting for comorbidity (odds ratio of age, per 10 year increment was 1.02 (95% credibility interval 1.01 to 1.04) and male sex (0.95 (0.91 to 1.00)). Comorbidity count was weakly associated with screen failure, but in an unexpected direction (0.97 per additional comorbidity (0.94 to 1.00), adjusted for age and sex). People who self-reported as black seemed to be slightly more likely to fail screening than people reporting as white (1.04 (0.99 to 1.09)); a weak effect that seemed to persist after adjustment for age, sex, and comorbidity count (1.05 (0.98 to 1.12)). The between-trial heterogeneity was generally low, evidence of heterogeneity by sex was noted across conditions (variation in odds ratios on log scale of 0.01-0.13).Conclusions Although the conclusions are limited by uncertainty about the completeness or accuracy of data collection among participants who were not randomised, we identified mostly weak associations with an increased likelihood of screen failure for age, sex, comorbidity count, and black race or ethnic group. Proportionate increases in screening these underserved populations may improve representation in trials.Trial registration number PROSPERO CRD42018048202.

Summary: Background: Robustly examining associations between long-term conditions may be important in identifying opportunities for intervention in multimorbidity but is challenging when evidence is limited. We have developed a Bayesian inference framework that is robust to sparse data and used it to quantify morbidity associations in the oldest old, a population with limited available data. Methods: We conducted a retrospective cross-sectional study of a representative dataset of primary care patients in Scotland as of March 2007. We included 40 long-term conditions and studied their associations in 12,009 individuals aged 90 and older, stratified by sex (3039 men, 8970 women). We analysed associations obtained with Relative Risk (RR), a standard measure in the literature, and compared them with our proposed measure, Associations Beyond Chance (ABC). To enable a broad exploration of interactions between long-term conditions, we built networks of association and assessed differences in their analysis when associations are estimated by RR or ABC. Findings: Our Bayesian framework was appropriately more cautious in attributing association when evidence is lacking, particularly in uncommon conditions. This caution in reporting association was also present in reporting differences in associations between sex and affected the aggregated measures of multimorbidity and network representations. Interpretation: Incorporating uncertainty into multimorbidity research is crucial to avoid misleading findings when evidence is limited, a problem that particularly affects small but important subgroups. Our proposed framework improves the reliability of estimations of associations and, more in general, of research into disease mechanisms and multimorbidity. Funding: National Institute for Health and Care Research.

Abstract Purpose To address the care needs of older adults, it is important to identify and understand the forms of care support older adults received. This systematic review aims to examine the social networks of older adults receiving informal or formal care and the factors that influenced their networks. Methods A systematic review was conducted by searching six databases from inception to January 31, 2023. The review included primary studies focusing on older adults receiving long-term care, encompassing both informal and formal care. To assess the risk of bias in the included studies, validated appraisal tools specifically designed for different study types were utilized. Network analysis was employed to identify the grouping of study concepts, which subsequently formed the foundation for describing themes through narrative synthesis. Results We identified 121 studies relating to the formal and informal care of older adults’ networks. A variety of social ties were examined by included studies. The most commonly examined sources of care support were family members (such as children and spouses) and friends. Several factors were consistently reported to influence the provision of informal care, including the intensity of networks, reciprocity, and geographical proximity. In terms of formal care utilization, older age and poor health status were found to be associated with increased use of healthcare services. Additionally, physical limitations and cognitive impairment were identified as factors contributing to decreased social engagement. Conclusion This review found that older people were embedded within a diverse network. The findings of this review emphasize the importance of recognizing and incorporating the diversity of social networks in care plans and policies to enhance the effectiveness of interventions and improve the overall well-being of older adults.

Abstract Background Measurement of multimorbidity in research is variable, including the choice of the data source used to ascertain conditions. We compared the estimated prevalence of multimorbidity and associations with mortality using different data sources. Methods A cross-sectional study of SAIL Databank data including 2,340,027 individuals of all ages living in Wales on 01 January 2019. Comparison of prevalence of multimorbidity and constituent 47 conditions using data from primary care (PC), hospital inpatient (HI), and linked PC-HI data sources and examination of associations between condition count and 12-month mortality. Results Using linked PC-HI compared with only HI data, multimorbidity was more prevalent (32.2% versus 16.5%), and the population of people identified as having multimorbidity was younger (mean age 62.5 versus 66.8 years) and included more women (54.2% versus 52.6%). Individuals with multimorbidity in both PC and HI data had stronger associations with mortality than those with multimorbidity only in HI data (adjusted odds ratio 8.34 [95% CI 8.02-8.68] versus 6.95 (95%CI 6.79-7.12] in people with ≥ 4 conditions). The prevalence of conditions identified using only PC versus only HI data was significantly higher for 37/47 and significantly lower for 10/47: the highest PC/HI ratio was for depression (14.2 [95% CI 14.1–14.4]) and the lowest for aneurysm (0.51 [95% CI 0.5–0.5]). Agreement in ascertainment of conditions between the two data sources varied considerably, being slight for five (kappa

Background Clinical guidelines commonly recommend preventative treatments for people above a risk threshold. Therefore, decision-makers must have faith in risk prediction tools and model-based cost-effectiveness analyses for people at different levels of risk. Two problems that arise are inadequate handling of competing risks of death and failing to account for direct treatment disutility (i.e. the hassle of taking treatments). We explored these issues using two case studies: primary prevention of cardiovascular disease using statins and osteoporotic fracture using bisphosphonates. Objectives Externally validate three risk prediction tools [QRISK®3, QRISK®-Lifetime, QFracture-2012 (ClinRisk Ltd, Leeds, UK)]; derive and internally validate new risk prediction tools for cardiovascular disease [competing mortality risk model with Charlson Comorbidity Index (CRISK-CCI)] and fracture (CFracture), accounting for competing-cause death; quantify direct treatment disutility for statins and bisphosphonates; and examine the effect of competing risks and direct treatment disutility on the cost-effectiveness of preventative treatments. Design, participants, main outcome measures, data sources Discrimination and calibration of risk prediction models (Clinical Practice Research Datalink participants: aged 25–84 years for cardiovascular disease and aged 30–99 years for fractures); direct treatment disutility was elicited in online stated-preference surveys (people with/people without experience of statins/bisphosphonates); costs and quality-adjusted life-years were determined from decision-analytic modelling (updated models used in National Institute for Health and Care Excellence decision-making). Results CRISK-CCI has excellent discrimination, similar to that of QRISK3 (Harrell’s c = 0.864 vs. 0.865, respectively, for women; and 0.819 vs. 0.834, respectively, for men). CRISK-CCI has systematically better calibration, although both models overpredict in high-risk subgroups. People recommended for treatment (10-year risk of ≥ 10%) are younger when using QRISK-Lifetime than when using QRISK3, and have fewer observed events in a 10-year follow-up (4.0% vs. 11.9%, respectively, for women; and 4.3% vs. 10.8%, respectively, for men). QFracture-2012 underpredicts fractures, owing to under-ascertainment of events in its derivation. However, there is major overprediction among people aged 85–99 years and/or with multiple long-term conditions. CFracture is better calibrated, although it also overpredicts among older people. In a time trade-off exercise (n = 879), statins exhibited direct treatment disutility of 0.034; for bisphosphonates, it was greater, at 0.067. Inconvenience also influenced preferences in best–worst scaling (n = 631). Updated cost-effectiveness analysis generates more quality-adjusted life-years among people with below-average cardiovascular risk and fewer among people with above-average risk. If people experience disutility when taking statins, the cardiovascular risk threshold at which benefits outweigh harms rises with age (≥ 8% 10-year risk at 40 years of age; ≥ 38% 10-year risk at 80 years of age). Assuming that everyone experiences population-average direct treatment disutility with oral bisphosphonates, treatment is net harmful at all levels of risk. Limitations Treating data as missing at random is a strong assumption in risk prediction model derivation. Disentangling the effect of statins from secular trends in cardiovascular disease in the previous two decades is challenging. Validating lifetime risk prediction is impossible without using very historical data. Respondents to our stated-preference survey may not be representative of the population. There is no consensus on which direct treatment disutilities should be used for cost-effectiveness analyses. Not all the inputs to the cost-effectiveness models could be updated. Conclusions Ignoring competing mortality in risk prediction overestimates the risk of cardiovascular events and fracture, especially among older people and those with multimorbidity. Adjustment for competing risk does not meaningfully alter cost-effectiveness of these preventative interventions, but direct treatment disutility is measurable and has the potential to alter the balance of benefits and harms. We argue that this is best addressed in individual-level shared decision-making. Study registration This study is registered as PROSPERO CRD42021249959. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: 15/12/22) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 4. See the NIHR Funding and Awards website for further award information. Plain language summary Before offering a medicine to prevent disease, prescribers must expect it to do more good than harm. This balance depends on how likely it is that the person will develop the disease we want to prevent. But people might first die for other reasons. We call this a ‘competing risk’. In most cases, the mathematical tools we use to estimate the chance of developing a disease do not account for competing risks. Another problem is that, when weighing up the benefits and harms of medicines, we ignore the hassle they cause patients, even when they do not cause side effects. We used two examples: statins to prevent heart disease and bisphosphonates to prevent fractures. First, we assessed if existing tools get predictions wrong by not accounting for competing risks. We found that they exaggerate the chance of heart attacks and strokes. However, the exaggeration is greatest among people who would clearly benefit from preventative treatment. So it may not change treatment decisions much. The fracture prediction tool we studied was very inaccurate, exaggerating risk among older people, but underestimating risk among younger people. We made a new fracture risk prediction tool. It gave better predictions, but it was still inaccurate for people aged > 85 years and those with several health problems. Next, we asked people questions designed to put a number on the hassle that statins and bisphosphonates cause. Most people thought that taking either is inconvenient, but the hassle factor for bisphosphonates is bigger. Finally, we updated the mathematical models that the National Institute for Health and Care Excellence used when recommending statins and bisphosphonates. We worked out if competing risks and the hassle of taking medicines make a difference to results. Statins remain a good idea for almost everyone, unless they really hate the idea of taking them. But bisphosphonates would do more harm than good for anyone who agrees with the hassle factor we found. Scientific summary Background Clinical guidelines help define and disseminate best practice. Guidelines increasingly use risk prediction tools to help target primary preventative treatments at people at highest risk. In National Institute for Health and Care Excellence (NICE) guidelines, the choice of risk threshold is commonly informed by model-based cost-effectiveness analyses (CEAs) for different levels of baseline risk. Risk prediction modelling and model-based CEA are therefore increasingly important for developing guidelines that recommend long-term preventative medicines, including primary prevention of cardiovascular disease (CVD) using statins and prevention of osteoporotic fracture using bisphosphonates. Risk prediction and competing mortality risk Most risk prediction models do not account for competing mortality risk, which is when someone dies of another condition (e.g. lung cancer) before experiencing the event being predicted (e.g. CVD or fracture). This can lead to overprediction of event rates among older people and those with multimorbidity. Model-based cost-effectiveness analysis Competing mortality risk is accounted for in model-based CEA, but whole-population estimates of competing mortality will not be correct at all levels of risk of CVD and fracture. Existing models also do not account for all harms, notably direct treatment disutility (DTD), which is the disutility arising from the hassles of taking treatments. Even small levels of DTD can be enough to outweigh relatively small lifetime benefits of primary prevention medication, but, to our knowledge, DTD impact has not been systematically estimated previously. Aim and objectives The overall aim was to improve the evidence generated from risk prediction models and model-based CEAs to inform decision-making for selecting primary prevention treatments for CVD and osteoporotic fracture. The prespecified objectives were to: externally validate the recommended risk prediction tools for primary prevention of CVD [QRISK®3 (ClinRisk Ltd, Leeds, UK)] and for osteoporotic fracture [QFracture-2012 (ClinRisk Ltd)] derive and internally validate new CVD and osteoporotic fracture risk prediction models accounting for competing risks of death externally validate the QRISK-Lifetime CVD risk prediction tool quantify the magnitude, variation and distribution of DTD in the general population and among people treated with statins or bisphosphonates examine the effect of accounting for competing risks and DTD on cost-effectiveness in the context of statins and bisphosphonates for the primary prevention of CVD and osteoporotic fracture, respectively. The prediction modelling protocol was approved by the Clinical Practice Research Datalink (CPRD) Independent Scientific Advisory Committee (reference number 16_248). The Health Research Authority approved the DTD elicitation study (Integrated Research Application System: 220,492) and granted ethics approval (Research Ethics Committee: 17/NW/0124). A systematic review for CEA model parameters was registered with PROSPERO (CRD42021249959). Methods Objective 1 methods For CVD modelling, CPRD GOLD data were used to define a cohort aged 25–84 years without CVD or prior statin prescription. The outcome was incident CVD. Multiple imputation was used to account for missing data. The performance of the published QRISK3–2017 model was evaluated in terms of discrimination (the ability of a tool to distinguish between those with and those without an event) and calibration (whether or not predicted risk is the same as observed risk) in the whole population, stratified by age and Charlson Comorbidity Index (CCI), and in subgroups with type 1 diabetes, type 2 diabetes and chronic kidney disease (CKD). Observed risk was estimated with and without accounting for competing risk (using Aalen–Johansen and Kaplan–Meier estimators, respectively). For fracture modelling, the cohort was aged 30–99 years (prior fracture or bisphosphonate treatment were allowed) with follow-up to specified fracture, death from non-fracture causes, deregistration or end of study. Two outcomes were defined: major osteoporotic fracture (MOF) and hip fracture. QFracture-2012 performance was evaluated as for QRISK3. For both cohorts, the earliest study entry date was 1 January 2004 and the end of the study was 31 March 2016. Objective 2 methods Using the same data set as objective 1, participants were randomly allocated to derivation and test data sets in a 2 : 1 ratio. For CVD, two Fine–Gray models were derived in the derivation data set and internally validated in the test data set, alongside QRISK3. The competing mortality risk model (CRISK) accounted for competing mortality only, whereas the competing mortality risk model with Charlson Comorbidity Index (CRISK-CCI) also included the modified CCI as a predictor. Model performance was examined using discrimination and calibration. For fracture, separate Fine–Gray models (CFracture) were estimated for MOF and hip fracture. Objective 3 methods The same data were used as for objective 1, but with the age range restricted to 30–84 years to match QRISK®-Lifetime (ClinRisk Ltd). As lifetime risk is not observed in this data set, model performance was evaluated at 10 years, and reclassification examined the characteristics of those recommended for treatment on the basis of a QRISK3 10-year risk of > 10%, a QRISK-Lifetime 10-year risk of > 10% and the QRISK-Lifetime highest risk, with thresholds chosen to recommend the same number of people for treatment as with QRISK3 > 10%. Objective 4 methods Two groups of participants were recruited to studies to elicit DTD of preventative statins and bisphosphonates: people with direct experience of taking one of the medicines and a sample of the general population. We described the process of taking each medicine (one tablet per day for statins, one tablet per week taken on an empty stomach with a requirement to stay upright for at least 30 minutes for bisphosphonates). Elicitation used time trade-off (TTO) (primary analysis) and best–worst scaling (BWS) (exploratory analysis) surveys iteratively developed using think-aloud interviews with 19 patients, and online pilot studies. Objective 5 methods For statins for the primary prevention of CVD, we modified the cohort-level decision-analytic model used in NICE’s lipid modification guideline [NICE. Lipid Modification: Cardiovascular Risk Assessment and the Modification of Blood Lipids for the Primary and Secondary Prevention of Cardiovascular Disease. Clinical Guideline (CG181). Methods, Evidence and Recommendations. July 2014. URL: https://web.archive.org/web/20220201050407/https://www.nice.org.uk/guidance/cg181/evidence/lipid-modification-update-full-guideline-pdf-243786637 (accessed 12 October 2022)]. General updates included rapid reviews to identify utility values and costs associated with CVD events, new regressions to predict baseline quality of life for people without CVD (based on Health Survey for England data) and type of first CVD event (based on data from objective 1), and inputs (costs, life expectancy) were updated to present-day values. For bisphosphonates for the prevention of fracture, we used the discrete-event simulation developed for NICE’s Technology Appraisal 464 [NICE. Bisphosphonates for Treating Osteoporosis. Technology Appraisal Guidance (TA464). London: NICE; 2017]. For both models, we explored competing risk by parameterising probability of non-cause-specific death using relative survival models adjusting for predicted risk (QRISK3 or QFracture-2012). We incorporated DTD as elicited in objective 4 under three assumptions (lifelong, time limited, diminishing over time). We explored how these factors alone or in combination affect the estimated value of the preventative medicines in terms of cost per quality-adjusted life-year (QALY). Results Objectives 1 and 2: predicting cardiovascular disease Discrimination of QRISK3 in the whole external validation cohort was excellent (Harrell’s c = 0.865 for women, 0.834 for men), and comparable to the previous internal validation. However, discrimination was worse among people with more comorbidity, and was poor to moderate among older people (e.g. c = 0.611 for women and 0.585 for men aged 75–84 years). Calibration in the whole population, ignoring competing risks, was very good, with minor overprediction. There was larger overprediction among older people, which was considerable after accounting for competing risks. Among people with type 1 diabetes, discrimination was excellent (c = 0.830 for women, 0.853 for men). There was evidence of overprediction at higher levels of predicted risk, which was larger after accounting for competing risks, although most overprediction happened well above the NICE 10% threshold for offering treatment. Discrimination among people with CKD was only moderate (women, c = 0.705; men, c = 0.671), but calibration was reasonable at recommended treatment thresholds. The new competing risk model (CRISK-CCI) had similar discrimination to QRISK3 in the whole population (women, c = 0.864; men, c = 0819), with the same pattern of worse discrimination among older people and those with more comorbidity. Calibration was systematically better than QRISK3, although, as with QRISK3, there was overprediction in some subgroups with high predicted risk. Objectives 1 and 2: predicting fracture Observed age-stratified incidences of both MOF and hip fracture were considerably higher in this study than in a previous external validation, which was partly explained by the use of hospital data in this study to ascertain fractures. Discrimination of QFracture-2012 in external validation was excellent among women (MOF, c = 0.813; hip fracture, c = 0.918) and good to excellent among men (MOF, c = 0.738; hip fracture, c = 0.888), similar to QFracture-2012 internal validation, but had poor to moderate discrimination among older people. Ignoring competing risks, QFracture-2012 showed serious underprediction in the whole population and in all subgroups of age and comorbidity, which was worse for hip fracture than for MOF. Accounting for competing risks reduced observed underprediction in the whole population, but there was very major overprediction among older people and at higher levels of predicted risk among people with more comorbidity. The new competing risk model (CFracture) had similar discrimination to QFracture-2012 in the internal validation cohort (women: c = 0.813 for MOF, c = 0.914 for hip fracture; men: c = 0.734 for MOF, c = 0.883 for hip fracture). CFracture was better calibrated than QFracture-2012 but showed overprediction at higher levels of predicted risk for MOF (both sexes) and for hip fracture (among men). CFracture calibration was poor among people aged 85–99 years for both outcomes. Objective 3: predicting lifetime cardiovascular disease risk Evaluated at 10 years’ follow-up, QRISK-Lifetime had excellent discrimination (women, c = 0.844; men, c = 0.808) in the whole population, with the same pattern as QRISK3 and CRISK-CCI of worse discrimination among older people and those with high comorbidity. QRISK-Lifetime underpredicted 10-year risk among people at higher predicted risk, particularly older people, implying that estimated lifetime risk will be underpredicted. A total of 5.3% of participants were recommended for treatment by both QRISK3 and QRISK-Lifetime, and 27.4% by one or the other, but not both. Participants recommended for treatment by QRISK-Lifetime were younger than those recommended by QRISK3 (mean age: women, 50.5 vs. 71.3 years, respectively; men, 46.3 vs. 63.8 years, respectively), were much more likely to have a strong family history of CVD (women: 36.3% vs. 6.3%, respectively; men: 20.0% vs. 7.2%, respectively) and had many fewer observed events during the 10-year follow-up (women with a CVD event: 4.0% vs. 11.9%, respectively; men with a CVD event: 4.3% vs. 10.8%, respectively). Objective 4: direct treatment disutility elicitation When measured by TTO, long-term statin use was associated with mean DTD of 0.034 among people willing to take statins; the equivalent number for bisphosphonates was significantly greater, at 0.067. The findings from the BWS experiment had face validity in that inconvenience influenced preferences. However, the estimated values for DTD are implausibly large. Consistent with previous studies, these findings suggest three distinct preference phenotypes: some people would avoid taking the medicines at all costs, some people see no problem with them and some people are willing to trade length of life to avoid treatment. The first group are unlikely to initiate treatment and the second group do not anticipate DTD; in the third group, depending on the individual’s strength of preference to avoid treatment and the magnitude of expected QALY gains from prevention, DTD may imply that a preventative medicine’s negative characteristics outweigh its benefits. Objective 5: model-based cost-effectiveness analysis General updates to the CVD model made high-intensity statins more cost-effective for primary prevention. Introducing accurate adjustment for competing risk of non-CVD death had the expected effect: more QALYs among people with below-average CVD risk for their sex and age (who experience lower rates of other-cause mortality) and fewer QALYs among people with above-average risk (whose non-CVD life expectancy is attenuated). However, the impact on incremental cost-effectiveness is minor, and statins remain almost universally cost-effective. Incorporating DTD has a more obvious effect, especially when we assume that it applies undiminished for as long as people take statins for primary prevention. Under that circumstance, the threshold at which expected long-term benefits outweigh DTD-related harm rises with age: for a 40-year-old, a 10-year risk of ≥ 8% would be enough to make treatment net beneficial whereas, for an 80-year-old, that figure rises to 38%. The model assessing bisphosphonates for the primary prevention of osteoporotic fragility fracture shows that we overestimate value for money among people at the highest risk if we do not adjust for competing risk of non-fracture death. However, this generally affects only the magnitude of expected net benefit among people for whom some degree of benefit is expected. Even among people at highest risk of fracture, average QALY gains associated with bisphosphonates are small and swamped by DTD of any duration. Consequently, it is impossible to identify any group of people for whom oral bisphosphonates represent an effective use of NHS resources, if we assume population-level average DTD for everyone to whom the decision applies. Conclusions Implications for healthcare Ignoring competing mortality in risk prediction overestimates the risk of CVD and fracture among older people and those with multimorbidity, which will lead to overestimation of the benefits of treatment. This affects fracture risk prediction more than CVD because CVD is a more substantial proportion of total mortality. The QFracture-2012 prediction tool simultaneously underestimates fracture risk among people without high competing mortality risk, partly because it did not include fractures recorded only in hospital data in its derivation. CVD and fracture risk prediction are improved by accounting for competing mortality risks, and transparency of the tools would be improved by fully publishing the codes used to define events and predictors. We have demonstrated an effective method of making accurate adjustment for competing risk of non-cause-specific death in decision-analytic CEAs. Although it made relatively little difference to the estimated cost-effectiveness of preventative interventions in the examples we explored, we have shown that it could potentially be important. Therefore, we recommend that modellers consider this issue when designing analyses of preventative treatments. Although we have demonstrated that DTD exists and has the potential to alter the balance of benefits and harms for preventative treatments, we do not recommend that population-level average DTD is incorporated in base-case CEAs. Rather, we recommend that decision-makers review scenarios with and scenarios without DTD and highlight its possible impact, enabling prescribers to engage in shared decision-making that gives appropriate weight to individual preferences. Research recommendations The excellent discrimination of QRISK3 and QFracture-2012 arises from including a very broad range of ages, but discrimination and calibration in subgroups are less good. Comparing models created in smaller age groups with whole-population models would be useful. Mortality is only one competing risk, and older people and those with multimorbidity are at risk of many different events. It is important to develop models that better account for multiple important events. Cost-effectiveness analysis of statins for the primary prevention of CVD could usefully be further modified to (1) enable stratification according to specific coexisting long-term conditions, (2) account for likely adherence to statins in practice and (3) update secondary transitions reflecting the subsequent natural history of CVD among people experiencing events. Future CEAs of bisphosphonates for the primary prevention of osteoporotic fragility fracture should explore different fracture risk prediction models, and use those based on demonstrable good ascertainment of fractures and accounting for competing mortality risk. Study registration This study is registered as PROSPERO CRD42021249959. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: 15/12/22) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 4. See the NIHR Funding and Awards website for further award information.

BackgroundFrailty is characterised by a reduced resilience to adversity. In this analysis we examined changes in frailty in people aged 50+ before and during a period of austere public spending in England.MethodsData from the English Longitudinal Study of Ageing 2002-2018 were analysed. Associations between austerity and frailty were examined using (1) Multilevel interrupted times series analysis (ITSA); and (2) Accelerated longitudinal modelling comparing frailty trajectories in people of the same age in 2002 and 2012.ResultsThe analysis included 16,410 people (mean age 67 years, 55% women), with mean frailty index score of 0.16. Mean scores in women (0.16) where higher than in men (mean 0.14), and higher in the poorest tertile (mean 0.20) than the richest (mean 0.12). In the ITSA, frailty index scores increased more quickly during austerity than before, with the additional increase in frailty 2012-2018 being similar in magnitude to the difference in mean frailty score between people aged 65-69 and 70-74 years. Steeper increases in frailty after 2012 were experienced across the wealth-spectrum and in both sexes but were greater in the very oldest (80+). In the accelerated longitudinal analysis, frailty was lower in 2012 than 2002, but increased more rapidly in the 2012 cohort compared to the 2002 cohort; markedly so in people aged 80+.ConclusionThe period of austerity politics was associated with steeper increases in frailty with age compared to the pre-austerity period, consistent with previously observed increases in mortality.

Objectives The aim of this study was to understand how patients experienced hypertension management, with or without blood pressure (BP) telemonitoring, during the COVID-19 pandemic.Design, methods, participants and setting This qualitative study conducted between April and November 2022 consisted of 43 semistructured telephone interviews (23 men and 20 women) from 6 primary care practices in one area of Scotland.Results From the views of 25 participants with experience of using the Connect Me telemonitoring service and 18 participants without such experience, 5 themes were developed. These were: (1) navigating access to services. There were challenges to gaining timely and/or in-person access to services and a reluctance to attend clinical settings because participants were aware of their increased risk of contracting the COVID-19 virus. (2) Adapting National Health Service services. All six practices had adapted care provision in response to potential COVID-19 transmission; however, these adaptations disrupted routine management of in-person primary care hypertension, diabetes and/or asthma checks. (3) Telemonitoring feedback. Telemonitoring reduced the need to attend in-person primary care practices and supported access to remote healthcare monitoring and feedback. (4) Self-management. Many non-telemonitoring participants were motivated to use self-management strategies to track their BP using home monitoring equipment. Also, participants were empowered to self-manage lifestyle and hypertension medication. (5) Experience of having COVID-19. Some participants contracting the COVID-19 virus experienced an immediate increase in their BP while a few experienced ongoing increased BP readings.Conclusions The COVID-19 pandemic disrupted routine in-person care for patients with hypertension. Both telemonitoring and some non-telemonitoring patients were motivated to self-manage hypertension, including self-adjusting medication; however, only those with access to telemonitoring had increased access to hypertension monitoring and feedback. BP telemonitoring permitted routine care to continue for participants in this study and may offer a service useful in pandemic proofing hypertension healthcare in the future.

Abstract Background Prediction of lifetime cardiovascular disease (CVD) risk is recommended in many clinical guidelines, but lifetime risk models are rarely externally validated. The aim of this study was to externally validate the QRiskLifetime incident CVD risk prediction tool. Methods Independent external validation of QRiskLifetime using Clinical Practice Research Datalink data, examining discrimination and calibration in the whole population and stratified by age, and reclassification compared to QRISK3. Since lifetime CVD risk is unobservable, performance was evaluated at 10-years’ follow-up, and lifetime performance inferred in terms of performance for in the different age-groups from which lifetime predictions are derived. Results One million, two hundreds sixty thousand and three hundreds twenty nine women and 1,223,265 men were included in the analysis. Discrimination was excellent in the whole population (Harrell’s-C = 0.844 in women, 0.808 in men), but moderate to poor stratified by age-group (Harrell’s C in people aged 30–44 0.714 for both men and women, in people aged 75–84 0.578 in women and 0.556 in men). Ten-year CVD risk was under-predicted in the whole population, and in all age-groups except women aged 45–64, with worse under-prediction in older age-groups. Compared to those at highest QRISK3 estimated 10-year risk, those with highest lifetime risk were younger (mean age: women 50.5 vs. 71.3 years; men 46.3 vs. 63.8 years) and had lower systolic blood pressure and prevalence of treated hypertension, but had more family history of premature CVD, and were more commonly minority ethnic. Over 10-years, the estimated number needed to treat (NNT) with a statin to prevent one CVD event in people with QRISK3 ≥ 10% was 34 in women and 37 in men, compared to 99 and 100 for those at highest lifetime risk. Conclusions QRiskLifetime underpredicts 10-year CVD risk in nearly all age-groups, so is likely to also underpredict lifetime risk. Treatment based on lifetime risk has considerably lower medium-term benefit than treatment based on 10-year risk.

IntroductionLinking free-text addresses to unique identifiers in a structural address database [the Ordnance Survey unique property reference number (UPRN) in the United Kingdom (UK)] is a necessary step for downstream geospatial analysis in many digital health systems, e.g., for identification of care home residents, understanding housing transitions in later life, and informing decision making on geographical health and social care resource distribution. However, there is a lack of open-source tools for this task with performance validated in a test data set.MethodsIn this article, we propose a generalisable solution (A Framework for Linking free-text Addresses to Ordnance Survey UPRN database, FLAP) based on a machine learning–based matching classifier coupled with a fuzzy aligning algorithm for feature generation with better performance than existing tools. The framework is implemented in Python as an Open Source tool (available at Link). We tested the framework in a real-world scenario of linking individual’s (n=771,588) addresses recorded as free text in the Community Health Index (CHI) of National Health Service (NHS) Tayside and NHS Fife to the Unique Property Reference Number database (UPRN DB).ResultsWe achieved an adjusted matching accuracy of 0.992 in a test data set randomly sampled (n=3,876) from NHS Tayside and NHS Fife CHI addresses. FLAP showed robustness against input variations including typographical errors, alternative formats, and partially incorrect information. It has also improved usability compared to existing solutions allowing the use of a customised threshold of matching confidence and selection of top n candidate records. The use of machine learning also provides better adaptability of the tool to new data and enables continuous improvement.DiscussionIn conclusion, we have developed a framework, FLAP, for linking free-text UK addresses to the UPRN DB with good performance and usability in a real-world task.

Background People with comorbidities are under-represented in randomised controlled trials, and it is unknown whether patterns of comorbidity are similar in trials and the community. Methods Individual-level participant data were obtained for 83 clinical trials (54,688 participants) for 16 index conditions from two trial repositories: Yale University Open Data Access (YODA) and the Centre for Global Clinical Research Data (Vivli). Community data (860,177 individuals) were extracted from the Secure Anonymised Information Linkage (SAIL) databank for the same index conditions. Comorbidities were defined using concomitant medications. For each index condition, we estimated correlations between comorbidities separately in trials and community data. For the six commonest comorbidities we estimated all pairwise correlations using Bayesian multivariate probit models, conditioning on age and sex. Correlation estimates from trials with the same index condition were combined into a single estimate. We then compared the trial and community estimates for each index condition. Results Despite a higher prevalence of comorbidities in the community than in trials, the correlations between comorbidities were mostly similar in both settings. On comparing correlations between the community and trials, 21% of correlations were stronger in the community, 10% were stronger in the trials and 68% were similar in both. In the community, 5% of correlations were negative, 21% were null, 56% were weakly positive and 18% were strongly positive. Equivalent results for the trials were 11%, 33%, 45% and 10% respectively. Conclusions Comorbidity correlations are generally similar in both the trials and community, providing some evidence for the reporting of comorbidity-specific findings from clinical trials.

Background Direct treatment disutility (DTD) represents an individual’s disutility associated with the inconvenience of taking medicine over a long period of time.Objectives The main aim of this study was to elicit DTD values for taking a statin or a bisphosphonate for primary prevention. A secondary aim was to understand factors which influence DTD values.Methods Design: We used a cross-sectional study consisting of time-trade off exercises embedded within online surveys. Respondents were asked to compare a one-off pill (‘Medicine A’) assumed to have no inconvenience and a daily pill (‘Medicine B’) over 10 years (statins) or 5 years (bisphosphonates).Setting: Individuals from National Health Service (NHS) primary care and the general population were surveyed using an online panel company.Participants: Two types of participants were recruited. First, a purposive sample of patients with experience of taking a statin (n=260) or bisphosphonate (n=100) were recruited from an NHS sampling frame. Patients needed to be aged over 30, have experience of taking the medicine of interest and have no diagnosis of dementia or of using dementia drugs. Second, a demographically balanced sample of members of the public were recruited for statins (n=376) and bisphosphonates (n=359).Primary and secondary outcome measures: Primary outcome was mean DTD. Regression analysis explored factors which could influence DTD values.Results A total of 879 respondents were included for analysis (514 for statins and 365 for bisphosphonates). The majority of respondents reported a disutility associated with medicine use. Mean DTD for statins was 0.034 and for bisphosphonates 0.067, respectively. Respondent characteristics including age and sex did not influence DTD. Experience of bisphosphonate-use reduced reported disutilities.Conclusions Statins and bisphosphonates have a quantifiable DTD. The size of estimated disutilities suggest they are likely to be important for cost-effectiveness, particularly in individuals at low-risk when treated for primary prevention.

Summary: Background: Understanding and quantifying the differences in disease development in different socioeconomic groups of people across the lifespan is important for planning healthcare and preventive services. The study aimed to measure chronic disease accrual, and examine the differences in time to individual morbidities, multimorbidity, and mortality between socioeconomic groups in Wales, UK. Methods: Population-wide electronic linked cohort study, following Welsh residents for up to 20 years (2000–2019). Chronic disease diagnoses were obtained from general practice and hospitalisation records using the CALIBER disease phenotype register. Multi-state models were used to examine trajectories of accrual of 132 diseases and mortality, adjusted for sex, age and area-level deprivation. Restricted mean survival time was calculated to measure time spent free of chronic disease(s) or mortality between socioeconomic groups. Findings: In total, 965,905 individuals aged 5–104 were included, from a possible 2.9 m individuals following a 5-year clearance period, with an average follow-up of 13.2 years (12.7 million person-years). Some 673,189 (69.7%) individuals developed at least one chronic disease or died within the study period. From ages 10 years upwards, the individuals living in the most deprived areas consistently experienced reduced time between health states, demonstrating accelerated transitions to first and subsequent morbidities and death compared to their demographic equivalent living in the least deprived areas. The largest difference were observed in 10 and 20 year old males developing multimorbidity (−0.45 years (99% CI: −0.45, −0.44)) and in 70 year old males dying after developing multimorbidity (−1.98 years (99% CI: −2.01, −1.95)). Interpretation: This study adds to the existing literature on health inequalities by demonstrating that individuals living in more deprived areas consistently experience accelerated time to diagnosis of chronic disease and death across all ages, accounting for competing risks. Funding: UK Medical Research Council, Health Data Research UK, and Administrative Data Research Wales.

Abstract Background The applicability of randomised controlled trials of pharmacological agents to older people with frailty/multimorbidity is often uncertain, due to concerns that trials are not representative. However, assessing trial representativeness is challenging and complex. We explore an approach assessing trial representativeness by comparing rates of trial serious adverse events (SAE) to rates of hospitalisation/death in routine care. Methods This was an observational analysis of individual (125 trials, n=122,069) and aggregate-level drug trial data (483 trials, n=636,267) for 21 index conditions compared to population-based routine healthcare data (routine care). Trials were identified from ClinicalTrials.gov . Routine care comparison from linked primary care and hospital data from Wales, UK (n=2.3M). Our outcome of interest was SAEs (routinely reported in trials). In routine care, SAEs were based on hospitalisations and deaths (which are SAEs by definition). We compared trial SAEs in trials to expected SAEs based on age/sex standardised routine care populations with the same index condition. Using IPD, we assessed the relationship between multimorbidity count and SAEs in both trials and routine care and assessed the impact on the observed/expected SAE ratio additionally accounting for multimorbidity. Results For 12/21 index conditions, the pooled observed/expected SAE ratio was

BackgroundPeople with comorbidities are underrepresented in clinical trials. Empirical estimates of treatment effect modification by comorbidity are lacking, leading to uncertainty in treatment recommendations. We aimed to produce estimates of treatment effect modification by comorbidity using individual participant data (IPD).Methods and findingsWe obtained IPD for 120 industry-sponsored phase 3/4 trials across 22 index conditions (n = 128,331). Trials had to be registered between 1990 and 2017 and have recruited ≥300 people. Included trials were multicentre and international. For each index condition, we analysed the outcome most frequently reported in the included trials. We performed a two-stage IPD meta-analysis to estimate modification of treatment effect by comorbidity. First, for each trial, we modelled the interaction between comorbidity and treatment arm adjusted for age and sex. Second, for each treatment within each index condition, we meta-analysed the comorbidity-treatment interaction terms from each trial. We estimated the effect of comorbidity measured in 3 ways: (i) the number of comorbidities (in addition to the index condition); (ii) presence or absence of the 6 commonest comorbid diseases for each index condition; and (iii) using continuous markers of underlying conditions (e.g., estimated glomerular filtration rate (eGFR)). Treatment effects were modelled on the usual scale for the type of outcome (absolute scale for numerical outcomes, relative scale for binary outcomes). Mean age in the trials ranged from 37.1 (allergic rhinitis trials) to 73.0 (dementia trials) and percentage of male participants range from 4.4% (osteoporosis trials) to 100% (benign prostatic hypertrophy trials). The percentage of participants with 3 or more comorbidities ranged from 2.3% (allergic rhinitis trials) to 57% (systemic lupus erythematosus trials). We found no evidence of modification of treatment efficacy by comorbidity, for any of the 3 measures of comorbidity. This was the case for 20 conditions for which the outcome variable was continuous (e.g., change in glycosylated haemoglobin in diabetes) and for 3 conditions in which the outcomes were discrete events (e.g., number of headaches in migraine). Although all were null, estimates of treatment effect modification were more precise in some cases (e.g., sodium-glucose co-transporter-2 (SGLT2) inhibitors for type 2 diabetes-interaction term for comorbidity count 0.004, 95% CI -0.01 to 0.02) while for others credible intervals were wide (e.g., corticosteroids for asthma-interaction term -0.22, 95% CI -1.07 to 0.54). The main limitation is that these trials were not designed or powered to assess variation in treatment effect by comorbidity, and relatively few trial participants had >3 comorbidities.ConclusionsAssessments of treatment effect modification rarely consider comorbidity. Our findings demonstrate that for trials included in this analysis, there was no empirical evidence of treatment effect modification by comorbidity. The standard assumption used in evidence syntheses is that efficacy is constant across subgroups, although this is often criticised. Our findings suggest that for modest levels of comorbidities, this assumption is reasonable. Thus, trial efficacy findings can be combined with data on natural history and competing risks to assess the likely overall benefit of treatments in the context of comorbidity.

Abstract Background Acute kidney injury (AKI) is common and associated with adverse outcomes as well as important healthcare costs. However, evidence examining the epidemiology of acute kidney disease (AKD)—recently defined as AKI persisting between 7 and 90 days—remains limited. The aims of this study were to establish the rates of early AKI recovery, progression to AKD and non-recovery; examine risk factors associated with non-recovery and investigate the association between recovery timing and adverse outcomes, in a population-based cohort. Methods All adult residents of Tayside & Fife, Scotland, UK, with at least one episode of community or hospital-managed AKI using KDIGO creatinine-based definition during the period 1 January 2010 to 31 December 2018 were identified. Logistic regression was used to examine factors associated with non-recovery, and Cox modelling was used to establish associations between AKI recovery timing and risks of mortality and development of de novo CKD. Results Over 9 years, 56,906 patients with at least one AKI episode were identified with 18,773 (33%) of these progressing to AKD. Of those progressing to AKD, 5059 (27%) had still not recovered at day 90 post AKI diagnosis. Risk factors for AKD included: increasing AKI severity, pre-existing cancer or chronic heart failure and recent use of loop diuretics. Compared with early AKI recovery, progression to AKD was associated with increased hazard of 1-year mortality and de novo CKD (HR = 1.20, 95% CI 1.13 to 1.26 and HR = 2.21, 95% CI 1.91 to 2.57 respectively). Conclusions These findings highlight the importance of early AKI recognition and management to avoid progression to AKD and long-term adverse outcomes.

BackgroundMultimorbidity prevalence rates vary considerably depending on the conditions considered in the morbidity count, but there is no standardised approach to the number or selection of conditions to include.Methods and findingsWe conducted a cross-sectional study using English primary care data for 1,168,260 participants who were all people alive and permanently registered with 149 included general practices. Outcome measures of the study were prevalence estimates of multimorbidity (defined as ≥2 conditions) when varying the number and selection of conditions considered for 80 conditions. Included conditions featured in ≥1 of the 9 published lists of conditions examined in the study and/or phenotyping algorithms in the Health Data Research UK (HDR-UK) Phenotype Library. First, multimorbidity prevalence was calculated when considering the individually most common 2 conditions, 3 conditions, etc., up to 80 conditions. Second, prevalence was calculated using 9 condition-lists from published studies. Analyses were stratified by dependent variables age, socioeconomic position, and sex. Prevalence when only the 2 commonest conditions were considered was 4.6% (95% CI [4.6, 4.6] p < 0.001), rising to 29.5% (95% CI [29.5, 29.6] p < 0.001) considering the 10 commonest, 35.2% (95% CI [35.1, 35.3] p < 0.001) considering the 20 commonest, and 40.5% (95% CI [40.4, 40.6] p < 0.001) when considering all 80 conditions. The threshold number of conditions at which multimorbidity prevalence was >99% of that measured when considering all 80 conditions was 52 for the whole population but was lower in older people (29 in >80 years) and higher in younger people (71 in 0- to 9-year-olds). Nine published condition-lists were examined; these were either recommended for measuring multimorbidity, used in previous highly cited studies of multimorbidity prevalence, or widely applied measures of "comorbidity." Multimorbidity prevalence using these lists varied from 11.1% to 36.4%. A limitation of the study is that conditions were not always replicated using the same ascertainment rules as previous studies to improve comparability across condition-lists, but this highlights further variability in prevalence estimates across studies.ConclusionsIn this study, we observed that varying the number and selection of conditions results in very large differences in multimorbidity prevalence, and different numbers of conditions are needed to reach ceiling rates of multimorbidity prevalence in certain groups of people. These findings imply that there is a need for a standardised approach to defining multimorbidity, and to facilitate this, researchers can use existing condition-lists associated with highest multimorbidity prevalence.

Abstract Background Recommended cardiovascular disease (CVD) prediction tools do not account for competing mortality risk and over-predict incident CVD in older and multimorbid people. The aim of this study was to derive and validate a competing risk model (CRISK) to predict incident CVD and compare its performance to that of QRISK3 in UK primary care. Methods We used UK linked primary care data from the Clinical Practice Research Datalink (CPRD) GOLD to identify people aged 25–84 years with no previous CVD or statin treatment split into derivation and validation cohorts. In the derivation cohort, we derived models using the same covariates as QRISK3 with Fine-Gray competing risk modelling alone (CRISK) and with Charlson Comorbidity score (CRISK-CCI) as an additional predictor of non-CVD death. In a separate validation cohort, we examined discrimination and calibration compared to QRISK3. Reclassification analysis examined the number of patients recommended for treatment and the estimated number needed to treat (NNT) to prevent a new CVD event. Results The derivation and validation cohorts included 989,732 and 494,865 women and 946,784 and 473,392 men respectively. Overall discrimination of CRISK and CRISK-CCI were excellent and similar to QRISK3 (for women, C-statistic = 0.863/0.864/0.863 respectively; for men 0.833/0.819/0.832 respectively). CRISK and CRISK-CCI calibration overall and in younger people was excellent. CRISK over-predicted in older and multimorbid people although performed better than QRISK3, whilst CRISK-CCI performed the best. The proportion of people reclassified by CRISK-CCI varied by QRISK3 risk score category, with 0.7–9.7% of women and 2.8–25.2% of men reclassified as higher risk and 21.0–69.1% of women and 27.1–57.4% of men reclassified as lower risk. Overall, CRISK-CCI recommended fewer people for treatment and had a lower estimated NNT at 10% risk threshold. Patients reclassified as higher risk were younger, had lower SBP and higher BMI, and were more likely to smoke. Conclusions CRISK and CRISK-CCI performed better than QRISK3. CRISK-CCI recommends fewer people for treatment and has a lower NNT to prevent a new CVD event compared to QRISK3. Competing risk models should be recommended for CVD primary prevention treatment recommendations.

Abstract Background Several common conditions have been widely recognised as risk factors for COVID-19 related death, but risks borne by people with rare diseases are largely unknown. Therefore, we aim to estimate the difference of risk for people with rare diseases comparing to the unaffected. Method To estimate the correlation between rare diseases and COVID-19 related death, we performed a retrospective cohort study in Genomics England 100k Genomes participants, who tested positive for Sars-Cov-2 during the first wave (16-03-2020 until 31-July-2020) of COVID-19 pandemic in the UK (n = 283). COVID-19 related mortality rates were calculated in two groups: rare disease patients (n = 158) and unaffected relatives (n = 125). Fisher’s exact test and logistic regression was used for univariable and multivariable analysis, respectively. Results People with rare diseases had increased risk of COVID19-related deaths compared to the unaffected relatives (OR [95% CI] = 3.47 [1.21– 12.2]). Although, the effect was insignificant after adjusting for age and number of comorbidities (OR [95% CI] = 1.94 [0.65–5.80]). Neurology and neurodevelopmental diseases was significantly associated with COVID19-related death in both univariable (OR [95% CI] = 4.07 [1.61–10.38]) and multivariable analysis (OR [95% CI] = 4.22 [1.60–11.08]). Conclusions Our results showed that rare disease patients, especially ones affected by neurology and neurodevelopmental disorders, in the Genomics England cohort had increased risk of COVID-19 related death during the first wave of the pandemic in UK. The high risk is likely associated with rare diseases themselves, while we cannot rule out possible mediators due to the small sample size. We would like to raise the awareness that rare disease patients may face increased risk for COVID-19 related death. Proper considerations for rare disease patients should be taken when relevant policies (e.g., returning to workplace) are made.

There is still limited understanding of how chronic conditions co-occur in patients with multimorbidity and what are the consequences for patients and the health care system. Most reported clusters of conditions have not considered the demographic characteristics of these patients during the clustering process. The study used data for all registered patients that were resident in Fife or Tayside, Scotland and aged 25 years or more on 1st January 2000 and who were followed up until 31st December 2018. We used linked demographic information, and secondary care electronic health records from 1st January 2000. Individuals with at least two of the 31 Elixhauser Comorbidity Index conditions were identified as having multimorbidity. Market basket analysis was used to cluster the conditions for the whole population and then repeatedly stratified by age, sex and deprivation. 318,235 individuals were included in the analysis, with 67,728 (21·3%) having multimorbidity. We identified five distinct clusters of conditions in the population with multimorbidity: alcohol misuse, cancer, obesity, renal failure, and heart failure. Clusters of long-term conditions differed by age, sex and socioeconomic deprivation, with some clusters not present for specific strata and others including additional conditions. These findings highlight the importance of considering demographic factors during both clustering analysis and intervention planning for individuals with multiple long-term conditions. By taking these factors into account, the healthcare system may be better equipped to develop tailored interventions that address the needs of complex patients.

BackgroundMultimorbidity is one of the greatest challenges facing health and social care systems globally. It is associated with high rates of health service use, adverse healthcare events, and premature death. Despite its importance, little is known about the effects of contextual determinants such as household and area characteristics on health and care outcomes for people with multimorbidity. This study protocol presents a plan for the examination of associations between individual, household, and area characteristics with important health and social care outcomes.MethodsThe study will use a cross-section of data from the SAIL Databank on 01 January 2019 and include all people alive and registered with a Welsh GP. The cohort will be stratified according to the presence or absence of multimorbidity, defined as two or more long-term conditions. Multilevel models will be used to examine covariates measured for individuals, households, and areas to account for social processes operating at different levels. The intra-class correlation coefficient will be calculated to determine the strength of association at each level of the hierarchy. Model outcomes will be any emergency department attendance, emergency hospital or care home admission, or mortality, within the study follow-up period.DiscussionHousehold and area characteristics might act as protective or risk factors for health and care outcomes for people with multimorbidity, in which case results of the analyses can be used to guide clinical and policy responses for effective targeting of limited resources.

BackgroundHealth-related quality of life metrics evaluate treatments in ways that matter to patients, so are often included in randomised clinical trials (hereafter trials). Multimorbidity, where individuals have 2 or more conditions, is negatively associated with quality of life. However, whether multimorbidity predicts change over time or modifies treatment effects for quality of life is unknown. Therefore, clinicians and guideline developers are uncertain about the applicability of trial findings to people with multimorbidity. We examined whether comorbidity count (higher counts indicating greater multimorbidity) (i) is associated with quality of life at baseline; (ii) predicts change in quality of life over time; and/or (iii) modifies treatment effects on quality of life.Methods and findingsIncluded trials were registered on the United States trials registry for selected index medical conditions and drug classes, phase 2/3, 3 or 4, had ≥300 participants, a nonrestrictive upper age limit, and were available on 1 of 2 trial repositories on 21 November 2016 and 18 May 2018, respectively. Of 124 meeting these criteria, 56 trials (33,421 participants, 16 index conditions, and 23 drug classes) collected a generic quality of life outcome measure (35 EuroQol-5 dimension (EQ-5D), 31 36-item short form survey (SF-36) with 10 collecting both). Blinding and completeness of follow up were examined for each trial. Using trials where individual participant data (IPD) was available from 2 repositories, a comorbidity count was calculated from medical history and/or prescriptions data. Linear regressions were fitted for the association between comorbidity count and (i) quality of life at baseline; (ii) change in quality of life during trial follow up; and (iii) treatment effects on quality of life. These results were then combined in Bayesian linear models. Posterior samples were summarised via the mean, 2.5th and 97.5th percentiles as credible intervals (95% CI) and via the proportion with values less than 0 as the probability (PBayes) of a negative association. All results are in standardised units (obtained by dividing the EQ-5D/SF-36 estimates by published population standard deviations). Per additional comorbidity, adjusting for age and sex, across all index conditions and treatment comparisons, comorbidity count was associated with lower quality of life at baseline and with a decline in quality of life over time (EQ-5D -0.02 [95% CI -0.03 to -0.01], PBayes > 0.999). Associations were similar, but with wider 95% CIs crossing the null for SF-36-PCS and SF-36-MCS (-0.05 [-0.10 to 0.01], PBayes = 0.956 and -0.05 [-0.10 to 0.01], PBayes = 0.966, respectively). Importantly, there was no evidence of any interaction between comorbidity count and treatment efficacy for either EQ-5D or SF-36 (EQ-5D -0.0035 [95% CI -0.0153 to -0.0065], PBayes = 0.746; SF-36-MCS (-0.0111 [95% CI -0.0647 to 0.0416], PBayes = 0.70 and SF-36-PCS -0.0092 [95% CI -0.0758 to 0.0476], PBayes = 0.631.ConclusionsTreatment effects on quality of life did not differ by multimorbidity (measured via a comorbidity count) at baseline-for the medical conditions studied, types and severity of comorbidities and level of quality of life at baseline, suggesting that evidence from clinical trials is likely to be applicable to settings with (at least modestly) higher levels of comorbidity.Trial registrationA prespecified protocol was registered on PROSPERO (CRD42018048202).

Objective To develop international consensus on the definition and measurement of multimorbidity in research.Design Delphi consensus study.Setting International consensus; data collected in three online rounds from participants between 30 November 2020 and 18 May 2021.Participants Professionals interested in multimorbidity and people with long term conditions were recruited to professional and public panels.Results 150 professional and 25 public participants completed the first survey round. Response rates for rounds 2/3 were 83%/92% for professionals and 88%/93% in the public panel, respectively. Across both panels, the consensus was that multimorbidity should be defined as two or more long term conditions. Complex multimorbidity was perceived to be a useful concept, but the panels were unable to agree on how to define it. Both panels agreed that conditions should be included in a multimorbidity measure if they were one or more of the following: currently active; permanent in their effects; requiring current treatment, care, or therapy; requiring surveillance; or relapsing-remitting conditions requiring ongoing care. Consensus was reached for 24 conditions to always include in multimorbidity measures, and 35 conditions to usually include unless a good reason not to existed. Simple counts were preferred for estimating prevalence and examining clustering or trajectories, and weighted measures were preferred for risk adjustment and outcome prediction.Conclusions Previous multimorbidity research is limited by inconsistent definitions and approaches to measuring multimorbidity. This Delphi study identifies professional and public panel consensus guidance to facilitate consistency of definition and measurement, and to improve study comparability and reproducibility.

Objectives To estimate the association between individual participant characteristics and attrition from randomised controlled trials.Design Meta-analysis of individual participant level data (IPD).Data sources Clinical trial repositories (Clinical Study Data Request and Yale University Open Data Access).Eligibility criteria for selecting studies Eligible phase 3 or 4 trials identified according to prespecified criteria (PROSPERO CRD42018048202).Main outcome measures Association between comorbidity count (identified using medical history or concomitant drug treatment data) and trial attrition (failure for any reason to complete the final trial visit), estimated in logistic regression models and adjusted for age and sex. Estimates were meta-analysed in bayesian linear models, with partial pooling across index conditions and drug classes.Results In 92 trials across 20 index conditions and 17 drug classes, the mean comorbidity count ranged from 0.3 to 2.7. Neither age nor sex was clearly associated with attrition (odds ratio 1.04, 95% credible interval 0.98 to 1.11; and 0.99, 0.93 to 1.05, respectively). However, comorbidity count was associated with trial attrition (odds ratio per additional comorbidity 1.11, 95% credible interval 1.07 to 1.14). No evidence of non-linearity (assessed via a second order polynomial) was seen in the association between comorbidity count and trial attrition, with minimal variation across drug classes and index conditions. At a trial level, an increase in participant comorbidity count has a minor impact on attrition: for a notional trial with high level of attrition in individuals without comorbidity, doubling the mean comorbidity count from 1 to 2 translates to an increase in trial attrition from 29% to 31%.Conclusions Increased comorbidity count, irrespective of age and sex, is associated with a modest increased odds of participant attrition. The benefit of increased generalisability of including participants with multimorbidity seems likely to outweigh the disadvantages of increased attrition.

Objective To externally evaluate the QFracture-2012 risk prediction tool for predicting the risk of major osteoporotic fracture and hip fracture.Design External validation cohort study.Setting UK primary care population. Linked general practice (Clinical Practice Research Datalink (CPRD) Gold), mortality registration (Office of National Statistics), and hospital inpatient (Hospital Episode Statistics) data, from 1 January 2004 to 31 March 2016.Participants 2 747 409 women and 2 684 730 men, aged 30-99 years, with up-to-standard linked data that had passed CPRD checks for at least one year.Main outcome measures Two outcomes were modelled based on those predicted by QFracture: major osteoporotic fracture and hip fracture. Major osteoporotic fracture was defined as any hip, distal forearm, proximal humerus, or vertebral crush fracture, from general practice, hospital discharge, and mortality data. The QFracture-2012 10 year predicted risk of major osteoporotic fracture and hip fracture was calculated, and performance evaluated versus observed 10 year risk of fracture in the whole population, and in subgroups based on age and comorbidity. QFracture-2012 calibration was examined accounting for, and not accounting for, competing risk of mortality from causes other than the major osteoporotic fracture.Results 2 747 409 women with 95 598 major osteoporotic fractures and 36 400 hip fractures, and 2 684 730 men with 34 321 major osteoporotic fractures and 13 379 hip fractures were included in the analysis. The incidence of all fractures was higher than in the QFracture-2012 internal derivation. Competing risk of mortality was more common than fracture from middle age onwards. QFracture-2012 discrimination in the whole population was excellent or good for major osteoporotic fracture and hip fracture (Harrell’s C statistic in women 0.813 and 0.918, and 0.738 and 0.888 in men, respectively), but was poor to moderate in age subgroups (eg, Harrell’s C statistic in women and men aged 85-99 years was 0.576 and 0.624 for major osteoporotic fractures, and 0.601 and 0.637 for hip fractures, respectively). Without accounting for competing risks, QFracture-2012 systematically under-predicted the risk of fracture in all models, and more so for major osteoporotic fracture than for hip fracture, and more so in older people. Accounting for competing risks, QFracture-2012 still under-predicted the risk of fracture in the whole population, but over-prediction was considerable in older age groups and in people with high comorbidities at high risk of fracture.Conclusions The QFracture-2012 risk prediction tool systematically under-predicted the risk of fracture (because of incomplete determination of fracture rates) and over-predicted the risk in older people and in those with more comorbidities (because of competing mortality). The current version of QFracture-2016 that is used by the UK's health service needs to be externally validated, particularly in people at high risk of death from other causes.

Introduction: Polypharmacy is increasingly common, and associated with undesirable consequences. Polypharmacy management necessitates balancing therapeutic benefits and risks, and varying clinical and patient priorities. Current guidance for managing polypharmacy is not supported by high quality evidence. The aim of the Improving Medicines use in People with Polypharmacy in Primary Care (IMPPP) trial is to evaluate the effectiveness of an intervention to optimise medication use for patients with polypharmacy in a general practice setting. Methods: This trial will use a multicentre, open-label, cluster-randomised controlled approach, with two parallel groups. Practices will be randomised to a complex intervention comprising structured medication review (including interprofessional GP/pharmacist treatment planning and patient-facing review) supported by performance feedback, financial incentivisation, clinician training and clinical informatics (intervention), or usual care (control). Patients with polypharmacy and triggering potentially inappropriate prescribing (PIP) indicators will be recruited in each practice using a computerised search of health records. 37 practices will recruit 50 patients, and review them over a 26-week intervention delivery period. The primary outcome is the mean number of PIP indicators triggered per patient at 26 weeks follow-up, determined objectively from coded GP electronic health records. Secondary outcomes will include patient reported outcome measures, and health and care service use. The main intention-to-treat analysis will use linear mixed effects regression to compare number of PIP indicators triggered at 26 weeks post-review between groups, adjusted for baseline (pre-randomisation) values. A nested process evaluation will explore implementation of the intervention in primary care. Ethics and dissemination: The protocol and associated study materials have been approved by the Wales REC 6, NHS Research Ethics Committee (REC reference 19/WA/0090), host institution and Health Research Authority. Research outputs will be published in peer-reviewed journals and relevant conferences, and additionally disseminated to patients and the public, clinicians, commissioners and policy makers. ISRCTN Registration: 90146150 (28/03/2019)

Objectives Multimorbidity poses a major challenge to healthcare systems worldwide, but there is no standardised approach to measuring multimorbidity. Existing studies include a median of 17 (IQR 11-23, range 2-285) long-term conditions (LTCs), making it difficult to compare or replicate research, particularly research examining the prevalence of multimorbidity. Approach In this cross-sectional study of 1,168,620 people from the Clinical Practice Research Datalink, we extracted data on 99 LTCs defined by validated code-sets, including all conditions recommended as core by a recent Delphi study. We examined how the prevalence of multimorbidity defined as ≥2 LTCs, varied when including different numbers of LTCs in the measure. We calculated multimorbidity prevalence when including only the two most prevalent LTCs, repeating the calculation for 3,4,5, through to 99 LTCs, added in order of descending prevalence. Results were stratified by age, sex, and socioeconomic status (SES). Results Using only the two most prevalent LTCs, 16.8% of people had multimorbidity, compared to 39.3% for 10 LTCs, 42.0% for 25 LTCs, 42.5% for 50 LTCs, 42.6% for 75 LTCs, and 42.6% for all 99 LTCs. Whole population prevalence was essentially stable once 46+ LTCs were included (estimated prevalence change from adding any additional LTC

Abstract Background The objective of this study was to investigate the relation of severe COVID-19 to prior drug prescribing. Methods Severe cases were defined by entry to critical care or fatal outcome. For this matched case-control study (REACT-SCOT), all 4251 cases of severe COVID-19 in Scotland since the start of the epidemic were matched for age, sex and primary care practice to 36,738 controls from the population register. Records were linked to hospital discharges since June 2015 and dispensed prescriptions issued in primary care during the last 240 days. Results Severe COVID-19 was strongly associated with the number of non-cardiovascular drug classes dispensed. This association was strongest in those not resident in a care home, in whom the rate ratio (95% CI) associated with dispensing of 12 or more drug classes versus none was 10.8 (8.8, 13.3), and in those without any of the conditions designated as conferring increased risk of COVID-19. Of 17 drug classes postulated at the start of the epidemic to be “medications compromising COVID”, all were associated with increased risk of severe COVID-19 and these associations were present in those without any of the designated risk conditions. The fraction of cases in the population attributable to exposure to these drug classes was 38%. The largest effect was for antipsychotic agents: rate ratio 4.18 (3.42, 5.11). Other drug classes with large effects included proton pump inhibitors (rate ratio 2.20 (1.72, 2.83) for = 2 defined daily doses/day), opioids (3.66 (2.68, 5.01) for = 50 mg morphine equivalent/day) and gabapentinoids. These associations persisted after adjusting for covariates and were stronger with recent than with non-recent exposure. Conclusions Severe COVID-19 is associated with polypharmacy and with drugs that cause sedation, respiratory depression, or dyskinesia; have anticholinergic effects; or affect the gastrointestinal system. These associations are not easily explained by co-morbidity. Measures to reduce the burden of mortality and morbidity from COVID-19 should include reinforcing existing guidance on reducing overprescribing of these drug classes and limiting inappropriate polypharmacy. Registration ENCEPP number EUPAS35558

Abstract Background The National Early Warning Score (NEWS2) is currently recommended in the UK for the risk stratification of COVID-19 patients, but little is known about its ability to detect severe cases. We aimed to evaluate NEWS2 for the prediction of severe COVID-19 outcome and identify and validate a set of blood and physiological parameters routinely collected at hospital admission to improve upon the use of NEWS2 alone for medium-term risk stratification. Methods Training cohorts comprised 1276 patients admitted to King’s College Hospital National Health Service (NHS) Foundation Trust with COVID-19 disease from 1 March to 30 April 2020. External validation cohorts included 6237 patients from five UK NHS Trusts (Guy’s and St Thomas’ Hospitals, University Hospitals Southampton, University Hospitals Bristol and Weston NHS Foundation Trust, University College London Hospitals, University Hospitals Birmingham), one hospital in Norway (Oslo University Hospital), and two hospitals in Wuhan, China (Wuhan Sixth Hospital and Taikang Tongji Hospital). The outcome was severe COVID-19 disease (transfer to intensive care unit (ICU) or death) at 14 days after hospital admission. Age, physiological measures, blood biomarkers, sex, ethnicity, and comorbidities (hypertension, diabetes, cardiovascular, respiratory and kidney diseases) measured at hospital admission were considered in the models. Results A baseline model of ‘NEWS2 + age’ had poor-to-moderate discrimination for severe COVID-19 infection at 14 days (area under receiver operating characteristic curve (AUC) in training cohort = 0.700, 95% confidence interval (CI) 0.680, 0.722; Brier score = 0.192, 95% CI 0.186, 0.197). A supplemented model adding eight routinely collected blood and physiological parameters (supplemental oxygen flow rate, urea, age, oxygen saturation, C-reactive protein, estimated glomerular filtration rate, neutrophil count, neutrophil/lymphocyte ratio) improved discrimination (AUC = 0.735; 95% CI 0.715, 0.757), and these improvements were replicated across seven UK and non-UK sites. However, there was evidence of miscalibration with the model tending to underestimate risks in most sites. Conclusions NEWS2 score had poor-to-moderate discrimination for medium-term COVID-19 outcome which raises questions about its use as a screening tool at hospital admission. Risk stratification was improved by including readily available blood and physiological parameters measured at hospital admission, but there was evidence of miscalibration in external sites. This highlights the need for a better understanding of the use of early warning scores for COVID.

Objectives To identify research priorities for primary care in Scotland following the COVID-19 pandemic.Design Modified James Lind Alliance methodology; respondents completed an online survey to make research suggestions and rank research themes in order of priority.Setting Scotland primary care.Participants Healthcare professionals in primary care in Scotland and members of primary care patient and public involvement groups. 512 respondents provided research suggestions; 8% (n=40) did not work in health or social care; of those who did work, 68.8% worked in primary care, 16.3% community care, 11.7% secondary care, 4.5% third sector, 4.2% university (respondents could select multiple options). Of those respondents who identified as healthcare professionals, 33% were in nursing and midwifery professions, 25% were in allied health professions (of whom 45% were occupational therapists and 35% were physiotherapists), 20% were in the medical profession and 10% were in the pharmacy profession.Main outcomes Suggestions for research for primary care made by respondents were categorised into themes and subthemes by researchers and ranked in order of priority by respondents.Results There were 1274 research suggestions which were categorised under 12 themes and 30 subthemes. The following five themes received the most suggestions for research: disease and illness (n=461 suggestions), access (n=202), workforce (n=164), multidisciplinary team (MDT; n=143) and integration (n=108). One hundred and three (20%) respondents to the survey participated in ranking the list of 12 themes in order of research priority. The five most highly ranked research priorities were disease and illness, health inequalities, access, workforce and MDTs. The disease and illness theme had the greatest number of suggestions for research and was scored the most highly in the ranking exercise. The subtheme ranked as the most important research priority in the disease and illness theme was ‘mental health’.Conclusions The themes and subthemes identified in this study should inform research funders so that the direction of primary healthcare is informed by evidence.

Objective (1) To estimate the pooled prevalence of multimorbidity in all age groups, globally. (2) To examine how measurement of multimorbidity impacted the estimated prevalence.Methods In this systematic review and meta-analysis, we conducted searches in nine bibliographic databases (PsycINFO, Embase, Global Health, Medline, Scopus, Web of Science, Cochrane Library, CINAHL and ProQuest Dissertations and Theses Global) for prevalence studies published between database inception and 21 January 2020. Studies reporting the prevalence of multimorbidity (in all age groups and in community, primary care, care home and hospital settings) were included. Studies with an index condition or those that did not include people with no long-term conditions in the denominator were excluded. Retrieved studies were independently reviewed by two reviewers, and relevant data were extracted using predesigned pro forma. We used meta-analysis to pool the estimated prevalence of multimorbidity across studies, and used random-effects meta-regression and subgroup analysis to examine the association of heterogeneous prevalence estimates with study and measure characteristics.Results 13 807 titles were screened, of which 193 met inclusion criteria for meta-analysis. The pooled prevalence of multimorbidity was 42.4% (95% CI 38.9% to 46.0%) with high heterogeneity (I2 >99%). In adjusted meta-regression models, participant mean age and the number of conditions included in a measure accounted for 47.8% of heterogeneity in effect sizes. The estimated prevalence of multimorbidity was significantly higher in studies with older adults and those that included larger numbers of conditions. There was no significant difference in estimated prevalence between low-income or middle-income countries (36.8%) and high-income countries (44.3%), or between self-report (40.0%) and administrative/clinical databases (52.7%).Conclusions The pooled prevalence of multimorbidity was significantly higher in older populations and when studies included a larger number of baseline conditions. The findings suggest that, to improve study comparability and quality of reporting, future studies should use a common core conditions set for multimorbidity measurement and report multimorbidity prevalence stratified by sociodemographics.PROSPERO registration numberCRD42020172409.

Abstract Background Research comparing sex differences in the effects of antipsychotic medications on acute ischemic heart disease (IHD) is limited and the findings ambiguous. This study aimed to investigate these associations within a primary care setting. Methods Hong Kong public general outpatient electronic records of patients aged 45+ during 2007–2010 were extracted, with the last consultation date as the baseline for a 4-year follow-up period to observe acute IHD hospitalizations (2011–2014). Antipsychotic use was defined as any prescription over the previous 12 months from a list of 16 antipsychotics, while acute IHD was defined by ICD-9: 410.00–411.89. Both sex-specific and sex-combined (both sexes) mixed-effects Cox models (random intercept across 74 clinics) were implemented to examine the association and test the interaction between antipsychotics and sex. Results Among 1,043,236 included patients, 17,780 (1.7%) were prescribed antipsychotics, and 8342 (0.8%) developed IHD. In sex-specific analyses, antipsychotic prescription was associated with a 32% increased hazard rate of acute IHD among women (95% CI 1.05–1.67) but not among men. A likelihood ratio test comparing sex-combined models with and without the interaction between antipsychotic use and sex suggested significant interaction (χ 2 = 4.72, P = 0.030). The association between antipsychotic use and IHD among women attenuated and became non-significant when haloperidol was omitted from the operationalization of antipsychotic use (HR = 1.23, 95% CI 0.95–1.60). Conclusion Our results suggest that antipsychotic prescription is moderately associated with an increased risk of acute IHD among women in primary care and this relationship may be explained by specific antipsychotics. Further research should observe and capture the potential intermediary mechanisms and the dose-response relationship of this association to provide more rigorous evidence to establish causality and inform clinical practices.

Abstract Background The generalisability of randomized controlled trials (RCTs) can be uncertain because the impact of exclusion criteria is rarely quantified. The aim of this study was to systematically review studies examining the percentage of clinical populations with a physical health condition who would be excluded by RCTs of treatments for that condition. Methods Medline and Embase were searched from inception to Feb 11th 2018. Two reviewers independently completed screening, full-text review, data extraction and risk-of-bias assessment. The primary outcome was the percentage of patients in the clinical population who would have been excluded from each examined trial. Subgroup analyses examined exclusion by population setting, publication date and funding source. Results Titles/abstracts (20,754) were screened, and 50 studies were included which reported exclusion rates from 305 trials of treatments in 31 physical conditions. Estimated rates of exclusion from trials varied from 0% to 100%, and the median exclusion rate was 77.1% of patients (interquartile range 55.5% to 89.0% exclusion). Median exclusion rates for trials in common chronic conditions were high, including hypertension 83.0%, type 2 diabetes 81.7%, chronic obstructive pulmonary disease 84.3%, and asthma 96.0%. The most commonly applied exclusion criteria related to age, co-morbidity and co-prescribing, whereas more implicit criteria relating to life expectancy or functional status were not typically examined. There was no evidence that exclusion varied by the nature of the clinical population in which exclusion was evaluated or trial funding source. There was no statistically significant change in exclusion rates in more recent compared with older trials. Conclusions The majority of trials of treatments for physical conditions examined excluded the majority of patients with the condition being treated. Almost a quarter of the trials studied excluded over 90% of patients, more than half of trials excluded at least three quarters of patients, and four out of five trials excluded at least half of patients. A limitation is that most studies applied only a subset of eligibility criteria, so exclusion rates are likely under-estimated. Exclusion from trials of older people and people with co-morbidity and co-prescribing is increasingly untenable given population aging and increasing multimorbidity. Trial registration PROSPERO registration CRD42016042282.

Background Clinical pathways are changing to incorporate support and appropriate follow-up for people to achieve remission of type 2 diabetes, but there is limited understanding of the prevalence of remission in current practice or patient characteristics associated with remission. Methods and findings We carried out a cross-sectional study estimating the prevalence of remission of type 2 diabetes in all adults in Scotland aged ≥30 years diagnosed with type 2 diabetes and alive on December 31, 2019. Remission of type 2 diabetes was assessed between January 1, 2019 and December 31, 2019. We defined remission as all HbA1c values Conclusions In this study, we found that 4.8% of people with type 2 diabetes who had at least 1 HbA1c ≥48 mmol/mol (6.5%) after diagnosis of diabetes and had at least 1 HbA1c recorded in 2019 had evidence of type 2 diabetes remission. Guidelines are required for management and follow-up of this group and may differ depending on whether weight loss and remission of diabetes were intentional or unintentional. Our findings can be used to evaluate the impact of future initiatives on the prevalence of type 2 diabetes remission. Mireille Captieux and co-workers report on population-level evidence for remission of type 2 diabetes in Scotland. Author summary Why was this study done? Feasibility of diabetes remission has been demonstrated in research settings and after bariatric surgery, but we do not know how many people in the general population achieve remission of type 2 diabetes. Informed decisions need to be made about which people are most likely to achieve and maintain remission; to do this, we need to better understand the characteristics of people who are currently in remission. Estimating the prevalence of remission of type 2 diabetes in Scotland in 2019 creates a baseline to evaluate the impact of future initiatives to support remission and for future studies of duration of remission and effect on risk of complications of diabetes. What did the researchers do and find? We calculated how many people were in remission of type 2 diabetes in 2019 in Scotland from a national type 2 diabetes register. This register contains 99% of people with diabetes in Scotland. We described the characteristics of people who were in remission of type 2 diabetes compared to people who were not in remission and created a mathematical model that shows the probability of achieving remission in 2019 based on these characteristics. We found that about 1 in 20 of people with type 2 diabetes in the study population were in remission of type 2 diabetes. Compared to people who did not achieve remission, people in remission of type 2 diabetes tended to be older; have a lower HbA1c at diagnosis; have never taken any glucose-lowering medication; have lost weight since the diagnosis of diabetes; and have had bariatric surgery. What do these findings mean? There is a sizeable proportion of people who achieve remission of type 2 diabetes outside research trials and without bariatric surgery. These people should be recognised and coded appropriately so they can be supported by their clinicians. The clinical progress of these people can now be followed by researchers. People who have not yet been prescribed drugs to treat diabetes may be the most appropriate group for clinicians to initiate discussions around remission and weight management options. Guidelines for supporting people who achieve remission of diabetes must recognise differences between people that lose weight intentionally and those that lose weight because of severe illness. Clinicians also need greater clarity on how to manage older or frailer people who achieve remission criteria.

Background: Care home residents often have multiple cognitive and physical impairments, and are at high risk of adverse drug events (ADEs). Aim: To describe excessive polypharmacy and potentially inappropriate prescribing predisposing care home residents to ADEs. Design & setting: A cross-sectional analysis of all dispensed prescriptions for 147 care home residents in Tayside and Fife, Scotland. Method: Prevalence of excessive polypharmacy was examined using multilevel logistic regression, by modelling associations between individual and care home predictors with excessive polypharmacy (≥10 drugs). Prescribing of drugs known to increase the risk of eight clinically important ADE categories was examined. Drugs prescribed within each ADE category, for each resident, were counted. Results: In total, 32.3% (n = 1444/4468) of residents had excessive polypharmacy, which was more common in residents aged 70–74 years (adjusted odds ratio [aOR] 1.86, 95% confidence interval [CI] = 1.04 to 3.34) and 80–84 years (aOR 1.75, 95% CI = 1.01 to 3.02), living in a residential care home (aOR 1.50, 95% CI = 1.19 to 1.88), and located in Fife (aOR 1.37, 95% CI = 1.09 to 1.71). Excessive polypharmacy was less common in residents with dementia (aOR 0.73, 95% CI = 0.64 to 0.84), and 8.9% (95% CI = 5.9% to 11.6%) of the variation was attributable to care home predictors. Potentially inappropriate prescribing of ≥2 drugs was seen across all ADE categories, with highest prevalence seen in drugs predisposing to constipation (35.8%), sedation (27.7%), and renal injury (18.0%). Conclusion: Excessive polypharmacy is common in care home residents and is associated with both individual and care home predictors. Potentially inappropriate prescribing of drugs that predisposed residents to all included ADE categories is common. Research is needed to support and evaluate safe care home prescribing practices.

BackgroundMost people with COVID-19 self-manage at home. However, the condition can deteriorate quickly, and some people may develop serious hypoxia with relatively few symptoms. Early identification of deterioration allows effective management with oxygen and steroids. Telemonitoring of symptoms and physiological signs may facilitate this. ObjectiveThe aim of this study was to design, implement, and evaluate a telemonitoring system for people with COVID-19 who are self-managing at home and are considered at significant risk of deterioration. MethodsA multidisciplinary team developed a telemonitoring protocol using a commercial platform to record symptoms, pulse oximetry, and temperature. If symptoms or physiological measures breached targets, patients were alerted and asked to phone for an ambulance (red alert) or for advice (amber alert). Patients attending COVID-19 assessment centers, who were considered fit for discharge but at risk of deterioration, were shown how to use a pulse oximeter and the monitoring system, which they were to use twice daily for 2 weeks. Patients could interact with the system via app, SMS, or touch-tone phone. Written guidance on alerts was also provided. Following consent, patient data on telemonitoring usage and alerts were linked to data on the use of service resources. Subsequently, patients who had either used or not used the telemonitoring service, including those who had not followed advice to seek help, agreed to brief telephone interviews to explore their views on, and how they had interacted with, the telemonitoring system. Interviews were recorded and analyzed thematically. Professionals involved in the implementation were sent an online questionnaire asking them about their perceptions of the service. ResultsWe investigated the first 116 patients who used the service. Of these patients, 71 (61.2%) submitted data and the remainder (n=45, 38.8%) chose to self-monitor without electronic support. Of the 71 patients who submitted data, 35 (49%) received 152 alerts during their 2-week observation. A total of 67 red alerts were for oxygen saturation (SpO2) levels of ≤93%, and 15 red alerts were because patients recorded severe breathlessness. Out of 71 patients, 14 (20%) were admitted to hospital for an average stay of 3.6 (SD 4.5) days. Of the 45 who used written guidance alone, 7 (16%) were admitted to hospital for an average stay of 4.0 (SD 4.2) days and 1 (2%) died. Some patients who were advised to seek help did not do so, some because parameters improved on retesting and others because they felt no worse than before. All patients found self-monitoring to be reassuring. Of the 11 professionals who used the system, most found it to be useful and easy to use. Of these 11 professionals, 5 (45%) considered the system “very safe,” 3 (27%) thought it “could be safer,” and 3 (27%) wished to have more experience with it before deciding. In total, 2 (18%) felt that SpO2 trigger thresholds were too high. ConclusionsSupported self-monitoring of patients with COVID-19 at home is reassuring to patients, is acceptable to clinicians, and can detect important signs of deterioration. Worryingly, some patients, because they felt well, occasionally ignored important signs of deterioration. It is important, therefore, to emphasize the importance of the early investigation and treatment of asymptomatic hypoxia at the time when patients are initiated and in the warning messages that are sent to patients.

Summary: Background: A systematic understanding of how multimorbidity has been constructed and measured is unavailable. This review aimed to examine the definition and measurement of multimorbidity in peer-reviewed studies internationally. Methods: We systematically reviewed studies on multimorbidity, via a search of nine bibliographic databases (Ovid [PsycINFO, Embase, Global Health, and MEDLINE], Web of Science, the Cochrane Library, CINAHL Plus, Scopus, and ProQuest Dissertations & Theses Global), from inception to Jan 21, 2020. Reference lists and tracked citations of retrieved articles were hand-searched. Eligible studies were full-text articles measuring multimorbidity for any purpose in community, primary care, care home, or hospital populations receiving a non-specialist service. Abstracts, qualitative research, and case series were excluded. Two reviewers independently reviewed the retrieved studies with conflicts resolved by discussion or a third reviewer, and a single researcher extracted data from published papers. To assess our objectives of how multimorbidity has been measured and examine variation in the chronic conditions included (in terms of number and type), we used descriptive analysis (frequencies, cross-tabulation, and negative binomial regression) to summarise the characteristics of multimorbidity studies and measures (study setting, source of morbidity data, study population, primary study purpose, and multimorbidity measure type). This systematic review is registered with PROSPERO, CRD420201724090. Findings: 566 studies were included in our review, of which 206 (36·4%) did not report a reference definition for multimorbidity and 73 (12·9%) did not report the conditions their measure included. The number of conditions included in measures ranged from two to 285 (median 17 [IQR 11–23). 452 (79·9%) studies reported types of condition within a single multimorbidity measure; most included at least one cardiovascular condition (441 [97·6%] of 452 studies), metabolic and endocrine condition (440 [97·3%]), respiratory condition (422 [93·4%]), musculoskeletal condition (396 [87·6%]), or mental health condition (355 [78·5%]) in their measure of multimorbidity. Chronic infections (123 [27·2%]), haematological conditions (110 [24·3%]), ear, nose, and throat conditions (107 [23·7%]), skin conditions (70 [15·5%]), oral conditions (19 [4·2%]), and congenital conditions (14 [3·1%]) were uncommonly included. Only eight individual conditions were included by more than half of studies in the multimorbidity measure used (diabetes, stroke, cancer, chronic obstructive pulmonary disease, hypertension, coronary heart disease, chronic kidney disease, and heart failure), with individual mental health conditions under-represented. Of the 566 studies, 419 were rated to be of moderate risk of bias, 107 of high risk of bias, and 40 of low risk of bias according to the Effective Public Health Practice Project quality assessment tool. Interpretation: Measurement of multimorbidity is poorly reported and highly variable. Consistent reporting of measure definitions should be required by journals, and consensus studies are needed to define core and study-dependent conditions to include in measures of multimorbidity. Funding: Health Data Research UK.

Summary: Background: Primary prevention of cardiovascular disease (CVD) is guided by risk-prediction tools, but these rarely account for the risk of dying from other conditions (ie, competing mortality risk). In England and Wales, the recommended risk-prediction tool is QRISK2, and a new version (QRISK3) has been derived and internally validated. We aimed to externally validate QRISK3 and to assess the effects of competing mortality risk on its predictive performance. Methods: For this retrospective population cohort study, we used data from the Clinical Practice Research Datalink. We included patients aged 25–84 years with no previous history of CVD or statin treatment who were permanently registered with a primary care practice, had up-to-standard data for at least 1 year, and had linkage to Hospital Episode Statistics discharge and Office of National Statistics mortality data. We compared the QRISK3-predicted 10-year CVD risk with the observed 10-year risk in the whole population and in important subgroups of age and multimorbidity. QRISK3 discrimination and calibration were examined with and without accounting for competing risks. Findings: Our study population included 1 484 597 women with 42 451 incident CVD events (4·9 cases per 1000 person-years of follow-up, 95% CI 4·89–4·99), and 1 420 176 men with 53 066 incident CVD events (6·7 cases per 1000 person-years, 6·66–6·78), with median follow-up of 5·0 years (IQR 1·9–9·2). Non-CVD death rose markedly with age (0·4% of women and 0·5% of men aged 25–44 years had a non-CVD death vs 20·1% of women and 19·6% of men aged 75–84 years). QRISK3 discrimination in the whole population was excellent (Harrell's C-statistic 0·865 in women and 0·834 in men) but was poor in older age groups (

Abstract Background Clinicians are less likely to prescribe guideline-recommended treatments to people with multimorbidity than to people with a single condition. Doubts as to the applicability of clinical trials of drug treatments (the gold standard for evidence-based medicine) when people have co-existing diseases (comorbidity) may underlie this apparent reluctance. Therefore, for a range of index conditions, we measured the comorbidity among participants in clinical trials of novel drug therapies and compared this to the comorbidity among patients in the community. Methods Data from industry-sponsored phase 3/4 multicentre trials of novel drug therapies for chronic medical conditions were identified from two repositories: Clinical Study Data Request and the Yale University Open Data Access project. We identified 116 trials (n = 122,969 participants) for 22 index conditions. Community patients were identified from a nationally representative sample of 2.3 million patients in Wales, UK. Twenty-one comorbidities were identified from medication use based on pre-specified definitions. We assessed the prevalence of each comorbidity and the total number of comorbidities (level of multimorbidity), for each trial and in community patients. Results In the trials, the commonest comorbidities in order of declining prevalence were chronic pain, cardiovascular disease, arthritis, affective disorders, acid-related disorders, asthma/COPD and diabetes. These conditions were also common in community-based patients. Mean comorbidity count for trial participants was approximately half that seen in community-based patients. Nonetheless, a substantial proportion of trial participants had a high degree of multimorbidity. For example, in asthma and psoriasis trials, 10–15% of participants had ≥ 3 conditions overall, while in osteoporosis and chronic obstructive pulmonary disease trials 40–60% of participants had ≥ 3 conditions overall. Conclusions Comorbidity and multimorbidity are less common in trials than in community populations with the same index condition. Comorbidity and multimorbidity are, nevertheless, common in trials. This suggests that standard, industry-funded clinical trials are an underused resource for investigating treatment effects in people with comorbidity and multimorbidity.

Following publication of the original manuscript [1], the authors noted several errors in Table 1. Details of the requested corrections are shown below:

Abstract Background Hospital admissions for Ambulatory Care Sensitive Conditions (ACSC) are those that could potentially be prevented by timely and effective disease management within primary care. ACSC admissions are increasingly used as performance indicators. However, key questions remain about the validity of these measures. The evidence to date has been inconclusive and limited to specific conditions. The aim of this study was to test the robustness of ACSC admissions as indicators of the quality of primary care. It is the first study to examine a wide range of ACSCs using longitudinal data which enables us to control for unmeasured characteristics which differ by practice but which are constant over time. Methods Using longitudinal data at the practice level, from 907 Scottish practices for the time period 1/4/2005 to 31/32012, we explored the relationships between the quality of primary care, and hospital admissions for multiple ACSCs controlling for a wide range of covariates including characteristics of GP practices, characteristics of the practice population, hospital effects and year effects. We examined the impact of two dimensions of quality of care: clinical quality of and access to daytime general practice. Generalised Estimating Equations taking the form of Negative Binomial regression models with the practice population included as the exposure term were estimated. Results We found that higher achievement on some clinical quality measures of primary care was associated with reduced ACSC emergency admissions. We also show that access to primary care was associated with ACSC emergency admissions. However, the effects were small and inconsistent and ACSC emergency admissions were associated with several confounding factors such as deprivation, rurality and distance to the hospital. Conclusions The results suggest caution in the use of crude ACSC admission rates as a performance indicator of quality of primary care.

Abstract Background Cognitive spectrum disorders (CSDs) are common in hospitalised older adults and associated with adverse outcomes. Their association with the maintenance of independent living has not been established. The aim was to establish the role of CSDs on the likelihood of living at home 30 days after discharge or being newly admitted to a care home. Methods A prospective cohort study with routine data linkage was conducted based on admissions data from the acute medical unit of a district general hospital in Scotland. 5570 people aged ≥ 65 years admitted from a private residence who survived to discharge and received the Older Persons Routine Acute Assessment (OPRAA) during an incident emergency medical admission were included. The outcome measures were living at home, defined as a private residential address, 30 days after discharge and new care home admission at hospital discharge. Outcomes were ascertained through linkage to routine data sources. Results Of the 5570 individuals admitted from a private residence who survived to discharge, those without a CSD were more likely to be living at home at 30 days than those with a CSD (93.4% versus 81.7%; difference 11.7%, 95%CI 9.7–13.8%). New discharge to a care home affected 236 (4.2%) of the cohort, 181 (76.7%) of whom had a CSD. Logistic regression modelling identified that all four CSD categories were associated with a reduced likelihood of living at home and an increased likelihood of discharge to a care home. Those with delirium superimposed on dementia were the least likely to be living at home (OR 0.25), followed by those with dementia (OR 0.43), then unspecified cognitive impairment (OR 0.55) and finally delirium (OR 0.57). Conclusions Individuals with a CSD are at significantly increased risk of not returning home after hospitalisation, and those with CSDs account for the majority of new admissions to care homes on discharge. Individuals with delirium superimposed on dementia are the most affected. We need to understand how to configure and deliver healthcare services to enable older people to remain as independent as possible for as long as possible and to ensure transitions of care are managed supportively.

Background: Cognitive impairment is common in older people admitted to hospital, but previous research has focused on single conditions. Objective: This project sits in phase 0/1 of the Medical Research Council Framework for the Development and Evaluation of Complex Interventions. It aims to develop an understanding of current health-care outcomes. This will be used in the future development of a multidomain intervention for people with confusion (dementia and cognitive impairment) in general hospitals. The research was conducted from January 2015 to June 2018 and used data from people admitted between 2012 and 2013. Design: For the review of outcomes, the systematic review identified peer-reviewed quantitative epidemiology measuring prevalence and associations with outcomes. Screening for duplication and relevance was followed by full-text review, quality assessment and a narrative review (141 papers). A survey sought opinion on the key outcomes for people with dementia and/or confusion and their carers in the acute hospital (n = 78). For the analysis of outcomes including cost, the prospective cohort study was in a medical admissions unit in an acute hospital in one Scottish health board covering 10% of the Scottish population. The participants (n = 6724) were older people (aged ≥ 65 years) with or without a cognitive spectrum disorder who were admitted as medical emergencies between January 2012 and December 2013 and who underwent a structured nurse assessment. ‘Cognitive spectrum disorder’ was defined as any combination of delirium, known dementia or an Abbreviated Mental Test score of

BackgroundPatients with multimorbidities have the greatest healthcare needs and generate the highest expenditure in the health system. There is an increasing focus on identifying specific disease combinations for addressing poor outcomes. Existing research has identified a small number of prevalent "clusters" in the general population, but the limited number examined might oversimplify the problem and these may not be the ones associated with important outcomes. Combinations with the highest (potentially preventable) secondary care costs may reveal priority targets for intervention or prevention. We aimed to examine the potential of defining multimorbidity clusters for impacting secondary care costs.Methods and findingsWe used national, Hospital Episode Statistics, data from all hospital admissions in England from 2017/2018 (cohort of over 8 million patients) and defined multimorbidity based on ICD-10 codes for 28 chronic conditions (we backfilled conditions from 2009/2010 to address potential undercoding). We identified the combinations of multimorbidity which contributed to the highest total current and previous 5-year costs of secondary care and costs of potentially preventable emergency hospital admissions in aggregate and per patient. We examined the distribution of costs across unique disease combinations to test the potential of the cluster approach for targeting interventions at high costs. We then estimated the overlap between the unique combinations to test potential of the cluster approach for targeting prevention of accumulated disease. We examined variability in the ranks and distributions across age (over/under 65) and deprivation (area level, deciles) subgroups and sensitivity to considering a smaller number of diseases. There were 8,440,133 unique patients in our sample, over 4 million (53.1%) were female, and over 3 million (37.7%) were aged over 65 years. No clear "high cost" combinations of multimorbidity emerged as possible targets for intervention. Over 2 million (31.6%) patients had 63,124 unique combinations of multimorbidity, each contributing a small fraction (maximum 3.2%) to current-year or 5-year secondary care costs. Highest total cost combinations tended to have fewer conditions (dyads/triads, most including hypertension) affecting a relatively large population. This contrasted with the combinations that generated the highest cost for individual patients, which were complex sets of many (6+) conditions affecting fewer persons. However, all combinations containing chronic kidney disease and hypertension, or diabetes and hypertension, made up a significant proportion of total secondary care costs, and all combinations containing chronic heart failure, chronic kidney disease, and hypertension had the highest proportion of preventable emergency admission costs, which might offer priority targets for prevention of disease accumulation. The results varied little between age and deprivation subgroups and sensitivity analyses. Key limitations include availability of data only from hospitals and reliance on hospital coding of health conditions.ConclusionsOur findings indicate that there are no clear multimorbidity combinations for a cluster-targeted intervention approach to reduce secondary care costs. The role of risk-stratification and focus on individual high-cost patients with interventions is particularly questionable for this aim. However, if aetiology is favourable for preventing further disease, the cluster approach might be useful for targeting disease prevention efforts with potential for cost-savings in secondary care.

UK care home residents are invisible in national datasets. The COVID-19 pandemic has exposed data failings that have hindered service development and research for years. Fundamental gaps, in terms of population and service demographics coupled with difficulties identifying the population in routine data are a significant limitation. These challenges are a key factor underpinning the failure to provide timely and responsive policy decisions to support care homes. In this commentary we propose changes that could address this data gap, priorities include: (1) Reliable identification of care home residents and their tenure; (2) Common identifiers to facilitate linkage between data sources from different sectors; (3) Individual-level, anonymised data inclusive of mortality irrespective of where death occurs; (4) Investment in capacity for large-scale, anonymised linked data analysis within social care working in partnership with academics; (5) Recognition of the need for collaborative working to use novel data sources, working to understand their meaning and ensure correct interpretation; (6) Better integration of information governance, enabling safe access for legitimate analyses from all relevant sectors; (7) A core national dataset for care homes developed in collaboration with key stakeholders to support integrated care delivery, service planning, commissioning, policy and research. Our suggestions are immediately actionable with political will and investment. We should seize this opportunity to capitalise on the spotlight the pandemic has thrown on the vulnerable populations living in care homes to invest in data-informed approaches to support care, evidence-based policy making and research.