BACKGROUND:Structured medication reviews (SMRs) are an essential component of medication optimization, especially for patients with multimorbidity and polypharmacy. However, the process remains challenging due to the complexities of patient data, time constraints, and the need for coordination among health care professionals (HCPs). This study explores HCPs' perspectives on the integration of artificial intelligence (AI)-assisted tools to enhance the SMR process, with a focus on the potential benefits of and barriers to adoption. OBJECTIVE:This study aims to identify the key user requirements for AI-assisted tools to improve the efficiency and effectiveness of SMRs, specifically for patients with multimorbidity, complex polypharmacy, and frailty. METHODS:A qualitative study was conducted involving focus groups and semistructured interviews with HCPs and patients in the United Kingdom. Participants included physicians, pharmacists, clinical pharmacologists, psychiatrists from primary and secondary care, a policy maker, and patients with multimorbidity. Data were analyzed using a hybrid inductive and deductive thematic analysis approach to identify themes related to AI-assisted tool functionality, workflow integration, user-interface visualization, and usability in the SMR process. RESULTS:Four major themes emerged from the analysis: innovative AI potential, optimizing electronic patient record visualization, functionality of the AI tool for SMRs, and facilitators of and barriers to AI tool implementation. HCPs identified the potential of AI to support patient identification and prioritizing those at risk of medication-related harm. AI-assisted tools were viewed as essential in detecting prescribing gaps, drug interactions, and patient risk trajectories over time. Participants emphasized the importance of presenting patient data in an intuitive format, with a patient interface for shared decision-making. Suggestions included color-coding blood results, highlighting critical medication reviews, and providing timelines of patient medical histories. HCPs stressed the need for AI tools to integrate seamlessly with existing electronic patient record systems and provide actionable insights without overwhelming users with excessive notifications or "pop-up" alerts. Factors influencing the uptake of AI-assisted tools included the need for user-friendly design, evidence of tool effectiveness (though some were skeptical about the predictive accuracy of AI models), and addressing concerns around digital exclusion. CONCLUSIONS:The findings highlight the potential for AI-assisted tools to streamline and optimize the SMR process, particularly for patients with multimorbidity and complex polypharmacy. However, successful implementation depends on addressing concerns related to workflow integration, user acceptance, and evidence of effectiveness. User-centered design is crucial to ensure that AI-assisted tools support HCPs in delivering high-quality, patient-centered care while minimizing cognitive overload and alert fatigue.

Codelists play a crucial role in ensuring accurate and standardized communication within healthcare. However, preparation of high-quality codelists is a rigorous and time-consuming process. The literature focuses on transparency of clinical codelists and overlooks the utility of automation. Here we present a Codelist Generation Framework that can automate generation of codelists with minimal input from clinical experts. We demonstrate the process using a specific project, DynAIRx, producing appropriate codelists and a framework allowing future projects to take advantage of automated codelist generation. Both the framework and codelist are publicly available. DynAIRx is an NIHR-funded project aiming to develop AIs to help optimise prescribing of medicines in patients with multiple long-term conditions. DynAIRx requires complex codelists to describe the trajectory of each patient, and the interaction between their conditions. We promptly generated ≈ 214 codelists for DynAIRx using the proposed framework and validated them with a panel of experts, significantly reducing the amount of time required by making effective use of automation. The framework reduced the clinician time required to validate codes, automatically shrunk codelists using trusted sources and added new codes for review against existing codelists. In the DynAIRx case study, a codelist of ≈ 14000 codes required only 7-9 hours of clinician’s time in the end (while existing methods takes months), and application of the automation framework reduced the workload by >80

INTRODUCTION:This retrospective open cohort study develops and externally validates a clinical prediction model (CPM) to predict the joint risk of two important outcomes occurring within the next year in people with epilepsy (PWE). These are: A) seizure-related emergency department or hospital admission; and B) epilepsy-related death. This will provide clinicians with a tool to predict either or both of these common outcomes. This has not previously been done despite both being potentially avoidable, interrelated, and devastating for patients and their families. We hypothesise that the CPM will identify individuals at high or low risk of either or both outcomes. We will guide clinicians on proposed actions to take based on the overall risk score. METHODS AND ANALYSIS:Routinely collected, anonymised, electronic health data from the following research platforms will be used: i) Clinical Practice Research Datalink (CPRD); ii) Secure Anonymised Information Linkage databank (SAIL); iii) Combined Intelligence for Population Health Action (CIPHA); and iv) TriNetX. We will study PWE aged ≥16 years having outcomes A and/or B between 2010-2024 within these datasets. Sample sizes of over 100,000 PWE are expected across these datasets. Candidate predictors will include demographic, lifestyle, clinical, and management variables. Logistic regression and multistate modelling will be used to develop a suitable CPM. The choice of modelling approach will be informed by consultation with clinicians and members of the public. We will assess the model's predictive performance using CPRD as a development dataset, and conduct external validation using SAIL, CIPHA, and TriNetX. CONCLUSIONS:This large study will develop and validate a CPM for PWE, creating an internationally generalisable tool for subsequent clinical implementation. It will predict the joint risk of acute admission and death in PWE. Mortality prediction is highlighted by NICE as a key recommendation for epilepsy research. The study has been co-developed by epilepsy researchers and members of the public affected by epilepsy.

OBJECTIVE:Deficiencies have been highlighted in acute hospital care for alcohol-related liver disease (ARLD). Such problems may be worse at weekends (WEs). Increased 30-day mortality for WE admissions has been reported for several acute conditions, but data for ARLD are limited. We aimed to compare patient and pathway characteristics between WE and weekday (WD) admissions and investigate the 'weekend effect' on mortality. METHODS:Retrospective cohort study (2008-2018) using linked electronic databases (Hospital Episode Statistics-Clinical Practice Research Datalink and death registration) including 17 575 first emergency admissions identified using the Liverpool ARLD algorithm. EXPOSURE:WE admission (Saturday or Sunday). MAIN OUTCOME:all-cause death within 30 days. Covariates included socio-demographic characteristics, pathway characteristics (pre-admission contacts and admission method) and markers of severity (recorded stage of liver disease, ascites and varices, comorbidity). Alternative risk-adjustment methods were used, including standard regression and propensity-weighted analysis (Inverse Probability of Treatment Weighting). RESULTS:3249 admissions (18.5%) were at WE. Unadjusted 30-day mortality was significantly higher for WE versus WD (17.1% vs 15.5%, p=0.018). All models demonstrated increased odds of death for WE admissions with adjusted ORs ranging from 1.15 to 1.23 (relative risk of 1.12-1.19). Causes of death did not vary by admission day and effect was consistent across subgroups. Findings were robust to sensitivity analyses restricting the cohort to patients admitted directly from Accident and Emergency department (A&E), or cirrhosis or ascites but not varices. CONCLUSION:First ARLD admissions at the WE experienced a 12-19% increase in 30-day mortality risk compared with WD. Although residual confounding cannot be excluded, this suggests the possibility of avoidable mortality among those hospitalised at WEs. Services should be alert to risks of WE effects when planning care.

Background Polycystic ovary syndrome (PCOS) is associated with adverse clinical outcomes that may differ according to PCOS phenotype. Methods Using UK Biobank data, we compared the incidence of type 2 diabetes (T2D), metabolic dysfunction associated steatotic liver disease, cardiovascular disease (CVD), hormone-dependent cancers, and dementia between PCOS participants and age- and body mass index-matched controls. We also compared multiorgan (liver, cardiac, and brain) magnetic resonance imaging (MRI) data and examined the impact of PCOS phenotype (hyperandrogenic and normoandrogenic) on these outcomes. Results We included 1008 women with PCOS (defined by diagnostic codes, self-reported diagnoses, or clinical/biochemical features of hyperandrogenism and a/oligoCmenorrhoea) and 5017 matched controls (5:1 ratio); median age, 61 years, body mass index, 28.4 kg/m². Adjusted Cox proportional hazard modeling demonstrated PCOS participants had greater incident T2D [hazard ratio (HR) 1.47; 95% confidence interval (CI), 1.11-1.95] and all-cause CVD (1.76; 1.35-2.30). No between-group differences existed for cancers or dementia. Liver MRI confirmed more PCOS participants had hepatic steatosis (proton density fat fraction >5.5%: 35.9 vs 23.9%; P = .02) and higher fibroinflammation (corrected T1 721.4 vs 701.5 ms; P = <.01) vs controls. No between-group difference existed for cardiac (biventricular/atrial structure and function) or brain (grey and white matter volumes) imaging. Normoandrogenic (but not hyperandrogenic) PCOS participants had greater incident all-cause CVD (1.82; 1.29-2.56) while hyperandrogenic (but not normoandrogenic) PCOS participants were more likely to have hepatic steatosis (8.96 vs 6.04 vs 5.23%; P = .03) with greater fibroinflammation (776.3 vs 707.7 vs 701.9 ms; P=<.01). Conclusion Cardiometabolic disease may be increased in PCOS patients with a disease phenotype-specific pattern.

Objectives Cardiovascular risk prediction tools developed for the general population often underperform for individuals with RA, and their predictive accuracy are unclear for other inflammatory conditions that also have increased cardiovascular risk. We investigated the performance of QRISK-3, the Framingham Risk Score (FRS) and the Reynolds Risk Score (RRS) in RA, psoriatic disease (PsA and psoriasis) and AS. We considered OA as a non-inflammatory comparator.Methods We utilized primary care records from the Clinical Practice Research Datalink (CPRD) Aurum database to identify individuals with each condition and calculated 10-year cardiovascular risk using each prediction tool. The discrimination and calibration of each tool was assessed for each disease.Results The time-dependent area under the curve (AUC) for QRISK3 was 0.752 for RA (95% CI 0.734-0.777), 0.794 for AS (95% CI 0.764-0.812), 0.764 for PsA (95% CI 0.741-0.791), 0.815 for psoriasis (95% CI 0.789-0.835) and 0.698 for OA (95% CI 0.670-0.717), indicating reasonably good predictive performance. The AUCs for the FRS were similar, and slightly lower for the RRS. The FRS was reasonably well calibrated for each condition but underpredicted risk for patients with RA. The RRS tended to underpredict CVD risk, while QRISK3 overpredicted CVD risk, especially for the most high-risk individuals.Conclusion CVD risk for individuals with RA, AS and psoriatic disease was generally less accurately predicted using each of the three CVD risk prediction tools than the reported accuracies in the original publications. Individuals with OA also had less accurate predictions, suggesting inflammation is not the sole reason for underperformance. Disease-specific risk prediction tools may be required.

Valproate is the most effective treatment for idiopathic generalised epilepsy. Currently, its use is restricted in women of childbearing potential owing to high teratogenicity. Recent evidence extended this risk to men's offspring, prompting recommendations to restrict use in everybody aged <55 years. This study will evaluate mortality and morbidity risks associated with valproate withdrawal by emulating a hypothetical randomised-controlled trial (called a "target trial") using retrospective observational data. The data will be drawn from ~250m mainly US patients in the TriNetX repository and ~60m UK patients in Clinical Practice Research Datalink (CPRD). These will be scanned for individuals aged 16-54 years with epilepsy and on valproate who either continued, switched to lamotrigine or levetiracetam, or discontinued valproate between 2014-2024, creating four groups. Randomisation to these groups will be emulated by baseline confounder adjustment using g-methods. Mortality and morbidity outcomes will be assessed and compared between groups over 1-10 years, employing time-to-first-event and recurrent events analyses. A causal prediction model will be developed from these data to aid in predicting the safest alternative antiseizure medications. Together, these findings will optimise informed decision-making about valproate withdrawal and alternative treatment selection, providing immediate and vital information for patients, clinicians and regulators.

Background: Population ageing has led to an increase in multimorbidity and polypharmacy. Some medications may need to be stopped, but patient attitudes towards deprescribing are poorly understood. This study explores attitudes towards (de)prescribing in patients with multimorbidity in the UK primary care. Methods: Patients with multimorbidity were invited to complete the Revised Patients Attitudes Towards Deprescribing (rPATD) Questionnaire using the Evergreen Life Personal Health Record App (Manchester, UK). The responses were linked to electronic health records. Anonymised data were analysed in a trusted research environment (University of Liverpool) for group comparisons and using multivariable logistic regression to identify factors associated with satisfaction with current medications. Results: A total 1,019 patients participated in the study (n=365 aged <65, 30% males; n=654 ≥65, 57% males). Most patients were satisfied with their current medications (74% aged <65, 70% aged ≥65) but were willing to stop one or more of their regular medicines if their doctor said it was possible (82%, 68% accordingly). Polypharmacy, use of antihypertensive drugs, and antidepressants were associated with patient-reported burden in taking medicines. Frailty did not influence patient deprescribing attitudes. Patients who were satisfied with current medications had fewer medications. Independent predictors of satisfaction with current medications were higher total involvement and appropriateness scores, and lower total burden score. Conclusions: Most patients with multimorbidity would consider stopping some of their medications, even when they are generally satisfied with the treatments they received. Frailty status does not imply willingness to stop medications. Clinicians should discuss medication deprescribing for shared decision. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study/project is funded by the National Institute for Health Research (NIHR) under its Programme Artificial Intelligence for Multiple and Long-Term Conditions (NIHR203986). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. IB is supported by NIHR as Senior Investigator award (NIHR205131).AW is partly funded by Health and Care Research Wales award (NHS-RTA-21-02) ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Newcastle North Tyneside Research Ethics Committee (REC reference:22/NE/0088) granted ethical approval for the DynAIRx study. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors

IntroductionStructured medication reviews (SMRs), introduced in the United Kingdom (UK) in 2020, aim to enhance shared decision-making in medication optimisation, particularly for patients with multimorbidity and polypharmacy. Despite its potential, there is limited empirical evidence on the implementation of SMRs, and the challenges faced in the process. This study is part of a larger DynAIRx (Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity) project which aims to introduce Artificial Intelligence (AI) to SMRs and develop machine learning models and visualisation tools for patients with multimorbidity. Here, we explore how SMRs are currently undertaken and what barriers are experienced by those involved in them.MethodsQualitative focus groups and semi-structured interviews took place between 2022-2023. Six focus groups were conducted with doctors, pharmacists and clinical pharmacologists (n = 21), and three patient focus groups with patients with multimorbidity (n = 13). Five semi-structured interviews were held with 2 pharmacists, 1 trainee doctor, 1 policy-maker and 1 psychiatrist. Transcripts were analysed using thematic analysis.ResultsTwo key themes limiting the effectiveness of SMRs in clinical practice were identified: 'Medication Reviews in Practice' and 'Medication-related Challenges'. Participants noted limitations to the efficient and effectiveness of SMRs in practice including the scarcity of digital tools for identifying and prioritising patients for SMRs; organisational and patient-related challenges in inviting patients for SMRs and ensuring they attend; the time-intensive nature of SMRs, the need for multiple appointments and shared decision-making; the impact of the healthcare context on SMR delivery; poor communication and data sharing issues between primary and secondary care; difficulties in managing mental health medications and specific challenges associated with anticholinergic medication.ConclusionSMRs are complex, time consuming and medication optimisation may require multiple follow-up appointments to enable a comprehensive review. There is a need for a prescribing support system to identify, prioritise and reduce the time needed to understand the patient journey when dealing with large volumes of disparate clinical information in electronic health records. However, monitoring the effects of medication optimisation changes with a feedback loop can be challenging to establish and maintain using current electronic health record systems.

Introduction Alcohol-related liver disease (ARLD) often presents for the first time as an emergency hospital admission. We examined time trends in characteristics, care processes and case fatality rates of first admissions for ARLD in England. Methods National population-based, observational study using CPRD-HES-ONS data, 2008/9–17/18. First emergency admissions ≥18 yrs were identified using Liverpool ARLD coding algorithm.1 Covariates: age, sex, deprivation status, case definition (coding pattern), stage of ARLD, non-liver comorbidity, coding for ascites and varices. We applied stratified survival analyses and binary logistic regression models to assess case-mix-adjusted associations between date of discharge and death. Results 17,575 first admissions (mean age: 53; 33% female; 32% from most deprived quintile; 47% with non-primary coding pattern; 13% with hepatic failure [HF]). During the year before admission, only 47% of GP consulters had alcohol-related problems documented (liver disease in just 14.7%) and alcohol-specific diagnoses were absent from 24% of prior emergency admission records. Case fatality rate was 15% in-hospital (HF: 39%) and 34% at one year (HF: 56%). Case-mix-adjusted odds of dying during index hospitalization reduced by 6% per year (aOR: 0.94; 95% CI: 0.93–0.96) and 4% per year at 365 days (aOR: 0.96; 95% CI: 0.95–0.97). There were regional variations in providing higher level care and in case fatality rates. Conclusions Despite improved prognosis of first admissions we found missed opportunities for earlier diagnosis in primary and secondary care. In 2017/18, one in seven were still dying during first hospitalisation, rising to one third within a year. Geographic inequalities require further investigation. Nationwide efforts are needed to promote earlier detection and intervention. (Funding: UK Department of Health - Connected Health Cities). Reference Dhanda A, Bodger K, et al. The liverpool alcohol-related liver disease algorithm identifies twice as many emergency admissions compared to standard methods when applied to Hospital Episode Statistics for England. Aliment Pharmacol Ther. 2023 Feb;57(4):368–377.

The emergence of castration-resistant prostate cancer remains an area of unmet clinical need. We recently identified a subpopulation of normal prostate progenitor cells, characterized by an intrinsic resistance to androgen deprivation and expression of LY6D. We here demonstrate that conditional deletion of PTEN in the murine prostate epithelium causes an expansion of transformed LY6D+ progenitor cells without impairing stem cell properties. Transcriptomic analyses of LY6D+ luminal cells identified an autocrine positive feedback loop, based on the secretion of amphiregulin (AREG)-mediated activation of mitogen-activated protein kinase (MAPK) signaling, increasing cellular fitness and organoid formation. Pharmacological interference with this pathway overcomes the castration-resistant properties of LY6D+ cells with a suppression of organoid formation and loss of LY6D+ cells in vivo. Notably, LY6D+ tumor cells are enriched in high-grade and androgen-resistant prostate cancer, providing clinical evidence for their contribution to advanced disease. Our data indicate that early interference with MAPK inhibitors can prevent progression of castration-resistant prostate cancer.

Introduction This retrospective open cohort study develops and externally validates a clinical prediction model (CPM) to predict the joint risk of two important outcomes occurring within the next year in people with epilepsy (PWE): A) seizure-related emergency department or hospital admission; and B) epilepsy-related death. This will provide clinicians with a tool to predict either or both of these common outcomes. This has not previously been done despite both being potentially avoidable, interrelated, and devastating for patients and their families. We hypothesise that the CPM will identify individuals at high or low risk of either or both outcomes. We will guide clinicians on proposed actions to take based on the overall risk score.Methods and analysis Routinely collected electronic health data from Clinical Practice Research Datalink (CPRD), Secure Anonymised Information Linkage databank (SAIL), Combined Intelligence for Population Health Action (CIPHA), and TriNetX research platforms will be used to identify PWE aged ≥16 years having outcomes A and/or B between 2010–2022. Data are held for 60 million patients in England on CPRD, 3.1m in Wales on SAIL, 2.6m in Cheshire and Merseyside on CIPHA, and 250m across 19 countries in TriNetX. Candidate predictors will include demographic, lifestyle, clinical, and management. Logistic regression and multistate modelling will be used to develop a suitable CPM (informed by clinician and public consultation), assessing predictive performance across development (CPRD) and external validation (SAIL, CIPHA, TriNetX) datasets.Conclusions This is the largest study to develop and validate a CPM for PWE, creating an internationally generalisable tool for subsequent clinical implementation. It is the first to predict the joint risk of acute admissions and death in PWE. Mortality prediction is highlighted by NICE as a key recommendation for epilepsy research. The study has been co-developed by epilepsy researchers and members of the public affected by epilepsy.Lay summary Some people with epilepsy (PWE) are at high risk of hospital admission or death because of seizures. If we give clinicians a tool to predict who, they’ll be in a better position to prevent it. Although statistical methods predicting future events are widely available, they haven’t yet been used to predict seizure-related hospital admission or death. Our study is the first to do this.We’ll analyse anonymised electronic research data from thousands of PWE in England. Among them, some will have been admitted to hospital or died because of seizures between 2010–2022. We’ll analyse their age, gender, ethnicity, features of their epilepsy, and medical conditions they developed in the year before being admitted to hospital or dying. From this, we’ll create a statistical tool to predict the chance of someone else with epilepsy being admitted to hospital or dying within a year. The tool’s external accuracy will be checked in Cheshire and Merseyside, Wales, North America, Europe, and other countries.Giving clinicians the tool should generate substantial impact for PWE. For example, emergency epilepsy clinics tend to be reserved for people experiencing a first seizure. However, given our prediction tool tells clinicians which people with an established diagnosis of epilepsy are at risk of seizure-related hospital admission or death within a year, it would provide strong justification for restructuring services such that these high-risk people are also seen in emergency clinics. A high-risk score could also prompt referral for epilepsy surgery sooner than previously considered. It could also prompt multidisciplinary team meetings between neurology and, for example, cardiology if the newly identified risks were cardiac. Such emergency interdisciplinary discussion would not normally happen for a person with epilepsy without good reason: providing evidence for an increased risk of death or hospital admission within a year due to a newly acquired cardiac problem would be good reason.### Competing Interest StatementThe authors have declared no competing interest.### Funding StatementThe salary of Gashirai Mbizvo is funded by an NIHR Clinical Lectureship (CL-2022-07-002). The funders played no role in the design or conduct of this protocol.### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The University of Liverpool Research Ethics decision tool was used to determine that ethical approval will not be required as the study described in this protocol consists of a secondary analysis of data that are anonymised by an external party (CPRD, SAIL, CIPHA, TriNetX) and provided to the research team in the fully anonymised format.I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesThis is a study protocol, therefore no data have been collected as yet. Once the study has been completed, we will use a public repository (eg GitHub) to make all diagnostic and outcome coding algorithms, metadata, and R analysis scripts used publicly available, facilitating external replication and adaptation.

Background An estimated 25% of GP patients within the UK have multimorbidity (two or more chronic conditions), a large proportion of which is attributable to non-communicable diseases, many of them preventable. There are known regional inequalities in health across England, including for chronic diseases. This study aimed to describe regional inequalities in multimorbidity incidence and prevalence. Methods We selected a random sample of 1m adults from the Clinical Practice Research Datalink (CPRD Aurum database) registered at participating GP practices within England between 2004 and 2019. Regions were defined by 2010 Strategic Health Authority boundaries as per the location of the participant’s general practice. Participants were linked to quintiles of the 2015 Index of Multiple Deprivation (IMD) as a measure of area-level socioeconomic deprivation. We used two measures of multimorbidity: a) basic multimorbidity: two or more chronic conditions; b) complex multimorbidity: at least three chronic conditions affecting at least three body systems. A list of 211 chronic conditions of interest, including long-term mental health conditions and chronic infections, was agreed by a multidisciplinary team. Using standard formulae, we calculated crude and age-sex standardised multimorbidity prevalence and incidence by geographical region. We used quasi-Poisson regression models to calculate risk ratios adjusted for year, sex, age, region, and IMD quintile. Analyses were conducted using R v4.0.4. Results Our final sample consisted of 989,421 adults: 48.7% male, with median age of 46 years (inter-quartile range 33–62). The overall crude prevalence of multimorbidity in England was 43.7% for basic, and 25.2% for complex multimorbidity over the 16-year study period. London had the lowest crude prevalence of both multimorbidity types (basic: 35.4%; complex: 18.3%), whilst the North East had the highest (basic: 48.6%; complex: 29.6%). In age-sex standardised results, prevalence was still highest in the North East, with London and the South East having the lowest prevalence. Similar regional inequalities were found in the incidence of both multimorbidity types. Compared to London, the North East had higher multimorbidity prevalence in risk ratios adjusted for socioeconomic deprivation and demographic factors (basic multimorbidity: 1.18 (95% confidence interval 1.16, 1.19); complex multimorbidity: 1.26 (95% confidence interval 1.24, 1.29). Conclusion There are regional inequalities in multimorbidity within England with higher burden in the North, compared to London and the South. These inequalities remained after adjusting for age and socioeconomic deprivation. Strategies aimed at addressing the social determinants of health are needed to reduce future burden on health and social care systems, particularly in the North of England.

Background The increasing burden of multimorbidity and its socioeconomic gradient poses unique challenges to the provision and structure of health care. We aimed to describe inequalities and trends over time in multimorbidity prevalence, incidence, and case fatality among adults of all ages in England using primary care electronic health records. Methods We used a random sample of 991 243 individuals from the Clinical Practice Research Datalink Aurum database registered at participating general practices within England between Jan 1, 2004, and Dec 31, 2019, linked to the 2015 English Index of Multiple Deprivation (IMD). We used the following two outcome measures: basic multimorbidity, comprising two or more chronic conditions; and complex multimorbidity, comprising at least three chronic conditions affecting at least three body systems. We calculated crude, age-standardised, and age-sex-standardised annual incidence, prevalence, and case fatality rates, along with median age of onset for both multimorbidity types. We calculated absolute and relative inequalities for each outcome. Findings In 2004, 30.8% of our study population had basic multimorbidity and 15.1% had complex multimorbidity. This increased to 52.8% and 32.7%, respectively, in 2019. Although the overall incidence of basic multimorbidity remained stable over the 16-year study period, the incidence among people of working age and the incidence of complex multimorbidity increased gradually. Socioeconomic deprivation was associated with an increased incidence of both multimorbidity types in working-age adults. The median age at onset of complex multimorbidity was 7 years younger for the most deprived quintile of the IMD compared with the least deprived quintile. Interpretation The burden of multimorbidity in England has increased substantially over the past 16 years with persistent inequalities, which are worse in working-age adults and for complex multimorbidity. Prevention efforts to reduce the onset and slow the progression of multimorbidity are essential to reduce the increasing impact on patients and health systems alike. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.

Abstract Background An estimated 25% of GP patients within the UK have multimorbidity, a large proportion of which is attributable to non-communicable diseases, many of them preventable. The heterogeneity of existing study methodologies limits comparisons to assess temporal trends. This study aims to use a large population-representative dataset to describe changes over time in multimorbidity incidence and prevalence. Methods We used two measures of multimorbidity a) basic: two or more chronic conditions; b) complex: at least three chronic conditions affecting at least three body systems. Chronic conditions for inclusion were discussed by a multidisciplinary team. A 1m random sample of patients registered between 2004 and 2019 at GP practices in England were drawn from the UK Clinical Practice Research Datalink. We calculated crude and age-sex standardised annual multimorbidity prevalence and incidence using standard formulae. Analyses were conducted using R v3.6.3. Participants will be linked to the 2015 Index of Multiple Deprivation to describe equity trends over time. Results Preliminary results show that age-sex standardised annual prevalence increased from 32.9% (95% CI: 32.7% - 33.1%) with basic multimorbidity and 14.9% (95% CI: 14.7%-15.0%) with complex multimorbidity in 2004 to 51.0% (95% CI: 50.8% - 51.3%) and 29.9% (95% CI: 29.7% - 30.1%) in 2019. Basic multimorbidity incidence per 10,000 person-years showed little change, however there was an increase in the incidence of complex multimorbidity from 322 (95% CI: 315- 330) to 418 (95% CI: 407 - 430). Conclusions The burden of multimorbidity has increased substantially over the last 15 years. Complex multimorbidity incidence and prevalence have increased more rapidly than for basic multimorbidity. This highlights the need for improved population-level prevention strategies to postpone and prevent the onset of long-term conditions. Next, we will assess whether there are socioeconomic differences in these temporal trends. Key messages The burden of multimorbidity increased between 2004 and 2019. The increase in incidence and prevalence of complex multimorbidity was greater than for basic multimorbidity.

Background An estimated 25% of GP patients within the UK have multimorbidity, a large proportion of which is attributable to non-communicable diseases, many of them preventable. The heterogeneity of existing study methodologies and definitions of multimorbidity limits comparisons to assess temporal trends. This study aims to use a large population-representative single dataset and disease list to describe changes over time in multimorbidity incidence and prevalence. Methods We selected a random sample of 1m adults from the Clinical Practice Research Datalink (CPRD Aurum database) registered at participating GP practices within England between 2004 and 2019. We used two measures of multimorbidity: a) basic multimorbidity: two or more chronic conditions; b) complex multimorbidity: at least three chronic conditions affecting at least three body systems. A multidisciplinary team discussed the list of chronic conditions of interest, including long-term mental health conditions and chronic infections. Using standard formulae, we calculated crude and age-sex standardised annual multimorbidity prevalence and incidence to assess trends over time. We also calculated the average age of onset for basic and complex multimorbidity. Analyses were conducted using R v3.6.3. Participants will be linked to quintiles of the 2015 Index of Multiple Deprivation as a measure of area-level socioeconomic deprivation to describe socioeconomic inequalities in temporal trends. Results Preliminary results show that age-sex standardised annual prevalence increased from 32.9% (95% CI: 32.7% - 33.1%) with basic multimorbidity and 14.9% (95% CI: 14.7%-15.0%) with complex multimorbidity in 2004, to 51.0% (95% CI: 50.8% - 51.3%) and 29.9% (95% CI: 29.7% - 30.1%) in 2019, or by 55.3% and 101.0% respectively. Basic multimorbidity incidence per 10,000 person-years showed little change from 644 (95% CI: 631 – 658) in 2004 to 669 (95% CI 648 – 690) in 2019. There was an increase in the incidence of complex multimorbidity from 322(95% CI: 315- 330) to 418 (95% CI: 407 – 430). The mean age of incident multimorbidity onset was 48.8 (95% CI: 48.7 – 48.8) years for basic and 57.5 years (95% CI: 57.5 – 57.6) for complex multimorbidity. Conclusion The prevalence of both basic and complex multimorbidity has increased substantially over the last 15 years. Complex multimorbidity incidence and prevalence have increased more rapidly than for basic multimorbidity. This highlights the need for improved population-level prevention strategies to postpone and prevent the onset of long-term conditions. Our next step is to assess whether there are socioeconomic differences in these temporal trends.