Cancer care for people with dementia: Literature overview and recommendations for practice and research.

As many countries experience population aging, patients with cancer are becoming older and have more preexisting comorbidities, which include prevalent, age-related, chronic conditions such as dementia. People living with dementia (PLWD) are vulnerable to health disparities, and dementia has high potential to complicate and adversely affect care and outcomes across the cancer trajectory. This report offers an overview of dementia and its prevalence among patients with cancer and a summary of the research literature examining cancer care for PLWD. The reviewed research indicates that PLWD are more likely to have cancer diagnosed at an advanced stage, receive no or less extensive cancer treatment, and have poorer survival after a cancer diagnosis. These cancer disparities do not necessarily signify inappropriately later diagnosis or lower treatment of people with dementia as a group, and they are arguably less feasible and appropriate targets for care optimization. The reviewed research indicates that PLWD also have an increased risk of cancer-related emergency presentations, lower quality processes of cancer-related decision making, accessibility-related barriers to cancer investigations and treatment, higher experienced treatment burden and higher caregiver burden for families, and undertreated cancer-related pain. The authors propose that optimal cancer care for PLWD should focus on proactively minimizing these risk areas and thus must be highly person-centered, with holistic decision making, individualized reasonable adjustments to practice, and strong inclusion and support of family carers. Comprehensive recommendations are made for clinical practice and future research to help clinicians and providers deliver best and equitable cancer care for PLWD and their families.

Multi-response Mendelian randomization: Identification of shared and distinct exposures for multimorbidity and multiple related disease outcomes.

The existing framework of Mendelian randomization (MR) infers the causal effect of one or multiple exposures on one single outcome. It is not designed to jointly model multiple outcomes, as would be necessary to detect causes of more than one outcome and would be relevant to model multimorbidity or other related disease outcomes. Here, we introduce multi-response Mendelian randomization (MR2), an MR method specifically designed for multiple outcomes to identify exposures that cause more than one outcome or, conversely, exposures that exert their effect on distinct responses. MR2 uses a sparse Bayesian Gaussian copula regression framework to detect causal effects while estimating the residual correlation between summary-level outcomes, i.e., the correlation that cannot be explained by the exposures, and vice versa. We show both theoretically and in a comprehensive simulation study how unmeasured shared pleiotropy induces residual correlation between outcomes irrespective of sample overlap. We also reveal how non-genetic factors that affect more than one outcome contribute to their correlation. We demonstrate that by accounting for residual correlation, MR2 has higher power to detect shared exposures causing more than one outcome. It also provides more accurate causal effect estimates than existing methods that ignore the dependence between related responses. Finally, we illustrate how MR2 detects shared and distinct causal exposures for five cardiovascular diseases in two applications considering cardiometabolic and lipidomic exposures and uncovers residual correlation between summary-level outcomes reflecting known relationships between cardiovascular diseases.

Genetic underpinning of the comorbidity between type 2 diabetes and osteoarthritis.

Multimorbidity is a rising public health challenge with important implications for health management and policy. The most common multimorbidity pattern is the combination of cardiometabolic and osteoarticular diseases. Here, we study the genetic underpinning of the comorbidity between type 2 diabetes and osteoarthritis. We find genome-wide genetic correlation between the two diseases and robust evidence for association-signal colocalization at 18 genomic regions. We integrate multi-omics and functional information to resolve the colocalizing signals and identify high-confidence effector genes, including FTO and IRX3, which provide proof-of-concept insights into the epidemiologic link between obesity and both diseases. We find enrichment for lipid metabolism and skeletal formation pathways for signals underpinning the knee and hip osteoarthritis comorbidities with type 2 diabetes, respectively. Causal inference analysis identifies complex effects of tissue-specific gene expression on comorbidity outcomes. Our findings provide insights into the biological basis for the type 2 diabetes-osteoarthritis disease co-occurrence.

Using clustering of genetic variants in Mendelian randomization to interrogate the causal pathways underlying multimorbidity from a common risk factor.

Mendelian randomization (MR) is an epidemiological approach that utilizes genetic variants as instrumental variables to estimate the causal effect of an exposure on a health outcome. This paper investigates an MR scenario in which genetic variants aggregate into clusters that identify heterogeneous causal effects. Such variant clusters are likely to emerge if they affect the exposure and outcome via distinct biological pathways. In the multi-outcome MR framework, where a shared exposure causally impacts several disease outcomes simultaneously, these variant clusters can provide insights into the common disease-causing mechanisms underpinning the co-occurrence of multiple long-term conditions, a phenomenon known as multimorbidity. To identify such variant clusters, we adapt the general method of agglomerative hierarchical clustering to multi-sample summary-data MR setup, enabling cluster detection based on variant-specific ratio estimates. Particularly, we tailor the method for multi-outcome MR to aid in elucidating the causal pathways through which a common risk factor contributes to multiple morbidities. We show in simulations that our "MR-AHC" method detects clusters with high accuracy, outperforming the existing methods. We apply the method to investigate the causal effects of high body fat percentage on type 2 diabetes and osteoarthritis, uncovering interconnected cellular processes underlying this multimorbid disease pair.

Use of Computation Ecosystems to Analyze the Kidney-Heart Crosstalk.

The identification of mediators for physiologic processes, correlation of molecular processes, or even pathophysiological processes within a single organ such as the kidney or heart has been extensively studied to answer specific research questions using organ-centered approaches in the past 50 years. However, it has become evident that these approaches do not adequately complement each other and display a distorted single-disease progression, lacking holistic multilevel/multidimensional correlations. Holistic approaches have become increasingly significant in understanding and uncovering high dimensional interactions and molecular overlaps between different organ systems in the pathophysiology of multimorbid and systemic diseases like cardiorenal syndrome because of pathological heart-kidney crosstalk. Holistic approaches to unraveling multimorbid diseases are based on the integration, merging, and correlation of extensive, heterogeneous, and multidimensional data from different data sources, both -omics and nonomics databases. These approaches aimed at generating viable and translatable disease models using mathematical, statistical, and computational tools, thereby creating first computational ecosystems. As part of these computational ecosystems, systems medicine solutions focus on the analysis of -omics data in single-organ diseases. However, the data-scientific requirements to address the complexity of multimodality and multimorbidity reach far beyond what is currently available and require multiphased and cross-sectional approaches. These approaches break down complexity into small and comprehensible challenges. Such holistic computational ecosystems encompass data, methods, processes, and interdisciplinary knowledge to manage the complexity of multiorgan crosstalk. Therefore, this review summarizes the current knowledge of kidney-heart crosstalk, along with methods and opportunities that arise from the novel application of computational ecosystems providing a holistic analysis on the example of kidney-heart crosstalk.

Management of Acute Coronary Syndrome in the Older Adult Population: A Scientific Statement From the American Heart Association.

Diagnostic and therapeutic advances during the past decades have substantially improved health outcomes for patients with acute coronary syndrome. Both age-related physiological changes and accumulated cardiovascular risk factors increase the susceptibility to acute coronary syndrome over a lifetime. Compared with younger patients, outcomes for acute coronary syndrome in the large and growing demographic of older adults are relatively worse. Increased atherosclerotic plaque burden and complexity of anatomic disease, compounded by age-related cardiovascular and noncardiovascular comorbid conditions, contribute to the worse prognosis observed in older individuals. Geriatric syndromes, including frailty, multimorbidity, impaired cognitive and physical function, polypharmacy, and other complexities of care, can undermine the therapeutic efficacy of guidelines-based treatments and the resiliency of older adults to survive and recover, as well. In this American Heart Association scientific statement, we (1) review age-related physiological changes that predispose to acute coronary syndrome and management complexity; (2) describe the influence of commonly encountered geriatric syndromes on cardiovascular disease outcomes; and (3) recommend age-appropriate and guideline-concordant revascularization and acute coronary syndrome management strategies, including transitions of care, the use of cardiac rehabilitation, palliative care services, and holistic approaches. The primacy of individualized risk assessment and patient-centered care decision-making is highlighted throughout.

Person-Centered Models for Cardiovascular Care: A Review of the Evidence: A Scientific Statement From the American Heart Association.

Cardiovascular disease remains the leading cause of death and disability in the United States and globally. Disease burden continues to escalate despite technological advances associated with improved life expectancy and quality of life. As a result, longer life is associated with multiple chronic cardiovascular conditions. Clinical guidelines provide recommendations without considering prevalent scenarios of multimorbidity and health system complexities that affect practical adoption. The diversity of personal preferences, cultures, and lifestyles that make up one's social and environmental context is often overlooked in ongoing care planning for symptom management and health behavior support, hindering adoption and compromising patient outcomes, particularly in groups at high risk. The purpose of this scientific statement was to describe the characteristics and reported outcomes in existing person-centered care delivery models for selected cardiovascular conditions. We conducted a scoping review using Ovid MEDLINE, Embase.com, Web of Science, CINAHL Complete, Cochrane Central Register of Controlled Trials through Ovid, and ClinicalTrials.gov from 2010 to 2022. A range of study designs with a defined aim to systematically evaluate care delivery models for selected cardiovascular conditions were included. Models were selected on the basis of their stated use of evidence-based guidelines, clinical decision support tools, systematic evaluation processes, and inclusion of the patient's perspective in defining the plan of care. Findings reflected variation in methodological approach, outcome measures, and care processes used across models. Evidence to support optimal care delivery models remains limited by inconsistencies in approach, variation in reimbursement, and inability of health systems to meet the needs of patients with chronic, complex cardiovascular conditions.

Epidemiology of RSV in Adults and Children with Medically-Attended Acute Respiratory Illness over Three Seasons.

BACKGROUND: Data on the true prevalence of RSV among medically-attended acute respiratory illnesses (MAARI) has been limited by the lack of regular clinical testing of mild to moderate illnesses. Here we present a prospective evaluation of the epidemiology of RSV-associated MAARI across age groups and multimorbidity status over three seasons, which is informative in light of the recommendations for shared decision-making for vaccination in older adults. METHODS: Ambulatory patients ≥6 months of age meeting a common MAARI case definition were prospectively enrolled in the Michigan Ford Influenza Vaccine Effectiveness (MFIVE) study, a subsite of the US Influenza Vaccine Effectiveness Network. All participants were tested by nasal-throat swab for RSV and influenza, including subtype, independently from clinician-directed testing. Participant illness characteristics and calculated Multimorbidity-Weighted Index (MWI) were collected by in-person survey and electronic medical record review. RESULTS: Over three surveillance seasons (fall 2017 to spring 2020), 9.9% (n=441) of 4,442 participants had RSV detected. RSV-associated MAARI was more prevalent than influenza for participants 6 months-4 years of age. Adults with RSV-MAARI had higher median MWI scores overall compared to influenza-MAARI and controls with neither virus (1.62, 0.40, and 0.64, respectively). CONCLUSIONS: RSV is a significant, underrecognized cause of MAARI in both children and adults presenting for ambulatory care. Multimorbidity is an important contributor to RSV-associated MAARI in outpatient adults, providing information to support shared clinical decision-making for vaccination.

Characterizing multimorbidity in ALIVE: comparing single and ensemble clustering methods.

Multimorbidity, defined as having 2 or more chronic conditions, is a growing public health concern, but research in this area is complicated by the fact that multimorbidity is a highly heterogenous outcome. Individuals in a sample may have a differing number and varied combinations of conditions. Clustering methods, such as unsupervised machine learning algorithms, may allow us to tease out the unique multimorbidity phenotypes. However, many clustering methods exist, and choosing which to use is challenging because we do not know the true underlying clusters. Here, we demonstrate the use of 3 individual algorithms (partition around medoids, hierarchical clustering, and probabilistic clustering) and a clustering ensemble approach (which pools different clustering approaches) to identify multimorbidity clusters in the AIDS Linked to the Intravenous Experience cohort study. We show how the clusters can be compared based on cluster quality, interpretability, and predictive ability. In practice, it is critical to compare the clustering results from multiple algorithms and to choose the approach that performs best in the domain(s) that aligns with plans to use the clusters in future analyses.

Long-term multimorbidity trajectories in older adults: The role of cancer, demographics, and health behaviors.

BACKGROUND: Multimorbidity is associated with premature mortality and excess health care costs. The burden of multimorbidity is highest among patients with cancer, yet trends and determinants of multimorbidity over time are poorly understood. METHODS: Via Medicare claims linked to Cancer Prevention Study II data, group-based trajectory modeling was used to compare National Cancer Institute comorbidity index score trends for cancer survivors and older adults without a cancer history. Among cancer survivors, multinomial logistic regression analyses evaluated associations between demographics, health behaviors, and comorbidity trajectories. RESULTS: In 82,754 participants (mean age, 71.6 years [SD, 5.1 years]; 56.9% female), cancer survivors (n = 11,265) were more likely than older adults without a cancer history to experience the riskiest comorbidity trajectories: (1) steady, high comorbidity scores (remain high; odds ratio [OR], 1.36; 95% CI, 1.29-1.45), and (2) high scores that increased over time (start high and increase; OR, 1.51; 95% CI, 1.38-1.65). Cancer survivors who were physically active postdiagnosis were less likely to fall into these two trajectories (OR, 0.73; 95% CI, 0.64-0.84, remain high; OR, 0.42; 95% CI, 0.33-0.53, start high and increase) compared to inactive survivors. Cancer survivors with obesity were more likely to have a trajectory that started high and increased (OR, 2.83; 95% CI, 2.32-3.45 vs. normal weight), although being physically active offset some obesity-related risk. Cancer survivors who smoked postdiagnosis were also six times more likely to have trajectories that started high and increased (OR, 6.86; 95% CI, 4.41-10.66 vs. never smokers). CONCLUSIONS: Older cancer survivors are more likely to have multiple comorbidities accumulated at a faster pace than older adults without a history of cancer. Weight management, physical activity, and smoking avoidance postdiagnosis may attenuate that trend.

Continuity and coordination of care for childhood cancer survivors with multiple chronic conditions: Results from the Childhood Cancer Survivor Study.

INTRODUCTION: Continuity and coordination-of-care for childhood cancer survivors with multiple chronic conditions are understudied but critical for appropriate follow-up care. METHODS: From April through June 2022, 800 Childhood Cancer Survivor Study participants with two or more chronic conditions (one or more severe/life-threatening/disabling) were emailed the "Patient Perceived Continuity-of-Care from Multiple Clinicians" survey. The survey asked about survivors' main (takes care of most health care) and coordinating (ensures follow-up) provider, produced three care-coordination summary scores (main provider, across multiple providers, patient-provider partnership), and included six discontinuity indicators (e.g., having to organize own care). Discontinuity (yes/no) was defined as poor care on one or more discontinuity item. Chi-square tests assessed associations between discontinuity and sociodemographics. Modified Poisson regression models estimated prevalence ratios (PRs) for discontinuity risk associated with the specialty and number of years seeing the main and coordinating provider, and PRs associated with better scores on the three care-coordination summary measures. Inverse probability weights adjusted for survey non-participation. RESULTS: A total of 377 (47%) survivors responded (mean age 48 years, 68% female, 89% non-Hispanic White, 78% privately insured, 74% ≥college graduate); 147/373 (39%) reported discontinuity. Younger survivors were more likely to report discontinuity (chi-square p = .02). Seeing the main provider ≤3 years was associated with more prevalent discontinuity (PR; 95%CI) (1.17; 1.02-1.34 vs ≥ 10 years). Cancer specialist main providers were associated with less prevalent discontinuity (0.81; 0.66-0.99 vs. primary care). Better scores on all three care-coordination summary measures were associated with less prevalent discontinuity: main provider (0.73; 0.64-0.83), across multiple providers (0.81; 0.78-0.83), patient-provider partnership (0.85; 0.80-0.89). CONCLUSIONS: Care discontinuity among childhood cancer survivors is prevalent and requires intervention.

Patterns of comorbidities in patients with atrial fibrillation and impact on management and long-term prognosis: an analysis from the Prospective Global GLORIA-AF Registry.

BACKGROUND: Clinical complexity, as the interaction between ageing, frailty, multimorbidity and polypharmacy, is an increasing concern in patients with AF. There remains uncertainty regarding how combinations of comorbidities influence management and prognosis of patients with atrial fibrillation (AF). We aimed to identify phenotypes of AF patients according to comorbidities and to assess associations between comorbidity patterns, drug use and risk of major outcomes. METHODS: From the prospective GLORIA-AF Registry, we performed a latent class analysis based on 18 diseases, encompassing cardiovascular, metabolic, respiratory and other conditions; we then analysed the association between phenotypes of patients and (i) treatments received and (ii) the risk of major outcomes. Primary outcome was the composite of all-cause death and major adverse cardiovascular events (MACE). Secondary exploratory outcomes were also analysed. RESULTS: 32,560 AF patients (mean age 70.0 ± 10.5 years, 45.4% females) were included. We identified 6 phenotypes: (i) low complexity (39.2% of patients); (ii) cardiovascular (CV) risk factors (28.2%); (iii) atherosclerotic (10.2%); (iv) thromboembolic (8.1%); (v) cardiometabolic (7.6%) and (vi) high complexity (6.6%). Higher use of oral anticoagulants was found in more complex groups, with highest magnitude observed for the cardiometabolic and high complexity phenotypes (odds ratio and 95% confidence interval CI): 1.76 [1.49-2.09] and 1.57 [1.35-1.81], respectively); similar results were observed for beta-blockers and verapamil or diltiazem. We found higher risk of the primary outcome in all phenotypes, except the CV risk factor one, with highest risk observed for the cardiometabolic and high complexity groups (hazard ratio and 95%CI: 1.37 [1.13-1.67] and 1.47 [1.24-1.75], respectively). CONCLUSIONS: Comorbidities influence management and long-term prognosis of patients with AF. Patients with complex phenotypes may require comprehensive and holistic approaches to improve their prognosis.

Prevalence and differences in the co-administration of drugs known to interact: an analysis of three distinct and large populations.

BACKGROUND: The co-administration of drugs known to interact greatly impacts morbidity, mortality, and health economics. This study aims to examine the drug-drug interaction (DDI) phenomenon with a large-scale longitudinal analysis of age and gender differences found in drug administration data from three distinct healthcare systems. METHODS: This study analyzes drug administrations from population-wide electronic health records in Blumenau (Brazil; 133 K individuals), Catalonia (Spain; 5.5 M individuals), and Indianapolis (USA; 264 K individuals). The stratified prevalences of DDI for multiple severity levels per patient gender and age at the time of administration are computed, and null models are used to estimate the expected impact of polypharmacy on DDI prevalence. Finally, to study actionable strategies to reduce DDI prevalence, alternative polypharmacy regimens using drugs with fewer known interactions are simulated. RESULTS: A large prevalence of co-administration of drugs known to interact is found in all populations, affecting 12.51%, 12.12%, and 10.06% of individuals in Blumenau, Indianapolis, and Catalonia, respectively. Despite very different healthcare systems and drug availability, the increasing prevalence of DDI as patients age is very similar across all three populations and is not explained solely by higher co-administration rates in the elderly. In general, the prevalence of DDI is significantly higher in women - with the exception of men over 50 years old in Indianapolis. Finally, we show that using proton pump inhibitor alternatives to omeprazole (the drug involved in more co-administrations in Catalonia and Blumenau), the proportion of patients that are administered known DDI can be reduced by up to 21% in both Blumenau and Catalonia and 2% in Indianapolis. CONCLUSIONS: DDI administration has a high incidence in society, regardless of geographic, population, and healthcare management differences. Although DDI prevalence increases with age, our analysis points to a complex phenomenon that is much more prevalent than expected, suggesting comorbidities as key drivers of the increase. Furthermore, the gender differences observed in most age groups across populations are concerning in regard to gender equity in healthcare. Finally, our study exemplifies how electronic health records' analysis can lead to actionable interventions that significantly reduce the administration of known DDI and its associated human and economic costs.

Co-designing care for multimorbidity: a systematic review.

BACKGROUND: The co-design of health care enables patient-centredness by partnering patients, clinicians and other stakeholders together to create services. METHODS: We conducted a systematic review of co-designed health interventions for people living with multimorbidity and assessed (a) their effectiveness in improving health outcomes, (b) the co-design approaches used and (c) barriers and facilitators to the co-design process with people living with multimorbidity. We searched MEDLINE, EMBASE, CINAHL, Scopus and PsycINFO between 2000 and March 2022. Included experimental studies were quality assessed using the Cochrane risk of bias tool (ROB-2 and ROBINS-I). RESULTS: We screened 14,376 reports, with 13 reports meeting the eligibility criteria. Two reported health and well-being outcomes: one randomised clinical trial (n = 134) and one controlled cohort (n = 1933). Outcome measures included quality of life, self-efficacy, well-being, anxiety, depression, functional status, healthcare utilisation and mortality. Outcomes favouring the co-design interventions compared to control were minimal, with only 4 of 17 outcomes considered beneficial. Co-design approaches included needs assessment/ideation (12 of 13), prototype (11 of 13), pilot testing (5 of 13) (i.e. focus on usability) and health and well-being evaluations (2 of 13). Common challenges to the co-design process include poor stakeholder interest, passive participation, power imbalances and a lack of representativeness in the design group. Enablers include flexibility in approach, smaller group work, advocating for stakeholders' views and commitment to the process or decisions made. CONCLUSIONS: In this systematic review of co-design health interventions, we found that few projects assessed health and well-being outcomes, and the observed health and well-being benefits were minimal. The intensity and variability in the co-design approaches were substantial, and challenges were evident. Co-design aided the design of novel services and interventions for those with multimorbidity, improving their relevance, usability and acceptability. However, the clinical benefits of co-designed interventions for those with multimorbidity are unclear.

Trajectories of health conditions and their associations with the risk of cognitive impairment among older adults: insights from a national prospective cohort study.

BACKGROUND: The associations between trajectories of different health conditions and cognitive impairment among older adults were unknown. Our cohort study aimed to investigate the impact of various trajectories, including sleep disturbances, depressive symptoms, functional limitations, and multimorbidity, on the subsequent risk of cognitive impairment. METHODS: We conducted a prospective cohort study by using eight waves of national data from the Health and Retirement Study (HRS 2002-2018), involving 4319 adults aged 60 years or older in the USA. Sleep disturbances and depressive symptoms were measured using the Jenkins Sleep Scale and the Centers for Epidemiologic Research Depression (CES-D) scale, respectively. Functional limitations were assessed using activities of daily living (ADLs) and instrumental activities of daily living (IADLs), respectively. Multimorbidity status was assessed by self-reporting physician-diagnosed diseases. We identified 8-year trajectories at four examinations from 2002 to 2010 using latent class trajectory modeling. We screened participants for cognitive impairment using the 27-point HRS cognitive scale from 2010 to 2018 across four subsequent waves. We calculated hazard ratios (HR) using Cox proportional hazard models. RESULTS: During 25,914 person-years, 1230 participants developed cognitive impairment. In the fully adjusted model 3, the trajectories of sleep disturbances and ADLs limitations were not associated with the risk of cognitive impairment. Compared to the low trajectory, we found that the increasing trajectory of depressive symptoms (HR = 1.39; 95% CI = 1.17-1.65), the increasing trajectory of IADLs limitations (HR = 1.88; 95% CI = 1.43-2.46), and the high trajectory of multimorbidity status (HR = 1.48; 95% CI = 1.16-1.88) all posed an elevated risk of cognitive impairment. The increasing trajectory of IADLs limitations was associated with a higher risk of cognitive impairment among older adults living in urban areas (HR = 2.30; 95% CI = 1.65-3.21) and those who smoked (HR = 2.77; 95% CI = 1.91-4.02) (all P for interaction < 0.05). CONCLUSIONS: The results suggest that tracking trajectories of depressive symptoms, instrumental functioning limitations, and multimorbidity status may be a potential and feasible screening method for identifying older adults at risk of cognitive impairment.

The medication-based Rx-Risk Comorbidity Index and risk of hip fracture - a nationwide NOREPOS cohort study.

BACKGROUND: Few previous studies have assessed overall morbidity at the individual level with respect to future risk of hip fracture. The aim of this register-based cohort study was to examine the association between morbidity measured by the medication-based Rx-Risk Comorbidity Index (Rx-Risk) and the risk of first hip fracture. METHODS: Individual-level data on medications dispensed from pharmacies (2005-2016) was retrieved from the Norwegian Prescription Database and used to calculate Rx-Risk for each calendar year. Information on first hip fractures (2006-2017) was obtained from a nationwide hip fracture database. Individuals ≥ 51 years who filled at least one prescription during the study period comprised the population at risk. Using Rx-Risk as a time-varying exposure variable, relative risk estimates were obtained by a negative binomial model. RESULTS: During 2006-2017, 94,104 individuals sustained a first hip fracture. A higher Rx-Risk was associated with increased risk of hip fracture within all categories of age and sex. Women with the highest Rx-Risk (> 25) had a relative risk of 6.1 (95% confidence interval (CI): 5.4, 6.8) compared to women with Rx-Risk ≤ 0, whereas the corresponding relative risk in women with Rx-Risk 1-5 was 1.4 (95% CI: 1.3, 1.4). Similar results were found in men. Women > 80 years with Rx-Risk 21-25 had the highest incidence rate (514 (95% CI: 462, 566) per 10, 000 person years). The relative increase in hip fracture risk with higher Rx-Risk was most pronounced in the youngest patients aged 51-65 years. CONCLUSIONS: Rx-Risk is a strong predictor of hip fracture in the general outpatient population and may be useful to identify individuals at risk in a clinical setting and in future studies.

The impact of adverse childhood experiences on multimorbidity: a systematic review and meta-analysis.

BACKGROUND: Adverse childhood experiences (ACEs) have been implicated in the aetiology of a range of health outcomes, including multimorbidity. In this systematic review and meta-analysis, we aimed to identify, synthesise, and quantify the current evidence linking ACEs and multimorbidity. METHODS: We searched seven databases from inception to 20 July 2023: APA PsycNET, CINAHL Plus, Cochrane CENTRAL, Embase, MEDLINE, Scopus, and Web of Science. We selected studies investigating adverse events occurring during childhood (< 18 years) and an assessment of multimorbidity in adulthood (≥ 18 years). Studies that only assessed adverse events in adulthood or health outcomes in children were excluded. Risk of bias was assessed using the ROBINS-E tool. Meta-analysis of prevalence and dose-response meta-analysis methods were used for quantitative data synthesis. This review was pre-registered with PROSPERO (CRD42023389528). RESULTS: From 15,586 records, 25 studies were eligible for inclusion (total participants = 372,162). The prevalence of exposure to ≥ 1 ACEs was 48.1% (95% CI 33.4 to 63.1%). The prevalence of multimorbidity was 34.5% (95% CI 23.4 to 47.5%). Eight studies provided sufficient data for dose-response meta-analysis (total participants = 197,981). There was a significant dose-dependent relationship between ACE exposure and multimorbidity (p < 0.001), with every additional ACE exposure contributing to a 12.9% (95% CI 7.9 to 17.9%) increase in the odds for multimorbidity. However, there was heterogeneity among the included studies (I2 = 76.9%, Cochran Q = 102, p < 0.001). CONCLUSIONS: This is the first systematic review and meta-analysis to synthesise the literature on ACEs and multimorbidity, showing a dose-dependent relationship across a large number of participants. It consolidates and enhances an extensive body of literature that shows an association between ACEs and individual long-term health conditions, risky health behaviours, and other poor health outcomes.

Experiences of hospital care for people with multiple long-term conditions: a scoping review of qualitative research.

BACKGROUND: Multiple long-term conditions-the co-existence of two or more chronic health conditions in an individual-present an increasing challenge to populations and healthcare systems worldwide. This challenge is keenly felt in hospital settings where care is oriented around specialist provision for single conditions. The aim of this scoping review was to identify and summarise published qualitative research on the experiences of hospital care for people living with multiple long-term conditions, their informal caregivers and healthcare professionals. METHODS: We undertook a scoping review, following established guidelines, of primary qualitative research on experiences of hospital care for people living with multiple long-term conditions published in peer-reviewed journals between Jan 2010 and June 2022. We conducted systematic electronic searches of MEDLINE, CINAHL, PsycInfo, Proquest Social Science Premium, Web of Science, Scopus and Embase, supplemented by citation tracking. Studies were selected for inclusion by two reviewers using an independent screening process. Data extraction included study populations, study design, findings and author conclusions. We took a narrative approach to reporting the findings. RESULTS: Of 8002 titles and abstracts screened, 54 papers reporting findings from 41 studies conducted in 14 countries were identified as eligible for inclusion. The perspectives of people living with multiple long-term conditions (21 studies), informal caregivers (n = 13) and healthcare professionals (n = 27) were represented, with 15 studies reporting experiences of more than one group. Findings included poor service integration and lack of person-centred care, limited confidence of healthcare professionals to treat conditions outside of their specialty, and time pressures leading to hurried care transitions. Few studies explored inequities in experiences of hospital care. CONCLUSIONS: Qualitative research evidence on the experiences of hospital care for multiple long-term conditions illuminates a tension between the desire to provide and receive person-centred care and time pressures inherent within a target-driven system focussed on increasing specialisation, reduced inpatient provision and accelerated journeys through the care system. A move towards more integrated models of care may enable the needs of people living with multiple long-term conditions to be better met. Future research should address how social circumstances shape experiences of care.

MorbidGCN: prediction of multimorbidity with a graph convolutional network based on integration of population phenotypes and disease network.

Exploring multimorbidity relationships among diseases is of great importance for understanding their shared mechanisms, precise diagnosis and treatment. However, the landscape of multimorbidities is still far from complete due to the complex nature of multimorbidity. Although various types of biological data, such as biomolecules and clinical symptoms, have been used to identify multimorbidities, the population phenotype information (e.g. physical activity and diet) remains less explored for multimorbidity. Here, we present a graph convolutional network (GCN) model, named MorbidGCN, for multimorbidity prediction by integrating population phenotypes and disease network. Specifically, MorbidGCN treats the multimorbidity prediction as a missing link prediction problem in the disease network, where a novel feature selection method is embedded to select important phenotypes. Benchmarking results on two large-scale multimorbidity data sets, i.e. the UK Biobank (UKB) and Human Disease Network (HuDiNe) data sets, demonstrate that MorbidGCN outperforms other competitive methods. With MorbidGCN, 9742 and 14 010 novel multimorbidities are identified in the UKB and HuDiNe data sets, respectively. Moreover, we notice that the selected phenotypes that are generally differentially distributed between multimorbidity patients and single-disease patients can help interpret multimorbidities and show potential for prognosis of multimorbidities.

Data-driven patient stratification of UK Biobank cohort suggests five endotypes of multimorbidity.

Multimorbidity generally refers to concurrent occurrence of multiple chronic conditions. These patients are inherently at high risk and often lead a poor quality of life due to delayed treatments. With the emergence of personalized medicine and stratified healthcare, there is a need to stratify patients right at the primary care setting. Here we developed multimorbidity analysis pipeline (MulMorPip), which can stratify patients into multimorbid subgroups or endotypes based on their lifetime disease diagnosis and characterize them based on demographic features and underlying disease-disease interaction networks. By implementing MulMorPip on UK Biobank cohort, we report five distinct molecular subclasses or endotypes of multimorbidity. For each patient, we calculated the existence of broad disease classes defined by Charlson's comorbidity classification using the International Classification of Diseases-10 encoding. We then applied multiple correspondence analysis in 77 524 patients from UK Biobank, who had multimorbidity of more than one disease, which resulted in five multimorbid clusters. We further validated these clusters using machine learning and were able to classify 20% model-blind test set patients with an accuracy of 97% and an average Jaccard similarity of 84%. This was followed by demographic characterization and development of interlinking disease network for each cluster to understand disease-disease interactions. Our identified five endotypes of multimorbidity draw attention to dementia, stroke and paralysis as important drivers of multimorbidity stratification. Inclusion of such patient stratification at the primary care setting can help general practitioners to better observe patients' multiple chronic conditions, their risk stratification and personalization of treatment strategies.