Complex multimorbidity in hemodialysis patients: Study in a metropolitan region in Brazil.

The objective of this article was to analyze the factors associated with complex multimorbidity (CMM) among hemodialysis patients in a metropolitan region in southeastern Brazil. To this end, a cross-sectional epidemiological survey was carried out with 1,024 individuals in the year 2019. CMM data were collected through the application of a questionnaire to hemodialysis patients. The binary logistic regression model was used to estimate odds ratios (OR) and 95% confidence intervals (95%CI) between independent variables and CMM. The prevalence of CMM was 81% and the results indicated that: living in cities with a low rate of general mortality (OR = 0.395, 95%CI = 0.179-0.870), being aged between 18 and 29 (OR = 0.402, 95%CI = 0.196-0.825), having an elementary education (OR = 0.536, 95%CI = 0.290-0.966) and assessing health as good/very good (OR = 0.446, 95%CI = 0.301-0.661) are factors that reduced the chances of having CMM, whereas a longer period of hemodialysis (OR = 1.779 and 95%CI = 1.057-2.997) increased the chances of CMM. The findings show that characteristics of the social and individual context are associated with CMM in hemodialysis patients, signaling the need for public health policies that include monitoring the complex multimorbidity condition among individuals undergoing hemodialysis treatment.

Association of the CUN-BAE body adiposity estimator and other obesity indicators with cardiometabolic multimorbidity: a cross-sectional study.

Cardiometabolic multimorbidity (CM), defined as the coexistence of two or three cardiometabolic disorders, is one of the most common and deleterious multimorbidities. This study aimed to investigate the association of Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE), body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR) with the prevalence of CM. The data were obtained from the 2021 health checkup database for residents of the Electronic Health Management Center in Xinzheng, Henan Province, China. 81,532 participants aged ≥ 60 years were included in this study. Logistic regression models were used to estimate the odd ratios (ORs) and 95% confidence intervals (CIs) for CUN-BAE, BMI, WC, and WHtR in CM. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminatory ability of different anthropometric indicators for CM. The multivariable-adjusted ORs (95% CIs) (per 1 SD increase) of CM were 1.799 (1.710-1.893) for CUN-BAE, 1.329 (1.295-1.364) for BMI, 1.343 (1.308-1.378) for WC, and 1.314 (1.280-1.349) for WHtR, respectively. Compared with BMI, WC and WHtR, CUN-BAE had the highest AUC in both males and females (AUC: 0.642; 95% CI 0.630-0.653 for males, AUC: 0.614; 95% CI 0.630-0.653 for females). CUN-BAE may be a better measure of the adverse effect of adiposity on the prevalence of CM than BMI, WC, and WHtR.

PReventing and Approaching Crises for frail community-dwelling patients Through Innovative Care (PRACTIC): protocol for an effectiveness cluster randomised controlled trial.

BACKGROUND: Demographic changes, with an increasing number and proportion of older people with multimorbidity and frailty, will put more pressure on home care services in municipalities. Frail multimorbid people receiving home care services are at high risk of developing crises, defined as critical challenges and symptoms, which demand immediate and new actions. The crises often result in adverse events, coercive measures, and acute institutionalisation. There is a lack of evidence-based interventions to prevent and resolve crises in community settings. METHODS: This is a participatory action research design (PAR) in a 6-month cluster randomised controlled trial (RCT). The trial will be conducted in 30 municipalities, including 150 frail community-dwelling participants receiving home care services judged by the services to be at risk of developing crisis. Each municipality (cluster) will be randomised to receive either the locally adapted TIME intervention (the intervention group) or care as usual (the control group). The Targeted Interdisciplinary Model for Evaluation and Treatment of Neuropsychiatric Symptoms (TIME) is a manual-based, multicomponent programme that includes a rigorous assessment of the crisis, one or more interdisciplinary case conferences, and the testing and evaluation of customised treatment measures. PAR in combination with an RCT will enhance adaptations of the intervention to the local context and needs. The primary outcome is as follows: difference in change between the intervention and control groups in individual goal achievement to resolve or reduce the challenges of the crises between baseline and 3 months using the PRACTIC Goal Setting Interview (PGSI). Among the secondary outcomes are the difference in change in the PGSI scale at 6 months and in neuropsychiatric symptoms (NPSs), quality of life, distress perceived by professional carers and next of kin, and institutionalisation at 3 and 6 months. DISCUSSION: Through customised interventions that involve patients, the next of kin, the social context, and health care services, crises may be prevented and resolved. The PReventing and Approaching Crises for frail community-dwelling patients Through Innovative Care (PRACTIC) study will enhance innovation for health professionals, management, and users in the development of new knowledge and a new adapted approach towards crises. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT05651659. Registered 15.12.22.

Tailored Self-Management App to Support Older Adults With Cancer and Multimorbidity: Development and Usability Testing.

BACKGROUND: Globally, cancer predominates in adults aged older than 60 years, and 70% of older adults have ≥1 chronic condition. Cancer self-management interventions can improve symptom management and confidence, but few interventions target the complex needs of older adults with cancer and multimorbidity. Despite growing evidence of digital health tools in cancer care, there is a paucity of theoretically grounded digital self-management supports for older adults. Many apps for older adults have not been co-designed with older adults to ensure that they are tailored to their specific needs, which would increase usability and uptake. OBJECTIVE: We aim to report on the user evaluations of a self- and symptom-management app to support older adults living with cancer and multimorbidity. METHODS: This study used Grey's self-management framework, a design thinking approach, and involved older adults with lived experiences of cancer to design a medium-fidelity app prototype. Older adults with cancer or caregivers were recruited through community organizations or support groups to participate in co-designing or evaluations of the app. Data from interviews were iteratively integrated into the design process and analyzed using descriptive statistics and thematic analyses. RESULTS: In total, 15 older adults and 3 caregivers (n=18) participated in this study: 10 participated (8 older adults and 2 caregivers) in the design of the low-fidelity prototype, and 10 evaluated (9 older adults and 1 caregiver) the medium-fidelity prototype (2 older adults participated in both phases). Participants emphasized the importance of tracking functions to make sense of information across physical symptoms and psychosocial aspects; a clear display; and the organization of notes and reminders to communicate with care providers. Participants also emphasized the importance of medication initiation or cessation reminders to mitigate concerns related to polypharmacy. CONCLUSIONS: This app has the potential to support the complex health care needs of older adults with cancer, creating a "home base" for symptom management and support. The findings from this study will position the researchers to conduct feasibility testing and real-world implementation.

Employment Status Following Heart Transplantation: Data From the Danish Nationwide Social Service Payment Register During 20 years.

Most studies on vocational rehabilitation after heart transplantation (HTX) are based on self-reported data. Danish registries include weekly longitudinal information on all public transfer payments. We intended to describe 20-year trends in employment status for the Danish heart-transplant recipients, and examine the influence of multimorbidity and socioeconomic position (SEP). Linking registry and Scandiatransplant data (1994-2018), we conducted a study in recipients of working age (19-63 years). The cohort contained 492 recipients (79% males) and the median (IQR) age was 52 years (43-57 years). Five years after HTX, 30% of the survived recipients participated on the labor market; 9% were in a flexible job with reduced health-related working capacity. Moreover, 60% were retired and 10% eligible for labor market participation were unemployed. Recipients with multimorbidity had a higher age and a lower prevalence of employment. Five years after HTX, characteristics of recipients with labor market participation were: living alone (27%) versus cohabitation (73%); low (36%) versus medium-high (64%) educational level; low (13%) or medium-high (87%) income group. Heart-transplant recipients with multimorbidity have a higher age and a lower prevalence of employment. Socioeconomically disadvantaged recipients had a lower prevalence of labor market participation, despite being younger compared with the socioeconomically advantaged.

The economic burden of multimorbidity: Protocol for a systematic review.

Multimorbidity, also known as multiple long-term conditions, leads to higher healthcare utilisation, including hospitalisation, readmission, and polypharmacy, as well as a financial burden to families, society, and nations. Despite some progress, the economic burden of multimorbidity remains poorly understood. This paper outlines a protocol for a systematic review that aims to identify and synthesise comprehensive evidence on the economic burden of multimorbidity, considering various definitions and measurements of multimorbidity, including their implications for future cost-of-illness analyses. The review will include studies involving people of all ages with multimorbidity without any restriction on location and setting. Cost-of-illness studies or studies that examined economic burden including model-based studies will be included, and economic evaluation studies will be excluded. Databases including Scopus (that includes PubMed/MEDLINE), Web of Science, CINAHL Plus, PsycINFO, NHS EED (including the HTA database), and the Cost-Effectiveness Analysis Registry, will be searched until March 2024. The risk of bias within included studies will be independently assessed by two authors using appropriate checklists. A narrative synthesis of the main characteristics and results, by definitions and measurements of multimorbidity, will be conducted. The total economic burden of multimorbidity will be reported as mean annual costs per patient and disaggregated based on counts of diseases, disease clusters, and weighted indices. The results of this review will provide valuable insights for researchers into the key cost components and areas that require further investigation in order to improve the rigour of future studies on the economic burden of multimorbidity. Additionally, these findings will broaden our understanding of the economic impact of multimorbidity, inform us about the costs of inaction, and guide decision-making regarding resource allocation and cost-effective interventions. The systematic review's results will be submitted to a peer-reviewed journal, presented at conferences, and shared via an online webinar for discussion.

Comorbidity and multimorbidity in patients with cirrhosis, hospitalised in an internal medicine ward: a monocentric, cross-sectional study.

OBJECTIVES: There are no data regarding the prevalence of comorbidity (ie, additional conditions in reference to an index disease) and multimorbidity (ie, co-occurrence of multiple diseases in which no one holds priority) in patients with liver cirrhosis. We sought to determine the rate and differences between comorbidity and multimorbidity depending on the aetiology of cirrhosis. DESIGN: This is a subanalysis of the San MAtteo Complexity (SMAC) study. We have analysed demographic, clinical characteristics and rate of comorbidity/multimorbidity of patients with liver cirrhosis depending on the aetiology-alcoholic, infectious and non-alcoholic fatty liver disease (NAFLD). A multivariable analysis for factors associated with multimorbidity was fitted. SETTING: Single-centre, cross-sectional study conducted in a tertiary referral, academic, internal medicine ward in northern Italy (November 2017-November 2019). PARTICIPANTS: Data from 1433 patients previously enrolled in the SMAC study were assessed; only those with liver cirrhosis were eventually included. RESULTS: Of the 1433 patients, 172 (median age 79 years, IQR 67-84; 83 females) had liver cirrhosis. Patients with cirrhosis displayed higher median Cumulative Illness Rating Scale (CIRS) comorbidity (4, IQR 3-5; p=0.01) and severity (1.85, IQR 16.-2.0; p<0.001) indexes and lower educational level (103, 59.9%; p=0.003). Patients with alcohol cirrhosis were significantly younger (median 65 years, IQR 56-79) than patients with cirrhosis of other aetiologies (p<0.001) and more commonly males (25, 75.8%). Comorbidity was more prevalent in patients with alcohol cirrhosis (13, 39.4%) and multimorbidity was more prevalent in viral (64, 81.0%) and NAFLD (52, 86.7%) cirrhosis (p=0.015). In a multivariable model for factors associated with multimorbidity, a CIRS comorbidity index >3 (OR 2.81, 95% CI 1.14 to 6.93, p=0.024) and admission related to cirrhosis (OR 0.19, 95% CI 0.07 to 0.54, p=0.002) were the only significant associations. CONCLUSIONS: Comorbidity is more common in alcohol cirrhosis compared with other aetiologies in a hospital, internal medicine setting.

Application of Machine Learning in Multimorbidity Research: Protocol for a Scoping Review.

BACKGROUND: Multimorbidity, defined as the coexistence of multiple chronic conditions, poses significant challenges to health care systems on a global scale. It is associated with increased mortality, reduced quality of life, and increased health care costs. The burden of multimorbidity is expected to worsen if no effective intervention is taken. Machine learning has the potential to assist in addressing these challenges since it offers advanced analysis and decision-making capabilities, such as disease prediction, treatment development, and clinical strategies. OBJECTIVE: This paper represents the protocol of a scoping review that aims to identify and explore the current literature concerning the use of machine learning for patients with multimorbidity. More precisely, the objective is to recognize various machine learning models, the patient groups involved, features considered, types of input data, the maturity of the machine learning algorithms, and the outcomes from these machine learning models. METHODS: The scoping review will be based on the guidelines of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). Five databases (PubMed, Embase, IEEE, Web of Science, and Scopus) are chosen to conduct a literature search. Two reviewers will independently screen the titles, abstracts, and full texts of identified studies based on predefined eligibility criteria. Covidence (Veritas Health Innovation Ltd) will be used as a tool for managing and screening papers. Only studies that examine more than 1 chronic disease or individuals with a single chronic condition at risk of developing another will be included in the scoping review. Data from the included studies will be collected using Microsoft Excel (Microsoft Corp). The focus of the data extraction will be on bibliographical information, objectives, study populations, types of input data, types of algorithm, performance, maturity of the algorithms, and outcome. RESULTS: The screening process will be presented in a PRISMA-ScR flow diagram. The findings of the scoping review will be conveyed through a narrative synthesis. Additionally, data extracted from the studies will be presented in more comprehensive formats, such as charts or tables. The results will be presented in a forthcoming scoping review, which will be published in a peer-reviewed journal. CONCLUSIONS: To our knowledge, this may be the first scoping review to investigate the use of machine learning in multimorbidity research. The goal of the scoping review is to summarize the field of literature on machine learning in patients with multiple chronic conditions, highlight different approaches, and potentially discover research gaps. The results will offer insights for future research within this field, contributing to developments that can enhance patient outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/53761.

Temporal change in multimorbidity prevalence, clustering patterns, and the association with mortality: findings from the China Kadoorie Biobank study in Jiangsu Province.

Objectives: The characteristics of multimorbidity in the Chinese population are currently unclear. We aimed to determine the temporal change in multimorbidity prevalence, clustering patterns, and the association of multimorbidity with mortality from all causes and four major chronic diseases. Methods: This study analyzed data from the China Kadoorie Biobank study performed in Wuzhong District, Jiangsu Province. A total of 53,269 participants aged 30-79 years were recruited between 2004 and 2008. New diagnoses of 15 chronic diseases and death events were collected during the mean follow-up of 10.9 years. Yule's Q cluster analysis method was used to determine the clustering patterns of multimorbidity. A Cox proportional hazards model was used to estimate the associations of multimorbidity with mortalities. Results: The overall multimorbidity prevalence rate was 21.1% at baseline and 27.7% at the end of follow-up. Multimorbidity increased more rapidly during the follow-up in individuals who had a higher risk at baseline. Three main multimorbidity patterns were identified: (i) cardiometabolic multimorbidity (diabetes, coronary heart disease, stroke, and hypertension), (ii) respiratory multimorbidity (tuberculosis, asthma, and chronic obstructive pulmonary disease), and (iii) mental, kidney and arthritis multimorbidity (neurasthenia, psychiatric disorders, chronic kidney disease, and rheumatoid arthritis). There were 3,433 deaths during the follow-up. The mortality risk increased by 24% with each additional disease [hazard ratio (HR) = 1.24, 95% confidence interval (CI) = 1.20-1.29]. Compared with those without multimorbidity at baseline, both cardiometabolic multimorbidity and respiratory multimorbidity were associated with increased mortality from all causes and four major chronic diseases. Cardiometabolic multimorbidity was additionally associated with mortality from cardiovascular diseases and diabetes, with HRs of 2.64 (95% CI = 2.19-3.19) and 28.19 (95% CI = 14.85-53.51), respectively. Respiratory multimorbidity was associated with respiratory disease mortality, with an HR of 9.76 (95% CI = 6.22-15.31). Conclusion: The prevalence of multimorbidity has increased substantially over the past decade. This study has revealed that cardiometabolic multimorbidity and respiratory multimorbidity have significantly increased mortality rates. These findings indicate the need to consider high-risk populations and to provide local evidence for intervention strategies and health management in economically developed regions.

Genetic effects and causal association analyses of 14 common conditions/diseases in multimorbidity patterns.

BACKGROUND: Multimorbidity has become an important health challenge in the aging population. Accumulated evidence has shown that multimorbidity has complex association patterns, but the further mechanisms underlying the association patterns are largely unknown. METHODS: Summary statistics of 14 conditions/diseases were available from the genome-wide association study (GWAS). Linkage disequilibrium score regression analysis (LDSC) was applied to estimate the genetic correlations. Pleiotropic SNPs between two genetically correlated traits were detected using pleiotropic analysis under the composite null hypothesis (PLACO). PLACO-identified SNPs were mapped to genes by Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA), and gene set enrichment analysis and tissue differential expression were performed for the pleiotropic genes. Two-sample Mendelian randomization analyses assessed the bidirectional causality between conditions/diseases. RESULTS: LDSC analyses revealed the genetic correlations for 20 pairs based on different two-disease combinations of 14 conditions/diseases, and genetic correlations for 10 pairs were significant after Bonferroni adjustment (P<0.05/91 = 5.49E-04). Significant pleiotropic SNPs were detected for 11 pairs of correlated conditions/diseases. The corresponding pleiotropic genes were differentially expressed in the brain, nerves, heart, and blood vessels and enriched in gluconeogenesis and drug metabolism, biotransformation, and neurons. Comprehensive causal analyses showed strong causality between hypertension, stroke, and high cholesterol, which drive the development of multiple diseases. CONCLUSIONS: This study highlighted the complex mechanisms underlying the association patterns that include the shared genetic components and causal effects among the 14 conditions/diseases. These findings have important implications for guiding the early diagnosis, management, and treatment of comorbidities.

Mental-somatic multimorbidity in trajectories of cognitive function for middle-aged and older adults.

INTRODUCTION: Multimorbidity may confer higher risk for cognitive decline than any single constituent disease. This study aims to identify distinct trajectories of cognitive impairment probability among middle-aged and older adults, and to assess the effect of changes in mental-somatic multimorbidity on these distinct trajectories. METHODS: Data from the Health and Retirement Study (1998-2016) were employed to estimate group-based trajectory models identifying distinct trajectories of cognitive impairment probability. Four time-varying mental-somatic multimorbidity combinations (somatic, stroke, depressive, stroke and depressive) were examined for their association with observed trajectories of cognitive impairment probability with age. Multinomial logistic regression analysis was conducted to quantify the association of sociodemographic and health-related factors with trajectory group membership. RESULTS: Respondents (N = 20,070) had a mean age of 61.0 years (SD = 8.7) at baseline. Three distinct cognitive trajectories were identified using group-based trajectory modelling: (1) Low risk with late-life increase (62.6%), (2) Low initial risk with rapid increase (25.7%), and (3) High risk (11.7%). For adults following along Low risk with late-life increase, the odds of cognitive impairment for stroke and depressive multimorbidity (OR:3.92, 95%CI:2.91,5.28) were nearly two times higher than either stroke multimorbidity (OR:2.06, 95%CI:1.75,2.43) or depressive multimorbidity (OR:2.03, 95%CI:1.71,2.41). The odds of cognitive impairment for stroke and depressive multimorbidity in Low initial risk with rapid increase or High risk (OR:4.31, 95%CI:3.50,5.31; OR:3.43, 95%CI:2.07,5.66, respectively) were moderately higher than stroke multimorbidity (OR:2.71, 95%CI:2.35, 3.13; OR: 3.23, 95%CI:2.16, 4.81, respectively). In the multinomial logistic regression model, non-Hispanic Black and Hispanic respondents had higher odds of being in Low initial risk with rapid increase and High risk relative to non-Hispanic White adults. CONCLUSIONS: These findings show that depressive and stroke multimorbidity combinations have the greatest association with rapid cognitive declines and their prevention may postpone these declines, especially in socially disadvantaged and minoritized groups.

Regular source of primary care and health services utilisation among Brazilian elderly with mental-physical multimorbidity.

BACKGROUND: In ageing populations, multimorbidity is a complex challenge to health systems, especially when the individuals have both mental and physical morbidities. Although a regular source of primary care (RSPC) is associated with better health outcomes, its relation with health service utilisation in elderly patients with mental-physical multimorbidity (MP-MM) is scarce. OBJECTIVE: This study explored the relations among health service utilisation, presence of RSPC and MP-MM among elderly Brazilians. METHODS: A national cross-sectional study performed with data from national representative samples from the Brazilian National Health Research (PNS, in Portuguese; Pesquisa Nacional de Saúde) carried out in 2013 with 11,177 elderly Brazilian people. MP-MM was defined as the presence of two or more morbidities, including at least one mental morbidity, and was evaluated using a list of 16 physical and mental morbidities. The RSPC was analysed by the presence of regular font of care in primary care and health service utilisation according to the demand for health services ≤ 15 days, medical consultation ≤ 12 months, and hospitalisation ≤ 1 year. Frequency description of variables and bivariate association were performed using Stata v.15.2 software. RESULTS: The majority of individuals was female (56.4%), and their mean age was 69.8 years. The observed prevalence of MP-MM was 12.2%. Individuals with MP-MM had higher utilisation of health services when compared to those without MP-MM. RSPC was present at 36.5% and was higher in women (37.8% vs. 34.9%). There was a lower occurrence of hospitalisation ≤ 1 year among MP-MM individuals with RSPC and without a private plan of health. CONCLUSION: Our findings demonstrate that RSPC can be an important component of care in elderly individuals with MP-MM because it was associated with lower occurrence of hospitalisation, mainly in those that have not a private plan of health. Longitudinal studies are necessary to confirm these findings.

Multimorbidity prevalence and health outcome prediction: assessing the impact of lookback periods, disease count, and definition criteria in health administrative data at the population-based level.

BACKGROUND: Health administrative databases play a crucial role in population-level multimorbidity surveillance. Determining the appropriate retrospective or lookback period (LP) for observing prevalent and newly diagnosed diseases in administrative data presents challenge in estimating multimorbidity prevalence and predicting health outcome. The aim of this population-based study was to assess the impact of LP on multimorbidity prevalence and health outcomes prediction across three multimorbidity definitions, three lists of diseases used for multimorbidity assessment, and six health outcomes. METHODS: We conducted a population-based study including all individuals ages > 65 years on April 1st, 2019, in Québec, Canada. We considered three lists of diseases labeled according to the number of chronic conditions it considered: (1) L60 included 60 chronic conditions from the International Classification of Diseases (ICD); (2) L20 included a core of 20 chronic conditions; and (3) L31 included 31 chronic conditions from the Charlson and Elixhauser indices. For each list, we: (1) measured multimorbidity prevalence for three multimorbidity definitions (at least two [MM2+], three [MM3+] or four (MM4+) chronic conditions); and (2) evaluated capacity (c-statistic) to predict 1-year outcomes (mortality, hospitalisation, polypharmacy, and general practitioner, specialist, or emergency department visits) using LPs ranging from 1 to 20 years. RESULTS: Increase in multimorbidity prevalence decelerated after 5-10 years (e.g., MM2+, L31: LP = 1y: 14%, LP = 10y: 58%, LP = 20y: 69%). Within the 5-10 years LP range, predictive performance was better for L20 than L60 (e.g., LP = 7y, mortality, MM3+: L20 [0.798;95%CI:0.797-0.800] vs. L60 [0.779; 95%CI:0.777-0.781]) and typically better for MM3 + and MM4 + definitions (e.g., LP = 7y, mortality, L60: MM4+ [0.788;95%CI:0.786-0.790] vs. MM2+ [0.768;95%CI:0.766-0.770]). CONCLUSIONS: In our databases, ten years of data was required for stable estimation of multimorbidity prevalence. Within that range, the L20 and multimorbidity definitions MM3 + or MM4 + reached maximal predictive performance.

What is the burden of multimorbidity and the factors associated with its occurrence in elderly Brazilians?

OBJECTIVE: To estimate the prevalence of multimorbidity in elderly people and its association with sociodemographic characteristics, lifestyle, and anthropometry. METHODS: This was a cross-sectional study using data from the National Health Survey, 2019. A total of 22,728 elderly individuals from all 27 Brazilian states were randomly selected. Poisson regression models with robust variance were employed, and a significance level of 5% was adopted. RESULTS: The prevalence of multimorbidity was 51.6% (95% CI: 50.4-52.7), with the highest estimates observed in the South and Southeast. Multimorbidity was associated with being female (aPR = 1.33; 95% CI: 1.27-1.39), being 80 years old or older (aPR = 1.12; 95% CI: 1.05-1.19), having low education (aPR = 1.16; 95% CI: 1.07-1.25), past cigarette use (aPR = 1.16; 95% CI: 1.11-1.21), insufficient physical activity (aPR = 1.13; 95% CI: 1.06-1.21), and screen use for 3 hours or more per day (aPR = 1.13; 95% CI: 1.08-1.18). CONCLUSION: Multimorbidity affects more than half of the elderly population in Brazil and is associated with social, demographic, and behavioral factors.

The Scope of Multimorbidity in Family Medicine: Identifying Age Patterns Across the Lifespan.

INTRODUCTION: Multimorbidity rates are both increasing in prevalence across age ranges, and also increasing in diagnostic importance within and outside the family medicine clinic. Here we aim to describe the course of multimorbidity across the lifespan. METHODS: This was a retrospective cohort study across 211,953 patients from a large northeastern health care system. Past medical histories were collected in the form of ICD-10 diagnostic codes. Rates of multimorbidity were calculated from comorbid diagnoses defined from the ICD10 codes identified in the past medical histories. RESULTS: We identify 4 main age groups of diagnosis and multimorbidity. Ages 0 to 10 contain diagnoses which are infectious or respiratory, whereas ages 10 to 40 are related to mental health. From ages 40 to 70 there is an emergence of alcohol use disorders and cardiometabolic disorders. And ages 70 to 90 are predominantly long-term sequelae of the most common cardiometabolic disorders. The mortality of the whole population over the study period was 5.7%, whereas the multimorbidity with the highest mortality across the study period was Circulatory Disorders-Circulatory Disorders at 23.1%. CONCLUSION: The results from this study provide a comparison for the presence of multimorbidity within age cohorts longitudinally across the population. These patterns of comorbidity can assist in the allocation to practice resources that will best support the common conditions that patients need assistance with, especially as the patients transition between pediatric, adult, and geriatric care. Future work examining and comparing multimorbidity indices is warranted.

Patient medication management, understanding and adherence during the transition from hospital to outpatient care - a qualitative longitudinal study in polymorbid patients with type 2 diabetes.

BACKGROUND: Continuity of care is under great pressure during the transition from hospital to outpatient care. Medication changes during hospitalization may be poorly communicated and understood, compromising patient safety during the transition from hospital to home. The main aims of this study were to investigate the perspectives of patients with type 2 diabetes and multimorbidities on their medications from hospital discharge to outpatient care, and their healthcare journey through the outpatient healthcare system. In this article, we present the results focusing on patients' perspectives of their medications from hospital to two months after discharge. METHODS: Patients with type 2 diabetes, with at least two comorbidities and who returned home after discharge, were recruited during their hospitalization. A descriptive qualitative longitudinal research approach was adopted, with four in-depth semi-structured interviews per participant over a period of two months after discharge. Interviews were based on semi-structured guides, transcribed verbatim, and a thematic analysis was conducted. RESULTS: Twenty-one participants were included from October 2020 to July 2021. Seventy-five interviews were conducted. Three main themes were identified: (A) Medication management, (B) Medication understanding, and (C) Medication adherence, during three periods: (1) Hospitalization, (2) Care transition, and (3) Outpatient care. Participants had varying levels of need for medication information and involvement in medication management during hospitalization and in outpatient care. The transition from hospital to autonomous medication management was difficult for most participants, who quickly returned to their routines with some participants experiencing difficulties in medication adherence. CONCLUSIONS: The transition from hospital to outpatient care is a challenging process during which discharged patients are vulnerable and are willing to take steps to better manage, understand, and adhere to their medications. The resulting tension between patients' difficulties with their medications and lack of standardized healthcare support calls for interprofessional guidelines to better address patients' needs, increase their safety, and standardize physicians', pharmacists', and nurses' roles and responsibilities.

Quantifying depressive symptoms on incidence of common chronic diseases and multimorbidity patterns in middle-aged and elderly Chinese adults.

BACKGROUND: Depressive symptoms are highly prevalent and increase risks of various morbidities. However, the extent to which depressive symptoms could account for incidence of these chronic conditions, in particular multimorbidity patterns, remains to be examined and quantified. METHODS: For this cohort analysis, we included 9024-14,093 participants aged 45 years and older from the China Health and Retirement Longitudinal Study (CHARLS). Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the longitudinal associations between depressive symptoms and 13 common chronic diseases and 4 multimorbidity patterns. Population attributable fractions (PAFs) combining the information on both exposure prevalence and risk association were estimated to quantify the magnitude of the burden of these conditions attributable to depressive symptoms. RESULTS: Depressive symptoms were associated with increased risks of liver disease, stroke, heart problem, asthma, diabetes, arthritis, kidney disease, chronic lung disease, digestive disease, dyslipidemia, and memory-related disease, and the adjusted HRs (95% CIs) and PAFs (95% CIs) ranged from 1.15 (1.05-1.26) to 1.64 (1.38-1.96) and 5% (0-10%) to 17% (6-28%), respectively. In addition, individuals with depressive symptoms had elevated risks of the cardiometabolic-cancer pattern, the cerebrovascular-memory pattern, the articular-visceral organ pattern, and the respiratory pattern, with respective HRs (95% CIs) of 1.26 (1.11-1.42), 1.34 (1.07-1.69), 1.45 (1.29-1.63), and 2.01 (1.36-2.96), and respective PAFs (95% CIs) of 5% (0-10%), 8% (-4-21%), 12% (7-17%), and 20% (5-35%). CONCLUSION: Depressive symptoms contribute substantially to the burden across a broad range of chronic diseases as well as different multimorbidity patterns in middle-aged and older Chinese.

The association between Chinese visceral adiposity index and cardiometabolic multimorbidity among Chinese middle-aged and older adults: a national cohort study.

Objective: This study aimed to explore the association between the Chinese visceral adiposity index (CVAI) and cardiometabolic multimorbidity in middle-aged and older Chinese adults. Methods: The data used in this study were obtained from a national cohort, the China Health and Retirement Longitudinal Study (CHARLS, 2011-2018 wave). The CVAI was measured using previously validated biomarker estimation formulas, which included sex, age, body mass index, waist circumference, triglycerides, and high-density lipoprotein cholesterol. The presence of two or more of these cardiometabolic diseases (diabetes, heart disease, and stroke) is considered as cardiometabolic multimorbidity. We used Cox proportional hazard regression models to examine the association between CVAI and cardiometabolic multimorbidity, adjusting for a set of covariates. Hazard ratios (HRs) and 95% confidence intervals (CIs) were used to show the strength of the associations. We also conducted a subgroup analysis between age and sex, as well as two sensitivity analyses. Receiver operator characteristic curves (ROC) were used to test the predictive capabilities and cutoff value of the CVAI for cardiometabolic multimorbidity. Results: A total of 9028 participants were included in the final analysis, with a mean age of 59.3 years (standard deviation: 9.3) and women accounting for 53.7% of the sample population. In the fully-adjusted model, compared with participants in the Q1 of CVAI, the Q3 (HR = 2.203, 95% CI = 1.039 - 3.774) and Q4 of CVAI (HR = 3.547, 95% CI = 2.100 - 5.992) were associated with an increased risk of cardiometabolic multimorbidity. There was no evidence of an interaction between the CVAI quartiles and sex or age in association with cardiometabolic multimorbidity (P >0.05). The results of both sensitivity analyses suggested that the association between CVAI and cardiometabolic multimorbidity was robust. In addition, the area under ROC and ideal cutoff value for CVAI prediction of cardiometabolic multimorbidity were 0.685 (95% CI = 0.649-0.722) and 121.388. Conclusion: The CVAI is a valid biomarker with good predictive capability for cardiometabolic multimorbidity and can be used by primary healthcare organizations in the future for early warning, prevention, and intervention with regard to cardiometabolic multimorbidity.

Further development and validation of the Multimorbidity Treatment Burden Questionnaire (MTBQ).

OBJECTIVES: To undertake further psychometric testing of the Multimorbidity Treatment Burden Questionnaire (MTBQ) and examine whether reversing the scale reduced floor effects. DESIGN: Survey. SETTING: UK primary care. PARTICIPANTS: Adults (≥18 years) with three or more long-term conditions randomly selected from four general practices and invited by post. MEASURES: Baseline survey: sociodemographics, MTBQ (original or version with scale reversed), Treatment Burden Questionnaire (TBQ), four questions (from QQ-10) on ease of completing the questionnaires. Follow-up survey (1-4 weeks after baseline): MTBQ, TBQ and QQ-10. Anonymous data collected from electronic GP records: consultations (preceding 12 months) and long-term conditions. The proportion of missing data and distribution of responses were examined for the original and reversed versions of the MTBQ and the TBQ. Intraclass correlation coefficient (ICC) and Spearman's rank correlation (Rs) assessed test-retest reliability and construct validity, respectively. Ease of completing the MTBQ and TBQ was compared. Interpretability was assessed by grouping global MTBQ scores into 0 and tertiles (>0). RESULTS: 244 adults completed the baseline survey (consent rate 31%, mean age 70 years) and 225 completed the follow-up survey. Reversing the scale did not reduce floor effects or data skewness. The global MTBQ scores had good test-retest reliability (ICC for agreement at baseline and follow-up 0.765, 95% CI 0.702 to 0.816). Global MTBQ score was correlated with global TBQ score (Rs 0.77, p<0.001), weakly correlated with number of consultations (Rs 0.17, p=0.010), and number of different general practitioners consulted (Rs 0.23, p<0.001), but not correlated with number of long-term conditions (Rs -0.063, p=0.330). Most participants agreed that both the MTBQ and TBQ were easy to complete and included aspects they were concerned about. CONCLUSION: This study demonstrates test-retest reliability and ease of completion of the MTBQ and builds on a previous study demonstrating good content validity, construct validity and internal consistency reliability of the questionnaire.