Identifying subgroups in heart failure patients with multimorbidity by clustering and network analysis.

This study presents a workflow for identifying and characterizing patients with Heart Failure (HF) and multimorbidity utilizing data from Electronic Health Records. Multimorbidity, the co-occurrence of two or more chronic conditions, poses a significant challenge on healthcare systems. Nonetheless, understanding of patients with multimorbidity, including the most common disease interactions, risk factors, and treatment responses, remains limited, particularly for complex and heterogeneous conditions like HF. We conducted a clustering analysis of 3745 HF patients using demographics, comorbidities, laboratory values, and drug prescriptions. Our analysis revealed four distinct clusters with significant differences in multimorbidity profiles showing differential prognostic implications regarding unplanned hospital admissions. These findings underscore the considerable disease heterogeneity within HF patients and emphasize the potential for improved characterization of patient subgroups for clinical risk stratification through the use of EHR data.

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.

A Technological Tool Aimed at Self-Care in Patients With Multimorbidity: Cross-Sectional Usability Study.

BACKGROUND: Information and communication technologies (ICTs) have been positioned as useful tools to facilitate self-care. The interaction between a patient and technology, known as usability, is particularly important for achieving positive health outcomes. Specific characteristics of patients with chronic diseases, including multimorbidity, can affect their interaction with different technologies. Thus, studying the usability of ICTs in the field of multimorbidity has become a key element to ensure their relevant role in promoting self-care. OBJECTIVE: The aim of this study was to analyze the usability of a technological tool dedicated to health and self-care in patients with multimorbidity in primary care. METHODS: A descriptive observational cross-sectional usability study was performed framed in the clinical trial in the primary care health centers of Madrid Health Service of the TeNDER (Affective Based Integrated Care for Better Quality of Life) project. The TeNDER technological tool integrates sensors for monitoring physical and sleep activity along with a mobile app for consulting the data collected and working with self-management tools. This project included patients over 60 years of age who had one or more chronic diseases, at least one of which was mild-moderate cognitive impairment, Parkinson disease, or cardiovascular disease. From the 250 patients included in the project, 38 agreed to participate in the usability study. The usability variables investigated were effectiveness, which was determined by the degree of completion and the total number of errors per task; efficiency, evaluated as the average time to perform each task; and satisfaction, quantified by the System Usability Scale. Five tasks were evaluated based on real case scenarios. Usability variables were analyzed according to the sociodemographic and clinical characteristics of patients. A logistic regression model was constructed to estimate the factors associated with the type of support provided for task completion. RESULTS: The median age of the 38 participants was 75 (IQR 72.0-79.0) years. There was a slight majority of women (20/38, 52.6%) and the participants had a median of 8 (IQR 7.0-11.0) chronic diseases. Thirty patients completed the usability study, with a usability effectiveness result of 89.3% (134/150 tasks completed). Among the 30 patients, 66.7% (n=20) completed all tasks and 56.7% (17/30) required personalized help on at least one task. In the multivariate analysis, educational level emerged as a facilitating factor for independent task completion (odds ratio 1.79, 95% CI 0.47-6.83). The median time to complete the total tasks was 296 seconds (IQR 210.0-397.0) and the median satisfaction score was 55 (IQR 45.0-62.5) out of 100. CONCLUSIONS: Although usability effectiveness was high, the poor efficiency and usability satisfaction scores suggest that there are other factors that may interfere with the results. Multimorbidity was not confirmed to be a key factor affecting the usability of the technological tool. TRIAL REGISTRATION: Clinicaltrials.gov NCT05681065; https://clinicaltrials.gov/study/NCT05681065.

Development of a person-centred care approach for persons with chronic multimorbidity in general practice by means of participatory action research.

BACKGROUND: The management of persons with multimorbidity challenges healthcare systems tailored to individual diseases. A person-centred care approach is advocated, in particular for persons with multimorbidity. The aim of this study was to describe the co-creation and piloting of a proactive, person-centred chronic care approach for persons with multimorbidity in general practice, including facilitators and challenges for successful implementation. METHODS: A participatory action research (PAR) approach was applied in 13 general practices employing four subsequent co-creation cycles between 2019 and 2021. The target population included adults with ≥3 chronic conditions. Participating actors were general practitioners (GPs), practice nurses (PNs), patients (target group), the affiliated care cooperation, representatives of a health insurer and researchers. Each cycle consisted of a try-out period in practice and a reflective evaluation through focus groups with healthcare providers, interviews with patients and analyses of routine care data. In each cycle, facilitators, challenges and follow-up actions for the next cycle were identified. Work satisfaction among GPs and PNs was measured pre and at the end of the final co-creation cycle. RESULTS: Identified essential steps in the person-centred chronic care approach include (1) appropriate patient selection for (2) an extended person-centred consultation, and (3) personalised goalsetting and follow-up. Key facilitators included improved therapeutic relationships, enhanced work satisfaction for care providers, and patient appreciation of extended time with their GP. Deliberate task division and collaboration between GPs and PNs based on patient, local setting, and care personnel is required. Challenges and facilitators for implementation encompassed a prioritisation tool to support GPs appropriately who to invite first for extended consultations, appropriate remuneration and time to conduct extended consultations, training in delivering person-centred chronic care available for all general practice care providers and an electronic medical record system accommodating comprehensive information registration. CONCLUSIONS: A person-centred chronic care approach targeting patients with multimorbidity in general practice was developed and piloted in co-creation with stakeholders. More consultation time facilitated better understanding of persons' situations, their functioning, priorities and dilemma's, and positively impacted work satisfaction of care providers. Challenges need to be tackled before widespread implementation. Future evaluation on the quadruple aims is recommended.

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.

Bidirectional association between falls and multimorbidity in middle-aged and elderly Chinese adults: a national longitudinal study.

This study explores the bidirectional association between multimorbidity and falls in Chinese middle-aged and elderly adults. Participants aged 45 and above from the China Health and Retirement Longitudinal Study were included. Binary logistic regression assessed the impact of chronic conditions on fall incidence (stage I), while multinomial logistic regression examined the relationship between baseline falls and multimorbidity (stage II). The fully adjusted odds ratios (ORs) for one, two, or three or more chronic conditions were 1.34, 1.65, and 2.02, respectively. Among participants without baseline falls, 28.61% developed two or more chronic conditions during follow-up, compared to 37.4% of those with a history of falls. Fully adjusted ORs for one, two, or three or more chronic conditions in those with a history of falls were 1.21, 1.38 and 1.70, respectively. The bidirectional relationship held in sensitivity and subgroup analyses. A bidirectional relationship exists between multimorbidity and falls in Chinese middle-aged and elderly adults. Strengthening chronic condition screening and treatment in primary healthcare may reduce falls risk, and prioritizing fall prevention and intervention in daily life is recommended.

Association of adiposity indicators with cardiometabolic multimorbidity risk in hypertensive patients: a large cross-sectional study.

Background: Cardiometabolic multimorbidity (CMM) has emerged as a prominent public health concern. Hypertensive patients are prone to develop comorbidities. Moreover, the accumulation of visceral adipose tissue is the main cause for the development of cardiometabolic diseases. The cardiometabolic index (CMI), lipid accumulation product (LAP), visceral adiposity index (VAI), and Chinese visceral adiposity index (CVAI) not only assess adipose tissue mass but also reflect adipose tissue dysfunction. So far, no study has been reported to evaluate the association of CMI, LAP, VAI, and CVAI with CMM risk in hypertensive patients. Therefore, this study aimed to assess the association between these adiposity indicators and the risk of CMM among Chinese hypertensive patients. Methods: In this cross-sectional study, a total of 229,287 hypertensive patients aged 35 years and older were included from the National Basic Public Health Service Project. All participants underwent a face-to-face questionnaire survey, physical examination, and the collection of fasting venous blood samples. Multivariable logistic regression models were performed to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Receiver operating characteristic curve was utilized to evaluate the identification ability for CMM. Results: After adjusting for confounders, each 1-standard deviation increase in CMI, LAP, VAI, and CVAI was associated with a 14%, 8%, 12%, and 54% increased risk of CMM, respectively. When comparing the highest quartile of these indicators with the lowest quartile, individuals in the highest quartile of CMM, LAP, VAI, and CVAI had a 1.39-fold (95% CI 1.30, 1.48), 1.28-fold (95% CI 1.19, 1.37), 1.37-fold (95% CI 1.29, 1.46), and 2.56-fold (95% CI 2.34, 2.79) increased risk of CMM after adjusting for potential confounders. Notably, a nonlinear association was observed for CMI, LAP, and VAI with the risk of CMM (all P nonlinearity < 0.001). CVAI exhibited the highest area under the receiver operating characteristic curve (AUC) among all the included adiposity indices in this analysis. Conclusion: This study indicated the significant positive association of CMI, LAP, VAI, and CVAI with the risk of CMM in hypertensive patients. Among these indicators, CVAI demonstrated the most robust performance in predicting CMM risk and may serve as a valuable tool for identifying CMM risk in Chinese hypertensive patients.

Implementation and external validation of the Cambridge Multimorbidity Score in the UK Biobank cohort.

BACKGROUND: Patients with multiple conditions present a growing challenge for healthcare provision. Measures of multimorbidity may support clinical management, healthcare resource allocation and accounting for the health of participants in purpose-designed cohorts. The recently developed Cambridge Multimorbidity scores (CMS) have the potential to achieve these aims using primary care records, however, they have not yet been validated outside of their development cohort. METHODS: The CMS, developed in the Clinical Research Practice Dataset (CPRD), were validated in UK Biobank participants whose data is not available in CPRD (the cohort used for CMS development) with available primary care records (n = 111,898). This required mapping of the 37 pre-existing conditions used in the CMS to the coding frameworks used by UK Biobank data providers. We used calibration plots and measures of discrimination to validate the CMS for two of the three outcomes used in the development study (death and primary care consultation rate) and explored variation by age and sex. We also examined the predictive ability of the CMS for the outcome of cancer diagnosis. The results were compared to an unweighted count score of the 37 pre-existing conditions. RESULTS: For all three outcomes considered, the CMS were poorly calibrated in UK Biobank. We observed a similar discriminative ability for the outcome of primary care consultation rate to that reported in the development study (C-index: 0.67 (95%CI:0.66-0.68) for both, 5-year follow-up); however, we report lower discrimination for the outcome of death than the development study (0.69 (0.68-0.70) and 0.89 (0.88-0.90) respectively). Discrimination for cancer diagnosis was adequate (0.64 (0.63-0.65)). The CMS performs favourably to the unweighted count score for death, but not for the outcomes of primary care consultation rate or cancer diagnosis. CONCLUSIONS: In the UK Biobank, CMS discriminates reasonably for the outcomes of death, primary care consultation rate and cancer diagnosis and may be a valuable resource for clinicians, public health professionals and data scientists. However, recalibration will be required to make accurate predictions when cohort composition and risk levels differ substantially from the development cohort. The generated resources (including codelists for the conditions and code for CMS implementation in UK Biobank) are available online.

Challenges Addressing Lung Cancer Screening for Patients With Multimorbidity in Primary Care: A Qualitative Study.

PURPOSE: Many individuals who are eligible for lung cancer screening have comorbid conditions complicating their shared decision-making conversations with physicians. The goal of our study was to better understand how primary care physicians (PCPs) factor comorbidities into their evaluation of the risks and benefits of lung cancer screening and into their shared decision-making conversations with patients. METHODS: We conducted semistructured interviews by videoconference with 15 PCPs to assess the extent of shared decision-making practices and explore their understanding of the intersection of comorbidities and lung cancer screening, and how that understanding informed their clinical approach to this population. RESULTS: We identified 3 themes. The first theme was whether to discuss or not to discuss lung cancer screening. PCPs described taking additional steps for individuals with complex comorbidities to decide whether to initiate this discussion and used subjective clinical judgment to decide whether the conversation would be productive and beneficial. PCPs made mental assessments that factored in the patient's health, life expectancy, quality of life, and access to support systems. The second theme was that shared decision making is not a simple discussion. When PCPs did initiate discussions about lung cancer screening, although some believed they could provide objective information, others struggled with personal biases. The third theme was that ultimately, the decision to be screened was up to the patient. Patients had the final say, even if their decision was discordant with the PCP's advice. CONCLUSIONS: Shared decision-making conversations about lung cancer screening differed substantially from the standard for patients with complex comorbidities. Future research should include efforts to characterize the risks and benefits of LCS in patients with comorbidities to inform guidelines and clinical application.

Augmenting K-Means Clustering With Qualitative Data to Discover the Engagement Patterns of Older Adults With Multimorbidity When Using Digital Health Technologies: Proof-of-Concept Trial.

BACKGROUND: Multiple chronic conditions (multimorbidity) are becoming more prevalent among aging populations. Digital health technologies have the potential to assist in the self-management of multimorbidity, improving the awareness and monitoring of health and well-being, supporting a better understanding of the disease, and encouraging behavior change. OBJECTIVE: The aim of this study was to analyze how 60 older adults (mean age 74, SD 6.4; range 65-92 years) with multimorbidity engaged with digital symptom and well-being monitoring when using a digital health platform over a period of approximately 12 months. METHODS: Principal component analysis and clustering analysis were used to group participants based on their levels of engagement, and the data analysis focused on characteristics (eg, age, sex, and chronic health conditions), engagement outcomes, and symptom outcomes of the different clusters that were discovered. RESULTS: Three clusters were identified: the typical user group, the least engaged user group, and the highly engaged user group. Our findings show that age, sex, and the types of chronic health conditions do not influence engagement. The 3 primary factors influencing engagement were whether the same device was used to submit different health and well-being parameters, the number of manual operations required to take a reading, and the daily routine of the participants. The findings also indicate that higher levels of engagement may improve the participants' outcomes (eg, reduce symptom exacerbation and increase physical activity). CONCLUSIONS: The findings indicate potential factors that influence older adult engagement with digital health technologies for home-based multimorbidity self-management. The least engaged user groups showed decreased health and well-being outcomes related to multimorbidity self-management. Addressing the factors highlighted in this study in the design and implementation of home-based digital health technologies may improve symptom management and physical activity outcomes for older adults self-managing multimorbidity.

How valid is a prescription-based multimorbidity index (Rx-risk) in predicting mortality in the Outcomes and Multimorbidity In Type 2 diabetes (OMIT) study? A nation-wide registry-based cohort study from Norway.

OBJECTIVE: The prescription-based Rx-risk index has previously been developed to measure multimorbidity. We aimed to adapt and evaluate the validity of the Rx-risk index in prediction of mortality among persons with type 2 diabetes. DESIGN: Registry-based study. SETTING: Adults with type 2 diabetes in Norway identified within the 'Outcomes and Multimorbidity In Type 2 diabetes' cohort, with linkage to prescriptions from the Norwegian Prescription Database and mortality from the Population Registry. PARTICIPANTS: We defined a calibration sample of 42 290 adults diagnosed with type 2 diabetes 1950-2013, and a temporal validation sample of 7085 adults diagnosed 2014-2016 to evaluate the index validity over time PRIMARY OUTCOME MEASURE: All-cause mortality METHODS: For the calibration sample, dispensed drug prescriptions in 2013 were used to define 44 morbidity categories. Weights were estimated using regression coefficients from a Cox regression model with 5 year mortality as the outcome and all morbidity categories, age and sex included as covariates. The Rx-risk index was computed as a weighted sum of morbidities. The validity of the index was evaluated using C-statistic and calibration plots. RESULTS: In the calibration sample, mean (SD) age at start of follow-up and duration of diabetes was 63.8 (12.4) and 10.1 (7.0) years, respectively. The overall C-statistic was 0.82 and varied from 0.74 to 0.85 when stratifying on age groups, sex, level of education and country of origin. In the validation sample, mean (SD) age and duration of diabetes was 59.7 (13.0) and 2.0 (0.8) years, respectively. Despite younger age, shorter duration of diabetes and later time period, the C-index was high both in the total sample (0.84) and separately for men (0.83) and women (0.84). CONCLUSIONS: The Rx-risk index showed good discrimination and calibration in predicting mortality and thus presents a valid tool to assess multimorbidity among persons with type 2 diabetes.

Characterizing Multimorbidity Prevalence and Adverse Outcomes in Ethnically and Culturally Diverse Sub-Populations in India: Gaps, Opportunities, and Future Directions.

India is a large middle-income country and has surpassed China in overall population, comprising 20% of the global population (over 1.43 billion people). India is experiencing a major demographic shift in its aging population. Chronic diseases are common among older adults and can be persistent over the life course, lead to the onset of disability, and be costly. Among older adults in India, the existence of multiple comorbid chronic conditions (i.e., multimorbidity) is rapidly growing and represents a burgeoning public health burden. Prior research identified greater rates of multimorbidity (e.g., overweight/obesity diabetes, hypertension, cardiovascular disease, stroke, and malignancies) in minority populations in the United States (U.S.); however, limited studies have attempted to characterize multimorbidity among older adult sub-populations residing in India. To address this gap, we conducted a narrative review of studies on multimorbidity using the data from the Longitudinal Aging Study of India (LASI), the largest nationally representative longitudinal survey study of adults in India. Our definition of multimorbidity was the presence of more than two conditions in the same person. Our findings, based on 15 reviewed studies, aim to (1) characterize the definition and measurement of multimorbidity and to ascertain its prevalence in ethnically and culturally diverse sub-populations in India; (2) identify adverse outcomes associated with multimorbidity in the Indian adult population; and (3) identify gaps, opportunities, and future directions.

Socioeconomic inequalities in accumulation of multimorbidity in England from 2019 to 2049: a microsimulation projection study.

BACKGROUND: There are socioeconomic inequalities in the prevalence of multimorbidity and its accumulation across the life course. Estimates of multimorbidity prevalence in English primary care increased by more than two-thirds from 2004 to 2019. We developed a microsimulation model to quantify current and projected multimorbidity inequalities in the English adult population. METHODS: We used primary care data for adults in England from the Clinical Practice Research Datalink Aurum database between 2004 and 2019, linked to the 2015 English Index of Multiple Deprivation (IMD), to model time individuals spent in four health states (healthy, one chronic condition, basic multimorbidity [two or more chronic conditions], and complex multimorbidity [three or more chronic conditions affecting three or more body systems]) by sex, age, IMD quintile, birth cohort, and region. We applied these transition times in a stochastic dynamic continuous-time microsimulation model to Office for National Statistics population estimates for adults aged 30-90 years. We calculated projected prevalence and cumulative incident cases from 2019 to 2049 by IMD quintile, age group (younger than 65 years vs 65 years and older), and years to be lived without multimorbidity at age 30 years. FINDINGS: Under the assumption that all chronic conditions were lifelong, and that once diagnosed there was no recovery, we projected prevalence of multimorbidity (basic or complex) increases by 34% from 53·8% in 2019 to 71·9% (95% uncertainty interval 71·8-72·0) in 2049. This rise equates to an 84% increase in the number of people with multimorbidity: from 19·2 million in 2019 to 35·3 million in 2049 (35·3 million to 35·4 million). This projected increase is greatest in the most deprived quintile, with an excess 1·07 million (1·04 million to 1·10 million) cumulative incident basic multimorbidity cases and 0·70 million (0·67 million to 0·74 million) complex multimorbidity cases over and above the projected cases for the least deprived quintile, largely driven by inequalities in those younger than 65 years. The median expected number of years to be lived without multimorbidity at age 30 years in 2019 is 15·12 years (14·62-16·01) in the least deprived IMD quintile and 12·15 years (11·61-12·60) in the most deprived IMD quintile. INTERPRETATION: The number of people living with multimorbidity will probably increase substantially in the next 30 years, a continuation of past observed increases partly driven by changing population size and age structure. Inequalities in the multimorbidity burden increase at each stage of disease accumulation, and are projected to widen, particularly among the working-age population. Substantial action is needed now to address population health and to prepare health-care and social-care systems for coming decades. FUNDING: University of Liverpool and National Institute for Health and Care Research School for Public Health Research.

Prevalence of multimorbidity and polypharmacy among adults and older adults: a systematic review.

Multimorbidity (multiple conditions) and polypharmacy (multiple medications) are increasingly common, yet there is a need to better understand the prevalence of co-occurrence. In this systematic review, we examined the prevalence of multimorbidity and polypharmacy among adults (≥18 years) and older adults (≥65 years) in clinical and community settings. Six electronic databases were searched, and 87 studies were retained after two levels of screening. Most studies focused on adults 65 years and older and were done in population-based community settings. Although the operational definitions of multimorbidity and polypharmacy varied across studies, consistent cut-points (two or more conditions and five or more medications) were used across most studies. In older adult samples, the prevalence of multimorbidity ranged from 4·8% to 93·1%, while the prevalence of polypharmacy ranged from 2·6% to 86·6%. High heterogeneity between studies indicates the need for more consistent reporting of specific lists of conditions and medications used in operational definitions.

Inter- and intrapopulation differences in the association between physical multimorbidity and depressive symptoms.

BACKGROUND: The association between physical multimorbidity and depression differs by populations. However, no direct inter- or intrapopulation comparison of the association has been conducted. Thus, this study aims to estimate the association in China and the United States and reveal inter- and intrapopulation differences in the association. METHODS: Middle-aged and older adults from the China Health and Retirement Longitudinal Study and the Health and Retirement Study were included. Physical multimorbidity was defined as the simultaneous presence of two or more chronic physical conditions and depressive symptoms was measured by the Center for Epidemiologic Studies Depression Scale. Generalized estimating equation model and stratification multilevel method were the main statistical models. RESULTS: The presence of physical multimorbidity was associated with a higher risk of depression in both China (RR = 1.360 [95 % CI: 1.325-1.395]) and the US (RR = 1.613 [95 % CI: 1.529-1.701]). For individuals at a low risk of multimorbidity, multimorbidity was associated with 47.4 % (95 % CI: 1.377-1.579) and 71.1 % (95 % CI: 1.412-2.074) increases in the likelihood of depression in China and the US. The effect size was smaller for individuals at a moderate or high risk. However, the cross-national differences were greater for those with a high risk of multimorbidity. LIMITATIONS: The self-report measures, attribution bias. CONCLUSIONS: Compared to Chinese adults, the presence of physical multimorbidity led to an additional increase in depressive symptoms for American counterparts. The association was stronger for individuals at a low risk of multimorbidity, but cross-national differences were observed mostly among individuals at a high risk.

Multimorbidity analysis with low condition counts: a robust Bayesian approach for small but important subgroups.

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

Dose-response relationship between sleep duration and cardiovascular metabolic multimorbidity among older adults in China: A nationwide survey.

AIMS AND OBJECTIVES: The purpose of this study was to explore the relationship between the duration of sleep per day and cardiovascular metabolic multimorbidity (CMM) in older adults and to identify how many hours of sleep per day can lead to a lower risk of CMM in older adults. BACKGROUND: CMM are a common syndrome in the older adults. There may be an association between sleep duration and CMM in older adults, with both insomnia and sleep deprivation having an impact on the health of older adults. Therefore, it is important to explore the possibility that older adults who sleep for a few hours per day may have a lower prevalence of CMM. METHODS: The study included 9710 older adults. The sleep duration in this study was assessed by the question "How many hours of sleep do you currently get in a day? ". Older adults were defined as having CMM when they had two or more of the five categories of hypertension, diabetes, heart disease, stroke or cardiovascular disease, dyslipidemia. We used multivariate logistic regression analysis to explore the association among sleep duration and CMM. Restrictive cubic splines were used to examine the shape of the association among sleep duration and the CMM. The STROBE checklist was used for this cross-sectional study. RESULTS: The mean age was 84.78 ± 11.73 years, with 55.5 % being female. Of the total sample, 21.3 % were CMM. When all covariates were adjusted, there was dose-response relationship between sleep duration and CMM. The dose-response relationship between CMM and sleep duration showed that older adults had a lower risk of cardiovascular and metabolic multimorbidity when they slept 9 h and 10 h per day. CONCLUSION: With the increasing population of older adults, the number of older adults suffering from CMM continues to rise, and adequate sleep time can effectively prevent the occurrence of CMM. We should pay attention to the sleep problem of the older adults. RELEVANCE TO CLINICAL PRACTICE: This study provided information for healthcare providers to identify circumstances that increase cardiovascular metabolic multimorbidity and suggest the appropriate sleep duration per day to reduce the risk of disease in older adults. PATIENT OR PUBLIC CONTRIBUTION: Because of the public database data used in this study, all data were collected by survey agency personnel, so this section is not applicable to this study.

Prevalence of and factors associated with multimorbidity among adults in Kuwait.

BACKGROUND: This cross-sectional study aimed to assess the prevalence of morbidity i.e., one morbidity and multimorbidity (≥ 2 morbid conditions) among adults in Kuwait and to examine the sociodemographic and lifestyle factors associated with morbidity as a multinomial outcome in the study population. METHODS: The data were collected from January 26, 2021, to February 3, 2021, using an electronic questionnaire, which was distributed on social media platforms. The consent form was attached with the questionnaire and the participants were requested to sign the consent form before completing the questionnaire. The prevalences (%) of each morbidity and multimorbidity were computed. Multivariable polychotomous logistic regression analysis was used to evaluate the association between the demographic and lifestyle factors with morbidity as a multinomial outcome. RESULTS: Of 3572 respondents included, 89% were Kuwaiti, 78.3% females and 66% were 21- 40 years old. The prevalence of multimorbidity and one morbidity respectively was 27.4% and 29.7%. The prevalence of multimorbidity with two, three, four or five ill-health conditions were 14.3%, 7.4%, 3.5%, and 1.2%, respectively. A higher prevalence of multimorbidity was among respondents over 60 years of age (71%) and Kuwaiti nationals (28.9%). The final multivariable polychotomous logistic regression model revealed that age, sex, nativity, sedentary lifestyle, smoking, and alcohol drinking were significantly (p < 0.05) associated with multimorbidity. However, age and alcohol drinking were significant (p < 0.05) predictors of one morbidity. CONCLUSION: This study provides evidence that multimorbidity is more prevalent among the elderly, females, and Kuwaiti nationals. Sedentary behaviour, smoking and alcohol consumption were significantly and independently associated with multimorbidity. These findings highlight the burden of multimorbidity and should be considered in the development of future prevention programs.