Multimorbidity patterns in low-middle and high income regions: a multiregion latent class analysis using ATHLOS harmonised cohorts

The effect of multimorbidity patterns on physical and cognitive function in diabetes patients: a longitudinal cohort of middle-aged and older adults in China

Background

The prevalence of diabetes has increased rapidly, and comorbid chronic conditions are common among diabetes patients. However, little is known about the pattern of multimorbidity in diabetes patients and the effect on physical and cognitive function. This study aimed to assess the disease clusters and patterns of multimorbidity in diabetes patients using a novel latent class analysis (LCA) approach in middle-aged and older adults and explore the association between different clusters of multimorbidity in diabetes and the effect on physical and cognitive function.

Methods

This national observational study included 1,985 diabetes patients from the four waves of the China Health and Retirement Longitudinal Study (CHARLS) in 2011 to 2018. Thirteen chronic diseases were used in latent class analysis to identify the patterns of multimorbidity in diabetes, which span the cardiovascular, physical, psychological, and metabolic systems. Cognitive function is assessed via a structured questionnaire in three domains: memory, executive function, and orientation. We combined activities of daily living (ADL) with instrumental activities of daily living (IADL) to measure physical function. Linear mixed models and negative binomial regression models were used to analyze the association between patterns of multimorbidity in diabetes and the effect on cognitive function and disability, respectively.

Results

A sample of 1,985 diabetic patients was identified, of which 1,889 (95.2%) had multimorbidity; their average age was 60.6 years (standard deviation (SD) = 9.5), and 53.1% were women. Three clusters were identified: "cardio-metabolic" (n = 972, 51.5%), "mental-dyslipidemia-arthritis" (n = 584, 30.9%), and "multisystem morbidity" (n = 333, 17.6%). Compared with diabetes alone, the "multisystem morbidity" class had an increased association with global cognitive decline. All patterns of multimorbidity were associated with an increased risk of memory decline and disability; however, the "multisystem morbidity" group also had the strongest association and presented a higher ADL-IADL disability (ratio = 4.22, 95% CI = 2.52, 7.08) and decline in memory Z scores (β = -0.322, 95% CI = -0.550, -0.095, p = 0.0058).

Conclusion

Significant longitudinal associations between different patterns of multimorbidity in diabetes patients and memory decline and disability were observed in this study. Future studies are needed to understand the underlying mechanisms and common risk factors for multimorbidity in diabetes patients and to propose treatments that are more effective.

Chronic disease and multimorbidity in the Chinese older adults' population and their impact on daily living ability: a cross-sectional study of the Chinese Longitudinal Healthy Longevity Survey (CLHLS)

BACKGROUND

Owing to an increase in life expectancy, it is common for the older adults to suffer from chronic diseases that can result in disability and a low quality of life. This study aimed to explore the influence of chronic diseases and multimorbidities on activities of daily living (ADLs) and instrumental ADLs (IADLs) in an older Chinese population.

METHODS

Based on the Chinese Longitudinal Healthy Longevity Survey (2018), 9,155 older adults aged 65 years and above were included in the study. A self-administered questionnaire was used to collect information on demographic characteristics, chronic diseases, ADLs, and IADLs. The impact of factors affecting ADL and IADL impairment in older adults was analysed using binary logistic regression.

RESULTS

In total, 66.3% participants had chronic diseases. Hypertension, heart disease, arthritis, diabetes and cerebrovascular disease were among the top chronic diseases. Of these, 33.7% participants had multimorbidities. The most common combination of the two chronic diseases was hypertension and heart disease (11.2%), whereas the most common combination of the three chronic diseases was hypertension, heart disease, and diabetes (3.18%). After categorising the older adults into four age groups, dementia, visual impairment, and hearing impairment were found to be more prevalent with increasing age. The prevalence of hypertension, heart disease, cerebrovascular disease, gastrointestinal ulcers, arthritis and chronic nephritis gradually increased with age until the age of 75 years, peaked in the 75-84 years age group, and then showed a decreasing trend with age. Multimorbidity prevalence followed a similar pattern. Regression analysis indicated that the increase in age group and the number of chronic diseases independently correlated with impairments in ADL as well as IADL. Additionally, gender, physical activity, educational background, obesity, depressive symptoms, and falls also had an impact on ADLs or IADLs.

CONCLUSION

Chronic diseases and multimorbidities are common in older adults, and it is important to note that aging, multimorbidity, obesity, and unhealthy lifestyle choices may interfere with ADLs or IADLs in older adults. Therefore, it is imperative that primary healthcare providers pay special attention to older adults and improve screening for multimorbidity and follow-up needs.

Multimorbidity patterns and disability and healthcare use in Europe: do the associations change with the regional socioeconomic status?

Multimorbidity, the concurrence of several chronic conditions, is a rising concern that increases the years lived with disability and poses a burden on healthcare systems. Little is known on how it interacts with socioeconomic deprivation, previously associated with poor health-related outcomes. We aimed to characterize the association between multimorbidity and these outcomes and how this relationship may change with socioeconomic development of regions. 55,915 individuals interviewed in 2017 were drawn from the Survey of Health, Ageing and Retirement in Europe, a population-based study. A Latent Class Analysis was conducted to fit multimorbidity patterns based on 16 self-reported conditions. Physical limitation, quality-of-life and healthcare utilization outcomes were regressed on those patterns adjusting for additional covariates. Those analyses were then extended to assess whether such associations varied with the region socioeconomic status. We identified six different patterns, labelled according to their more predominant chronic conditions. After the "healthy" class, the "metabolic" and the "osteoarticular" classes had the best outcomes involving limitations and the lowest healthcare utilization. The "neuro-affective-ulcer" and the "several conditions" classes yielded the highest probabilities of physical limitation, whereas the "cardiovascular" group had the highest probability of hospitalization. The association of multimorbidity over physical limitations appeared to be stronger when living in a deprived region, especially for metabolic and osteoarticular conditions, whereas no major effect differences were found for healthcare use. Multimorbidity groups do differentiate in terms of limitation and healthcare utilization. Such differences are exacerbated with socioeconomic inequities between regions even within Europe.

Epidemiology and impact of chronic disease multimorbidity in India: a systematic review and meta-analysis

Objectives

This is the first systematic review and meta-analysis of the prevalence of multimorbidity, its risk factors including socioeconomic factors, and the consequences of multimorbidity on health systems and broader society in India.

Methods

A systematic review of both published and grey literature from five databases (Medline, Embase, EBSCO, Scopus, and ProQuest) was conducted including original studies documenting prevalence or patient outcomes associated with multimorbidity among adults in India. We excluded studies that did not explicitly mention multimorbidity. Three independent reviewers did primary screening based on titles and abstracts followed by full-text review for potential eligibility. The risk of bias was independently assessed by two reviewers following the Appraisal Tool for Cross-Sectional Studies. We presented both qualitative and quantitative (through meta-analysis) summaries of the evidence. The protocol for this study was prospectively registered with PROSPERO (CRD42021257281).

Results

The review identified 5442 articles out of which 35 articles were finally included in this study. Twenty-three studies were based on the primary data while 12 used secondary data. Eleven studies were conducted in hospital/primary care setting while 24 were community-based. The pooled prevalence of multimorbidity based on (n=19) studies included for meta-analysis was 20% (95% CI: 19% to 20%). The most frequent outcomes were increased healthcare utilization, reduced health-related quality of life, physical and mental functioning.

Conclusion

We identified a wide variance in the magnitude of multimorbidity across age groups and regions with most of the studies from eastern India. Nation-wide studies, studies on vulnerable populations and interventions are warranted.

Latent class analyses of multimorbidity and all-cause mortality: A prospective study in Chilean adults

Multimorbidity patterns can lead to differential risks for all-cause mortality. Within the Chilean context, research on morbidity and mortality predominantly emphasizes individual diseases or combinations thereof, rather than specific disease clusters. This study aimed to identify multimorbidity patterns, along with their associations with mortality, within a representative sample of the Chilean population. 3,701 participants aged ≥18 from the Chilean National Health Survey 2009-2010 were included in this prospective study. Multimorbidity patterns were identified from 16 chronic conditions and then classified using latent class analyses. All-cause mortality data were extracted from the Chilean Civil Registry. The association of classes with all-cause mortality was carried out using Cox proportional regression models, adjusting by sociodemographic and lifestyle variables. Three classes were identified: a) Class 1, the healthiest (72.1%); b) Class 2, the depression/cardiovascular disease/cancer class (17.5%); and c) Class 3, hypertension/chronic kidney disease class (10.4%). Classes 2 and 3 showed higher mortality risk than the healthiest class. After adjusting, Class 2 showed 45% higher mortality risk, and Class 3 98% higher mortality risk, compared with the healthiest class. Hypertension appeared to be a critical underlying factor of all-cause morbidity. Particular combinations of chronic diseases have a higher excess risk of mortality than others.

Multimorbidity patterns in the German general population aged 40 years and over

AIM

The aim of this study was to identify and describe multimorbidity patterns among middle-aged and older community-dwelling individuals in Germany. Moreover, we aimed to determine potential gender differences in multimorbidity patterns.

METHODS

We analysed data from the most recent (sixth) wave (2017) of the large nationally representative German Ageing Survey (DEAS). Altogether n = 6,554 individuals participated, mean age was 62.0 (ranging from 43 to 92 years). Latent Class Analysis was performed to identify multimorbidity patterns, based on 13 chronic conditions and diseases. Multimorbidity was defined as the presence of at least two chronic conditions.

RESULTS

Altogether, 53.3% of individuals were multimorbid. We identified and clinically described five multimorbidity patterns: the relatively healthy class (45.1%), the high morbidity class (10.8%), the arthrosis/inflammatory/mental illnesses class (20.6%), the hypertension-metabolic illness class (21.7%), and the cardiovascular/cancer class (1.7%). Our analysis revealed that women compared to men have higher relative risk (IRR = 1.61, 95% CI 1.25-2.06) of being in the arthrosis/inflammatory/mental illnesses class, compared to the relatively healthy class. Furthermore, we found that, depending on which multimorbidity pattern individuals belong to, they differ greatly in terms of socio-demographic factors, health behaviour, and lifestyle factors.

CONCLUSIONS

We showed that the many chronic diseases cluster in a non-random way. Five clinically meaningful multimorbidity patterns were identified. Gender differences were apparent only in one class, namely in the arthrosis/inflammatory/mental illnesses class.

Healthy immigrants, unhealthy ageing? Analysis of health decline among older migrants and natives across European countries

The probability of having multiple chronic conditions simultaneously, or multimorbidity, tends to increase with age. Immigrants face a particularly high risk of unhealthy ageing. This study investigates the immigrant-native disparities in the speed of age-related chronic disease accumulation, focusing on the number of chronic health conditions; and considers the heterogeneity of this trajectory within immigrant populations by origin and receiving country. We use data from the Survey of Health, Ageing and Retirement in Europe from 2004 to 2020 on adults aged 50 to 79 from 28 European countries and employ both cross-sectional and longitudinal analyses. For longitudinal panel analyses, we use fixed-effects regression models to account for the unobserved heterogeneity related to individual characteristics including migration background. Our results indicate that immigrants report a higher number of chronic conditions at all ages relative to their native-born peers, but also that the immigrant-native differential in the number of chronic conditions decreases from age 65 onwards. When considering differences by origin country, we find that the speed of chronic disease accumulation is slower among immigrants from the Americas and the Asia and Oceania country groups than it is among natives. When looking at differences by receiving country group, we observe that the speed of accumulating chronic diseases is slower among immigrants in Eastern Europe than among natives, particularly at older ages. Our findings suggest that age-related trajectories of health vary substantially among immigrant populations by origin and destination country, which underscore that individual migration histories play a persistent role in shaping the health of ageing immigrant populations throughout the life course.

Topic modeling identifies novel genetic loci associated with multimorbidities in UK Biobank

Many diseases show patterns of co-occurrence, possibly driven by systemic dysregulation of underlying processes affecting multiple traits. We have developed a method (treeLFA) for identifying such multimorbidities from routine health-care data, which combines topic modeling with an informative prior derived from medical ontology. We apply treeLFA to UK Biobank data and identify a variety of topics representing multimorbidity clusters, including a healthy topic. We find that loci identified using topic weights as traits in a genome-wide association study (GWAS) analysis, which we validated with a range of approaches, only partially overlap with loci from GWASs on constituent single diseases. We also show that treeLFA improves upon existing methods like latent Dirichlet allocation in various ways. Overall, our findings indicate that topic models can characterize multimorbidity patterns and that genetic analysis of these patterns can provide insight into the etiology of complex traits that cannot be determined from the analysis of constituent traits alone.

Sociodemographic and lifestyle determinants of multimorbidity among community-dwelling older adults: findings from 346,760 SHARE participants

BACKGROUND

This study aimed to investigate the prevalence of multimorbidity and its associated factors among the older population in China to propose policy recommendations for the management of chronic diseases in older adults.

METHODS

This study was conducted based on the 2021 Shenzhen Healthy Ageing Research (SHARE), and involved analysis of 346,760 participants aged 65 or older. Multimorbidity is defined as the presence of two or more clinically diagnosed or non self-reported chronic diseases among the eight chronic diseases surveyed in an individual. The Logistic analysis was adopted to explore the potential associated factors of multimorbidity.

RESULTS

The prevalences of obesity, hypertension, diabetes, anemia, chronic kidney disease, hyperuricemia, dyslipidemia and fatty liver disease were 10.41%, 62.09%, 24.21%, 12.78%, 6.14%, 20.52%, 44.32%, and 33.25%, respectively. The prevalence of multimorbidity was 63.46%. The mean count of chronic diseases per participant was 2.14. Logistic regression indicated that gender, age, marriage status, lifestyle (smoking status, drinking status, and physical activity), and socioeconomic status (household registration, education level, payment method of medical expenses) were the common predictors of multimorbidity for older adults, among which, being women, married, or engaged in physical activity was found to be a relative determinant as a protective factor for multimorbidity after the other covariates were controlled.

CONCLUSION

Multimorbidity is prevalent among older adults in Chinese. Guideline development, clinical management,and public intervention should target a group of diseases instead of a single condition.

Social determinants of multimorbidity patterns: A systematic review

Social determinants of multimorbidity are poorly understood in clinical practice. This review aims to characterize the different multimorbidity patterns described in the literature while identifying the social and behavioral determinants that may affect their emergence and subsequent evolution. We searched PubMed, Embase, Scopus, Web of Science, Ovid MEDLINE, CINAHL Complete, PsycINFO and Google Scholar. In total, 97 studies were chosen from the 48,044 identified. Cardiometabolic, musculoskeletal, mental, and respiratory patterns were the most prevalent. Cardiometabolic multimorbidity profiles were common among men with low socioeconomic status, while musculoskeletal, mental and complex patterns were found to be more prevalent among women. Alcohol consumption and smoking increased the risk of multimorbidity, especially in men. While the association of multimorbidity with lower socioeconomic status is evident, patterns of mild multimorbidity, mental and respiratory related to middle and high socioeconomic status are also observed. The findings of the present review point to the need for further studies addressing the impact of multimorbidity and its social determinants in population groups where this problem remains invisible (e.g., women, children, adolescents and young adults, ethnic groups, disabled population, older people living alone and/or with few social relations), as well as further work with more heterogeneous samples (i.e., not only focusing on older people) and using more robust methodologies for better classification and subsequent understanding of multimorbidity patterns. Besides, more studies focusing on the social determinants of multimorbidity and its inequalities are urgently needed in low- and middle-income countries, where this problem is currently understudied.

Spatial patterns in sociodemographic factors explain to a large extent the prevalence of hypertension and diabetes in Aragon (Spain)

Introduction

The global burden of multi-morbidity has become a major public health challenge due to the multi stakeholder action required to its prevention and control. The Social Determinants of Health approach is the basis for the establishment of health as a cross-cutting element of public policies toward enhanced and more efficient decision making for prevention and management.

Objective

To identify spatially varying relationships between the multi-morbidity of hypertension and diabetes and the sociodemographic settings (2015-2019) in Aragon (a mediterranean region of Northeastern Spain) from an ecological perspective.

Materials and methods

First, we compiled data on the prevalence of hypertension, diabetes, and sociodemographic variables to build a spatial geodatabase. Then, a Principal Component Analysis (PCA) was performed to derive regression variables, i.e., aggregating prevalence rates into a multi-morbidity component (stratified by sex) and sociodemographic covariate into a reduced but meaningful number of factors. Finally, we applied Geographically Weighted Regression (GWR) and cartographic design techniques to investigate the spatial variability of the relationships between multi-morbidity and sociodemographic variables.

Results

The GWR models revealed spatial explicit relationships with large heterogeneity. The sociodemographic environment participates in the explanation of the spatial behavior of multi-morbidity, reaching maximum local explained variance (R2) of 0.76 in men and 0.91 in women. The spatial gradient in the strength of the observed relationships was sharper in models addressing men's prevalence, while women's models attained more consistent and higher explanatory performance.

Conclusion

Modeling the prevalence of chronic diseases using GWR enables to identify specific areas in which the sociodemographic environment is explicitly manifested as a driving factor of multi-morbidity. This is step forward in supporting decision making as it highlights multi-scale contexts of vulnerability, hence allowing specific action suitable to the setting to be taken.

Multimorbidity patterns in South Africa: A latent class analysis

Introduction

South Africa has the largest burden of HIV worldwide and has a growing burden of non-communicable diseases; the combination of which may lead to diseases clustering in ways that are not seen in other regions. This study sought to identify common disease classes and sociodemographic and lifestyle factors associated with each disease class.

Methods

Data were analyzed from the South African Demographic and Health Survey 2016. A latent class analysis (LCA) was conducted using nine disease conditions. Sociodemographic and behavioral factors associated with each disease cluster were explored. All analysis was conducted in Stata 15 and the LCA Stata plugin was used to conduct the latent class and regression analysis.

Results

Multimorbid participants were included (n = 2 368). Four disease classes were identified: (1) HIV, Hypertension and Anemia (comprising 39.4% of the multimorbid population), (2) Anemia and Hypertension (23.7%), (3) Cardiovascular-related (19.9%) and (4) Diabetes and Hypertension (17.0%). Age, sex, and lifestyle risk factors were associated with class membership. In terms of age, with older adults were less likely to belong to the first class (HIV, Hypertension and Anemia). Males were more likely to belong to Class 2 (Anemia and Hypertension) and Class 4 (Diabetes and Hypertension). In terms of alcohol consumption, those that consumed alcohol were less likely to belong to Class 4 (Diabetes and Hypertension). Current smokers were more likely to belong to Class 3 (Cardiovascular-related). People with a higher body mass index tended to belong to Class 3 (Cardiovascular-related) or the Class 4 (Diabetes and Hypertension).

Conclusion

This study affirmed that integrated care is urgently needed, evidenced by the largest disease class being an overlap of chronic infectious diseases and non-communicable diseases. This study also highlighted the need for hypertension to be addressed. Tackling the risk factors associated with hypertension could avert an epidemic of multimorbidity.

Impacts of social restrictions on mental health and health behaviours of individuals with multimorbidity during Covid-19 pandemic

Background

Social restrictions and their possible impact on lifestyle make people with multimorbidity (≥2 co-existing chronic conditions) more vulnerable to poor perceived mental health and health behaviours modifications during the COVID-19 pandemic.

Objective

To understand the mental health status and health behaviour modifications among individuals with multimorbidity during different levels of COVID-19 social restrictions.

Methods

Longitudinal multinational cohort study consisting of two online questionnaires with its first wave taken place while social restrictions were imposed (May 2020), and its second wave with less social restrictions in place (November 2020). Including 559 participants (wave 1) and 147 participants from wave 1 (wave 2) with an average age of 34.30±12.35 and 36.21±13.07 years old. Mostly females living in Canada, France, India and Lebanon.

Results

The prevalence of multimorbidity was 27.68% (wave 1) and 35.37% (wave 2). While social restrictions were imposed, people with multimorbidity were 2 to 3 times more likely to experience psychological distress, depressive symptoms, increased stress or isolation than those without multimorbidity. Health behaviours were also modified during this period with people with multimorbidity being more likely to reduce their physical activity and increased their fruit and vegetable consumption. In wave 2, regardless of multimorbidity status, sexual desire continuously decreased while stress and psychological distress increased.

Conclusion

Mental health and health behaviours modifications occurred while social restrictions were imposed and people with multimorbidity were more severely impacted than those without multimorbidity, indicating a need for a more adapted approach of care during socially restrictive periods for this population.

Evaluation of potentially inappropriate medication in older patients with cardiovascular diseases-STOPP/START-based study

Objective

This study aims to evaluate the use of STOPP/START criteria in the identification of Potentially inappropriate medication and potential prescribing omissions in older patients with cardiovascular diseases in Bulgaria. Excessive morbidity and mortality has been linked to drug-related problems and increased use of healthcare services and is an understudied problem for Bulgaria.

Materials and methods

A prospective, questionnaire-based study was conducted among 543 older patients across 25 pharmacies in Bulgaria. Socio-demographic characteristic, disease profile, symptoms, and medication data were collected. The questionnaire was developed for the purposes of the EUROAGEISM project. Out of all 543 patients, only those with documented cardio-vascular diseases were extracted and the medication profile per patient was evaluated for Potentially inappropriate medication (PIMs) and potentially prescribing omissions (PPOs) using STOPP/START criteria version 2. In addition, several risks for potentially inappropriate prescribing (PIPs), PPOs and PIMs were calculated with the focus being on the Odds and Risks to develop a PIP.

Results

Four hundred and twenty eight from 531 patients with known therapy for cardiovascular diseases (CVDs) were included in the analysis of PIP (40.52% aged 65-69 years, 61.88% female, 64% had up to 6 comorbidities, and 21.72% presenting with polypharmacy). A total of 71 PIMs in 64 patients with polypharmacy were identified during applying STOPP criteria. 56% of patients taking above five medicines daily had PIMs. The majority of PIMs (31%) were related to CVDs treatment, followed by PIMs in the treatment of endocrine diseases (22.54%), duplication of medicines (8.46%) and prolonged treatment with benzodiazepines (8.46%). Forty four PPOs were identified with START criteria. 22.72% were related to lack of proton pump inhibitors (PPI) in the presence of gastroesophageal disorders, and the same percentage was for lack of Calcium-vitamin D supplementation in osteoporosis. Applying the methodology of risks calculation the sample risk for PPO was 2.1% and for PIM 3.4%. At sample level the relative risk for PPO was 62% out of the risk for PIM and at population level varied between 42.8 and 89.8% and it is statistically significant. The number needed to treat for the event to happen is 77.5, meaning that at every 78 prescriptions there is a chance to appear PIP.

Conclusion

Application of methodologies for detection of potentially inappropriate prescribing is not part of routine clinical practice in Bulgaria. Our study demonstrates a high percentage of potentially inappropriate medication among older patients with polypharmacy. Along with the aging population in Bulgaria, economic burden of polypharmacy and the prevalence of cardiovascular diseases, it is especially important to address potentially inappropriate medication use in cardiovascular patients. There is a considerable necessity for implementation of measures for early detection of potentially inappropriate medication and potentially prescribing omission as a part of de-prescribing strategies in older patients.

Multimorbidity Patterns and Their Association with Social Determinants, Mental and Physical Health during the COVID-19 Pandemic

BACKGROUND

The challenge posed by multimorbidity makes it necessary to look at new forms of prevention, a fact that has become heightened in the context of the pandemic. We designed a questionnaire to detect multimorbidity patterns in people over 50 and to associate these patterns with mental and physical health, COVID-19, and possible social inequalities.

METHODS

This was an observational study conducted through a telephone interview. The sample size was 1592 individuals with multimorbidity. We use Latent Class Analysis to detect patterns and SF-12 scale to measure mental and physical quality-of-life health. We introduced the two dimensions of health and other social determinants in a multinomial regression model.

RESULTS

We obtained a model with five patterns (entropy = 0.727): 'Relative Healthy', 'Cardiometabolic', 'Musculoskeletal', 'Musculoskeletal and Mental', and 'Complex Multimorbidity'. We found some differences in mental and physical health among patterns and COVID-19 diagnoses, and some social determinants were significant in the multinomial regression.

CONCLUSIONS

We identified that prevention requires the location of certain inequalities associated with the multimorbidity patterns and how physical and mental health have been affected not only by the patterns but also by COVID-19. These findings may be critical in future interventions by health services and governments.

Patterns and Determinants of Multimorbidity in Older Adults: Study in Health-Ecological Perspective

(1) Background: Multimorbidity has become one of the key issues in the public health sector. This study aims to explore the patterns and health-ecological factors of multimorbidity in China to propose policy recommendations for the management of chronic diseases in the elderly. (2) Methods: A multi-stage random sampling method was used to conduct a questionnaire survey on 3637 older adults aged 60 and older in Shanxi, China. Association rule mining analysis (ARM) and network analysis were applied to analyze the patterns of multimorbidity. The health-ecological model was adopted to explore the potential associated factors of multimorbidity in a multidimensional perspective. A hierarchical multiple logistic model was employed to investigate the association strengths reflected by adjusted odds ratios and 95% confidence. (3) Results: Multimorbidity occurred in 20.95% of the respondents. The graph of network analysis showed that there were 6 combinations of chronic diseases with strong association strengths and 14 with moderate association strengths. The results of the ARM were similar to the network analysis; six dyadic chronic disease combinations and six triadic ones were obtained. Hierarchical multiple logistic regression indicated that innate personal traits (age, history of genetics, and body mass index), behavioral lifestyle (physical activity levels and medication adherence), interpersonal network (marital status), and socioeconomic status (educational level) were the common predictors of multimorbidity for older adults, among which, having no family history was found to be a relative determinant as a protective factor for multimorbidity after controlling the other covariates. (4) Conclusions: multimorbidity was prevalent in older adults and most disease combinations are associated with hypertension, followed by diabetes. This shows that diabetes and hypertension have a high prevalence among older adults and have a wide range of associations with other chronic diseases. Exploring the patterns and associated factors of multimorbidity will help the country prevent complications and avoid the unnecessary use of the health service, adopting an integrated approach to managing multimorbidity rather than an individual disease-specific approach and implementing different strategies according to the location of residence.

Association of multimorbidity patterns with incident disability and recovery of independence among middle-aged and older adults

OBJECTIVE

to identify multimorbidity patterns among middle-aged and older adults in China and examine how these patterns are associated with incident disability and recovery of independence.

METHODS

data were from The China Health and Retirement Longitudinal Study. We included 14,613 persons aged ≥45 years. Latent class analysis (LCA) was conducted to identify multimorbidity patterns with clinical meaningfulness. Multinomial logistic models were used to determine the adjusted association between multimorbidity patterns and incident disability and recovery of independence.

RESULTS

we identified four multimorbidity patterns: 'low morbidity' (67.91% of the sample), 'pulmonary-digestive-rheumatic' (17.28%), 'cardiovascular-metabolic-neuro' (10.77%) and 'high morbidity' (4.04%). Compared to the 'low morbidity' group, 'high morbidity' (OR = 2.63, 95% CI = 1.97-3.51), 'pulmonary-digestive-rheumatic' (OR = 1.89, 95% CI = 1.63-2.21) and 'cardiovascular-metabolic-neuro' pattern (OR = 1.61, 95% CI = 1.31-1.97) had higher odds of incident disability in adjusted multinomial logistic models. The 'cardiovascular-metabolic-neuro' (OR = 0.60, 95% CI = 0.44-0.81), 'high morbidity' (OR = 0.68, 95% CI = 0.47-0.98) and 'pulmonary-digestive-rheumatic' group (OR = 0.75, 95% CI = 0.60-0.95) had lower odds of recovery from disability than the 'low morbidity' group. Among people without disability, the 'cardiovascular-endocrine-neuro' pattern was associated with the highest 2-year mortality (OR = 2.42, 95% CI = 1.56-3.72).

CONCLUSIONS

multimorbidity is complex and heterogeneous, but our study demonstrates that clinically meaningful patterns can be obtained using LCA. We highlight four multimorbidity patterns with differential effects on incident disability and recovery from disability. These studies suggest that targeted prevention and treatment approaches are needed for people with multimorbidity.

Multimorbidity

Multimorbidity (two or more coexisting conditions in an individual) is a growing global challenge with substantial effects on individuals, carers and society. Multimorbidity occurs a decade earlier in socioeconomically deprived communities and is associated with premature death, poorer function and quality of life and increased health-care utilization. Mechanisms underlying the development of multimorbidity are complex, interrelated and multilevel, but are related to ageing and underlying biological mechanisms and broader determinants of health such as socioeconomic deprivation. Little is known about prevention of multimorbidity, but focusing on psychosocial and behavioural factors, particularly population level interventions and structural changes, is likely to be beneficial. Most clinical practice guidelines and health-care training and delivery focus on single diseases, leading to care that is sometimes inadequate and potentially harmful. Multimorbidity requires person-centred care, prioritizing what matters most to the individual and the individual's carers, ensuring care that is effectively coordinated and minimally disruptive, and aligns with the patient's values. Interventions are likely to be complex and multifaceted. Although an increasing number of studies have examined multimorbidity interventions, there is still limited evidence to support any approach. Greater investment in multimorbidity research and training along with reconfiguration of health care supporting the management of multimorbidity is urgently needed.

A longitudinal analysis of PM2.5 exposure and multimorbidity clusters and accumulation among adults aged 45-85 in China

While previous studies have emphasised the role of individual factors in understanding multimorbidity disparities, few have investigated contextual factors such as air pollution (AP). We first use cross-sectional latent class analysis (LCA) to assess the associations between PM2.5 exposure and multimorbidity disease clusters, and then estimate the associations between PM2.5 exposure and the development of multimorbidity longitudinally using growth curve modelling (GCM) among adults aged 45-85 in China. The results of LCA modelling suggest four latent classes representing three multimorbidity patterns (respiratory, musculoskeletal, cardio-metabolic) and one healthy pattern. The analysis shows that a 1 μg/m3 increase in cumulative exposure to PM2.5 is associated with a higher likelihood of belonging to respiratory, musculoskeletal or cardio-metabolic clusters: 2.4% (95% CI: 1.02, 1.03), 1.5% (95% CI: 1.01, 1.02) and 3.3% (95% CI: 1.03, 1.04), respectively. The GCM models show that there is a u-shaped association between PM2.5 exposure and multimorbidity, indicating that both lower and higher PM2.5 exposure is associated with increased multimorbidity levels. Higher multimorbidity in areas of low AP is explained by clustering of musculoskeletal diseases, whereas higher AP is associated with cardio-metabolic disease clusters. The study shows how multimorbidity clusters vary contextually and that PM2.5 exposure is more detrimental to health among older adults.

Nomogram for predicting the 12-year risk of ADL disability among older adults

OBJECTIVES

Previous studies have identified plenty of risk factors for activities of daily living (ADL). However, there are no reliable and widely available prediction models for ADL disability up to now. This study aimed to develop and validate a nomogram for predicting the 12-year risk of ADL disability in older adults.

METHODS

Data from 4,809 participants in the English Longitudinal Study of Ageing (ELSA) and 18,620 participants in the Survey of Health, Ageing and Retirement in Europe (SHARE) were used as training set and validation set, respectively. We used the least absolute shrinkage and selection operator (LASSO) and Cox regression to screen the predictors and develop the nomogram. The P value, concordance index (C-index), integrated area under the ROC (receiver operating characteristic) curve (AUC) and calibration curves were used to validate the nomogram.

RESULTS

During 12 years, 30.0% (n = 1,441) participants developed ADL disability in the training set, while the corresponding percentages were 18.5% in the validation set (n = 3,445). After screening, 13 variables were contained in the final prediction model. In ADL nomogram, the C-index and AUC were 0.744 ± 0.013 and 0.793 in internal valid ation, respectively, while in external validation, the C-index and AUC were 0.755 ± 0.009 and 0.796.

CONCLUSIONS

This study developed and validated a nomogram that predicts functional disability. The application of the predictive model could have important implications for patient prognosis and health care.

Studying trajectories of multimorbidity: a systematic scoping review of longitudinal approaches and evidence

OBJECTIVES

Multimorbidity-the co-occurrence of at least two chronic diseases in an individual-is an important public health challenge in ageing societies. The vast majority of multimorbidity research takes a cross-sectional approach, but longitudinal approaches to understanding multimorbidity are an emerging research area, being encouraged by multiple funders. To support development in this research area, the aim of this study is to scope the methodological approaches and substantive findings of studies that have investigated longitudinal multimorbidity trajectories.

DESIGN

We conducted a systematic search for relevant studies in four online databases (Medline, Scopus, Web of Science and Embase) in May 2020 using predefined search terms and inclusion and exclusion criteria. The search was complemented by searching reference lists of relevant papers. From the selected studies, we systematically extracted data on study methodology and findings and summarised them in a narrative synthesis.

RESULTS

We identified 35 studies investigating multimorbidity longitudinally, all published in the last decade, and predominantly in high-income countries from the Global North. Longitudinal approaches employed included constructing change variables, multilevel regression analysis (eg, growth curve modelling), longitudinal group-based methodologies (eg, latent class modelling), analysing disease transitions and visualisation techniques. Commonly identified risk factors for multimorbidity onset and progression were older age, higher socioeconomic and area-level deprivation, overweight and poorer health behaviours.

CONCLUSION

The nascent research area employs a diverse range of longitudinal approaches that characterise accumulation and disease combinations and to a lesser extent disease sequencing and progression. Gaps include understanding the long-term, life course determinants of different multimorbidity trajectories, and doing so across diverse populations, including those from low-income and middle-income countries. This can provide a detailed picture of morbidity development, with important implications from a clinical and intervention perspective.

Multimorbidity patterns and their related characteristics in European older adults: A longitudinal perspective

BACKGROUND

The concurrence of several chronic conditions is a rising concern that poses a serious burden on ageing populations. Analysing how these conditions appear together and how they change through time may provide useful information to design successful multimorbidity-management programs.

OBJECTIVE

To identify multimorbidity patterns and their related characteristics from a longitudinal perspective.

SUBJECTS

25,931 older adults aged 50+ drawn from the Survey of Health, Ageing and Retirement in Europe (SHARE), a population-based longitudinal European study.

METHODS

A sex-stratified Latent Transition Analysis was conducted to fit latent classes based on 15 self-reported chronic conditions across three time points. Health-related and socioeconomic variables were assessed as covariates of those patterns.

RESULTS

We identified 4 time-constant latent classes for each sex. A "severely impaired" class (with a weighted prevalence percentage of 7.24% for females and 3.30% for males at the first time point), a "metabolic" class (26.15% and 23.82%) and a "healthy" class (50.92% and 54.32%). The fourth class was named "osteoarticular" for females (15.70%) and "articular-COPD-ulcer" for males (18.56%). Age, smoke, material deprivation and a high body mass index were associated with worse health patterns, whereas education, being employed and physical activity were related to less multimorbid classes. Few class changes were detected when modelling transitions.

CONCLUSIONS

We reported information of multimorbidity classes and their characteristics that may help to develop targeted health strategies. Within a time window of four years, the identified latent classes were consistent between time points.

The association between clusters of chronic conditions and psychological well-being in younger and older people-A cross-sectional, population-based study from the Lolland-Falster Health Study, Denmark

Aim:

To investigate the association between clusters of conditions and psychological well-being across age groups.

Method:

This cross-sectional study used data collected in the Danish population-based Lolland-Falster Health Study. We included adults over the age of 18 years. Self-reported chronic conditions were divided into 10 groups of conditions. The primary outcome was psychological well-being (the WHO-5 Well-Being Index). Factor analysis constructed the clusters of conditions, and regression analysis investigated the association between clusters and psychological well-being.

Results:

Of 10,781 participants, 31.4% were between 18 and 49 years, 35.7% were between 50 and 64 years and 32.9% were above ≥65 years. 35.2% had conditions represented in 1 and 32.9% in at least 2 of 10 condition groups. Across age groups, living with one or more chronic conditions was associated with poorer psychological well-being. Two chronic condition patterns were identified; one comprised cardiovascular, endocrine, kidney, musculoskeletal and cancer conditions, the second mental, lung, neurological, gastrointestinal and sensory conditions. Both patterns were associated with poorer psychological well-being (Pattern 1: −4.5 (95% CI: −5.3 to −3.7), Pattern 2: −9.1 (95% CI −13.8 to −8.2). For pattern 2, participants ≥65 years had poorer psychological well-being compared to younger (−12.6 (95% CI −14.2 to −11.0) vs −6.6 (95% CI: −7.8 to −5.4) for 18–49 years and −8.7 (95% CI: −10.1 to −7.3) for 50–64 years, interaction: p ≤ 0.001)

Conclusion:

Living with one or more chronic conditions is associated with poorer psychological well-being. Findings point toward a greater focus on supporting psychological well-being in older adults with both mental and somatic conditions.

Diabetes multimorbidity combinations and disability in the Mexican Health and Aging Study, 2012-2015

PURPOSE

The aim of this study was to investigate the relationship between specific combinations of chronic conditions and disability in Mexican older adults with diabetes.

METHODS

This was a prospective cohort study of Mexican adults (n = 2558) with diabetes and aged 51 or older that used data from the 2012 and 2015 waves of the Mexican Health and Aging Study. The main outcome was an index that measured ability to perform activities of daily living and instrumental activities of daily living. The main independent variables were diabetes multimorbidity combinations, defined as diabetes and at least one other chronic condition. The authors calculated the prevalence of each multimorbidity combination present in the sample in 2012 and used negative binomial regression models to estimate the association of the most prevalent of these combinations with disability incidence in 2015.

RESULTS

The three most prevalent combinations were: 1) diabetes-hypertension (n = 637, 31.9%) 2) diabetes-hypertension-depression (n = 388, 19.4%) and 3) diabetes-depression (n = 211, 10.6%). In fully adjusted models comparing participants with specific multimorbidity combinations to participants with diabetes alone, the combinations that had an increased association with disability were diabetes-hypertension-depression, diabetes-depression and diabetes-hypertension-arthritis-depression. In nested models, the addition of arthritis to combinations including depression increased this association.

CONCLUSIONS

Consistent with prior studies, multimorbidity combinations including depression were associated with increased risk of disability. However, the effect size of this relationship was lower than what had been previously been reported internationally. This highlights the need for globally oriented multimorbidity research.