A Systematic Review of the Patterns of Associative Multimorbidity in Asia

Association between single and multiple cardiometabolic diseases and all-cause mortality among Chinese older adults: A prospective, nationwide cohort study

BACKGROUND AND AIM

Cardiometabolic diseases (CMDs) are leading causes of death and disability, but little is known about the additive mortality effects of multiple CMDs. This study aimed to examine the association between single and multiple CMDs and all-cause mortality among older Chinese population.

METHODS AND RESULTS

Using the Chinese Longitudinal Healthy Longevity Survey (CLHLS) database, we analyzed data from 2008 to 2018 to assess the relationship between CMDs and mortality. Cox regression models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for single and multiple CMDs. At baseline, 11,351 participants (56.9% female) aged 60 years or older were included. 11.91% of participants had a single CMD, 1.51% had two CMDs, and 0.22% had three CMDs. Over a decade follow-up, 8992 deaths (79.2%) were recorded. A dose-response relationship was observed, with the mortality risk increasing by 17% for each additional disease. The fully-adjusted HRs for all-cause mortality were 1.16, 1.36, and 2.03 for one, two, and three CMDs, respectively. Larger effects of single and multiple CMDs were observed in the male group (P = 0.015) and the younger senior group (P < 0.001).

CONCLUSIONS

This large-scale study found that CMDs multiply mortality risks, especially in younger seniors and males. The risk is highest when heart disease and stroke coexist, and diabetes further increases it. Public health efforts should prioritize evidence-based management and prevention of CMDs.

A call for caution when using network methods to study multimorbidity: an illustration using data from the Canadian Longitudinal Study on Aging

OBJECTIVES

To examine the impact of two key choices when conducting a network analysis (clustering methods and measure of association) on the number and type of multimorbidity clusters.

STUDY DESIGN AND SETTING

Using cross-sectional self-reported data on 24 diseases from 30,097 community-living adults aged 45-85 from the Canadian Longitudinal Study on Aging, we conducted network analyses using 5 clustering methods and 11 association measures commonly used in multimorbidity studies. We compared the similarity among clusters using the adjusted Rand index (ARI); an ARI of 0 is equivalent to the diseases being randomly assigned to clusters, and 1 indicates perfect agreement. We compared the network analysis results to disease clusters independently identified by two clinicians.

RESULTS

Results differed greatly across combinations of association measures and cluster algorithms. The number of clusters identified ranged from 1 to 24, with a low similarity of conditions within clusters. Compared to clinician-derived clusters, ARIs ranged from -0.02 to 0.24, indicating little similarity.

CONCLUSION

These analyses demonstrate the need for a systematic evaluation of the performance of network analysis methods on binary clustered data like diseases. Moreover, in individual older adults, diseases may not cluster predictably, highlighting the need for a personalized approach to their care.

Suicide risk of chronic diseases and comorbidities: A Korean case-control study

OBJECTIVES

Chronic diseases including mental disorders have been associated with suicide. This study broadens the approach by incorporating a comprehensive list of chronic diseases and a context of comorbidities and explored their associations with suicide.

METHODS

Data-linkage between death registry and Korean National Health Insurance data was conducted. Suicide cases (n = 64,099) between 2009 and 2013 were 1:4 matched for gender and age to an alive control (n = 256,396). A total of 92 individual diseases of 9 broad categories were identified from insurance claims data. Conditional logistic regression was applied to assess the associations, adjusting for mental and behavioral disorders and socioeconomic status.

RESULTS

Suicide cases frequently experienced chronic diseases (90.0 %) and comorbidities (74.6 %). Chronic diseases greatly increased suicide risk and, among these, mental and behavioral disorders showed the highest suicide risk (OR = 7.53, 95 % CI = 7.32-7.74) followed by cardiovascular (OR = 3.36, 95 % CI = 3.26-3.47). For individual diseases, gastritis and duodenitis were most prevalent (68.1 %) among suicide cases but depressive disorder showed the highest risk (OR = 4.95, 95 % CI = 4.79-5.12). Suicide risk was strong in comorbid status sometimes comparable to odds for mental and behavioral disorder alone (e.g., OR for cardiovascular and eye vision-related diseases = 4.01, 95 % CI = 3.86-4.17).

LIMITATIONS

Differentiation of comorbidity was limited to pairs between major disease categories, neglecting the heterogeneity within categories.

CONCLUSION

Chronic diseases, in particular comorbidity, showed strong associations with suicide. This suggests that those with comorbidities feel that they are pushed to the extreme line, supporting comprehensive interventions for them to address wider reasons including psychological and social problems, besides medical problems.

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.

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.

Multimorbidity Patterns and Associations with Gait, Balance and Lower Extremity Muscle Function in the Elderly: A Cross-Sectional Study in Northwest China

Purpose

Fall is a common geriatric syndrome leading to various adverse outcomes in the elderly. Gait and balance disorders and decreased lower extremity muscle function are the major intrinsic risk factors of falls, and studies suggested that they were closely related to the underlying chronic conditions. This study aimed to explore the patterns of multimorbidity and determine the associations of these multimorbidity patterns with gait, balance and lower extremity muscle function.

Patients and Methods

A cross-sectional survey of 4803 participants aged ≥60 years in Shaanxi Province, China was conducted and the self-reported chronic conditions were investigated. The 6-m walk test, timed-up-and-go test (TUG) and 5-sit-to-stand test (5-STS) were conducted to evaluate gait, balance, and lower extremity muscle function respectively. Latent class analysis was used to explore patterns of multimorbidity, and multivariate regression analysis was used to determine the associations of multimorbidity patterns with gait, balance, and lower extremity muscle function.

Results

Five multimorbidity patterns were identified: Degenerative Disease Class, Cardio-metabolic Class, Stroke-Respiratory-Depression Class, Gastrointestinal Class, and Very sick Class, and they were differently associated with gait and balance disorders and decreased lower extremity muscle function. In particular, the multimorbidity patterns of Degenerative Disease Class and Stroke-Respiratory-Depression Class were closely associated with all the three risk factors of falls.

Conclusion

There are significant differences in the impact of different multimorbidity patterns on the major intrinsic risk factors of falls in the elderly population, and appropriate multimorbidity patterns are closely related to the prediction of falls and can help to develop fall prevention strategies in the elderly.

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.

Prevalence and Patterns of Multimorbidity Among Adults Aged 18 Years and Older - China, 2018

What is already known about this topic?

Multimorbidity is becoming more common and poses a major challenge to healthcare systems. However, the prevalence and patterns of multimorbidity among Chinese adults aged ≥18 years are largely unknown.

What is added by this report?

This study found that 46.5% of Chinese adults had multimorbidity in 2018. And the prevalence of multimorbidity prevalence is increased with age. Prevalence of multimorbidity was higher among men, Han Chinese, adults with lower educational level, and those with lower household income. The most common multimorbidity pattern is a combination of three chronic conditions, hypertension, dyslipidemia, and obesity.

What are the implications for public health practices?

As multimorbidity diversifies characteristics and patterns, guideline development, clinical management, and public intervention should consider the complexity of multimorbidity.

Polypharmacy Patterns in Multimorbid Older People with Cardiovascular Disease: Longitudinal Study

(1) Introduction: Cardiovascular disease is associated with high mortality, especially in older people. This study aimed to characterize the evolution of combined multimorbidity and polypharmacy patterns in older people with different cardiovascular disease profiles. (2) Material and methods: This longitudinal study drew data from the Information System for Research in Primary Care in people aged 65 to 99 years with profiles of cardiovascular multimorbidity. Combined patterns of multimorbidity and polypharmacy were analysed using fuzzy c-means clustering techniques and hidden Markov models. The prevalence, observed/expected ratio, and exclusivity of chronic diseases and/or groups of these with the corresponding medication were described. (3) Results: The study included 114,516 people, mostly men (59.6%) with a mean age of 78.8 years and a high prevalence of polypharmacy (83.5%). The following patterns were identified: Mental, behavioural, digestive and cerebrovascular; Neuropathy, autoimmune and musculoskeletal; Musculoskeletal, mental, behavioural, genitourinary, digestive and dermatological; Non-specific; Multisystemic; Respiratory, cardiovascular, behavioural and genitourinary; Diabetes and ischemic cardiopathy; and Cardiac. The prevalence of overrepresented health problems and drugs remained stable over the years, although by study end, cohort survivors had more polypharmacy and multimorbidity. Most people followed the same pattern over time; the most frequent transitions were from Non-specific to Mental, behavioural, digestive and cerebrovascular and from Musculoskeletal, mental, behavioural, genitourinary, digestive and dermatological to Non-specific. (4) Conclusions: Eight combined multimorbidity and polypharmacy patterns, differentiated by sex, remained stable over follow-up. Understanding the behaviour of different diseases and drugs can help design individualised interventions in populations with clinical complexity.

Immunosenescence profiles of lymphocyte compartments and multiple long-term conditions (multimorbidity) in very old adults: The Newcastle 85+ Study

Immunosenescence, a decline in immune system function, has been linked to several age-related diseases and ageing syndromes. Very old adults (aged ≥ 85 years) live with multiple long-term conditions (MLTC, also known as multimorbidity)-a complex phenomenon of poor health defined by either counts, indices, or patterns, but little is known about the relationship between an ageing immune system and MLTC in this age group. We utilised baseline data from the Newcastle 85+ Study to investigate the associations between previously defined immunosenescence profiles of lymphocyte compartments and MLTC counts and patterns (from 16 chronic diseases/ageing syndromes). Seven hundred and three participants had MLTC and complete data for all 16 conditions, a median and mean of 5 (range 2-11) and 62.2% had ≥ 5 conditions. Three distinct MLTC patterns emerged by clustering: Cluster 1 ('Low frequency cardiometabolic-cerebrovascular diseases', n = 209), Cluster 2 ('High ageing syndromes-arthritis', n = 240), and Cluster 3 ('Hypertensive-renal impairment', n = 254). Although having a more senescent phenotype, characterised by higher frequency of CD4 and CD8 senescence-like effector memory cells and lower CD4/CD8 ratio, was not associated with MLTC compared with less senescent phenotype, the results warrant further investigation, including whether immunosenescence drives change in MLTC and influences MLTC severity in late adulthood.

Patterns of multimorbidity in association with falls among the middle-aged and older adults: results from the China Health and Retirement Longitudinal Study

Background

Chronic diseases are important risk factors of falls. However, most studies explored the effect of a single chronic disease on falls and few studies explored the combined effect of multiple chronic diseases on falls. In this study, we examined the associations between falls and multimorbidity and multimorbidity patterns.

Methods

Data collected between 2011 and 2018 were obtained from the China Health and Retirement Longitudinal Study (CHARLS). Multimorbidity was defined as the coexistence of ≥ 2 chronic diseases in the same person. The multimorbidity patterns were identified with exploratory factor analysis (EFA). The longitudinal associations of multimorbidity and multimorbidity patterns with falls were examined with generalized estimating equations methodology.

Results

Compared with patients without chronic conditions, patients with one, two, and ≥ 3 chronic diseases had 37%, 85%, and 175% increased risk of falls, respectively. The EFA identified four multimorbidity patterns and the factor scores in the cardiac-metabolic pattern [adjusted odds ratio (aOR): 1.16, 95% confidence interval (95% CI): 1.12–1.20)], visceral-arthritic pattern (aOR: 1.31, 95% CI: 1.28–1.35), respiratory pattern (aOR: 1.12, 95% CI: 1.10–1.16), and mental-sensory pattern (aOR: 1.31, 95% CI: 1.28–1.35) were all associated with a higher risk of falls.

Conclusion

Multimorbidity and multimorbidity patterns are related to falls. Older adults with multiple chronic diseases require early interventions to prevent falls.

Multimorbidity patterns and the relation to self-rated health among older Japanese people: a nationwide cross-sectional study

OBJECTIVES

Classifying individuals into multimorbidity patterns can be useful to identify the target population with poorer clinical outcomes. Self-rated health (SRH) is one of the core outcomes in multimorbidity patients. Although studies have reported that multimorbidity is associated with poor SRH, whether certain patterns have stronger associations remains unknown. Therefore, this study aimed to identify the prevalence and patterns of multimorbidity and investigate the association between multimorbidity patterns and SRH in an older Japanese population.

DESIGN

Cross-sectional study.

SETTING

Data were obtained from the 2013 Comprehensive Survey of Living Conditions, a nationally representative survey of the general Japanese population.

PARTICIPANTS

This study mainly examined 23 730 participants aged ≥65 years who were not hospitalised or institutionalised.

PRIMARY OUTCOME MEASURE

Poor SRH was defined as choosing 'not very good' or 'bad' from five options: 'excellent', 'fairly good', 'average', 'not very good' and 'bad'.

RESULTS

The prevalence of multimorbidity was 40.9% and that of poor SRH was 23.8%. Three multimorbidity patterns were identified by exploratory factor analysis: (1) degenerative/mental health, (3) malignant/digestive/urological/haematological and (3) cardiovascular/metabolic. Multivariable modified Poisson regression analysis revealed that high malignant/digestive/urological/haematological, degenerative/mental health and cardiovascular/metabolic pattern scores, corresponding to the number of affected body systems in each pattern, were significantly associated with poor SRH (adjusted risk ratio (aRR)=1.68, 95% CI: 1.60 to 1.76; aRR=1.63, 95% CI: 1.58 to 1.69; and aRR=1.31, 95% CI: 1.26 to 1.36, respectively). When including the Kessler 6 score, a screening scale for psychological distress, in the analysis, the association between each multimorbidity pattern score and poor SRH decreased.

CONCLUSIONS

Malignant/digestive/urological/haematological and degenerative/mental health patterns may be associated with a high risk for poor SRH. Further research should focus on interventions to improve SRH in multimorbidity patients.

Magnitude, pattern and correlates of multimorbidity among patients attending chronic outpatient medical care in Bahir Dar, northwest Ethiopia: The application of latent class analysis model

Objective

This study aimed to investigate the magnitude, pattern and associated factors of multimorbidity in Bahir Dar, northwest Ethiopia.

Methods

A multi-centered facility-based study was conducted among 1440 participants aged 40+ years attending chronic outpatient medical care. Two complementary methods (interview and review of medical records) were employed to collect data on socio-demographic, behavioral and disease related characteristics. The data were analyzed by STATA V.16 and R Software V.4.1.0. We fitted logistic regression and latent class analyses (LCA) models to identify the factors associated with multimorbidity and determine patterns of disease clustering, respectively. Statistical significance was considered at P-value <0.05.

Results

The magnitude of individual chronic conditions ranged from 1.4% (cancer) to 37.9% (hypertension), and multimorbidity was identified in 54.8% (95% CI = 52.2%-57.4%) of the sample. The likelihood of having multimorbidity was higher among participants aged 45–54 years (AOR: 1.6, 95%CI = 1.1, 2.2), 55–64 years (AOR: 2.6, 95%CI = 1.9, 3.6) and 65+ years (AOR: 2.6, 95%CI = 1.9, 3.6) compared to those aged 40–44 years. The odds of multimorbidity was also higher among individuals classified as overweight (AOR: 1.6, 95%CI = 1.2, 2.1) or obese (AOR: 1.9, 95%CI = 1.3, 3.0) than the normal weight category. Four patterns of multimorbidity were identified; the cardiovascular category being the largest class (50.2%) followed by the cardio-mental, (32.6%), metabolic (11.5%) and respiratory (5.7%) groups. Advanced age, being overweight and obesity predicted latent class membership, adjusting for relevant confounding factors.

Conclusions

The magnitude of multimorbidity in this study was high, and the most prevalent conditions shaped the patterns of multimorbidity. Advanced age, being overweight and obesity were the factors correlated with multimorbidity. Further research is required to better understand the burden of multimorbidity and related factors in the population, and to determine the impact of multimorbidity on individuals’ well-being and functioning.

Sociodemographic Differences in Multimorbidity: A Closer Look from Indonesian Family and Life Survey

The prevalence of multimorbidity, the coexistence of two or more chronic diseases, is expected to increase, including in Indonesia. This phenomenon is associated with increased life expectancy and incidence of non-communicable diseases. Therefore, this study assesses the burden of multimorbidity in Indonesia by sociodemographic factors. The researchers analyzed cross-sectional data from the latest wave of IFLS conducted in 2014, the IFLS wave 5. The researchers included individuals aged 15 and above with blood pressure measurements (n= 32.256) from 13,536 households. Meanwhile, the researchers excluded individuals with missing data on BMI (183 individuals) or who had biologically implausible or extreme values (n=6). The analyses were then conducted on 32,067 individuals. The analyses comprised the ten most common self-reported chronic diseases diagnosis in IFLS-5. The findings revealed that the prevalence of self-reported multimorbidity in Indonesia was relatively high, at 9.32% (n= 2.989), with the highest proportion of multimorbidity reported among the elderly. Approximately 2.76% of the respondents (n- 885) reported having three or more chronic diseases. The most common combinations were hypertension and digestive problem (2.15%, n= 689), followed by hypertension and arthritis (1.79%, n= 574), and hypertension and high cholesterol (1.68%, n= 539). When stratified by sociodemographic factors, the researchers found a higher proportion of multimorbidity among females (11.01%, n= 3,530) compared to males (7.41%, n= 2.376), elderly (21.54%, n= 3.530) compared to younger adults, and previous smokers (20.90%, n= 6.072). People who were obese and overweight also reported a higher prevalence of multimorbidity (13.73%, n= 4.403 and 9.3%, n= 2.998, respectively). In addition, those living in urban areas had a higher proportion of multimorbidity (10.33%, n= 3.313) compared to rural areas (7.2%, n= 2.527). In conclusion, this study uncovered a relatively high prevalence of multimorbidity. People living in urban areas were overweight/obese and those who had low SES had a higher proportion of multimorbidity. With the nature of self-reported data and previously reported underdiagnosis of chronic diseases, screening to examine multimorbidity is needed.