Similar multimorbidity patterns in primary care patients from two European regions: results of a factor analysis

Latent class analysis of chronic disease co-occurrence, clustering and their determinants in India using Study on global AGEing and adult health (SAGE) India Wave-2

Background

Understanding chronic disease prevalence, patterns, and co-occurrence is pivotal for effective health care planning and disease prevention strategies. In this paper, we aimed to identify the clustering of major non-communicable diseases among Indian adults aged ≥50 years based on their self-reported diagnosed non-communicable disease status and to find the risk factors that heighten the risk of developing the identified disease clusters.

Methods

We utilised data from the nationally representative survey Study on Global AGEing and Adult Health (SAGE Wave-2). The eligible sample size was 6298 adults aged ≥50 years. We conducted the latent class analysis to uncover latent subgroups of multimorbidity and the multinomial logistic regression to identify the factors linked to observed latent class membership.

Results

The latent class analysis grouped our sample of men and women >49 years old into three groups - mild multimorbidity risk (41%), moderate multimorbidity risk (30%), and severe multimorbidity risk (29%). In the mild multimorbidity risk group, the most prevalent diseases were asthma and arthritis, and the major prevalent disease in the moderate multimorbidity risk group was low near/distance vision, followed by depression, asthma, and lung disease. Angina, diabetes, hypertension, and stroke were the major diseases in the severe multimorbidity risk category. Individuals with higher ages had an 18% and 15% higher risk of having moderate multimorbidity and severe multimorbidity compared to those in the mild multimorbidity category. Females were more likely to have a moderate risk (3.36 times) and 2.82 times more likely to have severe multimorbidity risk.

Conclusions

The clustering of diseases highlights the importance of integrated disease management in primary care settings and improving the health care system to accommodate the individual's needs. Implementing preventive measures and tailored interventions, strengthening the health and wellness centres, and delivering comprehensive primary health care services for secondary and tertiary level hospitalisation may cater to the needs of multimorbid patients.

Learning prevalent patterns of co-morbidities in multichronic patients using population-based healthcare data

The prevalence of longstanding chronic diseases has increased worldwide, along with the average age of the population. As a result, an increasing number of people is affected by two or more chronic conditions simultaneously, and healthcare systems are facing the challenge of treating multimorbid patients effectively. Current therapeutic strategies are suited to manage each chronic condition separately, without considering the whole clinical condition of the patient. This approach may lead to suboptimal clinical outcomes and system inefficiencies (e.g. redundant diagnostic tests and inadequate drug prescriptions). We develop a novel methodology based on the joint implementation of data reduction and clustering algorithms to identify patterns of chronic diseases that are likely to co-occur in multichronic patients. We analyse data from a large adult population of multichronic patients living in Tuscany (Italy) in 2019 which was stratified by sex and age classes. Results demonstrate that (i) cardio-metabolic, endocrine, and neuro-degenerative diseases represent a stable pattern of multimorbidity, and (ii) disease prevalence and clustering vary across ages and between women and men. Identifying the most common multichronic profiles can help tailor medical protocols to patients' needs and reduce costs. Furthermore, analysing temporal patterns of disease can refine risk predictions for evolutive chronic conditions.

Patterns of multimorbidity in primary care electronic health records: A systematic review

Background

Multimorbidity, the coexistence of multiple chronic conditions in an individual, is a complex phenomenon that is highly prevalent in primary care settings, particularly in older individuals. This systematic review summarises the current evidence on multimorbidity patterns identified in primary care electronic health record (EHR) data.

Methods

Three databases were searched from inception to April 2022 to identify studies that derived original multimorbidity patterns from primary care EHR data. The quality of the included studies was assessed using a modified version of the Newcastle-Ottawa Quality Assessment Scale.

Results

Sixteen studies were included in this systematic review, none of which was of low quality. Most studies were conducted in Spain, and only one study was conducted outside of Europe. The prevalence of multimorbidity (i.e. two or more conditions) ranged from 14.0% to 93.9%. The most common stratification variable in disease clustering models was sex, followed by age and calendar year. Despite significant heterogeneity in clustering methods and disease classification tools, consistent patterns of multimorbidity emerged. Mental health and cardiovascular patterns were identified in all studies, often in combination with diseases of other organ systems (e.g. neurological, endocrine).

Discussion

These findings emphasise the frequent coexistence of physical and mental health conditions in primary care, and provide useful information for the development of targeted preventive and management strategies. Future research should explore mechanisms underlying multimorbidity patterns, prioritise methodological harmonisation to facilitate the comparability of findings, and promote the use of EHR data globally to enhance our understanding of multimorbidity in more diverse populations.

Assigning disease clusters to people: A cohort study of the implications for understanding health outcomes in people with multiple long-term conditions

Background

Identifying clusters of co-occurring diseases may help characterise distinct phenotypes of Multiple Long-Term Conditions (MLTC). Understanding the associations of disease clusters with health-related outcomes requires a strategy to assign clusters to people, but it is unclear how the performance of strategies compare.

Aims

First, to compare the performance of methods of assigning disease clusters to people at explaining mortality, emergency department attendances and hospital admissions over one year. Second, to identify the extent of variation in the associations with each outcome between and within clusters.

Methods

We conducted a cohort study of primary care electronic health records in England, including adults with MLTC. Seven strategies were tested to assign patients to fifteen disease clusters representing 212 LTCs, identified from our previous work. We tested the performance of each strategy at explaining associations with the three outcomes over 1 year using logistic regression and compared to a strategy using the individual LTCs.

Results

6,286,233 patients with MLTC were included. Of the seven strategies tested, a strategy assigning the count of conditions within each cluster performed best at explaining all three outcomes but was inferior to using information on the individual LTCs. There was a larger range of effect sizes for the individual LTCs within the same cluster than there was between the clusters.

Conclusion

Strategies of assigning clusters of co-occurring diseases to people were less effective at explaining health-related outcomes than a person's individual diseases. Furthermore, clusters did not represent consistent relationships of the LTCs within them, which might limit their application in clinical research.

Multimorbidity patterns in older persons and their association with self-reported quality of life and limitations in activities of daily living

BACKGROUND

As populations age, multimorbidity (the presence of two or more chronic morbidities) is increasingly more common. These evolving demographics demand further research into the identification of morbidity patterns in different settings as well as the longitudinal effects of these patterns.

METHODS

Prospectively collected data on 12,755 older persons aged 65+ years were derived from The Older Persons and Informal Caregivers Survey Minimum DataSet (TOPICS-MDS, www.topics-mds.eu). Latent class analyses were performed to identify unobserved relationship patterns between morbidities in older persons. Using linear mixed models, the average difference in health-related quality of life (EQ-5D) and general quality of life scores (Cantril's Self Anchoring Ladder) as well as limitations in Activities of Daily Living and Instrumental Activities of Daily Living (ADL/IADL) were examined over a 12-month period.

RESULTS

Five multimorbidity patterns were identified: sensory (n = 3882), cardio-metabolic (n = 2627), mental health (n = 920), osteo-articular (n = 4486), and system decline (n = 840). Relative to older persons in the sensory group, multimorbidity patterns did not have a strong effect on health-related quality of life, general quality of life or ADL/IADLs over a one-year period.

CONCLUSIONS

The observed multimorbidity patterns are similar to others based on different methodologies and study populations. When examining the effect of such patterns on quality of life, the EQ-5D and Cantril's Ladder may be insufficient outcome measures. Further investigations into the prognostic value of morbidity patterns would be of benefit.

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.

Competences of internal medicine specialists for the management of patients with multimorbidity. EFIM multimorbidity working group position paper

Patients with multimorbidity increasingly impact healthcare systems, both in primary care and in hospitals. This is particularly true in Internal Medicine. This population associates with higher mortality rates, polypharmacy, hospital readmissions, post-discharge syndrome, anxiety, depression, accelerated age-related functional decline, and development of geriatric syndromes, amongst others. Internists and Hospitalists, in one of their roles as Generalists, are increasingly asked to attend to these patients, both in their own Departments as well as in surgical areas. The management of polypathology and multimorbidity, however, is often complex, and requires specific clinical skills and corresponding experience. In addition, patients' needs, health-care environment, and routines have changed, so emerging and re-emerging specific competences and approaches are required to offer the best coordinated, continuous, and comprehensive integrated care to these populations, to achieve optimal health outcomes and satisfaction of patients, their relatives, and staff. This position paper proposes a set of emerging and re-emerging competences for internal medicine specialists, which are needed to optimally address multimorbidity now and in the future.

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.

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.

Trends of Multimorbidity Patterns over 16 Years in Older Taiwanese People and Their Relationship to Mortality

Understanding multimorbidity patterns is important in finding a common etiology and developing prevention strategies. Our aim was to identify the multimorbidity patterns of Taiwanese people aged over 50 years and to explore their relationship with health outcomes. This longitudinal cohort study used data from the Taiwan Longitudinal Study on Aging. The data were obtained from wave 3, and the multimorbidity patterns in 1996, 1999, 2003, 2007, and 2011 were analyzed separately by latent class analysis (LCA). The association between each disease group and mortality was examined using logistic regression. Four disease patterns were identified in 1996, namely, the cardiometabolic (18.57%), arthritis–cataract (15.61%), relatively healthy (58.92%), and multimorbidity (6.9%) groups. These disease groups remained similar in the following years. After adjusting all the confounders, the cardiometabolic group showed the highest risk for mortality (odds ratio: 1.237, 95% confidence interval: 1.040–1.472). This longitudinal study reveals the trend of multimorbidity among older adults in Taiwan for 16 years. Older adults with a cardiometabolic multimorbidity pattern had a dismal outcome. Thus, healthcare professionals should put more emphasis on the prevention and identification of cardiometabolic multimorbidity.

Multimorbidity patterns of chronic conditions and geriatric syndromes in older patients from the MoPIM multicentre cohort study

OBJECTIVES

To estimate the frequency of chronic conditions and geriatric syndromes in older patients admitted to hospital because of an exacerbation of their chronic conditions, and to identify multimorbidity clusters in these patients.

DESIGN

Multicentre, prospective cohort study.

SETTING

Internal medicine or geriatric services of five general teaching hospitals in Spain.

PARTICIPANTS

740 patients aged 65 and older, hospitalised because of an exacerbation of their chronic conditions between September 2016 and December 2018.

PRIMARY AND SECONDARY OUTCOME MEASURES

Active chronic conditions and geriatric syndromes (including risk factors) of the patient, a score about clinical management of chronic conditions during admission, and destination at discharge were collected, among other variables. Multimorbidity patterns were identified using fuzzy c-means cluster analysis, taking into account the clinical management score. Prevalence, observed/expected ratio and exclusivity of each chronic condition and geriatric syndrome were calculated for each cluster, and the final solution was approved after clinical revision and discussion among the research team.

RESULTS

740 patients were included (mean age 84.12 years, SD 7.01; 53.24% female). Almost all patients had two or more chronic conditions (98.65%; 95% CI 98.23% to 99.07%), the most frequent were hypertension (81.49%, 95% CI 78.53% to 84.12%) and heart failure (59.86%, 95% CI 56.29% to 63.34%). The most prevalent geriatric syndrome was polypharmacy (79.86%, 95% CI 76.82% to 82.60%). Four statistically and clinically significant multimorbidity clusters were identified: osteoarticular, psychogeriatric, cardiorespiratory and minor chronic disease. Patient-level variables such as sex, Barthel Index, number of chronic conditions or geriatric syndromes, chronic disease exacerbation 3 months prior to admission or destination at discharge differed between clusters.

CONCLUSIONS

In older patients admitted to hospital because of the exacerbation of chronic health problems, it is possible to define multimorbidity clusters using soft clustering techniques. These clusters are clinically relevant and could be the basis to reorganise healthcare circuits or processes to tackle the increasing number of older, multimorbid patients.

TRIAL REGISTRATION NUMBER

NCT02830425.

Exploring patient multimorbidity and complexity using health insurance claims data: a cluster analysis approach (Preprint)

Background

Although the trend of progressing morbidity is widely recognized, there are numerous challenges when studying multimorbidity and patient complexity. For multimorbid or complex patients, prone to fragmented care and high health care use, novel estimation approaches need to be developed.

Objective

This study aims to investigate the patient multimorbidity and complexity of Swiss residents aged ≥50 years using clustering methodology in claims data.

Methods

We adopted a clustering methodology based on random forests and used 34 pharmacy-based cost groups as the only input feature for the procedure. To detect clusters, we applied hierarchical density-based spatial clustering of applications with noise. The reasonable hyperparameters were chosen based on various metrics embedded in the algorithms (out-of-bag misclassification error, normalized stress, and cluster persistence) and the clinical relevance of the obtained clusters.

Results

Based on cluster analysis output for 18,732 individuals, we identified an outlier group and 7 clusters: individuals without diseases, patients with only hypertension-related diseases, patients with only mental diseases, complex high-cost high-need patients, slightly complex patients with inexpensive low-severity pharmacy-based cost groups, patients with 1 costly disease, and older high-risk patients.

Conclusions

Our study demonstrated that cluster analysis based on pharmacy-based cost group information from claims-based data is feasible and highlights clinically relevant clusters. Such an approach allows expanding the understanding of multimorbidity beyond simple disease counts and can identify the population profiles with increased health care use and costs. This study may foster the development of integrated and coordinated care, which is high on the agenda in policy making, care planning, and delivery.

Multimorbidity in the elderly in China based on the China Health and Retirement Longitudinal Study

This study aimed to investigate the spatial distribution and patterns of multimorbidity among the elderly in China. Data on the occurrence of 14 chronic diseases were collected for 9710 elderly participants in the 2015 waves of the China Health and Retirement Longitudinal Study (CHARLS). Web graph, Apriori algorithm, age-adjusted Charlson comorbidity index (AAC), and Spatial autocorrelation were used to perform the multimorbidity analysis. The multimorbidity prevalence rate was estimated as 49.64% in the elderly in China. Three major multimorbidity patterns were identified: [Asthma/Chronic lungs diseases]: (Support (S) = 6.17%, Confidence (C) = 63.77%, Lift (L) = 5.15); [Asthma, Arthritis, or rheumatism/ Chronic lungs diseases]: (S = 3.12%, C = 64.03%, L = 5.17); [Dyslipidemia, Hypertension, Arthritis or rheumatism/Heart attack]: (S = 3.96%, C = 51.56, L = 2.69). Results of the AAC analysis showed that the more chronic diseases an elderly has, the lower is the 10-year survival rate (P < 0.001). Global spatial autocorrelation showed a positive spatial correlation distribution for the prevalence of the third multimorbidity pattern in China (P = 0.032). The status of chronic diseases and multimorbidity among the elderly with a spatial correlation is a significant health issue in China.

Changes in Multimorbidity and Polypharmacy Patterns in Young and Adult Population over a 4-Year Period: A 2011-2015 Comparison Using Real-World Data

The pressing problem of multimorbidity and polypharmacy is aggravated by the lack of specific care models for this population. We aimed to investigate the evolution of multimorbidity and polypharmacy patterns in a given population over a 4-year period (2011–2015). A cross-sectional, observational study among the EpiChron Cohort, including anonymized demographic, clinical and drug dispensation information of all users of the public health system ≥65 years in Aragon (Spain), was performed. An exploratory factor analysis, stratified by age and sex, using an open cohort was carried out based on the tetra-choric correlations among chronic diseases and dispensed drugs during 2011 and compared with 2015. Seven baseline patterns were identified during 2011 named as: mental health, respiratory, allergic, mechanical pain, cardiometabolic, osteometabolic, and allergic/derma. Of the epidemiological patterns identified in 2015, six were already present in 2011 but a new allergic/derma one appeared. Patterns identified in 2011 were more complex in terms of both disease and drugs. Results confirmed the existing association between age and clinical complexity. The systematic associations between diseases and drugs remain similar regarding their clinical nature over time, helping in early identification of potential interactions in multimorbid patients with a high risk of negative health outcomes due to polypharmacy.

Multimorbidity Patterns in the General Population: Results from the EpiChron Cohort Study

The correct management of patients with multimorbidity remains one of the main challenges for healthcare systems worldwide. In this study, we analyze the existence of multimorbidity patterns in the general population based on gender and age. We conducted a cross-sectional study of individuals of all ages from the EpiChron Cohort, Spain (1,253,292 subjects), and analyzed the presence of systematic associations among chronic disease diagnoses using exploratory factor analysis. We identified and clinically described a total of 14 different multimorbidity patterns (12 in women and 12 in men), with some relevant differences in the functions of age and gender. The number and complexity of the patterns was shown to increase with age in both genders. We identified associations of circulatory diseases with respiratory disorders, chronic musculoskeletal diseases with depression and anxiety, and a very consistent pattern of conditions whose co-occurrence is known as metabolic syndrome (hypertension, diabetes, obesity, and dyslipidaemia), among others. Our results demonstrate the potential of using real-world data to conduct large-scale epidemiological studies to assess the complex interactions among chronic conditions. This could be useful in designing clinical interventions for patients with multimorbidity, as well as recommendations for healthcare professionals on how to handle these types of patients in clinical practice.

Characteristics, service use and mortality of clusters of multimorbid patients in England: a population-based study

BACKGROUND

Multimorbidity is associated with mortality and service use, with specific types of multimorbidity having differential effects. Additionally, multimorbidity is often negatively associated with participation in research cohorts. Therefore, we set out to identify clusters of multimorbidity patients and how they are differentially associated with mortality and service use across age groups in a population-representative sample.

METHODS

Linked primary and secondary care electronic health records contributed by 382 general practices in England to the Clinical Practice Research Datalink (CPRD) were used. The study included a representative set of multimorbid adults (18 years old or more, N = 113,211) with two or more long-term conditions (a total of 38 conditions were included). A random set of 80% of the multimorbid patients (N = 90,571) were stratified by age groups and clustered using latent class analysis. Consistency between obtained multimorbidity phenotypes, classification quality and associations with demographic characteristics and primary outcomes (GP consultations, hospitalisations, regular medications and mortality) was validated in the remaining 20% of multimorbid patients (N = 22,640).

RESULTS

We identified 20 patient clusters across four age strata. The clusters with the highest mortality comprised psychoactive substance and alcohol misuse (aged 18-64); coronary heart disease, depression and pain (aged 65-84); and coronary heart disease, heart failure and atrial fibrillation (aged 85+). The clusters with the highest service use coincided with those with the highest mortality for people aged over 65. For people aged 18-64, the cluster with the highest service use comprised depression, anxiety and pain. The majority of 85+-year-old multimorbid patients belonged to the cluster with the lowest service use and mortality for that age range. Pain featured in 13 clusters.

CONCLUSIONS

This work has highlighted patterns of multimorbidity that have implications for health services. These include the importance of psychoactive substance and alcohol misuse in people under the age of 65, of co-morbid depression and coronary heart disease in people aged 65-84 and of cardiovascular disease in people aged 85+.

Multimorbidity patterns in chronic older patients, potentially inappropriate prescribing and adverse drug reactions: protocol of the multicentre prospective cohort study MoPIM

INTRODUCTION

Multimorbidity is a major challenge for current healthcare systems and professionals. From the different approaches that have been proposed to analyse this issue, the hypothesis of the existence of association patterns of different chronic conditions is gaining visibility. In addition, multimorbidity can be associated to polypharmacy, which can lead to a higher risk of potentially inappropriate prescribing (PIP) and consequently to adverse drug reactions (ADRs). The general objective of this novel study is to identify the association between PIP, multimorbidity patterns, polypharmacy and the presence of ADRs in older patients admitted for exacerbation of chronic diseases.

METHODS AND ANALYSIS

The MoPIM (morbidity, potentially inappropriate medication) study is a multicentre prospective cohort study of an estimated sample of 800 older (≥65 years) patients admitted to five general hospitals in Spain due to an exacerbation of a chronic disease. Patients referred to home hospitalisation, admitted due to an acute process or with a fatal outcome expected at the time of admission are excluded. Sociodemographic data, chronic morbidities and geriatric syndromes, number of chronic prescribed medications, PIP at admission to hospital and on discharge, according to the newest screening tool of older screening tool of older person's potentially inappropriate prescriptions/screening tool to alert doctors to right treatment criteria, and ADRs during hospitalisation are being collected. Multimorbidity patterns will be identified using cluster analyses techniques, and the frequency of polypharmacy, PIP and ADRs will be calculated. Finally, the possible relationship between those indicators will be identified through bivariate and multivariate analyses.

ETHICS AND DISSEMINATION

The project has been approved by the clinical research ethics committees of each centre: Comité Ético de investigación Clínica del Parc Taulí, Comitè Ètic d'Investigació Clínica Osona per a la Recerca i Educació Sanitàries (FORES), Comité de Ètica de la Investigación con Medicamentos (CEIm)-Parc de Salut MAR, Comité Ético de Investigación Clínica de Euskadi, Comité de Ética de Investigación del Hospital Universitario de Canarias. The results will be actively and mainly disseminated through publication in peer-reviewed journals and communications in scientific conferences.

TRIAL REGISTRATION NUMBER

NCT02830425.

Use of latent class analysis to identify multimorbidity patterns and associated factors in Korean adults aged 50 years and older

Introduction

Multimorbidity associated with significant disease and economic burdens is common among the aged. We identified chronic disease multimorbidity patterns in Koreans 50 years of age or older, and explored whether such patterns were associated with particular sociodemographic factors and health-related quality-of-life.

Methods

The multimorbidity patterns of 10 chronic diseases (hypertension, dyslipidemia, stroke, osteoarthritis, tuberculosis, asthma, allergic rhinitis, depression, diabetes mellitus, and thyroid disease) were identified via latent class analysis of data on 8,370 Korean adults aged 50+ years who participated in the sixth Korean National Health and Nutrition Examination Survey (2013–2015). The associations between multimorbidity patterns, and sociodemographic factors and health-related quality of life, were subjected to regression analysis.

Results

Three patterns of multimorbidity were identified: 1) a relatively healthy group (60.4% of the population); 2) a ‘cardiometabolic conditions’ group (27.8%); and, 3) an ‘arthritis, asthma, allergic rhinitis, depression, and thyroid disease’ group (11.8%). The female (compared to male) gender was associated with an increased likelihood of membership of the cardiometabolic conditions group (odds ratio [OR] = 1.32, 95% confidence interval [CI] = 1.15–1.51) and (to a much greater extent) the arthritis, asthma, allergy, depression, and thyroid disease group (OR = 4.32, 95% CI = 3.30–5.66). Low socioeconomic status was associated with membership of the two multimorbidity classes. Membership of the arthritis, asthma, allergy, depression, and thyroid disease group was associated with a significantly poorer health-related quality-of-life than was membership of the other two groups.

Conclusion

The co-occurrence of chronic diseases was not attributable to chance. Multimorbidity patterns were associated with sociodemographic factors and quality-of-life. Our results suggest that targeted, integrated public health and clinical strategies dealing with chronic diseases should be based on an understanding of multimorbidity patterns; this would improve the quality-of-life of vulnerable multimorbid adults.

Do replicable profiles of multimorbidity exist? Systematic review and synthesis

This systematic review aimed to synthesise multimorbidity profiling literature to identify replicable and clinically meaningful groupings of multimorbidity. We searched six electronic databases (Medline, EMBASE, PsycINFO, CINAHL, Scopus, and Web of Science) for articles reporting multimorbidity profiles. The identified profiles were synthesised with multidimensional scaling, stratified by type of statistical analysis used in the derivation of profiles. The 51 studies that met inclusion criteria reported results of 98 separate analyses of multimorbidity profiling, with a total of 407 multimorbidity profiles identified. The statistical techniques used to identify multimorbidity profiles were exploratory factor analysis, cluster analysis of diseases, cluster analysis of people, and latent class analysis. Reporting of methodological details of statistical methods was often incomplete. The discernible groupings of multimorbidity took the form of both discrete categories and continuous dimensions. Mental health conditions and cardio-metabolic conditions grouped along identifiable continua in the synthesised results of all four methods. Discrete groupings of chronic obstructive pulmonary disease with asthma, falls and fractures with sensory deficits and of Parkinson's disease and cognitive decline where partially replicable (identifiable in the results of more than one method), while clustering of musculoskeletal conditions and clustering of reproductive systems were each observed only in one statistical approach. The two most replicable multimorbidity profiles were mental health conditions and cardio-metabolic conditions. Further studies are needed to understand aetiology and evolution of these multimorbidity groupings. Guidelines for strengthening the reporting of multimorbidity profiling studies are proposed.

Multimorbidity and health seeking behaviours among older people in Myanmar: A community survey

BACKGROUND

The world population is aging very rapidly and the impact is more severe in developing countries because of insufficient resources and low awareness of the challenges faced by older people. This study aimed to explore multimorbidity of older people in Myanmar and their health seeking behaviours.

METHODS

A community-based cross-sectional study was conducted in both urban and rural areas of Bago Region and Mon State during October 2016. A multistage sampling method was used to select 4,859 people aged 60 years and older. Participants were interviewed face-to-face using a questionnaire. Multinominal logistic regression was used to analyse data.

RESULTS

More than half of the study participants (57.9%) reported at least one chronic condition in the last year and 33.2% reported two or more conditions (multimorbidity). The common conditions were hypertension (67.3%), arthritis (24.7%), arrhythmia (14.7%), coronary heart disease (13.8%) and diabetes (13.7%). A majority (61.7%) of participants with a chronic condition took western medicine. Older people usually saw a doctor (60.2%) or health assistant (21.9%) at a nearby clinic or rural health center; 1.6% reported seeing uncredentialed medical persons. Factors associated with multimorbidity were being female (adjusted Prevalence Ratio (aPR) = 2.14, 95% confidence interval (CI) 1.63-2.82) and having fair (aPR = 2.20, 95% CI 1.59-3.04) or poor self-reported health (aPR = 3.93, 95% CI 2.79-5.52). Those with less than middle school education (aPR = 0.50, 95% CI 0.25-0.99) and those living in rural areas (aPR = 0.78, 95% CI 0.62-0.98) were less likely to have multimorbidity. Older people in rural areas had less access to health care than their urban counterparts.

CONCLUSION

Chronic conditions are common among older people in Myanmar, with higher prevalence in women and in urban areas. The lower prevalence of chronic conditions in those who live in rural areas may be related to living a more traditional lifestyle.

Patterns of multimorbid health conditions: a systematic review of analytical methods and comparison analysis

Background

The latest review of studies on multimorbidity patterns showed high heterogeneity in the methodology for identifying groups of multimorbid conditions. However, it is unclear how analytical methods used influence the identified multimorbidity patterns.

Methods

We undertook a systematic review of analytical methods used to identify multimorbidity patterns in PubMed and EMBASE from their inception to January 2017. We conducted a comparison analysis to assess the effect the analytical methods had on the multimorbidity patterns identified, using the Australian National Health Survey (NHS) 2007-08 data.

Results

We identified 13 194 studies and excluded 13 091 based on titles/abstracts. From the full-text reviews of the 103 remaining publications, we identified 41 studies that used five different analytical methods to identify multimorbid conditions in the studies. Thirty-seven studies (90%) adopted either the factor-analysis or hierarchical-clustering methods, but heterogeneity arises for the use of different proximity measures within each method to form clusters. Our comparison analysis showed the variation in identified groups of multimorbid conditions when applying the methods to the same NHS data. We extracted main similarities among the groupings obtained by the five methods: (i) cardiovascular and metabolic diseases, (ii) mental health problems and (iii) allergic diseases.

Conclusion

We showed the extent of effects for heterogeneous analytical methods on identification of multimorbidity patterns. However, more work is needed to guide investigators for choosing the best analytical method to improve the validity and generalizability of findings. Investigators should also attempt to compare results obtained by various methods for a consensus grouping of multimorbid conditions.

Patterns of Multimorbidity in Middle-Aged and Older Adults: An Analysis of the UK Biobank Data

OBJECTIVE

To assess the prevalence, disease clusters, and patterns of multimorbidity using a novel 2-stage approach in middle-aged and older adults from the United Kingdom.

PATIENTS AND METHODS

Data on 36 chronic conditions from 502,643 participants aged 40 to 69 years with baseline measurements between March 13, 2006, and October 1, 2010, from the UK Biobank were extracted. We combined cluster analysis and association rule mining to assess patterns of multimorbidity overall and by age, sex, and ethnicity. A maximum of 3 clusters and 30 disease patterns were mined. Comparisons were made using lift as the main measure of association.

RESULTS

Ninety-five thousand seven hundred-ten participants (19%) had 2 or more chronic conditions. The first cluster included only myocardial infarction and angina (lift=13.3), indicating that the likelihood of co-occurrence of these conditions is 13 times higher than in isolation. The second cluster consisted of 26 conditions, including cardiovascular, musculoskeletal, respiratory, and neurodegenerative diseases. The strongest association was found between heart failure and atrial fibrillation (lift=23.6). Diabetes was at the center of this cluster with strong associations with heart failure, chronic kidney disease, liver failure, and stroke (lift>2). The third cluster contained 8 highly prevalent conditions, including cancer, hypertension, asthma, and depression, and the strongest association was observed between anxiety and depression (lift=5.0).

CONCLUSION

Conditions such as diabetes, hypertension, and asthma are the epicenter of disease clusters for multimorbidity. A more integrative multidisciplinary approach focusing on better management and prevention of these conditions may help prevent other conditions in the clusters.

Comparative analysis of methods for identifying multimorbidity patterns: a study of 'real-world' data

OBJECTIVE

The aim was to compare multimorbidity patterns identified with the two most commonly used methods: hierarchical cluster analysis (HCA) and exploratory factor analysis (EFA) in a large primary care database. Specific objectives were: (1) to determine whether choice of method affects the composition of these patterns and (2) to consider the potential application of each method in the clinical setting.

DESIGN

Cross-sectional study. Diagnoses were based on the 263 corresponding blocks of the International Classification of Diseases version 10. Multimorbidity patterns were identified using HCA and EFA. Analysis was stratified by sex, and results compared for each method.

SETTING AND PARTICIPANTS

Electronic health records for 408 994 patients with multimorbidity aged 45-64 years in 274 primary health care teams from 2010 in Catalonia, Spain.

RESULTS

HCA identified 53 clusters for women, with just 12 clusters including at least 2 diagnoses, and 15 clusters for men, all of them including at least two diagnoses. EFA showed 9 factors for women and 10 factors for men. We observed differences by sex and method of analysis, although some patterns were consistent. Three combinations of diseases were observed consistently across sex groups and across both methods: hypertension and obesity, spondylopathies and deforming dorsopathies, and dermatitis eczema and mycosis.

CONCLUSIONS

This study showed that multimorbidity patterns vary depending on the method of analysis used (HCA vs EFA) and provided new evidence about the known limitations of attempts to compare multimorbidity patterns in real-world data studies. We found that EFA was useful in describing comorbidity relationships and HCA could be useful for in-depth study of multimorbidity. Our results suggest possible applications for each of these methods in clinical and research settings, and add information about some aspects that must be considered in standardisation of future studies: spectrum of diseases, data usage and methods of analysis.

Chronic Obstructive Pulmonary Disease (COPD) as a disease of early aging: Evidence from the EpiChron Cohort

BACKGROUND

Aging is an important risk factor for most chronic diseases. Patients with COPD develop more comorbidities than non-COPD subjects. We hypothesized that the development of comorbidities characteristically affecting the elderly occur at an earlier age in subjects with the diagnosis of COPD.

METHODS AND FINDINGS

We included all subjects carrying the diagnosis of COPD (n = 27,617), and a similar number of age and sex matched individuals without the diagnosis, extracted from the 727,241 records of individuals 40 years and older included in the EpiChron Cohort (Aragon, Spain). We compared the cumulative number of comorbidities, their prevalence and the mortality risk between both groups. Using network analysis, we explored the connectivity between comorbidities and the most influential comorbidities in both groups. We divided the groups into 5 incremental age categories and compared their comorbidity networks. We then selected those comorbidities known to affect primarily the elderly and compared their prevalence across the 5 age groups. In addition, we replicated the analysis in the smokers' subgroup to correct for the confounding effect of cigarette smoking. Subjects with COPD had more comorbidities and died at a younger age compared to controls. Comparison of both cohorts across 5 incremental age groups showed that the number of comorbidities, the prevalence of diseases characteristic of aging and network's density for the COPD group aged 56-65 were similar to those of non-COPD 15 to 20 years older. The findings persisted after adjusting for smoking.

CONCLUSION

Multimorbidity increases with age but in patients carrying the diagnosis of COPD, these comorbidities are seen at an earlier age.

A Latent Class Analysis of Multimorbidity and the Relationship to Socio-Demographic Factors and Health-Related Quality of Life. A National Population-Based Study of 162,283 Danish Adults

Objectives

To identify patterns of multimorbidity in the general population and examine how these patterns are related to socio-demographic factors and health-related quality of life.

Study design and setting

We used latent class analysis to identify subgroups with statistically distinct and clinically meaningful disease patterns in a nationally representative sample of Danish adults (N = 162,283) aged 16+ years. The analysis was based on 15 chronic diseases.

Results

Seven classes with different disease patterns were identified: a class with no or only a single chronic condition (59% of the population) labeled “1) Relatively Healthy” and six classes with a very high prevalence of multimorbidity labeled; “2) Hypertension” (14%); “3) Musculoskeletal Disorders” (10%); “4) Headache-Mental Disorders” (7%); “5) Asthma-Allergy” (6%); “6) Complex Cardiometabolic Disorders” (3%); and “7) Complex Respiratory Disorders” (2%). Female gender was associated with an increased likelihood of belonging to any of the six multimorbidity classes except for class 2 (Hypertension). Low educational attainment predicted membership of all of the multimorbidity classes except for class 5 (Asthma-Allergy). Marked differences in health-related quality of life between the seven latent classes were found. Poor health-related quality of life was highly associated with membership of class 6 (Complex Cardiometabolic Disorders) and class 7 (Complex Respiratory Disorders). Despite different disease patterns, these two classes had nearly identical profiles in relation to health-related quality of life.

Conclusion

The results clearly support that diseases tend to compound and interact, which suggests that a differentiated public health and treatment approach towards multimorbidity is needed.

Prevalence and Patterns of Multi-Morbidity in Serbian Adults: A Cross-Sectional Study

INTRODUCTION

Like many developing countries, Serbia is facing a growing burden of chronic diseases. Within such public health issue, multi-morbidity requires a special attention.

AIMS

This study investigated the prevalence of multi-morbidity in the Serbia population and assessed the co-occurrence of chronic diseases by age and gender.

METHODS

We analyzed data from the 2013 National Health Survey, which included 13,103 individuals ≥ 20 years old. Multi-morbidity patterns were identified by exploratory factor analysis of data on self-reported chronic diseases, as well as data on measured body weight and height. The analysis was stratified by age and gender.

RESULTS

Multi-morbidity was present in nearly one-third of respondents (26.9%) and existed in all age groups, with the highest prevalence among individuals aged 65 years and older (47.2% of men and 65.0% of women). Six patterns of multi-morbidity were identified: non-communicable, cardio-metabolic, respiratory, cardiovascular, aggregate, and mechanical/mental/metabolic. The non-communicable pattern was observed in both genders but only in the 20-44 years age group, while the aggregate pattern occurred only in middle-aged men. Cardio-metabolic and respiratory patterns were present in all age groups. Cardiovascular and mechanical/mental/metabolic patterns showed similar presentation in both men and women.

CONCLUSIONS

Multi-morbidity is a common occurrence among adults in Serbia, especially in the elderly. While several patterns may be explained by underlying pathophysiologies, some require further investigation and follow-up. Recognizing the complexity of multi-morbidity in Serbia is of great importance from both clinical and preventive perspectives given that it affects one-third of the population and may require adjustment of the healthcare system to address the needs of affected individuals.

Multimorbidity and Its Patterns according to Immigrant Origin. A Nationwide Register-Based Study in Norway

INTRODUCTION

As the flows of immigrant populations increase worldwide, their heterogeneity becomes apparent with respect to the differences in the prevalence of chronic physical and mental disease. Multimorbidity provides a new framework in understanding chronic diseases holistically as the consequence of environmental, social, and personal risks that contribute to increased vulnerability to a wide variety of illnesses. There is a lack of studies on multimorbidity among immigrants compared to native-born populations.

METHODOLOGY

This nationwide multi-register study in Norway enabled us i) to study the associations between multimorbidity and immigrant origin, accounting for other known risk factors for multimorbidity such as gender, age and socioeconomic levels using logistic regression analyses, and ii) to identify patterns of multimorbidity in Norway for immigrants and Norwegian-born by means of exploratory factor analysis technique.

RESULTS

Multimorbidity rates were lower for immigrants compared to Norwegian-born individuals, with unadjusted odds ratios (OR) and 95% confidence intervals 0.38 (0.37-0.39) for Eastern Europe, 0.58 (0.57-0.59) for Asia, Africa and Latin America, and 0.67 (0.66-0.68) for Western Europe and North America. Results remained significant after adjusting for socioeconomic factors. Similar multimorbidity disease patterns were observed among Norwegian-born and immigrants, in particular between Norwegian-born and those from Western European and North American countries. However, the complexity of patterns that emerged for the other immigrant groups was greater. Despite differences observed in the development of patterns with age, such as ischemic heart disease among immigrant women, we were unable to detect the systematic development of the multimorbidity patterns among immigrants at younger ages.

CONCLUSIONS

Our study confirms that migrants have lower multimorbidity levels compared to Norwegian-born. The greater complexity of multimorbidity patterns for some immigrant groups requires further investigation. Health care policies and practice will require a holistic approach for specific population groups in order to meet their health needs and to curb and prevent diseases.

Multimorbidity among registered immigrants in Norway: the role of reason for migration and length of stay

OBJECTIVES

International migration is rapidly increasing worldwide. However, the health status of migrants differs across groups. Information regarding health at arrival and subsequent periodic follow-up in the host country is necessary to develop equitable health care to immigrants. The objective of this study was to determine the impact of the length of stay in Norway and other sociodemographic variables on the prevalence of multimorbidity across immigrant groups (refugees, labour immigrants, family reunification immigrants and education immigrants).

METHODS

This is a register-based study merging data from the National Population Register and the Norwegian Health Economics Administration database. Sociodemographic variables and multimorbidity across the immigrant groups were compared using Persons' chi-square test and anova as appropriate. Several binary logistic regression models were conducted.

RESULTS

Multimorbidity was significantly lower among labour immigrants (OR (95% CI) 0.23 (0.21-0.26) and 0.45 (0.40-0.50) for men and women, respectively) and education immigrants (OR (95% CI) 0.40 (0.32-0.50) and 0.38 (0.33-0.43)) and higher among refugees (OR (95% CI) 1.67 (1.57-1.78) and 1.83 (1.75-1.92)), compared to family reunification immigrants. For all groups, multimorbidity doubled after a five-year stay in Norway. Effect modifications between multimorbidity and sociodemographic characteristics across the different reasons for migration were observed.

CONCLUSIONS

Multimorbidity was highest among refugees at arrival but increased rapidly among labour immigrants, especially females. Health providers need to ensure tailor-made preventive and management strategies that take into account pre-migration and post-migration experiences for immigrants in order to address their needs.

Global Multimorbidity Patterns: A Cross-Sectional, Population-Based, Multi-Country Study

BACKGROUND

Population ageing challenges health care systems due to the high prevalence and impact of multimorbidity in older adults. However, little is known about how chronic conditions present in certain multimorbidity patterns, which could have great impact on public health at several levels. The aim of our study was to identify and describe multimorbidity patterns in low-, middle-, and high-income countries.

METHODS

We analyzed data from the Collaborative Research on Ageing in Europe project (Finland, Poland, and Spain) and the World Health Organization's Study on Global Ageing and Adult Health (China, Ghana, India, Mexico, Russia, and South Africa). These cross-sectional studies obtained data from 41,909 noninstitutionalized adults older than 50 years. Exploratory factor analysis was performed to detect multimorbidity patterns. Additional adjusted binary logistic regressions were performed to identify associations between sociodemographic factors and multimorbidity.

RESULTS

Overall multimorbidity prevalence was high across countries. Hypertension, cataract, and arthritis were the most prevalent comorbid conditions. Two or three multimorbidity patterns were found per country. Several patterns were identified across several countries: "cardio-respiratory" (angina, asthma, and chronic obstructive pulmonary disease), "metabolic" (diabetes, obesity, and hypertension), and "mental-articular" (arthritis and depression).

CONCLUSIONS

A high prevalence of multimorbidity occurs in older adults across countries, with low- and middle-income countries gradually approaching the figures of richer countries. Certain multimorbidity patterns are present in several countries, which suggest that common underlying etiopathogenic factors may play a role. Deeper understanding of these patterns may lead to the development of preventive actions to diminish their prevalence and also give rise to new, comprehensive approaches for the management of these co-occurring conditions.

Multimorbidity and the inequalities of global ageing: a cross-sectional study of 28 countries using the World Health Surveys

BACKGROUND

Multimorbidity defined as the "the coexistence of two or more chronic diseases" in one individual, is increasing in prevalence globally. The aim of this study is to compare the prevalence of multimorbidity across low and middle-income countries (LMICs), and to investigate patterns by age and education, as a proxy for socio-economic status (SES).

METHODS

Chronic disease data from 28 countries of the World Health Survey (2003) were extracted and inter-country socio-economic differences were examined by gross domestic product (GDP). Regression analyses were applied to examine associations of education with multimorbidity by region adjusted for age and sex distributions.

RESULTS

The mean world standardized multimorbidity prevalence for LMICs was 7.8 % (95 % CI, 7.79 % - 7.83 %). In all countries, multimorbidity increased significantly with age. A positive but non-linear relationship was found between country GDP and multimorbidity prevalence. Trend analyses of multimorbidity by education suggest that there are intergenerational differences, with a more inverse education gradient for younger adults compared to older adults. Higher education was significantly associated with a decreased risk of multimorbidity in the all-region analyses.

CONCLUSIONS

Multimorbidity is a global phenomenon, not just affecting older adults in HICs. Policy makers worldwide need to address these health inequalities, and support the complex service needs of a growing multimorbid population.

Aging and Multimorbidity: New Tasks, Priorities, and Frontiers for Integrated Gerontological and Clinical Research

Aging is characterized by rising susceptibility to development of multiple chronic diseases and, therefore, represents the major risk factor for multimorbidity. From a gerontological perspective, the progressive accumulation of multiple diseases, which significantly accelerates at older ages, is a milestone for progressive loss of resilience and age-related multisystem homeostatic dysregulation. Because it is most likely that the same mechanisms that drive aging also drive multiple age-related chronic diseases, addressing those mechanisms may reduce the development of multimorbidity. According to this vision, studying multimorbidity may help to understand the biology of aging and, at the same time, understanding the underpinnings of aging may help to develop strategies to prevent or delay the burden of multimorbidity. As a consequence, we believe that it is time to build connections and dialogue between the clinical experience of general practitioners and geriatricians and the scientists who study aging, so as to stimulate innovative research projects to improve the management and the treatment of older patients with multiple morbidities.

Trajectories of multimorbidity: exploring patterns of multimorbidity in patients with more than ten chronic health problems in life course

Background

Physicians are frequently confronted with complex health situations of patients, but knowledge of intensive forms of multimorbidity and their development during life is lacking.

This study explores patterns and trajectories of chronic health problems of patients with multimorbidity particularly those with more than ten conditions and type and variety of organ systems involved in these patterns during life.

Method

Life time prevalence patterns of chronic health problems were determined in patients with illness trajectories accumulating more than ten chronic health problems during life as registered by general practitioners in the South of the Netherlands in the Registration Network Family Practices (RNH).

Results

Overall 4,560 subjects (5%) were registered with more than ten chronic health problems during their life (MM11+), accounting for 61,653 (20%) of the 302,808 registered health problems in the population (N = 87,837 subjects). More than 30% accumulates 4 or more chronic health conditions (MM4-5: 4–5 conditions (N = 14,199; 16.2%); MM6-10: 6–10 conditions (N = 14,365; 16.4%).

Gastro-intestinal, cardiovascular, locomotor, respiratory and metabolic conditions occur more frequently in the MM11+ patients than in the other patients, while the nature and variety of body systems involved in lifetime accumulation of chronic health problem clusters is both generic and specific. Regarding chronic conditions afflicting multiple sites throughout the body, the number of neoplasms seems low (N = 3,592; 5.8%), but 2,461 (49%) of the 4,560 subjects have registered at least one neoplasm condition during life. A similar pattern is noted for inflammation (N = 3,537, 78%), infection (N = 2,451, 54%) and injury (N = 3,401, 75%).

Conclusion

There are many challenges facing multimorbidity research, including the implementation of a longitudinal, life-time approach from a family practice perspective. The present study, although exploratory by nature, shows that both general and specific mechanisms characterize the development of multimorbidity trajectories. A small proportion of patients has a high number of chronic health problems (MM11+) and keeps adding health problems during life. However, GP’s need to realise that more than one third of their patients accumulate four or more chronic health problems (MM4-5 and MM6-10) during life.