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

Phenotypes of Polish primary care patients using hierarchical clustering: Exploring the risk of mortality in the LIPIDOGEN2015 study cohort

BACKGROUND

Comorbidities in primary care do not occur in isolation but tend to cluster together causing various clinically complex phenotypes. This study aimed to distinguish phenotype clusters and identify the risks of all-cause mortality in primary care.

METHODS

The baseline cohort of the LIPIDOGEN2015 sub-study involved 1779 patients recruited by 438 primary care physicians. To identify different phenotype clusters, we used hierarchical clustering and investigated differences between clinical characteristics and mortality between clusters. We then performed causal analyses using causal mediation analysis to explore potential mediators between different clusters and all-cause mortality.

RESULTS

A total of 1756 patients were included (mean age 51.2, SD 13.0; 60.3% female), with a median follow-up of 5.7 years. Three clusters were identified: Cluster 1 (n = 543) was characterised by overweight/obesity (body mass index ≥ 25 kg/m2), older (age ≥ 65 years), more comorbidities; Cluster 2 (n = 459) was characterised by non-overweight/obesity, younger, fewer comorbidities; Cluster 3 (n = 754) was characterised by overweight/obesity, younger, fewer comorbidities. Adjusted Cox regression showed that compared with Cluster 2, Cluster 1 had a significantly higher risk of all-cause mortality (HR 3.87, 95% CI: 1.24-15.91), whereas this was insignificantly different for Cluster 3. Causal mediation analyses showed that decreased protein thiol groups mediated the hazard effect of all-cause mortality in Cluster 1 compared with Cluster 2, but not between Clusters 1 and 3.

CONCLUSION

Overweight/obesity older patients with more comorbidities had the highest risk of long-term all-cause mortality, and in the young group population overweight/obesity insignificantly increased the risk in the long-term follow-up, providing a basis for stratified phenotypic risk management.

Clusters from chronic conditions in the Danish adult population

Multimorbidity, the presence of 2 or more chronic conditions in a person at the same time, is an increasing public health concern, which affects individuals through reduced health related quality of life, and society through increased need for healthcare services. Yet the structure of chronic conditions in individuals with multimorbidity, viewed as a population, is largely unmapped. We use algorithmic diagnoses and the K-means algorithm to cluster the entire 2015 Danish multimorbidity population into 5 clusters. The study introduces the concept of rim data as an additional tool for determining the number of clusters. We label the 5 clusters the Allergies, Chronic Heart Conditions, Diabetes, Hypercholesterolemia, and Musculoskeletal and Psychiatric Conditions clusters, and demonstrate that for 99.32% of the population, the cluster allocation can be determined from the diagnoses of 4-5 conditions. Clusters are characterized through most prevalent conditions, absent conditions, over- or under-represented conditions, and co-occurrence of conditions. Clusters are further characterized through socioeconomic variables and healthcare service utilizations. Additionally, geographical variations throughout Denmark are studied at the regional and municipality level. We find that subdivision into municipality levels suggests that the Allergies cluster frequency is positively associated with socioeconomic status, while the subdivision suggests that frequencies for clusters Diabetes and Hypercholesterolemia are negatively correlated with socioeconomic status. We detect no indication of association to socioeconomic status for the Chronic Heart Conditions cluster and the Musculoskeletal and Psychiatric Conditions cluster. Additional spatial variation is revealed, some of which may be related to urban/rural populations. Our work constitutes a step in the process of characterizing multimorbidity populations, leading to increased comprehension of the nature of multimorbidity, and towards potential applications to individual-based care, prevention, the development of clinical guidelines, and population management.

Multimorbidity Patterns, Hospital Uses and Mortality by Race and Ethnicity Among Oldest-Old Patients

BACKGROUNDS

Adults aged 85 years and older ("oldest-old") are perceived as survivors resilient to age-related risk factors. Although considerable heterogeneity has been often observed in this population, less is known about the unmet needs in health and healthcare service utilization for diverse patients in healthcare systems. We examined racial-ethnic variation in patterns of multimorbidity associated with emergency department (ED), clinic visits, and mortality among the oldest-old patients with multimorbidity.

METHODS

Administrative and clinical data from an integrated healthcare system for five years included 25,801 oldest-old patients with two or more chronic conditions. Hierarchical cluster analysis identified patterns of multimorbidity by four racial-ethnic groups (White, Black, Hispanic, & Other). Clusters associated with ED and clinic visits, and mortality were analyzed using generalized estimation equations and proportional hazards survival model, respectively.

RESULTS

Hypothyroidism, Alzheimer's disease and related dementia, bone & joint conditions, metabolism syndrome, and pulmonary-vascular clusters were commonly observed across the groups. While most clusters were significantly associated with ED and clinic visits among White patients, bone & joint conditions cluster was the most significantly associated with ED and clinic visits among Black (RR = 1.32, p <.01 for ED; RR = 1.67, p <.0001 for clinic) and Hispanic patients (RR = 1.36, p <.0001 for ED; RR = 1.39, p <.0001 for clinic). Similar patterns were observed in the relationship between multimorbidity clusters and mortality.

CONCLUSIONS

Patterns of multimorbidity and its significant association with the uses of ambulatory and emergency care varied by race-ethnicity. More studies are needed to explore barriers when minoritized patients are faced with the use of hospital services.

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.

Multimorbidity clusters in adults 50 years or older with and without a history of cancer: National Health Interview Survey, 2018

BACKGROUND

Multimorbidity is increasing among adults in the United States. Yet limited research has examined multimorbidity clusters in persons aged 50 years and older with and without a history of cancer. An increased understanding of multimorbidity clusters may improve the cancer survivorship experience for survivors with multimorbidity.

METHODS

We identified 7580 adults aged 50 years and older with 2 or more diseases-including 811 adults with a history of primary breast, colorectal, cervical, prostate, or lung cancer-from the 2018 National Health Interview Survey. Exploratory factor analysis identified clusters of multimorbidity among cancer survivors and individuals without a history of cancer (controls). Frequency tables and chi-square tests were performed to determine overall differences in sociodemographic characteristics, health-related characteristics, and multimorbidity between groups.

RESULTS

Cancer survivors reported a higher prevalence of having 4 or more diseases compared to controls (57% and 38%, respectively). Our analysis identified 6 clusters for cancer survivors and 4 clusters for controls. Three clusters (pulmonary, cardiac, and liver) included the same diseases for cancer survivors and controls.

CONCLUSIONS

Diseases clustered differently across adults ≥ 50 years of age with and without a history of cancer. Findings from this study may be used to inform clinical care, increase the development and dissemination of multilevel public health interventions, escalate system improvements, and initiate innovative policy reform.

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.

A scoping review finds a growing trend in studies validating multimorbidity patterns and identifies five broad types of validation methods

OBJECTIVES

Multimorbidity, the presence of two or more long-term conditions, is a growing public health concern. Many studies use analytical methods to discover multimorbidity patterns from data. We aimed to review approaches used in published literature to validate these patterns.

STUDY DESIGN AND SETTING

We systematically searched PubMed and Web of Science for studies published between July 2017 and July 2023 that used analytical methods to discover multimorbidity patterns.

RESULTS

Out of 31,617 studies returned by the searches, 172 were included. Of these, 111 studies (64%) conducted validation, the number of studies with validation increased from 53.13% (17 out of 32 studies) to 71.25% (57 out of 80 studies) in 2017-2019 to 2022-2023, respectively. Five types of validation were identified: assessing the association of multimorbidity patterns with clinical outcomes (n = 79), stability across subsamples (n = 26), clinical plausibility (n = 22), stability across methods (n = 7) and exploring common determinants (n = 2). Some studies used multiple types of validation.

CONCLUSION

The number of studies conducting a validation of multimorbidity patterns is clearly increasing. The most popular validation approach is assessing the association of multimorbidity patterns with clinical outcomes. Methodological guidance on the validation of multimorbidity patterns is needed.

Cardiometabolic and renal phenotypes and transitions in the United States population

Cardiovascular and renal conditions have both shared and distinct determinants. In this study, we applied unsupervised clustering to multiple rounds of the National Health and Nutrition Examination Survey from 1988 to 2018, and identified 10 cardiometabolic and renal phenotypes. These included a 'low risk' phenotype; two groups with average risk factor levels but different heights; one group with low body-mass index and high levels of high-density lipoprotein cholesterol; five phenotypes with high levels of one or two related risk factors ('high heart rate', 'high cholesterol', 'high blood pressure', 'severe obesity' and 'severe hyperglycemia'); and one phenotype with low diastolic blood pressure (DBP) and low estimated glomerular filtration rate (eGFR). Prevalence of the 'high blood pressure' and 'high cholesterol' phenotypes decreased over time, contrasted by a rise in the 'severe obesity' and 'low DBP, low eGFR' phenotypes. The cardiometabolic and renal traits of the US population have shifted from phenotypes with high blood pressure and cholesterol toward poor kidney function, hyperglycemia and severe obesity.

Epidemiology, mortality, and health service use of local-level multimorbidity patterns in South Spain

Multimorbidity -understood as the occurrence of chronic diseases together- represents a major challenge for healthcare systems due to its impact on disability, quality of life, increased use of services and mortality. However, despite the global need to address this health problem, evidence is still needed to advance our understanding of its clinical and social implications. Our study aims to characterise multimorbidity patterns in a dataset of 1,375,068 patients residing in southern Spain. Combining LCA techniques and geographic information, together with service use, mortality, and socioeconomic data, 25 chronicity profiles were identified and subsequently characterised by sex and age. The present study has led us to several findings that take a step forward in this field of knowledge. Specifically, we contribute to the identification of an extensive range of at-risk groups. Moreover, our study reveals that the complexity of multimorbidity patterns escalates at a faster rate and is associated with a poorer prognosis in local areas characterised by lower socioeconomic status. These results emphasize the persistence of social inequalities in multimorbidity, highlighting the need for targeted interventions to mitigate the impact on patients' quality of life, healthcare utilisation, and mortality rates.

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.

Analysis of Multiple Causes of Death: A Review of Methods and Practices

BACKGROUND

Research and reporting of mortality indicators typically focus on a single underlying cause of death selected from multiple causes recorded on a death certificate. The need to incorporate the multiple causes in mortality statistics-reflecting increasing multimorbidity and complex causation patterns-is recognized internationally. This review aims to identify and appraise relevant analytical methods and practices related to multiple causes.

METHODS

We searched Medline, PubMed, Scopus, and Web of Science from their incept ion to December 2020 without language restrictions, supplemented by consultation with international experts. Eligible articles analyzed multiple causes of death from death certificates. The process identified 4,080 items of which we reviewed 434 full-text articles.

RESULTS

Most articles we reviewed (76%, n = 332) were published since 2001. The majority of articles examined mortality by "any- mention" of the cause of death (87%, n = 377) and assessed pairwise combinations of causes (57%, n = 245). Since 2001, applications of methods emerged to group deaths based on common cause patterns using, for example, cluster analysis (2%, n = 9), and application of multiple-cause weights to re-evaluate mortality burden (1%, n = 5). We describe multiple-cause methods applied to specific research objectives for approaches emerging recently.

CONCLUSION

This review confirms rapidly increasing international interest in the analysis of multiple causes of death and provides the most comprehensive overview, to our knowledge, of methods and practices to date. Available multiple-cause methods are diverse but suit a range of research objectives. With greater availability of data and technology, these could be further developed and applied across a range of settings.

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.

Association between multimorbidity patterns and incident depression among older adults in Taiwan: the role of social participation

Background

Previous research has found different multimorbidity patterns that negatively affects health outcomes of older adults. However, there is scarce evidence, especially on the role of social participation in the association between multimorbidity patterns and depression. Our study aimed to explore the relationship between multimorbidity patterns and depression among older adults in Taiwan, including the social participation effect on the different multimorbidity patterns.

Methods

Data were retracted from the Taiwan longitudinal study on ageing (TLSA) for this population-based cohort study. 1,975 older adults (age > 50) were included and were followed up from 1996 to 2011. We used latent class analysis to determine participants’ multimorbidity patterns in 1996, whereas their incident depression was determined in 2011 by CES-D. Multivariable logistic regression was used to analyse the relationship between multimorbidity patterns and depression.

Results

The participants’ average age was 62.1 years in 1996. Four multimorbidity patterns were discovered through latent class analysis, as follows: (1) Cardiometabolic group (n = 93), (2) Arthritis-cataract group (n = 105), (3) Multimorbidity group (n = 128) and (4) Relatively healthy group (n = 1649). Greater risk of incident depression was found among participants in the Multimorbidity group (OR: 1.62; 95% CI: 1.02–2.58) than the Relatively healthy group after the multivariable analysis. Compare to participants in the relatively healthy group with social participation, participants in the arthritis-cataract group without social participation (OR: 2.22, 95% CI: 1.03–4.78) and the multimorbidity group without social participation (OR: 2.21, 95% CI: 1.14–4.30) had significantly increased risk of having depression.

Conclusion

Distinct multimorbidity patterns among older adults in Taiwan are linked with the incident depression during later life, and social participation functioned as a protective factor.

Associations of carotid artery flow parameters with MRI markers of cerebral small vessel disease and patterns of brain atrophy

OBJECTIVES

A growing body of evidence links age related brain pathologies to systemic vascular processes. We aimed to study the prevalence and interrelations between magnetic resonance imaging (MRI) markers of cerebral small vessel disease and patterns of brain atrophy, and their association to carotid duplex ultrasound flow parameters.

MATERIALS AND METHODS

We investigated a population based randomised cohort of older adults (n=391) aged 70-87, part of the Swedish Good Aging in Skåne Study. Peak systolic and end diastolic velocities of the carotid arteries were measured by ultrasound, and resistivity- and pulsatility indexes were calculated. Subjects with increased peak systolic velocity indicating carotid stenosis were excluded from analysis. Nine MRI findings were rated by visual scales: white matter changes, pontine white matter changes, microbleeds, lacunar infarctions, medial temporal lobe atrophy, global cortical atrophy, parietal atrophy, precuneus atrophy and central atrophy.

RESULTS

MRI pathologies were found in 80% of subjects. Mean end diastolic velocity in common carotid arteries was inversely associated with white matter hyperintensities (OR=0.92; p=0.004), parietal lobe atrophy (OR=0.94; p=0.039), global cortical atrophy (OR=0.90; p=0.013), precuneus atrophy (OR=0.94; p=0.022), "number of CSV pathologies" (β=-0.07; p<0.001) and "MRI-burden score" (β=-0.11; p<0.001), after adjustment for age and sex. The latter three were also associated with pulsatility and resistivity indexes.

CONCLUSIONS

Low carotid end diastolic velocity, as well as increased carotid resistivity and pulsatility, were associated with signs of cerebral small vessel disease and patterns of brain atrophy, indicating a vascular component in the process of brain aging.

Novel multimorbidity clusters in people with eczema and asthma: a population-based cluster analysis

Eczema and asthma are allergic diseases and two of the commonest chronic conditions in high-income countries. Their co-existence with other allergic conditions is common, but little research exists on wider multimorbidity with these conditions. We set out to identify and compare clusters of multimorbidity in people with eczema or asthma and people without. Using routinely-collected primary care data from the U.K. Clinical Research Practice Datalink GOLD, we identified adults ever having eczema (or asthma), and comparison groups never having eczema (or asthma). We derived clusters of multimorbidity from hierarchical cluster analysis of Jaccard distances between pairs of diagnostic categories estimated from mixed-effects logistic regressions. We analysed 434,422 individuals with eczema (58% female, median age 47 years) and 1,333,281 individuals without (55% female, 47 years), and 517,712 individuals with asthma (53% female, 44 years) and 1,601,210 individuals without (53% female, 45 years). Age at first morbidity, sex and having eczema/asthma affected the scope of multimorbidity, with women, older age and eczema/asthma being associated with larger morbidity clusters. Injuries, digestive, nervous system and mental health disorders were more commonly seen in eczema and asthma than control clusters. People with eczema and asthma of all ages and both sexes may experience greater multimorbidity than people without eczema and asthma, including conditions not previously recognised as contributing to their disease burden. This work highlights areas where there is a critical need for research addressing the burden and drivers of multimorbidity in order to inform strategies to reduce poor health outcomes.

In simulated data and health records, latent class analysis was the optimum multimorbidity clustering algorithm

BACKGROUND AND OBJECTIVES

To investigate the reproducibility and validity of latent class analysis (LCA) and hierarchical cluster analysis (HCA), multiple correspondence analysis followed by k-means (MCA-kmeans) and k-means (kmeans) for multimorbidity clustering.

METHODS

We first investigated clustering algorithms in simulated datasets with 26 diseases of varying prevalence in predetermined clusters, comparing the derived clusters to known clusters using the adjusted Rand Index (aRI). We then them investigated the medical records of male patients, aged 65 to 84 years from 50 UK general practices, with 49 long-term health conditions. We compared within cluster morbidity profiles using the Pearson correlation coefficient and assessed cluster stability using in 400 bootstrap samples.

RESULTS

In the simulated datasets, the closest agreement (largest aRI) to known clusters was with LCA and then MCA-kmeans algorithms. In the medical records dataset, all four algorithms identified one cluster of 20-25% of the dataset with about 82% of the same patients across all four algorithms. LCA and MCA-kmeans both found a second cluster of 7% of the dataset. Other clusters were found by only one algorithm. LCA and MCA-kmeans clustering gave the most similar partitioning (aRI 0.54).

CONCLUSION

LCA achieved higher aRI than other clustering algorithms.

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.

Multimorbidity patterns and function among adults in low- and middle-income countries: a scoping review protocol

BACKGROUND

A fifth of adults in low- and middle-income countries (LMICs) have multimorbid conditions, which are linked to socio-economic deprivation and aging. Multimorbidity is associated with high rates of functional problems and disability, increased healthcare utilization, and lower quality of life. Literature on multimorbidity and associations with function is mostly from high-income countries (HICs) and focused among older adults. Moreover, data regarding disease patterns and their impact on person-centered outcomes are limited. There is a need for research into understanding common patterns of multimorbidity, and their association with functional impairments, particularly in LMICs. Such information may contribute towards evidence-based and context-relevant strategic policy, planning, and delivery models for health and rehabilitation services, which is imperative in attaining Universal Health Coverage (UHC). The planned scoping review aims to provide an overview of the scope and nature of existing literature on multimorbidity patterns and function among adults in LMICs.

METHODS

A scoping review will be conducted using a five-step framework and reported according to the PRISMA-ScR guidelines. A comprehensive electronic search of PubMed/MEDLINE, Scopus, EBSCOhost, Scielo, Cochrane and Google Scholar will be conducted and updated from the last pilot search ran in September 2020. Studies of any design will be included if they are reported in English, published (between January 1976 and the last search date) in a peer-reviewed journal, and describe multimorbidity patterns and associations with physical functional impairments, activity limitations or participation restrictions among adults in LMICs. Search results will be independently screened by two reviewers and data extraction will cover study characteristics, participants' characteristics, multimorbidity measures, patterns analysis, and functional measures. Descriptive statistics and narrative synthesis will be used to synthesize and summarize findings.

DISCUSSION

Patients with multimorbidity have unique and cross-cutting needs, hence the need for integrated and person-centered approaches to policy, planning, and delivery of medical and rehabilitation services. Considering the shift towards UHC and primary healthcare-led management of chronic diseases, the proposed scoping review is timely. Findings will provide insights into the current extent and scope of multimorbidity research, and guide future inquiry in the field.

TRIAL REGISTRATION

Open Science Framework (OSF), https://osf.io/gcy7z/.

Multimorbidity patterns across race/ethnicity as stratified by age and obesity

The objective of our study is to assess differences in prevalence of multimorbidity by race/ethnicity. We applied the FP-growth algorithm on middle-aged and elderly cohorts stratified by race/ethnicity, age, and obesity level. We used 2016–2017 data from the Cerner HealthFacts electronic health record data warehouse. We identified disease combinations that are shared by all races/ethnicities, those shared by some, and those that are unique to one group for each age/obesity level. Our findings demonstrate that even after stratifying by age and obesity, there are differences in multimorbidity prevalence across races/ethnicities. There are multimorbidity combinations distinct to some racial groups—many of which are understudied. Some multimorbidities are shared by some but not all races/ethnicities. African Americans presented with the most distinct multimorbidities at an earlier age. The identification of prevalent multimorbidity combinations amongst subpopulations provides information specific to their unique clinical needs.

Different definitions of multimorbidity and their effect on prevalence rates: a retrospective study in German general practices

BACKGROUND

Multimorbidity is common among general practice patients and increases a general practitioner's (GP's) workload. But the extent of multimorbidity may depend on its definition and whether a time delimiter is included in the definition or not.

AIMS

The aims of the study were (1) to compare practice prevalence rates yielded by different models of multimorbidity, (2) to determine how a time delimiter influences the prevalence rates and (3) to assess the effects of multimorbidity on the number of direct and indirect patient contacts as an indicator of doctors' workload.

METHODS

This retrospective observational study used electronic medical records from 142 German general practices, covering 13 years from 1994 to 2007. The four models of multimorbidity ranged from a simple definition, requiring only two diseases, to an advanced definition requiring at least three chronic conditions. We also included a time delimiter for the definition of multimorbidity. Descriptive statistics, such as means and correlation coefficients, were applied.

FINDINGS

The annual percentage of multimorbid primary care patients ranged between 84% (simple model) and 16% (advanced model) and between 74% and 13% if a time delimiter was included. Multimorbid patients had about twice as many contacts annually than the remainder. The number of contacts were different for each model, but the ratio remained similar. The number of contacts correlated moderately with patient age (r = 0.35). The correlation between age and multimorbidity increased from model to model up to 0.28 while the correlations between contacts and multimorbidity varied around 0.2 in all four models.

CONCLUSION

Multimorbidity seems to be less prevalent in primary care practices than usually estimated if advanced definitions of multimorbidity and a temporal delimiter are applied. Although multimorbidity increases in any model a doctor's workload, it is especially the older person with multiple chronic diseases who is a challenge for the GP.

Age- and Sex-Specific Differences in Multimorbidity Patterns and Temporal Trends on Assessing Hospital Discharge Records in Southwest China: Network-Based Study

Background

Multimorbidity represents a global health challenge, which requires a more global understanding of multimorbidity patterns and trends. However, the majority of studies completed to date have often relied on self-reported conditions, and a simultaneous assessment of the entire spectrum of chronic disease co-occurrence, especially in developing regions, has not yet been performed.

Objective

We attempted to provide a multidimensional approach to understand the full spectrum of chronic disease co-occurrence among general inpatients in southwest China, in order to investigate multimorbidity patterns and temporal trends, and assess their age and sex differences.

Methods

We conducted a retrospective cohort analysis based on 8.8 million hospital discharge records of about 5.0 million individuals of all ages from 2015 to 2019 in a megacity in southwest China. We examined all chronic diagnoses using the ICD-10 (International Classification of Diseases, 10th revision) codes at 3 digits and focused on chronic diseases with ≥1% prevalence for each of the age and sex strata, which resulted in a total of 149 and 145 chronic diseases in males and females, respectively. We constructed multimorbidity networks in the general population based on sex and age, and used the cosine index to measure the co-occurrence of chronic diseases. Then, we divided the networks into communities and assessed their temporal trends.

Results

The results showed complex interactions among chronic diseases, with more intensive connections among males and inpatients ≥40 years old. A total of 9 chronic diseases were simultaneously classified as central diseases, hubs, and bursts in the multimorbidity networks. Among them, 5 diseases were common to both males and females, including hypertension, chronic ischemic heart disease, cerebral infarction, other cerebrovascular diseases, and atherosclerosis. The earliest leaps (degree leaps ≥6) appeared at a disorder of glycoprotein metabolism that happened at 25-29 years in males, about 15 years earlier than in females. The number of chronic diseases in the community increased over time, but the new entrants did not replace the root of the community.

Conclusions

Our multimorbidity network analysis identified specific differences in the co-occurrence of chronic diagnoses by sex and age, which could help in the design of clinical interventions for inpatient multimorbidity.

Empiric segmentation of high-risk patients: a structured literature review

OBJECTIVES

Empiric segmentation is a rapidly growing, learning health system approach that uses large health care system data sets to identify groups of high-risk patients who may benefit from similar interventions. We aimed to review studies that used data-driven approaches to segment high-risk patient populations and describe how their designs and findings can inform health care leaders who are interested in applying similar techniques to their patient populations.

STUDY DESIGN

Structured literature review.

METHODS

We searched for original research articles published since 2000 that identified high-risk adult patient populations and applied data-driven analyses to segment the population. Two reviewers independently extracted study population source and criteria for high-risk designation, segmentation method, data types included, model selection criteria, and model results from the identified studies.

RESULTS

Our search identified 224 articles, 12 of which met criteria for full review. Of these, 8 segmented high-risk patients and 4 segmented diagnoses without assigning patients to unique groups. Studies segmenting patients more often had clinically interpretable results. Common groups were defined by high prevalence of diabetes, cardiovascular disease, psychiatric conditions including substance use disorders, and neurologic disease (eg, stroke). Few studies incorporated patients' functional or social factors. Resulting patient and diagnosis clusters varied in ways closely linked to the model inputs, patient population inclusion criteria, and health care system context.

CONCLUSIONS

Empiric segmentation can yield clinically relevant groups of patients with complex medical needs. Segmentation results are context dependent, suggesting the need for careful design and interpretation of segmentation models to ensure that results can inform clinical care and program design in the target setting.

Adjustment for Multimorbidity in Estimations of the Burden of Diseases Using Korean NHIS Data

The current multimorbidity correction method in the Global Burden of Disease studies assumes the independent occurrence of diseases. Those studies use Monte-Carlo simulations to adjust for the presence of multiple disease conditions for all diseases. The present study investigated whether the above-mentioned assumption is reasonable based on the prevalence confirmed from actual data. This study compared multimorbidity-adjusted years of lived with disability (YLD) obtained by Monte-Carlo simulations and multimorbidity-adjusted YLD using multimorbidity prevalence derived from National Health Insurance Service data. The 5 most common diseases by sex and age groups were selected as diseases of interest. No significant differences were found between YLD estimations made using actual data and Monte-Carlo simulations, even though assumptions about the independent occurrence of diseases should be carefully applied. The prevalence was not well reflected according to disease characteristics in those under the age of 30, among whom there was a difference in YLD between the 2 methods. Therefore, when calculating the burden of diseases for Koreans over the age of 30, it is possible to calculate the YLD with correction for multimorbidity through Monte-Carlo simulation, but care should be taken with under-30s. It is useful to apply the efficiency and suitability of calibration for multiplicative methods using Monte-Carlo simulations in research on the domestic disease burden, especially in adults in their 30s and older. Further research should be carried out on multimorbidity correction methodology according to the characteristics of multiple diseases by sex and age.

Comorbidity patterns in institutionalized older adults affected by dementia

Introduction

Dementia is common in nursing homes (NH) residents. Defining dementia comorbidities is instrumental to identify groups of persons with dementia that differ in terms of health trajectories and resources consumption. We performed a cross‐sectional study to identify comorbidity patterns and their associated clinical, behavioral, and functional phenotypes in institutionalized older adults with dementia.

Methods

We analyzed data on 2563 Italian NH residents with dementia, collected between January 2014 and December 2018 using the multidimensional assessment instrument interRAI Long‐Term Care Facility (LTCF). A standard principal component procedure was used to identify comorbidity patterns. Linear regression analyses were used to ascertain correlates of expression of the different patterns.

Results

Among NH residents with dementia, we identified three different comorbidity patterns: (1) heart diseases, (2) cardiovascular and respiratory diseases and sensory impairments, and (3) psychiatric diseases. Older age significantly related to increased expression of the first two patterns, while younger patients displayed increased expression of the third one. Recent hospital admissions were associated with increased expression of the heart diseases pattern (β = 0.028; 95% confidence interval [CI] 0.003 to 0.05). Depressive symptoms and delirium episodes increased the expression of the psychiatric diseases pattern (β = 0.130, 95% CI 0.10 to 0.17, and β 0.130, CI 0.10 to 0.17, respectively), while showed a lower expression of the heart diseases pattern.

Discussion

We identified different comorbidity patterns within NH residents with dementia that differ in term of clinical and functional profiles. The prompt recognition of health needs associated to a comorbidity pattern may help improve long‐term prognosis and quality of life of these individuals.

Highlights

Defining dementia comorbidities patterns in institutionalized older adults is key.

Institutionalized older adults with dementia express different care needs.

Comorbidity patterns are instrumental to identify different patients’ phenotypes.

Phenotypes vary in terms of health trajectories and demand different care plans.

Prompt recognition of phenotypes in nursing homes can positively impact on outcomes.

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.

Phenotypes and subphenotypes of delirium: a review of current categorisations and suggestions for progression

Delirium is a clinical syndrome occurring in heterogeneous patient populations. It affects 45-87% of critical care patients and is often associated with adverse outcomes including acquired dementia, institutionalisation, and death. Despite an exponential increase in delirium research in recent years, the pathophysiological mechanisms resulting in the clinical presentation of delirium are still hypotheses. Efforts have been made to categorise the delirium spectrum into clinically meaningful subgroups (subphenotypes), using psychomotor subtypes such as hypoactive, hyperactive, and mixed, for example, and also inflammatory and non-inflammatory delirium. Delirium remains, however, a constellation of symptoms resulting from a variety of risk factors and precipitants with currently no successful targeted pharmacological treatment. Identifying specific clinical and biological subphenotypes will greatly improve understanding of the relationship between the clinical symptoms and the putative pathways and thus risk factors, precipitants, natural history, and biological mechanism. This will facilitate risk factor mitigation, identification of potential methods for interventional studies, and informed patient and family counselling. Here, we review evidence to date and propose a framework to identify subphenotypes. Endotype identification may be done by clustering symptoms with their biological mechanism, which will facilitate research of targeted treatments. In order to achieve identification of delirium subphenotypes, the following steps must be taken: (1) robust records of symptoms must be kept at a clinical level. (2) Global collaboration must facilitate large, heterogeneous research cohorts. (3) Patients must be clustered for identification, validation, and mapping of subphenotype stability.

Multimorbidity patterns in people with HIV

PURPOSE OF REVIEW

With the progressive aging of populations of people with HIV (PWH), multimorbidity is increasing. Multimorbidity patterns, that is groups of comorbidities that are likely to co-occur, may suggest shared causes or common risk factors. We review the literature regarding multimorbidity patterns identified with data-driven approaches and discuss the methodology and potential implications of the findings.

RECENT FINDINGS

Despite the substantial heterogeneity in the methods used to identify multimorbidity patterns, patterns of mental health problems, cardiovascular diseases, metabolic disorders and musculoskeletal problems are consistently reported in the general population, with patterns of mental health problems, cardiovascular diseases or metabolic disorders commonly reported in PWH. In addition to these, patterns of lifestyle-related comorbidities, such as sexually transmitted diseases, substance use (alcohol, recreational drugs and tobacco smoking) or their complications, seem to occur among PWH.

SUMMARY

Multimorbidity patterns could inform the development of appropriate guidelines for the prevention, monitoring and management of multiple comorbidities in PWH. They can also help to generate new hypotheses on the causes underlying previously known and unknown associations between comorbidities and facilitate the identification of risk factors and biomarkers for specific patterns.

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.

Characterizing chronological accumulation of comorbidities in healthy veterans: a computational approach

Understanding patient accumulation of comorbidities can facilitate healthcare strategy and personalized preventative care. We applied a directed network graph to electronic health record (EHR) data and characterized comorbidities in a cohort of healthy veterans undergoing screening colonoscopy. The Veterans Affairs Cooperative Studies Program #380 was a prospective longitudinal study of screening and surveillance colonoscopy. We identified initial instances of three-digit ICD-9 diagnoses for participants with at least 5 years of linked EHR history (October 1999 to December 2015). For diagnoses affecting at least 10% of patients, we calculated pairwise chronological relative risk (RR). iGraph was used to produce directed graphs of comorbidities with RR > 1, as well as summary statistics, key diseases, and communities. A directed graph based on 2210 patients visualized longitudinal development of comorbidities. Top hub (preceding) diseases included ischemic heart disease, inflammatory and toxic neuropathy, and diabetes. Top authority (subsequent) diagnoses were acute kidney failure and hypertensive chronic kidney failure. Four communities of correlated comorbidities were identified. Close analysis of top hub and authority diagnoses demonstrated known relationships, correlated sequelae, and novel hypotheses. Directed network graphs portray chronologic comorbidity relationships. We identified relationships between comorbid diagnoses in this aging veteran cohort. This may direct healthcare prioritization and personalized care.

Estimating and characterizing the burden of multimorbidity in the community: A comprehensive multistep analysis of two large nationwide representative surveys in France

BACKGROUND

Given the increasing burden of chronic conditions, multimorbidity is now a priority for healthcare and public health systems worldwide. Appropriate methodological approaches for assessing the phenomenon have not yet been established, resulting in inconsistent and incomplete descriptions. We aimed to estimate and characterize the burden of multimorbidity in the adult population in France in terms of number and type of conditions, type of underlying mechanisms, and analysis of the joint effects for identifying combinations with the most deleterious interaction effects on health status.

METHODS AND FINDINGS

We used a multistep approach to analyze cross-sectional and longitudinal data from 2 large nationwide representative surveys: 2010/2014 waves of the Health, Health Care, and Insurance Survey (ESPS 2010-2014) and Disability Healthcare Household Survey 2008 (HSM 2008), that collected similar data on 61 chronic or recurrent conditions. Adults aged ≥25 years in either ESPS 2010 (14,875) or HSM 2008 (23,348) were considered (participation rates were 65% and 62%, respectively). Longitudinal analyses included 7,438 participants of ESPS 2010 with follow-up for mortality (97%) of whom 3,798 were reinterviewed in 2014 (52%). Mortality, activity limitation, self-reported health, difficulties in activities/instrumental activities of daily living, and Medical Outcomes Study Short-Form 12-Item Health Survey were the health status measures. Multiple regression models were used to estimate the impact of chronic or recurrent conditions and multimorbid associations (dyads, triads, and tetrads) on health status. Etiological pathways explaining associations were investigated, and joint effects and interactions between conditions on health status measures were evaluated using both additive and multiplicative scales. Forty-eight chronic or recurrent conditions had an independent impact on mortality, activity limitations, or perceived heath. Multimorbidity prevalence varied between 30% (1-year time frame) and 39% (lifetime frame), and more markedly according to sex (higher in women), age (with greatest increases in middle-aged), and socioeconomic status (higher in less educated and low-income individuals and manual workers). We identified various multimorbid combinations, mostly involving vasculometabolic and musculoskeletal conditions and mental disorders, which could be explained by direct causation, shared or associated risk factors, or less frequently, confounding or chance. Combinations with the highest health impacts included diseases with complications but also associations of conditions affecting systems involved in locomotion and sensorial functions (impact on activity limitations), and associations including mental disorders (impact on perceived health). The interaction effects of the associated conditions varied on a continuum from subadditive and additive (associations involving cardiometabolic conditions, low back pain, osteoporosis, injury sequelae, depression, and anxiety) to multiplicative and supermultiplicative (associations involving obesity, chronic obstructive pulmonary disease, migraine, and certain osteoarticular pathologies). Study limitations included self-reported information on chronic conditions and the insufficient power of some analyses.

CONCLUSIONS

Multimorbidity assessments should move beyond simply counting conditions and take into account the variable impacts on health status, etiological pathways, and joint effects of associated conditions. In particular, the multimorbid combinations with substantial health impacts or shared risk factors deserve closer attention. Our findings also suggest that multimorbidity assessment and management may be beneficial already in midlife and probably earlier in disadvantaged groups.

Prevalence and Patterns of Multimorbidity in a Nationally Representative Sample of Older Chinese: Results From the China Health and Retirement Longitudinal Study

BACKGROUND

Multimorbidity has become a prominent problem worldwide; however, few population-based studies have been conducted among older Chinese with multimorbidity. This study aimed to examine the prevalence of multimorbidity and explore its common patterns among a nationally representative sample of older Chinese.

METHODS

This study used data from the China Health and Retirement Longitudinal Study and included 19,841 participants aged at least 50 years. The prevalence of individual chronic diseases and multimorbidity during 2011-2015 were evaluated among the entire cohort and according to residential regions and gender. The relationships between participants' demographic characteristics and multimorbidity were examined using logistic regression model. Patterns of multimorbidity were explored using hierarchical cluster analysis and association rule mining.

RESULTS

Multimorbidity occurred in 42.4% of the participants. The prevalence of multimorbidity was higher among women (odds ratio [OR] = 1.31, 95% confidence interval [CI]: 1.13-1.51) and urban residents (OR = 1.14, 95% CI: 1.02-1.27) than their respective counterparts after accounting for potential confounders of age, education, smoking, and alcohol consumption. Hierarchical cluster analysis revealed four common multimorbidity patterns: the vascular-metabolic cluster, the stomach-arthritis cluster, the cognitive-emotional cluster, and the hepatorenal cluster. Regional differences were found in the distributions of stroke and memory-related disease. Most combinations of conditions and urban-rural difference in multimorbidity patterns from hierarchical cluster analysis were also observed in association rule mining.

CONCLUSION

The prevalence and patterns of multimorbidity vary by gender and residential regions among older Chinese. Women and urban residents are more vulnerable to multimorbidity. Future studies are needed to understand the mechanisms underlying the identified multimorbidity patterns and their policy and interventional implications.

AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity

Multimorbidity refers to the coexistence of two or more chronic diseases in one person. Therefore, patients with multimorbidity have multiple and special care needs. However, in practice it is difficult to meet these needs because the organizational processes of current healthcare systems tend to be tailored to a single disease. To improve clinical decision making and patient care in multimorbidity, a radical change in the problem-solving approach to medical research and treatment is needed. In addition to the traditional reductionist approach, we propose interactive research supported by artificial intelligence (AI) and advanced big data analytics. Such research approach, when applied to data routinely collected in healthcare settings, provides an integrated platform for research tasks related to multimorbidity. This may include, for example, prediction, correlation, and classification problems based on multiple interaction factors. However, to realize the idea of this paradigm shift in multimorbidity research, the optimization, standardization, and most importantly, the integration of electronic health data into a common national and international research infrastructure is needed. Ultimately, there is a need for the integration and implementation of efficient AI approaches, particularly deep learning, into clinical routine directly within the workflows of the medical professionals.

Multimorbidity patterns among COVID-19 deaths: proposal for the construction of etiological models

OBJECTIVES

To describe patterns of multimorbidity among fatal cases of COVID-19, and to propose a classification of patients based on age and multimorbidity patterns to begin the construction of etiological models.

METHODS

Data of Colombian confirmed deaths of COVID-19 until June 11, 2020, were included in this analysis (n=1488 deaths). Relationships between COVID-19, combinations of health conditions and age were explored using locally weighted polynomial regressions.

RESULTS

The most frequent health conditions were high blood pressure, respiratory disease, diabetes, cardiovascular disease, and kidney disease. Dyads more frequents were high blood pressure with diabetes, cardiovascular disease or respiratory disease. Some multimorbidity patterns increase probability of death among older individuals, whereas other patterns are not age-related, or decrease the probability of death among older people. Not all multimorbidity increases with age, as is commonly thought. Obesity, alone or with other diseases, was associated with a higher risk of severity among young people, while the risk of the high blood pressure/diabetes dyad tends to have an inverted U distribution in relation with age.

CONCLUSIONS

Classification of individuals according to multimorbidity in the medical management of COVID-19 patients is important to determine the possible etiological models and to define patient triage for hospitalization. Moreover, identification of non-infected individuals with high-risk ages and multimorbidity patterns serves to define possible interventions of selective confinement or special management.

Learning multimorbidity patterns from electronic health records using Non-negative Matrix Factorisation

Multimorbidity, or the presence of several medical conditions in the same individual, has been increasing in the population - both in absolute and relative terms. Nevertheless, multimorbidity remains poorly understood, and the evidence from existing research to describe its burden, determinants and consequences has been limited. Previous studies attempting to understand multimorbidity patterns are often cross-sectional and do not explicitly account for multimorbidity patterns' evolution over time; some of them are based on small datasets and/or use arbitrary and narrow age ranges; and those that employed advanced models, usually lack appropriate benchmarking and validations. In this study, we (1) introduce a novel approach for using Non-negative Matrix Factorisation (NMF) for temporal phenotyping (i.e., simultaneously mining disease clusters and their trajectories); (2) provide quantitative metrics for the evaluation of these clusters and trajectories; and (3) demonstrate how the temporal characteristics of the disease clusters that result from our model can help mine multimorbidity networks and generate new hypotheses for the emergence of various multimorbidity patterns over time. We trained and evaluated our models on one of the world's largest electronic health records (EHR) datasets, containing more than 7 million patients, from which over 2 million where relevant to, and hence included in this study.

Europe's War against COVID-19: A Map of Countries' Disease Vulnerability Using Mortality Indicators

Specific and older age-associated comorbidities increase mortality risk in severe forms of coronavirus disease (COVID-19). We matched COVID-19 comorbidities with causes of death in 28 EU countries for the total population and for the population above 65 years and applied a machine-learning-based tree clustering algorithm on shares of death for COVID-19 comorbidities and for influenza and on their growth rates between 2011 and 2016. We distributed EU countries in clusters and drew a map of the EU populations’ vulnerabilities to COVID-19 comorbidities and to influenza. Noncommunicable diseases had impressive shares of death in the EU but with substantial differences between eastern and western countries. The tree clustering algorithm accurately indicated the presence of western and eastern country clusters, with significantly different patterns of disease shares of death and growth rates. Western populations displayed higher vulnerability to malignancy, blood-related diseases, and diabetes mellitus and lower respiratory diseases, while eastern countries’ populations suffered more from ischaemic heart, cerebrovascular, and circulatory diseases. Dissimilarities between EU countries were also present when influenza was considered. The heat maps of EU populations’ vulnerability to diseases based on mortality indicators constitute the basis for more targeted health policy strategies in a collaborative effort at the EU level.

Patterns of patients with multiple chronic conditions in primary care: A cross-sectional study

Objective

Our aim was to identify the patterns of multimorbidity among a group of patients who visited primary care in Singapore.

Methods

A cross-sectional study of electronic medical records was conducted on 437,849 individuals aged 0–99 years who visited National Healthcare Group Polyclinics from 1 Jul 2015 to 30 Jun 2016 for the management of chronic conditions. Patients’ health conditions were coded with the 10^th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10), and patient records were extracted for analysis. Patients’ diagnosis codes were grouped by exploratory factor analysis (EFA), and patterns of multimorbidity were then identified by latent class analysis (LCA).

Results

EFA identified 19 groups of chronic conditions. Patients with at least three chronic conditions were further separated into eight classes based on demographics and probabilities of various diagnoses. We found that older patients had higher probabilities of comorbid hypertension, kidney disease and ischaemic heart disease (IHD), while younger patients had a higher probability of comorbid obesity. Female patients had higher probabilities of comorbid arthritis and anaemia, while male patients had higher probabilities of comorbid kidney diseases and IHD. Indian patients presented with a higher probability of comorbid diabetes than Chinese and Malay patients.

Conclusions

This study demonstrated that patients with multimorbidity in primary care could be classified into eight patterns. This knowledge could be useful for more precise management of these patients in the multiethnic Asian population of Singapore. Programmes for early intervention for at-risk groups can be developed based on the findings.

Untangling the complexity of multimorbidity with machine learning

The prevalence of multimorbidity has been increasing in recent years, posing a major burden for health care delivery and service. Understanding its determinants and impact is proving to be a challenge yet it offers new opportunities for research to go beyond the study of diseases in isolation. In this paper, we review how the field of machine learning provides many tools for addressing research challenges in multimorbidity. We highlight recent advances in promising methods such as matrix factorisation, deep learning, and topological data analysis and how these can take multimorbidity research beyond cross-sectional, expert-driven or confirmatory approaches to gain a better understanding of evolving patterns of multimorbidity. We discuss the challenges and opportunities of machine learning to identify likely causal links between previously poorly understood disease associations while giving an estimate of the uncertainty on such associations. We finally summarise some of the challenges for wider clinical adoption of machine learning research tools and propose some solutions.

Prevalence and patterns of multimorbidity in the Jamaican population: A comparative analysis of latent variable models

Background

Evidence suggests that the single-disease paradigm does not accurately reflect the individual experience, with increasing prevalence of chronic disease multimorbidity, and subtle yet important differences in types of co-occurring diseases. Knowledge of multimorbidity patterns can aid clarification of individual-level burden and needs, to inform prevention and treatment strategies. This study aimed to estimate the prevalence of multimorbidity in Jamaica, identify population subgroups with similar and distinct disease profiles, and examine consistency in patterns identified across statistical techniques.

Methods

Latent class analysis (LCA) was used to examine multimorbidity patterns in a sample of 2,551 respondents aged 15–74 years, based on data from the nationally representative Jamaica Health and Lifestyle Survey 2007/2008 and self-reported presence/absence of 11 chronic conditions. Secondary analyses compared results with patterns identified using exploratory factor analysis (EFA).

Results

Nearly one-quarter of the sample (24.1%) were multimorbid (i.e. had ≥2 diseases), with significantly higher burden in females compared to males (31.6% vs. 16.1%; p<0.001). LCA revealed four distinct classes, including a predominant Relatively Healthy class, comprising 52.7% of the sample, with little to no morbidity. The remaining three classes were characterized by varying degrees and patterns of multimorbidity and labelled Metabolic (30.9%), Vascular-Inflammatory (12.2%), and Respiratory (4.2%). Four diseases determined using physical assessments (obesity, hypertension, diabetes, hypercholesterolemia) were primary contributors to multimorbidity patterns overall. EFA identified three patterns described as “Vascular” (hypertension, obesity, hypercholesterolemia, diabetes, stroke); “Respiratory” (asthma, COPD); and “Cardio-Mental-Articular” (cardiovascular disease, arthritis, mental disorders).

Conclusion

This first study of multimorbidity in the Caribbean has revealed a high burden of co-existing conditions in the Jamaican population, that is predominantly borne by females. Consistency across methods supports the validity of patterns identified. Future research into the causes and consequences of multimorbidity patterns can guide development of clinical and public health strategies that allow for targeted prevention and intervention.

Patterns of Multimorbidity in Adults: An Association Rules Analysis Using the Korea Health Panel

This study aimed to identify the prevalence and patterns of multimorbidity among Korean adults. A descriptive study design was used. Of 11,232 adults aged 18 and older extracted from the 2014 Korean Health Panel Survey, 7118 had one or more chronic conditions. The chronic conditions code uses the Korean Standard Classification of Diseases. Association rule analysis and network analysis were conducted to identify patterns of multimorbidity among 4922 participants with multimorbidity. The prevalence of multimorbidity in the overall population was 34.8%, with a higher prevalence among women (40.8%) than men (28.6%). Hypertension had the highest prevalence in both men and women. In men, diabetes mellitus and hypertension yielded the highest probability of comorbidity (10.04%). In women, polyarthrosis and hypertension yielded the highest probability of comorbidity (12.51%). The results of the network analysis in four groups divided according to gender and age showed different characteristics for each group. Public health practitioners should adopt an integrated approach to manage multimorbidity rather than an individual disease-specific approach, along with different strategies according to age and gender groups.

Identifying obesity-related multimorbidity combinations in the United States

Interest in understanding the effects of multimorbidity on outcomes has increased in recent years. This paper presents the most common obesity-related groupings of multimorbidity in the United States. Using Cerner HealthFacts data, we applied the frequent pattern growth algorithm to identify prevalent multimorbidity groupings of 3 or more diseases (one being obesity) by race using a dataset of 574 172 patients with obesity from all over the United States. We set the minimum prevalence to 10% and identified groupings of ICD10-CM diagnoses that occur in our dataset at or above the minimum prevalence level. We provide binomial proportion confidence interval estimates to demonstrate the validity of the proportions. We performed g-test for independence to validate differences in prevalence by race. We found 18 multimorbidity combinations with prevalence higher than or equal to 10%. Our results indicate that there are multiple common multimorbidities groupings for patients with obesity. Each multimorbidity combination is composed of diseases from the following clinical categories: endocrine, nutritional and metabolic diseases; diseases of the circulatory system; diseases of the digestive system; diseases of the nervous system; and diseases of the musculoskeletal system and connective tissue. For each multimorbidity pattern, the prevalence was found to be significantly different by race according to the g-test with P-value < .001. Most frequent patterns include essential hypertension or disorder of lipid metabolism. This study identifies common groupings of multimorbidity. We believe our data can be useful for those developing integrated care plans, particularly for those serving diverse communities.

Long-term outcome after ischemic stroke in relation to comorbidity - An observational study from the Swedish Stroke Register (Riksstroke)

Purpose

Comorbidity in stroke is common, but comprehensive reports are sparse. We describe prevalence of comorbidity and the prognostic impact on mortality and functional outcome in a large national ischemic stroke cohort.

Methods

We used outcome data from a long-term follow-up survey conducted in 2016 by the Swedish Stroke Register (Riksstroke). Those included in the study were 11 775 pre-stroke functionally independent patients with first-ever ischemic stroke followed up at three months and 12 months (all patients), and three years (2013 cohort) or five years (2011 cohort). Pre-stroke comorbidity data for 16 chronic conditions were obtained from the Swedish National Patient Register, the Swedish Prescribed Drugs Register and the Riksstroke register. Individuals were grouped according to number of conditions: none (0), low (1), moderate (2-3) or high (≥4). Co-occurrence was analysed using hierarchical clustering, and multivariable analyses were used to estimate the prognostic significance of individual conditions.

Results

The proportion of patients without comorbidity was 24.8%; 31.8% had low comorbidity; 33.5% had moderate comorbidity and 9.9% had high comorbidity. At 12 months, the proportion of poor outcome (dead or dependent: mRS ≥3) was 24.8% (no comorbidity), 34.7% (low), 45.2% (moderate) and 59.4% (high). At five years, these proportions were 37.7%, 50.3%, 64.3%, and 81.7%, respectively. There was clustering of cardiovascular conditions and substantial negative effects of dementia, kidney, and heart failure.

Conclusion

Comorbidity is common and has a strong impact on mortality and functional outcome. Our results highlight the need for health systems to shift focus to a comprehensive approach in stroke care that includes multimorbidity as a key component.

Medication patterns in older adults with multimorbidity: a cluster analysis of primary care patients

BACKGROUND

Older adults suffer from various chronic conditions which make them particularly vulnerable. The proper management of multiple drug use is therefore crucial. The aim of our study was to describe drug prescription and medication patterns in this population.

METHODS

A cross-sectional study in Barcelona (Spain) using electronic health records from 50 primary healthcare centres. Participants were aged 65 to 94 years, presenting multimorbidity (≥2 chronic diseases), and had been prescribed at least 1 drug for 6 months or longer during 2009. We calculated the prevalence of prescribed drugs and identified medication patterns using multiple correspondence analysis and k-means clustering. Analyses were stratified by sex and age (65-79, 80-94 years).

RESULTS

We studied 164,513 patients (66.8% women) prescribed a median of 4 drugs (interquartile range [IQR] = 3-7) in the 65-79 age-group and 6 drugs (IQR = 4-8) in the 80-94 age-group. A minimum of 45.9% of patients aged 65-79 years, and 61.8% of those aged 80-94 years, were prescribed 5 or more drugs. We identified 6 medication patterns, a non-specific one and 5 encompassing 8 anatomical groups (alimentary tract and metabolism, blood, cardiovascular, dermatological, musculo-skeletal, neurological, respiratory, and sensory organ).

CONCLUSIONS

Drug prescription is widespread among the elderly. Six medication patterns were identified, 5 of which were related to one or more anatomical group, with associations among drugs from different systems. Overall, guidelines do not accurately reflect the situation of the elderly multimorbid, new strategies for managing multiple drug uses are needed to optimize prescribing in these patients.

Multimorbidity patterns with K-means nonhierarchical cluster analysis

BACKGROUND

The purpose of this study was to ascertain multimorbidity patterns using a non-hierarchical cluster analysis in adult primary patients with multimorbidity attended in primary care centers in Catalonia.

METHODS

Cross-sectional study using electronic health records from 523,656 patients, aged 45-64 years in 274 primary health care teams in 2010 in Catalonia, Spain. Data were provided by the Information System for the Development of Research in Primary Care (SIDIAP), a population database. Diagnoses were extracted using 241 blocks of diseases (International Classification of Diseases, version 10). Multimorbidity patterns were identified using two steps: 1) multiple correspondence analysis and 2) k-means clustering. Analysis was stratified by sex.

RESULTS

The 408,994 patients who met multimorbidity criteria were included in the analysis (mean age, 54.2 years [Standard deviation, SD: 5.8], 53.3% women). Six multimorbidity patterns were obtained for each sex; the three most prevalent included 68% of the women and 66% of the men, respectively. The top cluster included coincident diseases in both men and women: Metabolic disorders, Hypertensive diseases, Mental and behavioural disorders due to psychoactive substance use, Other dorsopathies, and Other soft tissue disorders.

CONCLUSION

Non-hierarchical cluster analysis identified multimorbidity patterns consistent with clinical practice, identifying phenotypic subgroups of patients.