Reducing complexity: a visualisation of multimorbidity by combining disease clusters and triads

Choices of measures of association affect the visualisation and composition of the multimorbidity networks

BACKGROUND

Network analysis, commonly used to describe the patterns of multimorbidity, uses the strength of association between conditions as weight to classify conditions into communities and calculate centrality statistics. Our aim was to examine the robustness of the results to the choice of weight.

METHODS

Data used on 27 chronic conditions listed on Australian death certificates for women aged 85+. Five statistics were calculated to measure the association between 351 possible pairs: odds ratio (OR), lift, phi correlation, Salton cosine index (SCI), and normalised-joint frequency of pairs (NF). Network analysis was performed on the 10% of pairs with the highest weight according to each definition, the 'top pairs'.

RESULTS

Out of 56 'top pairs' identified, 13 ones were consistent across all statistics. In networks of OR and lift, three of the conditions which did not join communities were among the top five most prevalent conditions. Networks based on phi and NF had one or two conditions not part of any community. For the SCI statistics, all three conditions which did not join communities had prevalence below 3%. Low prevalence conditions were more likely to have high degree in networks of OR and lift but not SCI.

CONCLUSION

Use of different statistics to estimate weights leads to different networks. For exploratory purposes, one may apply alternative weights to identify a large list of pairs for further assessment in independent studies. However, when the aim is to visualise the data in a robust and parsimonious network, only pairs which are selected by multiple statistics should be visualised.

Musculoskeletal Diseases as the Most Prevalent Component of Multimorbidity: A Population-Based Study

Background/Objectives: Due to their high frequency, common risk factors, and similar pathogenic mechanisms, musculoskeletal disorders (MSDs) are more likely to occur with other chronic illnesses, making them a "component disorder" of multimorbidity. Our objective was to assess the prevalence of multimorbidity and to identify the most common clusters of diagnosis within multimorbidity states, with the primary hypothesis that the most common clusters of multimorbidity are MSDs. Methods: The current study employed data from a population-based 2019 European Health Interview Survey (EHIS). Multimorbidity was defined as a ≥2 diagnosis from the list of 17 chronic non-communicable diseases, and to define clusters, the statistical method of hierarchical cluster analysis (HCA) was performed. Results: Out of 13,178 respondents, multimorbidity was present among 4398 (33.4%). The HCA method yielded six multimorbidity clusters representing the most common diagnoses. The primary multimorbidity cluster, which was prevalent among both genders, age groups, incomes per capita, and statistical regions, consisted of three diagnoses: (1) lower spine deformity or other chronic back problem (back pain), (2) cervical deformity or other chronic problem with the cervical spine, and (3) osteoarthritis. Conclusions: Given the influence of musculoskeletal disorders on multimorbidity, it is imperative to implement appropriate measures to assist patients in relieving the physical discomfort and pain they endure. Public health information, programs, and campaigns should be utilized to promote a healthy lifestyle. Policymakers should prioritize the prevention of MSDs by encouraging increased physical activity and a healthy diet, as well as focusing on improving functional abilities.

Clusters of medical specialties around patients with multimorbidity - employing fuzzy c-means clustering to explore multidisciplinary collaboration

BACKGROUND

Hospital care organization, structured around medical specialties and focused on the separate treatment of individual organ systems, is challenged by the increasing prevalence of multimorbidity. To support the hospitals' realization of multidisciplinary care, we hypothesized that using machine learning on clinical data helps to identify groups of medical specialties who are simultaneously involved in hospital care for patients with multimorbidity.

METHODS

We conducted a cross-sectional study of patients in a Dutch general hospital and used a fuzzy c-means clustering algorithm for the analysis. We explored the patients' membership degrees in each cluster to identify subgroups of medical specialties that provide care to the same patients with multimorbidity. We used retrospectively collected electronic health record data from 2017. We extracted data from 22,133 patients aged ≥18 years who had received outpatient clinical care for two or more chronic and/ or oncological diagnoses.

RESULTS

We found six clusters of medical specialties and identified 22 subgroups. The clusters were labeled based on the specialties that most characterized them: 1. dermatology/ plastic surgery, 2. six specialties (gynecology/ rheumatology/ orthopedic surgery/ urology/ gastroenterology/ otorhinolaryngology), 3. pulmonology, 4. internal medicine/ cardiology/ geriatrics, 5. neurology/ physiatry (rehabilitation)/ anesthesiology, and 6. internal medicine. Most patients had a full or dominant membership to one of these clusters of medical specialties (11 subgroups), whereas fewer patients had a membership to two clusters. The prevalence of specific diagnosis groups, patient characteristics, and healthcare utilization differed between subgroups.

CONCLUSION

Our study shows that clusters and subgroups of medical specialties simultaneously involved in hospital care for patients with multimorbidity can be identified with fuzzy c-means cluster analysis using clinical data. Clusters and subgroups differed regarding the involved medical specialties, diagnoses, patient characteristics, and healthcare utilization. With this strategy, hospitals and medical specialists can further analyze which subgroups are target populations that might benefit from improved multidisciplinary collaboration.

Multi-Morbidity and Risk of Breast Cancer among Women in the UK Biobank Cohort

(Multi-)Morbidity shares common biological mechanisms or risk factors with breast cancer. This study aimed to investigate the association between the number of morbidities and patterns of morbidity and the risk of female breast cancer. Among 239,436 women (40-69 years) enrolled in the UK Biobank cohort who had no cancer history at baseline, we identified 35 self-reported chronic diseases at baseline. We assigned individuals into morbidity patterns using agglomerative hierarchical clustering analysis. We fitted Cox models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for breast cancer risk. In total, 58.4% of women had at least one morbidity, and the prevalence of multi-morbidity was 25.8%. During a median 7-year follow-up, there was no association between breast cancer risk (5326 cases) and either the number of morbidities or the identified clinically relevant morbidity patterns: no-predominant morbidity (reference), psychiatric morbidities (HR = 1.04, 95%CI 0.94-1.16), respiratory/immunological morbidities (HR = 0.98, 95%CI 0.90-1.07), cardiovascular/metabolic morbidities (HR = 0.93, 95%CI 0.81-1.06), and unspecific morbidities (HR = 0.98, 95%CI 0.89-1.07), overall. Among women younger than 50 years of age only, however, there was a significant association with psychiatric morbidity patterns compared to the no-predominant morbidity pattern (HR = 1.25, 95%CI 1.02-1.52). The other associations did not vary when stratifying by age at baseline and adherence to mammography recommendations. In conclusion, multi-morbidity was not a key factor to help identify patients at an increased risk of breast cancer.

Multimorbidity transitions and the associated healthcare cost among the Finnish adult population during a two-year follow-up

Background

Ageing of the population increases the prevalence and coexistence of many chronic diseases; a condition called multimorbidity. In Finland, information on the significance of multimorbidity and its relation to the sustainability of healthcare is scarce.

Aim

To assess the prevalence of multimorbidity, the transitions between patient groups with and without multiple diseases and the associated healthcare cost in Finland in 2017-2019.

Methods

A register-based cohort study covering all adults (n = 3,326,467) who used Finnish primary or specialised healthcare services in 2017. At baseline, patients were classified as 'non-multimorbid', 'multimorbid' or 'multimorbid at risk' based on the recordings of a diagnosis of interest. The costs were calculated using the care-related patient grouping and national standard rates. Transition plots were drawn to observe the transition of patients and costs between groups during the two-year follow-up.

Results

At baseline, 62% of patients were non-multimorbid, 23% multimorbid and 15% multimorbid at risk. In two years, the proportion of multimorbid patients increased, especially those at risk. Within the multimorbid at-risk group, total healthcare costs were greatest (€5,027 million), accounting for 62% of the total healthcare cost of the overall patient cohort in 2019. Musculoskeletal diseases, cardiometabolic diseases and tumours were the most common and expensive chronic diseases contributing to the onset of multimorbidity.

Conclusion

Multimorbidity is causing a heavy burden on Finnish healthcare. The estimates of its effect on healthcare usage and costs should be used to guide healthcare planning.

Patterns of patients with polypharmacy in adult population from Korea

Polypharmacy and its rising global prevalence is a growing public health burden. Using a large representative nationwide Korean cohort (N = 761,145), we conducted a retrospective cross-sectional study aiming to identify subpopulations of patients with polypharmacy and characterize their unique patterns through cluster analysis. Patients aged ≥ 30 years who were prescribed at least one medication between 2014 and 2018 were included in our study. Six clusters were identified: cluster 1 mostly included patients who were hospitalized for a long time (4.3 ± 5.3 days); cluster 2 consisted of patients with disabilities (100.0%) and had the highest mean number of prescription drugs (7.7 ± 2.8 medications); cluster 3 was a group of low-income patients (99.9%); cluster 4 was a group of high-income patients (80.2%) who frequently (46.4 ± 25.9 days) visited hospitals/clinics (7.3 ± 2.7 places); cluster 5 was mostly elderly (74.9 ± 9.8 years) females (80.3%); and cluster 6 comprised mostly middle-aged (56.4 ± 1.5 years) males (88.6%) (all P < 0.001). Patients in clusters 1-5 had more prescribed medications and outpatient visit days than those in cluster 6 (all P < 0.001). Given limited health care resources, individuals with any of the identified phenotypes may be preferential candidates for participation in intervention programs for optimal medication use.

Comparing machine learning algorithms for multimorbidity prediction: An example from the Elsa-Brasil study

BACKGROUND

Multimorbidity is a worldwide concern related to greater disability, worse quality of life, and mortality. The early prediction is crucial for preventive strategies design and integrative medical practice. However, knowledge about how to predict multimorbidity is limited, possibly due to the complexity involved in predicting multiple chronic diseases.

METHODS

In this study, we present the use of a machine learning approach to build cost-effective multimorbidity prediction models. Based on predictors easily obtainable in clinical practice (sociodemographic, clinical, family disease history and lifestyle), we build and compared the performance of seven multilabel classifiers (multivariate random forest, and classifier chain, binary relevance and binary dependence, with random forest and support vector machine as base classifiers), using a sample of 15105 participants from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). We developed a web application for the building and use of prediction models.

RESULTS

Classifier chain with random forest as base classifier performed better (accuracy = 0.34, subset accuracy = 0.15, and Hamming Loss = 0.16). For different feature sets, random forest based classifiers outperformed those based on support vector machine. BMI, blood pressure, sex, and age were the features most relevant to multimorbidity prediction.

CONCLUSIONS

Our results support the choice of random forest based classifiers for multimorbidity prediction.

Applying a biopsychosocial model to migraine: rationale and clinical implications

Migraine is a complex condition in which genetic predisposition interacts with other biological and environmental factors determining its course. A hyperresponsive brain cortex, peripheral and central alterations in pain processing, and comorbidities play a role from an individual biological standpoint. Besides, dysfunctional psychological mechanisms, social and lifestyle factors may intervene and impact on the clinical phenotype of the disease, promote its transformation from episodic into chronic migraine and may increase migraine-related disability.Thus, given the multifactorial origin of the condition, the application of a biopsychosocial approach in the management of migraine could favor therapeutic success. While in chronic pain conditions the biopsychosocial approach is already a mainstay of treatment, in migraine the biomedical approach is still dominant. It is instead advisable to carefully consider the individual with migraine as a whole, in order to plan a tailored treatment. In this review, we first reported an analytical and critical discussion of the biological, psychological, and social factors involved in migraine. Then, we addressed the management implications of the application of a biopsychosocial model discussing how the integration between non-pharmacological management and conventional biomedical treatment may provide advantages to migraine care.

The effect of disease co-occurrence measurement on multimorbidity networks: a population-based study

Background

Network analysis, a technique for describing relationships, can provide insights into patterns of co-occurring chronic health conditions. The effect that co-occurrence measurement has on disease network structure and resulting inferences has not been well studied. The purpose of the study was to compare structural differences among multimorbidity networks constructed using different co-occurrence measures.

Methods

A retrospective cohort study was conducted using four fiscal years of administrative health data (2015/16 – 2018/19) from the province of Manitoba, Canada (population 1.5 million). Chronic conditions were identified using diagnosis codes from electronic records of physician visits, surgeries, and inpatient hospitalizations, and grouped into categories using the Johns Hopkins Adjusted Clinical Group (ACG) System. Pairwise disease networks were separately constructed using each of seven co-occurrence measures: lift, relative risk, phi, Jaccard, cosine, Kulczynski, and joint prevalence. Centrality analysis was limited to the top 20 central nodes, with degree centrality used to identify potentially influential chronic conditions. Community detection was used to identify disease clusters. Similarities in community structure between networks was measured using the adjusted Rand index (ARI). Network edges were described using disease prevalence categorized as low (< 1%), moderate (1 to < 7%), and high (≥7%). Network complexity was measured using network density and frequencies of nodes and edges.

Results

Relative risk and lift highlighted co-occurrences between pairs of low prevalence health conditions. Kulczynski emphasized relationships between high and low prevalence conditions. Joint prevalence focused on highly-prevalent conditions. Phi, Jaccard, and cosine emphasized associations involving moderately prevalent conditions. Co-occurrence measurement differences significantly affected the number and structure of identified disease clusters. When limiting the number of edges to produce visually interpretable graphs, networks had significant dissimilarity in the percentage of co-occurrence relationships in common, and in their selection of the highest-degree nodes.

Conclusions

Multimorbidity network analyses are sensitive to disease co-occurrence measurement. Co-occurrence measures should be selected considering their intrinsic properties, research objectives, and the health condition prevalence relationships of greatest interest. Researchers should consider conducting sensitivity analyses using different co-occurrence measures.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01607-8.

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.

Multimorbidity and co-occurring musculoskeletal pain do not modify the effect of the SELFBACK app on low back pain-related disability

BACKGROUND

SELFBACK, an artificial intelligence (AI)-based app delivering evidence-based tailored self-management support to people with low back pain (LBP), has been shown to reduce LBP-related disability when added to usual care. LBP commonly co-occurs with multimorbidity (≥ 2 long-term conditions) or pain at other musculoskeletal sites, so this study explores if these factors modify the effect of the SELFBACK app or influence outcome trajectories over time.

METHODS

Secondary analysis of a randomized controlled trial with 9-month follow-up. Primary outcome is as follows: LBP-related disability (Roland Morris Disability Questionnaire, RMDQ). Secondary outcomes are as follows: stress/depression/illness perception/self-efficacy/general health/quality of life/physical activity/global perceived effect. We used linear mixed models for continuous outcomes and logistic generalized estimating equation for binary outcomes. Analyses were stratified to assess effect modification, whereas control (n = 229) and intervention (n = 232) groups were pooled in analyses of outcome trajectories.

RESULTS

Baseline multimorbidity and co-occurring musculoskeletal pain sites did not modify the effect of the SELFBACK app. The effect was somewhat stronger in people with multimorbidity than among those with LBP only (difference in RMDQ due to interaction, - 0.9[95 % CI - 2.5 to 0.6]). Participants with a greater number of long-term conditions and more co-occurring musculoskeletal pain had higher levels of baseline disability (RMDQ 11.3 for ≥ 2 long-term conditions vs 9.5 for LBP only; 11.3 for ≥ 4 musculoskeletal pain sites vs 10.2 for ≤ 1 additional musculoskeletal pain site); along with higher baseline scores for stress/depression/illness perception and poorer pain self-efficacy/general health ratings. In the pooled sample, LBP-related disability improved slightly less over time for people with ≥ 2 long-term conditions additional to LBP compared to no multimorbidity and for those with ≥4 co-occurring musculoskeletal pain sites compared to ≤ 1 additional musculoskeletal pain site (difference in mean change at 9 months = 1.5 and 2.2, respectively). All groups reported little improvement in secondary outcomes over time.

CONCLUSIONS

Multimorbidity or co-occurring musculoskeletal pain does not modify the effect of the selfBACK app on LBP-related disability or other secondary outcomes. Although people with these health problems have worse scores both at baseline and 9 months, the AI-based selfBACK app appears to be helpful for those with multimorbidity or co-occurring musculoskeletal pain.

TRIAL REGISTRATION

NCT03798288 . Date of registration: 9 January 2019.

Multimorbidity and mortality: A data science perspective

Background

With multimorbidity becoming the norm rather than the exception, the management of multiple chronic diseases is a major challenge facing healthcare systems worldwide.

Methods

Using a large, nationally representative database of electronic medical records from the United Kingdom spanning the years 2005–2016 and consisting over 4.5 million patients, we apply statistical methods and network analysis to identify comorbid pairs and triads of diseases and identify clusters of chronic conditions across different demographic groups. Unlike many previous studies, which generally adopt cross-sectional designs based on single snapshots of closed cohorts, we adopt a longitudinal approach to examine temporal changes in the patterns of multimorbidity. In addition, we perform survival analysis to examine the impact of multimorbidity on mortality.

Results

The proportion of the population with multimorbidity has increased by approximately 2.5 percentage points over the last decade, with more than 17% having at least two chronic morbidities. We find that the prevalence and the severity of multimorbidity, as quantified by the number of co-occurring chronic conditions, increase progressively with age. Stratifying by socioeconomic status, we find that people living in more deprived areas are more likely to be multimorbid compared to those living in more affluent areas at all ages. The same trend holds consistently for all years in our data. In general, hypertension, diabetes, and respiratory-related diseases demonstrate high in-degree centrality and eigencentrality, while cardiac disorders show high out-degree centrality.

Conclusions

We use data-driven methods to characterize multimorbidity patterns in different demographic groups and their evolution over the past decade. In addition to a number of strongly associated comorbid pairs (e.g., cardiac-vascular and cardiac-metabolic disorders), we identify three principal clusters: a respiratory cluster, a cardiovascular cluster, and a mixed cardiovascular-renal-metabolic cluster. These are supported by established pathophysiological mechanisms and shared risk factors, and largely confirm and expand on the results of existing studies in the medical literature. Our findings contribute to a more quantitative understanding of the epidemiology of multimorbidity, an important pre-requisite for developing more effective medical care and policy for multimorbid patients.

Exploring the non-communicable disease (NCD) network of multi-morbid individuals in India: A network analysis

Nationally representative evidence discussing the interplay of non-communicable diseases (diseases) are scarce in India. Therefore, the present study aims to fill this research void by providing empirical evidence on disease networking using a large nationally representative cross-sectional sample segregated by gender among older adults in India. The analysis utilized data on 10,606 multimorbid women and 7,912 multimorbid men from the Longitudinal Ageing Study in India (LASI), 2017-18. Multimorbidity was defined as the co-occurrence of two or more diseases in an individual using a list of 16 self-reported diseases. Weighted networks were visualized to illustrates the complex relationships between the diseases using network analysis. The findings suggest that women possess a higher burden of multimorbidity than men. Hypertension, musculoskeletal disorder, gastrointestinal disorder, diabetes mellitus, and skin diseases were reported as the most recurrent diseases. 'Hypertension-musculoskeletal disorder', 'diabetes mellitus-hypertension', 'gastrointestinal disorders-hypertension' and 'gastrointestinal disorders- musculoskeletal disorder' were recurrent disease combinations among the multimorbid individuals. The study generated compelling evidence to establish that there are statistically significant differences between the prevalence of diseases and how they interact with each other between women and men. These findings further accentuate that disease networks are slightly more complex among women. In totality, the study visualizes disease association, identifies the most influential diseases to the network, and those which acts as a bridge between other diseases, causing multimorbidity among the older adult population in India.

Another Look at Obesity Paradox in Acute Ischemic Stroke: Association Rule Mining

Though obesity is generally associated with the development of cardiovascular disease (CVD) risk factors, previous reports have also reported that obesity has a beneficial effect on CVD outcomes. We aimed to verify the existing obesity paradox through binary logistic regression (BLR) and clarify the paradox via association rule mining (ARM). Patients with acute ischemic stroke (AIS) were assessed for their 3-month functional outcome using the modified Rankin Scale (mRS) score. Predictors for poor outcome (mRS 3–6) were analyzed through BLR, and ARM was performed to find out which combination of risk factors was concurrently associated with good outcomes using maximal support, confidence, and lift values. Among 2580 patients with AIS, being obese (OR [odds ratio], 0.78; 95% CI, 0.62–0.99) had beneficial effects on the outcome at 3 months in BLR analysis. In addition, the ARM algorithm showed obese patients with good outcomes were also associated with an age less than 55 years and mild stroke severity. While BLR analysis showed a beneficial effect of obesity on stroke outcome, in ARM analysis, obese patients had a relatively good combination of risk factor profiles compared to normal BMI patients. These results may partially explain the obesity paradox phenomenon in AIS patients.

Genome-wide analysis identifies impaired axonogenesis in chronic overlapping pain conditions

Chronic pain is often present at more than one anatomical location, leading to chronic overlapping pain conditions (COPC). Whether COPC represents a distinct pathophysiology from the occurrence of pain at only one site is unknown. Using genome-wide approaches, we compared genetic determinants of chronic single-site vs. multisite pain in the UK Biobank. We found that different genetic signals underlie chronic single-site and multisite pain with much stronger genetic contributions for the latter. Among 23 loci associated with multisite pain, 9 loci replicated in the HUNT cohort, with the DCC netrin-1 receptor (DCC) as the top gene. Functional genomics identified axonogenesis in brain tissues as the major contributing pathway to chronic multisite pain. Finally, multimodal structural brain imaging analysis showed that DCC is most strongly expressed in subcortical limbic regions and is associated with alterations in the uncinate fasciculus microstructure, suggesting that DCC-dependent axonogenesis may contribute to COPC via cortico-limbic circuits.

Analysis of multimorbidity networks associated with different factors in Northeast China: a cross-sectional analysis

OBJECTIVES

This study aimed to identify and study the associations and co-occurrence of multimorbidity, and assessed the associations of diseases with sex, age and hospitalisation duration.

DESIGN

Cross-sectional.

SETTING

15 general hospitals in Jilin Province, China.

PARTICIPANTS

A total of 431 295 inpatients were enrolled through a cross-sectional study in Jilin Province, China.

PRIMARY OUTCOME MEASURES

The complex relationships of multimorbidity were presented as weighted networks.

RESULTS

The distributions of the numbers of diseases differed significantly by sex, age and hospitalisation duration (p<0.001). Cerebrovascular diseases (CD), hypertensive diseases (HyD), ischaemic heart diseases (IHD) and other forms of heart disease (OFHD) showed the highest weights in the multimorbidity networks. The connections between different sexes or hospitalisation duration and diseases were similar, while those between different age groups and diseases were different.

CONCLUSIONS

CD, HyD, IHD and OFHD were the central points of disease clusters and directly or indirectly related to other diseases or factors. Thus, effective interventions for these diseases should be adopted. Furthermore, different intervention strategies should be developed according to multimorbidity patterns in different age groups.

Multimorbidity measures from health administrative data using ICD system codes: A systematic review

BACKGROUND

We aimed to identify and characterize adult population-based multimorbidity measures using health administrative data and the International Classification of Diseases (ICD) codes for disease identification.

METHODS

We performed a narrative systematic review of studies using or describing development or validation of multimorbidity measures. We compared the number of diseases included in the measures, the process of data extraction (case definition) and the validation process. We assessed the methodological robustness using eight criteria, five based on general criteria for indicators (AIRE instrument) and three multimorbidity-specific criteria.

RESULTS

Twenty-two multimorbidity measures were identified. The number of diseases they included ranged from 5 to 84 (median = 20), with 19 measures including both physical and mental conditions. Diseases were identified using ICD codes extracted from inpatient and outpatient data (18/22) and sometimes including drug claims (10/22). The validation process relied mainly on the capacity of the measures to predict health outcome (5/22), or on the validation of each individual disease against a gold standard (8/22). Six multimorbidity measures met at least six of the eight robustness criteria assessed.

CONCLUSION

There is significant heterogeneity among the measures used to assess multimorbidity in administrative databases, and about a third are of low to moderate quality. A more consensual approach to the number of diseases or groups of diseases included in multimorbidity measures may improve comparison between regions, and potentially provide better control for multimorbidity-related confounding in studies.

Methods of analyzing patterns of multimorbidity using network analysis: a scoping review protocol

OBJECTIVE

The purpose of this review is to summarize the techniques used for network analysis of multimorbidity to inform development of a standard methodology.

INTRODUCTION

There is a growing trend of using network analysis to investigate relationships between chronic illnesses in people with multimorbidities. However, there is currently no recommended approach to calculating and displaying networks of chronic health conditions. This review intends to summarize the current literature to further the development of a standard methodology.

INCLUSION CRITERIA

Studies will be included if they investigated the relationships between multiple chronic health conditions without referring to an index condition, using network analysis techniques. Studies using both survey and administrative data will be included. Studies including biological or genomic data sets will not be included as they are out of scope.

METHODS

Databases searched will include MEDLINE, ScienceDirect, Scopus, and PsycINFO. All relevant publications will be included provided they were published before October 2020. Publications from all languages will be included where an appropriate translation in English can be obtained. Data extracted will include country of origin, type of data used, measure of association, software used, and notes on any specific points of methodological interest relevant to the review question.

A Network Analysis of Drug Combinations Associated with Acute Generalized Exanthematous Pustulosis (AGEP)

Acute generalized exanthematous pustulosis (AGEP) is a rare skin adverse drug reaction. The pathophysiology and causative drugs associated with AGEP are poorly understood, with the majority of studies in AGEP focusing on a single-drug-outcome association. We therefore aimed to explore and characterize frequently reported drug combinations associated with AGEP using the WHO pharmacovigilance database VigiBase. In this explorative cross-sectional study of a pharmacovigilance database using a data-driven approach, we assessed individual case safety reports (ICSR) with two or more drugs reported to VigiBase. A total of 2649 ICSRs reported two or more drugs. Cardiovascular drugs, including antithrombotics and beta-blockers, were frequently reported in combination with other drugs, particularly antibiotics. The drug pair of amoxicillin and furosemide was reported in 57 ICSRs (2.2%), with an O/E ratio of 1.3, and the combination of bisoprolol and furosemide was recorded 44 times (1.7%), with an O/E ratio of 5.5. The network analysis identified 10 different communities of varying sizes. The largest cluster primarily consisted of cardiovascular drugs. This data-driven and exploratory study provides the largest real-world assessment of drugs associated with AGEP to date. The results identify a high frequency of cardiovascular drugs, particularly used in combination with antibiotics.

Comparisons of disease cluster patterns, prevalence and health factors in the USA, Canada, England and Ireland

Background

Identification of those who are most at risk of developing specific patterns of disease across different populations is required for directing public health policy. Here, we contrast prevalence and patterns of cross-national disease incidence, co-occurrence and related risk factors across population samples from the U.S., Canada, England and Ireland.

Methods

Participants (n = 62,111) were drawn from the US Health and Retirement Study (n = 10,858); the Canadian Longitudinal Study on Ageing (n = 36,647); the English Longitudinal Study of Ageing (n = 7938) and The Irish Longitudinal Study on Ageing (n = 6668). Self-reported lifetime prevalence of 10 medical conditions, predominant clusters of multimorbidity and their specific risk factors were compared across countries using latent class analysis.

Results

The U.S. had significantly higher prevalence of multimorbid disease patterns and nearly all diseases when compared to the three other countries, even after adjusting for age, sex, BMI, income, employment status, education, alcohol consumption and smoking history. For the U.S. the most at-risk group were younger on average compared to Canada, England and Ireland. Socioeconomic gradients for specific disease combinations were more pronounced for the U.S., Canada and England than they were for Ireland. The rates of obesity trends over the last 50 years align with the prevalence of eight of the 10 diseases examined. While patterns of disease clusters and the risk factors related to each of the disease clusters were similar, the probabilities of the diseases within each cluster differed across countries.

Conclusions

This information can be used to better understand the complex nature of multimorbidity and identify appropriate prevention and management strategies for treating multimorbidity across countries.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11706-8.

VISEMURE: A Visual Analytics System for Making Sense of Multimorbidity Using Electronic Medical Record Data

Multimorbidity is a growing healthcare problem, especially for aging populations. Traditional single disease-centric approaches are not suitable for multimorbidity, and a holistic framework is required for health research and for enhancing patient care. Patterns of multimorbidity within populations are complex and difficult to communicate with static visualization techniques such as tables and charts. We designed a visual analytics system called VISEMURE that facilitates making sense of data collected from patients with multimorbidity. With VISEMURE, users can interactively create different subsets of electronic medical record data to investigate multimorbidity within different subsets of patients with pre-existing chronic diseases. It also allows the creation of groups of patients based on age, gender, and socioeconomic status for investigation. VISEMURE can use a range of statistical and machine learning techniques and can integrate them seamlessly to compute prevalence and correlation estimates for selected diseases. It presents results using interactive visualizations to help healthcare researchers in making sense of multimorbidity. Using a case study, we demonstrate how VISEMURE can be used to explore the high-dimensional joint distribution of random variables that describes the multimorbidity present in a patient population.

Multimorbidity clusters among long-term breast cancer survivors in Spain: Results of the SURBCAN study

The disease management of long-term breast cancer survivors (BCS) is hampered by the scarce knowledge of multimorbidity patterns. The aim of our study was to identify multimorbidity clusters among long-term BCS and assess their impact on mortality and health services use. We conducted a retrospective study using electronic health records of 6512 BCS from Spain surviving at least 5 years. Hierarchical cluster analysis was used to identify groups of similar patients based on their chronic diagnoses, which were assessed using the Clinical Classifications Software. As a result, multimorbidity clusters were obtained, clinically defined and named according to the comorbidities with higher observed/expected prevalence ratios. Multivariable Cox and negative binomial regression models were fitted to estimate overall mortality risk and probability of contacting health services according to the clusters identified. 83.7% of BCS presented multimorbidity, essential hypertension (34.5%) and obesity and other metabolic disorders (27.4%) being the most prevalent chronic diseases at the beginning of follow-up. Five multimorbidity clusters were identified: C1-unspecific (29.9%), C2-metabolic and neurodegenerative (28.3%), C3-anxiety and fractures (9.7%), C4-musculoskeletal and cardiovascular (9.6%) and C5-thyroid disorders (5.3%). All clusters except C5-thyroid disorders were associated with higher mortality compared to BCS without comorbidities. The risk of mortality in C4 was increased by 64% (adjusted hazard ratio 1.64, 95% confidence interval 1.52-2.07). Stratified analysis showed an increased risk of death among BCS with 5 to 10 years of survival in all clusters. These results help to identify subgroups of long-term BCS with specific needs and mortality risks and to guide BCS clinical practice regarding multimorbidity.

Development of Multimorbidity Over Time: An Analysis of Belgium Primary Care Data Using Markov Chains and Weighted Association Rule Mining

Background

The prevalence of multimorbidity is increasing in recent years, and patients with multimorbidity often have a decrease in quality of life and require more health care. The aim of this study was to explore the evolution of multimorbidity taking the sequence of diseases into consideration.

Methods

We used a Belgian database collected by extracting coded parameters and more than 100 chronic conditions from the Electronic Health Records of general practitioners to study patients older than 40 years with multiple diagnoses between 1991 and 2015 (N = 65 939). We applied Markov chains to estimate the probability of developing another condition in the next state after a diagnosis. The results of Weighted Association Rule Mining (WARM) allow us to show strong associations among multiple conditions.

Results

About 66.9% of the selected patients had multimorbidity. Conditions with high prevalence, such as hypertension and depressive disorder, were likely to occur after the diagnosis of most conditions. Patterns in several disease groups were apparent based on the results of both Markov chain and WARM, such as musculoskeletal diseases and psychological diseases. Psychological diseases were frequently followed by irritable bowel syndrome.

Conclusions

Our study used Markov chains and WARM for the first time to provide a comprehensive view of the relations among 103 chronic conditions, taking sequential chronology into consideration. Some strong associations among specific conditions were detected and the results were consistent with current knowledge in literature, meaning the approaches were valid to be used on larger data sets, such as National Health care Systems or private insurers.

Prognostic Factors for Health Outcomes After Exercise Therapy and Education in Individuals With Knee and Hip Osteoarthritis With or Without Comorbidities: A Study of 37,576 Patients Treated in Primary Care

OBJECTIVE

To identify prognostic factors for health outcomes following an 8-week supervised exercise therapy and education program for individuals with knee and hip osteoarthritis (OA) alone or with concomitant hypertension, heart or respiratory disease, diabetes mellitus, or depression.

METHODS

We included individuals with knee and/or hip OA from the Good Life With OsteoArthritis in Denmark (GLA:D) program. GLA:D consists of 2 patient education sessions and 12 supervised exercise therapy sessions. Before GLA:D, participants self-reported any comorbidities and were categorized into 8 comorbidity groups. Twenty-one potential prognostic factors (demographic information, clinical data, and performance-based physical function) gathered from participants and clinicians before the program were included. Outcomes were physical function using the 40-meter Fast-Paced Walk Test (FPWT), health-related quality of life using the 5-level EuroQol 5-domain (EQ-5D-5L) index score, and pain intensity using a visual analog scale (VAS; range 0-100) assessed before and immediately after the GLA:D program. Within each comorbidity group, associations of prognostic factors with outcomes were estimated using multivariable linear regression.

RESULTS

Data from 35,496 (40-meter FPWT) and 37,576 (EQ-5D-5L and VAS) participants were included in the analyses. Clinically relevant associations were demonstrated between age, self-efficacy, self-rated health, and pain intensity and change in 40-meter FPWT, EQ-5D-5L, or VAS scores across comorbidity groups. Furthermore, anxiety, education, physical function, and smoking were associated with outcomes among subgroups having depression or diabetes mellitus in addition to OA.

CONCLUSION

Age, self-efficacy, self-rated health, and pain intensity may be prognostic of change in health outcomes following an 8-week exercise therapy and patient education program for individuals with OA, irrespective of comorbidities.

Increases in multimorbidity with weight class in the United States

Little is known regarding how multimorbidity combinations associated with obesity change with increase in body weight. This study employed data from the national Cerner HealthFacts Data Warehouse to identify changes in multimorbidity patterns by weight class using network analysis. Networks were generated for 154 528 middle-aged patients in the following categories: normal weight, overweight, and classes 1, 2, and 3 obesity. The results show significant differences (P-value<0.05) in prevalence by weight class for all but three of 82 diseases considered. The percentage of patients with multimorbidity (excluding obesity) increases from in 55.1% in patients with normal weight, to 57.88% with overweight, 70.39% with Class 1 obesity, 73.99% with Class 2 obesity, and 71.68% in Class 3 obesity, increasing most substantially with the progression from overweight to class 1 obesity. Most prevalent disease clusters expand from only hypertension and dorsalgia in normal weight, to add joint disorders in overweight, lipidemias in class 1 obesity, diabetes in class 2 obesity, and sleep disorders and chronic kidney disease in class 3 obesity. Recognition of multimorbidity patterns associated with weight increase is essential for true precision care of obesity-associated chronic conditions and can help clinicians identify and address preclinical disease before additional complications arise.

An Association Rule Mining Analysis of Lifestyle Behavioral Risk Factors in Cancer Survivors with High Cardiovascular Disease Risk

We aimed to assess which lifestyle risk behaviors have the greatest influence on the risk of cardiovascular disease in cancer survivors and which of these behaviors are most prominently clustered in cancer survivors, using logistic regression and association rule mining (ARM). We analyzed a consecutive series of 897 cancer survivors from the Korean National Health and Nutritional Exam Survey (2012-2016). Cardiovascular disease risks were assessed using the atherosclerotic cardiovascular disease score (ASCVDs). We classified participants as being in a low-risk group if their calculated ASCVDs was less than 10% and as being in a high-risk group if their score was 10% or higher. We used association rule mining to analyze patterns of lifestyle risk behaviors by ASCVDs risk group, based upon public health recommendations described in the Alameda 7 health behaviors (current smoking, heavy drinking, physical inactivity, obesity, breakfast skipping, frequent snacking, and suboptimal sleep duration). Forty-two percent of cancer survivors had a high ASCVD. Current smoking (common odds ratio, 11.19; 95% confidence interval, 3.66-34.20, p < 0.001) and obesity (common odds ratio, 2.67; 95% confidence interval, 1.40-5.08, p < 0.001) were significant predictors of high ASCVD in cancer survivors within a multivariate model. In ARM analysis, current smoking and obesity were identified as important lifestyle risk behaviors in cancer survivors. In addition, various lifestyle risk behaviors co-occurred with smoking in male cancer survivors.

Disease-dependent variations in the timing and causes of readmissions in Germany: A claims data analysis for six different conditions

BACKGROUND

Hospital readmissions place a major burden on patients and health care systems worldwide, but little is known about patterns and timing of readmissions in Germany.

METHODS

We used German health insurance claims (AOK, 2011-2016) of patients ≥ 65 years hospitalized for acute myocardial infarction (AMI), heart failure (HF), a composite of stroke, transient ischemic attack, or atrial fibrillation (S/AF), chronic obstructive pulmonary disease (COPD), type 2 diabetes mellitus, or osteoporosis to identify hospital readmissions within 30 or 90 days. Readmissions were classified into all-cause, specific, and non-specific and their characteristics were analyzed.

RESULTS

Within 30 and 90 days, about 14-22% and 27-41% index admissions were readmitted for any reason, respectively. HF and S/AF contributed most index cases, and HF and COPD accounted for most all-cause readmissions. Distributions and ratios of specific to non-specific readmissions were disease-specific with highest specific readmissions rates among COPD and AMI.

CONCLUSION

German claims are well-suited to investigate readmission causes if longer periods than 30 days are evaluated. Conditions closely related with the primary disease are the most frequent readmission causes, but multiple comorbidities among readmitted cases suggest that a multidisciplinary care approach should be implemented vigorously addressing comorbidities already during the index hospitalization.

Low health literacy is associated with higher risk of type 2 diabetes: a cross-sectional study in Germany

Background

Low health literacy is believed to be associated with behaviours that increase the risk of type 2 diabetes. But there is limited knowledge on the relation between health literacy (HL) and diabetes risk, and whether improving HL could be a potential prevention strategy. Therefore, the main purpose of this study was to examine the link between HL and diabetes risk among non-diabetic adults.

Methods

We used data from the Hamburg Diabetes Prevention Survey, a population-based cross-sectional study in Germany. One thousand, two hundred and fifty-five non-diabetic subjects aged 18–60 years were eligible. The German Diabetes Risk Score (GDRS, ranging 0 to 123 points) was used to determine the individual risk of type 2 diabetes. The short version of the European Health Literacy Questionnaire (HLS-EU-Q16, ranging 0 to 16 points) was applied to assess the individual self-reported HL. Subjects were asked to self-estimate their diabetes risk, which was then compared with the calculated GDRS. Descriptive statistics were calculated to investigate group differences in the GDRS and self-estimated diabetes risk. Linear as well as logistic regression models were performed to analyse potential influencing variables of the GDRS as well as incorrect self-estimated diabetes risk. In three nested statistical models for each outcome, these analyses were adjusted for age, gender, educational level and the presence of chronic conditions.

Results

According to the criteria of the GDRS, 996 (79.4%) subjects showed “low risk”, 176 (14.0%) “still low risk”, 53 (4.2%) “elevated risk”, and 30 (2.4%) “high to very high risk” to develop type 2 diabetes within the next 5 years. In the statistical models including all control variables, subjects with “inadequate HL” scored 2.38 points higher on the GDRS (95% CI 0.378 to 4.336; P = 0.020) and had a 2.04 greater chance to estimate their diabetes risk incorrectly (OR 2.04; 95% CI 1.33 to 3.14; P = 0.001) compared to those with “sufficient HL”.

Conclusion

The risk of type 2 diabetes is increased in people with inadequate self-reported HL. People with high diabetes risk and inadequate HL might be provided with educational programs to improve diabetes knowledge and reduce behavioural risk factors.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-10508-2.

Associations between comorbidities and immediate and one-year outcomes following supervised exercise therapy and patient education - A cohort study of 24,513 individuals with knee or hip osteoarthritis

OBJECTIVE

To investigate if comorbidities are associated with change in health outcomes following an 8-week exercise and education program in knee and hip osteoarthritis (OA).

METHODS

We included 24,513 individuals with knee or hip OA from the Good Life with osteoArthritis in Denmark (GLA:D®). GLA:D® consists of two patient education sessions and 12 supervised exercise sessions. Before the program, individuals self-reported having one or more of 11 common comorbidities. Physical function was assessed using the 40-m Fast-Paced Walk Test (FPWT, m/sec) before and immediately after the program. Pain intensity and health-related quality of life was self-reported before, immediately after, and at 12 months post-intervention using a visual analogue scale (VAS, 0-100) and the EQ-5D-5L index (-0.624 to 1.000), respectively. Associations of comorbidity combinations with change in outcomes immediately and at 12 months was estimated using mixed linear regression.

RESULTS

Individuals with OA improved on average 0.12 m/s (95%CI 0.12 to 0.13) in 40-m FPWT, -12.7 mm (95%CI -13.2 to -12.2) in VAS, and 0.039 (95%CI 0.036 to 0.041) in EQ-5D-5L from before to immediately after the intervention with minor additional improvements at 12 months. Despite that individuals with comorbidities had worse baseline scores in all outcomes than individuals without comorbidities, they had similar levels of improvement immediately and 12 months after the intervention.

CONCLUSION

Comorbidities are not associated with worse nor better health outcomes following an 8-week exercise and education program in individuals with OA, suggesting exercise as a viable treatment option for individuals with OA, irrespective of comorbidities.

Five-year trajectories of multimorbidity patterns in an elderly Mediterranean population using Hidden Markov Models

This study aimed to analyse the trajectories and mortality of multimorbidity patterns in patients aged 65 to 99 years in Catalonia (Spain). Five year (2012–2016) data of 916,619 participants from a primary care, population-based electronic health record database (Information System for Research in Primary Care, SIDIAP) were included in this retrospective cohort study. Individual longitudinal trajectories were modelled with a Hidden Markov Model across multimorbidity patterns. We computed the mortality hazard using Cox regression models to estimate survival in multimorbidity patterns. Ten multimorbidity patterns were originally identified and two more states (death and drop-outs) were subsequently added. At baseline, the most frequent cluster was the Non-Specific Pattern (42%), and the least frequent the Multisystem Pattern (1.6%). Most participants stayed in the same cluster over the 5 year follow-up period, from 92.1% in the Nervous, Musculoskeletal pattern to 59.2% in the Cardio-Circulatory and Renal pattern. The highest mortality rates were observed for patterns that included cardio-circulatory diseases: Cardio-Circulatory and Renal (37.1%); Nervous, Digestive and Circulatory (31.8%); and Cardio-Circulatory, Mental, Respiratory and Genitourinary (28.8%). This study demonstrates the feasibility of characterizing multimorbidity patterns along time. Multimorbidity trajectories were generally stable, although changes in specific multimorbidity patterns were observed. The Hidden Markov Model is useful for modelling transitions across multimorbidity patterns and mortality risk. Our findings suggest that health interventions targeting specific multimorbidity patterns may reduce mortality in patients with multimorbidity.

Frailty as a predictor of mortality in older adults within 5 years of psychiatric admission

OBJECTIVES

Older adults with psychiatric disorders have a substantially lower life expectancy than age-matched controls. Knowledge of risk factors may lead to targeting treatment and interventions to reduce this gap in life expectancy. In this study, we investigated whether frailty independently predicts mortality in older patients following an acute admission to a geriatric psychiatry hospital.

METHODS

Clinical cohort study with a 5-year follow-up of 120 older patients admitted to a psychiatric hospital between February 2009 and September 2010. On admission, we assessed frailty with a frailty index (FI). We applied Cox regression analyses with time to death as the dependent variable, to examine whether the FI was a predictor for mortality, adjusted for age, sex, level of education, multimorbidity (Cumulative Illness Rating Scale for Geriatrics, CIRS-G scores), functional status (Barthel Index), neuropsychiatric symptoms (NPS), and severity of psychiatric symptoms at admission (Clinical Global Impressions Scale of Severity).

RESULTS

Of the 120 patients, 63 (53%) patients were frail (FI ≥ 0.25), and 59 (49%) had died within 5 years. The FI predicted mortality with a hazard ratio (HR) of 1.78 (95% CI, 1.06-2.98) per 0.1 point increase, independent of the covariates. Co-morbidity measured by the CIRS-G and functional status measured by the Barthel Index were not significantly associated.

CONCLUSIONS

Frailty was a strong predictor of mortality, independent of age, gender, multimorbidity, and functional status. This implies that frailty may be helpful in targeting inpatient psychiatric treatment and aftercare according to patients' life expectancy.

Sensitivity of comorbidity network analysis

OBJECTIVES

Comorbidity network analysis (CNA) is a graph-theoretic approach to systems medicine based on associations revealed from disease co-occurrence data. Researchers have used CNA to explore epidemiological patterns, differentiate populations, characterize disorders, and more; but these techniques have not been comprehensively evaluated. Our objectives were to assess the stability of common CNA techniques.

MATERIALS AND METHODS

We obtained seven co-occurrence data sets, most from previous CNAs, coded using several ontologies. We constructed comorbidity networks under various modeling procedures and calculated summary statistics and centrality rankings. We used regression, ordination, and rank correlation to assess these properties' sensitivity to the source of data and construction parameters.

RESULTS

Most summary statistics were robust to variation in link determination but somewhere sensitive to the association measure. Some more effectively than others discriminated among networks constructed from different data sets. Centrality rankings, especially among hubs, were somewhat sensitive to link determination and highly sensitive to ontology. As multivariate models incorporated additional effects, comorbid associations among low-prevalence disorders weakened while those between high-prevalence disorders shifted negative.

DISCUSSION

Pairwise CNA techniques are generally robust, but some analyses are highly sensitive to certain parameters. Multivariate approaches expose additional conceptual and technical limitations to the usual pairwise approach.

CONCLUSION

We conclude with a set of recommendations we believe will help CNA researchers improve the robustness of results and the potential of follow-up research.

Do comorbidities predict pain and function in knee osteoarthritis following an exercise intervention, and do they moderate the effect of exercise? Analyses of data from three randomized controlled trials

BACKGROUND

Although exercise is a core treatment for people with knee osteoarthritis (OA), it is currently unknown whether those with additional comorbidities respond differently to exercise than those without. We explored whether comorbidities predict pain and function following an exercise intervention in people with knee OA, and whether they moderate response to: exercise versus no exercise; and enhanced exercise versus usual exercise-based care.

METHODS

We undertook analyses of existing data from three randomized controlled trials (RCTs): TOPIK (n = 217), APEX (n = 352) and Benefits of Effective Exercise for knee Pain (BEEP) (n = 514). All three RCTs included: adults with knee pain attributable to OA; physiotherapy-led exercise; data on six comorbidities (overweight/obesity, pain elsewhere, anxiety/depression, cardiac problems, diabetes mellitus and respiratory conditions); the outcomes of interest (six-month Western Ontario and McMaster Universities Arthritis Index knee pain and function). Adjusted mixed models were fitted where data was available; otherwise linear regression models were used.

RESULTS

Obesity compared with underweight/normal body mass index was significantly associated with knee pain following exercise, as was the presence compared with absence of anxiety/depression. The presence of cardiac problems was significantly associated with the effect of enhanced versus usual exercise-based care for knee function, indicating that enhanced exercise may be less effective for improving knee function in people with cardiac problems. Associations for all other potential prognostic factors and moderators were weak and not statistically significant.

CONCLUSIONS

Obesity and anxiety/depression predicted pain and function outcomes in people offered an exercise intervention, but only the presence of cardiac problems might moderate the effect of exercise for knee OA. Further confirmatory investigations are required.

Health behaviour, social support, socio-economic status and the 5-year progression of multimorbidity: Results from the MultiCare Cohort Study

Background:

Multimorbidity in elderly patients is a major challenge for physicians, because of a high prevalence of and associations with many adverse outcomes. However, the mechanisms of progressing multimorbidity are not well-understood. The aim of our study was to determine if the progression of multimorbidity is influenced by health behaviour and social support and to analyse if the patients’ socio-economic status had an effect on these prognostic factors.

Methods:

The study was designed as prospective cohort study based on interviews of 158 GPs and 3189 patients randomly selected from GP records (response rate: 46.2%). Patients were aged 65–85 years at recruitment and observed in four waves of data collection (dropout rate: 41.5%). Statistical analyses of the ‘hot deck’ imputed data included multilevel mixed-effects linear regression allowing for random effects at the study centre and GP practice within study centre level.

Results:

Regarding cardiovascular and metabolic diseases, multimorbidity progressed more rapidly in patients who reported less physical activity (ß = −0.28; 95% confidence interval = −0.35 to −0.20), had more tobacco-related pack years (0.15; 0.07–0.22) and consumed less alcohol (−0.21; −0.31 to −0.12) at baseline. Multimorbidity related to psychiatric and pain-related disorders progressed more rapidly if the patients had less perceived social support (−0.31; −0.55 to −0.07) and reported less physical activity (−0.08; −0.15 to −0.02) at baseline. Education and income only slightly modified the effects of these variables.

Conclusion:

Depending on the multimorbidity cluster, different strategies should be used for slowing down the progression of multimorbidity. Changing lifestyle and increasing social support are beneficial for the entire group of elderly multimorbid patients – regardless of their socio-economic status.

Registration:

ISRCTN89818205

Multiple chronic conditions at a major urban health system: a retrospective cross-sectional analysis of frequencies, costs and comorbidity patterns

OBJECTIVE

To (1) examine the burden of multiple chronic conditions (MCC) in an urban health system, and (2) propose a methodology to identify subpopulations of interest based on diagnosis groups and costs.

DESIGN

Retrospective cross-sectional study.

SETTING

Mount Sinai Health System, set in all five boroughs of New York City, USA.

PARTICIPANTS

192 085 adult (18+) plan members of capitated Medicaid contracts between the Healthfirst managed care organisation and the Mount Sinai Health System in the years 2012 to 2014.

METHODS

We classified adults as having 0, 1, 2, 3, 4 or 5+ chronic conditions from a list of 69 chronic conditions. After summarising the demographics, geography and prevalence of MCC within this population, we then described groups of patients (segments) using a novel methodology: we combinatorially defined 18 768 potential segments of patients by a pair of chronic conditions, a sex and an age group, and then ranked segments by (1) frequency, (2) cost and (3) ratios of observed to expected frequencies of co-occurring chronic conditions. We then compiled pairs of conditions that occur more frequently together than otherwise expected.

RESULTS

61.5% of the study population suffers from two or more chronic conditions. The most frequent dyad was hypertension and hyperlipidaemia (19%) and the most frequent triad was diabetes, hypertension and hyperlipidaemia (10%). Women aged 50 to 65 with hypertension and hyperlipidaemia were the leading cost segment in the study population. Costs and prevalence of MCC increase with number of conditions and age. The disease dyads associated with the largest observed/expected ratios were pulmonary disease and myocardial infarction. Inter-borough range MCC prevalence was 16%.

CONCLUSIONS

In this low-income, urban population, MCC is more prevalent (61%) than nationally (42%), motivating further research and intervention in this population. By identifying potential target populations in an interpretable manner, this segmenting methodology has utility for health services analysts.

Investigation of multimorbidity and prevalent disease combinations in older Irish adults using network analysis and association rules

Multimorbidity (the presence of multiple medical conditions) is well known to increase with age. People with multimorbidities often have higher physical and functional decline as well as increased mortality. Despite growing evidence that integrated and collaborative care improves many undesirable outcomes of multimorbidity, the majority of health systems are based around treating individual diseases. A pattern analysis of comorbidities using network graphs and a novel use of association rules was conducted to investigate disease associations on 6101 Irish adults aged 50+. The complex network of morbidities and differences in the prevalence and interactions of these morbidities by sex was also assessed. Gender specific differences in disease prevalence was found for 22/31 medical conditions included in this study. Females had a more complex network of disease associations than males with strong associations found between arthritis, osteoporosis and thyroid issues among others. To assess the strength of these associations we provide probabilities of being diagnosed with a comorbid condition given the presence of an index morbidity for 639 pairwise combinations. This information can be used to guide clinicians in deciding which comorbidities should be incorporated into comprehensive assessments in addition to anticipating likely future morbidities and thus developing prevention strategies.

Associations Between Multimorbidity and Cerebrospinal Fluid Amyloid: A Cross-Sectional Analysis of the European Prevention of Alzheimer's Dementia (EPAD) V500.0 Cohort

BACKGROUND

Multimorbidity (the co-occurrence of multiple chronic conditions) is increasingly common, especially among people with dementia. Few neuroimaging studies have explored amyloid biomarkers in people with multimorbidity.

OBJECTIVE

We aimed to conduct the first study of the association between multimorbidity and cerebrospinal fluid amyloid-β42 (CSF Aβ).

METHOD

The European Prevention of Alzheimer's Dementia (EPAD) Longitudinal Cohort Study V500.0 dataset includes volunteers aged ≥50 years from 12 sites. Participants undergo detailed phenotyping, including CSF measures and a self-reported medical history. Using logistic and linear regression analyses, we explored the association between multimorbidity and continuous chronic condition count with CSF Aβ positivity (Aβ42 <1000pg/ml) and continuous CSF Aβ concentration. All models were adjusted for age, sex, APOE status, education, and family history of dementia.

RESULTS

Among 447 eligible participants without dementia, the mean (SD) age was 66.6 (6.6) years, 234 (52.3%) were women, and 157 (35.1%) were amyloid positive. With chronic conditions regarded as pseudo-continuous, each additional condition carried a decreased likelihood of amyloid positivity (OR = 0.82, 95% CI: 0.68-0.97; p = 0.026). With CSF Aβ as a continuous variable, each additional condition was associated with an increase of 54.2 pg/ml (95% CI: 9.9-98.5, p = 0.017). Having ≥2 conditions was inversely associated with amyloid positivity (OR 0.59, 95% CI: 0.37-0.95, p = 0.030) compared to one or none.

CONCLUSION

Our findings suggest that the established association between multimorbidity and dementia may be due to a pathway other than amyloid. However, this cross-sectional study does not allow us to make causal inferences. Longitudinal work is required to confirm the inverse association found.

Health Benefits of Anti-aging Drugs

Aging, as a physiological process mediated by numerous regulatory pathways and transcription factors, is manifested by continuous progressive functional decline and increasing risk of chronic diseases. There is an increasing interest to identify pharmacological agents for treatment and prevention of age-related disease in humans. Animal models play an important role in identification and testing of anti-aging compounds; this step is crucial before the drug will enter human clinical trial or will be introduced to human medicine. One of the main goals of animal studies is better understanding of mechanistic targets, therapeutic implications and side-effects of the drug, which may be later translated into humans. In this chapter, we summarized the effects of different drugs reported to extend the lifespan in model organisms from round worms to rodents. Resveratrol, rapamycin, metformin and aspirin, showing effectiveness in model organism life- and healthspan extension mainly target the master regulators of aging such as mTOR, FOXO and PGC1α, affecting autophagy, inflammation and oxidative stress. In humans, these drugs were demonstrated to reduce inflammation, prevent CVD, and slow down the functional decline in certain organs. Additionally, potential anti-aging pharmacologic agents inhibit cancerogenesis, interfering with certain aspects of cell metabolism, proliferation, angioneogenesis and apoptosis.

Sensitivity for multimorbidity: The role of diagnostic uncertainty of physicians when evaluating multimorbid video case-based vignettes

Background

Multimorbidity can be defined as the co-occurrence of two or more chronic medical conditions in one person. Within the diagnostic process, accurately detecting a multimorbid disease pattern still poses a major challenge for most physicians, and is known as a source of diagnostic uncertainty.

Objective

We investigated, how sensitive, confident, and accurate physicians are in diagnosing multimorbid versus monomorbid patients.

Methods

We created eight video case-based vignettes, which differed in type of morbidity (multimorbid versus monomorbid), field of medical specialization (somatic versus mental), and relatedness of underlying diseases (causally related versus unrelated). In total, 74 physicians (GPs, residents in an emergency department and psychiatrists) watched three to five randomly allocated video cases and had to generate suspected diagnoses at the end of each of three video sequences. Additionally, participating physicians rated subjective confidence for all mentioned diagnoses and for three sequences per case with the help of confidence profiles.

Results

Altogether, physicians made a large number of accurate diagnoses (69%). Nevertheless, the overall number of underdiagnosed multimorbid cases (misses) was significantly higher (71%) than over-diagnosed monomorbid cases (false alarms) (7%).

Discussion

According to Signal Detection Theory, GPs and psychiatrists both showed lower detection performance for medical cases that lay beyond their own field of specialization. Remarkably, residents show the highest sensitivity for multimorbid cases with an approximately identically detection performance d' slightly over 1 for both field of medical specialization (somatic and mental). Furthermore, higher uncertainty in diagnosing multimorbid cases is related to lower confidence especially at the beginning of a diagnostic process, as well as to unrelated and therefore probably rare disease pattern. Several limitations of the study and the video case-based vignettes are described within the discussion section.

Conclusions

Physicians have to be sensitized for multimorbidity even more, and have to be taught in the prevalence of existing disease combinations. Communicating uncertainty with other specialists could be helpful when faced with a sometimes “fuzzy” pattern of symptoms.

Underlying mechanisms of complex interventions addressing the care of older adults with multimorbidity: a realist review

OBJECTIVES

To understand how and why effective multi-chronic disease management interventions influence health outcomes in older adults 65 years of age or older.

DESIGN

A realist review.

DATA SOURCES

Electronic databases including Medline and Embase (inception to December 2017); and the grey literature.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

We considered any studies (ie, experimental quasi-experimental, observational, qualitative and mixed-methods studies) as long as they provided data to explain our programme theories and effectiveness review (published elsewhere) findings. The population of interest was older adults (age ≥65 years) with two or more chronic conditions.

ANALYSIS

We used the Realist And MEta-narrative Evidence Syntheses: Evolving Standards (RAMESES) quality and publication criteria for our synthesis aimed at refining our programme theories such that they contained multiple context-mechanism-outcome configurations describing the ways different mechanisms fire to generate outcomes. We created a 3-step synthesis process grounded in meta-ethnography to separate units of data from articles, and to derive explanatory statements across them.

RESULTS

106 articles contributed to the analysis. We refined our programme theories to explain multimorbidity management in older adults: (1) care coordination interventions with the best potential for impact are team-based strategies, disease management programmes and case management; (2) optimised disease prioritisation involves ensuring that clinician work with patients to identify what symptoms are problematic and why, and to explore options that are acceptable to both clinicians and patients and (3) optimised patient self-management is dependent on patients' capacity for selfcare and to what extent, and establishing what patients need to enable selfcare.

CONCLUSIONS

To optimise care, both clinical management and patient self-management need to be considered from multiple perspectives (patient, provider and system). To mitigate the complexities of multimorbidity management, patients focus on reducing symptoms and preserving quality of life while providers focus on the condition that most threaten morbidity and mortality.

PROSPERO REGISTRATION NUMBER

CRD42014014489.

Are big data analytics helpful in caring for multimorbid patients in general practice? - A scoping review

BACKGROUND

The treatment of multimorbid patients is one crucial task in general practice as multimorbidity is highly prevalent in this setting. However, there is little evidence how to treat these patients and consequently there are but a few guidelines that focus primarily on multimorbidity. Big data analytics are defined as a method that obtains results for high volume data with high variety generated at high velocity. Yet, the explanatory power of these results is not completely understood. Nevertheless, addressing multimorbidity as a complex condition might be a promising field for big data analytics. The aim of this scoping review was to evaluate whether applying big data analytics on patient data does already contribute to the treatment of multimorbid patients in general practice.

METHODS

In January 2018, a review searching the databases PubMed, The Cochrane Library, and Web of Science, using defined search terms for "big data analytics" and "multimorbidity", supplemented by a search of grey literature with Google Scholar, was conducted. Studies were not filtered by type of study, publication year or language. Validity of studies was evaluated independently by two researchers.

RESULTS

In total, 2392 records were identified for screening. After title and abstract screening, six articles were included in the full-text analysis. Of those articles, one reported on a model generated with big data techniques to help caring for one group of multimorbid patients. The other five articles dealt with the analysis of multimorbidity clusters. No article defined big data analytics explicitly.

CONCLUSIONS

Although the usage of the phrase "Big Data" is growing rapidly, there is nearly no practical use case for big data analysis techniques in the treatment of multimorbidity in general practice yet. Furthermore, in publications addressing big data analytics, the term is rarely defined. However, possible models and algorithms to address multimorbidity in the future are already published.

Family practitioners' top medical priorities when managing patients with multimorbidity: a cross-sectional study

Background

Managing multiple chronic and acute conditions in patients with multimorbidity requires setting medical priorities. How family practitioners (FPs) rank medical priorities between highly, moderately, or rarely prevalent chronic conditions (CCs) has never been described. The authors hypothesised that there was no relationship between the prevalence of CCs and their medical priority ranking in individual patients with multimorbidity.

Aim

To describe FPs’ medical priority ranking of conditions relative to their prevalence in patients with multimorbidity.

Design & setting

This cross-sectional study of 100 FPs in Switzerland included patients with ≥3 CCs on a predefined list of 75 items from the International Classification of Primary Care 2 (ICPC-2); other conditions could be added. FPs ranked all conditions by their medical priority.

Method

Priority ranking and distribution were calculated for each condition separately and for the top three priorities together.

Results

The sample contained 888 patients aged 28–98 years (mean 73), of which 48.2% were male. Included patients had 3–19 conditions (median 7; interquantile range [IQR] 6–9). FPs used 74/75 CCs from the predefined list, of which 27 were highly prevalent (>5%). In total, 336 different conditions were recorded. Highly prevalent CCs were only the top medical priority in 66%, and the first three priorities in 33%, of cases. No correlation was found between prevalence and the ranking of medical priorities.

Conclusion

FPs faced a great diversity of different conditions in their patients with multimorbidity, with nearly every condition being found at nearly every rank of medical priority, depending on the patient. Medical priority ranking was independent of the prevalence of CCs.

Multimorbidity patterns and 5-year overall mortality: Results from a claims data-based observational study

Background:

Multimorbidity is prevalent and related to adverse outcomes. The effect on mortality is disputed, possibly because studies show differences in the diseases which operationalize multimorbidity. The aim of this study is to analyze the effects of three multimorbidity patterns (representing subgroups of diseases) on mortality.

Methods:

We conducted a longitudinal observational study based on insurance claims data of ambulatory care from 2005 to 2009. Analyses are based on 46 chronic conditions with a prevalence ≥1%. We included 52,217 females and 71,007 males aged 65+ and insured by the Gmünder ErsatzKasse throughout 2004. Our outcome was 5-year overall mortality documented as exact time of death. We calculated hazard ratios by Cox regression analyses with time-dependent covariates. Three statistical models were analyzed: (a) the individual diseases, (b) the number of diseases in multimorbidity patterns, and (c) a count of all diseases, all calculated separately for genders and adjusted for age.

Results:

During the study period, 12,473 males (17.6%) and 7,457 females (14.3%) died. The general effect of multimorbidity on mortality was small (females: 1.02, 1.01–1.02; males: 1.04, 1.03–1.04). The number of neuropsychiatric disorders was related to higher mortality (1.33, 1.30–1.36; 1.46, 1.43–1.50). Cardiovascular and metabolic disorders had inconsistent effects (0.99, 0.97–1.01; 1.08, 1.07–1.09). Psychiatric, psychosomatic, and pain-related disorders were related to higher life expectancy (0.87, 0.86–0.89; 0.88, 0.87–0.90).

Conclusions:

Chronic diseases have heterogeneous effects on mortality and generalized measures of multimorbidity reflect and even out the effects of the single diseases. In multimorbidity studies, a careful selection of diseases is therefore important.

Using item response theory with health system data to identify latent groups of patients with multiple health conditions

A critical step toward tailoring effective interventions for heterogeneous and medically complex patients is to identify clinically meaningful subgroups on the basis of their comorbid conditions. We applied Item Response Theory (IRT), a potentially useful tool to identify clinically meaningful subgroups, to characterize phenotypes within a cohort of high-risk patients. This was a retrospective cohort study using 68,400 high-risk Veteran’s Health Administration (VHA) patients. Thirty-one physical and mental health diagnosis indicators based on ICD-9 codes from patients’ inpatient, outpatient VHA and VA-paid community care claims. Results revealed 6 distinct subgroups of high-risk patients were identified: substance use, complex mental health, complex diabetes, liver disease, cancer with cardiovascular disease, and cancer with mental health. Multinomial analyses showed that subgroups significantly differed on demographic and utilization variables which underscored the uniqueness of the groups. Using IRT models with clinical diagnoses from electronic health records permitted identification of diagnostic constellations among otherwise undifferentiated high-risk patients. Recognizing distinct patient profiles provides a framework from which insights into medical complexity of high-risk patients can be explored and effective interventions can be tailored.

Lifetime trajectory simulation of chronic disease progression and comorbidity development

INTRODUCTION

Comorbidity is common in elderly patients and it imposes heavy burden on both individual and the whole healthcare system. This study aims to gain insights of comorbidity development by simulating the lifetime trajectory of disease progression from single chronic disease to comorbidity.

METHODS

Eight health states spanning from no chronic condition to comorbidity are considered in this study. Disease progression network is constructed based on the seven-year retrospective data of around 700,000 residents living in Singapore central region. Microsimulation is applied to simulate the process of aging and disease progression of a synthetic new-born cohort for the entire lifetime.

RESULTS

Among the 40 unique trajectories observed from the simulation, the top 10 trajectories covers 60% of the cohort. Timespan of most trajectories from birth to death is 80 years. Most people progress to at risk at late 30 s, develop the first chronic condition at 50 s or 60 s, and then progress to complications at 70 s. It is also observed that the earlier one person develops chronic conditions, the more life-year-lost is incurred.

DISCUSSION

The lifetime disease progression trajectory constructed for each person in the cohort describes how a person starts healthy, becomes at risk, then progresses to one or more chronic conditions, and finally deteriorates to various complications over the years. This study may help us have a better understanding of chronic disease progression and comorbidity development, hence add values to chronic disease prevention and management.

Multimorbidity: The Brazilian Longitudinal Study of Aging (ELSI-Brazil)

OBJECTIVE

To evaluate the occurrence and factors associated with multimorbidity among Brazilians aged 50 years and over.

METHODS

This is a cross-sectional study in a nation-based cohort of the non-institutionalized population in Brazil. Data were collected between 2015 and 2016. Multimorbidity was assessed from a list of 19 morbidities, which were categorized into ≥ 2 and ≥ 3 diseases. The analysis included the calculation of frequencies and the most frequent 10 pairs and triplets of combinations of diseases. The crude and adjusted analyses evaluated the demographic, socioeconomic, behavioral, and contextual variables (area of residence, geopolitical region, and coverage of the Family Health Strategy) using Poisson regression.

RESULTS

From the total of 9,412 individuals, 67.8% (95%CI 65.6–69.9) and 47.1% (95%CI 44.8–49.4) showed ≥ 2 and ≥ 3 diseases, respectively. In the adjusted analysis, women, older persons, and those who did not consume alcohol had increased multimorbidity. There were no associations with race, area of residence, geopolitical region, and coverage of the Family Health Strategy. The 10 pairs (frequencies observed between 11.6% and 23.2%) and the 10 triplets (frequencies observed between 4.9% and 9.5%) of the most frequent diseases mostly included back problems (15 times) and systemic arterial hypertension (11 times). All combinations were statistically higher than expected by chance.

CONCLUSIONS

The occurrence of multimorbidity was high even among younger individuals (50 to 59 years). Approximately two in three (≥ 2 diseases) and one in two (≥ 3 diseases) individuals aged 50 years and over presented multimorbidity, which represents 26 and 18 million persons in Brazil, respectively. We observed high frequencies of combinations of morbidities.

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.

Applications of network analysis to routinely collected health care data: a systematic review

Objective

To survey network analyses of datasets collected in the course of routine operations in health care settings and identify driving questions, methods, needs, and potential for future research.

Materials and Methods

A search strategy was designed to find studies that applied network analysis to routinely collected health care datasets and was adapted to 3 bibliographic databases. The results were grouped according to a thematic analysis of their settings, objectives, data, and methods. Each group received a methodological synthesis.

Results

The search found 189 distinct studies reported before August 2016. We manually partitioned the sample into 4 groups, which investigated institutional exchange, physician collaboration, clinical co-occurrence, and workplace interaction networks. Several robust and ongoing research programs were discerned within (and sometimes across) the groups. Little interaction was observed between these programs, despite conceptual and methodological similarities.

Discussion

We use the literature sample to inform a discussion of good practice at this methodological interface, including the concordance of motivations, study design, data, and tools and the validation and standardization of techniques. We then highlight instances of positive feedback between methodological development and knowledge domains and assess the overall cohesion of the sample.