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Abstract Tools from network science can be utilized to study relations between diseases. Different studies focus on different types of inter-disease linkages. One of them is the comorbidity patterns derived from large-scale longitudinal data of hospital discharge records. Researchers seek to describe comorbidity relations as a network to characteri...
Contexts in source publication
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... 1 presents the histogram of the number of ICD9-coded diagnoses per hospital visit. Table 1 presents the top 10 disease in the data set with highest prevalence. Figure 2 depicts the histogram of the prevalence of the diseases in our data set. ...
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... is of note that eigenvector centrality tends to capture nodes with high in-strength. The OER and IPFP methods predominantly pick up pregnancy-related Tables 19 and 20, the aggregated in-strength and out-strength of this category of nodes is not high as compared to other categories (it ranks among the bot- tom 5 in both cases), but interestingly, in terms of the weights of within-category links, this category has an outstandingly large share (it ranks second, after the diseases of the circulatory system). This means that the pregnancy-related nodes form a cohesive sub- network. ...
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... authority would be a disease that follows many other diseases. Table 10 presents the results for the top 5 diseases in terms of hubness in different networks. Comparing the results in Table 10 with those of Table 6, we observe that the hub-ness scores for all categories correlate highly with those of eigenvector centrality, except DF and Salience. ...
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... 10 presents the results for the top 5 diseases in terms of hubness in different networks. Comparing the results in Table 10 with those of Table 6, we observe that the hub-ness scores for all categories correlate highly with those of eigenvector centrality, except DF and Salience. Table 11 presents the corre- lation matrix for hubness. ...
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... the results in Table 10 with those of Table 6, we observe that the hub-ness scores for all categories correlate highly with those of eigenvector centrality, except DF and Salience. Table 11 presents the corre- lation matrix for hubness. The hubness score is important in comorbidity studies because the hubs that the HITS algorithm nominates are universal senders, and in the context of comorbidity studies, these would pertain to diseases that substantially increase the risk of many other diseases, demanding more prevention and care. ...
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... hubness score is important in comorbidity studies because the hubs that the HITS algorithm nominates are universal senders, and in the context of comorbidity studies, these would pertain to diseases that substantially increase the risk of many other diseases, demanding more prevention and care. Table 11 shows that there is good agreement between the hub scores of different methods, so despite their structural differences, the hub score is robust and can be reliably used. The two usual exceptions are present here as well: the IPFP method, and the Salience method. ...
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... are expected because their tasks are different: the hubness scores of the IPFP method pertain to an alternative inflow-outflow comorbidity matrix where the prevalences are controlled for, and the Salience method only focuses on distances and trajectories rather than actual disease-disease relations. Table 12 presents the top 5 nodes for authority, and Table 13 presents the correlation matrix for authority scores. For the authority index too, there is good agreement between every method except IPFP and Salience. ...
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... are expected because their tasks are different: the hubness scores of the IPFP method pertain to an alternative inflow-outflow comorbidity matrix where the prevalences are controlled for, and the Salience method only focuses on distances and trajectories rather than actual disease-disease relations. Table 12 presents the top 5 nodes for authority, and Table 13 presents the correlation matrix for authority scores. For the authority index too, there is good agreement between every method except IPFP and Salience. ...
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... this measure is helpful in detecting this structural feature of diseases. Table 14 presents the top 5 nodes with highest betweenness centrality in the constructed networks. In the raw, OER, φ, DF, and Gloss networks, the top nodes are those with extremely high prevalence. ...
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... results of the IPFP method are less intuitive, consistent with the results for the previous measures. Table 15 presents the correlations across different networks. ...
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... get a better intuition into the network measures that we used to characterize diseases, we investigate the correlation between each network measure and disease prevalence in every constructed network. The results are presented in Table 16. In the raw network, every measure has a strong positive association with disease prevalence. ...
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... due to the presence of higher-order trajectories, more than one link can together pertain to a single patient, thus the prevalence information lost. However, Table 16 shows a strong correlation between the out-degree of diseases (about 0.95) and their prevalence. So if we did not have the prevalence data, we could use out-degree as a proxy for prevalence. ...
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... highlights the strength of the directed char- acterization of the network over the undirected versions considered in the literature, because in addition to association between disease pairs, the distinction between the sta- tistical properties of the two directions sheds light on which of the two diseases is more probable to cause the other, or at least to precede the other in the causal network that subsumes them both besides other covariates. Table 17 pertains to disease pairs that have OER > 1.5 and are related via shared PPI or genes as deemed by Ref. Park et al. (2009). The table presents the percentage of such disease pairs that are deemed significant by different constructed networks. ...
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... three meth- ods have a local focus, therefore, they are potent in detecting such link-based relations. The other methods, however, focus more on the global structure of the network, and as Table 17 shows, have poor performance for detecting such disease pairs, which matches the expectation. ...
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... now construct a coarse-grained picture of the disease network based on the standard 17 categories of the ICD9 coding scheme (World Health Organization 2004). The list of the 17 categories are presented in Table 18, along with the number of 3-digit ICD9 codes contained within each category, percentage of 3-digit ICD9 codes contained within each category, number of diagnoses in the data set that pertain to diseases within each category, and the percentage of such diagnoses. The network properties of the 17-node coarse-grained networks are summarized in Table 19, which presents the percentage of the total link weight that flows into and out of The percentage of the total link strength of the constructed networks that flows into and out of each of the 17 disease categories in the coarse-grained network each category. ...
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... list of the 17 categories are presented in Table 18, along with the number of 3-digit ICD9 codes contained within each category, percentage of 3-digit ICD9 codes contained within each category, number of diagnoses in the data set that pertain to diseases within each category, and the percentage of such diagnoses. The network properties of the 17-node coarse-grained networks are summarized in Table 19, which presents the percentage of the total link weight that flows into and out of The percentage of the total link strength of the constructed networks that flows into and out of each of the 17 disease categories in the coarse-grained network each category. Table 20 presents the percentages of the total link weight of the network that falls within each disease category, that is, pertaining to links that connect two nodes that both belong to the same disease category. ...
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... Table 21, we present the rankings of the disease categories in terms of what fraction of the total link weight of the network flows out of each category. Except for the IPFP network, the highest out-strength of all networks either belongs to category 11 (com- plications of pregnancy, childbirth, and the puerperium) or category 7 (disease of the circulatory system). ...
Citations
... However, concerns remain about the reproducibility of findings across diverse EHR systems, potentially hindering the translation of these insights into clinical practice. Network analysis has emerged as a valuable tool for studying multimorbidity 10 . By modeling diseases as nodes and their co-occurrences as connections, these networks offer insights into disease relationships, clusters, and potential progression patterns. ...
Background: Electronic health records (EHR) are increasingly used for studying multimorbidities. However, concerns about accuracy, completeness, and EHRs being primarily designed for billing and administration raise questions about the consistency and reproducibility of EHR-based multimorbidity research.
Methods: Utilizing phecodes to represent the disease phenome, we analyzed pairwise
comorbidity strengths using a dual logistic regression approach and constructed multimorbidity as an undirected weighted graph. We assessed the consistency of the multimorbidity networks within and between two major EHR systems at local (nodes and edges), meso (neighboring patterns), and global (network statistics) scales. We present case studies to identify disease clusters and uncover clinically interpretable disease relationships. We provide an interactive web
tool and a knowledge base combing data from multiple sources for online multimorbidity
analysis.
Findings: Analyzing data from 500,000 patients across Vanderbilt University Medical Center and Mass General Brigham health systems, we observed a strong correlation in disease frequencies ( Kendall's t = 0.643) and comorbidity strengths (Pearson r = 0.79). Consistent network statistics across EHRs suggest a similar structure of multimorbidity networks at various scales. Comorbidity strengths and similarities of multimorbidity connection patterns align with the disease genetic correlations. Graph-theoretic analyses revealed a consistent core-periphery structure, implying efficient network clustering through threshold graph construction. Using hydronephrosis as a case study, we demonstrated the network's ability to uncover clinically
relevant disease relationships and provide novel insights.
... The analysis of multimorbidity has recently been catalyzed by the massive collection of patient health information on diagnoses, medication, and results of laboratory tests in electronic health records (EHR), and other clinical registries. Comorbidity networks have been established as tools to analyze multimorbidity in such datasets 11,12 . Age and sex-specific analyses can further be conducted to address age-and sex-dependent associations between diagnoses 13,14 . ...
We aim to comprehensively identify typical life-spanning trajectories and critical events that impact patients’ hospital utilization and mortality. We use a unique dataset containing 44 million records of almost all inpatient stays from 2003 to 2014 in Austria to investigate disease trajectories. We develop a new, multilayer disease network approach to quantitatively analyze how cooccurrences of two or more diagnoses form and evolve over the life course of patients. Nodes represent diagnoses in age groups of ten years; each age group makes up a layer of the comorbidity multilayer network. Inter-layer links encode a significant correlation between diagnoses ( p < 0.001, relative risk > 1.5), while intra-layers links encode correlations between diagnoses across different age groups. We use an unsupervised clustering algorithm for detecting typical disease trajectories as overlapping clusters in the multilayer comorbidity network. We identify critical events in a patient’s career as points where initially overlapping trajectories start to diverge towards different states. We identified 1260 distinct disease trajectories (618 for females, 642 for males) that on average contain 9 (IQR 2–6) different diagnoses that cover over up to 70 years (mean 23 years). We found 70 pairs of diverging trajectories that share some diagnoses at younger ages but develop into markedly different groups of diagnoses at older ages. The disease trajectory framework can help us to identify critical events as specific combinations of risk factors that put patients at high risk for different diagnoses decades later. Our findings enable a data-driven integration of personalized life-course perspectives into clinical decision-making.
... In addition, we constructed a disease network (DN) based on comorbidity results obtained using ICD-10. DNs are a network structure that can be used to reveal potential links between diseases with similar characteristics, providing a theoretical and practical basis for a deeper understanding of disease relationships and promoting personalized medicine [14]. ...
Objective
Acute kidney injury (AKI) is a clinical syndrome that occurs as a result of a dramatic decline in kidney function caused by a variety of etiological factors. Its main biomarkers, serum creatinine and urine output, are not effective in diagnosing early AKI. For this reason, this study provides insight into this syndrome by exploring the comorbidities of AKI, which may facilitate the early diagnosis of AKI. In addition, organ crosstalk in AKI was systematically explored based on comorbidities to obtain clinically reliable results.
Methods
We collected data from the Medical Information Mart for Intensive Care-IV database on patients aged ≥\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge$$\end{document} 18 years in intensive care units (ICU) who were diagnosed with AKI using the criteria proposed by Kidney Disease: Improving Global Outcomes. The Apriori algorithm was used to mine association rules on the diagnoses of 55,486 AKI and non-AKI patients in the ICU. The comorbidities of AKI mined were validated through the Electronic Intensive Care Unit database, the Colombian Open Health Database, and medical literature, after which comorbidity results were visualized using a disease network. Finally, organ diseases were identified and classified from comorbidities to investigate renal crosstalk with other distant organs in AKI.
Results
We found 579 AKI comorbidities, and the main ones were disorders of lipoprotein metabolism, essential hypertension, and disorders of fluid, electrolyte, and acid-base balance. Of the 579 comorbidities, 554 were verifiable and 25 were new and not previously reported. In addition, crosstalk between the kidneys and distant non-renal organs including the liver, heart, brain, lungs, and gut was observed in AKI with the strongest heart-kidney crosstalk, followed by lung-kidney crosstalk.
Conclusion
The comorbidities mined in this study using association rules are scientific and may be used for the early diagnosis of AKI and the construction of AKI predictive models. Furthermore, the organ crosstalk results obtained through comorbidities may provide supporting information for the management of short- and long-term treatment practices for organ dysfunction.
... Network analysis has been a powerful approach to decipher complex multimorbidity patterns (Strayer, Zhang, et al., 2023;Fotouhi et al., 2018;Aguado et al., 2020). Our recent work has demonstrated the interoperability of EHR-based multimorbidities and multimorbidity networks when compared across distinct EHR systems using standardized diagnostic codes (e.g., ICD9 or ICD10) Strayer, Vessels, et al., 2023). ...
Motivation: Multimorbidity, characterized by the simultaneous occurrence of multiple diseases in an individual, is an increasing global health concern, posing substantial challenges to healthcare systems. Comprehensive understanding of disease-disease interactions and intrinsic mechanisms behind multimorbidity can offer opportunities for innovative prevention strategies, targeted interventions, and personalized treatments. Yet, there exist limited tools and datasets that characterize multimorbidity patterns across different populations. To bridge this gap, we used large-scale electronic health record (EHR) systems to develop the Phenome-wide Multi-Institutional Multimorbidity Explorer (PheMIME), which facilitates research in exploring and comparing multimorbidity patterns among multiple institutions, potentially leading to the discovery of novel and robust disease associations and patterns that are interoperable across different systems and organizations.
Results: PheMIME integrates summary statistics from phenome-wide analyses of disease multimorbidities. These are currently derived from three major institutions: Vanderbilt University Medical Center, Massachusetts General Brigham, and the UK Biobank. PheMIME offers interactive exploration of multimorbidity through multi-faceted visualization. Incorporating an enhanced version of associationSubgraphs, PheMIME enables dynamic analysis and inference of disease clusters, promoting the discovery of multimorbidity patterns. Once a disease of interest is selected, the tool generates interactive visualizations and tables that users can delve into multimorbidities or multimorbidity networks within a single system or compare across multiple systems. The utility of PheMIME is demonstrated through a case study on Schizophrenia.
Availability and implementation: The PheMIME knowledge base and web application are accessible at https://prod.tbilab.org/PheMIME/. A comprehensive tutorial, including a use-case example, is available at https://prod.tbilab.org/PheMIME_supplementary_materials/. Furthermore, the source code for PheMIME can be freely downloaded from https://github.com/tbilab/PheMIME.
Data availability statement: The data underlying this article are available in the article and in its online web application or supplementary material.
... This study is a preliminary excursion into the various factors that influence the development of comorbidities post mild TBI. In the future, we intend to build upon this work to study pairwise disease co-occurrences at scale using graph networks and network analysis (Fotouhi et al., 2018;Ljubic et al., 2020;Lee and Park, 2021). In doing so, a larger avenue of analysis is opened to visualize and understand disease comorbidities as differences in network structures across scales (Fotouhi et al., 2018;Ljubic et al., 2020;Lee and Park, 2021). ...
... In the future, we intend to build upon this work to study pairwise disease co-occurrences at scale using graph networks and network analysis (Fotouhi et al., 2018;Ljubic et al., 2020;Lee and Park, 2021). In doing so, a larger avenue of analysis is opened to visualize and understand disease comorbidities as differences in network structures across scales (Fotouhi et al., 2018;Ljubic et al., 2020;Lee and Park, 2021). Comorbidities are understood as unique spatial organisations of statistically significant interconnected disease nodes with varying depths of connectedness (Fotouhi et al., 2018;Kim et al., 2018;Lee and Park, 2021). ...
... In doing so, a larger avenue of analysis is opened to visualize and understand disease comorbidities as differences in network structures across scales (Fotouhi et al., 2018;Ljubic et al., 2020;Lee and Park, 2021). Comorbidities are understood as unique spatial organisations of statistically significant interconnected disease nodes with varying depths of connectedness (Fotouhi et al., 2018;Kim et al., 2018;Lee and Park, 2021). Network structural properties will be compared across time, TBI severity, and demographic conditions. ...
Traumatic brain injury (TBI) is associated with an increased risk of long-lasting health-related complications. Survivors of brain trauma often experience comorbidities which could further dampen functional recovery and severely interfere with their day-to-day functioning after injury. Of the three TBI severity types, mild TBI constitutes a significant proportion of total TBI cases, yet a comprehensive study on medical and psychiatric complications experienced by mild TBI subjects at a particular time point is missing in the field. In this study, we aim to quantify the prevalence of psychiatric and medical comorbidities post mild TBI and understand how these comorbidities are influenced by demographic factors (age, and sex) through secondary analysis of patient data from the TBI Model Systems (TBIMS) national database. Utilizing self-reported information from National Health and Nutrition Examination Survey (NHANES), we have performed this analysis on subjects who received inpatient rehabilitation at 5 years post mild TBI. Our analysis revealed that psychiatric comorbidities (anxiety, depression, and post-traumatic stress disorder (PTSD)), chronic pain, and cardiovascular comorbidities were common among survivors with mild TBI. Furthermore, depression exhibits an increased prevalence in the younger compared to an older cohort of subjects whereas the prevalence of rheumatologic, ophthalmological, and cardiovascular comorbidities was higher in the older cohort. Lastly, female survivors of mild TBI demonstrated increased odds of developing PTSD compared to male subjects. The findings of this study would motivate additional analysis and research in the field and could have broader implications for the management of comorbidities after mild TBI.
... To mitigate the effect of random diagnosis occurrences in a patient's records, we used Fisher's Exact Test to meas- www.nature.com/scientificreports/ ure the statistical significance of the tendency of a disease in preceding another disease, as described in Fotouhi et al.(2018) 34 . We applied the Benjamini-Hochberg method for multiple testing correction and considered only links with adjusted p-value < 0.05. ...
A better understanding of the sequential and temporal aspects in which diseases occur in patient’s lives is essential for developing improved intervention strategies that reduce burden and increase the quality of health services. Here we present a network-based framework to study disease relationships using Electronic Health Records from > 9 million patients in the United States Veterans Health Administration (VHA) system. We create the Temporal Disease Network, which maps the sequential aspects of disease co-occurrence among patients and demonstrate that network properties reflect clinical aspects of the respective diseases. We use the Temporal Disease Network to identify disease groups that reflect patterns of disease co-occurrence and the flow of patients among diagnoses. Finally, we define a strategy for the identification of trajectories that lead from one disease to another. The framework presented here has the potential to offer new insights for disease treatment and prevention in large health care systems.
... Québec (Canada), which found that a large share of the overall link weight in a coarse 17-node network was accounted for by the category "complications of pregnancy, childbirth, and the puerperium" but not by the category "certain conditions originating in the perinatal period" [22]. In our study, chapters in the network that were highly connected with other chapters (i.e., chapters with high outflow) included L Diseases of the skin and subcutaneous tissue, G Diseases of the nervous system, and E Endocrine, nutritional and metabolic diseases. ...
Background:
Reducing the considerable non-communicable disease (NCD) burden in the aging Japanese population depends on better understanding of the comorbid and temporal relationships between different NCDs.
Objective:
We aimed to identify associations between NCDs and temporal patterns of NCDs in Japan using data from a large medical claims database.
Methods:
The study used three-digit International Classification of Diseases, Tenth Revision codes for NCDs for employees and their dependents included in the MinaCare database, which covers the period since 2010. Associations between pairs of NCDs were assessed by calculating risk ratios. The calculated risk ratios were used to create a network of closely associated NCDs (risk ratio > 15, statistically significant) and to assess temporal patterns of NCD diagnoses (risk ratio ≥ 5). The Infomap algorithm was used to identify clusters of diseases for different sex and age strata.
Results:
The analysis included 4,200,254 individuals (age < 65 years: 98%). Many of the temporal associations and patterns of the diseases of interest identified in this study were previously known. Regarding the diseases of interest, these associations can be classified as comorbidities, early manifestations initially diagnosed as something else, diseases attributable to or that cause the disease of interest, or caused by pharmacological treatment. International Classification of Diseases, Tenth Revision chapters that were most associated with other chapters included L Diseases of the skin and subcutaneous tissue. In the age-stratified and gender-stratified networks, clusters with the highest numbers of International Classification of Diseases, Tenth Revision codes included I Diseases of the circulatory system and F Mental and behavioral disorders.
Conclusions:
Our findings reinforce established associations between NCDs and underline the importance of comprehensive NCD care.
... Recommended indicators include 2×2 contingency table-based measures such as odds ratio, relative risk, or observed-to-expected ratio (O/E ratio). 26,29,30 However, these indicators have some disadvantages when trying to construct and visualize a network. First of all, they are not bounded and can extend to positive infinity. ...
... Several studies addressed this issue and tried to delineate the pattern of disease progression. 29,30,59,60 They are challenging tasks since access to sensitive datasets is restricted and the methodology to analyze dynamic networks is refined. Besides, temporal order does not necessarily guarantee the causal relationship. ...
Objective:
The nature of physical comorbidities in patients with mental illness may differ according to diagnosis and personal characteristics. We investigated this complexity by conventional logistic regression and network analysis.
Methods:
A health insurance claims data in Korea was analyzed. For every combination of psychiatric and physical diagnoses, odds ratios were calculated adjusting age and sex. From the patient-diagnosis data, a network of diagnoses was constructed using Jaccard coefficient as the index of comorbidity.
Results:
In 1,017,024 individuals, 77,447 (7.6%) were diagnosed with mental illnesses. The number of physical diagnoses among them was 11.2, which was 1.6 times higher than non-psychiatric groups. The most noticeable associations were 1) neurotic illnesses with gastrointestinal/pain disorders and 2) dementia with fracture, Parkinson's disease, and cerebrovascular accidents. Unexpectedly, the diagnosis of metabolic syndrome was only scarcely found in patients with severe mental illnesses (SMIs). However, implicit associations between metabolic syndrome and SMIs were suggested in comorbidity networks.
Conclusion:
Physical comorbidities in patients with mental illnesses were more extensive than those with other disease categories. However, the result raised questions as to whether the medical resources were being diverted to less serious conditions than more urgent conditions in patients with SMIs.
... It therefore remains unclear how different diseases interact with one another over the lifetime of a patient with SMI. Recent evidence has shown that network models provide a powerful means to characterize interactions among diseases [27][28][29] and explore temporal progression trajectories for complex conditions such as diabetes and cardiovascular diseases [10,30,31,11,32]. These methods often represent patient-diagnosis data as a one-mode network, in which each node represents a disease and an edge links two nodes if two diseases have a strongly statistical correlation, such as co-occurrences [33,29,32] and sequential associations [10,11], which enables us to examine relationships of diseases/symptoms, e.g., disease progression paths, by measuring structural properties of these networks. ...
... Following previous studies [30,43,13], all ICD-10 codes were rounded to 3 characters as the first 3 characters capture the main category of a diagnosis. Unlike one-mode network models that merely capture relationships among a single type of nodes (e.g., diseases) [33,29,30,28,31,11,32], the proposed bipartite network models jointly represent information on both patients and diseases, as well as their relationships, which provides a natural representation and preserves more information in patient-diagnosis data. ...
... One might wonder whether the complex processes leading to the decreased distances are (a) disease progression [30,28,31,11], i.e., a patient with some existing conditions tends to suffer from another condition, or (b) selection effects [61], i.e., some patients tend to be diagnosed with the same conditions due to common characteristics such as demographic and genetic attributes. To approach this, we examine the efficiencies of patient nodes and disease nodes respectively. ...
Multimorbidity is a major factor contributing to increased mortality among people with severe mental illnesses (SMI). Previous studies either focus on estimating prevalence of a disease in a population without considering relationships between diseases or ignore heterogeneity of individual patients in examining disease progression by looking merely at aggregates across a whole cohort. Here, we present a temporal bipartite network model to jointly represent detailed information on both individual patients and diseases, which allows us to systematically characterize disease trajectories from both patient and disease centric perspectives. We apply this approach to a large set of longitudinal diagnostic records for patients with SMI collected through a data linkage between electronic health records from a large UK mental health hospital and English national hospital administrative database. We find that the resulting diagnosis networks show disassortative mixing by degree, suggesting that patients affected by a small number of diseases tend to suffer from prevalent diseases. Factors that determine the network structures include an individual’s age, gender and ethnicity. Our analysis on network evolution further shows that patients and diseases become more interconnected over the illness duration of SMI, which is largely driven by the process that patients with similar attributes tend to suffer from the same conditions. Our analytic approach provides a guide for future patient-centric research on multimorbidity trajectories and contributes to achieving precision medicine.
... Multimorbidity networks consist of age-and sex-specific clusters of diseases, i.e., groups of diagnoses that often co-occur with each other (e.g., the metabolic syndrome co-occurring with cardiovascular diseases; common mental disorders co-occurring with substance abuse) [20,24]. In addition, these networks often contain diagnoses that connect with many diagnoses from other clusters across the entire diagnostic spectrum; so called network hubs [25]. The hub diseases were shown to have positive association with mortality [18]. ...
Background:
Young refugees are at increased risk of labor market marginalization (LMM). We sought to examine whether the association of multimorbidity patterns and LMM differs in refugee youth compared to Swedish-born youth and identify the diagnostic groups driving this association.
Methodology:
We analyzed 249,245 individuals between 20-25 years, on 31 December 2011, from a combined Swedish registry. Refugees were matched 1:5 to Swedish-born youth. A multimorbidity score was computed from a network of disease co-occurrences in 2009-2011. LMM was defined as disability pension (DP) or >180 days of unemployment during 2012-2016. Relative risks (RR) of LMM were calculated for 114 diagnostic groups (2009-2011). The odds of LMM as a function of multimorbidity score were estimated using logistic regression.
Results:
2841 (1.1%) individuals received DP and 16,323 (6.5%) experienced >180 annual days of unemployment during follow-up. Refugee youth had a marginally higher risk of DP (OR (95% CI): 1.59 (1.52, 1.67)) depending on their multimorbidity score compared to Swedish-born youth (OR (95% CI): 1.51 (1.48, 1.54)); no differences were found for unemployment (OR (95% CI): 1.15 (1.12, 1.17), 1.12 (1.10, 1.14), respectively). Diabetes mellitus and influenza/pneumonia elevated RR of DP in refugees (RRs (95% CI) 2.4 (1.02, 5.6) and 1.75 (0.88, 3.45), respectively); most diagnostic groups were associated with a higher risk for unemployment in refugees.
Conclusion:
Multimorbidity related similarly to LMM in refugees and Swedish-born youth, but different diagnoses drove these associations. Targeted prevention, screening, and early intervention strategies towards specific diagnoses may effectively reduce LMM in young adult refugees.
