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Chronic diseases are often comorbid and present a weighty burden for communities in the 21st century. The present investigation depicted patterns of multimorbidity in the general population and examined its association with the individual- and area-level factors in an urban sample of non-elderly adults of Brazil. Data were from the cross-sectional...
Context in source publication
Context 1
... significantly complained of more diseases than men, as well as more multimorbidity. Figure 2 shows the number of comorbid conditions by sex and age bracket. A salient dose-response gradient between age and number of morbidities was observed for both sexes: the higher the age bracket, the greater the proportion of concomitant conditions. ...
Citations
... These findings show the magnitude of the mental condition in the issue of multimorbidity and its outcomes. Mental conditions appear to carry more weight and increase the burden of morbidity when present in these patients [14,40]. It appears as if the presence of a mental condition in a patient with MM had a greater impact on negative outcomes than the two physical medical illnesses. ...
Background
In ageing populations, multimorbidity is a complex challenge to health systems, especially when the individuals have both mental and physical morbidities. Although a regular source of primary care (RSPC) is associated with better health outcomes, its relation with health service utilisation in elderly patients with mental-physical multimorbidity (MP-MM) is scarce.
Objective
This study explored the relations among health service utilisation, presence of RSPC and MP-MM among elderly Brazilians.
Methods
A national cross-sectional study performed with data from national representative samples from the Brazilian National Health Research (PNS, in Portuguese; Pesquisa Nacional de Saúde) carried out in 2013 with 11,177 elderly Brazilian people. MP-MM was defined as the presence of two or more morbidities, including at least one mental morbidity, and was evaluated using a list of 16 physical and mental morbidities. The RSPC was analysed by the presence of regular font of care in primary care and health service utilisation according to the demand for health services ≤ 15 days, medical consultation ≤ 12 months, and hospitalisation ≤ 1 year. Frequency description of variables and bivariate association were performed using Stata v.15.2 software.
Results
The majority of individuals was female (56.4%), and their mean age was 69.8 years. The observed prevalence of MP-MM was 12.2%. Individuals with MP-MM had higher utilisation of health services when compared to those without MP-MM. RSPC was present at 36.5% and was higher in women (37.8% vs. 34.9%). There was a lower occurrence of hospitalisation ≤ 1 year among MP-MM individuals with RSPC and without a private plan of health.
Conclusion
Our findings demonstrate that RSPC can be an important component of care in elderly individuals with MP-MM because it was associated with lower occurrence of hospitalisation, mainly in those that have not a private plan of health. Longitudinal studies are necessary to confirm these findings.
... As different analytical methods adjust for multimorbidity by chance to different extents, it is anticipated that multimorbid groups of conditions from different studies vary, limiting comparability to the literature. Most Brazilian studies still focus on disease counts and rely their results about multimorbidity patterns on techniques, such as factor analysis, [19][20][21] principal component analysis, 54 association rule 22,23 and hierarchical cluster. 55 The soft technique employed in the present study has the main advantage, it places individuals and not their diseases at the center of the analyses for assessing multimorbidity patterns. ...
Background
To identify multimorbidity patterns, by sex, according to sociodemographic and lifestyle in ELSA-Brasil.
Methods
Cross-sectional study with 14,516 participants from ELSA-Brasil (2008–2010). Fuzzy c-means was used to identify multimorbidity patterns of 2+ chronic morbidities, where the consequent morbidity had to occur in at least 5% of all cases. Association rule (O/E≥1.5) was used to identify co-occurrence of morbidities, in each cluster, by sociodemographic and lifestyle factors.
Results
The prevalence of multimorbidity was higher in women (73.7%) compared to men (65.3%). Among women, cluster 1 was characterized by hypertension/diabetes (13.2%); cluster 2 had no overrepresented morbidity; and cluster 3 all participants had kidney disease. Among men, cluster 1 was characterized by cirrhosis/hepatitis/obesity; cluster 2, most combinations included kidney disease/migraine (6.6%); cluster 3, no pattern reached association ratio; cluster 4 predominated co-occurrence of hypertension/rheumatic fever, and hypertension/dyslipidemia; cluster 5 predominated diabetes and obesity, and combinations with hypertension (8.8%); and cluster 6 presented combinations of diabetes/hypertension/heart attack/angina/heart failure. Clusters were characterized by higher prevalence of adults, married and participants with university degrees.
Conclusion
Hypertension/diabetes/obesity were highly co-occurred, in both sexes. Yet, for men, morbidities like cirrhosis/hepatitis were commonly clustered with obesity and diabetes; and kidney disease was commonly clustered with migraine and common mental disorders. The study advances in understanding multimorbidity patterns, benefiting simultaneously or gradually prevention of diseases and multidisciplinary care responses.
... For instance, the cardiometabolic burden was lower in rural areas. Territorial characteristics such as the level of crime and violence in the area were also found to be associated with a pattern of chronic pain and respiratory diseases in men (101). In these areas of higher economic deprivation and lower socioeconomic status (i.e., lower household income and lower education levels), cardiometabolic, respiratory, mental and musculoskeletal patterns were more prevalent (104). ...
Social determinants of multimorbidity are poorly understood in clinical practice. This review aims to characterize the different multimorbidity patterns described in the literature while identifying the social and behavioral determinants that may affect their emergence and subsequent evolution. We searched PubMed, Embase, Scopus, Web of Science, Ovid MEDLINE, CINAHL Complete, PsycINFO and Google Scholar. In total, 97 studies were chosen from the 48,044 identified. Cardiometabolic, musculoskeletal, mental, and respiratory patterns were the most prevalent. Cardiometabolic multimorbidity profiles were common among men with low socioeconomic status, while musculoskeletal, mental and complex patterns were found to be more prevalent among women. Alcohol consumption and smoking increased the risk of multimorbidity, especially in men. While the association of multimorbidity with lower socioeconomic status is evident, patterns of mild multimorbidity, mental and respiratory related to middle and high socioeconomic status are also observed. The findings of the present review point to the need for further studies addressing the impact of multimorbidity and its social determinants in population groups where this problem remains invisible (e.g., women, children, adolescents and young adults, ethnic groups, disabled population, older people living alone and/or with few social relations), as well as further work with more heterogeneous samples (i.e., not only focusing on older people) and using more robust methodologies for better classification and subsequent understanding of multimorbidity patterns. Besides, more studies focusing on the social determinants of multimorbidity and its inequalities are urgently needed in low- and middle-income countries, where this problem is currently understudied.
... In this sense, other factors, mainly socio-economic, play an essential role in this trend, showing an advance of 10-15 years in the age of onset of multimorbidity among people with fewer socio-economic resources [21][22][23] . The degree and pattern of multimorbidity are particularly associated with social and demographic factors such as gender, age, educational level, income and, in general, any type of social inequality 1,12,16,[24][25][26][27][28] . Recent work shows that multimorbidity is strongly associated with socioeconomic factors 21 and this condition appears 10-15 years earlier in disadvantaged classes 1 . ...
... As the value of k was increased, it could be observed that the density of the network was considerably reduced. The most clearly maintained relationships were as follows: (1) lumbar pain-cervical pain; (2) anxiety-depression; (3) allergy-asthma-respiratory problems; (4) haemorrhoids-constipation; (5) prostate problems-incontinence; (6) osteoarthritis-osteoporosis; (7) heart attack-coronary heart disease; (8) hypertension-diabetes-obesity; (9) hypertension-diabetes [or other heart problems]-cataracts; (10) hypertension-diabetes-cholesterol; (11) osteoarthritis-osteoporosis-cataracts; (12) allergy-hypertension-cataracts; or (13) cataracts-osteoarthritis-allergies, among other weaker relationships. Thus, although in general terms the previously detected structure of interrelationships was maintained, some less intuitive (and so interesting) associations were lost. ...
... For www.nature.com/scientificreports/ men (Fig. 4A), the more relevant diseases were: urinary incontinence (14), arthrosis (6), cataracts (16), cervical pain (8) kidney disease (29), osteoporosis (27), and diabetes (12). In parallel, the closeness centrality index (that quantify indirect connections a node has with other nodes) showed a similar relevance for these variables. ...
Multimorbidity can be defined as the presence of two or more chronic diseases in an individual. This condition is associated with reduced quality of life, increased disability, greater functional impairment, increased health care utilisation, greater fragmentation of care and complexity of treatment, and increased mortality. Thus, understanding its epidemiology and inherent complexity is essential to improve the quality of life of patients and to reduce the costs associated with multi-pathology. In this paper, using data from the European Health Survey, we explore the application of Mixed Graphical Models and its combination with social network analysis techniques for the discovery and classification of complex multimorbidity patterns. The results obtained show the usefulness and versatility of this approach for the study of multimorbidity based on the use of graphs, which offer the researcher a holistic view of the relational structure of data with variables of different types and high dimensionality.
... We examined multimorbidity patterns in each country, by showing the size of the bubble and its associated percentage of individuals with each dyad of disease. 44 We estimated the relationships between multimorbidity and OOPE for medicine using log-linear models where a constant, equals to one, was added to the outcome variable prior to the log transformation to reduce skewness. 12 To interpret the coefficients estimated from the model, we converted the coefficients to a percentage change in outcome. ...
Introduction:
Using nationally representative survey data from China and India, this study examined (1) the distribution and patterns of multimorbidity in relation to socioeconomic status and (2) association between multimorbidity and out-of-pocket expenditure (OOPE) for medicines by socioeconomic groups.
Methods:
Secondary data analysis of adult population aged 45 years and older from WHO Study on Global Ageing and Adult Health (SAGE) India 2015 (n=7397) and China Health and Retirement Longitudinal Study (CHARLS) 2015 (n=11 570). Log-linear, two-parts, zero-inflated and quantile regression models were performed to assess the association between multimorbidity and OOPE for medicines in both countries. Quantile regression was adopted to assess the observed relationship across OOPE distributions.
Results:
Based on 14 (11 self-reported) and 9 (8 self-reported) long-term conditions in the CHARLS and SAGE datasets, respectively, the prevalence of multimorbidity in the adult population aged 45 and older was found to be 63.4% in China and 42.2% in India. Of those with any long-term health condition, 38.6% in China and 20.9% in India had complex multimorbidity. Multimorbidity was significantly associated with higher OOPE for medicines in both countries (p<0.05); an additional physical long-term condition was associated with a 18.8% increase in OOPE for medicine in China (p<0.05) and a 20.9% increase in India (p<0.05). Liver disease was associated with highest increase in OOPE for medicines in China (61.6%) and stroke in India (131.6%). Diabetes had the second largest increase (China: 58.4%, India: 91.6%) in OOPE for medicines in both countries.
Conclusion:
Multimorbidity was associated with substantially higher OOPE for medicines in China and India compared with those without multimorbidity. Our findings provide supporting evidence of the need to improve financial protection for populations with an increased burden of chronic diseases in low-income and middle-income countries.
... Peripheral psychiatric and neurological disorders, along with chronic heart disease, COPD and bronchiectasis, and arthropathy and arthritis, corresponding to 23% of patients. Wang et al. (2019) reported patterns of multimorbidity using principal component analysis (PCA) as a clustering method. They used data from a local survey in a large city of Brazil, including 2,713 subjects. ...
... However, it was remarkable how varied results were regarding the clusters recovered. For example, arthritis and other painful conditions have been reported to co-occur with depressive disorders (Miguel et al., 2012), but the algorithms used only grouped arthritis with mental disorders in one (Wang et al., 2019) of the studies. Similarly, only one study grouped mental disorders with metabolic syndrome (Violan et al., 2018), which is one association expected to be found widely due to mechanisms of metabolic alterations via psychiatric medication use. ...
Objective
The presence of two or more chronic diseases results in worse clinical outcomes than expected by a simple combination of diseases. This synergistic effect is expected to be higher when combined with some conditions, depending on the number and severity of diseases. Multimorbidity is a relatively new term, with the first fundamental definitions appearing in 2015. Studies usually define it as the presence of at least two chronic medical illnesses. However, little is known regarding the relationship between mental disorders and other non-psychiatric chronic diseases. This review aims at investigating the association between some mental disorders and non-psychiatric diseases, and their pattern of association.
Methods
We performed a systematic approach to selecting papers that studied relationships between chronic conditions that included one mental disorder from 2015 to 2021. These were processed using Covidence, including quality assessment.
Results
This resulted in the inclusion of 26 papers in this study. It was found that there are strong associations between depression, psychosis, and multimorbidity, but recent studies that evaluated patterns of association of diseases (usually using clustering methods) had heterogeneous results. Quality assessment of the papers generally revealed low quality among the included studies.
Conclusions
There is evidence of an association between depressive disorders, anxiety disorders, and psychosis with multimorbidity. Studies that tried to examine the patterns of association between diseases did not find stable results.
Systematic review registration
https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021216101, identifier: CRD42021216101.
... In [50] added fuzziness upon k-means algorithm to estimate clusters of patients as well as membership matrix indicating the membership degree of a patient to a given cluster. In [48] a multilevel analysis of the influence of individual and area level factors on patterns of physical-mental multimorbidity and healthcare used in the general population. Applying this method allows detecting the isolated and combined influence of variables of each level on the outcome variables. ...
Aim: Multi-morbidity remains poorly understood due to the multifactorial complexity of this phenomenon and the lack of a standardized methodology for building and analysing Multimorbidity network. A comparative analysis of methods of modeling Multimorbidity network in literature may help to understand the pros and cons of these methods, then to facilitate a consensus about a standardized methodology. We propose to study two approaches for building Multimorbidity network focusing in their technical specificities. Subject and Methods: We propose to model Multimorbidity using Ising Model, a Markov Random field based approach, and to compare its performance to the approach consisting in building a network of co-occurence using pairwise association strength estimated by Multimorbidity Coefficient. Besides, we illustrate how to use network science techniques to extract structural knowledge from Multimorbidity network. Results: The results show that the Ising model is able to detect a similar structural patern as the approach of computing Multimorbidity coefficient for all paires of diseases. An evaluation of the stability and precision of the obtained comorbidity network has proved its reliability. Conclusion: Defining methods and algorithms of detecting Multimorbidity network in formal language may help interdisciplinary cooperative research. Ising Model is a machine learning based on a probabilistic formalism capable of detecting the same pattern as traditional approaches in Multimorbidity research literature. Understanding how diseases co-occur at the same time will help physicians to reason on multimorbidity burden as a complex system rather than reasoning on diseases as single and isolated entities.
... NCD multimorbidity leads to poor quality of life [10][11][12], disability [13], increased healthcare utilization [14], high economic burden [14] and reduced physical [15] and mental competence [16]. The World Health Organization (WHO) notes the coexisting NCD burden is higher for those in low-and middlelevel economies, including India [17]. ...
Escalating non-communicable disease multimorbidity rates among older adults is an emerging public health concern in India, but the literature sparsely addresses the epidemiology of multimorbidity. We explore levels, patterns, combinations and pre-dictors of multimorbidity among older adults using information on 59,764 individuals , aged 45 years and older, from the first wave of Longitudinal Ageing Study in India (LASI), 2017-2018. We computed multimorbidity score for sixteen non-com-municable diseases to identify frequently occurring morbidity patterns (dyads and triads) and assess the relationship between multimorbidity and selected background characteristics. Near third of the older adult population is affected by multimorbidity, with hypertension, gastrointestinal disorders, musculoskeletal disorders, diabetes, and skin diseases being the most common. Policymakers should seek strategies to increase early detection and prevention of chronic diseases, delay the age at onset of disease for those who are not affected, and improve management for those affected with multiple disease conditions.
... The most common approach to measuring multimorbidity is disease counts. Part of the problem in choosing an accurate measure is the abstract nature of the concept of multimorbidity and the relationship between diseases (Wang et al., 2019). The problem has been intensified by the paucity of studies that have directly compared the performance of different measures in a longitudinal period and the impact of the variables on each other (Huntley et al., 2012). ...
Background
Depression, anxiety, and binge eating are common psychiatric symptoms among people with obesity. Although many studies seek to understand the mechanisms of association between these psychiatric symptoms, there is no still consensus about the longitudinal association.
Methods
155 patients (124 women) were recruited from a university-based bariatric center and evaluated over three waves (T0-T1-T2). In the last period the sample comprised 126 (104 women) participants. Trained clinicians assessed psychiatric symptoms by telephone interview using measurement scales. Partial Least Squares (PLS) was applied to investigate the path effects between anxiety, depression and binge eating symptoms over time.
Results
The results of path coefficients (β) showed that the effect of anxiety on depression was constantly significant in all periods T0 (β =0.74), T1 (β =0.71), and T2 (β =0.67). Anxiety had an effect on binge eating in T0 (β =0.39) and T2 (β =0.26) but not in T1. Binge eating affected depressive symptoms only in T2 (β =0.22). Two carry-over-effects were significant binge eating in T0-T1 (β=0.41) and T1-T2 (β=0.19).
Limitations
Telephone interviews, social isolation due to the pandemic and the social desirability may have contributed to collection and information biases.
Conclusion
Anxiety has significant path effects on depression and binge eating. Binge eating was shown to be the most unstable symptom over time. The time factor seems to contribute to path effects between the psychiatric symptoms. The results draw attention to the fact that psychiatric symptoms must be evaluated and treated in association with each other, and investigated over time.
... In [15] added fuzziness upon k-means algorithm to estimate clusters of patients as well as membership matrix indicating the membership degree of a patient to a given cluster. In [16] a multilevel analysis of the influence of individual-and area-level factors on patterns of physical-mental multimorbidity and health-care use in the general population. Applying this method allows detecting the isolated and combined influence of variables of each level on the outcome variable. ...
... explaining the observed co-occurencing of diseases }. Parameters θ encode the parameters related to the chosen model: it could be logistic coefficients or Bayesian networks probabilistic table, degree of polynomial regression, Hidden Markov models [24], latent class [25], principal component analysis [16], threshold in significant associations [18,26]. Technically, statistical models R are grouped as family of equations and H is framed based on assumptions underlying the problem of interest. ...
Multimorbidity is one of the major problems in the modern medical system. The more conditions the patient has, the greater the psychological pressure will be. We propose a formal definition of the general case of Multimorbidity Disease Network detection. Based on pairwise association method, we constructed an undirected weighted graph of co-occurrence for comorbidity based on the socio-psychological profile existing in a real data set. Based on the obtained network, we used the centrality analysis of the network nodes to conduct a mesoscopic-analysis, and used the community detection algorithm to determine potential components of the network. The main results show first, that algorithms used can be helpful for extracting models of multimorbidity. Second, that aging process not only affects the number of diseases, but can also influence Multimorbidity Burden and its complexity pattern.
