Description of multimorbidity clusters of admitted patients in medical departments of a general hospital

Relationship between the characteristics of Japanese physicians involved in medical care for older adults and their approaches to treating older patients with multimorbidity

One countermeasure against the increasing prevalence of multimorbidity is the need to provide clinical education and training that considers the characteristics of physicians. We conducted a questionnaire survey to determine the relationship between physicians' characteristics and their approach to treating older patients with multimorbidity. A total of 3300 geriatric specialists and primary care specialists in Japan were enrolled. A 4-point Likert scale was used to score the following items: difficult diseases (43 items), difficult patient backgrounds (14 items), important clinical factors (32 items), and important clinical management (32 items). Exploratory factor analysis was performed to examine the constructs in each of the scales Diseases, Backgrounds, Clinical Factors, and Clinical Management, and group comparisons by physician characteristics were conducted. A total of 778 respondents were included in the analysis. Six factors for Diseases, two factors for Patient Background, four factors for Clinical Factors, and two factors for Clinical Management were explored as patterns. Group comparison between mean scores for each factor and the characteristics of responding physicians showed statistically significant differences in at least one factor for all patterns in terms of years of experience as a physician (26 years or less, 27 years or more), the clinical setting (providing or not providing home medical care), and sex (male or female). Our results suggest a need for clinical education and training that takes into account not only physicians' experience and clinical setting, but also their sex.

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

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

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

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

From designing minimum data set to developing kidney transplantation registry in Iran

Objective

Currently, population growth and increasing life expectancy are becoming one of the biggest public health challenges in the world, which has increased the prevalence of chronic diseases such as end-stage renal disease and the need for kidney transplantation. The use of a variety of registries has the potential to determine the effectiveness of clinical care and costs and improve the quality of patient care. The aim of this study is to design minimum data set to develop a kidney transplantation registry in Iran to improve the quality of care for people with end-stage renal disease.

Methods

The present research is descriptive-applied. The minimum data set was reviewed and evaluated in expert panel meetings. The various elements of the minimum data set were discussed, and specialists in urology, nephrology, health information management, and medical informatics presented their views.

Results

The characteristics of the kidney transplantation registry in the form of eight axes of purpose, structure, data sources, minimum data set, classification systems, data processing and reporting, distribution and access to information, and data quality were extracted and finally these characteristics were approved by experts. The relevant tables were validated and were within the acceptable range from the point of view of experts.

Conclusion

In developing a kidney transplantation registry in Iran, the necessary requirements and features for designing a web-based registry have been considered. The prototype of this registry in the country will help to collect higher quality data. It is hoped that by developing this registry, a step will be taken to better manage the information on people with end-stage renal disease, provide better services to these patients, and facilitate related research.

Social determinants of multimorbidity patterns: A systematic review

Social determinants of multimorbidity are poorly understood in clinical practice. This review aims to characterize the different multimorbidity patterns described in the literature while identifying the social and behavioral determinants that may affect their emergence and subsequent evolution. We searched PubMed, Embase, Scopus, Web of Science, Ovid MEDLINE, CINAHL Complete, PsycINFO and Google Scholar. In total, 97 studies were chosen from the 48,044 identified. Cardiometabolic, musculoskeletal, mental, and respiratory patterns were the most prevalent. Cardiometabolic multimorbidity profiles were common among men with low socioeconomic status, while musculoskeletal, mental and complex patterns were found to be more prevalent among women. Alcohol consumption and smoking increased the risk of multimorbidity, especially in men. While the association of multimorbidity with lower socioeconomic status is evident, patterns of mild multimorbidity, mental and respiratory related to middle and high socioeconomic status are also observed. The findings of the present review point to the need for further studies addressing the impact of multimorbidity and its social determinants in population groups where this problem remains invisible (e.g., women, children, adolescents and young adults, ethnic groups, disabled population, older people living alone and/or with few social relations), as well as further work with more heterogeneous samples (i.e., not only focusing on older people) and using more robust methodologies for better classification and subsequent understanding of multimorbidity patterns. Besides, more studies focusing on the social determinants of multimorbidity and its inequalities are urgently needed in low- and middle-income countries, where this problem is currently understudied.

Network Structure of Comorbidity Patterns in U.S. Adults with Depression: A National Study Based on Data from the Behavioral Risk Factor Surveillance System

Background

People with depression are at increased risk for comorbidities; however, the clustering of comorbidity patterns in these patients is still unclear.

Objective

The aim of the study was to identify latent comorbidity patterns and explore the comorbidity network structure that included 12 chronic conditions in adults diagnosed with depressive disorder.

Methods

A cross-sectional study was conducted based on secondary data from the 2017 behavioral risk factor surveillance system (BRFSS) covering all 50 American states. A sample of 89,209 U.S. participants, 29,079 men and 60,063 women aged 18 years or older, was considered using exploratory graphical analysis (EGA), a statistical graphical model that includes algorithms for grouping and factoring variables in a multivariate system of network relationships.

Results

The EGA findings show that the network presents 3 latent comorbidity patterns, i.e., that comorbidities are grouped into 3 factors. The first group was composed of 7 comorbidities (obesity, cancer, high blood pressure, high blood cholesterol, arthritis, kidney disease, and diabetes). The second pattern of latent comorbidity included the diagnosis of asthma and respiratory diseases. The last factor grouped 3 conditions (heart attack, coronary heart disease, and stroke). Hypertension reported higher measures of network centrality.

Conclusion

Associations between chronic conditions were reported; furthermore, they were grouped into 3 latent dimensions of comorbidity and reported network factor loadings. The implementation of care and treatment guidelines and protocols for patients with depressive symptomatology and multimorbidity is suggested.