Clustering of comorbidities

Prevalence and patterns of comorbidities in older people with type 2 diabetes in Australian primary care settings

OBJECTIVE

The aim of this study was to identify the prevalence and patterns of comorbidity in community-dwelling older people with type 2 diabetes mellitus (T2DM) attending general practice settings in Australia.

METHODS

This study involved a cross-sectional analysis using the Bettering the Evaluation and Care of Health (BEACH) sub-study data. In a series of sub-studies, a representative sample of general practitioners was asked to record all diagnosed chronic conditions for patients at 40 consecutive encounters using structured paper-based recording forms. The dataset was analysed with descriptive analyses, and exploratory factor analyses were applied to examine comorbidity patterns.

RESULTS

Of the 14,042 patients aged 65 years or older, 2688 had a diagnosis of T2DM (19%). Of the 2688 patients with T2DM, hypertension was present in 67% (95% CI: 64.6-70.0), followed by arthritis 52% (95% CI: 48.8-54.8), hyperlipidaemia 45% (95% CI: 41.8-47.9), ischemic heart disease, 23% (95% CI: 20.7-24.9), depression 16% (95% CI: 48.8-54.8), atrial fibrillation 10% (95% CI: 8.9-11.6), congestive heart failure 7% (95% CI: 6.0-8.1), stroke/cerebrovascular accident 7% (95% CI: 5.4-8.2) and peripheral vascular disease 5% (95% CI: 4.4-6.2). We identified two comorbidity patterns among older people with T2DM. The first were psychological and musculoskeletal conditions and the second were cardiovascular conditions and chronic renal failure.

CONCLUSIONS

The prevalence of cardiovascular and non-cardiovascular comorbidities in community-dwelling older people with T2DM was high. Adequate primary care strategies should be in place to support the long-term care for this population.

Patient Factors Associated with Missed Otolaryngology Appointments at an Urban Safety-Net Hospital

OBJECTIVE

To determine if patient factors related to ethnicity, socioeconomic status (SES), medical comorbidities, or appointment characteristics increase the risk of missing an initial adult otolaryngology appointment.

METHODS

This study is a retrospective case control study at Boston Medical Center (BMC) in Boston, Massachusetts, that took place in 2019. Patient demographic and medical comorbidity data as well as appointment characteristic data were collected and compared between those that attended their initial otolaryngology appointment versus those who missed their initial appointment. Chi-square and ANOVA tests were used to calculate differences between attendance outcomes. Multivariate analysis was used to compare the odds of missing an appointment based on various patient- and appointment-related factors.

RESULTS

Patients who were more likely to miss their appointments were more often female, of lower education, disabled, not employed, Black or Hispanic, and Spanish-speaking. Spring and Fall appointments were more likely to be missed. When a multivariate regression was conducted to control for social determinants of health (SDOH) such as race, insurance status, employment, and education status, the odds of females, Spanish-speaking, students, and disabled patients missing their appointment were no longer statistically significant.

CONCLUSION

A majority of patients at BMC come from lower SES backgrounds and have multiple medical comorbidities. Those who reside closer to BMC, often areas of lower average income, had higher rates of missed appointments. Interventions such as decreasing lag time, providing handicap-accessible free transportation, and increasing accessibility of telemedicine for patients could help improve attendance rates at BMC.

LEVEL OF EVIDENCE

IV Laryngoscope, 2024.

Chronic Disease Patterns and Their Relationship With Health-Related Quality of Life in South Korean Older Adults With the 2021 Korean National Health and Nutrition Examination Survey: Latent Class Analysis

BACKGROUND

Improved life expectancy has increased the prevalence of older adults living with multimorbidities, which likely deteriorates their health-related quality of life (HRQoL). Understanding which chronic conditions frequently co-occur can facilitate person-centered care tailored to the needs of individuals with specific multimorbidity profiles.

OBJECTIVE

The study objectives were to (1) examine the prevalence of multimorbidity among Korean older adults (ie, those aged 65 years and older), (2) investigate chronic disease patterns using latent class analysis, and (3) assess which chronic disease patterns are more strongly associated with HRQoL.

METHODS

A sample of 1806 individuals aged 65 years and older from the 2021 Korean National Health and Nutrition Examination Survey was analyzed. Latent class analysis was conducted to identify the clustering pattern of chronic diseases. HRQoL was assessed by an 8-item health-related quality of life scale (HINT-8). Multiple linear regression was used to analyze the association with the total score of the HINT-8. Logistic regression analysis was performed to evaluate the odds ratio of having problems according to the HINT-8 items.

RESULTS

The prevalence of multimorbidity in the sample was 54.8%. Three chronic disease patterns were identified: relatively healthy, cardiometabolic condition, arthritis, allergy, or asthma. The total scores of the HINT-8 were the highest in participants characterized as arthritis, allergy, or asthma group, indicating the lowest quality of life.

CONCLUSIONS

Current health care models are disease-oriented, meaning that the management of chronic conditions applies to a single condition and may not be relevant to those with multimorbidities. Identifying chronic disease patterns and their impact on overall health and well-being is critical for guiding integrated care.

Perspectives on common chronic diseases in adult cancer patients in South Africa

There is a rising noncommunicable disease (NCD) burden in low- and middle-income countries. Sub-Saharan Africa (SSA) bears a higher burden than the global average with South Africa (SA) enduring the highest regional burden. SA among other southern African countries also bears a high prevalence of HIV and other chronic communicable diseases. Having a perspective on common chronic diseases in the ever-increasing numbers of adult cancer patients in SA will inform our understanding of approaches to better manage them. This commentary reviews regional and national studies and data of low- and middle-income countries and particularly SA on the chronic infectious and NCD multimorbidity burden among adult cancer patients. It also reflects on the considerable health system challenges of managing discordant multimorbidity among adult cancer patients within the SA Public Health System. Despite the critical need to better manage the growing MM burden in general and particularly the high prevalence of discordant multimorbidity among cancer patients, there is a dearth of research into MM management generally and in LMICs particularly.

Multimorbidity patterns and the association with health status of the oldest-old in long-term care facilities in China: a two-step analysis

BACKGROUND

The increasing prevalence of multimorbidity has created a serious global public health problem in aging populations. Certain multimorbidity patterns across different age ranges and their association with health status remain unclear. The main aim of this study is to identify multimorbidity patterns discrepancies and associated health status between younger-old and oldest-old.

METHODS

The Ethics Committee of Nanjing Medical University approved the study protocol (No.2019-473). Convenience sampling method was used to recruit older adults aged ≥ 60 years with multimorbidity from July to December 2021 from 38 Landsea long-term care facilities in China. The multimorbidity patterns were analyzed using network analysis and two-step cluster analysis. One-Way ANOVA was utilized to explore their association with health status including body function, activity of daily living, and social participation. A Sankey diagram visualized the flow of health status within different multimorbidity patterns. This study is reported following the STROBE guidelines.

RESULTS

A total of 214 younger-old (60-84 years) and 173 oldest-old (≥ 85 years) were included. Leading coexisting diseases were cardiovascular disease (CD), metabolic and endocrine disease (MED), neurological disease (ND), and orthopedic disease (OD). Cluster 1 (53, 24.8%) of CD-ND (50, 94.3%; 31, 58.8%), cluster 2 (39, 18.2%) of MED-ND-CD (39, 100%; 39, 100%; 37, 94.9%), cluster 3 (37, 17.3%) of OD-CD-MED-ND (37, 100%; 33, 89.2%; 27, 73.0%; 16, 43.2%), and cluster 4 (34, 15.9%) of CD-MED (34, 100%; 34, 100%) were identified in the younger-old. In the oldest-old, the primary multimorbidity patterns were: cluster 1 (33, 19.1%) of CD-respiratory disease-digestive disease-urogenital disease (CD-RD-DSD-UD) (32, 97.0%; 9, 27.3%; 8, 24.2%; 7, 21.2%), cluster 2 (42, 24.3%) of ND-CD-MED (42, 100%; 35, 83.3%; 14, 33.3%), cluster 3 (28, 16.2%) of OD-CD-MED (28, 100%; 25, 89.3%; 18, 64.3%), and cluster 4 (35, 20.2%) of CD-MED (35, 100%; 35, 100%). Younger-old with CD-ND or MED-ND-CD, and oldest-old with ND-CD-MED have worse health status compared with other multimorbidity patterns (e.g., CD-MED and OD-CD-MED).

CONCLUSION

Discrepancies in common patterns of multimorbidity across age groups suggest that caregivers in long-term care facilities should consider changes in multimorbidity patterns with ageing when developing prevention plans for individualized management. Neurological disease concurrent with other diseases was the major determinant of health status, especially for the oldest-old. Interventions targeting multimorbidity need to be focused, yet generic. It is essential to assess complex needs and health outcomes that arise from different multimorbidity patterns and manage them through an interdisciplinary approach and consider their priorities to gain high-quality primary care for older adults living in long-term care facilities.

Diabetes and Multiple Long-term Conditions: A Review of Our Current Global Health Challenge

Use of effective treatments and management programs is leading to longer survival of people with diabetes. This, in combination with obesity, is thus contributing to a rise in people living with more than one condition, known as multiple long-term conditions (MLTC or multimorbidity). MLTC is defined as the presence of two or more long-term conditions, with possible combinations of physical, infectious, or mental health conditions, where no one condition is considered as the index. These include a range of conditions such as cardiovascular diseases, cancer, chronic kidney disease, arthritis, depression, dementia, and severe mental health illnesses. MLTC has major implications for the individual such as poor quality of life, worse health outcomes, fragmented care, polypharmacy, poor treatment adherence, mortality, and a significant impact on health care services. MLTC is a challenge, where interventions for prevention and management are lacking a robust evidence base. The key research directions for diabetes and MLTC from a global perspective include system delivery and care coordination, lifestyle interventions and therapeutic interventions.

Clustering by multiple long-term conditions and social care needs: a cross-sectional study among 10 026 older adults in England

BACKGROUND   : People with multiple long-term conditions (MLTC) face health and social care challenges. This study aimed to classify people by MLTC and social care needs (SCN) into distinct clusters and quantify the association between derived clusters and care outcomes. METHODS : A cross-sectional study was conducted using the English Longitudinal Study of Ageing, including people with up to 10 MLTC. Self-reported SCN was assessed through 13 measures of difficulty with activities of daily living, 10 measures of mobility difficulties and whether health status was limiting earning capability. Latent class analysis was performed to identify clusters. Multivariable logistic regression quantified associations between derived MLTC/SCN clusters, all-cause mortality and nursing home admission. RESULTS: Our study included 9171 people at baseline with a mean age of 66.3 years; 44.5% were men. Nearly 70.8% had two or more MLTC, the most frequent being hypertension, arthritis and cardiovascular disease. We identified five distinct clusters classified as high SCN/MLTC through to low SCN/MLTC clusters. The high SCN/MLTC included mainly women aged 70-79 years who were white and educated to the upper secondary level. This cluster was significantly associated with higher nursing home admission (OR=8.71; 95% CI: 4.22 to 18). We found no association between clusters and all-cause mortality. CONCLUSIONS: We have highlighted those at risk of worse care outcomes, including nursing home admission. Distinct clusters of individuals with shared sociodemographic characteristics can help identify at-risk individuals with MLTC and SCN at primary care level.

Classification of patients with osteoarthritis through clusters of comorbidities using 633 330 individuals from Spain

OBJECTIVES

To explore clustering of comorbidities among patients with a new diagnosis of OA and estimate the 10-year mortality risk for each identified cluster.

METHODS

This is a population-based cohort study of individuals with first incident diagnosis of OA of the hip, knee, ankle/foot, wrist/hand or 'unspecified' site between 2006 and 2020, using SIDIAP (a primary care database representative of Catalonia, Spain). At the time of OA diagnosis, conditions associated with OA in the literature that were found in ≥1% of the individuals (n = 35) were fitted into two cluster algorithms, k-means and latent class analysis. Models were assessed using a range of internal and external evaluation procedures. Mortality risk of the obtained clusters was assessed by survival analysis using Cox proportional hazards.

RESULTS

We identified 633 330 patients with a diagnosis of OA. Our proposed best solution used latent class analysis to identify four clusters: 'low-morbidity' (relatively low number of comorbidities), 'back/neck pain plus mental health', 'metabolic syndrome' and 'multimorbidity' (higher prevalence of all studied comorbidities). Compared with the 'low-morbidity' cluster, the 'multimorbidity' cluster had the highest risk of 10-year mortality (adjusted hazard ratio [HR]: 2.19 [95% CI: 2.15, 2.23]), followed by the 'metabolic syndrome' cluster (adjusted HR: 1.24 [95% CI: 1.22, 1.27]) and the 'back/neck pain plus mental health' cluster (adjusted HR: 1.12 [95% CI: 1.09, 1.15]).

CONCLUSION

Patients with a new diagnosis of OA can be clustered into groups based on their comorbidity profile, with significant differences in 10-year mortality risk. Further research is required to understand the interplay between OA and particular comorbidity groups, and the clinical significance of such results.

Multimorbidity of cardiometabolic diseases: a cross-sectional study of patterns, clusters and associated risk factors in sub-Saharan Africa

OBJECTIVE

To determine the patterns of cardiometabolic multimorbidity and associated risk factors in sub-Saharan Africa (SSA).

DESIGN

We used data from the WHO STEPwise approach to non-communicable disease risk factor surveillance cross-sectional surveys conducted between 2014 and 2017.

PARTICIPANTS

The participants comprised 39, 658 respondents aged 15-69 years randomly selected from nine SSA countries using a multistage stratified sampling design.

PRIMARY OUTCOME MEASURE

Using latent class analysis and agglomerative hierarchical clustering algorithms, we analysed the clustering of cardiometabolic diseases (CMDs) including high blood sugar, hypercholesterolaemia, hypertension and cardiovascular diseases (CVDs) such as heart attack, angina and stroke. Clusters of lifestyle risk factors: harmful salt intake, physical inactivity, obesity, tobacco and alcohol use were also computed. Prevalence ratios (PR) from modified Poisson regression were used to assess the association of cardiometabolic multimorbidity with sociodemographic and lifestyle risk factors.

RESULTS

Two distinct classes of CMDs were identified: relatively healthy group with minimal CMDs (95.2%) and cardiometabolic multimorbidity class comprising participants with high blood sugar, hypercholesterolaemia, hypertension and CVDs (4.8%). The clusters of lifestyle risk factors included alcohol, tobacco and harmful salt consumption (27.0%), and physical inactivity and obesity (5.8%). The cardiometabolic multimorbidity cluster exhibited unique sociodemographic and lifestyle risk profiles. Being female (PR=1.7, 95% CI (1.5 to 2.0), middle-aged (35-54 years) (3.9 (95% CI 3.2 to 4.8)), compared with age 15-34 years, employed (1.2 (95% CI 1.1 to 1.4)), having tertiary education (2.5 (95% CI 2.0 to 3.3)), vs no formal education and clustering of physical inactivity and obesity (2.4 (95% CI 2.0 to 2.8)) were associated with a higher likelihood of cardiometabolic multimorbidity.

CONCLUSION

Our findings show that cardiometabolic multimorbidity and lifestyle risk factors cluster in distinct patterns with a disproportionate burden among women, middle-aged, persons in high socioeconomic positions, and those with sedentary lifestyles and obesity. These results provide insights for health systems response in SSA to focus on these clusters as potential targets for integrated care.

Healthy lifestyle choices and microvascular complications: New insights into diabetes management

Yogini Chudasama and Kamlesh Khunti discuss new evidence, published in PLOS Medicine, highlighting the importance of healthy lifestyle behaviours in diabetes management.

1000 Days: The "WeCare Generation" Program-The Ultimate Model for Improving Human Mental Health and Economics: The Study Protocol

INTRODUCTION

The COVID-19 pandemic stressed the necessity of a new resilience of the human population and health system. The "WeCare Generation" program is a new proposal of territorial intervention, with a new paradigm, on the diseases of the human body and mind.

BACKGROUND

In recent decades, the independent strands of investigation on brain plasticity and early trauma consequences have demonstrated that traumatic experiences in the period from pregnancy to the age of 3 years have an enormous impact on an individual's future development, and both physical and mental health. Research shows that adverse child experiences (ACEs) are associated with a strong risk of conditions such as: harmful alcohol use, smoking, illicit drug use, high body-mass index, depression, anxiety, interpersonal violence, cancer, type 2 diabetes, cardiovascular diseases, stroke respiratory diseases and, as a consequence, to a high financial cost in Italy and also across Europe (1-9% GDP) and the USA (total annual costs estimated to be USD 581 billion in Europe and USD 748 billion in North America). All this suggests that an early intervention on that traumatized-slice of population leads to multiplied savings.

METHODS

A multi-center, randomized, controlled trial was designed. The parents of the future neonatal population (from pregnancy to delivery) with trauma will be enrolled, and randomized to treatment, or control arm. The article describes in detail how the primary outpoint (cost to the national health system), and some secondary outpoints, will be collected.

DISCUSSION

An overall rate of return on investment (ROI) statistically significant 13.0% per annum with an associated benefit/cost ratio (BCR) of 6.3 is expected as the primary outcome of the "WeCare Generation" program. Our proposed model predicts a new medical paradigm aiming to empower new generations, with a strong return on economy and health.

Immunosenescence profiles of lymphocyte compartments and multiple long-term conditions (multimorbidity) in very old adults: The Newcastle 85+ Study

Immunosenescence, a decline in immune system function, has been linked to several age-related diseases and ageing syndromes. Very old adults (aged ≥ 85 years) live with multiple long-term conditions (MLTC, also known as multimorbidity)-a complex phenomenon of poor health defined by either counts, indices, or patterns, but little is known about the relationship between an ageing immune system and MLTC in this age group. We utilised baseline data from the Newcastle 85+ Study to investigate the associations between previously defined immunosenescence profiles of lymphocyte compartments and MLTC counts and patterns (from 16 chronic diseases/ageing syndromes). Seven hundred and three participants had MLTC and complete data for all 16 conditions, a median and mean of 5 (range 2-11) and 62.2% had ≥ 5 conditions. Three distinct MLTC patterns emerged by clustering: Cluster 1 ('Low frequency cardiometabolic-cerebrovascular diseases', n = 209), Cluster 2 ('High ageing syndromes-arthritis', n = 240), and Cluster 3 ('Hypertensive-renal impairment', n = 254). Although having a more senescent phenotype, characterised by higher frequency of CD4 and CD8 senescence-like effector memory cells and lower CD4/CD8 ratio, was not associated with MLTC compared with less senescent phenotype, the results warrant further investigation, including whether immunosenescence drives change in MLTC and influences MLTC severity in late adulthood.

Magnitude, pattern and correlates of multimorbidity among patients attending chronic outpatient medical care in Bahir Dar, northwest Ethiopia: The application of latent class analysis model

Objective

This study aimed to investigate the magnitude, pattern and associated factors of multimorbidity in Bahir Dar, northwest Ethiopia.

Methods

A multi-centered facility-based study was conducted among 1440 participants aged 40+ years attending chronic outpatient medical care. Two complementary methods (interview and review of medical records) were employed to collect data on socio-demographic, behavioral and disease related characteristics. The data were analyzed by STATA V.16 and R Software V.4.1.0. We fitted logistic regression and latent class analyses (LCA) models to identify the factors associated with multimorbidity and determine patterns of disease clustering, respectively. Statistical significance was considered at P-value <0.05.

Results

The magnitude of individual chronic conditions ranged from 1.4% (cancer) to 37.9% (hypertension), and multimorbidity was identified in 54.8% (95% CI = 52.2%-57.4%) of the sample. The likelihood of having multimorbidity was higher among participants aged 45–54 years (AOR: 1.6, 95%CI = 1.1, 2.2), 55–64 years (AOR: 2.6, 95%CI = 1.9, 3.6) and 65+ years (AOR: 2.6, 95%CI = 1.9, 3.6) compared to those aged 40–44 years. The odds of multimorbidity was also higher among individuals classified as overweight (AOR: 1.6, 95%CI = 1.2, 2.1) or obese (AOR: 1.9, 95%CI = 1.3, 3.0) than the normal weight category. Four patterns of multimorbidity were identified; the cardiovascular category being the largest class (50.2%) followed by the cardio-mental, (32.6%), metabolic (11.5%) and respiratory (5.7%) groups. Advanced age, being overweight and obesity predicted latent class membership, adjusting for relevant confounding factors.

Conclusions

The magnitude of multimorbidity in this study was high, and the most prevalent conditions shaped the patterns of multimorbidity. Advanced age, being overweight and obesity were the factors correlated with multimorbidity. Further research is required to better understand the burden of multimorbidity and related factors in the population, and to determine the impact of multimorbidity on individuals’ well-being and functioning.

Correlation Analysis between Hemoglobin and C-Reactive Protein in Patients Admitted to an Emergency Unit

Anemia and inflammation are common clinical conditions in emergency departments. This study explored a cohort of patients admitted to the emergency department with a particular interest in determining the frequency of anemia and inflammation and the association between hemoglobin (Hb) and C-reactive protein (CRP) concentrations. The study included 125 patients categorized according to their demographic (gender and age) and clinical condition (Hb and CRP concentrations, pathological background, and diagnostic). We found that anemia and inflammation were simultaneously present in 36.0% of the cohort, reaching 67.0% in patients that were subsequently hospitalized. The Hb level was significantly lower in patients with elevated concentration of CRP when compared to individuals with normal CRP levels (11.58 ± 2.23 vs. 13.25 ± 1.80, p = 0.001); furthermore, we found a significantly negative correlation between Hb concentration and the CRP level (rs = −0.42, p < 0.001). The linear regression model applied to the cohort showed that CRP levels explain 15% of Hb variations. The sensitivity of the CRP/Hb ratio (cut-off = 1.32) as a predictor of hospitalization was 80.0%, with a specificity of 68.4% for all patients. These findings confirmed the prevalence of anemia and inflammation and identified a moderate but significant association between Hb and serum CRP in a heterogeneous group of patients admitted to the emergency department.