Prevalence and patterns of multimorbidity in the Jamaican population: A comparative analysis of latent variable models

A scoping review finds a growing trend in studies validating multimorbidity patterns and identifies five broad types of validation methods

OBJECTIVES

Multimorbidity, the presence of two or more long-term conditions, is a growing public health concern. Many studies use analytical methods to discover multimorbidity patterns from data. We aimed to review approaches used in published literature to validate these patterns.

STUDY DESIGN AND SETTING

We systematically searched PubMed and Web of Science for studies published between July 2017 and July 2023 that used analytical methods to discover multimorbidity patterns.

RESULTS

Out of 31,617 studies returned by the searches, 172 were included. Of these, 111 studies (64%) conducted validation, the number of studies with validation increased from 53.13% (17 out of 32 studies) to 71.25% (57 out of 80 studies) in 2017-2019 to 2022-2023, respectively. Five types of validation were identified: assessing the association of multimorbidity patterns with clinical outcomes (n = 79), stability across subsamples (n = 26), clinical plausibility (n = 22), stability across methods (n = 7) and exploring common determinants (n = 2). Some studies used multiple types of validation.

CONCLUSION

The number of studies conducting a validation of multimorbidity patterns is clearly increasing. The most popular validation approach is assessing the association of multimorbidity patterns with clinical outcomes. Methodological guidance on the validation of multimorbidity patterns is needed.

Exploring multimorbidity clusters in relation to healthcare use and its impact on self-rated health among older people in India

The conventional definition of multimorbidity may not address the complex treatment needs resulting from interactions between multiple conditions, impacting self-rated health (SRH). In India, there is limited research on healthcare use and SRH considering diverse disease combinations in individuals with multimorbidity. This study aims to identify multimorbidity clusters related to healthcare use and determine if it improves the self-rated health of individuals in different clusters. This study extracted information from cross-sectional data of the first wave of the Longitudinal Ageing Study in India (LASI), conducted in 2017-18. The study participants were 31,373 people aged ≥ 60 years. A total of nineteen chronic diseases were incorporated to identify the multimorbidity clusters using latent class analysis (LCA) in the study. Multivariable logistic regression was used to examine the association between identified clusters and healthcare use. A propensity score matching (PSM) analysis was utilised to further examine the health benefit (i.e., SRH) of using healthcare in each identified cluster. LCA analysis identified five different multimorbidity clusters: relatively healthy' (68.72%), 'metabolic disorder (16.26%), 'hypertension-gastrointestinal-musculoskeletal' (9.02%), 'hypertension-gastrointestinal' (4.07%), 'complex multimorbidity' (1.92%). Older people belonging to the complex multimorbidity [aOR:7.03, 95% CI: 3.54-13.96] and hypertension-gastrointestinal-musculoskeletal [aOR:3.27, 95% CI: 2.74-3.91] clusters were more likely to use healthcare. Using the nearest neighbor matching method, results from PSM analysis demonstrated that healthcare use was significantly associated with a decline in SRH across all multimorbidity clusters. Findings from this study highlight the importance of understanding multimorbidity clusters and their implications for healthcare utilization and patient well-being. Our findings support the creation of clinical practice guidelines (CPGs) focusing on a patient-centric approach to optimize multimorbidity management in older people. Additionally, finding suggest the urgency of inclusion of counseling and therapies for addressing well-being when treating patients with multimorbidity.

Multimorbidity patterns and health-related quality of life in Jamaican adults: a cross sectional study exploring potential pathways

Introduction

Multimorbidity and health-related quality of life (HRQoL) are intimately linked. Multiple chronic conditions may adversely affect physical and mental functioning, while poorer HRQoL may contribute to the worsening course of diseases. Understanding mechanisms through which specific combinations of diseases affect HRQoL outcomes can facilitate identification of factors which are amenable to intervention. Jamaica, a middle-income country with high multimorbidity prevalence, has a health service delivery system dominated by public sector provision via a broad healthcare network. This study aims to examine whether multimorbidity classes differentially impact physical and mental dimensions of HRQoL in Jamaicans and quantify indirect effects on the multimorbidity-HRQoL relationship that are mediated by health system factors pertaining to financial healthcare access and service use.

Materials and methods

Latent class analysis (LCA) was used to estimate associations between multimorbidity classes and HRQoL outcomes, using latest available data from the nationally representative Jamaica Health and Lifestyle Survey 2007/2008 (N = 2,551). Multimorbidity measurement was based on self-reported presence/absence of 11 non-communicable diseases (NCDs). HRQoL was measured using the 12-item short-form (SF-12) Health Survey. Mediation analyses guided by the counterfactual approach explored indirect effects of insurance coverage and service use on the multimorbidity-HRQoL relationship.

Results

LCA revealed four profiles, including a Relatively Healthy class (52.7%) characterized by little to no morbidity and three multimorbidity classes characterized by specific patterns of NCDs and labelled Metabolic (30.9%), Vascular-Inflammatory (12.2%), and Respiratory (4.2%). Compared to the Relatively Healthy class, Vascular-Inflammatory class membership was associated with lower physical functioning (β = -5.5; p < 0.001); membership in Vascular-Inflammatory (β = -1.7; p < 0.05), and Respiratory (β = -2.5; p < 0.05) classes was associated with lower mental functioning. Significant mediated effects of health service use, on mental functioning, were observed for Vascular-Inflammatory (p < 0.05) and Respiratory (p < 0.05) classes.

Conclusion

Specific combinations of diseases differentially impacted HRQoL outcomes in Jamaicans, demonstrating the clinical and epidemiological value of multimorbidity classes for this population, and providing insights that may also be relevant to other settings. To better tailor interventions to support multimorbidity management, additional research is needed to elaborate personal experiences with healthcare and examine how health system factors reinforce or mitigate positive health-seeking behaviours, including timely use of services.

CHRONIC MULTIMORBIDITY OF LOW BACK PAIN OR OTHER CHRONIC BACK DISORDERS IN THE REPUBLIC OF CROATIA

The aim was to assess the prevalence of chronic multimorbidity in patients with chronic low back pain or other chronic back disorders (BD). We analyzed data from the population-based cross-sectional European Health Interview Survey (EHIS) performed in the Republic of Croatia 2014-2015 by the Croatian Institute of Public Health. Outcome was the point-prevalence of chronic multimorbidity defined as having ≥2 chronic illnesses out of 14 contained in the EHIS questionnaire, after adjustment for ten sociodemographic, anthropometric and lifestyle confounders. Amoung fourteen targeted illnesses were asthma, allergies, hypertension, urinary incontinence, kidney diseases, coronary heart disease or angina pectoris, neck disorder, arthrosis, chronic obstructive pulmonary disease, diabetes mellitus, myocardial infarction, stroke, depression, and the common category "other". We analyzed data on 268 participants with BD and 511 without it. Participants with BD had a significantly higher relative risk of any chronic multimorbidity (RRadj=2.12; 95% CI 1.55, 2.99; p<0.001), as well as of non-musculoskeletal chronic multimorbidity (RRadj=2.29; 95% CI 1.70, 3.08; p=0.001) than participants without BD. All chronic comorbidities except for asthma and liver cirrhosis were significantly more prevalent in participants with BD than in participants without BD. In the population with BD, the participants with multimorbidity had three to four times higher odds for unfavorable self-reported health outcomes than the participants with no comorbid conditions, whereas the existence of only one comorbidity was not significantly associated with a worse outcome compared to the population with no comorbidities. In conclusion, the population suffering from BD has a higher prevalence of chronic multimorbidity than the population without BD and this multimorbidity is associated with unfavorable health outcomes.

Global and regional prevalence of multimorbidity in the adult population in community settings: a systematic review and meta-analysis

BACKGROUND

Knowing the prevalence of multimorbidity among adults across continents is a crucial piece of information for achieving Sustainable Development Goal 3.4, which calls for reducing premature death due to non-communicable diseases. A high prevalence of multimorbidity indicates high mortality and increased healthcare utilization. We aimed to understand the prevalence of multimorbidity across WHO geographic regions among adults.

METHODS

We performed a systematic review and meta-analysis of surveys designed to estimate the prevalence of multimorbidity among adults in community settings. We searched PubMed, ScienceDirect, Embase and Google Scholar databases for studies published between January 1, 2000, and December 31, 2021. The random-effects model estimated the pooled proportion of multimorbidity in adults. Heterogeneity was quantified using I² statistics. We performed subgroup analyses and sensitivity analyses based on continents, age, gender, multimorbidity definition, study periods and sample size. The study protocol was registered with PROSPERO (CRD42020150945).

FINDINGS

We analyzed data from 126 peer-reviewed studies that included nearly 15.4 million people (32.1% were male) with a weighted mean age of 56.94 years (standard deviation of 10.84 years) from 54 countries around the world. The overall global prevalence of multimorbidity was 37.2% (95% CI = 34.9-39.4%). South America (45.7%, 95% CI = 39.0-52.5) had the highest prevalence of multimorbidity, followed by North America (43.1%, 95% CI = 32.3-53.8%), Europe (39.2%, 95% CI = 33.2-45.2%), and Asia (35%, 95% CI = 31.4-38.5%). The subgroup study highlights that multimorbidity is more prevalent in females (39.4%, 95% CI = 36.4-42.4%) than males (32.8%, 95% CI = 30.0-35.6%). More than half of the adult population worldwide above 60 years of age had multimorbid conditions (51.0%, 95% CI = 44.1-58.0%). Multimorbidity has become increasingly prevalent in the last two decades, while the prevalence appears to have stayed stable in the recent decade among adults globally.

INTERPRETATION

The multimorbidity patterns by geographic regions, time, age, and gender suggest noticeable demographic and regional differences in the burden of multimorbidity. According to insights about prevalence among adults, priority is required for effective and integrative interventions for older adults from South America, Europe, and North America. A high prevalence of multimorbidity among adults from South America suggests immediate interventions are needed to reduce the burden of morbidity. Furthermore, the high prevalence trend in the last two decades indicates that the global burden of multimorbidity continues at the same pace. The low prevalence in Africa suggests that there may be many undiagnosed chronic illness patients in Africa.

FUNDING

None.

Identifying non-communicable disease multimorbidity patterns and associated factors: a latent class analysis approach

OBJECTIVE

In the absence of adequate nationally-representative empirical evidence on multimorbidity, the existing healthcare delivery system is not adequately oriented to cater to the growing needs of the older adult population. Therefore, the present study identifies frequently occurring multimorbidity patterns among older adults in India. Further, the study examines the linkages between the identified patterns and socioeconomic, demographic, lifestyle and anthropometric correlates.

DESIGN

The present findings rest on a large nationally-representative sample from a cross-sectional study.

SETTING AND PARTICIPANTS

The study used data on 58 975 older adults (45 years and older) from the Longitudinal Ageing Study in India, 2017-2018.

PRIMARY AND SECONDARY OUTCOME MEASURES

The study incorporated a list of 16 non-communicable diseases to identify commonly occurring patterns using latent class analysis. The study employed multinomial logistic regression models to assess the association between identified disease patterns with unit-level socioeconomic, demographic, lifestyle and anthropometric characteristics.

RESULTS

The present study demonstrates that older adults in the country can be segmented into six patterns: 'relatively healthy', 'hypertension', 'gastrointestinal disorders-hypertension-musculoskeletal disorders', 'musculoskeletal disorders-hypertension-asthma', 'metabolic disorders' and 'complex cardiometabolic disorders'. Additionally, socioeconomic, demographic, lifestyle and anthropometric factors are significantly associated with one or more identified disease patterns.

CONCLUSIONS

The identified classes 'hypertension', 'metabolic disorders' and 'complex cardiometabolic disorders' reflect three stages of cardiometabolic morbidity with hypertension as the first and 'complex cardiometabolic disorders' as the last stage of disease progression. This underscores the need for effective prevention strategies for high-risk hypertension group. Also, targeted interventions are essential to reduce the burden on the high-risk population and provide equitable health services at the community level.

Social determinants of multimorbidity in Jamaica: application of latent class analysis in a cross-sectional study

Background

Non-communicable disease (NCD) multimorbidity is associated with impaired functioning, lower quality of life and higher mortality. Susceptibility to accumulation of multiple NCDs is rooted in social, economic and cultural contexts, with important differences in the burden, patterns, and determinants of multimorbidity across settings. Despite high prevalence of individual NCDs within the Caribbean region, exploration of the social epidemiology of multimorbidity remains sparse. This study aimed to examine the social determinants of NCD multimorbidity in Jamaica, to better inform prevention and intervention strategies.

Methods

Latent class analysis (LCA) was used to examine social determinants of identified multimorbidity patterns in a sample of 2551 respondents aged 15–74 years, from the nationally representative Jamaica Health and Lifestyle Survey 2007/2008. Multimorbidity measurement was based on self-reported presence/absence of 11 chronic conditions. Selection of social determinants of health (SDH) was informed by the World Health Organization’s Commission on SDH framework. Multinomial logistic regression models were used to estimate the association between individual-level SDH and class membership.

Results

Approximately one-quarter of the sample (24.05%) were multimorbid. LCA revealed four distinct profiles: a Relatively Healthy class (52.70%), with a single or no morbidity; and three additional classes, characterized by varying degrees and patterns of multimorbidity, labelled Metabolic (30.88%), Vascular-Inflammatory (12.21%), and Respiratory (4.20%). Upon controlling for all SDH (Model 3), advancing age and recent healthcare visits remained significant predictors of all three multimorbidity patterns (p < 0.001). Private insurance coverage (relative risk ratio, RRR = 0.63; p < 0.01) and higher educational attainment (RRR = 0.73; p < 0.05) were associated with lower relative risk of belonging to the Metabolic class while being female was a significant independent predictor of Vascular-Inflammatory class membership (RRR = 2.54; p < 0.001). Material circumstances, namely housing conditions and features of the physical and neighbourhood environment, were not significant predictors of any multimorbidity class.

Conclusion

This study provides a nuanced understanding of the social patterning of multimorbidity in Jamaica, identifying biological, health system, and structural determinants as key factors associated with specific multimorbidity profiles. Future research using longitudinal designs would aid understanding of disease trajectories and clarify the role of SDH in mitigating risk of accumulation of diseases.

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

OBJECTIVE

We aim to identify patterns of disease clusters among inpatients of a general hospital and to describe the characteristics and evolution of each group.

METHODS

We used two data sets from the CMBD (Conjunto mínimo básico de datos - Minimum Basic Hospital Data Set (MBDS)) of the Lucus Augusti Hospital (Spain), hospitalisations and patients, realising a retrospective cohort study among the 74 220 patients discharged from the Medic Area between 01 January 2000 and 31 December 2015. We created multimorbidity clusters using multiple correspondence analysis.

RESULTS

We identified five clusters for both gender and age. Cluster 1: alcoholic liver disease, alcoholic dependency syndrome, lung and digestive tract malignant neoplasms (age under 50 years). Cluster 2: large intestine, prostate, breast and other malignant neoplasms, lymphoma and myeloma (age over 70, mostly males). Cluster 3: malnutrition, Parkinson disease and other mobility disorders, dementia and other mental health conditions (age over 80 years and mostly women). Cluster 4: atrial fibrillation/flutter, cardiac failure, chronic kidney failure and heart valve disease (age between 70-80 and mostly women). Cluster 5: hypertension/hypertensive heart disease, type 2 diabetes mellitus, ischaemic cardiomyopathy, dyslipidaemia, obesity and sleep apnea, including mostly men (age range 60-80). We assessed significant differences among the clusters when gender, age, number of chronic pathologies, number of rehospitalisations and mortality during the hospitalisation were assessed (p<0001 in all cases).

CONCLUSIONS

We identify for the first time in a hospital environment five clusters of disease combinations among the inpatients. These clusters contain several high-incidence diseases related to both age and gender that express their own evolution and clinical characteristics over time.

Diabetes multimorbidity combinations and disability in the Mexican Health and Aging Study, 2012-2015

PURPOSE

The aim of this study was to investigate the relationship between specific combinations of chronic conditions and disability in Mexican older adults with diabetes.

METHODS

This was a prospective cohort study of Mexican adults (n = 2558) with diabetes and aged 51 or older that used data from the 2012 and 2015 waves of the Mexican Health and Aging Study. The main outcome was an index that measured ability to perform activities of daily living and instrumental activities of daily living. The main independent variables were diabetes multimorbidity combinations, defined as diabetes and at least one other chronic condition. The authors calculated the prevalence of each multimorbidity combination present in the sample in 2012 and used negative binomial regression models to estimate the association of the most prevalent of these combinations with disability incidence in 2015.

RESULTS

The three most prevalent combinations were: 1) diabetes-hypertension (n = 637, 31.9%) 2) diabetes-hypertension-depression (n = 388, 19.4%) and 3) diabetes-depression (n = 211, 10.6%). In fully adjusted models comparing participants with specific multimorbidity combinations to participants with diabetes alone, the combinations that had an increased association with disability were diabetes-hypertension-depression, diabetes-depression and diabetes-hypertension-arthritis-depression. In nested models, the addition of arthritis to combinations including depression increased this association.

CONCLUSIONS

Consistent with prior studies, multimorbidity combinations including depression were associated with increased risk of disability. However, the effect size of this relationship was lower than what had been previously been reported internationally. This highlights the need for globally oriented multimorbidity research.