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

Multimorbidity, health Literacy, and quality of life among older adults in an urban slum in India: a community-based cross-sectional study

BACKGROUND

India is experiencing a rising burden of chronic disease multimorbidity due to an aging population and epidemiological transition. Older adults residing in urban slums are especially vulnerable due to challenges in managing multimorbidity amid deprived living conditions. This study aimed to assess the prevalence of multimorbidity, associated health literacy, and quality of life impact in this population.

METHODS

A community-based cross-sectional study was conducted among 800 adults aged ≥ 65 years in an urban slum in Gujarat, India. Data on sociodemographics, physical and mental health conditions, health literacy (HLS-SF-47), quality of life (Short Form-12 scale), and social determinants of health were collected. Multimorbidity is ≥ 2 physical or mental health conditions in one person.

RESULTS

The prevalence of multimorbidity was 62.5% (500/800). Multimorbidity was significantly associated with lower physical component summary (PCS) and mental component summary (MCS) scores on the SF-12 (p < 0.001). After adjusting for sociodemographic variables, the odds ratio of 0.81 indicates that for every 1 unit increase in the health literacy score, the odds of having multimorbidity decrease by 19%. Older age within the older adult cohort (per year increase) was associated with greater odds of multimorbidity (AOR 1.05, 95% CI 1.02-1.09). Physical inactivity (AOR 1.68, 95% CI 1.027-2.77) and lack of social support (AOR 1.57, 95% CI 1.01-2.45) also increased the likelihood of multimorbidity.

CONCLUSION

There is a substantial burden of multimorbidity among urban slum dwellers aged ≥ 65 years in India, strongly linked to modifiable risk factors like poor health literacy and social determinants of health. Targeted interventions are essential to alleviate this disproportionate burden among urban slum older adults.

Latent class analysis of chronic disease co-occurrence, clustering and their determinants in India using Study on global AGEing and adult health (SAGE) India Wave-2

Background

Understanding chronic disease prevalence, patterns, and co-occurrence is pivotal for effective health care planning and disease prevention strategies. In this paper, we aimed to identify the clustering of major non-communicable diseases among Indian adults aged ≥50 years based on their self-reported diagnosed non-communicable disease status and to find the risk factors that heighten the risk of developing the identified disease clusters.

Methods

We utilised data from the nationally representative survey Study on Global AGEing and Adult Health (SAGE Wave-2). The eligible sample size was 6298 adults aged ≥50 years. We conducted the latent class analysis to uncover latent subgroups of multimorbidity and the multinomial logistic regression to identify the factors linked to observed latent class membership.

Results

The latent class analysis grouped our sample of men and women >49 years old into three groups - mild multimorbidity risk (41%), moderate multimorbidity risk (30%), and severe multimorbidity risk (29%). In the mild multimorbidity risk group, the most prevalent diseases were asthma and arthritis, and the major prevalent disease in the moderate multimorbidity risk group was low near/distance vision, followed by depression, asthma, and lung disease. Angina, diabetes, hypertension, and stroke were the major diseases in the severe multimorbidity risk category. Individuals with higher ages had an 18% and 15% higher risk of having moderate multimorbidity and severe multimorbidity compared to those in the mild multimorbidity category. Females were more likely to have a moderate risk (3.36 times) and 2.82 times more likely to have severe multimorbidity risk.

Conclusions

The clustering of diseases highlights the importance of integrated disease management in primary care settings and improving the health care system to accommodate the individual's needs. Implementing preventive measures and tailored interventions, strengthening the health and wellness centres, and delivering comprehensive primary health care services for secondary and tertiary level hospitalisation may cater to the needs of multimorbid patients.

Risk factors of multimorbidity among older adults in India: A systematic review and meta-analysis

Background

Multimorbidity among older adults is a growing concern in India. Multimorbidity is defined as the coexistence of two or more chronic health conditions in an individual. Primary studies have been conducted on risk factors of multimorbidity in India, but no systematic review has been conducted on this topic. This systematic review aimed to synthesize the existing evidence on risk factors of multimorbidity among older adults in India.

Methods

The JBI and Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines were followed. Several databases were searched for published and unpublished studies until August 03, 2022. The screening of titles and abstracts and full texts, data extraction, and quality assessment were conducted by two independent reviewers. Any disagreements were resolved through discussion or by involving a third reviewer. Data synthesis was conducted using narrative synthesis and random effects meta-analysis, where appropriate.

Results

Out of 8781 records identified from the literature search, 16 and 15 studies were included in the systematic review and meta-analysis, respectively. All included studies were cross-sectional, and 10 met a critical appraisal score of more than 70%. Broadly, sociodemographic, lifestyle, and health conditions-related factors were explored in these studies. The pooled odds of multimorbidity were higher in people aged ≥70 years compared to 60-69 years (odds ratio (OR) 1.51; 95% confidence interval (CI) 1.20-1.91), females compared to males (1.38; 1.09-1.75), single, divorced, separated, and widowed compared to married (1.29; 1.11-1.49), economically dependent compared to economically independent (1.54; 1.21-1.97), and smokers compared to non-smokers (1.33; 1.16-1.52) and were lower in working compared to not working (0.51; 0.36-0.72).

Conclusion

This systematic review and meta-analysis provided a comprehensive picture of the problem by synthesizing the existing evidence on risk factors of multimorbidity among older adults in India. These synthesized sociodemographic and lifestyle factors should be taken into consideration when developing health interventions for addressing multimorbidity among older adults in India.

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.

Burden, determinants, consequences and care of multimorbidity in rural and urbanising Telangana, India: protocol for a mixed-methods study within the APCAPS cohort

INTRODUCTION

The epidemiological and demographic transitions are leading to a rising burden of multimorbidity (co-occurrence of two or more chronic conditions) worldwide. Evidence on the burden, determinants, consequences and care of multimorbidity in rural and urbanising India is limited, partly due to a lack of longitudinal and objectively measured data on chronic health conditions. We will conduct a mixed-methods study nested in the prospective Andhra Pradesh Children and Parents' Study (APCAPS) cohort to develop a data resource for understanding the epidemiology of multimorbidity in rural and urbanising India and developing interventions to improve the prevention and care of multimorbidity.

METHODS AND ANALYSIS

We aim to recruit 2100 APCAPS cohort members aged 45+ who have clinical and lifestyle data collected during a previous cohort follow-up (2010-2012). We will screen for locally prevalent non-communicable, infectious and mental health conditions, alongside cognitive impairments, disabilities and frailty, using a combination of self-reported clinical diagnosis, symptom-based questionnaires, physical examinations and biochemical assays. We will conduct in-depth interviews with people with varying multimorbidity clusters, their informal carers and local healthcare providers. Deidentified data will be made available to external researchers.

ETHICS AND DISSEMINATION

The study has received approval from the ethics committees of the National Institute of Nutrition and Indian Institute of Public Health Hyderabad, India and the London School of Hygiene and Tropical Medicine, UK. Meta-data and data collection instruments will be published on the APCAPS website alongside details of existing APCAPS data and the data access process (www.lshtm.ac.uk/research/centres-projects-groups/apcaps).

Concurrent Undernutrition and Overnutrition within Indian Families between 2006 and 2021

Background

The double burden of malnutrition (DBM), characterized by concurrent undernutrition and overnutrition, is a growing global concern. Families share resources and eating behaviors and programs often target households, yet evidence of the DBM at the family level is scarce.

Objectives

This study examined trends and inequality in the intrahousehold DBM in India between 2006 and 2021.

Methods

Data were from 3 waves of India's National Family Health Survey (NFHS 2006, 2016, and 2021). We examined 3 types of household member (with children aged <5 y) combinations: mother-child (N = 328,039 across 3 waves), father-child, and parent (mother and father)-child (N = 47,139 for each pair). The DBM was defined as one or more individuals with undernutrition (either wasting or stunting in children or underweight in adults) and one or more overweight individuals within the same household. DBM was examined over time, at national and subnational levels, and by residence and wealth.

Results

Nearly all DBM was in the form of an overweight parent and an undernourished weight or stunted child. The prevalence of parent-child DBM increased from 15% in 2006 to 26% in 2021. Father-child pairs experienced the most rapid DBM increase, from 12% in 2006 to 22% in 2021, an 83% increase, driven by increasing overweight among men. In 2021, the DBM was highest in North-Eastern and Southern states, and among relatively rich households from urban areas. The increase in the DBM was faster in rural areas and among poor households compared with that in urban areas and rich households. Urban-rural and rich-poor inequalities in the DBM have decreased over time.

Conclusions

The intrahousehold DBM has increased over time, affecting 1 in 4 households in India in 2021. Family-based interventions that can simultaneously address child underweight and parent overweight are required to address India's increasing intrahousehold DBM.

Multimorbidity Patterns and Associations with Gait, Balance and Lower Extremity Muscle Function in the Elderly: A Cross-Sectional Study in Northwest China

Purpose

Fall is a common geriatric syndrome leading to various adverse outcomes in the elderly. Gait and balance disorders and decreased lower extremity muscle function are the major intrinsic risk factors of falls, and studies suggested that they were closely related to the underlying chronic conditions. This study aimed to explore the patterns of multimorbidity and determine the associations of these multimorbidity patterns with gait, balance and lower extremity muscle function.

Patients and Methods

A cross-sectional survey of 4803 participants aged ≥60 years in Shaanxi Province, China was conducted and the self-reported chronic conditions were investigated. The 6-m walk test, timed-up-and-go test (TUG) and 5-sit-to-stand test (5-STS) were conducted to evaluate gait, balance, and lower extremity muscle function respectively. Latent class analysis was used to explore patterns of multimorbidity, and multivariate regression analysis was used to determine the associations of multimorbidity patterns with gait, balance, and lower extremity muscle function.

Results

Five multimorbidity patterns were identified: Degenerative Disease Class, Cardio-metabolic Class, Stroke-Respiratory-Depression Class, Gastrointestinal Class, and Very sick Class, and they were differently associated with gait and balance disorders and decreased lower extremity muscle function. In particular, the multimorbidity patterns of Degenerative Disease Class and Stroke-Respiratory-Depression Class were closely associated with all the three risk factors of falls.

Conclusion

There are significant differences in the impact of different multimorbidity patterns on the major intrinsic risk factors of falls in the elderly population, and appropriate multimorbidity patterns are closely related to the prediction of falls and can help to develop fall prevention strategies in the elderly.