Latent Class Analysis of Chronic Disease Co-occurrence, Clustering and their determinants in India using SAGE India, Wave-2

Neha Shri
Saurabh Singh, International Institute for Population Sciences

This study seeks to shed light on the factors associated with the self-reported multi-morbidity latent classes in India. Methods: The present study utilizes data from nationally representative survey "Study on Global AGEing and Adult Health (SAGE-Wave 2, 2015)". The eligible sample size was 6,298 adults aged 50 years and older. Latent Class Analysis was performed to uncover latent subgroups of multi-morbidity. Multinomial logistic regression was carried -out to identify the factors linked to observed latent class membership. Results: The LCA grouped our sample of men and women over the age of 49into three groups: mild MM risk (30%), moderate MM risk (41%), and severe MM risk (29%). In the mild MM risk group, the most prevalent diseases were asthma and arthritis, and the major prevalent disease in the moderate MM risk group is low near/distance vision, followed by depression, asthma, and lung disease. Angina, diabetes, hypertension, and stroke were the major diseases in the severe MM risk category. Individuals with higher ages were at increased risk of having moderate MM risk OR: 1.18*** (1.16–1.19) and severe MM risk OR: 1.15*** (1.13–1.16). Females were 3.36% more likely to have a moderate relative risk ratio (RRR: 3.36*** CI: 2.67–4.25). The clustering of diseases highlights the importance of integrated disease management in primary care settings and improving the healthcare system to accommodate the ' 'individual's needs. Implementing preventive measures and tailored interventions, strengthening the health and wellness centers, and delivering comprehensive primary health care services may cater to the needs of multimorbid patients.

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 Presented in Session 29. Flash session Morbidity