Abstract
BACKGROUND: There is a lack of substantive evidence on the epidemiology of multimorbidity in low and middle-income countries (LMICs). India is also undergoing through the major demographic shift marked by the rapidly aging population and followed by significantly elevated implications to multimorbidity risk. These implications include an increased risk of functional limitations, poor quality of life, and delayed mortality pattern but expanding morbidity burden among older adults. This study aimed to characterize the distribution, pattern and further provide an understanding of the conditional role of risk factors for multimorbidity susceptibility among older adults in India. METHODS: The study utilized data on 66,606 individuals form the national representative Longitudinal Ageing Study in India (LASI), Wave - 1, of individuals aged 45 years and above. To understands the distribution, prevalence and pattern of morbidities and multimorbidity we characterized and calculated the weighted frequency distribution, chi-square test for association and stacked area plot for relative burden of multimorbidity over age. Finally, we constructed the Classification and Regression Trees (CART) model to further identify the optimal leading covariates and their conditional role on multimorbidity susceptibility among older adults in India. RESULTS: Hypertension showed the highest prevalence (26.72%) and highest age-specific relative proportional share, followed by myopia (24.2%), hypermetropia (20.75%) and gastrointestinal condition (17.98%). Single morbidity prevalence was 23.78%, and multimorbidity was 50.94% and the relative prevalence share of multimorbidity over age expanded by 25.16% from the 45-49 to 85+ age group. The CART model identified that childhood health, place of residence, age, body mass index, caste category and education level were the leading risk factors for multimorbidity susceptibility among older adults in India. The highest susceptibility of multimorbidity with a risk of 0.57 was observed among individuals who had moderate to poor childhood health. Conversely, those adults who had good childhood health, resided in rural areas, underweighted or normal BMI and belonged to the Schedule tribe had the lowest risk of multimorbidity (0.19). CONCLUSION: This study offers a comprehensive analysis of multimorbidity among older adults in India, revealing a risk burden driven by aging, poor childhood health, and socioeconomic disparities. By using an inclusive morbidity framework and CART modeling, it underscores the multifactorial nature of multimorbidity and highlights key interconnected determinants for future policy interventions.