Spatial pattern and state-level variation in multimorbidity among older adults in India: a spatial and multilevel analysis of the Longitudinal Ageing Study in India (LASI)
DOI:
https://doi.org/10.63671/ijsssr.v4i3.618Keywords:
India, LASI, Moran’s I, multilevel analysis, multimorbidity, older adultsAbstract
Multimorbidity is rising among older Indians, and states differ in epidemiological transition. Whether it is spatially clustered, and whether state of residence matters beyond individual characteristics, remains untested. The study mapped and tested spatial autocorrelation of multimorbidity and nine chronic conditions across states/UTs and quantified state-level variation. Wave 1 (2017–18) of the Longitudinal Ageing Study in India provided 31,810 adults aged ≥60 from 36 states/UTs. Multimorbidity was ≥2 of nine self-reported, physician-diagnosed conditions. State prevalence was mapped; global and local Moran’s I assessed clustering; a two-level random-intercept logistic model estimated adjusted odds ratios (AOR), intraclass correlation (ICC) and median odds ratio (MOR). Prevalence was 24.1% (range 6.5–51.8%). Multimorbidity was spatially clustered (Moran’s I = 0.430, pseudo-p < 0.001), as were diabetes, bone/joint disease, hypertension and high cholesterol. A hot spot lay in the south and a cold spot in Assam. Urban residence (AOR 1.56), ADL limitation (1.63), IADL limitation (1.46), education (1.34) and working (1.40) raised the odds; caste category lowered them (0.72). State-level variance was 0.202 (ICC 5.8%; MOR 1.54). Multimorbidity is geographically structured and varies between states beyond individual composition. Self-reported diagnoses may understate burden in low-prevalence areas. State-differentiated care and surveillance are warranted.
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