Emerging Multimorbidity Patterns and Its Linkages With Selected Health Outcomes Among Working-Agegroup Population: Findings From Odisha, India

Abstract

The study utilized data on 2912 individuals in the age-group 15-64 years collected under the burden of diseases study among patients attending public health care settings of Odisha, India. The findings suggested that 2.4% of the individuals in the working age-group were affected with multimorbidity. We utilized a latent class analysis (LCA) to identify commonly occurring disease clusters. Based on the LCA model fits, i.e., lowest AIC and BIC values, two latent disease classes were identified. These classes were named low co-morbidity and Hypertension-Diabetes-Arthritis; based on the item responseprobabilities. Binary logistic regression adjusted for age, sex, ethnicity, educationlevel, marital status, socio-economic status, residence, and health insurance, highlighted thatage, belonging to a non-aboriginal ethnicity and urban area increased the risk of being in the‘Hypertension-Diabetes-Arthritis’group compared to ‘low-comorbidity’ group. Furthermore, 50% of the individual in the ‘Hypertension-Diabetes-Arthritis’ group reportedpoor quality of life, whereas 30% reported poor self-rated health (SRH) compared to only11% reporting poor SRH in the ‘low-comorbidity’ group. Additionally, the mean healthscore reported by the individuals in the ‘Hypertension-Diabetes-Arthritis’ group was 39.9(scale 0-100) compared to 46.9 by their counterparts.

Representing multimorbid disease progressions using directed hypergraphs

“…With access to population-scale EHR data, and as the pattern of health and disease is changing in our population [8], there is a growing focus on establishing statistical and analytical frameworks to sufficiently model population-level multimorbidity cohorts. Greater availability of computational resources and a diverse set of analytical approaches has ushered in a range of analyses on multimorbidity data, which includes disease and population clustering [9, 10, 11, 12, 13, 14] and network medicine [15, 16, 17, 18, 19].…”