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Identifying non-communicable disease multimorbidity patterns and associated factors: a latent class analysis approach
Parul Puri1, Shri Kant Singh2, Sanghamitra Pati3
1Department of Survey Research and Data Analytics, International Institute for Population Sciences, Mumbai, Maharashtra, India parulpuri93@gmail.com.
Objective:
In the absence of adequate nationally-representative empirical evidence on multimorbidity, the existing healthcare delivery system is not adequately oriented to cater to the growing needs of the older adult population. Therefore, the present study identifies frequently occurring multimorbidity patterns among older adults in India. Further, the study examines the linkages between the identified patterns and socioeconomic, demographic, lifestyle and anthropometric correlates.
Design:
The present findings rest on a large nationally-representative sample from a cross-sectional study.
Setting And Participants:
The study used data on 58 975 older adults (45 years and older) from the Longitudinal Ageing Study in India, 2017-2018.
Primary And Secondary Outcome Measures:
The study incorporated a list of 16 non-communicable diseases to identify commonly occurring patterns using latent class analysis. The study employed multinomial logistic regression models to assess the association between identified disease patterns with unit-level socioeconomic, demographic, lifestyle and anthropometric characteristics.
Results:
The present study demonstrates that older adults in the country can be segmented into six patterns: 'relatively healthy', 'hypertension', 'gastrointestinal disorders-hypertension-musculoskeletal disorders', 'musculoskeletal disorders-hypertension-asthma', 'metabolic disorders' and 'complex cardiometabolic disorders'. Additionally, socioeconomic, demographic, lifestyle and anthropometric factors are significantly associated with one or more identified disease patterns.
Conclusions:
The identified classes 'hypertension', 'metabolic disorders' and 'complex cardiometabolic disorders' reflect three stages of cardiometabolic morbidity with hypertension as the first and 'complex cardiometabolic disorders' as the last stage of disease progression. This underscores the need for effective prevention strategies for high-risk hypertension group. Also, targeted interventions are essential to reduce the burden on the high-risk population and provide equitable health services at the community level.
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