Identifying non-communicable disease multimorbidity patterns and associated factors: a latent class analysis approach
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 multimorbi...
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BMJ Publishing Group
2022-07-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/12/7/e053981.full |
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| author | Sanghamitra Pati Parul Puri Shri Kant Singh |
| author_facet | Sanghamitra Pati Parul Puri Shri Kant Singh |
| author_sort | Sanghamitra Pati |
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| description | 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. |
| format | Article |
| id | doaj-art-4fdffd6a9afb4a31b44672d07b532950 |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2022-07-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-4fdffd6a9afb4a31b44672d07b5329502025-01-30T12:45:12ZengBMJ Publishing GroupBMJ Open2044-60552022-07-0112710.1136/bmjopen-2021-053981Identifying non-communicable disease multimorbidity patterns and associated factors: a latent class analysis approachSanghamitra Pati0Parul Puri1Shri Kant Singh29 Department of Health Research, Indian Council of Medical Research Chandrasekharpur, Bhubaneswar, Orissa, India1 Department of Survey Research and Data Analytics, International Institute for Population Sciences, Mumbai, Maharashtra, India1 Department of Survey Research and Data Analytics, International Institute for Population Sciences, Mumbai, Maharashtra, IndiaObjective 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.https://bmjopen.bmj.com/content/12/7/e053981.full |
| spellingShingle | Sanghamitra Pati Parul Puri Shri Kant Singh Identifying non-communicable disease multimorbidity patterns and associated factors: a latent class analysis approach BMJ Open |
| title | Identifying non-communicable disease multimorbidity patterns and associated factors: a latent class analysis approach |
| title_full | Identifying non-communicable disease multimorbidity patterns and associated factors: a latent class analysis approach |
| title_fullStr | Identifying non-communicable disease multimorbidity patterns and associated factors: a latent class analysis approach |
| title_full_unstemmed | Identifying non-communicable disease multimorbidity patterns and associated factors: a latent class analysis approach |
| title_short | Identifying non-communicable disease multimorbidity patterns and associated factors: a latent class analysis approach |
| title_sort | identifying non communicable disease multimorbidity patterns and associated factors a latent class analysis approach |
| url | https://bmjopen.bmj.com/content/12/7/e053981.full |
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