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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 multimorb...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9277367/ https://www.ncbi.nlm.nih.gov/pubmed/35820748 http://dx.doi.org/10.1136/bmjopen-2021-053981 |
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author | Puri, Parul Singh, Shri Kant Pati, Sanghamitra |
author_facet | Puri, Parul Singh, Shri Kant Pati, Sanghamitra |
author_sort | Puri, Parul |
collection | PubMed |
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 | Online Article Text |
id | pubmed-9277367 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-92773672022-07-28 Identifying non-communicable disease multimorbidity patterns and associated factors: a latent class analysis approach Puri, Parul Singh, Shri Kant Pati, Sanghamitra BMJ Open Public Health 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. BMJ Publishing Group 2022-07-12 /pmc/articles/PMC9277367/ /pubmed/35820748 http://dx.doi.org/10.1136/bmjopen-2021-053981 Text en © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Public Health Puri, Parul Singh, Shri Kant Pati, Sanghamitra Identifying non-communicable disease multimorbidity patterns and associated factors: a latent class analysis approach |
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 |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9277367/ https://www.ncbi.nlm.nih.gov/pubmed/35820748 http://dx.doi.org/10.1136/bmjopen-2021-053981 |
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