Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Puri, Parul, Singh, Shri Kant, Pati, Sanghamitra
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
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
_version_ 1784745963842174976
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
work_keys_str_mv AT puriparul identifyingnoncommunicablediseasemultimorbiditypatternsandassociatedfactorsalatentclassanalysisapproach
AT singhshrikant identifyingnoncommunicablediseasemultimorbiditypatternsandassociatedfactorsalatentclassanalysisapproach
AT patisanghamitra identifyingnoncommunicablediseasemultimorbiditypatternsandassociatedfactorsalatentclassanalysisapproach