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Multimorbidity patterns and their relationship to mortality in the US older adult population

BACKGROUND: Understanding patterns of multimorbidity in the US older adult population and their relationship with mortality is important for reducing healthcare utilization and improving health. Previous investigations measured multimorbidity as counts of conditions rather than specific combination...

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Autores principales: Zheng, D. Diane, Loewenstein, David A., Christ, Sharon L., Feaster, Daniel J., Lam, Byron L., McCollister, Kathryn E., Curiel-Cid, Rosie E., Lee, David J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7816983/
https://www.ncbi.nlm.nih.gov/pubmed/33471812
http://dx.doi.org/10.1371/journal.pone.0245053
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author Zheng, D. Diane
Loewenstein, David A.
Christ, Sharon L.
Feaster, Daniel J.
Lam, Byron L.
McCollister, Kathryn E.
Curiel-Cid, Rosie E.
Lee, David J.
author_facet Zheng, D. Diane
Loewenstein, David A.
Christ, Sharon L.
Feaster, Daniel J.
Lam, Byron L.
McCollister, Kathryn E.
Curiel-Cid, Rosie E.
Lee, David J.
author_sort Zheng, D. Diane
collection PubMed
description BACKGROUND: Understanding patterns of multimorbidity in the US older adult population and their relationship with mortality is important for reducing healthcare utilization and improving health. Previous investigations measured multimorbidity as counts of conditions rather than specific combination of conditions. METHODS: This cross-sectional study with longitudinal mortality follow-up employed latent class analysis (LCA) to develop clinically meaningful subgroups of participants aged 50 and older with different combinations of 13 chronic conditions from the National Health Interview Survey 2002–2014. Mortality linkage with National Death Index was performed through December 2015 for 166,126 participants. Survival analyses were conducted to assess the relationships between LCA classes and all-cause mortality and cause specific mortalities. RESULTS: LCA identified five multimorbidity groups with primary characteristics: “healthy” (51.5%), “age-associated chronic conditions” (33.6%), “respiratory conditions” (7.3%), “cognitively impaired” (4.3%) and “complex cardiometabolic” (3.2%). Covariate-adjusted survival analysis indicated “complex cardiometabolic” class had the highest mortality with a Hazard Ratio (HR) of 5.30, 99.5% CI [4.52, 6.22]; followed by “cognitively impaired” class (3.34 [2.93, 3.81]); “respiratory condition” class (2.14 [1.87, 2.46]); and “age-associated chronic conditions” class (1.81 [1.66, 1.98]). Patterns of multimorbidity classes were strongly associated with the primary underlying cause of death. The “cognitively impaired” class reported similar number of conditions compared to the “respiratory condition” class but had significantly higher mortality (3.8 vs 3.7 conditions, HR = 1.56 [1.32, 1.85]). CONCLUSION: We demonstrated that LCA method is effective in classifying clinically meaningful multimorbidity subgroup. Specific combinations of conditions including cognitive impairment and depressive symptoms have a substantial detrimental impact on the mortality of older adults. The numbers of chronic conditions experienced by older adults is not always proportional to mortality risk. Our findings provide valuable information for identifying high risk older adults with multimorbidity to facilitate early intervention to treat chronic conditions and reduce mortality.
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spelling pubmed-78169832021-01-28 Multimorbidity patterns and their relationship to mortality in the US older adult population Zheng, D. Diane Loewenstein, David A. Christ, Sharon L. Feaster, Daniel J. Lam, Byron L. McCollister, Kathryn E. Curiel-Cid, Rosie E. Lee, David J. PLoS One Research Article BACKGROUND: Understanding patterns of multimorbidity in the US older adult population and their relationship with mortality is important for reducing healthcare utilization and improving health. Previous investigations measured multimorbidity as counts of conditions rather than specific combination of conditions. METHODS: This cross-sectional study with longitudinal mortality follow-up employed latent class analysis (LCA) to develop clinically meaningful subgroups of participants aged 50 and older with different combinations of 13 chronic conditions from the National Health Interview Survey 2002–2014. Mortality linkage with National Death Index was performed through December 2015 for 166,126 participants. Survival analyses were conducted to assess the relationships between LCA classes and all-cause mortality and cause specific mortalities. RESULTS: LCA identified five multimorbidity groups with primary characteristics: “healthy” (51.5%), “age-associated chronic conditions” (33.6%), “respiratory conditions” (7.3%), “cognitively impaired” (4.3%) and “complex cardiometabolic” (3.2%). Covariate-adjusted survival analysis indicated “complex cardiometabolic” class had the highest mortality with a Hazard Ratio (HR) of 5.30, 99.5% CI [4.52, 6.22]; followed by “cognitively impaired” class (3.34 [2.93, 3.81]); “respiratory condition” class (2.14 [1.87, 2.46]); and “age-associated chronic conditions” class (1.81 [1.66, 1.98]). Patterns of multimorbidity classes were strongly associated with the primary underlying cause of death. The “cognitively impaired” class reported similar number of conditions compared to the “respiratory condition” class but had significantly higher mortality (3.8 vs 3.7 conditions, HR = 1.56 [1.32, 1.85]). CONCLUSION: We demonstrated that LCA method is effective in classifying clinically meaningful multimorbidity subgroup. Specific combinations of conditions including cognitive impairment and depressive symptoms have a substantial detrimental impact on the mortality of older adults. The numbers of chronic conditions experienced by older adults is not always proportional to mortality risk. Our findings provide valuable information for identifying high risk older adults with multimorbidity to facilitate early intervention to treat chronic conditions and reduce mortality. Public Library of Science 2021-01-20 /pmc/articles/PMC7816983/ /pubmed/33471812 http://dx.doi.org/10.1371/journal.pone.0245053 Text en © 2021 Zheng et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Zheng, D. Diane
Loewenstein, David A.
Christ, Sharon L.
Feaster, Daniel J.
Lam, Byron L.
McCollister, Kathryn E.
Curiel-Cid, Rosie E.
Lee, David J.
Multimorbidity patterns and their relationship to mortality in the US older adult population
title Multimorbidity patterns and their relationship to mortality in the US older adult population
title_full Multimorbidity patterns and their relationship to mortality in the US older adult population
title_fullStr Multimorbidity patterns and their relationship to mortality in the US older adult population
title_full_unstemmed Multimorbidity patterns and their relationship to mortality in the US older adult population
title_short Multimorbidity patterns and their relationship to mortality in the US older adult population
title_sort multimorbidity patterns and their relationship to mortality in the us older adult population
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7816983/
https://www.ncbi.nlm.nih.gov/pubmed/33471812
http://dx.doi.org/10.1371/journal.pone.0245053
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