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Clustering of chronic disease risks among people accessing community mental health services

This study identified clusters of chronic disease risks and explored associations between clusters and demographic characteristics and mental health conditions, among people accessing community mental health services. Data from a cross-sectional telephone survey of Australian mental health consumers...

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Autores principales: Regan, Casey, Fehily, Caitlin, Campbell, Elizabeth, Bowman, Jenny, Faulkner, Jack, Oldmeadow, Christopher, Bartlem, Kate
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9256721/
https://www.ncbi.nlm.nih.gov/pubmed/35813396
http://dx.doi.org/10.1016/j.pmedr.2022.101870
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author Regan, Casey
Fehily, Caitlin
Campbell, Elizabeth
Bowman, Jenny
Faulkner, Jack
Oldmeadow, Christopher
Bartlem, Kate
author_facet Regan, Casey
Fehily, Caitlin
Campbell, Elizabeth
Bowman, Jenny
Faulkner, Jack
Oldmeadow, Christopher
Bartlem, Kate
author_sort Regan, Casey
collection PubMed
description This study identified clusters of chronic disease risks and explored associations between clusters and demographic characteristics and mental health conditions, among people accessing community mental health services. Data from a cross-sectional telephone survey of Australian mental health consumers (n = 567) were analysed. Clusters were identified based on tobacco smoking (53.5%), harmful chronic alcohol consumption (20.1%), harmful acute alcohol consumption (43.5%), inadequate fruit and vegetable intake (66.0%), inadequate physical activity (75.5%), inadequate strength activity (81.8%), and high body mass index (BMI) (67.9%), using latent class analysis. Multinomial logistic regression examined associations between cluster membership and participant characteristics. Three groups were identified: Cluster 1 (19.05%) had < 0.5 probabilities for most risks; Cluster 2 (34.04%) had high probabilities of all risks, particularly tobacco smoking and both types of harmful alcohol consumption; and Cluster 3 (46.91%) had high probabilities of both inadequate physical and strength activity, inadequate fruit and vegetable intake, and high BMI. Compared to Cluster 1 membership, participants with higher education were less likely to be in either Cluster 2 or 3, females or those over 55 were more likely to be in Cluster 3, those with a substance use disorder were more likely to be in Cluster 2, and those with a personality disorder were less likely to be in Cluster 3. The clustering patterns reinforce the importance of addressing multiple chronic disease risks for people with a mental health condition. Preventive care interventions targeting clusters of risks may help reduce the burden of chronic disease among this high-risk population.
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spelling pubmed-92567212022-07-07 Clustering of chronic disease risks among people accessing community mental health services Regan, Casey Fehily, Caitlin Campbell, Elizabeth Bowman, Jenny Faulkner, Jack Oldmeadow, Christopher Bartlem, Kate Prev Med Rep Regular Article This study identified clusters of chronic disease risks and explored associations between clusters and demographic characteristics and mental health conditions, among people accessing community mental health services. Data from a cross-sectional telephone survey of Australian mental health consumers (n = 567) were analysed. Clusters were identified based on tobacco smoking (53.5%), harmful chronic alcohol consumption (20.1%), harmful acute alcohol consumption (43.5%), inadequate fruit and vegetable intake (66.0%), inadequate physical activity (75.5%), inadequate strength activity (81.8%), and high body mass index (BMI) (67.9%), using latent class analysis. Multinomial logistic regression examined associations between cluster membership and participant characteristics. Three groups were identified: Cluster 1 (19.05%) had < 0.5 probabilities for most risks; Cluster 2 (34.04%) had high probabilities of all risks, particularly tobacco smoking and both types of harmful alcohol consumption; and Cluster 3 (46.91%) had high probabilities of both inadequate physical and strength activity, inadequate fruit and vegetable intake, and high BMI. Compared to Cluster 1 membership, participants with higher education were less likely to be in either Cluster 2 or 3, females or those over 55 were more likely to be in Cluster 3, those with a substance use disorder were more likely to be in Cluster 2, and those with a personality disorder were less likely to be in Cluster 3. The clustering patterns reinforce the importance of addressing multiple chronic disease risks for people with a mental health condition. Preventive care interventions targeting clusters of risks may help reduce the burden of chronic disease among this high-risk population. 2022-06-27 /pmc/articles/PMC9256721/ /pubmed/35813396 http://dx.doi.org/10.1016/j.pmedr.2022.101870 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Regular Article
Regan, Casey
Fehily, Caitlin
Campbell, Elizabeth
Bowman, Jenny
Faulkner, Jack
Oldmeadow, Christopher
Bartlem, Kate
Clustering of chronic disease risks among people accessing community mental health services
title Clustering of chronic disease risks among people accessing community mental health services
title_full Clustering of chronic disease risks among people accessing community mental health services
title_fullStr Clustering of chronic disease risks among people accessing community mental health services
title_full_unstemmed Clustering of chronic disease risks among people accessing community mental health services
title_short Clustering of chronic disease risks among people accessing community mental health services
title_sort clustering of chronic disease risks among people accessing community mental health services
topic Regular Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9256721/
https://www.ncbi.nlm.nih.gov/pubmed/35813396
http://dx.doi.org/10.1016/j.pmedr.2022.101870
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