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Comorbidity profiles of patients experiencing homelessness: A latent class analysis

Individuals experiencing homelessness are known to have increased rates of healthcare utilization when compared to the average patient population, often attributed to their complex health care needs and under or untreated comorbid conditions. With increasing focus on hospital readmissions among acut...

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Autores principales: Subedi, Keshab, Ghimire, Shweta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9128947/
https://www.ncbi.nlm.nih.gov/pubmed/35609060
http://dx.doi.org/10.1371/journal.pone.0268841
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author Subedi, Keshab
Ghimire, Shweta
author_facet Subedi, Keshab
Ghimire, Shweta
author_sort Subedi, Keshab
collection PubMed
description Individuals experiencing homelessness are known to have increased rates of healthcare utilization when compared to the average patient population, often attributed to their complex health care needs and under or untreated comorbid conditions. With increasing focus on hospital readmissions among acute care settings, a better understanding of these comorbidity patterns and their impacts on acute care utilization could help improve quality of care. This study aims to identify distinct comorbidity profiles of homeless patients, and to explore the correlates of the identified comorbidity profiles and their impact on hospital readmission. This is a retrospective analysis using electronic health records (EHR) of patients experiencing homelessness encountered in the hospitals of ChristianaCare from 2015 to 2019 (N = 3445). Latent class analysis (LCA) was used to identify the comorbidity profiles of homeless patients. The mean age of the study population was 44-year, and the majority were male (63%). The most prevalent comorbid conditions were tobacco use (77%), followed by depression (58%), drug use disorder (56%), anxiety disorder (50%), hypertension (44%), and alcohol use disorder (43%). The LCA model identified 4 comorbidity classes—“relatively healthy” class with 31% of the patients, “medically-comorbid with SUD” class with 15% of the patients, “substance use disorder (SUD)” class with 39%, and “Medically comorbid” class with 15% of the patients. The Kaplan-Meir curves of probability of readmission against time from the index visits were significantly different for the four classes (p<0.001). The multivariable Cox proportional hazard model adjusted for age, sex, race, ethnicity, and insurance type showed that the hazard for readmission among patients in medically comorbid with SUD class is 3.16 (CI: 2.72, 3.67) times higher than the patients in the relatively healthy class.
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spelling pubmed-91289472022-05-25 Comorbidity profiles of patients experiencing homelessness: A latent class analysis Subedi, Keshab Ghimire, Shweta PLoS One Research Article Individuals experiencing homelessness are known to have increased rates of healthcare utilization when compared to the average patient population, often attributed to their complex health care needs and under or untreated comorbid conditions. With increasing focus on hospital readmissions among acute care settings, a better understanding of these comorbidity patterns and their impacts on acute care utilization could help improve quality of care. This study aims to identify distinct comorbidity profiles of homeless patients, and to explore the correlates of the identified comorbidity profiles and their impact on hospital readmission. This is a retrospective analysis using electronic health records (EHR) of patients experiencing homelessness encountered in the hospitals of ChristianaCare from 2015 to 2019 (N = 3445). Latent class analysis (LCA) was used to identify the comorbidity profiles of homeless patients. The mean age of the study population was 44-year, and the majority were male (63%). The most prevalent comorbid conditions were tobacco use (77%), followed by depression (58%), drug use disorder (56%), anxiety disorder (50%), hypertension (44%), and alcohol use disorder (43%). The LCA model identified 4 comorbidity classes—“relatively healthy” class with 31% of the patients, “medically-comorbid with SUD” class with 15% of the patients, “substance use disorder (SUD)” class with 39%, and “Medically comorbid” class with 15% of the patients. The Kaplan-Meir curves of probability of readmission against time from the index visits were significantly different for the four classes (p<0.001). The multivariable Cox proportional hazard model adjusted for age, sex, race, ethnicity, and insurance type showed that the hazard for readmission among patients in medically comorbid with SUD class is 3.16 (CI: 2.72, 3.67) times higher than the patients in the relatively healthy class. Public Library of Science 2022-05-24 /pmc/articles/PMC9128947/ /pubmed/35609060 http://dx.doi.org/10.1371/journal.pone.0268841 Text en © 2022 Subedi, Ghimire https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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
Subedi, Keshab
Ghimire, Shweta
Comorbidity profiles of patients experiencing homelessness: A latent class analysis
title Comorbidity profiles of patients experiencing homelessness: A latent class analysis
title_full Comorbidity profiles of patients experiencing homelessness: A latent class analysis
title_fullStr Comorbidity profiles of patients experiencing homelessness: A latent class analysis
title_full_unstemmed Comorbidity profiles of patients experiencing homelessness: A latent class analysis
title_short Comorbidity profiles of patients experiencing homelessness: A latent class analysis
title_sort comorbidity profiles of patients experiencing homelessness: a latent class analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9128947/
https://www.ncbi.nlm.nih.gov/pubmed/35609060
http://dx.doi.org/10.1371/journal.pone.0268841
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