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Identifying Unique Subgroups of High-Cost Patients With Schizophrenia: A Population-Based Study Using Latent Class Analysis

Schizophrenia does not present uniformly among patients and as a result this patient population is characterized by a diversity in the type and amount of healthcare supports needed for daily functioning. Despite this, little work has been completed to understand the heterogeneity that exists among t...

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Autores principales: Stewart, Andrew J, Patten, Scott B, Fiest, Kirsten M, Williamson, Tyler S, Wick, James P, Ronksley, Paul E
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10291413/
https://www.ncbi.nlm.nih.gov/pubmed/37377884
http://dx.doi.org/10.1177/11786329231183317
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author Stewart, Andrew J
Patten, Scott B
Fiest, Kirsten M
Williamson, Tyler S
Wick, James P
Ronksley, Paul E
author_facet Stewart, Andrew J
Patten, Scott B
Fiest, Kirsten M
Williamson, Tyler S
Wick, James P
Ronksley, Paul E
author_sort Stewart, Andrew J
collection PubMed
description Schizophrenia does not present uniformly among patients and as a result this patient population is characterized by a diversity in the type and amount of healthcare supports needed for daily functioning. Despite this, little work has been completed to understand the heterogeneity that exists among these patients. In this work we used a data-driven approach to identify subgroups of high-cost patients with schizophrenia to identify potentially actionable interventions for the improvement of outcomes and to inform conversations on how to most efficiently allocate resources in an already strained system. Administrative health data was used to conduct a retrospective analysis of “high-cost” adult patients with schizophrenia residing in Alberta, Canada in 2017. Costs were derived from inpatient encounters, outpatient primary care and specialist encounters, emergency department encounters, and drug costs. Latent class analysis was used to group patients based on their unique clinical profiles. Latent class analysis of 1659 patients revealed the following patient groups: (1) young, high-needs males early in their disease course; (2) actively managed middle-aged patients; (3) elderly patients with multiple chronic conditions and polypharmacy; (4) unstably housed males with low treatment rates; (5) unstably housed females with high acute care use and low treatment rates. This taxonomy may be used to inform policy, including the identification of interventions most likely to improve care and reduce health spending for each subgroup.
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spelling pubmed-102914132023-06-27 Identifying Unique Subgroups of High-Cost Patients With Schizophrenia: A Population-Based Study Using Latent Class Analysis Stewart, Andrew J Patten, Scott B Fiest, Kirsten M Williamson, Tyler S Wick, James P Ronksley, Paul E Health Serv Insights Original Research Schizophrenia does not present uniformly among patients and as a result this patient population is characterized by a diversity in the type and amount of healthcare supports needed for daily functioning. Despite this, little work has been completed to understand the heterogeneity that exists among these patients. In this work we used a data-driven approach to identify subgroups of high-cost patients with schizophrenia to identify potentially actionable interventions for the improvement of outcomes and to inform conversations on how to most efficiently allocate resources in an already strained system. Administrative health data was used to conduct a retrospective analysis of “high-cost” adult patients with schizophrenia residing in Alberta, Canada in 2017. Costs were derived from inpatient encounters, outpatient primary care and specialist encounters, emergency department encounters, and drug costs. Latent class analysis was used to group patients based on their unique clinical profiles. Latent class analysis of 1659 patients revealed the following patient groups: (1) young, high-needs males early in their disease course; (2) actively managed middle-aged patients; (3) elderly patients with multiple chronic conditions and polypharmacy; (4) unstably housed males with low treatment rates; (5) unstably housed females with high acute care use and low treatment rates. This taxonomy may be used to inform policy, including the identification of interventions most likely to improve care and reduce health spending for each subgroup. SAGE Publications 2023-06-24 /pmc/articles/PMC10291413/ /pubmed/37377884 http://dx.doi.org/10.1177/11786329231183317 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Research
Stewart, Andrew J
Patten, Scott B
Fiest, Kirsten M
Williamson, Tyler S
Wick, James P
Ronksley, Paul E
Identifying Unique Subgroups of High-Cost Patients With Schizophrenia: A Population-Based Study Using Latent Class Analysis
title Identifying Unique Subgroups of High-Cost Patients With Schizophrenia: A Population-Based Study Using Latent Class Analysis
title_full Identifying Unique Subgroups of High-Cost Patients With Schizophrenia: A Population-Based Study Using Latent Class Analysis
title_fullStr Identifying Unique Subgroups of High-Cost Patients With Schizophrenia: A Population-Based Study Using Latent Class Analysis
title_full_unstemmed Identifying Unique Subgroups of High-Cost Patients With Schizophrenia: A Population-Based Study Using Latent Class Analysis
title_short Identifying Unique Subgroups of High-Cost Patients With Schizophrenia: A Population-Based Study Using Latent Class Analysis
title_sort identifying unique subgroups of high-cost patients with schizophrenia: a population-based study using latent class analysis
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10291413/
https://www.ncbi.nlm.nih.gov/pubmed/37377884
http://dx.doi.org/10.1177/11786329231183317
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