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Two-year change in latent classes of comorbidity among high-risk Veterans in primary care: a brief report
BACKGROUND: Segmentation models such as latent class analysis are an increasingly popular approach to inform group-tailored interventions for high-risk complex patients. Multiple studies have identified clinically meaningful high-risk segments, but few have evaluated change in groupings over time. O...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9652993/ https://www.ncbi.nlm.nih.gov/pubmed/36371216 http://dx.doi.org/10.1186/s12913-022-08757-x |
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author | Hutchins, Franya Thorpe, Joshua Zhao, Xinhua Zhang, Hongwei Rosland, Ann-Marie |
author_facet | Hutchins, Franya Thorpe, Joshua Zhao, Xinhua Zhang, Hongwei Rosland, Ann-Marie |
author_sort | Hutchins, Franya |
collection | PubMed |
description | BACKGROUND: Segmentation models such as latent class analysis are an increasingly popular approach to inform group-tailored interventions for high-risk complex patients. Multiple studies have identified clinically meaningful high-risk segments, but few have evaluated change in groupings over time. OBJECTIVES: To describe population-level and individual change over time in latent comorbidity groups among Veterans at high-risk of hospitalization in the Veterans Health Administration (VA). RESEARCH DESIGN: Using a repeated cross-sectional design, we conducted a latent class analysis of chronic condition diagnoses. We compared latent class composition, patient high-risk status, and patient class assignment in 2018 to 2020. SUBJECTS: Two cohorts of eligible patients were selected: those active in VA primary care and in the top decile of predicted one-year hospitalization risk in 2018 (n = 951,771) or 2020 (n = 978,771). MEASURES: Medical record data were observed from January 2016–December 2020. Latent classes were modeled using indicators for 26 chronic health conditions measured with a 2-year lookback period from study entry. RESULTS: Five groups were identified in both years, labeled based on high prevalence conditions: Cardiometabolic (23% in 2018), Mental Health (18%), Substance Use Disorders (16%), Low Diagnosis (25%), and High Complexity (10%). The remaining 8% of 2018 patients were not assigned to a group due to low predicted probability. Condition prevalence overall and within groups was stable between years. However, among the 563,725 patients identified as high risk in both years, 40.8% (n = 230,185) had a different group assignment in 2018 versus 2020. CONCLUSIONS: In a repeated latent class analysis of nearly 1 million Veterans at high-risk for hospitalization, population-level groups were stable over two years, but individuals often moved between groups. Interventions tailored to latent groups need to account for change in patient status and group assignment over time. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-022-08757-x. |
format | Online Article Text |
id | pubmed-9652993 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-96529932022-11-15 Two-year change in latent classes of comorbidity among high-risk Veterans in primary care: a brief report Hutchins, Franya Thorpe, Joshua Zhao, Xinhua Zhang, Hongwei Rosland, Ann-Marie BMC Health Serv Res Research BACKGROUND: Segmentation models such as latent class analysis are an increasingly popular approach to inform group-tailored interventions for high-risk complex patients. Multiple studies have identified clinically meaningful high-risk segments, but few have evaluated change in groupings over time. OBJECTIVES: To describe population-level and individual change over time in latent comorbidity groups among Veterans at high-risk of hospitalization in the Veterans Health Administration (VA). RESEARCH DESIGN: Using a repeated cross-sectional design, we conducted a latent class analysis of chronic condition diagnoses. We compared latent class composition, patient high-risk status, and patient class assignment in 2018 to 2020. SUBJECTS: Two cohorts of eligible patients were selected: those active in VA primary care and in the top decile of predicted one-year hospitalization risk in 2018 (n = 951,771) or 2020 (n = 978,771). MEASURES: Medical record data were observed from January 2016–December 2020. Latent classes were modeled using indicators for 26 chronic health conditions measured with a 2-year lookback period from study entry. RESULTS: Five groups were identified in both years, labeled based on high prevalence conditions: Cardiometabolic (23% in 2018), Mental Health (18%), Substance Use Disorders (16%), Low Diagnosis (25%), and High Complexity (10%). The remaining 8% of 2018 patients were not assigned to a group due to low predicted probability. Condition prevalence overall and within groups was stable between years. However, among the 563,725 patients identified as high risk in both years, 40.8% (n = 230,185) had a different group assignment in 2018 versus 2020. CONCLUSIONS: In a repeated latent class analysis of nearly 1 million Veterans at high-risk for hospitalization, population-level groups were stable over two years, but individuals often moved between groups. Interventions tailored to latent groups need to account for change in patient status and group assignment over time. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-022-08757-x. BioMed Central 2022-11-12 /pmc/articles/PMC9652993/ /pubmed/36371216 http://dx.doi.org/10.1186/s12913-022-08757-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Hutchins, Franya Thorpe, Joshua Zhao, Xinhua Zhang, Hongwei Rosland, Ann-Marie Two-year change in latent classes of comorbidity among high-risk Veterans in primary care: a brief report |
title | Two-year change in latent classes of comorbidity among high-risk Veterans in primary care: a brief report |
title_full | Two-year change in latent classes of comorbidity among high-risk Veterans in primary care: a brief report |
title_fullStr | Two-year change in latent classes of comorbidity among high-risk Veterans in primary care: a brief report |
title_full_unstemmed | Two-year change in latent classes of comorbidity among high-risk Veterans in primary care: a brief report |
title_short | Two-year change in latent classes of comorbidity among high-risk Veterans in primary care: a brief report |
title_sort | two-year change in latent classes of comorbidity among high-risk veterans in primary care: a brief report |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9652993/ https://www.ncbi.nlm.nih.gov/pubmed/36371216 http://dx.doi.org/10.1186/s12913-022-08757-x |
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