Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Hutchins, Franya, Thorpe, Joshua, Zhao, Xinhua, Zhang, Hongwei, Rosland, Ann-Marie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
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
_version_ 1784828594856394752
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
work_keys_str_mv AT hutchinsfranya twoyearchangeinlatentclassesofcomorbidityamonghighriskveteransinprimarycareabriefreport
AT thorpejoshua twoyearchangeinlatentclassesofcomorbidityamonghighriskveteransinprimarycareabriefreport
AT zhaoxinhua twoyearchangeinlatentclassesofcomorbidityamonghighriskveteransinprimarycareabriefreport
AT zhanghongwei twoyearchangeinlatentclassesofcomorbidityamonghighriskveteransinprimarycareabriefreport
AT roslandannmarie twoyearchangeinlatentclassesofcomorbidityamonghighriskveteransinprimarycareabriefreport