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Predicting Hospice Transitions in Dementia Caregiving Dyads: An Exploratory Machine Learning Approach
BACKGROUND AND OBJECTIVES: Hospice programs assist people with serious illness and their caregivers with aging in place, avoiding unnecessary hospitalizations, and remaining at home through the end-of-life. While evidence is emerging of the myriad of factors influencing end-of-life care transitions...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9701063/ https://www.ncbi.nlm.nih.gov/pubmed/36452051 http://dx.doi.org/10.1093/geroni/igac051 |
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author | Sullivan, Suzanne S Bo, Wei Li, Chin-Shang Xu, Wenyao Chang, Yu-Ping |
author_facet | Sullivan, Suzanne S Bo, Wei Li, Chin-Shang Xu, Wenyao Chang, Yu-Ping |
author_sort | Sullivan, Suzanne S |
collection | PubMed |
description | BACKGROUND AND OBJECTIVES: Hospice programs assist people with serious illness and their caregivers with aging in place, avoiding unnecessary hospitalizations, and remaining at home through the end-of-life. While evidence is emerging of the myriad of factors influencing end-of-life care transitions among persons living with dementia, current research is primarily cross- sectional and does not account for the effect that changes over time have on hospice care uptake, access, and equity within dyads. RESEARCH DESIGN AND METHODS: Secondary data analysis linking the National Health and Aging Trends Study to the National Study of Caregiving investigating important social determinants of health and quality-of-life factors of persons living with dementia and their primary caregivers (n = 117) on hospice utilization over 3 years (2015–2018). We employ cutting-edge machine learning approaches (correlation matrix analysis, principal component analysis, random forest [RF], and information gain ratio [IGR]). RESULTS: IGR indicators of hospice use include persons living with dementia having diabetes, a regular physician, a good memory rating, not relying on food stamps, not having chewing or swallowing problems, and whether health prevents them from enjoying life (accuracy = 0.685; sensitivity = 0.824; specificity = 0.537; area under the curve (AUC) = 0.743). RF indicates primary caregivers’ age, and the person living with dementia’s income, census division, number of days help provided by caregiver per month, and whether health prevents them from enjoying life predicts hospice use (accuracy = 0.624; sensitivity = 0.713; specificity = 0.557; AUC = 0.703). DISCUSSION AND IMPLICATIONS: Our exploratory models create a starting point for the future development of precision health approaches that may be integrated into learning health systems that prompt providers with actionable information about who may benefit from discussions around serious illness goals-for-care. Future work is necessary to investigate those not considered in this study—that is, persons living with dementia who do not use hospice care so additional insights can be gathered around barriers to care. |
format | Online Article Text |
id | pubmed-9701063 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-97010632022-11-29 Predicting Hospice Transitions in Dementia Caregiving Dyads: An Exploratory Machine Learning Approach Sullivan, Suzanne S Bo, Wei Li, Chin-Shang Xu, Wenyao Chang, Yu-Ping Innov Aging Special Issue: Nursing Science Interventions in Aging BACKGROUND AND OBJECTIVES: Hospice programs assist people with serious illness and their caregivers with aging in place, avoiding unnecessary hospitalizations, and remaining at home through the end-of-life. While evidence is emerging of the myriad of factors influencing end-of-life care transitions among persons living with dementia, current research is primarily cross- sectional and does not account for the effect that changes over time have on hospice care uptake, access, and equity within dyads. RESEARCH DESIGN AND METHODS: Secondary data analysis linking the National Health and Aging Trends Study to the National Study of Caregiving investigating important social determinants of health and quality-of-life factors of persons living with dementia and their primary caregivers (n = 117) on hospice utilization over 3 years (2015–2018). We employ cutting-edge machine learning approaches (correlation matrix analysis, principal component analysis, random forest [RF], and information gain ratio [IGR]). RESULTS: IGR indicators of hospice use include persons living with dementia having diabetes, a regular physician, a good memory rating, not relying on food stamps, not having chewing or swallowing problems, and whether health prevents them from enjoying life (accuracy = 0.685; sensitivity = 0.824; specificity = 0.537; area under the curve (AUC) = 0.743). RF indicates primary caregivers’ age, and the person living with dementia’s income, census division, number of days help provided by caregiver per month, and whether health prevents them from enjoying life predicts hospice use (accuracy = 0.624; sensitivity = 0.713; specificity = 0.557; AUC = 0.703). DISCUSSION AND IMPLICATIONS: Our exploratory models create a starting point for the future development of precision health approaches that may be integrated into learning health systems that prompt providers with actionable information about who may benefit from discussions around serious illness goals-for-care. Future work is necessary to investigate those not considered in this study—that is, persons living with dementia who do not use hospice care so additional insights can be gathered around barriers to care. Oxford University Press 2022-08-11 /pmc/articles/PMC9701063/ /pubmed/36452051 http://dx.doi.org/10.1093/geroni/igac051 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of The Gerontological Society of America. 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 reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Special Issue: Nursing Science Interventions in Aging Sullivan, Suzanne S Bo, Wei Li, Chin-Shang Xu, Wenyao Chang, Yu-Ping Predicting Hospice Transitions in Dementia Caregiving Dyads: An Exploratory Machine Learning Approach |
title | Predicting Hospice Transitions in Dementia Caregiving Dyads: An Exploratory Machine Learning Approach |
title_full | Predicting Hospice Transitions in Dementia Caregiving Dyads: An Exploratory Machine Learning Approach |
title_fullStr | Predicting Hospice Transitions in Dementia Caregiving Dyads: An Exploratory Machine Learning Approach |
title_full_unstemmed | Predicting Hospice Transitions in Dementia Caregiving Dyads: An Exploratory Machine Learning Approach |
title_short | Predicting Hospice Transitions in Dementia Caregiving Dyads: An Exploratory Machine Learning Approach |
title_sort | predicting hospice transitions in dementia caregiving dyads: an exploratory machine learning approach |
topic | Special Issue: Nursing Science Interventions in Aging |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9701063/ https://www.ncbi.nlm.nih.gov/pubmed/36452051 http://dx.doi.org/10.1093/geroni/igac051 |
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