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Predicting Hospice Transitions in Dementia Caregiving Dyads: An Exploratory Machine Learning Approach
Suzanne S Sullivan1, Wei Bo2, Chin-Shang Li1
1School of Nursing, University at Buffalo, Buffalo, New York, USA.
Hospice care access for individuals with dementia is influenced by health, socioeconomic factors, and caregiver support. Machine learning models identified key predictors for hospice utilization over three years.
Area of Science:
- Gerontology
- Health Services Research
- Dementia Care
Background:
- Hospice programs support individuals with serious illnesses and their caregivers, facilitating aging in place and end-of-life care at home.
- Existing research on dementia care transitions is often cross-sectional, lacking longitudinal data on hospice uptake, access, and equity.
Purpose of the Study:
- To analyze longitudinal factors influencing hospice utilization among persons with dementia and their caregivers.
- To identify social determinants of health and quality-of-life indicators impacting hospice care access.
Main Methods:
- Secondary data analysis using the National Health and Aging Trends Study and National Study of Caregiving (2015-2018).
- Employed machine learning techniques including correlation matrix analysis, principal component analysis, random forest (RF), and information gain ratio (IGR).
Main Results:
- Information Gain Ratio identified predictors of hospice use: diabetes, regular physician, good memory, not using food stamps, no chewing/swallowing issues, and health not limiting life enjoyment.
- Random Forest identified predictors: caregiver age, dementia patient's income, census division, monthly caregiver support days, and health not limiting life enjoyment.
Conclusions:
- Exploratory models provide a foundation for precision health approaches in serious illness care.
- Further research is needed to understand barriers to hospice care for individuals with dementia who do not utilize these services.
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