Dawn M Guthrie1, Nicole Williams1, Cheryl Beach2, Colleen J Maxwell3, Deborah Mills4, Lori Mitchell5, R Colin Reid6, Jeffrey W Poss3. 1. Wilfrid Laurier University, Waterloo, Ontario, Canada. 2. Fraser Health, Surrey, British Columbia, Canada. 3. University of Waterloo, Ontario, Canada. 4. Island Health, Victoria, British Columbia, Canada. 5. Winnipeg Regional Health Authority, Manitoba, Canada. 6. University of British Columbia-Okanagan, Kelowna, British Columbia, Canada.
Abstract
Objective: The main objective was to develop a decision-support tool to assess the risk of caregiver burden, the Caregiver Risk Evaluation (CaRE) algorithm. Methods: Home care clients were assessed using the Resident Assessment Instrument for Home Care (RAI-HC). Their caregiver completed the 12-item Zarit Burden Interview (ZBI), the main dependent measure, which was linked to the RAI-HC. Results: In the sample (n = 344), 48% were aged 85+ years and 61.6% were female. The algorithm can be collapsed into four categories (low, moderate, high, and very high risk). Relative to the low-risk group, clients in the very high-risk group had an odds ratio of 5.16 (95% confidence interval: [2.05, 12.9]) for long-term care admission, after adjusting for client age, sex, and regional health authority. Discussion: The CaRE algorithm represents a new tool to be used by home care clinicians as they proactively plan for the needs of clients and their caregivers.
Objective: The main objective was to develop a decision-support tool to assess the risk of caregiver burden, the Caregiver Risk Evaluation (CaRE) algorithm. Methods: Home care clients were assessed using the Resident Assessment Instrument for Home Care (RAI-HC). Their caregiver completed the 12-item Zarit Burden Interview (ZBI), the main dependent measure, which was linked to the RAI-HC. Results: In the sample (n = 344), 48% were aged 85+ years and 61.6% were female. The algorithm can be collapsed into four categories (low, moderate, high, and very high risk). Relative to the low-risk group, clients in the very high-risk group had an odds ratio of 5.16 (95% confidence interval: [2.05, 12.9]) for long-term care admission, after adjusting for client age, sex, and regional health authority. Discussion: The CaRE algorithm represents a new tool to be used by home care clinicians as they proactively plan for the needs of clients and their caregivers.
Entities:
Keywords:
analysis-regression models; caregiving-informal; home and community based care and services; interRAI
Authors: Dawn M Guthrie; Nicole Williams; Cheryl Beach; Emma Buzath; Joachim Cohen; Anja Declercq; Kathryn Fisher; Brant E Fries; Donna Goodridge; Kirsten Hermans; John P Hirdes; Hsien Seow; Maria Silveira; Aynharan Sinnarajah; Susan Stevens; Peter Tanuseputro; Deanne Taylor; Christina Vadeboncoeur; Tracy Lyn Wityk Martin Journal: PLoS One Date: 2022-04-07 Impact factor: 3.240