Predicting the burden of family caregivers from their individual characteristics

ABSTRACT

This study examined the association between caregivers’ burdens and their individual characteristics and identified characteristics that are useful for predicting the level of caregiver burden. We successfully surveyed 387 family caregivers, having them complete the caregiver burden inventory scale (CBI) and an individual characteristic questionnaire. When we compared the average CBI scores between groups with a particular individual characteristic (including caring for older adult(s), educational level, employment status, place of birth, marital status, financial status, need for family support, need for friend support, and need for nonprofit organizational support), we found a significant difference in the average scores. From a logistic regression model, with burden level as the outcome, we found that caring for older adult(s), educational level, employment status, place of birth, financial situation, and need for nonprofit organizational support were significant predictors of the burden level of caregivers. The research findings suggest that certain individual characteristics can be adopted for identifying and quantifying caregivers who may have a higher level of burden. The findings are useful to uncover caregivers who may need prompt support and social care.

KEYWORDS:

• Caregiver burden
• caring responsibility
• diagnosis
• informal caregiver
• informatics

Acknowledgments

The authors thank the HKSKH Lady MacLehose Centre for their assistance with this project and the family caregivers who participated.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethics approval

The study was approved by the ethical committee in the Hong Kong University of Science and Technology. All procedures performed in this study were in accordance with the ethical standards of the HKSKH Lady MacLehose Centre.

Data availability statement

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Additional information

Funding

The work described in this paper was partially supported from The Hong Kong University of Science and Technology research grant “Big Data Analytics on Social Research” (grant number CEF20BM04).