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Triaging Factors of Caregiver Depression through Chi-Square Automatic Interaction Detection (CHAID) Analysis
Journal of Health Care for the Poor and Underserved
|June 15, 2026
Summary
Caregiver depression is prevalent, affecting over 25% of US caregivers. Younger caregivers and those with lower incomes experience higher rates, highlighting key factors for targeted support and resource allocation.
Area of Science:
- Public Health
- Gerontology
- Mental Health Research
Background:
- Caregiver depression is a significant public health issue, with over 25% of US caregivers reporting a diagnosis.
- This rate is substantially higher than the general adult population (less than 10%).
Purpose of the Study:
- To identify key demographic and caregiving variables associated with depression rates among caregivers.
- To understand the hierarchy of factors predicting caregiver depression for resource allocation.
Main Methods:
- Cross-sectional analysis of the 2022-2023 Behavioral Risk Factor Surveillance Survey (BRFSS).
- Involved 30,961 surveyed caregivers, examining 23 variables.
- Utilized chi-square automatic interaction detection (CHAID) for variable significance.
Main Results:
- Caregiver age emerged as the most significant predictor, with 35.3% of those under 55 reporting depression versus 21.6% of those 55 and older (p < .001).
- Income level was the second most significant factor, with 35.4% of caregivers earning less than $50,000 reporting depression compared to 21.6% earning $50,000 or more (p < .001).
Conclusions:
- Caregiver age and income are critical factors influencing depression rates.
- Findings can inform targeted community interventions and resource allocation for high-risk caregiver populations.
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