Keywords
Risk profiling, Caregiving, Chronic stress

Technologies
R, Clustering, MICE, KAMILA

Informal caregivers provide unpaid support to sick, disabled or elderly relatives, often at significant cost to their own health. As populations age, understanding which caregivers face the greatest health risk is increasingly important. Existing research treats caregivers as a homogeneous group, obscuring risk across individuals with different personalities, caregiving intensities and biological profiles.

This project applies six clustering algorithms to identify distinct health risk profiles among informal caregivers using data from the UK Household Longitudinal Study, spanning demographics, caregiving intensity, psychological wellbeing, lifestyle and biomarkers.

Three profiles emerged consistently. A Psychologically Distressed group characterised by high neuroticism and distress, a high-intensity group defined by in-household care, low income and high care hours and a low-risk group. Long-term illness rates differed significantly across profiles.

Cross-method convergence confirms the structure reflects genuine population heterogeneity. Findings support targeted caregiver interventions rather than population-level approaches.