Urusov, Alexey2023-08-282023-08-282023-08-282023-08-23http://hdl.handle.net/10012/19771This study examined the family and individual-level predictors of caregiver/child health and social service utilization expenditures during the COVID-19 pandemic. A sample of UK caregivers (n = 418) provided reports on their families and two of their children between the ages of 5-18 (n = 836) during May and November of 2020. Caregiver report measures included COVID-19 distress, family functioning, caregiver distress, social support, child functional impairment, social and health service utilization expenditures, and demographic variables. Kruskall-Wallis non-parametric tests revealed significant group differences among families in relation to service expenditures based on family social support, caregiver distress, and child impairment. Zero-inflated negative binomial regressions revealed that for the younger child, COVID-19 stressors were the most important predictor of service expenditures. For the older child, functional impairment in different areas (e.g., school, home) was the most important predictor. For the caregiver, their own mental health, and demographic characteristics (e.g., relationship status, age), were the most important predictors. For the whole family, child impairment played the biggest role in predicting service utilization expenditures. These results demonstrate the importance of considering family and individual variables in relation to social and health service utilization expenditures. These outcomes highlight the importance of supporting families with prevention and early intervention initiatives that consider systemic factors across the family ecology, especially during large-scale social disruptions. Additionally, the findings highlight that there are multiple family processes at work associated with family well-being and the resulting societal healthcare expenditures.enfamily functioningfamily stressmental healthservice useservice expendituressocial supportCOVID-19Family Health Service Utilization Patterns: Analysis of Predictors, Economic Costs, and Preventative FactorsMaster Thesis