466 - The Impact of Discrimination in Medical Settings on Mental Health Among Caregivers of Children with Special Healthcare Needs

Publication Number: 466.6814

George Verdelis, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States; Ryan Coller, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States; Elizabeth Patterson, University of Utah School of Medicine, Orem, UT, United States; Amanda K. Gatewood, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, United States; Danielle Gerber, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States; Casey O'Hare, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States; Stefanie G. Ames, University of Utah School of Medicine, Salt Lake City, UT, United States

Background: Caregivers of children with special healthcare needs (CSHCN) face challenges, including discrimination, that may impact their mental health and well-being as they navigate interactions with healthcare systems. Better understanding caregiver mental health and discrimination in medical settings is needed to drive change.

Objective: To evaluate caregiver mental health in a national cohort of CSHCN and examine its association with caregiver characteristics and exposure to discrimination in medical settings.

Design/Methods: This is a cross-sectional survey study with data from the 9 sites in the Family CIRCLE cohort of caregiver-child dyads. Eligible participants were children aged 0–15.5 years with ≥1 complex chronic condition (CCC) and ≥2 healthcare encounters in the past year. Children who received an organ transplant, were undergoing active cancer treatment, or had only a neonatal CCC were excluded. Caregivers, aged 18 or older, completed an online survey in English or Spanish including child and caregiver demographics, the Everyday Discrimination Scale (EDS) adapted for a medical setting, and health assessments. The primary outcome is caregiver response to the question “In general, how is your mental or emotional health?” dichotomized into favorable (excellent, very good, good) and unfavorable (fair, poor). A priori determined characteristics, as well as EDS responses categorized into quartiles, were evaluated in a multivariable logistic regression including random effects for participant site.

Results: 601/718 (83.7%) caregivers had complete data and were included in this analysis. In this cohort, 129 (21.5%) reported unfavorable mental health while 472 (78.5%) reported favorable mental health. 41.1% (n=247) of caregivers reported no discrimination (EDS score of 7) (Table 1). In the multivariable analysis, discrimination was significantly associated with caregiver mental health (p <.001); caregivers in the highest quartile of discrimination had an aOR of 2.6 (95% CI: 1.52 - 4.38) of unfavorable mental health compared to those in the lowest quartile (EDS score of 7). Caregiver disability was associated with unfavorable mental health with an aOR of 3.0 (95% CI: 1.49 - 6.16), relative to those without disability. Caregiver age was also significant in the multivariable model (Table 2).

Conclusion(s): Approximately 1/5 of caregivers of CSHCN report poor or fair mental health. While some caregiver characteristics were associated with poor mental health, experiencing discrimination in the medical setting emerged as a potentially modifiable factor associated with worse mental health among caregivers of CSHCN.

Multivariable Logistic Regression Model of Predictors of Caregiver Mental Health
Table 2 Logistic Regression.pdfThis table presents a multivariable logistic regression model examining the association between caregiver mental health and predictors, with discrimination quartile as the primary exposure of interest. Higher odds ratios (aOR) indicate increased likelihood of fair or poor mental health, adjusting for each predictor shown with a random effect for site. Odds ratios and 95% confidence intervals (CI) are provided.

Multivariable Logistic Regression Model of Predictors of Caregiver Mental Health
Table 1 Participant Characteristics.pdfThis table presents a multivariable logistic regression model examining the association between caregiver mental health and predictors. Higher odds ratios (aOR) indicate increased odds of fair or poor mental health, adjusting for each predictor shown with a random effect for site. Odds ratios and 95% confidence intervals (CI) are provided.