Socioeconomic Metrics and Their Unique Role in Predicting Cognitive and Functional Morbidity in Racially and Ethnically Diverse Patients with Epilepsy
Abstract number :
3.112
Submission category :
11. Behavior/Neuropsychology/Language / 11B. Pediatrics
Year :
2024
Submission ID :
58
Source :
www.aesnet.org
Presentation date :
12/9/2024 12:00:00 AM
Published date :
Authors :
Presenting Author: Jenna Chiang, PsyD – Children's Hospital Los Angeles / University of Southern California
Ashley Whitaker, PhD, ABPP-CN – Children's Hospital Los Angeles
Anastasia Harrell, PsyD – Children's Hospital Los Angeles
Leanne Mendoza, PsyD – Children's Hospital Los Angeles
Michele Van Hirtum-Das, MD – Children's Hospital Los Angeles
Latanya Agurs, MD – Children's Hospital Los Angeles
Madeline Kahan, MD – Children's Hospital Los Angeles
Brittany Jordan, MD – Children's Hospital Los Angeles
Asri Yuliati, MD – Children's Hospital Los Angeles
Sucheta Joshi, MD, MS, FAES – Children's Hospital Los Angeles
Rationale: Children and adolescents with epilepsy from disadvantaged backgrounds (education, income, employment, and housing quality) are at higher risk for adverse outcomes due to psychosocial factors (e.g., access to resources, medical adherence) and neurodevelopmental differences (e.g., milestones, cortical maturation). Socioeconomic disadvantage is a unique predictor of intellectual morbidity across pediatric populations, including pediatric epilepsy, above and beyond diagnostic/treatment variables. This study aims to replicate prior findings regarding relationships between socioeconomic status (SES) proxies (i.e., parental education; median household income, MHI), disadvantage (using the area deprivation index, ADI), and neuropsychological (NP) outcomes among patients with pediatric epilepsy, with a primarily minority and lower SES sample for added generalizability.
Methods: MHI and ADI quintiles (1-5, 5 being most disadvantaged) were estimated for 75 patients with pediatric onset epilepsy who underwent NP evaluation (ages 1-22; x̄=13.1 years, SD=4.9 years; 62.3% male). Parental education was collected from most patients as a second SES proxy. NP outcomes included estimated IQ (calculated from Wechsler Vocabulary/Matrix Reasoning) and/or adaptive functioning (AF; Adaptive Behavior Assessment System or Behavior Assessment System for Children). Given violations of normality across SES proxies, medians, interquartile ranges (IQR), and non-parametric multivariate Kruskal-Wallis (MKW) tests were utilized.
Results: The majority of the sample (77.3%) identified as belonging to minority ethnic and/or racial groups (62.7% Latinx, 5.3% Asian, 2.7% Black, 2.7% bi/multiracial, 4% Other) from predominantly middle to high disadvantaged neighborhoods (ADI 3-5, N=48, 64.0%). Unsurprisingly, given the overrepresentation of disadvantage and lower SES among minority populations, significant racial/ethnic disparities were noted in ADI [χ2(5)=22.398, p< .001], MHI [χ2(5)=20.731, p< .001], and parental education [χ2(5)=23.123, p< .001]. MHI (median=$85,354, IQR=$33,351), ADI (median=3, IQR=2), and parental education (highest grade median=14, IQR=4) were all correlated, with positive correlations between MHI and parental education [r(75)=.454, p< .001] and negative correlations between MHI and ADI [r(75)=-.642, p< .001] as well as parental education and ADI [r(75)=-.421, p< .001]. IQ and AF were also significantly correlated, r(48)=.452, p< .001; however, neither parental education nor ADI predicted IQ (SS=45-137; x̄=91.43, SD=18.474) or AF (SS=47-115; x̄ =84.93, SD=16.430). Conversely, while MHI did not predict AF [F(1,57)=3.298, p=.075], it did predict IQ [F(1,58)=6.606, p=.013].
Conclusions: Consistent with prior research in other pediatric populations (e.g., oncology), findings reveal ADI does not predict cognitive prognosis among children and adolescents with epilepsy in the same way as MHI. Given the heterogeneous sociodemographic sample characteristics, these findings allow for greater generalizability and indicate potential diversity-related protective factors (e.g., multigenerational living in the context of poorer housing quality).
Funding: N/A
Behavior