Date Presented 4/1/2017
A study of youths with intellectual disability and traumatic brain injury showed no differences in receipt of transition services between minority and nonminority youths after adjusting for socioeconomic status. Informed occupational therapists can develop culturally responsive transition plans.
Primary Author and Speaker: Kelli Williams Gary
Contributing Authors: Adam Sima, Katherine Inge, Paul Wehman
PURPOSE: The purpose of this study is to determine differences in participation in high school transition services between racial and ethnic minority and White youths after adjusting for demographic and socioeconomic factors. This study attempts to answer the following research questions: (1) What is the difference between White and non-White youths in participation in high school services to assist youth with intellectual disability (ID) and traumatic brain injury (TBI) in transitioning from high school to adulthood? (2) What is the predictive relationship between sociodemographic variables and participation in high school services that assist youth with ID and TBI in transitioning from high school to adulthood?
DESIGN: The design of this study is a quantitative retrospective cohort study of a longitudinal data set. Participants were selected from more than 10,000 youths with disabilities enrolled in the National Longitudinal Transition Study–2 database. Weighted averages of adolescents with ID and TBI were categorized as minority (43.3%) and nonminority (56.7%).
METHOD: Simple logistic regression was fit to predict occupational therapy (OT), assistive technology, career counseling, transportation services, and social work for each demographic and socioeconomic variable while incorporating the sample weights. Next, a multivariable logistic regression model predicted race/ethnicity. New sample weights generated propensity scores, and calculated differences between minority and nonminority youths were determined. Separate logistic regressions were fit comparing minority with nonminority youths with new sample weights.
RESULTS: Prior to incorporating propensity scores, racial and ethnic minority youths with ID and TBI were 59% less likely to participate in OT than their White peers. The sole predictive factor for any transition service was household education (p < .0001); youths whose parents and guardians had postsecondary education had nearly three times more odds of receiving any service than those with high school education (odds ratio = 2.92, 95% confidence interval [1.60, 5.31]). Additionally, higher household education and income were significant predictors of greater participation in OT (p < .001) and transportation services (p = .009). Higher percentage of students eligible for discounted lunch corresponded with lower participation in OT (p =.007). Families who spoke only English at home were less likely to receive transportation services (p < .001), assistive technology (p = .035), and social work (p < .0001). Lastly, higher percentage of minorities at the youth’s school indicated higher participation in transportation services (p = .043). Once propensity scores were incorporated, racial and ethnic minorities were no longer less likely to participate in any service than White peers; however, income, percent of students qualifying for discounted lunch, and language spoken at home were all related to race.
CONCLUSION: Socioeconomic factors with unique analytical methods should be used to determine racial and ethnic differences related to service outcomes for adolescents with disabilities. Although racial and ethnic difference existed initially, when socioeconomic factors were accounted for, the differences diminished to nonsignificance. This is important to enable occupational therapists to understand the complex interrelationships among social, economic, environmental, and cultural factors and to intervene to limit the effects of health inequities in order to deliver culturally responsive transition services.
References
American Occupational Therapy Association. (2013). AOTA’s statement on health disparities. American Journal of Occupational Therapy, 67(6 Suppl.), S7–S8. https://doi.org/10.5014/ajot.2013.67S7
Mitra, R., & Reiter, J. P. (2012). A comparison of two methods of estimating propensity scores after multiple imputation. Statistical Methods in Medical Research, 25, 188–204. https://doi.org/10.1177/0962280212445945
Newman, L., Wagner, M., Knokey, A.-M., Marder, C., Nagle, K., Shaver, D., . . . Schwarting, M. (2011). The post–high school outcomes of young adults with disabilities up to 8 years after high school: A report from the National Longitudinal Transition Study–2 (NLTS2). Menlo Park, CA: SRI International. Retrieved from https://nlts2.sri.com/reports/2011_09_02/index.html
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