Youth with disabilities transitioning from foster care: Examining prevalence and predicting positive outcomes

Journal Pre-proofs Youth with disabilities transitioning from foster care: Examining prevalence and predicting positive outcomes Leah P. Cheatham, Karen A. Randolph, Laura Boltz PII: DOI: Reference:

S0190-7409(19)31045-X https://doi.org/10.1016/j.childyouth.2020.104777 CYSR 104777

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Children and Youth Services Review

Received Date: Revised Date: Accepted Date:

22 September 2019 14 January 2020 14 January 2020

Please cite this article as: L.P. Cheatham, K.A. Randolph, L. Boltz, Youth with disabilities transitioning from foster care: Examining prevalence and predicting positive outcomes, Children and Youth Services Review (2020), doi: https://doi.org/10.1016/j.childyouth.2020.104777

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YOUTH WITH DISABILITIES TRANSITIONING FROM FOSTER CARE

Youth with disabilities transitioning from foster care: Examining prevalence and predicting positive outcomes

Leah P. Cheathama, Karen A. Randolphb and Laura Boltza

a University

of Alabama School of Social Work, Box 870314, Tuscaloosa, AL 35487

[email protected] and [email protected] b Florida

State University College of Social Work, 296 Champions Way, University Center C –

Suite 2500, Tallahassee, FL 32306 [email protected]

Corresponding Author:

Leah P. Cheatham [email protected] Box 870314, Tuscaloosa, AL 35487

Declarations of Interest: None. Disclosure: This manuscript has been adapted from a published dissertation (Cheatham, 2016).

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ABSTRACT Youth with disabilities are overrepresented among youth transitioning out of the foster care system (Slayter, 2016), yet few studies specifically examine the needs of former foster youth with disabilities as they transition into adulthood. Addressing this gap, the current study provides a more nuanced account of foster youth with disabilities’ transitions into adulthood. Using two national databases (NYTD and AFCARS), this study: (1) describes the prevalence of disability among older youth in foster care (age 17) and (2) investigates differences in educational and employment outcomes at age 21 among youth with and without disability diagnoses, with attention toward distinguishing emotional from non-emotional diagnoses. These lines of inquiry provide information about the experiences and needs of older foster youth with disabilities so that practices and policies aimed toward improving educational and employment outcomes can be appropriately tailored to this substantial population of youth exiting care. Keywords: foster care; disabilities; youth aging out; transitions to adulthood; educational disparities; employment disparities; productive engagement; high school completion

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INTRODUCTION Each year, nearly 20,000 young people exit from the child welfare system upon reaching the age of emancipation and thus immediately confront the challenges that come with adulthood (Annie E. Casey Foundation, 2018). Yet, too many young people experience great strife during this transitional period: these youth are at heightened risk for early parenting, drug use, joblessness, homelessness, and incarceration (Courtney & Hughes Heuring, 2005). Given the problematic outcomes that may arise from the experience of instant adulthood, youth exiting the child welfare system as a result of age (or “youth aging out”) are receiving heightened attention, with aims to promote normative achievements such as education and employment (e.g., Foster Care Independence Act of 1999; Fostering Connections to Success and Increasing Adoptions Act of 2008). The need for these efforts is clear; large-scale studies find that youth aging out achieve dramatically lowered rates of educational attainment and employment relative to peers outside of the child welfare system (see Okpych et al., 2017; Courtney et al., 2009; Pecora et al., 2005). However, a sizeable segment of this population has been largely overlooked: youth aging out with disabilities (Blakeslee et al., 2013). While estimates vary, approximately one third of youth in the child welfare system have a disability (Slayter, 2016). It is well established that experiencing a disability imposes barriers to educational achievement and employment among youth not in the child welfare system (Blackorby & Wagner, 1996; Goodman et al., 2011; U.S. Department of Labor, 2011; Wagner et al., 2005). Researchers have separately attended to transitions of youth aging out as well as the

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general population of youth transitioning with disabilities, yet few have examined the ways in which these populations converge or, more specifically, to how the complexities of these characteristics (e.g., specific disability diagnoses) might shape youth with disabilities’ transition from the foster care system into adulthood (Blakeslee et al., 2013). Given the substandard educational enrollment and employment among both youth aging out and youth with disabilities as well as the recognition that these outcomes are critical to setting one’s life course (Furstenberg et al., 2004), this study aims to describe the prevalence of youth aging out with disabilities and examine the role disability plays in predicting educational- and employment-related transitions into adulthood among youth aging out of care. LITERATURE Disability Prevalence among Older Foster Youth Children and youth with disabilities are notably overrepresented within the child welfare system. While precise estimates are scarce, Slayter (2016) reported that 53% of transition-aged foster youth in the U.S. carry a physical, cognitive, or emotional disability diagnosis compared to 10% of youth within the general population (Brault, 2012). Other more localized studies have lent support to these statistics: Wulczyn, Smithgall, and Chen (2009) found that 50% of middle school-aged youth engaged with the Chicago child welfare system reported a disability, while Hill (2012) found that 60% of transition-aged youth preparing to exit the Minnesota child welfare system reported a disability. These statistics are not surprising, as increased risk for abuse among children with disabilities has been well documented (American Academy of Pediatrics, 2001; Sullivan & Knutson, 2000; Wescott & Jones, 1999). Furthermore, because children with disabilities are less likely to find permanency within the system, they are also overrepresented among older foster youth in out-of-home placements (Center for Advanced

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Studies in Child Welfare, 2012; Hill, 2012; Rhode Island Kids Count, 2006; Slayter, 2016). While understudied, foster youth with disabilities undoubtedly face unique circumstances during their transitions to adulthood, often complicated by their particular disability diagnoses (Hill & Stenjhem, 2006). For instance, Katz and Courtney (2015) found that former foster youth with mental health diagnoses reported higher levels of unmet needs relative to other youth upon leaving care. Moreover, few youth carry just one disability diagnosis. Estimates indicate that among youth in foster care with a disability, two-thirds receive more than one diagnosis (Steinberg & Hylton, 1998), further complicating our understanding of how disability diagnoses may impact an individual. Disability Type and the Prevalence of Emotional Issues Recent attention toward the mental and physical health effects of foster care placement suggests that youth in foster care are at increased risk of experiencing adverse childhood experiences (or ACEs, see Centers for Disease Control & Prevention, 2019) and mental health challenges, including ADD/ADHD, anxiety, depression, and behavioral health problems (Turney & Wildeman, 2016). Because youth in care experience ACEs at rates higher than the general population (Bruskas & Tessin, 2013), they may also experience higher rates of trauma-related mental health challenges, such as post-traumatic stress disorder (Kolko et al., 2010; Salazar et al., 2013). In contrast to studies noting increased risk of abuse and child welfare involvement for youth with existing disabilities (Slayter, 2016; Sullivan & Knutson, 2000), findings surrounding the prevalence of mental health and trauma among foster youth suggest that the experience of foster care could also trigger new or secondary mental health and behavior problems during and after foster care placement. Given that particular diagnoses will impose different barriers and accommodations required by youth during transitions from high school into college or

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employment, more knowledge is needed regarding the implications of disability diagnosis type for youth exiting foster care (Morton, 2018; Vaccaro et al., 2015).

Education and Employment as Indicators of Well-Being Important life events, such as educational attainment or obtaining employment, typically characterize an individual’s transition into adulthood (Furstenberg et al., 2004). Large-scale studies of both youth aging out and youth transitioning with disabilities have focused on educational attainment and employment outcomes in quantifying successful transitions to adulthood. For example, the CalYOUTH study (Courtney et al., 2016) tracks youth transitioning from the California foster care system into adulthood and documents important shortcomings for foster youth. At age 19, foster youth in California completed high school or received a GED at a rate 20% lower than youth in the general population (66% vs. 88%) and attended at least one year of college at a rate 30% below youth in the general population (24% vs. 53%) (Courtney et al, 2016). At age 21, foster youth in California were employed at a rate 10% below their general population peers (54% vs. 65%) (Courtney et al., 2018). At the national level, data from the National Longitudinal Transition Study tracking youth with disabilities transitioning into adulthood show disparities in high school graduation rates between youth with distinct disability diagnoses. Only 56% of youth diagnosed with emotional disturbance graduate high school, compared to 95% of youth diagnosed with visual impairments (Wagner et al., 2005). When viewed against their general population peers in 2015, youth with disabilities’ graduation rate, as a whole, was nearly 20% lower than the national average (Congressional Research Service, 2017). Because both youth leaving care and youth with disabilities struggle to achieve transitional milestones at rates on par with their general

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population peers, education and employment remain key areas of interest in promoting wellbeing and gauging progress among these vulnerable groups of youth. The World Health Organization (2001) advocates for the use of outcome measures that focus on participation in normative activities for people without disabilities as proxies for wellbeing among people with disabilities. Thus, “normative outcomes”, such as high school completion, college enrollment and employment, are positive indicators of participation and should be considered within efforts to understand differences between youth transitioning into adulthood with and without disabilities. Beyond empirical rationales for examining educational and employment outcomes, the call for this line of inquiry is strengthened by the substantial social investments made toward improving these outcomes among youth aging out and youth with disabilities. For example, the Foster Care Independence Act (1999), or “Chafee Program,” targets transitional outcomes by increasing state funding to provide independent living (IL) services for foster youth as they age out of care. These services, provided at the individual state’s discretion, include educational support and employment training services. Additionally, the Fostering Connections Act (2008) provides states the opportunity to gain federal reimbursement for extending foster care services to youth up to age 21. For youth with disabilities in the general population, the Individuals with Disabilities Education Act (IDEA, 2004) authorizes funding for states to provide transitional support to students with special education needs and strengthens policy by requiring services specifically designed to meet students’ post-secondary goals. With academic, political, and fiscal attention turned toward educational and employment outcomes of both youth aging out of the child welfare system and the general population of youth with disabilities, examination of these outcomes specifically among youth aging out with disabilities is timely and important.

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Examining how these youth fare in our current system, accounting for system-level predictors of known influence on academic and employment success among youth with disabilities leaving care, will enhance our ability to meet their needs. System-Level Factors Influencing Academic Achievement and Employment As a result of the investments made toward promoting education and employment of both youth aging out of care and youth with disabilities, much is already known about the factors which help and hinder youth in achieving success in these transitions. The following discussion summarizes the impact of important system-level factors relevant to understanding transitional success of youth aging out of care with disabilities. Number of Placement Changes. Youth with multiple placement changes while in care experience academic challenges (Clemens et al., 2016) and behavioral problems (Sullivan et al., 2010)—both of which could influence their abilities to successfully transition to adulthood. Youth with disabilities are at increased risk for placement changes relative to youth without disabilities in the child welfare system (Rosenberg & Robinson, 2004), drawing more attention to the importance of placement stability as a mechanism for promoting success among youth with disabilities in foster care. Special Education. The intent of the IDEA (within special education), the Chafee Program, and the Fostering Connections Act are consistent: to assist youth in making successful transitions into early adulthood. However, these policies are falling short of their stated goals, evidenced by early adult outcomes for both youth with disabilities and foster youth exiting care, which remain poor relative to their peers (Janus, 2009; Osgood, Foster, & Courtney, 2010). These shortcomings are likely, in part, attributable to a lack of coordination among the multiple services received by foster youth with disabilities in special education as they prepare to exit care

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and pursue post-secondary education. While each policy calls for collaboration across service delivery systems, evidence of this is scarce (Geenen & Powers, 2006; Palladino, 2006). Hence, attention toward the effects of the current special education services system for youth in foster care is warranted. Extended Foster Care Involvement. Pursuant to the Fostering Connections Act (2008), states may access federal funds to provide additional foster care services to youth beyond the age of 18 (until 21). Youth in 46 states are permitted to remain in foster care beyond age 18, in most states until age 21 (though funding and eligibility schemes differ by state: only 24 states have opted to use federal funds to extend care as of December, 2016) (Child Welfare Information Gateway, 2017). Preliminary findings on the impact of this policy are promising—additional time in care is associated with improved academic and employment outcomes (Courtney & Hook, 2017; Courtney et al., 2018). Independent Living Services. The success of the extended foster care policy may be tied, in part, to independent living services made available to youth who remain in care past the age of 18. Okpych (2015) notes that youth with disabilities are more likely to receive services than other youth in care. Yet, evidence documenting the effects of Chafee-funded independent living (IL) services on educational and employment outcomes is limited and inconclusive (Courtney et al., 2011a-b; Okpych, 2015; U.S. Dept. of Health and Human Services, 2008a-b). Thus, IL service receipt remains an open question in understanding factors that help and hinder youth with disabilities in care, and is worthy of further study. Access to Healthcare. Strong associations exist between access to health, healthcare, and successful transitions for young adults within the general population—specifically within education and employment (see Adler & Newman, 2002). These associations take on a new level

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of urgency for young people with complex health needs who, historically, have been less likely to be insured (Fishman, 2001). Thanks to the Affordable Care Act (2010), youth aging out with disabilities are covered under Medicaid until their 26th birthday. However, many youth are unaware of their coverage and, as a result, may defer important medical care (Galewitz, 2018). Access to and awareness of healthcare for youth with disabilities leaving care is instrumental toward ensuring success in high school, college and beyond. Current Study Despite the prevalence of disability among youth involved in the child welfare system and the clear potential for complication as these youth leave foster care and transition toward adulthood, many studies have either failed to specifically examine this group or excluded youth with specific disabilities (e.g., individuals with developmental disorders, severe mental illness, physical disabilities, or cognitive impairments) (Blakeslee et al., 2013; Courtney & Hughes Heuring, 2005; Pecora et al., 2005). The few studies of youth aging out that examine disability show that youth aging out with disabilities are less likely to graduate high school or report postsecondary enrollment or employment than either youth aging out or youth with disabilities, alone (Anctil et al., 2007, Mares & Kroner, 2011; Salazar et al., 2019). Anctil and others (2007) found that, among of sample of youth in foster care, those with disabilities fared poorly across a number of indicators of well-being compared to youth without disabilities, including: educational attainment, job earnings, job satisfaction, and job turnover. Mares and Kroner (2011) found that youth aging out with mental health issues were half as likely to graduate high school, gain employment, or achieve independent living, relative to their peers yet cognitive impairments had no disadvantage. Examining data from the National Youth in Transition Study, Salazar and colleagues (2019) identified disability status as one of six key factors linked to post-secondary

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educational enrollment, suggesting that youth aging out with a disability diagnosis are less likely to attend college than youth aging out without a disability diagnosis. While these findings clearly highlight the importance of attending to disability among youth transitioning out of foster care, only Mares and Kroner (2011) distinguished among different types of disabilities. Their findings underscore a need to investigate the ways in which the experiences of being a young person with disabilities and also a ward of the state affect youth as they progress from adolescence into adulthood. Thus, more nuanced understandings of disability are needed to fully understand the challenges youth aging out face in pursuing education and employment and to identify targeted services to improve outcomes among these youth. Using national administrative data, the current study seeks to: 1) explore the prevalence and characteristics of foster youth with disabilities as they transition from care and into adulthood (ages 17-21); and 2) examine disability diagnosis type as a predictor of education and employment among foster youth transitioning from care, while controlling for other relevant individual- and system-level predictors. METHODS Data Data Sources This study utilized two national datasets: The National Youth in Transition Database (NYTD, 2014 Outcomes Cohort, ages 17, 19 and 21) merged with the Adoption and Foster Care Analysis Reporting System (AFCARS, 2014). The NYTD provides state-by-state cross-sectional information about Chafee-funded Independent Living (IL) service use (e.g., academic, employment, financial, health, and social support services) and longitudinal transitional

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outcomes (including academic and employment outcomes) of youth aging out of care (Children’s Bureau, 2019). The AFCARS data provide cross-sectional information, including measures of disability type (i.e., physical, sensory, mental, emotional, and other), placement history, and other information pertinent to youth’s ability to successfully transition from care (National Data Archive on Child Abuse and Neglect [NDACAN], 2019). Both the NYTD and AFCARS data are publicly available and, when merged together, present new opportunities to track outcomes among a national sample of foster youth leaving care, including foster youth with disabilities. Data Collection As required by the Chafee Act (42 USC §677), NYTD data are collected annually from all 50 states, as well as Puerto Rico and the District of Columbia, on both services received and outcomes attained by youth aging out of foster care. The NYTD Services file captures crosssectional data reported by case workers about services received annually by youth while the NYTD Outcomes file captures longitudinal cohort-based reports from youth (i.e., for the 2014 cohort, baseline data are collected at age 17; first follow-up data are collected at age 19[2016], and second and final follow-up data are collected at age 21[2018]). States are given discretion as to the modality of outcome data collection (e.g., in-person, telephone, or online). States can choose to draw a probabilistic sample for follow-up data collection. Fifteen states opted to select a sample of youth in Waves II and III of the NYTD 2014 cohort. AFCARS data are also collected annually from all 50 states and Puerto Rico, in accordance with federal law (45 CFR 1355.40). The AFCARS provide case-level data, reported by caseworkers, regarding all foster youth in custody of state child protective services (NDACAN, 2019). Study Sample

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Wave I (2014) of the NYTD provides baseline information for 16,480 youth; 12,309 of the original baseline cohort were eligible for follow-up at Waves II and III (ages 19 and 21). Response rates of youth at Waves II and III were 72% and 64%, respectively (calculated from wave-specific survey-eligible populations; see NDACAN, 2019 for a full description of NYTD). This current study is based on data from the NYTD 2014 cohort youth who provided valid responses during the 2018 (age 21) follow-up (n = 7,797). Of these 7,797 youth, 97% returned valid matches within the 2014 AFCARS data (n = 7,538). Additional information about Wavespecific non-response can be found through the NDACAN website (2020). Cases with missing data across any measures in logistic regression models (n = 421; 5.9%) were excluded from the final sample. Analysis of excluded cases revealed differences in racial composition, number of placement changes and receipt of any IL services. Youth retained in the final analytic sample were more diverse (𝑥2(3) = 8.537, p < .05), experienced more placement changes (𝑥2(3) = 20.084, p < .001), and received IL services at a higher rate than those excluded from the final analysis (𝑥2(1) = 10.800, p < .01). No differences were noted across gender, disability, special education, health insurance, or extended foster care participation. The final analytic sample included 7,117 youth (see Figure 1 for additional data merge details).

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 Figure 1. Flowchart Illustrating Data Merge Process NYTD Outcomes File (2014 Cohort-eligible & 19/21 Follow-up Eligible) Used as master file N=12,309

Cohort-eligible youth with no response at Wave 3 n=4,512

Excluded non-responding at Wave 3 (2018)

Revised NYTD Outcomes (N=7,797) merged with NYTD Services (2011-2018) (n=6,845 matched Service records)

NYTD Outcomes & Services file Merged with AFCARS (2014) Foster Care file N=7,538

Final Sample, excluding all cases with missing data N=7,117

Because service and special education data were reported only for youth receiving Chafee services within a fiscal year, individuals without a matched record from 2011-2018 (n=952) were coded as “0” for all Chafee services and “no report” for special education services.

Excludes individuals from the NYTD Outcomes & Services file who did not have matched records within the 2014 AFCARS Foster Care file. (n=259)

Excludes those with missing data across measures of interest (n=421; 5.9%)

Figure 1. Flowchart illustrating data merge process; including documentation of cases retained (left) and excluded (right) within each step of the data merge process.

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Measurement Dependent Variables Transitional outcomes within the current study were conceptualized as high school completion and productive engagement, which relate to age appropriate benchmarks for participation among young adults ages of 18 to 21 (WHO, 2001). This focus acknowledges the movement among disability scholars advocating for the use of “normative outcomes,” such as high school or GED completion, college enrollment, and employment as proxy indicators of quality of life among young adults with disabilities (Brown, Keith, & Schalock, 2004). High school completion captured whether the participant’s highest level of education achieved was less than high school (coded as 0) or high school—including equivalent certification—and beyond (coded as 1). This dichotomous variable was obtained through Wave III participant reports within the NYTD (2018). Because distinctions between high school degrees and equivalency degrees (e.g., GED) are not provided within the NYTD, no distinction was made between alternative completion statuses in the current study. Productive engagement measured whether the participant reported enrollment in postsecondary education, part-time employment (between 1 and 34 hours per week), or full-time employment (at least 35 hours per week). Any positive reports of these 3 outcomes in any combination were coded as “1”. For example, a participant earning their GED while working 10 hours per week would be coded “1” for productively engaged. Similarly, a young person enrolled in college but not working would also be coded as “1”. Youth reporting no post-secondary enrollment and less than one hour of employment per week served as a reference group (coded as “0”). Therefore, a participant earning their GED but not working would be coded

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as “0”. This dichotomous variable was created via data through Wave III participant reports within the NYTD (2018). Independent Variables – Individual-level Factors Disability type was captured through the AFCARS (2014) case report data. These data included the following diagnoses: (1) Physical disability diagnosis; (2) Sensory disability diagnosis (i.e., visual or hearing disability); (3) Mental retardation diagnosis; (4) Emotional disability diagnosis (i.e., DSM diagnosis); and (5) “Other Medical” diagnosis. Additionally, a sizeable number of youth (n = 544, or 7.6%) in the final sample had not yet received a diagnosis through the child welfare system (as noted in AFCARS, 2014). Youth without a diagnosis were categorized as not having a disability within this analysis, likely leading to underestimation of disability prevalence within this study. The AFCARS disability categorizations presented yet another analytic challenge as they were not mutually exclusive. In fact, approximately half of youth reporting a disability diagnosis within the AFCARS indicated more than one diagnosis (consistent with prior research; see Steinberg & Hylton, 1998). Within this study, we sought to not only describe prevalence and characteristics of foster youth with disabilities, but also understand differences in educational and employment outcomes among individuals with differing disability diagnoses. Therefore, the degree of overlap and imbalanced sample sizes among these specific diagnostic categories presented both analytic and practical challenges, necessitating development of a mutually exclusive disability categorization. Informed by the extant literature on youth with disabilities transitioning into adulthood while also accounting for analytic methods, an alternate conceptualization of disability type was tested. Prior literature has noted that transition-aged youth with emotional disabilities achieve at

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lower levels than their peers with and without disabilities (see Wagner et al., 2005). Recognizing the challenges faced by general population youth with emotional disabilities, as well as the fact that emotional diagnoses are prevalent among foster youth (37%; NYTD, 2014) in comparison to general population youth (20% of youth ages 13-18; NAMI, 2016), a scheme was adopted that partitioned foster youth with emotional disabilities from their peers with non-emotional disabilities, while continuing to attend to possible differences among youth with multiple diagnoses. Disability type was operationalized as follows: (1) Emotional-only diagnosis (i.e., youth aging out with only one diagnosis—emotional disability; n = 1,549); (2) Emotional-plus diagnoses (i.e., youth aging out with an emotional disability diagnosis along with any other disability diagnosis or combination of diagnoses; n = 1,058); and (3) Non-emotional diagnoses (i.e., youth aging out with no emotional diagnosis but with any other diagnosis or combination of diagnoses; n = 796). Youth with no diagnosis (n = 3,714) served as the reference category within logistic regression analyses. Race/ethnicity was measured as non-Hispanic Black, non-Hispanic Other, or Hispanic (any race), with non-Hispanic White as the reference group (AFCARS, 2014). Gender was captured as a dichotomous measure (NYTD, 2014), where males served as the reference group. Age was measured in calendar months as the time elapsed from the date of birth to the date of last outcome report (NYTD, 2018). Independent Variables – System-level Factors Measurement of special education was challenged by the structure of the NYTD Services dataset. Because report of services (and other accompanying variables, including special education receipt) was predicated upon receiving at least one Chafee-funded service, youth who

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did not receive a service during the years included within this study (2014-2018) did not have valid information regarding receipt of special education. Thus, information on receipt of special education services was collected from FY 2011 through FY 2018 within the NYTD Services data. While this approach increased the scope of time examined beyond the years of the study (2014-2018), the nature of special education services in combination with the sample age restrictions were such that report of special education receipt in any of these years was likely a reliable measure, because eligibility for such services would likely remain relatively constant during the years of secondary education. This broadened approach captured a total of 6,224 valid reports of special education receipt—26% reported receiving special education services (n = 1,884). Yet, these data are incomplete as 12% of the sample (n = 877) did not report receiving Chafee-funded IL services between FY 2011 and 2018, resulting in missing information for youth retained in the final analytic sample within the special education variable. Receipt of special education services was measured as a dichotomous variable, where those whose administrative record indicated ever receiving these services were coded as “1”; those who did not receive special education or who did not report valid data served as the reference group. Recognizing the relationships between the number of placement changes youth experience and their ability to achieve educational goals (Clemens et al., 2016; Rosenberg & Robinson, 2004), the number of placement changes is measured as a continuous variable within full analyses (ranging from 1 to 68; M = 5.4 [SD = 5.9]) and as a categorical variable within descriptive analyses (1-2, 3-5, 6-10, or 11+ placement settings). To address potential associations between healthcare and educational and employment outcomes, access to healthcare was measured as a dichotomous variable where those who reported “Yes” to receiving Medicaid or any other type of health insurance were coded as “1”

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(NYTD, 2014). This study also includes a measure of receiving Chaffee Independent Living (IL) services, where youth receiving any IL service during the study period were coded as “1”. Finally, to control for being in foster care at age 19, a measure indicating youth placement in any Title IV-B/E agency at Wave II (2016) was included and coded as “1”. Analysis First, univariate and bivariate descriptive statistics (through Pearson chi-square difference tests or t-tests tests) were produced to identify differences in individual- and system-level characteristics among foster youth, as well as bivariate group differences across educational and employment outcomes. Next, logistic regression analyses predicting the odds of high school completion and productive engagement by disability type (emotional-only, emotional-plus, and non-emotional) were conducted, while controlling for relevant individual-and system-level covariates (entered as separate variable blocks into the model). All analyses were conducted using SPSS v. 24 (IBM Corp, 2016). RESULTS Descriptive Findings Individual-level Characteristics Table 1 presents a description of the final analytic sample (N = 7,117). Females represented 56% of the sample, while 42% of respondents were non-Hispanic White, 28% were non-Hispanic Black, 21% were Hispanic (of any race), and 9% were another non-Hispanic race or ethnicity not previously captured (including those reporting more than one race). As respondents were sampled by age (within 45 days of their 17th, 19th and 21st birthdays), age (in months) varied little within sample (SD = 2.51 months), with an average age of 251 months, or slightly under 21 years, when outcome data were collected (calculated at Wave III [2018]). Of

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particular interest within this study, 48% of the sample carried a disability diagnosis at Wave I (2014). These diagnoses included physical (1%), sensory (6%), mental (4%), emotional (i.e., DSM diagnosis; 37%), or other medical conditions (22%). The majority of youth with a disability carried an emotional diagnosis or other medical diagnosis. Thirty-one percent of the sample had only one diagnosis on record, approximately 17% had more than one diagnosis (and as many as four diagnoses). System-level Characteristics Twenty-seven percent of youth in the sample reported receipt of special education services between ages 14 and 21, where 61% of youth reported receiving no special education services. As previously discussed (see measures, p. 13), special education data were missing for 12% of the total sample (876 youth). Over one third (37%) of youth experienced 1 or 2 placement changes during the episode of foster care recorded in 2014 (around their 17th birthday). Another third experienced 3-5 placement changes during this same period, and the remaining third experienced 6 or more placement changes (with over 10 % of these youth experiencing 11 or more settings). Over 90% of youth reported either Medicaid coverage or other health insurance coverage at Wave I. At Wave II (2016), 37% of youth remained in foster care at the age of 19, whereas at Wave III (age 21), only 18% remained in care. Approximately 88% of the sample reported receiving at least one IL service between fiscal years 2011 and 2018. Transitional Outcomes Nearly 80% of youth at age 21 had completed high school or GED, while 66% were productively engaged (i.e., enrolled in post-secondary education or employed).

YOUTH WITH DISABILITIES TRANSITIONING FROM FOSTER CARE Table 1. Description of analytic sample. Individual Factors (Wave I, except Age – determined at Wave III)

Total Sample (%) (N = 7,117)

Sex Male Female

44.0 56.0

White Black Hispanic/Latinx Other

42.2 28.2 21.0 8.5

(mean months)

251.3

Yes

47.8

Mental Sensory Physical Emotional Other Medical

3.7 5.8 1.2 36.6 21.7

1 Diagnosis 2 Diagnoses 3+ Diagnoses

31.2 12.5 4.1

Race/Ethnicity

Age Disability Disability Type

Disability Cooccurrence

Mutually Exclusive Disability Type Emotional ONLY Emotional PLUS NON-Emotional System-level Factors (Waves I, II & III, ages 17, 19 & 21)

21.8 14.9 11.2

Special Education (FY2011-2018) Yes No Not reported

26.5 61.2 12.3

1-2 3-5 6-10 11+

37.4 30.2 19.6 12.8

% w/any type coverage

92.5

Receiving care age 19 Receiving care age 21

36.6 18.4

Received at least 1 service

87.7

Graduated HS or GED

79.0

Enrolled—any intensity

22.9

Part-time Full-time

27.2 33.1

Working or PS. Enrolled

65.8

Number of placement changes (1-68)

Health Insurance (W1, age 17) Extended Foster Care (WII & WIII) IL Service (FY2011-2018) Outcomes (Wave III, age 21) Educational Attainment Post-secondary Enrollment Employment Productive Engagement a. b.

Data for White, Black, and Other races do not include Hispanics. Hispanic ethnicity includes persons of any race. Percentages reported for post-secondary enrollment, part-time and full-time employment do not equal 100% as these variables were not included in final analyses and, therefore, were not considered when excluding missing cases.

21

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Within the productive engagement measure, 23% of youth were enrolled in some type of postsecondary education (e.g., vocational training, 2-year, or 4-year college programs), 27% of youth were employed part-time (1-34 hours/week), and 33% of youth were employed full-time (35 or more hours/week). Bivariate Findings As previously discussed, the overlap of disability diagnoses for youth within this sample was notable. In fact, 17% of the total sample (and 35% of the sample of youth with disability diagnoses) documented more than one, and up to four, disability diagnoses. Addressing this cooccurrence, an alternate, mutually exclusive categorization of three disability diagnosis types was employed within bivariate and logistic regression analyses, grouping youth with only emotional disabilities (22%), youth with an emotional diagnosis plus at least one other nonemotional diagnosis (15%), and youth with a non-emotional diagnosis (11%). As reported in Table 2, significant differences across disability diagnosis types were noted by gender and race/ethnicity. Interestingly, males, who comprised a minority of the total sample (44%), found themselves in a slight majority (52%) among those reporting an emotional diagnosis with any additional diagnosis (i.e., “emotional-plus”). White youth were represented at higher rates among those with emotional-only or emotional-plus diagnoses while underrepresented among those with a non-emotional diagnosis. When compared to the distribution of Black youth across other disability diagnostic types, slightly fewer Black youth carried an emotional-only diagnosis. Despite representing only 21% of this sample, 37% of Hispanic youth were diagnosed with a non-emotional condition, where only 16% of Hispanic youth were diagnosed with emotional-only conditions.

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Interesting associations were revealed when examining differences in special education receipt by disability type (see Table 2). While all disability types analyzed received special education at higher rates than their peers without disabilities, individuals with an emotional-plus diagnosis received these services at a far higher rate (44%) than their peers with emotional-only diagnoses (31%) or non-emotional diagnoses (31%). Significant differences were also noted across disability types with respect to the number of placement changes youth experienced. While it remains uncertain where statistical differences were detected across categories, the trends within these data suggest that individuals with emotional disability diagnoses (alone or in combination with another diagnosis) experience more removals than do their peers who experience non-emotional disabilities or their peers without disabilities. Additionally, individuals with non-emotional diagnoses accessed extended foster care at the highest rate among all groups examined within this sample. Analyses by disability diagnosis type also revealed significant differences across all education-related outcomes: Youth with non-emotional diagnoses completed high school at rates higher than their peers with or without diagnoses (see Table 2). These youth were also enrolled in post-secondary education at rates higher than their peers with and without disabilities. While higher enrollment rates among youth with non-emotional disabilities are important, what may be equally important are the poor enrollment rates among those with emotional diagnoses (emotional-only and emotional-plus, alike). Youth within the sample carrying an emotional diagnosis of any type report high school completion and post-secondary enrollment at lower rates than their peers with no disability diagnosis or non-emotional diagnoses.

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Table 2. Descriptive information about final sample, understood by types and combinations of diagnoses (1: No diagnosis, 2: Emotional diagnosis only, 3: Emotional and other diagnosis, and 4: Non-emotional diagnosis). No Dx Emotional Emotional Dx Non-Emotional Total Dx ONLY + Other Dx (n=3,714) (n=1,549) (n=1,058) (n=796) (N=7,117) Individual Factors Sex (%)*** Male

42.6

42.9

51.5

42.8

44.0

Female

57.4

57.1

48.5

57.2

56.0

Race/Ethnicity (%)*** White

44.0

48.0

39.4

26.3

42.2

Black

29.0

25.3

29.0

29.3

28.2

Hispanic/Latinx

18.9

16.1

23.4

37.3

21.0

Other

8.1

10.6

8.1

7.2

8.5

251.4

251.5

251.1

250.9

251.3

18.7

31.0

44.2

30.5

26.5

No

67.0

58.6

45.2

60.7

61.2

Not reported

14.4

10.4

10.6

8.8

12.3

1-2

43.2

28.5

24.5

44.8

37.4

3-5

31.5

30.1

26.3

29.7

30.2

6-10

16.6

22.8

26.5

18.3

19.6

11+

8.8

18.6

22.7

7.2

12.8

% w/any coverage

92.4

92.0

92.1

94.2

92.5

30.1

32.0

50.1

58.5

36.6

13.8

16.5

23.9

36.1

18.4

85.6

89.6

89.4

91.2

87.7

79.3

76.8

77.3

83.4

79.0

Enrolled-any intensity

24.6

19.1

19.0

27.6

22.9

Part-time

26.7

27.3

27.3

29.4

27.2

Full-time***

36.4

31.2

26.0

31.0

33.1

68.6

62.7

59.3

67.7

65.8

Age (mean months) System-level Factors (%) Special Education*** Yes

Number of placement changes***

Health Insurance Foster Care at 19*** Receiving care Foster Care at 21*** Receiving care IL Service FY2011-2018*** Received ANY service Outcomes (%) Educational Attainment** Graduated HS or GED Post-secondary Enrollment*** Employment

Productive Engagement*** Working or PS Enrolled a. b.

*p < .05; **p < .01; ***p < .001 Percentages reported for post-secondary enrollment, part-time and full-time employment do not equal 100% as these variables were not included in final, multivariate analyses and, therefore, were not considered when excluding missing cases.

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Trends of higher educational achievement among youth with non-emotional conditions translate to higher rates of productive engagement, while trends of lower educational achievement among those with emotional diagnoses translate into lower rates of productive engagement. Logistic Regression Findings Tables 3 and 4 present results for the logistic regression analyses testing relationships between disability type and both high school completion (Table 3) and productive engagement (Table 4). Exponentiated beta coefficients, or odds ratios, along with the associated standard errors and confidence intervals of each predictor are included. Predictors and covariates were entered into the models as conceptual groupings, beginning with disability diagnoses (Model A), followed by individual-level covariates (Model B), and concluding with system-level covariates (Model C), comprising the full model. High School Completion When compared to youth in the sample with no disability diagnosis, having an emotionalonly diagnosis decreased the odds of high school completion at age 21 by 14% (p < .05). Conversely, having a non-emotional diagnosis increased odds of high school completion at age 21 by 31% (see Model A, Table 3). These relationships remained stable and significant even after controlling for individual-level predictors thought to affect rates of high school completion (see Model B, Table 3). Yet, upon accounting for system-level predictors (Model C), these relationships between disability diagnoses shifted in magnitude and significance. Youth with a non-emotional diagnosis retained only 15% greater odds of high school completion relative to youth with no diagnosis, and this relationship no longer reached (or trended toward) significance. While odds of youth with an emotional-only diagnosis completing high school remained stable

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across Models A, B and C, this previously significant relationship merely trended toward significance within the full model (p < .1). Full Model. Disability type was not a significant predictor of high school completion at the designated alpha level (p < .05). Yet, all disability types trended in the directions highlighted within bivariate analyses: emotional diagnoses were associated with lower odds of high school completion and non-emotional diagnoses were associated with higher odds of high school completion. Other individual-level covariates within the model reached significance, including gender, where females demonstrated 17% increased odds of high school completion over males (p < .01), as well as race and ethnicity, where Hispanics experienced 16% decreased odds of high school completion relative to Whites (p < .05). Additionally, four system-level covariates were significantly associated with high school completion. Youth receiving special education services had 31% lower odds of high school completion than those who did not receive or did not report special education service receipt (p < .001). Placement instability was significantly linked to high school completion, where each additional placement change decreased odds of completion by 3% (p < .001). Youth in foster care at age 19 experienced 2 times higher odds of high school completion at age 21 as compared to youth who were no longer in care at 19 (p < .001). Chafeefunded IL service receipt was also associated with increased high school completion, where receiving any service between ages 14 and 21 increased odds of high school completion by 51% (p < .001). Neither health insurance nor number of removals from the home were significant predictors of high school completion within this sample.

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Table 3. Logistic regression analysis examining effect of disability diagnosis type on high school completion. OR Individual Factors Disability Diagnosis Emotional ONLY Emotional PLUS NON-Emotional No Diagnosis (ref.) Gender Female Male (reference) Race/Ethnicity Black Hispanic/Latino Other White (ref.) Age (months) System Factors Special Education Receiving service No service/Not reported (ref.) Number of Placement Changes Health Insurance Medicaid/Other Foster Care at 19 Receiving care IL Service Receipt (2011-2018) Received ANY service Constant Log Likelihood Log Likelihood x2 Nagelkerke R2 0.4%

0.86* 0.89 1.31** ---

MODEL A SE CI (95%) 0.07 0.08 0.10 ---

0.75 – 0.99 0.75 – 1.05 1.07 – 1.60 ---

OR

MODEL B SE CI (95%)

OR

0.86* 0.90 1.31* ---

0.07 0.08 0.11 ---

0.75 – 0.99 0.76 – 1.06 1.06– 1.60 ---

0.93 0.94 1.14 ---

0.08 0.09 0.11 ---

0.80 – 1.07 0.79 – 1.12 0.93 – 1.41 ---

1.23**

0.06

1.09 – 1.37

1.17**

0.06

1.04 – 1.32

---

---

---

---

0.07 0.07 0.11 --0.01

0.81 – 1.07 0.85 – 1.17 0.79 – 1.21 --0.97 – 1.01

0.88 0.84* 0.93 --1.01

0.07 0.08 0.11 --0.01

0.76 – 1.01 0.72 – 0.99 0.75 – 1.16 --0.99 – 1.03

0.69*** --0.97***

0.07 --0.01

0.60 – 0.79 --0.96 – 0.98

1.05

0.11

0.84 – 1.30

2.00***

0.07

1.74 – 2.29

1.51*** 0.39 -7090.41 239.60***

0.09 2.99

1.27 – 1.79

--0.94 1.01 0.97 --0.99

3.84*** -7308.48 16.26**

0.04

53.46 -7294.26 30.48*** 0.7%

MODEL C (Full Model) SE CI (95%)

---

2.94 5.1%

Note. ~p < .1, *p < .05; **p < .01; ***p < .001

Productive Engagement When compared to youth in the sample with no diagnosis of disability, having emotionalonly or emotional-plus diagnoses decreased the odds of productive engagement at age 21 by 23% and 33%, respectively (p < .001; see Model A, Table 4). Youth with a non-emotional diagnosis experienced no difference in odds of productive engagement from those without a diagnosis in this sample. The significance and directions of the associations between disability diagnosis type and productive engagement remained constant across Models B and C, yet the magnitude of

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disadvantage imparted by emotional-only and emotional-plus diagnoses lessened after accounting for individual- and system-level covariates. Full Model. As noted in Model C (Table 4), both emotional-only and emotional-plus diagnoses were associated with decreased odds of productive engagement at age 21. Youth with an emotional-only diagnosis experienced 15% lower odds of productive engagement than their peers without disability diagnoses (p < .05), while youth with an emotional-plus diagnosis experienced 27% lower odds of productive engagement than youth without diagnoses (p < .001). Youth with a non-emotional diagnosis demonstrated no significant differences in productive engagement, yet trended toward lowered productive engagement relative to their peers without diagnoses (p < .1). Unlike the previous model predicting high school completion, race and gender were not significant predictors of productive engagement. However, age reached significance, but predicted little substantive difference in outcomes (one month increase in age was associated with 2% decreased odds of productive engagement, p < .05). Just as within the model predicting high school completion, system-level covariates reaching significance included special education receipt, foster care enrollment at age 19, and IL service receipt. Again, receipt of any special education services between 2011-2018 was associated with 47% lowered odds of productive engagement at age 21 compared to those not receiving or not reporting special education services (p < .001). Placement instability significantly predicted high school completion, where each additional placement change decreased odds of completion by 3% (p < .001). Both foster care enrollment (age 19) and IL service receipt were associated with over 50% increased odds of productive engagement at age 21 relative to those not in foster care at age 19 and those not receiving IL services between 2011-2018 (p < .001).

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Table 4. Logistic regression analysis examining effect of disability diagnosis type on productive engagement. OR Individual Factors Disability Diagnosis Emotional ONLY Emotional PLUS NON-Emotional No Diagnosis (ref.) Gender Female Male (ref.) Race/Ethnicity Black Hispanic/Latino Other White (ref.) Age (months) System Factors Special Education Receiving service No service/Not reported (ref.) Number of Placement Changes Health Insurance Medicaid/Other Foster Care at 19 Receiving care IL Service Receipt (2011-2018) Received ANY service Constant Log Likelihood Log Likelihood x2 Nagelkerke R2

0.77*** 0.67*** 0.96 ---

MODEL A SE CI (95%) 0.06 0.07 0.08 ---

0.68 – 0.87 0.58 – 0.77 0.82 – 1.13 ---

OR

MODEL C (Full Model) SE CI (95%)

0.06 0.07 0.09 ---

0.69 – 0.88 0.57 – 0.75 0.76 – 1.06 ---

0.85* 0.73*** 0.85~ ---

0.07 0.08 0.09 ---

0.74 – 0.96 0.63 – 0.85 0.72 – 1.10 ---

1.08

0.05

0.98 – 1.19

0.99

0.05

0.89 – 1.10

---

---

---

---

0.06 0.07 0.09 --0.01

0.93 – 1.19 1.14 – 1.49 0.80 – 1.15 --0.94 – 0.98

1.00 1.11 0.91 --0.98*

0.06 0.07 0.10 --0.01

0.88 – 1.13 0.97 – 1.29 0.76 – 1.10 --0.96 – 0.99

0.53*** --0.98***

0.06 --0.00

0.47 – 0.60 --0.97 – 0.99

1.17

0.10

0.97 – 1.41

1.58***

0.06

1.41 – 1.77

0.08 2.59

1.35 – 1.84

1.05 1.30*** 0.96 --0.96***

0.04

OR

0.78*** 0.66*** 0.90~ ---

---

2.19*** -9099.99 40.62*** 0.8%

MODEL B SE CI (95%)

37951.82 -9063.13 77.48*** 1.5%

2.53

---

1.58*** 468.85* -8833.75 305.56*** 5.8%

Note. ~p < .1, *p < .05; **p < .01; ***p < .001

DISCUSSION Prevalence of Disability among Youth Aging Out Basic yet important information regarding the prevalence of disability among older foster youth emerged from this research: Of the 7,117 youth surveyed at age 21, 3,403 (48%) were identified as having at least one disability diagnosis at age 17. To provide context, a recent study documenting the prevalence of disability within the foster care system, as a whole, showed that fewer youth (32%) were diagnosed with a disability (Slayter, 2016). These data, when viewed alongside data presented herein, lend additional support to the idea that youth with disabilities are less likely to achieve permanency within the child welfare system; hence, leading to

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overrepresentation of youth with disabilities aging out (48% of 17-year-old youth in foster care, relative to 32% of all youth in foster care). In light of the poor educational and employment outcomes experienced by youth with disabilities aging out of care, this overrepresentation raises concern from practice and policy perspectives. Yet, findings relating to disability as an homogeneous concept (assuming similarity among all with “disability status”) are only a first step toward understanding the issues faced by youth aging out with disabilities. One of the most striking findings in this study was the excellent performance of youth with non-emotional disability diagnoses across all transitional outcomes. Youth with physical, sensory, mental, or other medical diagnoses (and no DSM/emotional diagnosis) in this sample completed high school and enrolled in college at rates higher than their peers without disabilities. While youth without disability diagnoses also excelled, youth with emotional disabilities fell behind their peers without disability diagnosis across all transitional outcomes. These findings reveal the importance of examining disability as a multi-faceted measure, as opposed to a singular class. Additional results reveal that youth with disability diagnoses generally receive services at equal or higher rates relative to their peers without disability diagnoses—similar to findings from a previous study on IL service receipt among youth in care (Okpych, 2015). Furthermore, the current data show that youth with non-emotional disabilities (which may be more visible) receive services at a rate slightly higher than their peers with emotional disabilities or without disabilities. The only exception is with special education, where youth with an emotional diagnosis plus another diagnosis receive the highest rate of services (44%).

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Educational and Employment Outcomes Examination of disability by diagnosis type (i.e., emotional-only, emotional-plus, and non-emotional) in a multivariate context supported findings of disadvantage among older foster youth with emotional diagnoses—particularly relating to productive engagement at age 21. The findings of poorer educational and employment outcomes among youth with emotional diagnoses are consistent with prior research. Wagner et al (2005) found that general population youth with emotional disturbances were among the most likely of youth with disabilities to leave high school without a degree and also less likely than their peers with disabilities to report college enrollment and employment. Similarly, prior research has documented decreased odds of high school completion (Mares & Kroner, 2011) and challenges with independent living (Leathers & Testa, 2006) among youth aging out of foster care with mental health conditions—a group which may be closely aligned with the youth diagnosed with emotional disabilities (emotional-only and emotional-plus) in the current study. Turning to system-level factors, it is important to note that youth who were engaged in foster care at age 19 and who had received at least one IL service fared better in educational and employment outcomes than those who had left care or not received IL services. These findings converge with evidence from the CalYOUTH study, where youth who remained in care longer were more likely to graduate high school and enroll in college, and also reported enhanced financial security (Courtney et al., 2018). Further, in a national sample of youth leaving care, Kim et al. (2019) found that increases in IL service receipt were associated with increased odds of high school graduation (25%), post-secondary enrollment (20%) and full-time work (24%). While foster care engagement and IL service receipt supported positive youth outcomes, it is troubling to note that special education enrollment was associated with decreased odds of

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educational and employment achievement. Geenen and Powers (2006) report that receipt of special education among youth in care is plagued by inconsistencies and challenges, often attributed to the silo-like structure of the current service delivery system—observations which echo within the policy literature (Hill, 2009). The current study lends support to these concerns about agencies’ abilities to effectively collaborate toward transition planning for youth in care receiving special education services. Limitations In order to interpret the findings accurately, several limitations must be considered. First, the analyses account for only a small fraction of the variance in outcomes, suggesting the omission of variables relevant to predicting education and employment of foster youth with disabilities (e.g., disability severity, receipt of individualized education plan, receipt of Social Security Income, etc.). Furthermore, future research should consider follow-up beyond age 21 where post-secondary education and employment can be continually assessed. Third, the data utilized within this study are not without limitations: While the NYTD data provide national information about youth leaving foster care, these data are not nationally representative. Also, the NYTD systematically excluded youth residing in psychiatric settings, youth residing in any type of delinquency program, as well as tribal youth not receiving any Title IV-E funded services (Blackeslee et al., 2013), challenging the generalizability of these findings. Finally, because NYTD data were self-reported, the validity of the data is questionable. The AFCARS data, which provide broad disability diagnoses, do not fully capture the heterogeneity of youth aging out with disabilities, as no measures of disability severity are reported. Because a sizeable number of youth had “not yet received a diagnosis” within the AFCARS (n = 544), disability prevalence estimates are likely conservative. Additionally, because youths’ disabilities are often

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confused or overlooked within the child welfare system (Lightfoot & LaLiberte, 2006; Shannon & Agorastou, 2006), disabilities may be inconsistently reported or underreported within AFCARS. Yet, despite these limitations, these national databases provide the best available source of information necessary to advance knowledge of older foster youth with disabilities—a group rarely studied (Blackeslee et al., 2013). Implications Findings from this study suggest that disability remains an issue of concern when promoting positive transitions into adulthood. In particular, youth with any emotional (i.e., DSM) diagnosis within the child welfare system appear to be at risk of falling behind during their transition into adulthood. These inter-diagnostic differences emphasize the importance of operationalizing the experience of disability as more nuanced than often-used dichotomous representations permit. While the AFCARS data provide broad diagnostic categorizations for youth, future research should attend to more specific diagnoses and their effects on foster youth’s academic and employment outcomes (see Okpych & Courtney, 2018). Cross-system data sharing is one avenue by which to facilitate these lines of inquiries (see Carson et al., 2010). Youth with disabilities, as a whole, showed more favorable outcomes in high school as compared to productive engagement beyond high school. This trend may be attributable to benefits conferred from IL services and continued engagement with the foster care system, which have been shown to promote high school graduation and college enrollment (Kim et al., 2019; Okpych & Courtney, 2019). As demonstrated in Table 3, disparities in high school completion diminish for those with disability diagnoses after controlling for special education, IL service receipt and continued engagement in foster care, suggesting that these service-based factors may be influential in helping these youth complete high school. However, these trends did not extend

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to productive engagement, leading one to question whether services are more effectively targeting high school completion as compared to post-secondary enrollment and employment. Emerging evidence suggests gains in post-secondary education and employment are associated with continued participation in extended foster care (Courtney et al., 2018) and specific IL services and supports (Kim et al., 2019). Yet, additional research is needed to confirm these promising findings. Furthermore, given the marginal rates of participation in extended foster care documented in this study (37% at age 19 and 18% at age 21), research attending to the reasons why youth choose to opt-in or opt-out of extended care is needed to advance our ability to ensure youth receive these valuable supports. Disparities in productive engagement among youth with emotional disability diagnoses in this study should amplify growing concerns about the challenges faced by foster youth in the transition to adulthood as a result of trauma and poor mental health. As highlighted by Morton (2018), few studies directly address that role of trauma for youth after care. Yet, those that do suggest links between trauma-related mental health diagnoses and later hardship in postsecondary educational environments (Morton, 2018; Okpych & Courtney, 2018). As noted by Salazar and colleagues (2013), one-third of youth leaving foster care report experiencing their most serious traumas around age 16—in most cases, just before leaving care. It is therefore unsurprising that, when making the transition away from a system of care intended to provide space and support for healing, youth with emotional disabilities (and unresolved trauma) struggle to succeed. Leaving care may jeopardize youth’s access to health insurance and healthcare, including mental health services (McMillen & Raghavan, 2009), and may shift the burden of advocacy for services and accommodations from the foster care system to the youth, themselves. Whether due to costs of care, stigma associated with mental healthcare (Brannan & Heflinger,

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2006; Mukolo et al., 2010), or hesitancy to reach out for help (Okpych & Courtney, 2018), many youth with emotional disabilities will find this new responsibility of self-care to be a significant, and in some cases, insurmountable challenge. Given the unique challenges faced by youth with emotional disabilities after care, researchers and policymakers must identify mechanisms through which to normalize continued access to healthcare and other supportive services well beyond youth’s high school graduation. Findings of disadvantage among youth with emotional disabilities across domains of higher education and employment are also concerning when viewed in the context of federal legislation aimed to advance the rights of individuals with disabilities. While great strides have been made in providing educational and workplace accommodations for individuals with disabilities (e.g. IDEA, 2004; ADA, 1990), it appears that these accommodations have favored individuals with visible non-emotional disabilities over often invisible emotional disabilities (Stefan, 2001). For instance, the required accommodations for an individual presenting with a physical disability that necessitates use of a wheelchair are straightforward: physical access to the structural environment of school or work through ramps, automated doorways, etc. In contrast, the required accommodations for an individual with an emotional disability that limits their social interactions are less obvious. Educators and employers may struggle to identify appropriate accommodations for these individuals; or worse yet, the adult services needed by students or employees with emotional disabilities may be hard to access or non-existent. In Morton’s recent qualitative study of barriers faced by former foster youth in college (2018), numerous students discussed mental health needs that went unaddressed due to limitations of student health insurance coverage or long waiting lists for counseling services on campus. Ambiguity and unavailability of mental health accommodations for individuals with emotional

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disabilities may limit opportunities for a substantial population of youth leaving care. Hence, policies promoting awareness of mental health challenges in addition to policies providing financial supports or incentives to making mental health services available and accessible to youth aging out will be key to addressing disparities among former foster youth in higher education and employment. Findings relating to the general prevalence of disability among youth aging out also require the attention of practitioners who interact with this sizeable population of youth. Child welfare caseworkers, independent living coordinators, and specialized service providers undoubtedly interface with these youth throughout their journey from foster care toward independence. Yet, few of these professionals receive explicit training regarding strategies to best engage youth with disabilities (see Baladerian, 2006). Beyond the prevalence of disability among youth aging out, there are significant practice implications relating to the prevalence of emotional disability among these youth. Because 37% of youth in this study (and 76% of youth with a disability in this study) report emotional disability diagnoses (alone or in combination with other diagnoses), specific understanding of these conditions should be considered requisite knowledge for culturally competent practice among child welfare and independent living professionals (National Council on Disability, 2008). In the past decade, child welfare systems have recognized the need to attend to the traumas experienced by youth in care, and conversations about the impact of trauma after leaving care are emerging (see Morton, 2018; Okpych & Courtney, 2018; Salazar et al., 2014). Yet, trauma-informed care is only a starting point to a longer system-wide conversation about ways to ensure equal opportunity among foster youth with emotional disabilities or mental health conditions. As a part of the discourse, attention to emerging interventions addressing trauma and

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related mental health needs of former foster youth will be critical. One promising model, Better Futures, specifically aims to improve post-secondary educational outcomes among youth aging out with mental health issues (Phillips et al., 2015). Another program, entitled My Life, incorporates self-determination skills as a mechanism to support positive transitions among youth with disabilities aging out of foster care (Geenen et al., 2013). By incorporating knowledge of disabilities into service provision, youth may receive more appropriately tailored services that enable them to thrive during their transitions out of care toward adulthood. Conclusion Recognizing a gap in knowledge regarding youth exiting foster care, this study provides information about the prevalence and implications of disability diagnoses within a national sample of foster youth transitioning into adulthood. Conservatively estimated, youth with disabilities comprise 48% of youth preparing to leave care, and 76% of these youth with disabilities identify with an emotional disability (or 37% within the total sample of youth). Findings show that, at age 21, foster youth with emotional disabilities fare poorly during the post-secondary transition (college and employment) when compared to peers with non-emotional disabilities and peers without disabilities. These findings emphasize the need for additional services and supports to facilitate transitions to college and employment for youth with emotional challenges as they exit the foster care system. Additionally, this study highlights the importance of examining disability in a more granular way than data often allow. By recognizing the heterogeneity of this sizeable population of older youth with disabilities in foster care, along with disadvantages experienced by youth with emotional disability diagnoses, practices and policies may be more effectively developed and tailored to meet the needs of youth as they transition from care into adulthood.

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ACKNOWLEDGEMENTS Special thanks to the National Data Archive on Child Abuse and Neglect for access to and assistance with the NYTD and AFCARS data, as well as the Doris Duke Charitable Foundation Fellowship Program for the Promotion of Child Well-being for the generous funding and support of the dissertation which served as a foundation of this study.

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Author Statement Leah Cheatham: Conceptualization, Methodology, Formal Analysis, Investigation, Resources, Data Curation, Writing – Original Draft, Writing – Review & Editing, Supervision, Project Administration, and Funding Acquisition. Karen Randolph: Resources, Writing – Review & Editing, Supervision, Project Administration, and Funding Acquisition. Laura Boltz: Resources and Writing – Review & Editing.

YOUTH WITH DISABILITIES TRANSITIONING FROM FOSTER CARE Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

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