Identifying Transition-Age Youth with Disabilities Using Existing Surveys. July 10, Todd Honeycutt David Wittenburg

Transcription

1 Identifying Transition-Age Youth with Disabilities Using Existing Surveys July 10, 2012 Todd Honeycutt David Wittenburg

2 Subcontract Number: A; T&M Mathematica Reference Number: Submitted to: National Institute on Disability and Rehabilitation Research U.S. Department of Education 400 Maryland Ave., SW Mailstop PCP-6038 Washington, DC Project Officer: David Keer Identifying Transition-Age Youth with Disabilities Using Existing Surveys July 10, 2012 Todd Honeycutt David Wittenburg Submitted by: Mathematica Policy Research P.O. Box 2393 Princeton, NJ Telephone: (609) Facsimile: (609) Project Director: Debra Wright

3 This study was supported by the National Institute on Disability and Rehabilitation Research, U.S. Department of Education, through its Rehabilitation Research and Training Center on Disability Statistics and Demographics grant to Hunter College, CUNY (Grant No. H133B A).

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5 Identifying Transition-age Youth with Disabilities Using Existing Surveys Mathematica Policy Research IDENTIFYING TRANSITION-AGE YOUTH WITH DISABILITIES USING EXISTING SURVEYS Vocational, educational, and social transitions from youth to adulthood can be difficult for many young adults because of issues related to separating from parents, developing autonomy, increasing responsibilities, and moving from one set of institutions to another. Youth with disabilities face additional barriers in this transition period which we define as occurring between ages 16 and 24 because of potential issues with accessible environments and medical issues. Additionally, many youth in this group are moving into an adult-based service delivery system that can influence their ability to obtain education, vocational training, and accommodations (for example, availability of specialized school services such as special education) (Davies, Rupp, & Wittenburg, 2009); health care as a result of the transition from pediatric to adult health care (Field & Jette, 2007); and income supports (Wittenburg & Loprest, 2007). This transition period is important because the choices made during it, particularly around education, employment, and social activities, can have important lifelong implications. A key problem in examining issues facing transition-age youth with disabilities is a lack of consistent data for this population, particularly regarding outcomes. Several surveys have information on transitioning youth, but the availability and content of questions related to disability status, including health, degree of impairment, and functional status, vary substantially. While several studies on subgroups of youth with disabilities (for example, those receiving special education) are available, the lack of comparative data creates challenges in tracking how the broader population of youth with disabilities is faring, especially compared to youth without disabilities. Researchers have identified a need for more complete data and analysis using a unified framework (see, for example, Halfon et al., 2012). This paper provides a framework to identify definitions of disability within existing surveys for the purpose of producing statistics on the characteristics and outcomes of transition-age youth with disabilities. This framework applies the International Classification of Functioning, Disability and Health (ICF) to statistics for this population. The primary advantage of the ICF conceptualization of disability is that it allows for a systematic and comprehensive review of statistics on transition-age youth from available surveys. Using this framework, we present and contrast prevalence statistics from eight publicly available surveys, examine the overlapping of different definitions of disability, and show how prevalence rates of youth with disabilities differ by residence type and by geographic area. Our framework builds on a similar application of the ICF model conducted to produce statistics for adults with disabilities (Weathers, 2009). By adopting a similar approach, the findings for youth can eventually be compared to prevalence and employment rates of adults with disabilities, including official prevalence and employment for adults with disabilities from the Bureau of Labor Statistics. This paper fills existing gaps in the literature by exploring prevalence rates based on different definitions of disability, and it capitalizes on recent advances in improving and standardizing survey questions regarding disabilities. In addition, our statistics illustrate how different definitions of disability influence disability prevalence rates and how those rates vary across key subgroups, such as state of residence and institutional living. The surveys we reviewed include several potential identifiers of disability status that have important implications for estimates of disability prevalence among transition-age youth. One key finding is that the prevalence of youth-based impairments, such as learning disabilities, is not 1

6 Identifying Transition-age Youth with Disabilities Using Existing Surveys Mathematica Policy Research explicitly included in adult definitions, which has important implications for thinking about a youth s transition into adulthood. In part, this might explain why broad definitions of child disability, which include a mix of adult- and child-based impairments, vary substantially in prevalence. For example, applying a broad assessment that includes impairments, activity limitations, and participation restrictions to nationally representative population surveys, our estimates of those youth who have any disability range from 5 percent to 11 percent. When we include an additional disability category capturing youth with special need indicators such as those involved with special education or who have special health needs in the survey with the most detailed disability questions, the prevalence increases by 2.5 percentage points (from 11.3 percent to 13.8 percent). In general, the prevalence rates for definitions that are commonly measured for youth and adults, such as limitations in activities of daily living, are substantially lower among youth, which is to be expected given the prevalence of these conditions increases with age. We also find that youth with disabilities are overrepresented in institutional group quarters (such as adult and juvenile detention facilities and residential schools); about one in four youths in such facilities has a disability. Despite this overrepresentation, however, including this subgroup in estimates of disability prevalence has little affect on those estimates, as the actual size of that subgroup relative to the overall population of youth is small. This paper is structured as follows. Section I presents the issues involved in defining disability for transition-age youth. Section II reviews publicly available surveys and their disability measures and assesses the advantages and disadvantages of those surveys. Sections III and IV present our methods for this paper and our results on the prevalence of transition-age youth with disabilities. Section V concludes with a review of our findings, comments on the study s strengths and limitations, and a discussion of the implications for policy involving transition-age youth with disabilities. I. Disability Definitions for Transition-Age Youth The published literature includes several estimates of disability prevalence for transition-age youth that vary substantially across data sources. In general population surveys, such as the Survey of Income and Program Participation (SIPP), the prevalence rate of work limitations is approximately 3 percent for youth ages 18 to 24 (Wittenburg & Nelson, 2006). However, when disability status is defined by other limitations, such as instrumental activities of daily living, the prevalence rate increases threefold to approximately 9 percent for the same age group. In surveys that include child-specific conditions, such as the National Health Interview Survey (NHIS), which asks about limitations in usual activities due to chronic conditions, the prevalence rate is approximately 8 percent (Halfon et al., 2012). In surveys that assess the use of special services (for example, an accommodation or the use of a special health care service), the prevalence rates are much higher, though they vary by source. For example, one study found that special health care needs among youth ages 12 to 17 varied from 16 percent in the National Survey of Children with Special Health Care Needs (NS-CSHCN) to 24 percent in the Medical Expenditures Panel Survey (MEPS) (Bethell, Read, Blumberg, & Newacheck, 2008). Finally, substantial variation also exists in the number of youth with disabilities who participate in programs and services. For example, in 2005, over 6 million individuals ages 6 to 21 (9.1 percent of the total population in that age range) received special education services under the Individuals with Disabilities Education Act (IDEA) (U.S. Department of Education, 2010), which is substantially more than the 1.1 million youth under the age of 18 who in that same year received Supplemental Security Income (SSI), a means-tested income support for youth with disabilities who have significant limitations (Social Security Administration, 2011). 2

7 Identifying Transition-age Youth with Disabilities Using Existing Surveys Mathematica Policy Research We use the framework presented in Weathers (2009) as a starting point for conceptualizing data about disability for youth. Weathers applies the disability measures contained in five public surveys to the ICF classifications (World Health Organization, 2001) to calculate and compare statistics for working-age adults with disabilities. Using this framework, Weathers found that statistics for the working-age population differ widely because of variations in survey designs; differences in the wording and number of questions addressing similar concepts; and variations in the type, duration, and severity of health conditions identified as representing a disability. The ICF model posits that disability is a function of one s health, environment, and personal factors (see the left portion of Figure 1). Specifically, a health condition may result in a disability through an impairment that affects one s body structure or function, an activity limitation that affects one s ability to take care of oneself, or a participation limitation that affects one s ability to engage socially. For any individual, a condition may result in a disability in one or more areas. For example, a physical impairment could also result in activity limitations in dressing oneself as well as participation limitations in working, but it does not necessarily do so. Environmental and personal factors can exacerbate a disability or eliminate it altogether. For example, an individual with a learning disability may have a disability as measured by impairment, but the condition may not affect her ability in other activities such as work because of appropriate environmental supports. We adapt Weathers s approach to disability statistics by expanding the participation restrictions to include youth-specific items, such as youth-specific conditions, and adding another participation component special need indicators to account for the roles youth play and the supports to which they have access (right side of Figure 1). This framework for defining youth disability is flexible enough to cast a broad net and contrast different definitions, thereby allowing us to see that some variables used in surveys to measure disability only show a part of the total picture. This framework shares many similarities with the one used recently by Halfon et al. (2012), who developed a disability framework to identify data collection needs to measure statistics for youth with disabilities. They defined disability as an environmentally contextualized health-related limitation in a child s existing or emergent capacity to perform developmentally appropriate activities and participate, as desired in society (pp. 32). The definitions we propose below share similarities with the Halfon et al. definition, though our definitions seek to use current definitions based on specific survey concepts to examine the potential sources in variations across surveys. Definitions of disability for youth may vary from those used for adults as measured in surveys in several key areas: Daily Participation Activities. Several surveys include information on youth-specific activities that represent important aspects of the youths environment such as schooling. By comparison, work participation is commonly used for identifying adults with disabilities because of the important role of work in adults lives. That social activity might not be as important to youth, however. By adding these youth-based definitions, we can examine other possible limitations that youth might have before entering adult life. Youth-Specific Conditions. Some surveys, particularly those focused on youth, include information on conditions found more frequently among youth than among adults. For example, learning disabilities and attention-deficit hyperactivity disorder are major conditions that limit school participation of youth (Field & Jette, 2007), but these conditions may not be included in adult-oriented surveys. 3

8 Identifying Transition-age Youth with Disabilities Using Existing Surveys Mathematica Policy Research Special Need or Youth Program Indicators. Most surveys also include information on possible special need indicators for youth who need services or supports beyond those generally provided to other youth such as special education services and cash benefits from Social Security disability programs. In addition, surveys may use a special set of questions to identify children with special health care needs those young people with chronic physical, developmental, behavioral, and emotional conditions that require health and related services of a type or amount beyond that usually required by children. In Figure 1, we present special need indicators as a separate category from participation restrictions because it involves youths participation in society and also reflects the environmental supports for youth, which can affect disability measurement. These definitions may capture some youth who do not report limitations in other disability categories. For example, a youth with a learning disorder might receive special education services. This youth s condition may not interfere with her activities or participation, particularly with access to appropriate secondary school accommodations. Such a youth would likely not be identified through impairment, activity limitation, and participation restriction questions, and so is identified only by including the special education question. As we will show, using this framework furthers our ability to identify youth with disabilities in public surveys, though the studies vary in the extent to which we can include these additional components. II. Publicly Available Data that Include Transition-Age Youth with Disabilities Several options exist for producing disability statistics from publicly available longitudinal and cross-sectional surveys that include information on transition-age youth with disabilities as part of a large sample from the general population (Table 1). A major advantage of these surveys is that they allow comparisons of youth with and without disabilities. As discussed in more detail below, the amount of information on disability questions and the nature of the specific sample (for example, the entire population versus a population of youth) vary substantially across surveys. In addition, the type of data varies between the longitudinal and cross-sectional studies. In general, longitudinal surveys track individuals over time, important for those interested in tracking dynamic phenomena such as outcomes following the onset of a disability. The relevant longitudinal surveys include the MEPS; the Panel Study of Income Dynamics (PSID); the SIPP; and the National Longitudinal Survey of Youth 1997 (NLSY97), a panel of youth ages 12 to 18 at the time of their first survey. Conversely, cross-sectional surveys provide point-in-time estimates and typically have larger sample sizes than do longitudinal surveys, which can be useful in looking at subgroups of youth with disabilities such as populations by state. These surveys include the American Community Survey (ACS), Current Population Survey (CPS), the decennial census, the National Health and Nutrition Examination Survey (NHANES), the NHIS, and the National Survey of Children s Health (NSCH). Several existing studies of transition-age youth with disabilities use longitudinal or crosssectional data or both. Horvath-Rose, Stapleton, and O Day (2004) used cross-sectional estimates from the CPS to track trends in employment and other outcomes. Shandra and Hogan (2008) capitalized on the longitudinal nature of the NLTS97 to examine youth identified as having a disability in the first wave of data collection by following them for up to eight years to assess the relationship between the use of school- and work-based programs and later employment outcomes. The CDC has published detailed information on disability from the NHIS for youth ages 5 to 17 4

9 Identifying Transition-age Youth with Disabilities Using Existing Surveys Mathematica Policy Research across a range of demographic and health care characteristics (Pastor, Reuben, & Loeb, 2009), and Halfon et al. (2012) use the NHIS data in their treatment of childhood disability trends and issues. As shown in Table 1, each of these surveys has its advantages and disadvantages for producing disability statistics. For example, while the SIPP tracks individuals for up to four years and has one of the most comprehensive sets of disability measures, those measures are only included periodically, and the sample size can be small, particularly for youth and for certain definitions of disability. We provide more details about each source, including the sponsor and sample size, in Appendices A.1 (longitudinal surveys) and A.2 (cross-sectional surveys). An alternative to surveys that study the general population are those that provide a more indepth look at youth with disabilities and their circumstances. A major advantage of these surveys is that they often provide in-depth information on the characteristics or outcomes of a specific group, such as youth in special education programs. However, a drawback is that these surveys do not include samples of youth without disabilities that could be used in comparisons. The National Longitudinal Transition Survey 2 (NLTS2) tracks for 10 years a population of youth ages 13 to 16 in 2000 who received special education services. The National Survey of Children with Special Health Care Needs (NS-CSHCN) is a cross-sectional survey of households that include children with special health care needs; it focuses on health care access and use. Finally, the National Survey of SSI Children and Families (NSCF) includes information on the experiences of current and former recipients of children s SSI benefits. An example of research using these sources concerns the longterm outcomes for youth receiving SSI benefits, which linked data from the NSCF to SSA administrative data (Hemmeter, Kauff, & Wittenburg, 2009). More information about these sources can be found in Appendix A.3. In Table 2, we summarize the disability-related content in all these surveys according to the four major categories shown in Figure 1: Impairments. All surveys include questions on impairments, but they differ widely in the number of indicators, the specific impairments, and the duration of the condition, particularly regarding physical and mental impairments. For example, as shown in Appendices B.2 and B.3, the NSCH includes very detailed information on certain physical and mental impairment indicators specific to youth, such as asthma and learning disabilities, and the SIPP has 10 items on physical activity and addresses several mental conditions, such as mental retardation and learning disabilities. In contrast, the ACS and CPS which are frequently used for disability research and official disability statistics only include relatively broad indicators of whether a person has a physical or mental condition. While there are some similarities in sensory condition questions across surveys (see Appendix B.1), some studies include important qualifiers that affect prevalence rates. For example, while most surveys ask about current difficulties with seeing, hearing, or speaking and include qualifiers such as even when wearing glasses, the NLSY97 asks if a person ever had trouble with these functions a difference in both duration and severity that is broadly inclusive. The NLSY97 could therefore include those whose vision is 20/20 with eyeglasses and those who required speech therapy in elementary school but had no problems afterward. For these reasons, some of the data in this study must be qualified or the disability prevalence statistics will be inflated. Activity Limitations. Almost all surveys include measures of activity limitations. As with impairments, the ACS and CPS use just one broad question, asking about difficulty dressing or bathing (see Appendix B.4). Other surveys, such as the SIPP and NHIS, 5

10 Identifying Transition-age Youth with Disabilities Using Existing Surveys Mathematica Policy Research include a more detailed list of activity measures such as getting around inside the house and toileting. Though not indicated in Table 2, surveys also differ in the degree of difficulty the subject experiences in this area, with some asking about needing assistance for the activity in addition to just having difficulty. Participation Limitations. Surveys contain several measures related to four types of participation: (1) functional limitations such as difficulty doing errands outside the home or preparing one s own meals; (2) work limitations; (3) school limitations; and (4) other participation limitations such as housework or play. Almost all surveys include measures of functional limitations though they differ in whether they assess these limitations with one item (the ACS, CPS, and NLTS2 all ask about doing errands or getting to places outside the home) or with multiple items (the SIPP asks about tasks such as preparing meals and keeping track of money). Most surveys include a work limitation question: the SIPP asks about having a condition that limits the kind or amount of work; the CPS asks about having a condition that prevents work or limits the kind or amount of work ; and the NSCF asks about a condition preventing work or school. However, the ACS dropped its work limitation question after 2007, and two of the three disability-specific surveys (the NLTS2 and the NS-CSHCN) do not ask about work limitations. School and other limitation questions are less frequent. Though both of these areas are important for youth, only the MEPS, SIPP and NSCH include questions in both of them. Special Need and Program Indicators. Surveys have a variety of special need and program indicators, but these often vary by survey. Only four surveys include questions regarding special health needs (see the questions for the NSCH and NSCF listed in Appendix B.6). Six surveys include measures on special education, but they differ on whether they ask about current involvement or involvement at any time in the subject s life. Most surveys include questions on SSI and SSDI, though even this is not consistently measured across sources (the NLSY97 combines SSI with other welfare income sources) or may apply to anyone in the household receiving such income, rather than to the individual youth. Finally, only one survey the NSCF includes measures on special needs and program characteristics. Some survey questions overlap these categories, which makes cross-survey comparisons challenging for certain definitions. For example, the NLSY97 asks about mental or emotional conditions that limit school attendance, schoolwork, or paid employment. Asking only about specific participation limitations is very different than the approach other surveys use to identify mental impairments; it even differs from the approach the NLSY97 uses for sensory and physical conditions. Another example is with the NSCF, which asks about work and school limitations in the same question; though both are measures of participation limitation, we cannot assess each component separately since the question conflates the two kinds of limitations. As indicated by Tables 1 and 2, each survey answers different kinds of questions. As a result, several factors should be considered in selecting a data source. First, survey questions may depend on a broad or specific definition of disability. For example, the ACS provides a broad definition, but its data may not capture all youth with activity limitations other than in dressing and bathing and may not identify all youth receiving special education services. Second, survey questions do not always include youth of a specific age range. For example, the NSCH and NS-CSHCN include only those 17 years old and younger. Third, surveys vary in the extent to which one can observe annual trends, individual changes, or state estimates (see Appendices A.1 through A.3). For example, the CPS includes several cross-sections that can be used to analyze historical trends but changes in 6

11 Identifying Transition-age Youth with Disabilities Using Existing Surveys Mathematica Policy Research question content limit the ability to do so with the ACS; the ACS includes large samples that can be used to produce state and county level estimates but none of the longitudinal studies do. A final issue regarding survey choice is that each includes different kinds of outcomes, largely related to its intended use (Table 3). We list broadly whether a survey includes any measures on three outcomes human capital development (such as employment and education), at-risk outcomes (such as involvement with the criminal justice system or unmarried pregnancy), and other outcomes relevant for youth (such as social skills and health access). Those interested in specific types of outcomes may be limited in their survey choice, and therefore the disability definitions to which they have access. III. Method We selected eight surveys with sufficiently large samples to produce estimates for key subgroups of youth with disabilities and with the ability to address our research questions and illustrate a specific issue or topic relevant to disability statistics for transition-age youth. 1 As outlined earlier, several data sources provide promising options for producing statistics on youth outcomes. However, to provide comparisons that can be readily understood by a broad policy audience, we focused on those data sources that have been most commonly used in previous disability-related studies for adults and youth. As a starting point, we selected the ACS, CPS, NHIS, and SIPP, all of which were used by Weathers (2009). This provides a straightforward framework for researchers interested in comparing our estimates for transition-age youth to existing published estimates for adults. We then included four data sources commonly used in studies of youth with and without disabilities: the NLSY97, NLTS2, NSCF, and NSCH. While we also reviewed other data sources that could produce disability statistics, we did not attempt to include them. 2 For the NHIS, NLTS2, and NSCF, we used results from existing reports (NHIS, 2011; NLTS2: Wagner, Newman, Cameto, & Levine, 2006; Wagner et al., 2003a; Wagner et al., 2003b; NSCF: Wittenburg & Loprest, 2007) rather than conduct our own analyses of the data; this approach allowed us to expand the number of sources included in this study. A key goal of our analysis is to understand how data on the prevalence of disability varies across data sources. This information provides important context regarding how each data source and disability definition can be used in research and suggests potential implications in understanding outcomes for this population. 1 Because we draw on data from several published reports, it is not possible to produce standard errors for all statistics. However, we will include 90 percent margins of error, when available, for many of the tables in the final version of this report. We also provide sample sizes for each of our statistics when available, which provides the reader additional context for the size of the survey sample. 2 We excluded the PSID, MEPS, and the NHANES from the analysis because of the potential complexity in analyzing the data from those sources and the potentially small number of youths with disabilities that can be studied based on them. Despite the rich data available from the NS-CSHCN on children with special needs, we omitted it from this analysis because of the lack of comparison with children without special needs and its limitation in terms of the ages of the population studied. 7

12 Identifying Transition-age Youth with Disabilities Using Existing Surveys Mathematica Policy Research A. Study Sample Includes Youth between 16 and 24 We focused the analysis on youth between 16 and 24, though our ability to generate estimates for the entire age range varied by survey: All ages (16 to 24): The CPS, SIPP, and ACS included youth in the full age range of interest. Ages 18 to 24: Though the NHIS includes information on people of all ages, the published results we accessed showed information for 18 to 24 year olds. Ages 17 to 22: The cohort of youth tracked by the NLSY97 is within a five-year age range; in the wave we selected for analysis (wave 6), subjects ages were between 17 and 22. Under age 18: The maximum age for youth in the NSCH is 17, so that the sample available included only 16- and 17-year-olds. Because we used previously released reports for the NLTS2 sample, we only had disability data for youth during the first wave of data collection (2002), when they were between the ages of 13 and 17. The NSCF contains two age cohorts; we selected youth ages 13 to 17, corresponding to those who were SSI beneficiaries in December 2000 (Wittenburg & Loprest, 2007). The years covered by the surveys ranged from 2001 to When multiple years were available, we used the most recent one. 3 For the NLSY97, we chose to analyze data from wave 6 (2002) because it was the first wave to have disability questions in which the cohort (17 to 22 years old) was in the desired age range. We used data from the first wave of NLST2 for reasons discussed in the last bulleted point. B. Survey Measures Matched Disability Concepts in Figure 1 To generate prevalence estimates, we matched the concepts of disability in our model to measures in each data source. We identified three impairment categories (sensory, physical, and mental), one activity limitation category, and four participation limitation categories (functional, work, school, and other). A key extension of the ICF model to youth is the inclusion of special need indicators, and we used four indicators: youth with special health care needs, youth who ever received special education services, youth receiving SSI benefits, and youth receiving SSDI benefits. These definitions have been used in previous studies to identify youth with disabilities. We realize, however, that many youth involved in special education or who have special health care needs will have less severe health issues than others. For example, many youth have asthma, but that condition need not result in disability if they have access to the right supports such as inhalers. Similarly, youth receiving special education most commonly have learning disorders, which may not greatly interfere in activities or social interactions outside school. Expanding our definition of disability using special need indicators may therefore increase prevalence statistics to include those with less severe conditions. 3 We selected two years of CPS data to improve the precision of estimates based on small samples, such as for states. 8

13 Identifying Transition-age Youth with Disabilities Using Existing Surveys Mathematica Policy Research The overall prevalence of disability among youth is thus sensitive to the measures included in or excluded from the definition; we use two overall prevalence measures for this study. The first definition includes our impairment, activity limitation, and participation limitation categories and reflects the ICF model of disability. This approach is a more typical way to identify those who have a disability. The second definition is a broader measure that includes the special need indicators along with those three categories. We present the survey questions used for our analyses for each disability definition in Appendices B.1 to B.6. C. Prevalence Estimates Based on Disability Definitions For each disability definition, we calculated the prevalence of the population identified as having that disability using cross-sectional or point-in-time disability estimates, even for longitudinal surveys. This approach allowed us to more easily contrast prevalence data across the different surveys. We show prevalence for the entire age range from each survey. Where appropriate, for each statistic, we calculated margins of error to identify the likely range of sampling error. The interval defined by a point estimate plus or minus its margin of error is the 90 percent confidence interval for the population value. For all results, we used weights for our statistics so the estimates represent national totals. Additionally, we excluded data from Puerto Rico from ACS estimates because no other survey included data from the territory. D. Special Topics: Residence, Overlapping Disability Definitions, and Variation Across States Disability prevalence tells an important part of the story for transitioning youth, but these results raise further questions about underlying issues with prevalence. We extended our analysis of prevalence by exploring issues related to residence in households and group quarters, overlapping of disability definitions, and variations in prevalence by state. First, we conducted additional analyses using the ACS to show how including residents of group quarters affects overall prevalence and the extent to which transition-age youth with disabilities reside in group quarters. While all surveys provide estimates for youth that live in households, they differ in the extent to which they include individuals in noninstitutional group quarters (such as group homes and educational dormitories), and only one survey (the ACS) includes individuals in institutional group quarters (such as juvenile detention centers). We used SIPP data to examine the overlap of disability definitions across surveys. The SIPP is an ideal data source for this analysis because it contains information on at least one variable in each of the four categories of disability summarized in Figure 1. The analysis illustrates both the overall disability prevalence as well as the overlap of multiple definitions. Finally, we used the CPS to examine disability prevalence rates across states. 4 statistics only for overall disability definitions. We examine 4 Both the ACS and the CPS allow estimates for each state and the District of Columbia; we selected the CPS for this task because of the ability to obtain estimates for work limitation in addition to the other concepts shared across the two surveys. 9

14 Identifying Transition-age Youth with Disabilities Using Existing Surveys Mathematica Policy Research IV. Results Because of important differences in disability definitions and sample selection, we split our comparisons of disability prevalence from our eight selected surveys into four tables. Table 4 includes information from general population surveys that include the broad age range of transitionage youth (the ACS, CPS, NHIS, NLYS97, and SIPP). While there are important differences between these data sets, they include samples and disability definitions that are comparable within our framework. Table 5 includes information from the NLTS2 and the NSCF, which have samples of youth who participate in a program (special education or SSI) based on their disability status. Unlike the surveys in Table 4, these two studies do not include youth without disabilities for comparisons. Table 6 explores differences in demographic characteristics among youth with various disability definitions. Finally, in Table 7, we present data from the NSCH, which because it only includes information on a limited portion of transition-age youth (those ages 16 to 17) makes comparisons to other surveys limited. However, because the NSCH has many measures concerning the severity of disability, we present several comparisons within the NSCH to illustrate how prevalence changes when alternative methods are used to define severity. All surveys show data for those who live in households; the ACS also includes data from those who reside in group quarters. A. How Does Disability Prevalence Vary Across Sources and Definitions? Definitions based on impairment and activity limitations that are most similar to the definitions used to produce official disability estimates for adults in the CPS and ACS generally produce prevalence rates below 5 percent (Table 4). The specific definitions for sensory, physical, or mental impairments identify relatively small portions of transition-age youth and only a small proportion report a participation limitation regarding work. The SIPP, NHIS, and NLSY97 have broader measures of impairments than the ACS and CPS, and the SIPP includes several functional measures not available in the other surveys, which can increase the overall prevalence rates based on any disability definitions. The SIPP estimates on physical and mental impairments tend to be higher than those from the ACS and the CPS because the SIPP asks 10 questions related to physical impairments compared to one question in those two surveys; in addition, the mental impairment question in that study asks about specific conditions rather than using more general language. The NHIS, which uses a similar battery of questions as the SIPP, shows high prevalence for physical impairments. The higher prevalence rate in the NHIS relative to the SIPP is consistent with findings for adults. We speculate that health-related surveys, which focus respondents attention on health questions, tend to result in respondents answering more affirmatively when asked about potential issues. Finally, results from the NLSY97 suggest that more than one in 10 youth ages 17 to 22, has sensory or physical impairments. These high rates might be due to both the very low severity and the relatively long duration of the impairment. For example, the NLSY97 asks about having trouble seeing, hearing, or speaking rather than having serious difficulty, as in surveys like the SIPP. The NLSY97 question also asks about ever having trouble, while other surveys ask only about current difficulty. Similarly, on physical impairment, the NLSY97 asks about having deformed or missing body parts and having a diagnosis of one of nine chronic conditions; 8.4 percent of youth have asthma, which influences the high prevalence rate. Activity limitation prevalence is consistently less than 1 percent across all surveys that measure this limitation. Estimates for participation limitations vary widely by survey (Table 4). To identify functional limitations, the ACS, CPS, and NHIS have only one question (focused on going outside the home), and the prevalence ranges from 0.9 to 2.0. The SIPP, despite including a range of questions about 10

15 Identifying Transition-age Youth with Disabilities Using Existing Surveys Mathematica Policy Research functional limitations (such as keeping track of money and preparing meals), has a similar prevalence (1.4 percent). The NLSY97 has the highest prevalence, at 3.1 percent; it asks those with sensory, physical, or mental impairments if those impairments currently limit their activities. We identified those whose impairments limit them a lot as having a functional limitation. This approach to functional limitations with the NLSY97 is very broad, and likely fewer individuals would respond affirmatively to the functional limitation questions contained in other surveys. Only two surveys in Table 4 include information on work limitations. The CPS shows that 2.7 percent of youth reported having a work limitation, while the SIPP shows that 5.8 percent did. 5 Only the SIPP contains information on other kinds of participation limitation. It shows that 8 percent of youth ages 16 and 17 had a school limitation and 2 percent of youth at any age had a housework limitation. The special need indicators from the surveys included in Table 4 suggest that relatively few youth no more than 2 percent reported receiving either SSI or SSDI. Rates are somewhat higher for SSI than SSDI in most cases, which is expected given that those younger than 18 will not have a sufficient work history to qualify for SSDI (though some might qualify for such reasons as being a dependent). About one in 10 youth ages 16 to 19 had ever been involved in special education services, according to the SIPP. We identify the proportion of youth who reported any of the disability definitions in the last two rows of Table 4. The overall disability prevalence for the ACS is slightly higher than that for the CPS (6.3 percent versus 5.3 percent), largely because the prevalence rates of impairments is higher in the ACS. The SIPP estimates, at 11.3 percent, are almost twice as high as those from the ACS and CPS; the main drivers of this difference are the addition of school limitations and the higher proportion of youth reporting work limitations. Finally, the overall disability prevalence from the NLSY97 indicates that one in 5 transition-age youth have a disability; this high number reflects the large number of youth reporting sensory and physical impairments, which as noted above, is based on a very broad definition of severity. Overall prevalence increases when including the special need indicators, with special education having a large influence. For the two surveys where SSI and SSDI are the only special need indicators, the changes are modest: 6 percent (0.3 percentage points) for the CPS (perhaps because of the inclusion of the work limitation measure) and 11 percent (0.6 percentage points) in the ACS. The SIPP has special education as well as SSI and SSDI; the overall prevalence in that study increases from 11.3 to 13.8 percent. This 2.5 percentage point change likely reflects the fact that many individuals in special education or who receive disability income do not identify themselves as having a disability as defined in the surveys, as will be shown later. The increase in the overall prevalence using the NLSY97 is negligible, which reflects the large overall prevalence in the other categories and the relative small number of respondents who report SSI or other welfare income (the two measures are combined in the survey). When we look at two special need populations (special education students ages 13 to 17 and SSI recipients ages 14 to 17), four key themes emerge (Table 5). First, the special education population is much larger than the SSI population (1.8 million special education youth and 192,000 child SSI recipients), which is not surprising given that the target population for special education is much 5 The SIPP work limitation prevalence is also greater than the CPS prevalence for working-age adults (Weathers, 2009). 11

16 Identifying Transition-age Youth with Disabilities Using Existing Surveys Mathematica Policy Research larger. Second, consistent with other sources, over three-quarters of youth participating in special education and SSI had a mental impairment. However, youth with mental impairments represent very different populations across the two groups (Wagner et al., 2003a; Wittenburg & Loprest, 2007). For special education students, the majority of youth with mental impairments has learning disabilities; for the SSI population, the majority has been diagnosed with mental retardation. Third, compared to special education students, youth receiving SSI benefits have more severe disabilities, as noted by the higher prevalence of activity limitations and functional limitations. For example, 26 percent of child SSI recipients reported an activity limitation; only 1.3 percent of special education youth did. Additionally, 91 percent report a special health need (for example, help with routine needs). This finding is not unexpected given that the medical criteria that a youth must satisfy to become eligible for SSI distinguishes these youth from others who might report some other limitation in a survey or who receive special education services. Finally, the overlap between special education and SSI is quite high. About one in 8 special education students received SSI, and a majority of SSI youth about 3 of every 4 had been involved in special education. Youth identified through different disability definitions vary in their demographic and other characteristics (Table 6). In general, the gender composition is evenly split for youth ages 16 to 24, youth with any impairment, and youth who reported work limitations. However, special education and SSI participants differed in that approximately two-thirds of both groups are male. Special education and SSI participants are also less likely to be white relative to the groups with impairments and work limitations. Table 6 also shows the distribution of impairment types for the various groups of youth. By definition, youth with work limitations have a lower prevalence of impairments than youth with any impairment; however, fewer youth with work limitations have mental impairments than the general distribution of impairments observed among youth with any impairment would suggest. That is, the proportion of youth with sensory or physical impairments among youth with work limitations is more similar to the proportion among youth with any limitations than the proportion with mental impairments across the two groups. As Table 5 also showed, mental conditions are the predominant impairment for special education students and SSI recipients, further suggesting differences in the youth who are identified through varying disability definitions. In Table 7, we illustrate how disability prevalence rates are sensitive to both the definition of impairment and its severity using detailed information on these characteristics from the NSCH. This analysis provides some additional insights into the differences we observed across surveys in Table 4, especially the relatively high prevalence rates in the NLSY97, which had relatively modest definitions of impairment. The NSCH asks about specific conditions for sensory (hearing, vision, or speech problem), physical (such as asthma and diabetes), and mental (such as learning disability and depression) impairments, as well as the severity of each condition (mild, moderate, or severe). The first data column of Table 7 shows responses for having any condition; the rates for each condition are high when compared to the impairment rates from the ACS, CPS, and SIPP in Table 4 and closer to the NHIS (for physical impairments) and NLSY97 (for sensory and physical impairments). For example, 17.8 percent of youth report any mental impairment, 14.2 percent report any physical impairment, and 34.4 percent report some type of disability. This high overall prevalence illustrates how very broadly stated identifiers capture samples of youth who might have conditions that are not severe. The prevalence is attenuated when only moderate or severe impairments are counted (the second data column) or when the definition is limited to severe impairments (the third data column). For example, only 2.5 percent of youth reported a severe mental impairment, and only 1.5 percent reported a severe physical condition. These findings indicate that the choice of using mild, moderate, and severe impairments for disability identification has important implications for who is counted as having an impairment. 12

17 Identifying Transition-age Youth with Disabilities Using Existing Surveys Mathematica Policy Research The NSCH also includes information that examines the interaction of several other indicators, including special health needs and program indicators such as special education. A large number of youth (22.5 percent) report a special health care need, and 11.2 percent report special education participation. These two indicators, particularly the former, may be capturing a population not typically identified through traditional disability definitions; in addition, these individuals may receive sufficient environmental supports or have personal factors that keep them from meeting a disability threshold. This intersection between personal factors, environmental factors, and disability for individuals with special need indicators represents a promising direction for further research into youth with disabilities, such as which conditions or impairments result in a youth having a special health need but not reporting an impairment, activity limitation, or participation limitation. This information could be especially helpful in identifying whether surveys such as the ACS should add information on additional indicators to identify other youth with disabilities. B. How Do the Different Disability Definitions in the Framework Overlap? To show that disability is not a homogenous issue for youth, we mapped the overlap among selected disability definitions to understand the uniqueness across the disability categories. The findings in this section suggest that multiple measures are needed to identify youth with disabilities. In addition, school-based measures are an important aspect of disability for transition-age youth and may identify a unique set of youth that other measures, particularly work-limitation measures, do not identify. Figure 2 presents the overall prevalence of any impairment (sensory, physical, or mental) or work limitation among all youth ages 16 to 24 in the 2004 SIPP, including the overlap between these two categories. We chose these two categories because they are more commonly found in the literature and had relatively higher prevalence rates compared to other definitions. The prevalence of any impairment is larger than that of work limitation (8.5 percent compared to 5.8 percent). While a small portion of young adults reported the presence of both any impairment and a work limitation (3.8 percent), these individuals represent a large proportion of those reporting any kind of disability. In Figures 3a through 3d, we examine how the definitions of any impairment and work limitations overlap with other disability definitions in our framework. We show the overlap for those over and under age 18 because the school limitation questions are only asked of those under age 18, and the relationship between school limitations and these other definitions, particularly work, is important. We find that the majority of youth who report any impairment or a work limitation also report disability using one other definition. Among youth ages 18 to 24 who reported any impairments, about half (51 percent) reported another type of disability, predominantly a work limitation (46 percent). Among those with a work limitation, 75 percent reported other types of disability, with most reporting any impairment (66 percent). When we turn to youth ages 16 and 17, a higher proportion (77 to 79 percent) reported other types of disability. This is likely because of the inclusion of school limitations and special education. More than half of the youth with any impairment also reported disability using these two definitions, while the proportion of youth reporting work limitations and other disability definitions are roughly similar to the proportions reported by 18- to 24-year-olds. These figures also show that some proportion of youth reporting disability using one definition will not report any other disability definition. For example, about half of youth ages 18 to 24 with any limitation did not report a disability using any other definition. Multiple measures are needed to capture the population of youth with disabilities. 13

18 Identifying Transition-age Youth with Disabilities Using Existing Surveys Mathematica Policy Research The prevalence rates for youth with disabilities change substantially when switching from more adult-based definitions such as work to youth-based definitions, such as limitations in school. The SIPP data show that 17.5 percent of youth ages 16 and 17, about whom we have youth-specific variables, reported a disability based on either adult criteria or a youth-specific definition (Figure 4; this figure excludes youth who had housework disabilities or SSI disability benefits). When looking only at those who reported conditions or limitations that met the adult criteria (having an impairment, an activity limitation, a functional limitation, and/or a work limitation), 10.3 percent had a disability. When using a youth-specific definition (having a school limitation or ever receiving special education services), 12.9 percent of youth had a disability. Interestingly, despite relatively similar adult and youth prevalence rates, there was relatively limited overlap in adult and youth definitions; about 1 in 3 youth with any child disability had an adult disability. This finding is important because it indicates that many adult-based definitions typically used in research do not necessarily capture childhood disabilities. As youth age, the focus on their impairments (such as learning impairments) may change, particularly as they move into the workplace, but the issue does not go away. Hence, childhood disabilities may often go unmeasured by studies that use certain adult-based definitions. C. How Does the Inclusion of Residents of Group Quarters Affect Prevalence? In Table 8, we use data from the ACS to illustrate the effect that the inclusion of institutional residence has on prevalence rates. This analysis is especially important given that most general surveys, such as the NHIS, CPS, and NHIS (see Table 4), do not include those who reside in group quarters. Hence, it is important to have an understanding of the potential effect of this exclusion in other surveys on who is counted in disability prevalence rates among youth. Inclusion of residents of institutional and noninstitutional group quarters does not greatly affect prevalence estimates derived from household residents, but youth with disabilities make up a very large proportion of institutional group quarters residents. The ACS includes information on three residence types: households, noninstitutional group quarters, and institutional group quarters. Most surveys, such as the SIPP, cover the first and second types but not the third. Disability prevalence in the ACS (using the broadest measure that includes special need indicators) for 16- to 24-year-olds increased slightly, from 6.1 percent for household residents to 6.3 percent when including group quarters residents. Among noninstitutional group quarters residents, which includes groups such as youth in college dormitories, the disability prevalence (5.7 percent) was slightly below the national average. The pattern for those in institutional group quarters, of which correctional facilities are likely the predominant type, is the opposite: though this is a relatively small population (just one of every 100 youth), almost one in 4 of these residents has a disability. This institutional population is of key concern because it suggests that youth with disabilities may be overrepresented in correctional facilities (though we cannot be certain because we cannot identify the specific type of facility from the ACS public use files). Earlier findings from the special education and child SSI populations also indicated that many youth in these programs have a history of involvement with the criminal justice system (Rangarajan et al., 2009). The table also shows data by age group, which illustrates that the number of young adults living in noninstitutionalized group quarters (such as colleges) increases substantially for youth ages 18 to 24 compared to those ages 16 and 17. Disability prevalence for youths ages 18 to 24 in noninstitutional quarters is 5.7 percent, which is slightly lower than the overall prevalence rate in the household population (6.5 percent), indicating that youth with disabilities are underrepresented in these quarters. Nonetheless, the key issue, as noted above, is that many youth (22.9 percent) in institutional group quarters report a disability. 14

19 Identifying Transition-age Youth with Disabilities Using Existing Surveys Mathematica Policy Research D. To What Extent Does Prevalence for Youth Vary by State? The prevalence of youth with disabilities varies considerably by state, with implications for the provision of services to these individuals and their participation in programs. The CPS includes eight of the disability definitions in our framework, including work limitation and the special need indicators of SSI and SSDI receipt, which are combined to provide the overall disability measure presented in Figure 5. We ranked states from high to low to show the range of prevalence across states and to identify a state s relative position more easily. Using the broadest measure of disability, we found a threefold difference between the state with the highest and lowest prevalence among its 16- to 24-year-olds. Nevada had the fewest youth with disabilities (3.3±1.3 percent) followed by Colorado (3.7±1.7 percent) and then by California, North Dakota, and Hawaii. West Virginia had the highest prevalence at 8.7±3.2 percent, followed by four states (Maine, Oklahoma, Mississippi, and Arkansas) with prevalence above 7 percent. Published results for adults have a similar range across states, though adults had higher prevalence (for example, ACS state estimates for adults ages 21 to 64 ranged from 7.2 percent in Hawaii to 18.7 percent in West Virginia [Erickson, Lee, & von Schrader, 2010]). These results raise several interesting questions. First, what factors are influencing the disability prevalence across states? It is not clear why one state should have higher prevalence of youth with, for example, mental impairments than another state. Environmental factors such as poverty, access to medical services, school programs, and state policies for youth with disabilities may influence these prevalence statistics. Poverty may be a critical factor in understanding the influence that factors have, both because having a disability may lead to poverty and because poverty may lead to disability. Second, to what extent do youth with disabilities in states with higher prevalence have access to services (such as special education and vocational rehabilitation) that is equal to those available to youth in states with lower prevalence? Potentially, states with lower prevalence may have more resources to support their youth with disabilities in their transition to adulthood, with subsequent implications for the experiences of those youth as adults. V. Discussion In this paper, we present a framework that can be used to organize and contrast disability measures of transition-age youth from existing surveys. We use this framework to identify youth with disabilities in eight publicly available surveys, calculate prevalence rates, and explore issues related to varying the definitions, group quarters residence, and state differences. The following are our key findings: Though few surveys target youth with disabilities, many publicly available surveys include information that could be used as the basis for future research. These data sources will be particularly important for comparisons made using future studies of special populations such as the upcoming NLTS2012, which will include updated information on special education students. Disability prevalence changes substantially based on specific measures for adults (e.g., work limitations) and youth (such as school limitations and learning disabilities). This change has important implications for measuring disability status as youth transition into adulthood, as presumably their childhood disability would remain, though most adult disability metrics would not capture childhood limitations. 15

20 Identifying Transition-age Youth with Disabilities Using Existing Surveys Mathematica Policy Research Disability prevalence varies across surveys, with higher rates generally in surveys that had more moderate definitions of severity or more questions related to functional impairments. Although prevalence varies across definitions, there are substantial overlaps in disability definitions. The majority of youth who report some type of impairment also tend to report a limitation in another area, such as with work, school, or activities of daily living. Youth identified through varying disability definitions likely vary across other important demographic characteristics. Including youth with special need indicators those involved with special education, who receive SSI or SSDI, or who have special health needs increases disability prevalence. Special health needs and special education participants have the largest such effect, with SSI and SSDI having a more modest effect due to the relatively smaller number of participants in these programs in comparison to the overall number of youth in the age group. Youth with disabilities who are involved in special education programs or who receive SSI benefits overwhelmingly have mental impairments (though each group has different types of mental impairments), and many youth in one program are also in the other. Disability prevalence is not greatly affected by the addition of youth who reside in group quarters, as this population is small and residents in noninstitutional group quarters are less likely to have a disability. However, youth with disabilities are overrepresented in institutional group quarters; about one in 4 youth in such residences has a disability. Disability prevalence rates vary substantially (from 3 percent to 9 percent) across states using a CPS-based definition, indicating potential key differences in states needs for serving youth with disabilities. Our analysis provides a conceptual framework for classifying youth with disabilities according to the ICF disability model and uses different data sources to interpret prevalence rates. While substantial variation exists, much of this variation can readily be explained by understanding the sources of data and the questions used in the surveys. Our framework provides a sorting mechanism to interpret statistics from a wide range of studies. Despite the richness they provide, the surveys vary considerably in terms of how youth with disabilities are identified and the outcomes that can be observed, which influences how these surveys can be used for research. Even for our analyses, we were not able to use the same data source to answer all of our research questions. For example, we could only use the ACS to explore group quarters residence; and the NSCH had the best information to explore issues related to the degree of sensory, physical, and mental impairments. Most definitions of disability have a degree of uniqueness, in that youth identified by one definition may not be identified through others, though this varied by definition. For instance, youth with school limitations more often are involved in special education than are youth with work limitations, and having a school limitation does not necessarily mean that a youth will also have a limitation in another social sphere, such as work. These findings suggest two important points that researchers and others should be aware of when using or interpreting statistics for youth with disabilities. First, somewhat obviously, having one disability definition does not necessarily mean having other disability definitions. Second, research on youth with disabilities may omit a significant portion of such youth depending on the survey and the measures used, especially when youth-based measures are not included in the survey. 16