Disability status—experiencing a functional limitation caused by a health condition—is dynamic throughout the life cycle, even during adolescence and young adulthood. We use data from the 1997 cohort of the National Longitudinal Survey of Youth to better understand these dynamics, examining how health condition and limitation statuses evolve during adolescence and young adulthood as well as how changes in these characteristics are related to survey nonresponse and attrition. Health condition and limitation dynamics are evident in our data: the proportion of sample members who reported having a limitation in their activities for any interview increased from approximately 12 % during the initial interview (when sample members were 12 to 17 years old) to almost 25 % 13 years later. Multivariate analyses revealed that women are more likely than men to report changes in health condition or limitation status. Those with mild limitations were relatively less likely than those without limitations or with severe limitations to experience changes in limitation status. Somewhat surprisingly, a survival analysis of survey participation outcomes found limited correlation among health conditions, limitations, and either missing a survey interview for the first time or permanently leaving the survey sample.
This is a preview of subscription content, access via your institution.
The weights used in the study are either included in the NLSY97 data or constructed using a program obtained from the Bureau of Labor Statistics (BLS). The NLSY97 includes the weights that make the entire NLSY97 sample nationally representative. However, for our analyses that involve subsamples of NLSY97 respondents, we must construct custom weights. The program from the BLS can create weights that make any subsample of NLSY97 respondents nationally representative.
A key assumption of a proportional hazard model is that when an explanatory variable’s value changes, the hazard function moves relative to the baseline hazard—an assumption that is testable (Grambsch and Therneau 1994). Results (not shown) revealed that the proportional hazard assumption was not rejected for the attrition analysis but was rejected for the first missed interview analysis. We therefore also estimated the first missed interview survival model assuming an underlying distribution for the hazard function. Using the Akaike information criterion to compare model results across distributional assumptions, we found that the Gompertz survival distribution fit best. However, the results from the Gompertz survival model did not differ qualitatively from the proportional hazard model results. Hence, to minimize the number of models we need to describe in this article, we present only the proportional hazard model results for the first missed interview analysis.
For a diagnostic test, in all regression analyses, we assessed the colinearity of the variables of interest using the variance inflation factor (VIF). We did not find a VIF large enough to warrant a concern of multicolinearity.
Ahern, K., & Le Brocque, R. (2005). Methodological issues in the effects of attrition: Simple solutions for social scientists. Field Methods, 17, 43–69.
Allison, P. D. (2001). Missing data. Thousand Oaks, CA: Sage.
Bethell, C. D., Read, D., Blumberg, S. J., & Newacheck, P. W. (2008). What is the prevalence of children with special health care needs? Toward an understanding of variations in findings and methods across three national surveys. Maternal and Child Health Journal, 12, 1–14.
Brault, M. W. (2012). Americans with disabilities: 2010 (Current Population Report P70-131). Washington, DC: U.S. Census Bureau.
Currie, J., & Kahn, R. (2012). Children with disabilities: Introducing the issue. Future of Children, 22(1), 3–11.
Davey, A., Shanahan, M. J., & Schafer, J. L. (2001). Correcting for selective nonresponse in the National Longitudinal Survey of Youth using multiple imputation. Journal of Human Resources, 36, 500–519.
de Graaf, R., van Dorsselaer, S., Tuithof, M., & ten Have, M. (2013). Sociodemographic and psychiatric predictors of attrition in a prospective psychiatric epidemiological study among the general population. Result of the Netherlands Mental Health Survey and Incidence Study-2. Comprehensive Psychiatry, 54, 1131–1139.
Grambsch, P. M., & Therneau, T. M. (1994). Proportional hazards tests in diagnostics based on weighted residuals. Biometrika, 81, 515–526.
Halfon, N., & Newacheck, P. W. (2010). Evolving notions of childhood chronic illness. Journal of the American Medical Association, 303, 665–666.
Hemmeter, J., & Gilby, E. (2009). The age-18 redetermination and postredetermination participation in SSI. Social Security Bulletin, 69(4), 1–25.
Honeycutt, T., & Wittenburg, D. (2012). Identifying transition-age youth with disabilities using existing surveys. Princeton, NJ: Mathematica Policy Research.
Jamoom, E., Horner-Johnson, W., Suzuki, R., Andresen, E. M., & Campbell, V. A. (2008). Age at disability onset and self-reported health status. BMC Public Health, 8, 10. doi:10.1186/1471-2458-8-10
Jeličić, H., Phelps, E., & Lerner, R. M. (2010). Why missing data matter in the Longitudinal Study of Adolescent Development: Using the 4-H Study to understand the uses of different missing data methods. Journal of Youth and Adolescence, 39, 816–835.
Little, R. J., & Rubin, D. B. (1987). Statistical analysis with missing data. New York, NY: John Wiley & Sons.
Loprest, P. J., & Maag, E. (2007). The relationship between early disability onset and education and employment. Journal of Vocational Rehabilitation, 26, 49–62.
Macurdy, T., Mroz, T., & Gritz, R. M. (2001). An evaluation of the National Longitudinal Survey of Youth. Journal of Human Resources, 33, 345–436.
Mann, D. R., & Honeycutt, T. (2014). Is timing everything? Disability onset of youth and their outcomes as young adults. Journal of Disability Policy Studies, 25, 117–129.
Nagi, S. (1965). Some conceptual issues is disability and rehabilitation. In M. Sussman (Ed.), Sociology and rehabilitation (pp. 100–113). Washington, DC: American Sociological Association.
Osgood, W., Foster, M., & Courtney, M. E. (2010). Vulnerable populations and the transition to adulthood. Future of Children, 20(1), 209–229.
Rubin, D. B. (1976). Inference and missing data. Biometrika, 63, 581–592.
Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York, NY: John Wiley & Sons.
Schoeni, R. F., Stafford, F., McGonagle, K. A., & Andreski, P. (2013). Response rates in national surveys. ANNALS of the American Academy of Political and Social Science, 645, 60–87.
Shandra, C. L. (2011). Life course transitions among adolescents with and without disabilities: A longitudinal examination of expectations and outcomes. International Journal of Sociology, 41, 67–86.
Shandra, C. L., & Hogan, D. P. (2008). School-to-work program participation and the post-high school employment of young adults with disabilities. Journal of Vocational Rehabilitation, 29, 117–130.
Torvik, F. A., Rognmo, K., & Tambs, K. (2012). Alcohol use and mental distress as predictors of non-response in a general population health survey: The HUNT Study. Social Psychiatry and Psychiatric Epidemiology, 47, 805–816.
U.S. Government Accountability Office (U.S. GAO). (2012). Students with disabilities: Better federal coordination could lessen challenges in the transition from high school (Publication No. GAO-12-594). Washington, DC: U.S. GAO.
Verbugge, L. M., & Jette, A. M. (1994). The disablement process. Social Science & Medicine, 38, 1–14.
Weathers, R. R., II. (2009). The disability data landscape. In A. Houtenville, D. C. Stapleton, R. R. Weathers II, & R. V. Burkhauser (Eds.), Counting working-age people with disabilities (pp. 27–68). Kalamazoo, MI: Upjohn Institute.
World Health Organization (WHO). (2001). International classification of functioning, disability and health. Geneva, Switzerland: WHO.
Zhivan, N. A., Ang, A., Amaro, H., Vega, W., & Markides, K. S. (2012). Ethnic/race differences in the attrition of older American survey respondents: Implications for health-related research. Health Services Research, 47, 241–254.
The authors appreciate the assistance of Nora Paxton for programming support, Jody Schimmel Hyde for helpful comments on the analysis, and Jane Nelson for production support. Funding for this study was provided by the Research and Training Center on Disability Statistics and Demographics (StatsRRTC) at the University of New Hampshire, which is funded by the U.S. Department of Education, National Institute for Disability and Rehabilitation Research (NIDRR) (Grant No. H133B100015). The contents do not necessarily represent the policy of the U.S. Department of Education and you should not assume endorsement by the federal government (Edgar, 75.620 (b)).
About this article
Cite this article
Mann, D.R., Honeycutt, T. Understanding the Disability Dynamics of Youth: Health Condition and Limitation Changes for Youth and Their Influence on Longitudinal Survey Attrition. Demography 53, 749–776 (2016). https://doi.org/10.1007/s13524-016-0469-7
- Transition to adulthood
- Disability changes
- Longitudinal surveys
- Survey attrition