Disability Based Inequalities in Adolescent Health and Well-Being

Context

Exposure to persistent socioeconomic disadvantage and social exclusion are an all too common reality for the 200,000 plus children with disabilities living in Canada today1,2,3,4. Disability-based inequality may result in far-reaching adverse consequences for this population. However, the impact of childhood disadvantage on young people with disability as they come of age, and pathways or mechanisms underlying these inequalities, remain poorly understood. Evidence suggests the subjective well-being (SWB) of these children is likely to be lower than their peers5,6,7. Emerging evidence also suggests that differences in well-being may be contingent on the social and material conditions under which young people with disability are living rather than any inherent association with their disability8,9,10. Findings from this work indicate that disability-based inequalities in the subjective well-being of youth may be socially patterned and preventable, and therefore, unjust.

Preliminary Data

Based on public use National Longitudinal Survey of Children and Youth (NLSCY) cycle one micro-data, we know that 811 children (3.7%) in the original NLSCY cohort were parent-identified as living with a disability in 1995 (item HLT-Q45B). Disparities in socioeconomic disadvantage and social exclusion are evident within this initial sample. Compared with their non-disabled peers, a greater proportion of the children with disabilities were living in low income households (28.4% vs. 24.0% respectively) and/or in a neighbourhood deemed by parents to be a poor place to bring up children (8.0% vs. 4.7% respectively). Further, a greater proportion of 10 and 11 year old disabled youth reported being bullied (7.4% vs. 1.8% respectively), and feeling like an 'outsider' (13.2% vs. 4.8% respectively). Children identified as disabled in this sample suffered poorer well-being than their same age peers and were reported to be less satisfied with their lives by their parents (OR = 0.35, 95% CI 0.29‑0.42, p<0.001).

Study aim and hypotheses

This study is designed replicate and extend the work of Emerson et al. (2009) to advance understanding of disability-based inequalities in the subjective well-being (SWB) of adolescents. The study will test hypotheses derived from the latent path model depicted below. Eight principal hypotheses will be tested:

  1. The socioeconomic differential between children with and without disabilities will increase between early childhood and adolescence.
  2. The relationship between child disability (DS) and socioeconomic disadvantage (SeD) is at least partially explained by low parental employment (WFP).
  3. Children with disabilities will report lower SWB in adolescence in comparison to non-disabled peers.
  4. The relationship between DS and SWB is at least partially explained by higher lifetime exposures to SeD and low social participation (SP).
  5. Children and youth with disabilities will be exposed to higher levels of family stress than their non-disabled peers, in early childhood, middle childhood and adolescence.
  6. The relationship between child disability and family stress processes (FSP) is at least partially explained by SeD.
  7. The relationship between SeD and SWB in Canadian youth, with and without disabilities, will be mediated by FSP; i.e. family stress processes are a primary mechanism linking socioeconomic exposures to the SWB of Canadian youth.
  8. Socioeconomic exposures and social participation will moderate the relationship between DS and SWB in adolescence; i.e. under conditions of relative socioeconomic advantage and high social participation, no disability-based difference in SWB will be found.

Methodology

The study method is secondary analysis of longitudinal NLSCY data. The SSHRC RDC Program has approved an application for access to NLSCY original cohort confidential micro files cycles (Approved Sept 19, 2011; Project ID: 11-SSH-UAB-2876). Analysis will be limited to the estimated 6,000+ children in the longitudinal sample, including an estimated 250-300 children and youth with disabilities, for whom data was collected at ages 4/5, 8/9 and 14/15. All data analysis will be conducted under the guidance of Dr N.G. Narasimha Prasad, Statistics Centre, University of Alberta.

After screening the data, descriptive statistics will be computed and demographic profiles generated for children with and without disabilities at 3 points in the life course: early childhood, middle childhood and adolescence. These profiles will include, for example, frequency and distribution statistics for each group on each manifest variable. The measurement model we then be evaluated using confirmatory factor analysis (with robust weighted least squares estimation) 11, 12. Once the measurement model is established, multiple group analyses will be conducted to determine the consistency of the model across children with and without disabilities. The correlation matrix for all latent variables will then be generated and the full latent variable path model and study hypotheses will be tested using the longitudinal structural equation modelling (LSEM) module in STATA version 12. To assess overall goodness of fit, we will compute the chi-square test statistic, the comparative fit index (criterion value >.95)13, and the root mean square error of approximation (criterion value <.60)14.

Contribution to the advancement of knowledge

This study will add to a growing body of knowledge demonstrating inequalities in the well-being of Canadian children with disabilities. Longitudinal analysis of a Canadian cohort from early childhood through adolescence addresses a gap in extant knowledge by enhancing our understanding of disability based inequities in the distribution of well-being in Canada and identifying mechanisms linking disability, disadvantage and subjective well-being over time.

Works Cited

  1. Hanvey, L. (2001). Children and Youth with Special Needs. Ottawa, ON: Canadian Council on Social Development.
  2. Statistics Canada. (2006). Participation and Activity Limitation Survey. Catalogue no. 89-628-x.
  3. Statistics Canada. (2008). Participation and Activity Limitation Survey 2006: Families of Children with Disabilities in Canada. Ottawa, CAN: Statistics Canada, Social and Aboriginal Statistics Division. (Catalogue no. 89-628-X no. 009)
  4. Canadian Institute of Child Health. (2000). The Health of Canada's Children: A CICH Profile - 3rd Edition. Available from http://www.cich.ca/Publications_monitoring.html
  5. Dickinson, H.O., Parkinson, K.N., Ravens-Sieberer, U., Schirripa, G., Thyen, U., Arnaud, C., Beckung, E., Fauconnier, J., McManus, V., Michelsen, S.I., Parkes, J. & Colver, A.F. (2007). Self-reported quality of life of 8-12-year-old children with cerebral palsy: a cross-sectional European study. The Lancet, 369(9580), 2171-2178. DOI: 10.1016/S0140-6736(07)61013-7\
  6. Lindstrom, B. & Eriksson, B. (1993). Quality of life for children with disabilities. Soz Praventivmed, 38, 83-89.
  7. Watson, S.M.R. & Keith, K.D. (2002). Comparing the quality of life of school-age children with and without disabilities. Mental Retardation, 40, 304-12.
  8. Emerson, E., Honey, A. & Llewellyn, G. (2008). The Well-Being and Aspirations of Australian Adolescents and Young Adults with a Long-term Health Condition, Disability or Impairment. Retrieved Sept 1, 2010 from The Australian Research Alliance for Children & Youth (ARACY): http://www.afdsrc.org/publications/
  9. Emerson E., Honey A., Madden R., Llewellyn G. (2009). The Well-Being of Australian Adolescents and Young Adults with Self-Reported Long-Term Health Conditions, Impairments or Disabilities: 2001 and 2006. Australian Journal of Social Issues, 44(1), 37-53.
  10. Emerson, E., Llewellyn, G., Honey, A. & Kariuki, M. (in press). The lower well-being of young Australian adults with self-reported disability reflects their poorer living conditions rather than the presence of health conditions or impairments. Australian & New Zealand Journal of Public Health.
  11. Anderson, J.C. & Gerbing D.W. (1988). Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach, Psychological Bulletin, 103, 411-23.
  12. Flora, D.B. & Curran, P.J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychologival Methods, 9, 466-491.
  13. Bentler, P.M. (1990). Comparative Fit Indexes in Structural Models. Psychological Bulletin, 107 (2), 238-46.
  14. Steiger, J.H. (2000). Point estimation, hypothesis testing, and interval estimation using the RMSEA: Some comments and a reply to Hayduk and Glaser. Structural Equation Modeling, 7, 149-162.