Methods: A repeated cross-sectional design is used with data appended from the 2011-2015 National Health Interview Survey (NHIS). The study sample includes low-income individuals ages 13-26 with a household income below the 200% poverty line (N=213,650). Their health care needs are measured by two dichotomous variables as responses to questions of whether the respondent (1) delayed health care and/or (2) did not get health care in the last 12 months due to cost. Two independent variables are disability status and transition into adulthood. A binary variable of disability (1=with, 0=without a disability) is assessed by presence of any functional limitation. Transition to adulthood is measured by age: Those ages between 13-17 are assigned a value of “0”, and others are assigned a value of “1”. Logistic regression is run to predict the probability of having unmet health care needs using disability status, transition to adulthood and the interaction term of these two variables, controlling for other socioeconomic and demographic covariates.
Results: About nine percent of this sample report having disabilities. The odds for children with disabilities to report delayed health care is 1.33 times that for those without disabilities. Similarly, young adults with disabilities have a greater likelihood to report having delayed health care than those without disabilities (odds ratio = 3.09). The interaction term of disability status and transition to young adulthood is significant (p<.001), indicating a statistical difference between the two odds ratios. The model yields similar results when using the second outcome measure indicating not getting health care needed, with the two odds ratios being 1.33 and 3.60, respectively.
Implications: Children with disabilities, compared to those without disabilities, have greater unmet health care needs as they transition to young adulthood. This is likely to negatively affect their physical health and other health-related outcomes. It is important for future research to identify risk factors associated with access to health care and to understand how these factors interact with different insurance programs. Such knowledge would be immensely important for developing interventions specific to this population.