Prevalence and Prediction of ASD in Non-Hispanic White and Non-Hispanic Black Children: Results from the 2017 National Survey of Children’s Health

Background: Despite the increasing rates of ASD prevalence in the United States, data from 2014-2016 reports that black children are less likely to be identified with ASD than white children (Baio et al., 2018; Xu et al., 2018). This difference in ASD prevalence may be because over 50% of black children with ASD are initially misidentified with another developmental condition (Mandell et al., 2007). Race may also influence when children are identified with ASD, although findings on this topic are mixed (Daniels & Mandell, 2014), suggesting that other demographic variables play a role in age of ASD identification.

Objectives: This study utilized data from the 2017 National Survey of Children’s Health (NSCH) to calculate prevalence rates of ASD and related developmental conditions in Non-Hispanic White (NHW) and Non-Hispanic Black (NHB) children, and to determine which commonly-studied demographic variables independently predict age of ASD identification.

Methods: Primary caregivers living within the United States were randomly selected to complete demographic items about their families. The total sample included 14,593 NHW children (7,529M; 7,064F) and 1,290 NHB children (652M; 638F), ages 0–17 years. Caregivers reported whether a doctor, health care provider, or educator had ever told them that their child has ASD, and how old their child was at time of ASD identification. Based on this item, 422 NHW children (334M; 88F) and 35 NHB children (28M; 7F) were identified with ASD. Caregivers also reported whether their child had ever been identified with another developmental condition.

Results: Calculated rates of ASD prevalence showed that NHW children were more likely to be identified with ASD (3,028/100,000) than NHB children (2,713/100,000). However, NHB children were more likely to be identified with another related developmental condition, such as ADHD/ADD, speech disorder, conduct/behavioral problems, developmental delay, or intellectual disability, than NHW children (Figure 1). A linear hierarchical regression model demonstrated that when controlling for current age, having mild ASD symptoms, being female, and not having health insurance significantly predicted later age of ASD identification. Race, socioeconomic status, and geographical location did not significantly predict age of ASD identification (Table 1). Overall, the model accounted for 22.7% of variance in age of ASD identification.

Conclusions: Differences in ASD prevalence by race may reflect racial biases in identification, as NHB children are more likely to be identified with a related developmental condition instead of ASD. However, results of the regression model imply that race does not independently predict age of ASD identification. Other demographic variables (sex, ASD symptom severity, health insurance status) may have greater influence on age of ASD identification. Because this model only accounted for a portion of variance in age of ASD identification, future studies should continue to investigate which variables predict delayed ASD identification so that they can be targeted in public health program design. Additionally, these results should be interpreted with caution due to the low sample size of NHB children. Further efforts should be made to recruit more NHB children in the 2018 NSCH so that results better reflect the United States population.