Changes in the Prevalence of Autism Spectrum Disorder in Utah: A Cohort Approach

Poster Presentation
Saturday, May 12, 2018: 11:30 AM-1:30 PM
Hall Grote Zaal (de Doelen ICC Rotterdam)
A. V. Bakian1, C. A. Palmer1, C. Kingsbury2, N. Taxin2, W. M. McMahon1 and D. A. Bilder1, (1)Psychiatry, University of Utah, Salt Lake City, UT, (2)Bureau of Children with Special Health Care Needs, Utah Department of Health, Salt Lake City, UT
Background: Accurate autism spectrum disorder (ASD) prevalence estimates are critical for driving policy decisions such as planning for ASD-related services. U.S.-based efforts to identify children with ASD and estimate ASD prevalence often use cross-sectional study designs that are limited to a specific age (e.g. age 8). Recent research suggests that approximately 1/3 of children with ASD are not identified until after age eight. Delayed identification may be related to ASD severity, race/ethnicity and/or socioeconomic status. Hence, some prevalence estimates may be overly-conservative and biased. A cohort study design in which children from specific birth cohorts are ascertained for ASD at different ages may yield more accurate and unbiased ASD prevalence estimates.

Objectives: 1) Estimate ASD prevalence bi-annually in the 1994 birth cohort in Utah starting at age 8 through age 16, and 2) compare ASD prevalence across study years.

Methods: Data for this study was acquired from the Utah Registry of Autism and Developmental Disabilities (URADD), a passive, population-based ASD surveillance system. Children born in 1994 in a four county (Davis, Salt Lake, Utah and Tooele) Utah surveillance region were ascertained biannually for ASD starting at age 8 through age 16 based on a community medical ASD diagnosis and/or autism special education eligibility. Overall ASD prevalence and ASD prevalence by gender were estimated in surveillance years (SY) 2002, 2004, 2006, 2008, and 2010; the adjusted Wald method based on a normal approximation was used to estimate 95% confidence intervals. Chi-square tests were used to compare the prevalence of ASD across years.

Results: 159 and 557 children from the 1994 birth cohort were identified with ASD at age 8 (SY2002) and age 16 (SY2010), respectively. The female to male ratio was 1:6 in SY2002 and 1:4 in SY2010. ASD prevalence increased 237% between SY2002 (age 8) and SY2010 (age 16) and was 6.1/1000 8-year-old children in SY2002 and 18.8/1000 16-year-old children in 2010 (Figure). In comparison, the prevalence of ASD among 8-year-old children in SY2010 was 19% lower (prevalence =15.8/1000; 95% confidence interval (CI): 14.5-17.1; p = 0.004) than that of 16-year-olds. The odds of being identified with ASD was three times greater among 16-year-olds compared to 8-year-olds from the 1994 birth cohort (Odds ratio: 3.14; 95% CI: 2.63-3.74; p<0.0001).

Conclusions: Utah’s ASD prevalence increased significantly between SY2002 and SY2010 within the 1994 birth cohort. The current study demonstrates that ASD prevalence continues to increase following early childhood and well into adolescence. This pattern may be the result of multiple factors including improved ASD awareness, recognition, referral and access to services. Further research is needed to identify the specific drivers of within cohort increases in prevalence. ASD prevalence among 8-year-old children in Utah is a conservative estimate; policy makers should consider basing decisions on prevalence estimates that incorporate a wider age range including adolescents.

See more of: Epidemiology
See more of: Epidemiology