Socioeconomic disparities in the age of first diagnosis of autism spectrum disorder (ASD) in Metropolitan Atlanta

1. Socioeconomic disparities in the age of first diagnosis of autism spectrum disorder (ASD) in Metropolitan Atlanta Sally M. Brocksen, PhD Kimberly Kimiko Powell, PhD, RD “The findings and conclusions in this presentation are those of the presenter and do not represent those of the Centers for Disease Control and Prevention”

2. Background and Purpose • For children with an autism spectrum disorder (ASD), early identification is crucial in providing better developmental outcomes. • The impact of socioeconomic and demographic factors needs to be examined to determine if an ASD diagnosis is delayed within certain populations; postponing treatment and intervention services. • This study examines whether there are differences among children meeting the Metropolitan Atlanta Developmental Disabilities Program (MADDSP) surveillance case definition of ASD.

3. Study design • Children identified in the 2000 MADDSP study year as having an ASD were linked with the 2000 census data to analyze SES and demographic factors. • Block group census data on income, education, occupation, employment, poverty status and residential stability were analyzed using principal component analysis (PCA) to create a SES variable. • Additional regression analyses on demographic factors (e.g. race, gender) were conducted to evaluate differences among children identified as having an ASD.

4. MADDSP Design • Ongoing, population-based, active monitoring program based on record review • Mental retardation, cerebral palsy, vision impairment and hearing loss; autism spectrum disorders since 1996 • Children aged 3-10 years, 1991-1994; 8 year olds in 1996, 2000, 2002 and future study years • Multiple sources (educational, clinical, service) • Five counties in metro Atlanta

5. MADDSP ASD Clinician Review Process • Case status determined by systematic review of abstracted information by autism/DD clinicians. • Behavioral coding scheme developed based on DSM-IV, TR (2000) criteria for Autistic Disorder and PDD-NOS. • All evaluation records for a child were compiled and behaviors scored individually. • Criteria were summarized across evaluations to determine case status. • Questionable cases are re-reviewed.

6. Methods • Children who meet the case definition of ASD may or may not have a previous clinical diagnosis. • Children with this previous clinical diagnosis are compared with children who have not received a clinical diagnosis but meet the MADDSP case definition of having an ASD.

7. Methods • Since individual level economic data was not available this study used area-based measurements from the 2000 census data to create a community socioeconomic index. • A validated method used by Krieger (1992) was employed to create socioeconomic profiles of the neighborhoods (block groups) in which individuals live.

8. Methods Principal Component analysis (PCA) was used to rank communities from low to high SES and classified into tertiles using the following variables: • occupational class • percent working class, professional class and unemployed • income • percent low income and percent high income • poverty • percent below poverty line • education • percent low and high education • stability • percent movement of houses and counties over five years

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13. Summary • A child having ASD with an IQ > 70 is associated with being in the highest SES tertile; whereas a child having ASD with an IQ < 70 is associated with being in the lowest SES tertile. • Children in the lowest SES tertile are 4 times more likely to have not received an ASD diagnosis before the age of 5. • Findings are similar to the results by Karapurkar Bhasin & Schendel (in press) who examined sociodemographic risk factors using 1996 MADDSP study year data.

14. Future studies • Conduct analysis on future MADDSP study years to look for trends in the diagnosing of ASD in children from different socioeconomic groups. • Analyze the impact of specific census variables related to SES (e.g. median household income). • Link data to birth certificate files to gain information related to maternal age and education. • Look at differences based on where the child’s record was obtained (school sources vs. clinical sources).

15. Strengths and limitations • Strengths: • Used population based data to classify children as ASD. • The creation of socioeconomic status variables using multiple measurements. • Limitations: • No individual level economic data is available, therefore census data was used to approximate the socioeconomic status of identified children.

16. Public Health Implications • Early identification of ASD is crucial to promoting optimal developmental outcomes for children with ASD. • Strategies need to be developed to assess and target the needs of children from all socioeconomic strata.

17. Andrew Autry Jon Baio Claudia M. Bryant Matt Cahill Afiya Celestine Nancy Doernberg Shryl Epps Lekeisha Jones Rita Lance Charmaine McKenzie Catherine Rice Fiona Steele Darlene Sowemimo Melody Stevens Melissa Talley Ignae Thomas Kim Van Naarden-Braun Anita Washington Victoria Washington Laquita Williams Susan Williams Marshalyn Yeargin-Allsopp Acknowledgements