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

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Ongoing, population-based, active monitoring program based on record review ... the case definition of ASD may or may not have a previous clinical diagnosis. ... – PowerPoint PPT presentation

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Title: 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

9
Results
  N
Total number of children meeting the case definition of ASD 285
 
Children with a previous ASD diagnosis on a clinical evaluation 115 40.4
Children without a previous ASD diagnosis 170 59.6

ASD with IQ gt70 161 31.1
ASD IQ lt 70 107 20.7
Diagnosis before the age of five 56 19.6
10
Results
Previous ASD diagnosis Previous ASD diagnosis No previous ASD diagnosis No previous ASD diagnosis ASD with IQ gt 70 ASD with IQ gt 70 ASD with IQ lt 70 ASD with IQ lt 70 Diagnosis before the age of five Diagnosis before the age of five
n n n n n
Sex
Male 104 26.3 137 43.2 144 35.7 82 20.3 50 48.1
Female 11 9.6 33 25.0 17 14.9 25 21.9 6 51.9
Race
White (non Hispanic 61 42.7 82 57.3 97 48.5 38 19.0 26 42.6
Black (non Hispanic) 38 37.3 64 62.7 41 20.1 57 27.9 22 57.9
Other 11 42.3 15 57.7 14 31.8 10 22.7 5 60.0
11
Results
SES Tertile 1 1 2 2 3 3
n n n Total p-value
Total ASD cases 115 40.8 96 34.0 71 25.2 282 .001
Children with a previous ASD diagnosis 52 45.6 38 33.3 24 21.1 114 .299
Children without a previous ASD diagnosis 63 37.5 58 34.5 47 28.0 168 .299
ASD and IQgt70 79 49.4 52 32.5 29 18.1 160 .000
ASD and IQ lt 70 32 30.5 33 31.4 40 38.1 105 .204
Diagnosis before the age of five 26 47.3 21 39.2 8 14.5 55 .229
12
Results
  No previous ASD diagnosis No previous ASD diagnosis No ASD diagnosis before the age of 5 No ASD diagnosis before the age of 5 ASD with IQ gt70   ASD with IQ gt70  
  AOR p-value AOR p-value AOR p-value
Race, Black (non Hispanic) .92 (.49-1.74) .799 .31 (.11-.86) .025 .38 (.22-.65) .000
Race, Other .87 (.36-2.11) .762 .65 (.16-2.68) .553 .64 (.31-1.33) .234
Highest SES tertile .66 (.36-1.21) .182 1.05 (.41-2.65) .923 1.7 (.64-1.78) .801
Lowest SES tertile 1.21 (.61-2.41) .586 4.18 (1.26-13.9) .020 .60 (.34-1.08) .807
Male .40 (.19-.85) .018 1.57 (.40-6.25) .519 2.7 (1.53-4.90) .001
13
Summary
  • A child having ASD with an IQ gt 70 is associated
    with being in the highest SES tertile whereas a
    child having ASD with an IQ lt 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 childs
    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
Acknowledgements
  • 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
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