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Table 1.  

Site Surveillance area description Denominator No. of children aged 4 yrs with ASD ASD prevalence(95% CI)* % of children who had an ASD diagnosis % of children who had ASD special education eligibility % of children who had an ASD ICD code
Arizona Part of one county in metropolitan Phoenix 13,929 141 10.1 (8.6–11.9) 92.9 24.1 44.0
Arkansas 21 counties in central Arkansas 15,387 183 11.9 (10.3–13.7) 92.3 17.5 79.2
California Part of one county in metropolitan San Diego 16,796 698 41.6 (38.6–44.7) 81.2 88.3 54.7
Georgia Two counties in metropolitan Atlanta 23,040 340 14.8 (13.3–16.4) 75.0 48.2 60.0
Maryland Five counties in suburban Baltimore 19,818 233 11.8 (10.3–13.4) 70.4 51.9 73.4
Minnesota Parts of three counties in the Twin Cities metropolitan area 10,529 240 22.8 (20.1–25.8) 48.3 80.4 47.5
Missouri Five counties in metropolitan St. Louis 24,521 338 13.8 (12.4–15.3) 95.6 3.3 97.6
New Jersey Part of two counties in New York metropolitan area 17,286 342 19.8 (17.8–22.0) 98.0 17.8 73.7
Tennessee 11 counties in middle Tennessee 25,335 497 19.6 (18.0–21.4) 75.1 39.0 89.7
Utah Three counties in northern Utah 25,064 229 9.1 (8.0–10.4) 76.9 24.0 87.8
Wisconsin Eight counties in southeastern Wisconsin 28,689 513 17.9 (16.4–19.5) 80.3 25.5 77.2
Total   220,394 3,754 17.0 (16.5–17.6) 80.5 42.9 72.0

Table 1. Prevalence of autism spectrum disorder per 1,000 children aged 4 years and percentage of children who had an autism spectrum disorder diagnosis, special education eligibility, or an International Classification of Diseases code — Autism and Developmental Disabilities Monitoring Network, 11 sites, 2018

Abbreviations: ASD = autism spectrum disorder; CI = confidence interval; ICD = International Classification of Diseases.
*95% CIs were calculated using the Wilson score method.
Denominator excludes school districts that were not included in the surveillance area, calculated from National Center for Education Statistics enrollment counts of kindergarteners during the 2018–19 school year.

Table 2.  

Site Male ASD prevalence (95% CI)* Female ASD prevalence (95% CI)* Male-to-female prevalence ratio (95% CI)*,†
Arizona 16.6 (13.8–19.8) 3.5 (2.4–5.2) 4.7 (3.1–7.3)
Arkansas 19.2 (16.4–22.5) 4.1 (2.9–5.9) 4.6 (3.2–6.8)
California 63.6 (58.6–68.9) 18.2 (15.5–21.3) 3.5 (2.9–4.2)
Georgia 23.5 (20.9–26.5) 5.7 (4.5–7.3) 4.1 (3.1–5.4)
Maryland 18.2 (15.8–21.0) 5.0 (3.8–6.6) 3.7 (2.7–5.0)
Minnesota 34.8 (30.2–40.0) 10.6 (8.1–13.7) 3.3 (2.4–4.4)
Missouri 20.2 (17.9–22.8) 7.1 (5.7–8.8) 2.8 (2.2–3.6)
New Jersey 30.3 (26.9–34.1) 8.8 (7.0–11.0) 3.5 (2.7–4.5)
Tennessee 29.2 (26.4–32.2) 9.8 (8.2–11.7) 3.0 (2.4–3.7)
Utah 13.7 (11.8–15.9) 4.3 (3.3–5.7) 3.2 (2.3–4.3)
Wisconsin 26.2 (23.7–28.9) 9.0 (7.6–10.7) 2.9 (2.4–3.5)
Total 25.9 (25.0–26.9) 7.7 (7.2–8.2) 3.4 (3.1–3.6)

Table 2. Prevalence of autism spectrum disorder per 1,000 children aged 4 years, by sex — Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2018

Abbreviations: ASD = autism spectrum disorder; CI = confidence interval.
*95% CIs were calculated using the Wilson score method.
Prevalence significantly higher among males than females at all sites (prevalence ratio 95% CIs do not include 1.0).

Table 3.  

Site ASD prevalence (95% CI) Prevalence ratio (95% CI)
White, non-Hispanic Black, non-Hispanic Hispanic Asian/Pacific Islander American Indian/Alaska Native White, non-Hispanic to Black, non-Hispanic White, non-Hispanic to Hispanic White, non-Hispanic to Asian/Pacific Islander Black, non-Hispanic to Hispanic Black, non-Hispanic to Asian/Pacific Islander Hispanic to Asian/Pacific Islander
Arizona 13.2 (10.6–16.4) § 8.5 (6.5–11.3) § § § 1.6 (1.1–2.2) § § § §
Arkansas 11.4 (9.5–13.7) 10.5 (7.8–14.2) § § § 1.1 (0.8–1.5) § § § § §
California 26.0 (21.9–30.8) 30.6 (23.1–40.5) 45.5 (41.1–50.3) 41.7 (34.3–50.6) § 0.8 (0.6–1.2) 0.6 (0.5–0.7) 0.6 (0.5–0.8) 0.7 (0.5–0.9) 0.7 (0.5–1.0) 1.1 (0.9–1.4)
Georgia 12.0 (9.6–15.0) 17.1 (14.7–20.0) 10.0 (7.7–13.0) 17.7 (12.9–24.3) § 0.7 (0.5–0.9) 1.2 (0.9–1.7) 0.7 (0.5–1.0) 1.7 (1.3–2.3) 1.0 (0.7–1.4) 0.6 (0.4–0.9)
Maryland 9.1 (7.5–11.1) 13.5 (10.6–17.1) 8.8 (5.5–14.0) 15.5 (10.8–22.3) § 0.7 (0.5–0.9) 1.0 (0.6–1.7) 0.6 (0.4–0.9) 1.5 (0.9–2.6) 0.9 (0.6–1.3) 0.6 (0.3–1.0)
Minnesota 16.6 (13.6–20.3) 23.5 (18.4–30.0) 24.4 (17.0–34.8) 30.8 (21.3–44.5) § 0.7 (0.5–1.0) 0.7 (0.5–1.0) 0.5 (0.4–0.8) 1.0 (0.6–1.5) 0.8 (0.5–1.2) 0.8 (0.5–1.3)
Missouri 13.1 (11.4–14.9) 11.5 (9.2–14.5) § 30.0 (20.7–43.3) § 1.1 (0.9–1.5) § 0.4 (0.3–0.6) § 0.4 (0.3–0.6) §
New Jersey 15.2 (12.0–19.2) 20.0 (16.6–24.1) 20.6 (17.4–24.4) 16.4 (10.1–26.5) § 0.8 (0.6–1.0) 0.7 (0.6–1.0) 0.9 (0.5–1.6) 1.0 (0.8–1.2) 1.2 (0.7–2.0) 1.3 (0.8–2.1)
Tennessee 16.8 (14.9–18.9) 21.1 (17.4–25.5) 24.8 (20.1–30.5) 20.0 (12.5–31.8) § 0.8 (0.6–1.0) 0.7 (0.5–0.9) 0.8 (0.5–1.4) 0.9 (0.6–1.1) 1.1 (0.6–1.7) 1.2 (0.7–2.1)
Utah 7.7 (6.5–9.1) § 10.8 (8.3–13.9) 9.9 (5.7–17.2) § § 0.7 (0.5–1.0) 0.8 (0.4–1.4) § § 1.1 (0.6–2.0)
Wisconsin 12.7 (11.1–14.5) 18.1 (15.0–22.0) 30.4 (25.7–36.0) 21.9 (15.8–30.3) § 0.7 (0.6–0.9) 0.4 (0.3–0.5) 0.6 (0.4–0.8) 0.6 (0.5–0.8) 0.8 (0.6–1.2) 1.4 (1.0–2.0)
Total 12.9 (12.3–13.6) 16.6 (15.4–17.8) 21.1 (19.8–22.4) 22.7 (20.3–25.4) 11.5 (7.2–18.4) 0.8 (0.7–0.9) 0.6 (0.6–0.7) 0.6 (0.5–0.6) 0.8 (0.7–0.9) 0.7 (0.6–0.8) 0.9 (0.8–1.1)

Table 3. Prevalence of autism spectrum disorder per 1,000 children aged 4 years, by race/ethnicity* — Autism and Developmental Disabilities Monitoring Network, 11 sites, 2018

Abbreviations: ASD = autism spectrum disorder; CI = confidence interval.
*Excludes children of other (including multiracial) or unknown race.
95% CIs were calculated using the Wilson score method.
§Estimate was suppressed because standard error for prevalence was ≥30% of estimate or prevalence ratio was based on an estimate that was suppressed.
Significant prevalence ratio (unrounded 95% CIs do not include 1.0).

Table 4.  

Site/Characteristic No. with intellectual disability information With co-occurring intellectual disability
No. (%)*
Site
Arizona 94 54 (57.4)
Arkansas 121 75 (62.0)
California 542 125 (23.1)
Georgia 192 115 (59.9)
Maryland 118 90 (76.3)
Minnesota 159 90 (56.6)
Missouri 63 31 (49.2)
New Jersey 205 114 (55.6)
Tennessee 241 162 (67.2)
Utah 90 53 (58.9)
Wisconsin 178 124 (69.7)
Total 2,003 1,033 (51.6)
Sex
Female 434 230 (53.0)
Male 1,569 803 (51.2)
Race/Ethnicity
White, non-Hispanic 744 371 (49.9)
Black, non-Hispanic 394 264 (67.0)
Asian/Pacific Islander 169 81 (47.9)
Hispanic 554 252 (45.5)
Median household income tertile§
Low 685 411 (60.0)
Middle 679 359 (52.9)
High 631 256 (40.6)

Table 4. Presence of co-occurring intellectual disability among children aged 4 years with autism spectrum disorder and available intellectual disability information, by site and selected characteristics — Autism and Developmental Disabilities Monitoring Network, 11 sites, 2018

*Chi-square p values for comparisons: male to female: p = 0.54; White to Asian/Pacific Islander: p = 0.71; White to Hispanic: p = 0.13; White to Black, Asian/Pacific Islander to Black, Hispanic to Black: p<0.01; Cochran-Armitage test for trend for median household income tertile: p<0.01.
Excludes children of other (including multiracial) or unknown race.
§Limited to 3,534 children with median household income information (218 children were not able to be matched to census tract and two children were living in census tracts with suppressed median household income estimates).

Table 5.  

Site/Characteristic No. with evaluation Evaluated by age 36 mos
No. (%)*
Site
Arizona 141 115 (81.6)
Arkansas 182 124 (68.1)
California 679 504 (74.2)
Georgia 285 211 (74.0)
Maryland 183 152 (83.1)
Minnesota 228 156 (68.4)
Missouri 334 248 (74.3)
New Jersey 338 250 (74.0)
Tennessee 476 322 (67.6)
Utah 202 134 (66.3)
Wisconsin 473 314 (66.4)
Total 3,521 2,530 (71.9)
Sex
Female 791 564 (71.3)
Male 2,730 1,966 (72.0)
Race/Ethnicity
White, non-Hispanic 1,393 1,003 (72.0)
Black, non-Hispanic 706 499 (70.7)
Asian/Pacific Islander 280 199 (71.1)
Hispanic 894 653 (73.0)
Co-occurring intellectual disability
Intellectual disability 1,028 803 (78.1)
No intellectual disability 967 770 (79.6)
Unknown 1,526 957 (62.7)
Median household income tertile §
Low 1,274 911 (71.5)
Middle 1,165 860 (73.8)
High 999 750 (75.1)

Table 5. Percentage of children aged 4 years with autism spectrum disorder who had earliest recorded evaluation by age 36 months, by site and selected characteristics — Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2018

*Chi-square p values for comparisons: sex: p = 0.73; race: p = 0.75; intellectual disability to no intellectual disability: p = 0.44; intellectual disability to unknown, no intellectual disability to unknown: p<0.01; low to high median household income: p = 0.06; Cochran-Armitage test for trend for median household income tertile: p = 0.45.
Excludes children of other (including multiracial) or unknown race.
§Limited to 3,534 children with median household income information (218 children were not able to be matched to census tract, and two children were living in census tracts with suppressed median household income estimates).

Table 6.  

Site Low MHI tertile Middle MHI tertile High MHI tertile
Rate (95% CI) Rate (95% CI) Rate (95% CI)
Arizona§ 9.0 (7.0–11.6) 6.1 (4.4–8.6) 5.0 (3.4–7.4)
Arkansas 8.9 (7.2–11.0) 10.6 (8.1–14.0) 15.0 (9.1–24.6)
California§ 36.3 (31.5–41.7) 27.6 (24.2–31.6) 26.1 (22.9–29.7)
Georgia 9.3 (7.5–11.5) 9.7 (7.7–12.2) 12.2 (9.8–15.1)
Maryland 7.4 (4.6–12.0) 8.3 (6.4–10.9) 7.8 (6.3–9.7)
Minnesota 18.1 (14.3–23.0) 15.8 (12.6–19.8) 14.6 (11.4–18.6)
Missouri§ 9.4 (7.5–11.7) 12.3 (10.2–14.9) 13.2 (10.9–16.1)
New Jersey 17.3 (14.7–20.3) 19.9 (15.6–25.5) 18.3 (15.1–22.3)
Tennessee§ 14.6 (12.3–17.2) 15.6 (13.3–18.2) 10.1 (7.8–13.0)
Utah§ 11.9 (8.9–15.9) 5.9 (4.6–7.7) 4.4 (3.3–5.8)
Wisconsin§ 14.2 (12.2–16.5) 12.3 (10.2–14.7) 9.3 (7.4–11.7)
Total§ 13.8 (13.0–14.6) 12.8 (12.1–13.7) 11.7 (11.0–12.5)

Table 6. Cumulative incidence of autism spectrum disorder diagnosis or eligibility by age 48 months per 1,000 children aged 4 years, by site and median household income tertile* — Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2018

Abbreviations: CI = confidence interval; MHI = median household income.
*Limited to 3,534 children with MHI information (218 children were not able to be matched to census tract and two children were living in census tracts with suppressed MHI estimates).
95% CIs were calculated using the Wilson score method.
§Cochran-Armitage test for trend p<0.05.

Table 7.  

Site No. of children aged 4 yrs with suspected ASD Suspected ASD prevalence rate (95% CI)* Ratio of ASD prevalence to suspected ASD prevalence
Arizona 68 4.9 (3.9–6.2) 2:1
Arkansas 72 4.7 (3.7–5.9) 3:1
California 6 0.4 (0.2–0.8) 116:1
Georgia 69 3.0 (2.4–3.8) 5:1
Maryland 48 2.4 (1.8–3.2) 5:1
Minnesota 23 2.2 (1.5–3.3) 10:1
Missouri 36 1.5 (1.1–2.0) 9:1
New Jersey 37 2.1 (1.6–2.9) 9:1
Tennessee 33 1.3 (0.9–1.8) 15:1
Utah 86 3.4 (2.8–4.2) 3:1
Wisconsin 102 3.6 (2.9–4.3) 5:1
Total 580 2.6 (2.4–2.9) 6:1

Table 7. Prevalence of suspected autism spectrum disorder per 1,000 children aged 4 years — Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2018

Abbreviations: ASD = autism spectrum disorder; CI = confidence interval.
*95% CIs were calculated using the Wilson score method.

Table 8.  

Characteristic Children with ASD Children with suspected ASD*
No. (%) No. (%)
Sex
Female 830 (22.1) 140 (24.1)
Male 2,924 (77.9) 440 (75.9)
Race/Ethnicity
White, non-Hispanic 1,487 (42.6) 272 (49.5)
Black, non-Hispanic 771 (22.1) 127 (23.1)
Asian/Pacific Islander 302 (8.6) 25 (4.6)
Hispanic 932 (26.7) 125 (22.8)
Intellectual disability§
Intellectual disability 1,033 (51.6) 106 (41.4)
Evaluation
Earliest recorded evaluation by age ≤36 mos 2,530 (71.9) 401 (69.1)
Median household income tertile**
Low 1,309 (37.0) 183 (32.3)
Middle 1,199 (33.9) 204 (36.0)
High 1,026 (29.0) 179 (31.6)

Table 8. Characteristics of children aged 4 years with autism spectrum disorder and with suspected autism spectrum disorder — Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2018

Abbreviation: ASD = autism spectrum disorder.
*Chi-square p values for comparisons: sex: p = 0.30; race: p<0.01; intellectual disability: p<0.01; evaluation: p = 0.61; Chi-square test of distribution of median household income tertile among children with ASD: p<0.01; Chi-square test of distribution of median household income tertile among children with suspected ASD: p = 0.38.
Excludes children of other (including multiracial) or unknown race.
§Limited to children with intellectual disability data available.
Limited to children with an evaluation (N = 3,521 children with ASD; N = 580 children with suspected ASD).
**Limited to 3,534 children with median household income information (218 children were not able to be matched to census tract and two children were living in census tracts with suppressed median household income estimates).

CME / ABIM MOC / CE

Early Identification of Autism Spectrum Disorder Among Children Aged 4 Years — Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2018

  • Authors: Kelly A. Shaw, PhD; Matthew J. Maenner, PhD; Amanda V. Bakian, PhD; Deborah A. Bilder, MD; Maureen S. Durkin, DrPH, PhD; Sarah M. Furnier, MS; Michelle M. Hughes, PhD; Mary Patrick, MPH; Karen Pierce, PhD; Angelica Salinas, MS; Josephine Shenouda, MS; Alison Vehorn, MS; Zachary Warren, PhD; Walter Zahorodny, PhD; John N. Constantino, MD; Monica DiRienzo, MA; Amy Esler, PhD; Robert T. Fitzgerald, PhD; Andrea Grzybowski, MS; Allison Hudson; Margaret H. Spivey; Akilah Ali, MPH; Jennifer G. Andrews, PhD; Thaer Baroud, MHSA, MA; Johanna Gutierrez; Libby Hallas, MS; Jennifer Hall-Lande, PhD; Amy Hewitt, PhD; Li-Ching Lee, PhD; Maya Lopez, MD; Kristen Clancy Mancilla; Dedria McArthur, MPH; Sydney Pettygrove, PhD; Jenny N. Poynter, PhD; Yvette D. Schwenk, MS; Anita Washington, MPH; Susan Williams; Mary E. Cogswell, DrPH
  • CME / ABIM MOC / CE Released: 3/31/2022
  • THIS ACTIVITY HAS EXPIRED FOR CREDIT
  • Valid for credit through: 3/31/2023, 11:59 PM EST
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Target Audience and Goal Statement

This activity is intended for public health officials, pediatricians, psychiatrists, neurologists, family practice clinicians, nurses, and other clinicians caring for children with or at risk for autism spectrum disorder (ASD).

The goal of this activity is to describe prevalence of ASD, associated intellectual disability, and early identification of ASD among children aged 4 years in the United States in 2018, according to an analysis of surveillance data from all 11 Autism and Developmental Disabilities Monitoring Network (ADDM) Network sites.

Upon completion of this activity, participants will:

  1. Describe prevalence of autism spectrum disorder (ASD) and associated intellectual disability among children aged 4 years in the United States in 2018, according to an analysis of surveillance data from all 11 Autism and Developmental Disabilities Monitoring Network (ADDM) Network sites
  2. Determine early identification of ASD and prevalence of suspected ASD among children aged 4 years in the United States in 2018, according to an analysis of surveillance data from all 11 ADDM Network sites
  3. Identify clinical and public health implications of prevalence of ASD, associated intellectual disability and other characteristics, and early identification of ASD among children aged 4 years in the United States in 2018, according to an analysis of surveillance data from all 11 ADDM Network sites


Faculty

  • Kelly A. Shaw, PhD

    National Center on Birth Defects and Developmental Disabilities
    Centers for Disease Control and Prevention (CDC)
    Atlanta, Georgia

    Disclosures

    Disclosure: Kelly A. Shaw, PhD, has disclosed no relevant financial relationships.

  • Matthew J. Maenner, PhD

    National Center on Birth Defects and Developmental Disabilities
    Centers for Disease Control and Prevention (CDC)
    Atlanta, Georgia

    Disclosures

    Disclosure: Matthew J. Maenner, PhD, has disclosed no relevant financial relationships.

  • Amanda V. Bakian, PhD

    University of Utah School of Medicine
    Salt Lake City, Utah

    Disclosures

    Disclosure: Amanda V. Bakian, PhD, has disclosed no relevant financial relationships.

  • Deborah A. Bilder, MD

    University of Utah School of Medicine
    Salt Lake City, Utah

    Disclosures

    Disclosure: Deborah A. Bilder, MD, has disclosed no relevant financial relationships.

  • Maureen S. Durkin, DrPH, PhD

    University of Wisconsin
    Madison, Wisconsin

    Disclosures

    Disclosure: Maureen S. Durkin, DrPH, PhD, has disclosed no relevant financial relationships.

  • Sarah M. Furnier, MS

    University of Wisconsin
    Madison, Wisconsin

    Disclosures

    Disclosure: Sarah M. Furnier, MS, has disclosed no relevant financial relationships.

  • Michelle M. Hughes, PhD

    National Center on Birth Defects and Developmental Disabilities
    Centers for Disease Control and Prevention (CDC)
    Atlanta, Georgia

    Disclosures

    Disclosure: Michelle M. Hughes, PhD, has disclosed no relevant financial relationships.

  • Mary Patrick, MPH

    National Center on Birth Defects and Developmental Disabilities
    Centers for Disease Control and Prevention (CDC)
    Atlanta, Georgia

    Disclosures

    Disclosure: Mary Patrick, MPH, has disclosed no relevant financial relationships.

  • Karen Pierce, PhD

    University of California, San Diego

    Disclosures

    Disclosure: Karen Pierce, PhD, has disclosed no relevant financial relationships.

  • Angelica Salinas, MS

    University of Wisconsin
    Madison, Wisconsin

    Disclosures

    Disclosure: Angelica Salinas, MS, has disclosed no relevant financial relationships.

  • Josephine Shenouda, MS

    Rutgers New Jersey Medical School
    Newark, New Jersey

    Disclosures

    Disclosure: Josephine Shenouda, MS, has disclosed no relevant financial relationships.

  • Alison Vehorn, MS

    Vanderbilt University Medical Center
    Nashville, Tennessee

    Disclosures

    Disclosure: Alison Vehorn, MS, has disclosed no relevant financial relationships.

  • Zachary Warren, PhD

    Vanderbilt University Medical Center
    Nashville, Tennessee

    Disclosures

    Disclosure: Zachary Warren, PhD, has disclosed the following relevant financial relationships:
    Consulting fees: Adaptive Technology Consulting; Roche
    Participation on a data safety monitoring board or advisory board: Roche IDSMB.

  • Walter Zahorodny, PhD

    Rutgers New Jersey Medical School
    Newark, New Jersey

    Disclosures

    Disclosure: Walter Zahorodny, PhD, has disclosed no relevant financial relationships.

  • John N. Constantino, MD

    Washington University
    St Louis, Missouri

    Disclosures

    Disclosure: John N. Constantino, MD, has disclosed no relevant financial relationships.

  • Monica DiRienzo, MA

    National Center on Birth Defects and Developmental Disabilities
    Centers for Disease Control and Prevention (CDC)
    Atlanta, Georgia

    Disclosures

    Disclosure: Monica DiRienzo, MA, has disclosed no relevant financial relationships.

  • Amy Esler, PhD

    University of Minnesota
    Minneapolis, Minnesota

    Disclosures

    Disclosure: Amy Esler, PhD, has disclosed no relevant financial relationships.

  • Robert T. Fitzgerald, PhD

    Washington University
    St. Louis, Missouri

    Disclosures

    Disclosure: Robert T. Fitzgerald, PhD, has disclosed no relevant financial relationships.

  • Andrea Grzybowski, MS

    University of California, San Diego

    Disclosures

    Disclosure: Andrea Grzybowski, MS, has disclosed no relevant financial relationships.

  • Allison Hudson

    University of Arkansas for Medical Sciences
    Little Rock, Arkansas

    Disclosures

    Disclosure: Allison Hudson has disclosed no relevant financial relationships.

  • Margaret H. Spivey

    Johns Hopkins University
    Baltimore, Maryland
     

    Participation by Ms Margaret H. Spivey does not constitute or imply endorsement by the Johns Hopkins University or the Johns Hopkins Hospital and Health System.

    Disclosures

    Disclosure: Margaret H. Spivey, has disclosed no relevant financial relationships.

  • Akilah Ali, MPH

    National Center on Birth Defects and Developmental Disabilities
    Centers for Disease Control and Prevention (CDC)
    Atlanta, Georgia
    Oak Ridge Institute for Science and Education
    Oak Ridge, Tennessee

    Disclosures

    Disclosure: Akilah Ali, MPH, has disclosed no relevant financial relationships.

  • Jennifer G. Andrews, PhD

    University of Arizona
    Tucson, Arizona

    Disclosures

    Disclosure: Jennifer G. Andrews, PhD, has disclosed no relevant financial relationships.

  • Thaer Baroud, MHSA, MA

    University of Arkansas for Medical Sciences
    Little Rock, Arkansas

    Disclosures

    Disclosure: Thaer Baroud, MHSA, MA, has disclosed no relevant financial relationships.

  • Johanna Gutierrez

    University of Utah School of Medicine
    Salt Lake City, Utah

    Disclosures

    Disclosure: Johanna Gutierrez has disclosed no relevant financial relationships.

  • Libby Hallas, MS

    University of Minnesota
    Minneapolis, Minnesota

    Disclosures

    Disclosure: Libby Hallas, MS, has disclosed no relevant financial relationships.

  • Jennifer Hall-Lande, PhD

    University of Minnesota
    Minneapolis, Minnesota

    Disclosures

    Disclosure: Jennifer Hall-Lande, PhD, has disclosed no relevant financial relationships.

  • Amy Hewitt, PhD

    University of Minnesota
    Minneapolis, Minnesota

    Disclosures

    Disclosure: Amy Hewitt, PhD, has disclosed no relevant financial relationships.

  • Li-Ching Lee, PhD

    Johns Hopkins University
    Baltimore, Maryland
     

    Participation by Dr Li-Ching Lee, PhD, does not constitute or imply endorsement by the Johns Hopkins University or the Johns Hopkins Hospital and Health System.

    Disclosures

    Disclosure: Li-Ching Lee, PhD, has disclosed no relevant financial relationships.

  • Maya Lopez, MD

    University of Arkansas for Medical Sciences
    Little Rock, Arkansas

    Disclosures

    Disclosure: Maya Lopez, MD, has disclosed no relevant financial relationships.

  • Kristen Clancy Mancilla

    University of Arizona
    Tucson, Arizona

    Disclosures

    Disclosure: Kristen Clancy Mancilla has disclosed no relevant financial relationships.

  • Dedria McArthur, MPH

    National Center on Birth Defects and Developmental Disabilities
    Centers for Disease Control and Prevention (CDC)
    Atlanta, Georgia

    Disclosures

    Disclosure: Dedria McArthur, MPH, has disclosed no relevant financial relationships.

  • Sydney Pettygrove, PhD

    University of Arizona
    Tucson, Arizona

    Disclosures

    Disclosure: Sydney Pettygrove, PhD, has disclosed no relevant financial relationships.

  • Jenny N. Poynter, PhD

    University of Minnesota
    Minneapolis, Minnesota

    Disclosures

    Disclosure: Jenny N. Poynter, PhD, has disclosed no relevant financial relationships.

  • Yvette D. Schwenk, MS

    University of Arkansas for Medical Sciences
    Little Rock, Arkansas

    Disclosures

    Disclosure: Yvette D. Schwenk, MS, has disclosed no relevant financial relationships.

  • Anita Washington, MPH

    National Center on Birth Defects and Developmental Disabilities
    Centers for Disease Control and Prevention (CDC)
    Atlanta, Georgia

    Disclosures

    Disclosure: Anita Washington, MPH, has disclosed no relevant financial relationships.

  • Susan Williams

    National Center on Birth Defects and Developmental Disabilities
    Centers for Disease Control and Prevention (CDC)
    Atlanta, Georgia

    Disclosures

    Disclosure: Susan Williams has disclosed no relevant financial relationships.

  • Mary E. Cogswell, DrPH

    National Center on Birth Defects and Developmental Disabilities
    Centers for Disease Control and Prevention (CDC)
    Atlanta, Georgia

    Disclosures

    Disclosure: Mary E. Cogswell, DrPH, has disclosed no relevant financial relationships.

CME Author

  • Laurie Barclay, MD

    Freelance writer and reviewer
    Medscape, LLC

    Disclosures

    Disclosure: Laurie Barclay, MD, has disclosed the following relevant financial relationships:
    Stock, stock options, or bonds: AbbVie Inc. (former)

CME Reviewer/Nurse Planner

  • Leigh A. Schmidt, MSN, RN, CMSRN, CNE, CHCP

    Associate Director, Accreditation and Compliance
    Medscape, LLC

    Disclosures

    Disclosure: Leigh A. Schmidt, MSN, RN, CMSRN, CNE, CHCP, has disclosed no relevant financial relationships.


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CME / ABIM MOC / CE

Early Identification of Autism Spectrum Disorder Among Children Aged 4 Years — Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2018

Authors: Kelly A. Shaw, PhD; Matthew J. Maenner, PhD; Amanda V. Bakian, PhD; Deborah A. Bilder, MD; Maureen S. Durkin, DrPH, PhD; Sarah M. Furnier, MS; Michelle M. Hughes, PhD; Mary Patrick, MPH; Karen Pierce, PhD; Angelica Salinas, MS; Josephine Shenouda, MS; Alison Vehorn, MS; Zachary Warren, PhD; Walter Zahorodny, PhD; John N. Constantino, MD; Monica DiRienzo, MA; Amy Esler, PhD; Robert T. Fitzgerald, PhD; Andrea Grzybowski, MS; Allison Hudson; Margaret H. Spivey; Akilah Ali, MPH; Jennifer G. Andrews, PhD; Thaer Baroud, MHSA, MA; Johanna Gutierrez; Libby Hallas, MS; Jennifer Hall-Lande, PhD; Amy Hewitt, PhD; Li-Ching Lee, PhD; Maya Lopez, MD; Kristen Clancy Mancilla; Dedria McArthur, MPH; Sydney Pettygrove, PhD; Jenny N. Poynter, PhD; Yvette D. Schwenk, MS; Anita Washington, MPH; Susan Williams; Mary E. Cogswell, DrPHFaculty and Disclosures
THIS ACTIVITY HAS EXPIRED FOR CREDIT

CME / ABIM MOC / CE Released: 3/31/2022

Valid for credit through: 3/31/2023, 11:59 PM EST

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Abstract and Introduction

Abstract

Problem/Condition: Autism spectrum disorder (ASD).

Period Covered: 2018.

Description of System: The Autism and Developmental Disabilities Monitoring Network is an active surveillance program that estimates ASD prevalence and monitors timing of ASD identification among children aged 4 and 8 years. This report focuses on children aged 4 years in 2018, who were born in 2014 and had a parent or guardian who lived in the surveillance area in one of 11 sites (Arizona, Arkansas, California, Georgia, Maryland, Minnesota, Missouri, New Jersey, Tennessee, Utah, and Wisconsin) at any time during 2018. Children were classified as having ASD if they ever received 1) an ASD diagnostic statement (diagnosis) in an evaluation, 2) a special education classification of ASD (eligibility), or 3) an ASD International Classification of Diseases (ICD) code. Suspected ASD also was tracked among children aged 4 years. Children who did not meet the case definition for ASD were classified as having suspected ASD if their records contained a qualified professional's statement indicating a suspicion of ASD.

Results: For 2018, the overall ASD prevalence was 17.0 per 1,000 (one in 59) children aged 4 years. Prevalence varied from 9.1 per 1,000 in Utah to 41.6 per 1,000 in California. At every site, prevalence was higher among boys than girls, with an overall male-to-female prevalence ratio of 3.4. Prevalence of ASD among children aged 4 years was lower among non-Hispanic White (White) children (12.9 per 1,000) than among non-Hispanic Black (Black) children (16.6 per 1,000), Hispanic children (21.1 per 1,000), and Asian/Pacific Islander (A/PI) children (22.7 per 1,000). Among children aged 4 years with ASD and information on intellectual ability, 52% met the surveillance case definition of co-occurring intellectual disability (intelligence quotient ≤70 or an examiner's statement of intellectual disability documented in an evaluation). Of children aged 4 years with ASD, 72% had a first evaluation at age ≤36 months. Stratified by census-tract–level median household income (MHI) tertile, a lower percentage of children with ASD and intellectual disability was evaluated by age 36 months in the low MHI tertile (72%) than in the high MHI tertile (84%). Cumulative incidence of ASD diagnosis or eligibility received by age 48 months was 1.5 times as high among children aged 4 years (13.6 per 1,000 children born in 2014) as among those aged 8 years (8.9 per 1,000 children born in 2010). Across MHI tertiles, higher cumulative incidence of ASD diagnosis or eligibility received by age 48 months was associated with lower MHI. Suspected ASD prevalence was 2.6 per 1,000 children aged 4 years, meaning for every six children with ASD, one child had suspected ASD. The combined prevalence of ASD and suspected ASD (19.7 per 1,000 children aged 4 years) was lower than ASD prevalence among children aged 8 years (23.0 per 1,000 children aged 8 years).

Interpretation: Groups with historically lower prevalence of ASD (non-White and lower MHI) had higher prevalence and cumulative incidence of ASD among children aged 4 years in 2018, suggesting progress in identification among these groups. However, a lower percentage of children with ASD and intellectual disability in the low MHI tertile were evaluated by age 36 months than in the high MHI group, indicating disparity in timely evaluation. Children aged 4 years had a higher cumulative incidence of diagnosis or eligibility by age 48 months compared with children aged 8 years, indicating improvement in early identification of ASD. The overall prevalence for children aged 4 years was less than children aged 8 years, even when prevalence of children suspected of having ASD by age 4 years is included. This finding suggests that many children identified after age 4 years do not have suspected ASD documented by age 48 months.

Public Health Action: Children born in 2014 were more likely to be identified with ASD by age 48 months than children born in 2010, indicating increased early identification. However, ASD identification among children aged 4 years varied by site, suggesting opportunities to examine developmental screening and diagnostic practices that promote earlier identification. Children aged 4 years also were more likely to have co-occurring intellectual disability than children aged 8 years, suggesting that improvement in the early identification and evaluation of developmental concerns outside of cognitive impairments is still needed. Improving early identification of ASD could lead to earlier receipt of evidence-based interventions and potentially improve developmental outcomes.

Introduction

Autism spectrum disorder (ASD) is a developmental disability characterized by deficits in social communication and interaction and the presence of restricted interests and repetitive behaviors. Early routine screening for ASD and other developmental concerns is recommended by the American Academy of Pediatrics[1] because early evaluation, diagnosis, and evidence-based interventions could enhance short-term and long-term developmental outcomes for young children with ASD.[2–6] Because of the potential to improve outcomes, increasing the proportion of all children who receive a developmental screening by age 36 months and the proportion of children with ASD who receive special services by age 48 months are included as Healthy People 2030 goals.[7]

Since 2010, CDC has conducted active population-based surveillance of ASD among children aged 4 years as a subset of the Autism and Developmental Disabilities Monitoring (ADDM) Network to better understand early identification of ASD in communities. Surveillance among children aged 4 years in 2016[8] indicated similar prevalence among children of different racial and ethnic groups, consistent with trends among children aged 8 years.[9] In addition, ASD identification measured by cumulative incidence of diagnosis by age 48 months was higher among children aged 4 years (born in 2012) compared with children aged 8 years (born in 2008), suggesting increased early identification of ASD among the younger cohort.[8] Cumulative incidence represents the number of children identified at or before each month of age divided by the total population and, unlike median age calculations, it reflects differences in prevalence and allows direct age-by-age comparison over time and between groups of children.[9,10]

For surveillance year 2018, ASD surveillance among children aged 4 years expanded from a subset to all ADDM Network sites. For this age group, sites ascertained children with characteristics meeting the ASD case definition as well as those who were suspected of having ASD by a qualified provider.

This report focuses on early identification of children with ASD and presents the estimated prevalence of ASD and suspected ASD among children aged 4 years, cumulative incidence of ASD identified by age 48 months, and characteristics of children aged 4 years with ASD and suspected ASD identified by ADDM Network sites in 2018. These data can be used to track trends and support efforts to ensure children with ASD are identified and receive necessary evidence-based interventions as early as possible.