Autism 201: The State of Autism in 2019

Jim Mancini MS, CCC-SLP

• What does inclusion mean to you?

Updating Our

Understanding of

Risks for Autism

Sara Jane Webb

Professor

Psychiatry & Behavioral Sciences, Psychology & Neuroscience

University of Washington

Seattle Children’s Research Institute Autism 200

Risk?

Infant Behaviors

Prenatal & Perinatal

Genetic

Environmental

Measurement of Risk

Infant behaviors| Social Attention Markers of Risk

• 6 & 12 months

• Decreased attention to the face,

• Altered time to learn about a face,

• Decreased brain activity to social information

• 12 – 24 months Early Red Flags for autism

• Poor eye contact

• Lack of attention to others

• Difficulties with joint attention

• Failure to differentiate and respond to emotions

• Poor imitation

Updating our understanding --> Infant Behaviors

LeBarton ES, Landa RJ. (2018). Infant motor skill predicts later expressive language and autism spectrum disorder diagnosis. Infant Behav Dev.

Motor skills in infancy are related to object exploration, social communication and social skills.

• N=140 infants (51 LR and 89 HR) • 6 month Peabody Development Motor Scales

• Less mature Visual – Motor integration was related to later ASD

• Gross motor (stationary) and grasping were related to 3 year old language ability

Updating our understanding --> Infant Behaviors

Wolff J.J. et al. (2018). A longitudinal study of parent-reported sensory responsiveness in toddlers at-risk for autism. J. Child Psychol. Psychiatry

DSM-5 added Sensory Symptoms to the RRB domain

‘hyper-responsivity or hypo-responsivity to sensory input or unusual interests in sensory aspects of the environment’ (DSM-5)

• IBIS N=331 HR & n=135 LR

• Sensory Experiences Questionnaire (SEQ) @ 12 months

• Children later diagnosed with ASD • higher in sensory hyper‐responsivity & tactile stimulation

• Increased in sensory issues over time

Globally ~13 million infants are born premature per year. 1 in 10 infants in the US are born premature.

Agrawal S. et al. (2018). Prevalence of Autism Spectrum Disorder in Preterm Infants: A Meta-analysis. Pediatrics

Updating our understanding --> Pre-natal and Perinatal Risk

Who is at risk for developing ASD?

• Meta Analysis of 18 studies • N=3366 pre-term infants

• 25-32 weeks

• 719 -1565 g

• Follow up 1.5 to 21 years

• Prevalence of ASD • 7% in pre-terms

• 1.5% general population

Updating our understanding --> Prenatal Risk

Croen LA, et al. (2019). Family history of immune conditions and autism spectrum and developmental disorders: Findings from the study to explore early development. Autism Res.

Risk for ASD has been associated with immune system response & higher rates of ASD are found in those with a family history of autoimmune diseases.

Study to Explore Early Development

• California, Colorado, Georgia, Maryland, North Carolina, Pennsylvania

• Children born 2003-2006; English and Spanish speaking

Mothers @ Delivery

ASD (n=633) DD (n=984) TD (n=915)

Allergy 50.7% 47.6% 50.6%

Asthma 29.9%* 28.5% 25.5%*

Any Autoimmune 19.8% 20.7%* 16.9%*

Eczema / Psoriasis 13.4%* 12.4% 10.4%*

Updating our understanding --> Measurement of Risk

Janvier Y.M. et al. (2018). The Developmental Check-In: Development and initial testing of an autism screening tool targeting young children from underserved communities. Autism

Children with autism spectrum disorder from low-income, minority families or those with limited English proficiency are diagnosed at a later age, or not at all, compared with their more advantaged peers.

• N=376 24-60 mos • Federally qualified health centers

• Daycares

• Full evaluations regardless of scores

• The Developmental Check-in • Pictures (line draw & photo)

• English & Spanish

• Based on known red flags

• from ADOS and STAT

• Cut-off of 7.5 • sensitivity of .66 (True Positive Rate) and specificity of .76 (True Negative

Rate).

Risk?

Infant Behaviors

Prenatal & Perinatal

Genetic

Environmental

Measurement of Risk

Motor

Sensory

Asthma &

Autoimmune

Prematurity

Picture Based

Screeners

School and Medical Collaboration to evaluate children for Autism in Rural Communities

Amy Carlsen, RN January 17, 2019

Support for this work

• Support for this work comes from the University of Washington LEND program, the Washington State Department of Health (DOH), and the WA State Medical Home Partnerships Project (MHPP)

• This includes the federal AS3D Grant which has the goals of:

• improving access to interventions for children, youth and families with ASD/DD

• empowering families to partner with health professionals in decision making

• strengthening state level leadership and systems integration needed to assure timely identification and access to services

Lewis County School Medical Autism Review Team (SMART) – it all started here

How and Why:

• Lewis County Autism Coalition

• Workgroup to address challenges families faced to get a timely ASD diagnosis:

• Long wait lists at Mary Bridge and Seattle Children’s, UW

• Travel issues for families to go out of county – cost, time

• Support and encouragement from MB, Seattle Children’s, UW, and the WA State Dept. of Health

• Workgroup with help from Amy Carlsen, RN and UW graduate fellows created forms, processes

School Medical Autism Review Team Process Oct 2018

O t h e r E v a l u a t i o n s

Clinic Evaluations

SMART Data Review

No Evaluation Indicated ~ Refer

family back to community,

educational resources, Primary

Care Doctor

Evaluation Indicated ~

Determination of possible

evidence of ASD

Family shares

concerns about

their child to their

Primary Care

Doctor

Primary Care Doctor

& family agree for

the family to contact

SMART Coordinator

SMART

Coordinator &

the family begin

the data

collection

process

Medical Evaluations

School Evaluations;

IE P, IFSP

SMART administer

recommended

assessments-ADOS

STAT, as needed

Center of E xcellence (COE)

Evaluation scheduled

Results provided to family

preferred providers and

other providers of family

choice

SMART – School Medical Autism Review Team

ASD – Autism Spectrum Disorder ADOS – Autism Diagnostic

Observation Schedule COE – Center of E xcellence

Parent to Parent

recommended as a

family resource

What is a Center of Excellence?

• A Center of Excellence (COE) is any medical practice, psychology practice, or multidisciplinary assessment team that has received the COE training from the Health Care Authority or has been judged by the HCA to be qualified to write a prescription for ABA services. There are many different ways for children and youth to get a diagnosis of autism. In order for a child or youth to be eligible for ABA therapy through Apple Health/Medicaid, a recognized Center of Excellence (COE) must conduct a comprehensive evaluation, and write an order for Applied Behavior Analysis therapy, within the last two years.

More Information: https://www.hca.wa.gov/billers-

providers/programs-and-

services/autism-and-applied-

behavior-analysis-aba-therapy

Contact: Rebecca (Becky)Peters

ABA Clinical Program Manager

CQCT HCA ABA@hca.wa.gov

Tel: 360-725-1194

Centers of Excellence and the Health Care Authority

Who’s SMART?

• Pediatrician

• Special Education Director

• School Psychologists

• Speech Therapists

• Birth to Three

• ABA Therapist

• Coordinator, can be a RN, Community Health Worker, or Family Resource Coordinator

• Others at times: OT, other school/medical, parent representative

Why SMART Works

• Local people with passion

• Know many of the children and families

• Variety of agencies with broad knowledge – child development, medical, educator, IEP/evaluations etc.

• Coordinator keeps us organized and connects with families and agencies.

• Open discussion – all input welcome and valued

• BENEFITS wider than autism-

• sharing info, knowledge, resources between schools, pediatricians and community services

Variation - Community Resources and Need

Criteria for children seen • Age, specific to practice or insurance, ASD concerns or broader Coordinator • Role and funding Diagnosis/eligibility for what systems/services – depends on who diagnoses • For Developmental Disability Administration (DDA)? For Medicaid?

For other insurance? For Special Education Services under the Autism category?

Outcome from evaluation • Final Diagnosis or clear pathway to further evaluation

Future Opportunities

• Center of Excellence Training in Vancouver, WA on April 26-27

• Providers who are interested in being part of the SMART process, but want more training can visit Seattle Children’s Hospital for more training

• Talks are underway to bring an Autism ECHO project to Washington State to support COE trained providers to increase their knowledge and comfort with diagnosing and providing ongoing care of children with autism locally

• There is a partnership with the WA State Health Care Authority to support a change in the WAAC code to make an autism diagnosis by a COE provider valid for DDA services

• There is work being done with the HCA to increase Medicaid reimbursement for ABA therapy to attract more ABA providers

Why does this matter to me?

For more information:

WA State Medical Home Partnerships Project

• Amy Carlsen carlsa@uw.edu

• Kate Orville orville@uw.edu

Bits, Bytes, Bots:

Autism Technology in Review

2018

Frederick Shic, Ph.D. SCRI / UWSOM Pediatrics

UW CSE

A Brief Background

Seattle Children’s Innovative Technologies Lab

Technology in Autism Research: Translational Science

Clinical Experimental Development

Technology Development

Between-group Differences

Diagnostics

Individual Variation

Phenotyping

Trajectories

Interventions

Seattle Children’s Innovative Technologies Lab

0

5000

10000

15000

20000

25000

30000

1990 1995 2000 2005 2010 2015 2020

Year

#Publications/Year: Autism Technology

A) VoiceMatch B) VoiceChart C) Library

Figure 2: Screenshots from the Completed Phase I Prototype

Superpower Glass

https://www.smithsonianmag.com/innovation/superpower-glass-helps-kids-autism-understand-emotions-180970080/

Superpower Glass

https://www.smithsonianmag.com/innovation/superpower-glass-helps-kids-autism-understand-emotions-180970080/

Superpower Glass

• 24 families of children with ASD consented • 5 withdrew (younger children, Age = 7.5 (2.5) years

• 5 did not meet study requirements (3 sessions/week, 20 min/session)

• Age 8.5 (4.0) years, ABIQ = 75 (10)

• N=14, 9.6 (3.4), ABIQ=95 (22), 4-20 weeks (average 10 weeks)

• Results: • Increases in performance on emotion guessing game

• Decreases in SRS-2

• Not relation to intensity, IQ

• 12 of 14 families reported increases in eye contact

• Limitations: • No control group

In-home Social Robots

Participant Information

• 14 families of children with ASD

• 2 withdrew • Unrelated health issue

• Technical difficulties

• Children: • 5 females and 7 male

• Age: 6 years to 12 years (M = 9.02, SD = 1.41)

• Nonverbal IQ ≥ 70 • Differential Ability Scales (M =

94.17, SD = 20.06)

(Scassellati et al., SciRob

2018)

• Autonomous: 30 days without contact

• Adaptive: alters curriculum and engagement patterns to match strengths and preferences of individual child

• In-home: complex and challenging environment

• Tests generalized skill use: high bar for showing learning; new context, standardized clinician validated protocol

Differences from Prior Art

(Scassellati et al., SciRob

2018)

• Autonomous: 30 days without contact

• Adaptive: alters curriculum and engagement patterns to match strengths and preferences of individual child

• In-home: complex and challenging environment

• Tests generalized skill use: high bar for showing learning; new context, standardized clinician validated protocol

Skill Performance Improvements

127 hours of data, 837 games

played

Parent ratings

Clinical-Relevant Assessment

• Bean & Eigsti’s joint attention probes

• Clinician administered

• Naturalistic assessment

• For school-age children

• Similar to ADOS

Bean, J. L., & Eigsti, I. M. (2012). Assessment of joint attention in school-age children and adolescents. Research in Autism Spectrum Disorders, 6(4), 1304-1310.

t(8)=2.863, p = 0.02

(Scassellati et al., SciRob

2018)

Mobile Computer Vision for Phenotyping

Mobile Computer Vision for Phenotyping

Mobile Computer Vision for Phenotyping

Mobile Computer Vision for Phenotyping

Computer vision captures atypical attention in toddlers with ASD

Computer vision captures atypical attention in toddlers with ASD

Computer vision captures atypical attention in toddlers with ASD

Technology as supports for secondary schools

Technology as supports for secondary schools

• Positives:

• Makes learning easier (87%) and fun (85%)

• Ability to reach parents (66%)

• Used to relax (84%) and play with friends (49%)

• Communication:

• Friends (81%)

• Family members (74%)

• Keep up with what’s going on (55%)

• Make new friends (47%)

• Find people with same interests (47%)

• Avoid talking to people (32%)

• Communicate with Teachers (36%)

• Barriers:

– “Cyberslacking”/distraction (58%)

– Not permitted in all classes (44%)

Technology as supports for secondary schools

• Benefits:

• Increases in independence

• Facilitates social opportunities

• Reduce stress and anxiety

• Recommendations:

• Technology-aided instruction – evidence based (Wong et al., 2014)

• Teachers/institutions will need to facilitate

• Coaching and supports for how technology can be incorporated

• A shift towards focusing on how to use technology responsibly (Aakash et al., 2015)

Notable other works

• Rapp, A., Cena, F., Castaldo, R., Keller, R., & Tirassa, M. (2018). Designing technology for spatial needs: Routines, control and social competences of people with autism. International Journal of Human-Computer Studies, 120, 49–65. https://doi.org/10.1016/j.ijhcs.2018.07.005

• 12 adults with ASD interviewed

• Overarching theme of managing uncertainty: Crystalized, Safe, Social Aspects

• Lee, C. S. C., Lam, S. H. F., Tsang, S. T. K., Yuen, C. M. C., & Ng, C. K. M. (2018). The Effectiveness of Technology-Based Intervention in Improving Emotion Recognition Through Facial Expression in People with Autism Spectrum Disorder: a Systematic Review. Review Journal of Autism and Developmental Disorders, 5(2), 91–104. https://doi.org/10.1007/s40489-017-0125-1

• 8 studies

• Coaching support/focus on face features = effective

Areas of advancement

• Wearables

• Virtual reality

• Augmented reality • Video games

• Robots.. Lots of robots

• “Biomarker” technologies

• Eye Tracking

• EEG and other brain-based techniques

• Computational Behavioral Vision

• SGDs/AAC

• Things we didn’t expect: • Kapur, A., Kapur, S., & Maes, P. (2018). AlterEgo: A

Personalized Wearable Silent Speech Interface. In 23rd International Conference on Intelligent User Interfaces (pp. 43–53). New York, NY, USA: ACM. https://doi.org/10.1145/3172944.3172977

Technology in Autism Research: Translational Science

Clinical Experimental Development

Technology Development

Between-group Differences

Diagnostics

Individual Variation

Phenotyping

Trajectories

Interventions

Technology in Autism Research: Translational Science

Clinical Experimental Development

Technology Development

Between-group Differences

Diagnostics

Individual Variation

Phenotyping

Trajectories

Interventions

Adham Atyabi Ph.D Senior Post-doc

Jay Martini, PhD Postdoc Associate

Minah Kim B.A. Clinical Research Associate

Sarah Corrigan MA Clinical Research Associate

Fred Shic Ph.D Principal Troublemaker

Maddy Aubertine BA Clinical Research Associate

Kelsey Jackson MSc Clinical Research Associate

Emily Neuhaus, PhD Clinician

fNIRS Eye Tracking

Video Games/Apps Data Analytics

Chat with us: scitl@seattlechildrens.org

What’s worked... awesomely

60 Other projects: Tablet-based Eye Tracking, Hololens,

Screening for Developmental Risk

Chat with us: scitl@seattlechildrens.org