The revised version of the Sequential Intercept Model is a tool for victims, offenders, families and providers as it follows both the offender and victim perspectives as they navigate the criminal justice system. CREDIT A.J. Drexel Autism Institute

Autistic individuals interact with the justice system at high rates as both victims and offenders. With a grant from the International Society for Autism Research (INSAR) in late 2019, Drexel University’s A.J. Drexel Autism Institute explored ways to improve interactions between autistic individuals and the justice system. The funding and collaboration led to the recently published policy brief, “Autism and the Criminal Justice System: Policy Opportunities and Challenges,” with wide reaching recommendations and considerations for the broader justice system.

Recommendations from the policy brief include provisions of trauma-informed supports for autistic victims and a need for nations to sign and ratify the United Nations Convention on the Rights of Persons with Disabilities. The brief also includes recommendations that are specific to different aspects of the justice system – community services, law enforcement, confinement and re-entry – that are focused on novel programing like diversionary efforts to keep autistic people out of jail, alternatives to policing and the use of peer supports.

The policy recommendations are intended to address all individuals who are impacted by the overrepresentation of autistic individuals in the justice system. This includes autistic individuals – whether a victim or witness – and their family members and caregivers. Justice system professionals who interact with autistic individuals, community service providers and other involved professionals, also need guidance, support and new practices to effectively support autistic individuals who interact with the justice system. These recommendations offer strategies for the field and focus on generating and implementing equity-informed solutions.

“This international policy brief is an important first step to ensuring equitable access to justice for autistic individuals,” said Lindsay Shea, DrPH, leader of the Policy and Analytics Center in the Autism Institute. “Future research is needed at each stage of the criminal justice system that identifies evidence-based practices and focuses on solutions.”

Anchored by a revised version of the Sequential Intercept Model (SIM), which organizes each stage of the justice system as specific intercepts, the policy brief leverages the predictable pathways that offenders and victims follow throughout the justice system. This allows for policies that address a complex and disconnected system and guides the direction of future research.

The collaboration between the International Society for Autism Research and the Policy and Analytics Center led to an international team of stakeholders from 10 countries across multiple continents convening regularly over the course of a year to produce policy recommendations at each intercept and throughout the entire justice system. The team of stakeholders was comprised of members of the autism community, including autistic self-advocates and family members, along with researchers, policymakers and justice system professionals.

Two workgroups also focused on revising the Sequential Intercept Model and developing a global survey to measure and characterize interactions between autistic individuals and the justice system across all stages.

Autistic self-advocates and justice system professionals reviewed the text of the recommendations and policy briefs for accessibility and feedback.

“We hope this brief can help spark a discourse focusing on preventing root causes for offending and levying justice grounded in supporting and rehabilitating rather than punishing,” said Shea.

Shea added that policy changes are needed globally, as well as reimagining the role of justice.

The Policy and Analytics Center and the stakeholders of the Global Autism and Criminal Justice Consortium that helped produce this brief will continue to lead the charge on this front.

For more information, click here.

Medical insurance claims might do more than help pay for health concerns; they could help predict them, according to new findings from an interdisciplinary Penn State research team published in BMJ Health & Care Informatics. The researchers developed machine learning models that assess the connections among hundreds of clinical variables, including doctor visits and health care services for seemingly unrelated medical conditions, to predict the likelihood of autism spectrum disorder in young children. 

“Insurance claim data, which is de-identified and widely available in marketing scan datasets, provides thorough, longitudinal medical details about the patient,” said corresponding author Qiushi Chen, assistant professor of industrial and manufacturing engineering in the Penn State College of Engineering. “The scientific literature in the field suggests that kids with autism spectrum disorder also often have higher rates of clinical symptoms, such as different types of infections, gastrointestinal problems, seizures, as well as behavior indications. Those symptoms are not a cause of autism but are often manifested among kids with autism especially at young ages, so we were inspired to synthesize the medical information to quantify and predict that associated likelihood.”

The researchers fed the data into machine learning models, training it to assess hundreds of variables to find correlations that are related to an increased likelihood for autism spectrum disorder. 

“Autism spectrum disorder is a developmental disability,” said co-author Guodong Liu, associate professor of public health sciences, of psychiatry and behavioral health and of pediatrics at Penn State College of Medicine. “It takes observation and several screenings for a clinician to make a diagnosis. The process is usually lengthy, and many kids miss the window for early interventions — the most effective way to improve outcomes.” 

One of the commonly used screening tools to help identify young children with an elevated likelihood of autism spectrum disorder is called the Modified Checklist for Autism in Toddlers (M-CHAT), which is normally given at routine well-child visits at 18 and 24 months old. It consists of 20 questions focused on behaviors related to eye contact, social interactions and some physical milestones such as walking. Guardians answer based on their observations, but, according to Chen, development varies so significantly at these ages that the tool may misidentify children. As a result, children often are not officially diagnosed until they are four or five years old, meaning they miss years of potential early interventions. 

“Our new model, which quantifies the sum of identified risk factors together to inform the likelihood level, is already comparable to — and in some cases even slightly better than — the existing screening tool,” Chen said. “When we combine the model with the screening tool, we have a very promising approach for clinicians.” 

According to Liu, it would be practically feasible to integrate the model with the screening tool for clinical use. 

“A unique strength of this work is that this clinical informatics approach can be easily incorporated into the clinical flow,” Liu said. “The prediction model could be embedded in a hospital’s Electronic Health Record system, which is used to chart patient health, as a clinical decision support tool to flag the high-risk children so that both clinicians and the families could take actions sooner.” 

This work, funded by the National Institutes of Health, the Penn State Social Science Research Institute and the Penn State College of Engineering, is the basis of a new $460,000 grant awarded to Chen and Whitney Guthrie, clinical psychologist at the Children’s Hospital of Philadelphia Center for Autism Research and assistant professor of psychiatry and pediatrics at the University of Pennsylvania Perelman School of Medicine, by the National Institute of Mental Health. 

They are using the new grant to analyze precisely how well the combined hospital record data and screen results predict autism diagnoses, as well as exploring other potential screening tools that could better equip clinicians to help their patients. 

“Not only is the current tool missing many children on the autism spectrum, but many children who are detected by our screening tools experience long waitlists because of our limited diagnosis capacity,” Guthrie said. “Although it does detect many children, the M-CHAT also has very high rates of false positives and false negatives, which means that many autistic children are missed, and other children are referred for an autism evaluation when they may not need one. Both problems contribute to the long wait — often many months or even years — for further evaluation. The consequences for children who are missed by our current screening tools are particularly important because delayed diagnosis often means that children miss the window for early intervention entirely. Pediatricians need better screening tools to accurately identify all children who need an autism evaluation as early as possible.”

Part of the problem is the limited number of psychologists, developmental pediatricians and other experts in pediatric development who can make an autism spectrum disorder diagnosis. According to Chen, the solution may exist in industrial engineering. 

“The key idea is improving how we use resources,” Chen said. “With Dr. Guthrie’s clinical expertise and my group’s modeling capabilities, we aim to develop a tool that primary care physicians without specialized training can apply to make confident assessments to diagnose children as early as possible in order to get the care they need as soon as possible.” 

Autistic person reacts to NEW autistic character- Bruno (we DO talk about Bruno today)

Ben shares his thoughts and perspective as a non-speaking autistic adult. Note that this interview has been significantly edited down for the convenience of the viewer. Ben uses a letterboard in this interview to communicate, and Sarah, his Communication and Regulation Partner (CRP) helps. I left in some snippets to help the audience understand a little about how Ben uses letterboard. Please don’t get confused thinking that Ben gave a long answer with just a few points at the board! He spent a lot of time and effort to be able to share his thoughts in a real-time conversation with me, and I am so, so appreciative of his perspective and willingness to participate.