Assessing the validity of administrative health data for the identification of children and youth with autism spectrum disorder in Ontario.
Ontario’s best health-admin algorithm captures only half of youth with ASD—supplement with direct screening before allocating services.
01Research in Context
What this study did
The team built an algorithm that flags autism cases in Ontario health records. They tested how well the code spots kids and teens who truly have ASD.
They compared the algorithm list with real diagnoses. The goal was to see if billing codes alone can find every child who needs services.
What they found
The best code set caught only half of the youth with ASD. It missed one out of every two kids who actually had the diagnosis.
The codes were very good at avoiding false alarms. When the algorithm said 'yes,' it was right almost every time.
How this fits with other research
Mierau et al. (2026) used a different Ontario record set and found the same gap. Their EMR method also under-counted ASD, so the problem is not just billing codes.
Moss et al. (2009) looked at toddler screeners and saw poor sensitivity too. Whether you use paper forms or big-data codes, single-step tools miss many cases.
Koh et al. (2014) showed the M-CHAT can work if you pick the right scoring rule. The lesson: refine the cut-offs or add a second step before you decide.
Why it matters
If you rely on ministry lists to plan therapy spots, half of the kids who need help may never get called. Run your own quick screen at intake. Add a parent questionnaire or brief ADOS module even when the record already says 'no ASD.' A two-step check beats a single code every time.
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02At a glance
03Original abstract
Population-level identification of children and youth with ASD is essential for surveillance and planning for required services. The objective of this study was to develop and validate an algorithm for the identification of children and youth with ASD using administrative health data. In this retrospective validation study, we linked an electronic medical record (EMR)-based reference standard, consisting 10,000 individuals aged 1-24 years, including 112 confirmed ASD cases to Ontario administrative health data, for the testing of multiple case-finding algorithms. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and corresponding 95% confidence intervals (CI) were calculated for each algorithm. The optimal algorithm was validated in three external cohorts representing family practice, education, and specialized clinical settings. The optimal algorithm included an ASD diagnostic code for a single hospital discharge or emergency department visit or outpatient surgery, or three ASD physician billing codes in 3 years. This algorithm's sensitivity was 50.0% (95%CI 40.7-88.7%), specificity 99.6% (99.4-99.7), PPV 56.6% (46.8-66.3), and NPV 99.4% (99.3-99.6). The results of this study illustrate limitations and need for cautious interpretation when using administrative health data alone for the identification of children and youth with ASD. LAY SUMMARY: We tested algorithms (set of rules) to identify young people with ASD using routinely collected administrative health data. Even the best algorithm misses more than half of those in Ontario with ASD. To understand this better, we tested how well the algorithm worked in different settings (family practice, education, and specialized clinics). The identification of individuals with ASD at a population level is essential for planning for support services and the allocation of resources. Autism Res 2021, 14: 1037-1045. © 2021 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals LLC.
Autism research : official journal of the International Society for Autism Research, 2021 · doi:10.1001/jamapediatrics.2018.0082