Assessing the validity of administrative health data for the identification of children and youth with autism spectrum disorder in Ontario.
Jennifer D Brooks, Jasleen Arneja, Longdi Fu and 9 others
PMID 33694293WHAT IT FOUND
Ontario administrative health data identified only 50.0% of children and youth with autism, and 56.6% of those flagged actually had autism.
Billing data alone misses many and may mislabel others.
Key findings
01The optimal algorithm had sensitivity 50.0%, specificity 99.6%, positive predictive value 56.6%, and negative predictive value 99.4%.
02In the 2016 Ontario population aged 1-24 years, the algorithm identified 36,713 of 3,960,763 children and youth as having ASD, a prevalence of 0.93%.
03False positives tended to have other neurodevelopmental or mental health conditions, and about half of false negatives were recorded as having Asperger's Syndrome.
STILL TO COME
How it was doneWhat they foundWhat it means for PTsWhat it means for OTs
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What it does not show
The optimal algorithm found only 50.0% of children and youth with autism, and only 56.6% of those it flagged actually had autism. Administrative health data alone were not enough to identify all children and youth with autism. The physician billing code used is not specific to autism. The algorithms could not include education data or services delivered by other health professionals, including occupational and physical therapists. The study could not distinguish autism with and without intellectual disability or language impairment. The validation cohorts had their own uncertainty: primary care autism status was identified by another electronic record algorithm, and the kindergarten measure recorded existing diagnoses rather than screening. The study focused on children and youth, not adults.
Declared interests
The authors declared no conflicts of interest. The study was funded by the Ontario Brain Institute.
The easy way to misread this
Do not conclude administrative health data can reliably identify all children and youth with autism. The best algorithm found 50.0% of known cases and confirmed autism in only 56.6% of those it flagged.