Assessing the validity of administrative health data for the identification of children and youth with autism spectrum disorder in Ontario. Issue 5 (10th March 2021)

• Record Type: Journal Article
• Authors: Brooks, Jennifer D.; Arneja, Jasleen; Fu, Longdi; Saxena, Farah E.; Tu, Karen; Pinzaru, Virgiliu Bogdan; Anagnostou, Evdokia; Nylen, Kirk; Saunders, Natasha R.; Lu, Hong; McLaughlin, John; Bronskill, Susan E.
• Is Part Of: Autism research. Volume 14:Issue 5(2021)
• Page Start: 1037
• Page End: 1045
• Publication Date: 2021-03-10
• Subjects: administrative health data -- algorithm -- autism -- Ontario; Autism -- Periodicals; Autism -- Research -- Periodicals; 616.85882005
• DOI: 10.1002/aur.2491
• Languages: English
• ISSNs: 1939-3792

Abstract

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.