Identification of Pediatric Autism Spectrum Disorder Cases Using Health Administrative Data
Bibliographic record
Abstract
Administrative data are frequently used to identify Autism Spectrum Disorder (ASD) cases in epidemiological studies. However, validation studies on this mode of case ascertainment have lacked access to high-quality clinical diagnostic data and have not followed published reporting guidelines. We report on the diagnostic accuracy of using readily available health administrative data for pediatric ASD case ascertainment. The validation cohort included almost all the ASD-positive children born in British Columbia, Canada from April 1, 2000 to December 31, 2009 and consisted of 8,670 children in total. 4,079 ASD-positive and 2,787 ASD-negative children were identified using Autism Diagnostic Observation Schedule (ADOS) and Autism Diagnostic Interview-Revised (ADI-R) assessments done through the British Columbia Autism Assessment Network (BCAAN). An additional 1,804 ADOS/ADI-R assessed ASD-positive children were identified using Ministry of Education records. This prospectively collected clinical data (the diagnostic gold standard) was then linked to each child's physician billing and hospital discharge data. The diagnostic accuracy of 11 algorithms that used the administrative data to assign ASD case status was assessed. For all algorithms, high positive predictive values (PPVs) were observed alongside low values for other measures of diagnostic accuracy illustrating that PPVs alone are not an adequate measure of diagnostic accuracy. We show that British Columbia's health administrative data cannot reliably be used to discriminate between children with ASD and children with other developmental disorders. Utilizing these data may result in misclassification bias. Methodologically sound, region-specific validation studies are needed to support the use of administrative data for ASD case ascertainment. Autism Res 2020, 13: 456-463. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Health administrative data are frequently used to identify Autism Spectrum Disorder (ASD) cases for research purposes. However, previous validation studies on this sort of case identification have lacked access to high-quality clinical diagnostic data and have not followed published reporting guidelines. We show that British Columbia's health administrative data cannot reliably be used to discriminate between children with ASD and children with other developmental disorders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".