Concordance of EDI-based prevalence rates of health disorders with administrative data in two Canadian provinces
Bibliographic record
Abstract
IntroductionPopulation level data provide unprecedented opportunities to explore low-frequency health disorders, yet few suitable sources exist for young children. Early Development Instrument (EDI) data on child development in kindergarten in publically-funded schools in Canada include information on children’s health disorders that may be used as a source for prevalence estimates.
 Objectives and ApproachWe aimed to examine the concordance of EDI-based prevalence rates of health disorders with administrative data in two Canadian provinces by linking data at various levels. In Manitoba, individual EDI data were linked with health and educational data containing information on diagnosis prior to kindergarten for six health disorders: Autism Spectrum Disorder (ASD), Fetal Alcohol Spectrum Disorder (FASD), Attention Deficit and Hyperactivity Disorder (ADHD), Cerebral Palsy (CP), Anxiety, and Asthma. In Ontario, the prevalence rate of ASD based on the EDI was compared to the prevalence rate obtained from regional ASD service providers, matched on area geocodes.
 ResultsIn Manitoba, the total number of children with one of the six diagnoses in the linked database was 10,181. EDI data from 2011 and 2013 demonstrated concordance rates ranging from 0% (Anxiety) to 37% (ASD) between EDI and pre-kindergarten individual administrative data for the prevalence of the health disorders. In Ontario, interrater reliability was established with 2010-2012 EDI data in 12 regions (total number of children with ASD based on the EDI =1,329) to examine consistency among the two data sources. Results showed a “fair” concordance rate for the two sources of ASD prevalence information (Kappa = 0.329; p < 0.001), with rates varying from 0.85% to 1.05%. Linkages with subsequent cohorts of children are ongoing and will be examined for consistency with current results.
 Conclusion/ImplicationsWhile the results are somewhat lower than expected, they established feasibility of linkages in 2 provinces and will be repeated in others, potentially with a broader time-frame (up to 1-2 years post-kindergarten). This will further inform successful utilization of existing data sources to monitor the prevalence of children’s health disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".