Prevalence and incidence of autism in children and adolescents in Manitoba, Canada: An updated estimate using population-based administrative health data from 2011 to 2022
Bibliographic record
Abstract
OBJECTIVES: Estimates of autism prevalence are critical for informing evidence-based decisions, allocating resources, and developing effective strategies to support autistic individuals and their families. In Canada, such estimates remain limited, with the most recent population-based data on autism prevalence and incidence in Manitoba spanning 2004-2015, underscoring the need for more current data. METHODS: We used linked, whole-population administrative health and clinical data to develop a validated identification algorithm. We determined annual prevalence and incidence rates of autism among Manitoba children and adolescents aged 0-17 from 2011 to 2022, and conducted regression modelling to examine changes over time, adjusting for sex, geography, and socioeconomic variables. RESULTS: We identified 9396 children and adolescents diagnosed with autism during the study period. The prevalence of autism diagnoses was 0.58% (95% CI 0.55-0.60) in 2011 and 1.67% (95% CI 1.63-1.72) in 2022. The incidence of autism diagnoses was 0.79/1000 (95% CI 0.69-0.90) in 2011 and 3.06/1000 (95% CI 2.87-3.27) in 2022. We found statistically significant year-over-year increases in both prevalence and incidence. CONCLUSIONS: Increasing autism prevalence indicates a pressing public health need for sustained investment in specialized healthcare services and supports that promote the full inclusion of autistic people in society. Strengthening surveillance systems across Canada is essential for generating high-quality population-based data to inform policy development and resource allocation and ensuring the health and social needs of autistic people and their families are met.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".