Prevalence of autism among adults in Canada: results from a simulation modelling study
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence of autism among adults living in Canada. DESIGN: A Monte Carlo simulation modelling approach was employed. Input parameters included adult population estimates and mortality rates; autism population all-cause mortality risk ratios; and autism prevalence estimates derived from child and youth data due to the lack of adult data. This approach was executed through 10 000 simulations, with each iteration generating a distinct data scenario. Prevalence estimates were reported as the mean with the 2.5th and 97.5th percentiles, corresponding to a 95% simulation interval (SI). SETTING: Where possible, Canadian data sources were used, including the 2019 Canadian Health Survey on Children and Youth and Statistics Canada mortality rates and population estimates. PRIMARY OUTCOME MEASURE: National prevalence estimates of autistic adults living in private dwellings in Canada, with variations in prevalence by sex at birth and province/territory considered. RESULTS: The findings suggest the prevalence of autism among adults in Canada to be 1.8% (95% SI 1.6%, 2.0%). National prevalence estimates by sex at birth were 0.7% (95% SI 0.6%, 0.9%) for females and 2.9% (95% SI 2.6%, 3.2%) for males. Provincial/territorial estimates ranged from 0.7% in Saskatchewan (95% SI 0.3%, 1.3%) to 3.6% in New Brunswick (95% SI 2.4%, 5.1%). CONCLUSIONS: The limited availability of data on autistic adults constrains our ability to fully understand and address their unique needs. In this study, autism prevalence was estimated based on diagnosed cases, which excludes individuals without a formal diagnosis. Additionally, other factors such as data availability and methodological assumptions may influence the modelling of prevalence estimates. As a result, our findings should be interpreted within the context of these limitations. Nevertheless, this study provides a valuable reference point for understanding autism prevalence among adults in Canada.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".