Estimation de la prévalence et du taux d’incidence du trouble du spectre de l’autisme (TSA). Comparaison interprovinciale
Bibliographic record
Abstract
Objective The prevalence of diagnosed autism spectrum disorders (ASD) has risen steadily over time. There is therefore a need for the monitoring of treated ASD for timely policy making. The objective of this study is to report and compare over a 10-year period the prevalence and incidence rate of diagnosed ASD in four Canadian provinces. Methods This study utilized data from the provinces of Manitoba, Ontario, Quebec and Nova Scotia with access to linked administrative database sources used in the Canadian Chronic Diseases Surveillance Systems to assess the prevalence and incidence rate of a physician diagnosis of ASD. Estimates were produced using health datasets for outpatient and inpatient care (Med-Echo in Quebec, the Canadian Institute of Health Information Discharge Abstract Database in the three other provinces, plus the Ontario Mental Health Reporting System). Dates of service, diagnosis, and physician specialty were extracted. The target population consisted of all residents aged 24 and under eligible for healthcare coverage under provincial law between 1999 and 2012. To be considered as having ASD, an individual had to have at least one physician claim or hospital discharge abstract indicating one of the following: ICD-9 codes 299.0 to 299.9 or their ICD-10 equivalents, F84.0 to F84.9. The estimates were presented in yearly brackets between 1999-2000 and 2011-2012 by sex and age groups. The main analyses focused on those aged 17 years or less, with the 18 to 24 years group added to show the subsequent progression of the disorder. Results Our findings show that the annual prevalence of ASD rose steadily between 1999 and 2012 in all provinces and for all age groups although this increase varied across Canadian provinces. There were higher annual prevalence estimates in Ontario (4.8 per 1,000) and Nova Scotia (4.2 per 1,000) compared to Quebec (3.0 per 1,000) and Manitoba (2.5 per 1,000), among persons aged 17 years and younger in 2011. As compared to 1999, Quebec and Ontario reported a fivefold and fourfold increase in 2010-2012, the highest among provinces. The prevalence was four times higher in boys than in girls. By age group, the highest prevalence was observed in those aged between 1 to 4 and 5 to 9 years depending on the province. ASD was generally diagnosed before age 10. Incident cases were more frequently diagnosed by pediatricians followed by either psychiatrists or general practitioners depending on the province. Conclusion Our research confirms that ASD has risen steadily in terms of prevalence and incidence rate and that it varies considerably across provinces. It also demonstrates that health administrative databases can be used as registers for ASD. Information derived from these databases could support and monitor development of improved coordination and shared care to meet the continuous and changing needs of patients and families over time. Implication for future research include exploring the etiology of ASD in more recent cohorts as well as investigating the association between variations in health service availability and the prevalence of ASD.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".