Estimation de la prévalence et du taux d’incidence du trouble du spectre de l’autisme (TSA). Comparaison interprovinciale
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,006 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».