The Impact of Diagnostic Timing on Healthcare Use: Statistical Trends Among Early- and Late-diagnosed Autistic Youth
Notice bibliographique
Résumé
Background: Autism is a neurodevelopmental condition that now affects 1 in 50 Canadian children, with prevalence rising over the past 20 years. A disparity continues to exist in the ratio of males to females who have an autism diagnosis, with rates as high as four autistic males for every one autistic female. There is also an increasing trend of children and adolescents receiving their autism diagnosis much later in childhood and into adolescence, most often in youth without cognitive or developmental delays and mild autistic traits. A late autism diagnosis (i.e., after 6 years of age) is often associated with multiple co-occurring mental health challenges such as depression, anxiety, non-suicidal self-injury, and suicidal thoughts leading to a higher likelihood of accessing healthcare resources than non-autistic peers. Healthcare administration databases allow for researchers to determine trends in healthcare service usage, and while there is some literature on how much autistic children and adolescents are accessing healthcare services, there has been almost no exploration into the healthcare utilization of late diagnosed autistic people. This is the first study to determine and compare the number of encounters with the healthcare system between early and late diagnosed autistic children and adolescents (herein, youth), and characterize the referral reasons for these encounters. Objectives: The present study extended previous literature on healthcare service access in autistic children and adolescents and: (1) examined if late diagnosed autistic youth access more healthcare services than early diagnosed autistic youth; and (2) investigated if late diagnosed autistic youth have more distinct referral reasons compared to early diagnosed autistic youth. Methods: Participants were identified from a larger file review study; 147 children and adolescents diagnosed with autism spectrum disorder (herein autism) at the Alberta Health Services (AHS) Autism Diagnostic Clinic were included in the study (median age at assessment = 5.0 years, 24.49% female). Records from two administrative databases for participants were obtained: Practitioner Claims and the National Ambulatory Care Reporting System (NACRS), which include information on physician visits, emergency department visits, same-day surgery, visits to outpatient clinics, mental health services, urgent care, and public health clinics. A series of logistic regression analyses were conducted to determine if early versus late diagnosis predicted frequency of healthcare encounters, as well as the number of unique reasons for accessing services, while controlling for age. Sex was also added in the logistic regressions to determine if sex moderated either of these associations. Results: Sample sizes for diagnostic timing groups (early vs. late diagnosed) were comparable (53.74% early diagnosed), as well as a comparable sex distribution (25.0% female in late diagnosed group, 21.5% female in early diagnosed group). When controlling for age of first encounter, a late diagnosis predicted 59% more psychiatric visits than the early diagnosed group (β = 0.47, p = .0243). However, when sex was added as a moderator, this effect only approached significance (p = .066). Diagnostic timing and sex were not significant predictors in the number of visits to the emergency department, outpatient clinics, or overall physician claims. In looking at reasons for health care encounters, diagnostic timing and sex were not significant predictors of the number of distinct reasons for accessing healthcare services. Conclusions: This study is the first of its kind to examine the impact of timing of receiving an autism diagnosis and sex on the frequency of healthcare visits, as well as the unique reasons for accessing healthcare services. Findings highlight that late diagnosed autistic youth accessed a significantly higher number of psychiatric services; however, this effect diminished after adding sex as a moderator into the analysis. Findings from this study can inform post-diagnosis supports as well as training for medical professionals to better support late diagnosed autistic youth.
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 | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,002 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 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 ».