Cost-Effectiveness Analysis of Wait Time Reduction for Intensive Behavioural Intervention in Ontario
Notice bibliographique
Résumé
Abstract BACKGROUND: In Ontario, the Autism Intervention Program funds intensive behavioral intervention (IBI) for children severely affected by autism spectrum disorder (ASD). Accessing IBI before age four is associated with significantly better outcomes compared with later access; however, the average wait time for this program is 2.7 years. There have been no analyses modeling lifetime cost-effectiveness of wait time reduction for IBI. OBJECTIVES: 1) Model the change in starting age for IBI with halved and eliminated wait times; 2) Perform a cost-effectiveness analysis (CEA) comparing wait time reduction and elimination to the current status quo from both provincial government and societal perspectives. DESIGN/METHODS: Published wait list statistics were used to calculate average starting age for IBI for current wait time, wait time halved, and wait time eliminated. The target population was children diagnosed with severe ASD. The outcome modeled was independence measured in dependency-free life years (DFLYs) to age 65. To derive this, expected IQ was modeled for each comparator based on probability of early (< age 4) or late (4 or older) access to IBI. Probabilities of having an IQ in the normal (70+) or intellectual disability range (<70) were calculated. IQ strata were assigned probabilities of achieving an Independent (60 DFLYs), Semi-Dependent (30 DFLYs) or Dependent (0 DFLYs) outcome. Costs were determined from provincial government and societal perspectives. Parameters were inputted into a decision analytic model, with an annual discount rate of 3% applied to costs and DFLYs. Incremental cost-effectiveness ratios (ICERs) were determined for each strategy. One-way and probabilistic sensitivity analyses were performed to assess the impact of model uncertainty. RESULTS: Average starting ages for IBI were determined to be 5.24 years for current wait time, 3.89 years for wait time halved, and 2.71 years for wait time eliminated. From the provincial government perspective, eliminated wait time was the dominant strategy, generating the most DFLYs for $6,500 less per individual than current wait time. From the societal perspective, eliminated wait time again dominated the other strategies, with lifetime savings of $38,000 per individual compared with current wait time. ICERs were most sensitive to uncertainty associated with probability of having an IQ greater than 70 and probability of achieving an independent outcome when IQ was greater than 70. CONCLUSION: The results suggest that funding expanded program capacity would optimize the likelihood of positive IBI outcomes, improve future independence, and lessen the cost burden from provincial and societal perspectives.
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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 | 0,004 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».