Cost-Effectiveness Analysis of Wait Time Reduction for Intensive Behavioural Intervention in Ontario
Bibliographic record
Abstract
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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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".