Estimating Cumulative Health Care Costs of Childhood and Adolescence Autism Spectrum Disorder in Ontario, Canada: A Population-Based Incident Cohort Study
Bibliographic record
Abstract
BACKGROUND: Few studies have estimated cumulative health care costs post-diagnosis for individuals with autism spectrum disorder (ASD). OBJECTIVES: Using an incidence-based approach, the objective of this analysis was to estimate cumulative costs of ASD to the Ontario health care system of children and adolescents. METHODS: Using administrative health records from Ontario, Canada's most populous province, a retrospective, population-based, incident cohort study of children and adolescents aged 0-19 years old diagnosed with ASD was undertaken to estimate cumulative health care costs of ASD to the health care system from 2010 to 2019. Cumulative health care costs in 2021 Canadian dollars (CAD) from diagnosis to death or end of observation period were estimated using a consistent estimator based on the inverse probability weighting technique. Cumulative health care costs (and respective 95% confidence intervals [CI]) were estimated for 1, 5 and 10 years post-diagnosis by sex, age group and health service. RESULTS: In 2010, there were 2867 diagnosed cases of ASD; in 2019, the number of incident cases had risen to 6072. The first year (i.e., 1-year) post-diagnosis cost of ASD was $4710.18 CAD (95% CI 4560.28-4860.08); just under a third of costs were for physician services. Total cumulative 5- and 10-year discounted costs were $16,025.95 CAD (15,371.64-16,680.26) and $32,635.76 CAD (28,906.94-36,364.58), respectively. Mean costs were higher for females and older age groups. CONCLUSIONS: These results suggest that costs of ASD are high in the year of diagnosis and then increase at a steady rate thereafter. This information will help with future resource planning within the health care sector to ensure individuals with ASD are supported once their diagnosis is established.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".