Large increase in <scp>ASD</scp> prevalence in <scp>Israel</scp> between 2017 and 2021
Bibliographic record
Abstract
Accurate estimation of annual changes in autism spectrum disorders (ASD) prevalence is critical for planning the expansion of diagnostic, education, and intervention services at an adequate rate. Previous studies from Israel have reported that ASD prevalence among 8-year-old children has increased from estimates of 0.3% in 2008 to 0.65% in 2015 and 1.3% in 2018. Here, we analyzed data acquired from the National Insurance Institute of Israeli (NII), a governmental organization that approves and monitors all ASD children who receive welfare services in Israel, and Clalit Health Services (CHS), the largest Health Maintenance Organization in Israel that provides health services to ~52% of the population. Data from both sources included annual data files from 2017 to 2021 containing the number of ASD cases per year of birth for 1-17-year-old children. This allowed us to estimate annual ASD prevalence among 3.5 million children born between 2000 and 2020 in Israel. Both data sources revealed a nearly two-fold increase in ASD prevalence among 1-17-year-old children from 2017 to 2021. Estimated prevalence rates differed across age groups with 2-3-year-old (day-care) children increasing from 0.27% to 1.19% (>4 fold change), 4-6-year-old (pre-school) children increasing from 0.8% to 1.83%, and 8-year-old children increasing from 0.82% to 1.56% in NII data. These results demonstrate that autism prevalence continues to increase in Israel with a shift towards diagnosis at earlier ages. These findings highlight the challenge facing health and education service providers in meeting the needs of a rapidly growing autism population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".