Epidemiology and Healthcare Resource Utilization of Rett Syndrome in Canada: The Ontario Experience
Bibliographic record
Abstract
BACKGROUND: Rett Syndrome (RTT) is an X-linked neurodevelopmental disorder, characterized by the gradual loss of motor, verbal and social skills. This study describes the epidemiology and healthcare resource utilization (HCRU) of RTT in Ontario, Canada. METHODS: Rett Syndrome (RTT) cases (≥ one ICD-10-CA code F84.2) were identified utilizing the Institute for Clinical Evaluative Sciences (ICES) data. Incident cases were identified between September 2017 and August 2023, while prevalent cases were captured from April 2002 to August 2023. Prevalent cases identified before September 2017 were indexed on that date. Demographic and clinical characteristics were collected and analyzed descriptively. Prevalence and incidence were calculated. Healthcare resource utilization (HCRU) was analyzed as the number of cases with at least one touchpoint and the number of touchpoints. RESULTS: In total, 246 RTT cases were indexed; 40% from central Ontario, 95% female, median age 21 years. From September 2017 to August 2023, 57 incident cases and 257 prevalent cases were reported in Ontario. Common comorbidities included developmental disability (85.4%), epilepsy (49.6%) and gastrointestinal symptoms (42.3 %). Most patients had at least one outpatient visit (primary care 96.7%, specialist 86.6%), emergency department visit (76.8%) and inpatient hospitalization (54.5%). During the 5-year follow-up period, most cases (95.1%) had at least one public claim for all-cause medication. Disease-specific medication claims included antibiotics (69.1%) and anti-seizure medications (73.6%). CONCLUSION: This study provides population-based estimates of RTT in Ontario. Findings highlight the high burden of illness in RTT in terms of comorbidity prevalence and HCRU. Further research may identify opportunities to improve healthcare outcomes in this 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".