Electroconvulsive Therapy Variability Across Ontario 2007–2023: A Population Level Study: Variabilité de la thérapie électroconvulsive en Ontario de 2007 à 2023 : une étude au sein de la population
Bibliographic record
Abstract
ObjectiveElectroconvulsive therapy (ECT) is an important but underused treatment for severe psychiatric illnesses. We sought to examine the variability of ECT utilization at a population level and between several subgroups. We also sought to quantify the impact of the COVID-19 pandemic on ECT utilization.MethodsWe used population level data from Ontario to examine all ECT procedures administered from 1 January 2007 to 31 December 2023. Our primary measure of variability was the rate of ECT procedures per 1,000 population. We included three subgroups at time of ECT procedure: age (18-39, 40-64, and 65+), biologic sex (male/female), and Ontario Health (OH) region of residence (West, Central, Toronto, East, North West, North East). To quantify the impact of the COVID-19 pandemic we calculated the change in ECT rate from 2019 to 2020 (acute effect) and 2019 to 2023 (persistent effect).ResultsThere were 450,381 ECT procedures delivered during the observation period. The yearly rate of ECT increased from 1.69 per 1,000 in 2007 to a peak of 3.08 per 1,000 in 2019. In 2023 the greatest per capita rates of ECT use were in the 65+ age group, female sex, and North East geographic region. In 2023, the rates of ECT use in different geographic regions ranged from 1.28 (North West) to 4.19 per 1,000 (North East). The COVID-19 pandemic resulted in an immediate 26.73%, followed by a 17.47% persistent drop in the rate of ECT with notable regional heterogeneity.ConclusionsWhile ECT use increased over time, there were differences in this increase between age groups, biological sex, and geographic regions. The COVID-19 pandemic had significant immediate and persistent impacts on the rates of ECT use highlighting the need for ongoing population level monitoring of this important treatment.Plain Language Summary TitleElectroconvulsive therapy volume in Ontario from 2007 to 2023.
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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 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".