Visits to the emergency department by community-dwelling people with dementia during the first 2 waves of the COVID-19 pandemic in Ontario: a repeated cross-sectional analysis
Bibliographic record
Abstract
Background: Community-dwelling people with dementia have been affected by COVID-19 pandemic health risks and control measures that resulted in worsened access to health care and service cancellation. One critical access point in health systems is the emergency department. We aimed to determine the change in weekly rates of visits to the emergency department of community-dwelling people with dementia in Ontario during the first 2 waves of the COVID-19 pandemic compared with historical patterns. Methods: We conducted a population-based repeated cross-sectional study and used health administrative databases to compare rates of visits to the emergency department among community-dwelling people with dementia who were aged 40 years and older in Ontario during the first 2 waves of the COVID-19 pandemic (March 2020–February 2021) with the rates of a historical period (March 2019–February 2020). Weekly rates of visits to the emergency department were evaluated overall, by urgency and by chapter from the International Statistical Classification of Diseases and Related Health Problems, 10th Revision. We used Poisson models to compare pandemic and historical rates at the week of the lowest rate during the pandemic period and the latest week. Results: We observed large immediate declines in rates of visits to the emergency department during the COVID-19 pandemic (rate ratio [RR] 0.50, 95% confidence interval [CI] 0.47–0.53), which remained below historical levels by the end of the second wave (RR 0.88, 95% CI 0.83–0.92). Rates of both nonurgent (RR 0.33, 95% CI 0.28–0.39) and urgent (RR 0.51, 95% CI 0.48–0.55) visits to the emergency department also declined and remained low (RR 0.68, 95% CI 0.59–0.79, RR 0.91, 95% CI 0.86–0.96), respectively. Visits for injuries, and circulatory, respiratory and musculoskeletal diseases declined and remained below historical levels. Interpretation: Prolonged reductions in visits to the emergency department among people with dementia during the first 2 pandemic waves raise concerns about patients who delay seeking acute care services. Understanding the long-term effects of these reductions requires further research.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".