Trends in Delirium and New Antipsychotic and Benzodiazepine Use Among Hospitalized Older Adults Before and After the Onset of the COVID-19 Pandemic
Bibliographic record
Abstract
Importance: The COVID-19 pandemic caused large disruptions to health care for hospitalized older adults. The incidence and management of delirium may have been affected by high rates of COVID-19 infection, staffing shortages, overwhelmed hospital capacity, and changes to visitor policies. Objective: To measure changes in rates of delirium and related medication prescribing during the COVID-19 pandemic among hospitalized older adults. Design, Setting, and Participants: This population-based, repeated cross-sectional study used linked databases to measure rates of delirium and related medication prescriptions among adults aged 66 years or older hospitalized before and during the COVID-19 pandemic (January 1, 2017, to March 31, 2022) in Ontario, Canada. Exposure: The first 2 years of the COVID-19 pandemic (March 1, 2020, to March 31, 2022). Main Outcomes and Measures: The main outcomes were weekly rates of delirium per 1000 admitted population and monthly rates of new antipsychotic and benzodiazepine prescriptions per 1000 discharged population. Observed rates were compared with projected rates based on modeling from 3 years before pandemic onset. Results: Among 2 128 411 hospitalizations of older adults over the 5-year study period (50.7% female; mean [SD] age, 78.9 [8.3] years), absolute rates of delirium increased from 35.9 per 1000 admitted population during the prepandemic period to 41.5 per 1000 admitted population throughout the pandemic. The adjusted rate ratio (ARR) of delirium during the pandemic compared with the projected rate was 1.15 (95% CI, 1.11-1.19). Monthly rates of new antipsychotic prescriptions increased from 6.9 to 8.8 per 1000 discharged population and new benzodiazepine prescriptions from 4.4 to 6.0 per 1000 discharged population and were significantly higher during the pandemic compared with projected rates (antipsychotics: ARR, 1.28; 95% CI, 1.19-1.38; benzodiazepines: ARR, 1.37; 95% CI, 1.20-1.57). Rates were highest during pandemic waves 1 (March to June 2020), 3 (March to June 2021), and 5 (December 2021 to February 2022) and remained elevated above projected levels throughout the first 2 years of the pandemic. Conclusions and Relevance: In this repeated cross-sectional study of hospitalized older adults, there was a temporal association between COVID-19 pandemic onset and significant increases in rates of delirium in the hospital and new antipsychotic and benzodiazepine prescriptions after hospital discharge. Rates remained elevated over 2 years. Pandemic-related changes such as visitor restrictions, staff shortages, isolation practices, and reduced staff time at the bedside may have contributed to these trends.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".