The COVID-19 Pandemic and Dementia: a Multijurisdictional Meta-Analysis of the Impact of the First Two Pandemic Waves on Acute Health-care Utilization and Mortality in Canada
Bibliographic record
Abstract
Background: Previous studies on the impact of the coronavirus disease 2019 (COVID-19) pandemic on persons living with dementia (PLWD) were mostly conducted in a single jurisdiction or focused on a limited number of outcomes. Our study estimates the impact of the first two pandemic waves on emergency department (ED) visits (all-cause/ambulatory care sensitive conditions), hospitalizations (all-cause/30-day readmissions), and all-cause mortality in four Canadian jurisdictions. Methods: Using administrative databases from Alberta, Ontario, Saskatchewan, and Quebec, we assembled two closed retrospective cohorts (2019/pre-pandemic control and 2020/pandemic) of PLWD aged 65+. Within community and nursing home settings, the rates of the above-mentioned outcomes in three pandemic periods (first wave, interim period, second wave) were compared to the corresponding pre-pandemic periods. We performed random effects meta-analyses on the provincial incident rate ratios. Results: Pre-pandemic and pandemic cohorts included 167,095 vs. 173,240 (community) and 93,374 vs. 92,434 (nursing home) individuals, respectively. During the first wave, community and nursing home populations experienced significant declines in the rates of all-cause ED visits (36% vs. 40%) and hospitalizations (25% vs. 22%), which persisted in the following periods in the community. These declines were greater for the rates of ambulatory care sensitive condition ED visits and 30-day readmissions. Mortality was 36% higher in nursing homes (first wave) and 13% higher in the community (second wave). Conclusions: It is key to prepare for future health crises and ensure that PLWD receive necessary care and services and do not have such a high mortality rate. Attention should be equally given to PLWD living in their homes and nursing homes.
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 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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.035 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".