Effect of long-term care and pandemic wave on relative risk of COVID-19-related infection, hospitalization and mortality in people living with dementia: A population-based cohort study
Bibliographic record
Abstract
BackgroundPeople living with dementia (PLWD) are vulnerable to serious COVID-19 illness and death but the contribution of various factors including long-term care (LTC), pandemic wave, hospitalization, comorbidities, and underlying neurological health remains unclear.ObjectiveTo investigate the relative risk of SARS-CoV-2 infection, hospitalization, and mortality (COVID-19 and non-COVID-19) in PLWD compared to those without dementia, by living circumstance and pandemic wave, while controlling for additional risk factors.MethodsA cohort of people 65 and up with dementia, including Alzheimer's disease, was propensity score matched to a control cohort using linked population-level health records. Relative risk of outcomes was estimated using adjusted Cox proportional hazards modelling. The modifying effects of LTC residence and pandemic wave on all outcomes, and of COVID-19-related hospitalization on COVID-19 mortality were investigated.ResultsCompared to controls without dementia, PLWD had higher risk of infection and COVID-19 mortality whether they lived in LTC or not. For PLWD in LTC, relative risk was often reduced or not significantly different when stratified by wave but remained higher for PLWD not in LTC (32-93%). In LTC, likelihood of hospitalization was 53-64% lower for PLWD compared to those without dementia. PLWD not hospitalized for COVID-19 had higher COVID-19 mortality than non-hospitalized, non-dementia controls both in and not in LTC (32% and 477%, respectively).ConclusionsPLWD repeatedly had higher risk of COVID-19 infection and mortality, but risk varied with changing pandemic circumstances and living environment. Higher mortality may have been associated with reduced hospital transfers, complex care needs and neurological health.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".