Association between vaccination and preventive routines on COVID-19-related mortality in nursing home facilities: a population-based systematic retrospective chart review
Bibliographic record
Abstract
Abstract Background: Older and frail individuals are at high risk of dying from COVID-19, and residents in nursing homes (NHs) are overrepresented in death rates. We explored four different periods during the COVID-19 pandemic to analyze the effects of improved preventive routines and vaccinations, respectively, on mortality in NHs. Methods: We undertook a population-based systematic retrospective chart review comprising 136 NH facilities in southeast Sweden. All residents, among these facilities, who died within 30 days after a laboratory-verified COVID-19 diagnosis during four separate 92-day periods representing early pandemic (second quarter 2020), middle of the pandemic (fourth quarter 2020), early post-vaccination phase (first quarter 2021), and the following post-vaccination phase (second quarter 2021). Mortality together with electronic chart data on demographic variables, comorbidity, frailty, and cause of death was collected. Results: The number of deaths during the four periods was 104, 120, 34 and 4, respectively, with a significant reduction in the two post-vaccination periods (P < 0.001). COVID-19 was assessed as the dominant cause of death in 20 (19%), 19 (16%), 4 (12%) and 1 (3%) residents in each period (P < 0.01). The respective median age in the four studied periods varied between 87and 89 years, and three or more diagnoses besides COVID-19 were present in 70–90% of the respective periods’ study population. Considerable or severe frailty was found in all residents. Conclusions: Vaccination against COVID-19 seems associated with a reduced number of deaths in NHs. We could not demonstrate an effect on mortality merely from the protective routines that were undertaken.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".