Evaluation of a multisectoral intervention to mitigate the risk of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) transmission in long-term care facilities
Bibliographic record
Abstract
OBJECTIVE: A Canadian health authority implemented a multisectoral intervention designed to control severe acute respiratory coronavirus virus 2 (SARS-CoV-2) transmission during long-term care facility (LTCF) outbreaks. The primary objective was to evaluate the effectiveness of the intervention 14 days after implementation. DESIGN: Quasi-experimental, segmented regression analysis. INTERVENTION: A series of outbreak measures classified into 4 categories: case and contact management, proactive case detection, rigorous infection control practices and resource prioritization and stewardship. METHODS: A mixed-effects segmented Poisson regression model was fitted to the incidence rate of coronavirus disease 2019 (COVID-19), calculated every 2 days, within each facility and case type (staff vs residents). For each facility, the outbreak time period was segmented into an early outbreak period (within 14 days of the intervention) and postintervention period (beyond 14 days following the intervention). Model outputs quantified COVID-19 incidence trend and rate changes between these 2 periods. A secondary model was constructed to identify effect modification by case type. RESULTS: The significant upward trend in COVID-19 incidence rate during the early outbreak period (rate ratio [RR], 1.07; 95% confidence interval [CI], 1.03-1.11; P < .001) reversed during the postintervention period (RR, 0.73; 95% CI, 0.67-0.80; P < .001). The average trend did not differ by case type during the early outbreak period (P > .05) or the postintervention period (P > .05). However, staff had a 70% larger decrease in the average rate of COVID-19 during the postintervention period than residents (RR, 0.30; 95% CI, 0.10-0.88; P < .05). CONCLUSIONS: Our study provides evidence for the effectiveness of this intervention to reduce the transmission of COVID-19 in LTCFs. This intervention can be adapted and utilized by other jurisdictions to protect the vulnerable individuals in LTCFs.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 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".