1879. Detection of COVID-19 Outbreaks in Long-Term Care Homes Using Built Environment Testing for SARS-CoV-2: A Multicentre Prospective Study
Bibliographic record
Abstract
Abstract Background Environmental surveillance of SARS-CoV-2 via wastewater has become an invaluable tool for population-level surveillance of COVID-19. More highly resolved environmental sampling approaches may also be useful for surveillance. Built environment sampling may provide a spatially refined approach for surveillance of COVID-19 in congregate living settings. Methods We conducted a prospective study of 10 long-term care homes (LTCHs) in both urban and rural settings in Ontario Canada between September 2021 and April 2022. Floor surfaces were sampled weekly at multiple locations within each building and were analyzed for the presence of SARS-CoV-2 using qPCR. The exposure variable was detection of SARS-CoV-2 on floors. The primary outcome was the presence of a COVID-19 outbreak. We calculated the test characteristics of the presence of SARS-CoV-2 on floors for detection of COVID-19 outbreaks. Results We followed 10 LTCHs for 214 cumulative weeks, and collected 3,219 swabs from 183 unique locations. Overall, 15 COVID-19 outbreaks occurred with 74.9 cumulative weeks of outbreaks. During time periods when there were outbreaks of COVID-19 the proportion of floor swabs positive for SaRS-CoV-2 was 50.8% (95% CI: 47.7-53.9). During time periods where there were no outbreaks of COVID-19 the proportion of floor swabs positive was 15.8% (95% CI:14.3-17.3). Using the proportion of positive floor swabs for SARS-CoV-2 to predict COVID-19 outbreak status for a given week, the area under the receiver operating curve was 0.84 (95% CI: 0.76-0.92). Using thresholds of ≥10%, ≥30%, and ≥50%, the prevalence of floor swabs positive for SARS-CoV-2 yielded positive predictive values for outbreak of 0.52 (0.43-0.61), 0.65 (0.53-0.75), and 0.72 (0.58-0.83) respectively, and negative predictive values of 0.93 (0.86-0.97), 0.85 (0.78-0.91), and 0.80 (0.73-0.86) respectively (Figure 1). 13 outbreaks had floor sampling performed in the week prior to them being identified, and of these 7 (54%) had positive swab proportions exceeding 30% in the week prior to the outbreak. Figure 1.Test characteristics of built environment floor swabs for predicting COVID-19 outbreaks in LTCH. Figure 1. Test characteristics of different thresholds for percentage of floor swabs positive for SARS-CoV-2 at a given LTCH for predicting active COVID-19 outbreak in the same building in the same week. NPV = negative predictive value, PPV = positive predictive value, Sens = sensitivity, Spec = specificity. Conclusion Detection of SARS-CoV-2 on floors is strongly associated with COVID-19 outbreaks in LTCHs. These data suggest a potential role for floor sampling in improving early outbreak detection and management. Disclosures Michael Fralick, MD, ProofDx: Advisor/Consultant Doug Manuel, MD, PhD, World Bank: Advisor/Consultant.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".