Connectivity between long-term care homes and subsequent SARS-CoV-2 outbreaks
Bibliographic record
Abstract
Abstract Objectives To describe the relationship between individual workers employed at more than one LTCH (inter-LTCH connectivity) across long-term care homes (LTCH) and SARS-CoV-2 outbreaks. Design A retrospective cohort study using long-term care home surveillance and mobile geolocation data. Setting Using data observed between February 26 th , 2020, and August 31 st , 2020, from Ontario, the province where close to one-third of the Canada’s SARS-CoV-2 cases among long-term care homes residents were reported. Participants We included all 179 LTCH in the Greater Toronto Area (population 6.7 million, where close to 50% of Ontario population resides). Exposures The main exposure of interest was the inter-LTCH connectivity, generated from geographic position system location data procured across apps on different platforms. Main outcomes and measures Three outcomes were examined: 1) at least one SARS-CoV-2 diagnosis among residents, 2) cumulative cases among residents in each facility, and 3) time to first outbreak. Results The median degree of connectivity for LTCH that experienced an outbreak (59%; 106/179) was 1.2 times the degree of those without an outbreak (6 compared to 5). LTCH with higher inter-LTCH connectivity also had larger numbers of residents and beds, and were more likely to have for-profit ownership. After adjusting for facility-level and neighbourhood-level factors, every additional connection to another LTCH increased the odds of an outbreak in the respective LTCH by 8% (adjusted odds ratio=1.08, 90% credible interval [CrI]: 1.02-1.09). Inter-LTCH connectivity was also associated with higher risk of earlier occurrence of a first SARS-CoV-2 case (adjusted hazard ratio=1.05, 90%CrI: 1.02-1.09), but not with outbreak size. Conclusions and Relevance Staff cohorting was associated with reduced importation risk of SARS-CoV-2 cases into LTCH. However, findings suggest that once importation has occurred, other facility-level factors including facility infrastructure and staff benefits are more important in shaping outbreak size. Implementing these structural strategies to meet the LTCH workers and residents’ needs are pivotal to prevent and manage future respiratory virus outbreaks. Key points Question Were movement of long-term care homes (LTCH) workers between facilities (staff connectivity) associated with the risk, size, and timing of SARS-CoV-2 outbreaks in these facilities during the first wave of the COVID-19 pandemic. Finding After adjusting for facility-level and neighbourhood-level factors, a higher degree of staff connectivity between LTCH was associated with a greater risk of outbreaks (2.2-fold the risk of a LTCH connected with 10 more other LTCHs) and a higher risk of experiencing an earlier outbreak (1.7-fold the hazard with 10 more staff connections with other LTCH). However, we did not observe an association between connectivity and the size of outbreaks. Meaning “One-site” strategy to cohort staff by facility and minimizing movement may reduce risk of pathogen importation. However, structural strategies (e.g. improve facility design and infrastructure) to reduce nosocomial transmission within these facilities remain pivotal to prevent and manage future respiratory virus outbreaks.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".