Single- versus shared-occupancy bedrooms in long-term care homes during the COVID-19 pandemic: A regional cohort study of 355 facilities in British Columbia, Canada
Bibliographic record
Abstract
Long-term care homes (LTCHs) around the world have been severely impacted by COVID-19 outbreaks with exceptionally high case loads and fatalities relative to the general public. A growing body of researchers, policy makers, and advocates have raised concern that the design and operation of these specialized 24-hour eldercare facilities may be partly responsible for risk of infection from transmissible diseases. While by no means the only factor in healthcare associated infections (HAIs), bedroom occupancy has been suggested as a potential determinant due to the disparities of exposure in shared bedrooms with two or more residents when compared to the isolation provided by single-occupancy bedrooms. This cohort study examines the role of bedroom occupancy on resident attack rates (RAR) in LTCHs in British Columbia (BC), Canada, by linking public health data from the BC Centre for Disease Control (BCCDC) and administrative survey data from the BC Office of the Seniors Advocate (BCOSA). During the observation period which extended from March 5, 2020–February 9, 2022 (707 days), 333 outbreaks were reported at 200 of the 355 BCLTCHs (56.3%). A total of 2,519 staff cases, 4,367 resident cases, and 960 resident deaths were reported (22.0% case fatality rate). Correlation analyses show that single-occupancy bedrooms had a weak, inverse correlation with COVID-19 infections among residents, whereas number of staff cases and highest RAR of any encountered outbreak were strongly correlated with resident infections. Counter to the perception that LTCH residents of shared bedrooms were at far greater risk, these observations suggest the bedroom occupancy was a minor factor contributing to the spread of COVID-19 in BCLTCHs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".