Association between nursing home crowding and outbreak-associated respiratory infection and death prior to the COVID-19 pandemic between 2014 and 2019 in Ontario, Canada
Bibliographic record
Abstract
Abstract Importance Resident crowding in nursing homes is associated with larger SARS-CoV-2 outbreaks. However, this association has not been previously documented for non-SARS-CoV-2 respiratory infections. Objective We sought to measure the association between nursing home crowding and respiratory infections in Ontario nursing homes prior to the COVID-19 pandemic. Design, Setting, and Participants We conducted a retrospective cohort study of nursing home residents in Ontario, Canada over a five-year period prior to the COVID-19 pandemic, between September 2014 and August 2019. Exposure Using administrative data, we estimated the crowding index equal to the mean number of residents per bedroom and bathroom (residents / [0.5*bedrooms+0.5*bathrooms]). Outcomes The incidence of outbreak-associated infections and mortality per 100 nursing home residents per year. We also examined infection and mortality outcomes for outbreaks due to 7 specific pathogens: coronaviruses (OC43, 229E, NL63, HKU1), influenza A, influenza B, human metapneumovirus, parainfluenza virus, respiratory syncytial virus, rhinovirus/enterovirus. Results There was one or more respiratory outbreak in 93.9% (588/626) nursing homes in Ontario. There were 4,921 outbreaks involving 64,829 cases of respiratory infection, and 1,969 deaths. Outbreaks attributable to a single identified pathogen were principally caused by influenza A (29%), rhinovirus (11.7%), influenza B (8.1%), and respiratory syncytial virus (6.1%). Among homes, 42.7% (251/588) homes had a high crowding index (≥ 2.0). After adjustment, more crowded homes had higher outbreak-associated respiratory infection incidence (aRR 1.89; 95% 1.64-2.18) and mortality incidence (aRR 2.28; 95% 1.84-2.84). More crowded homes had higher adjusted estimates of the incidence of infection and mortality for each of the 7 respiratory pathogens examined. Conclusions and Relevance Residents of crowded nursing homes experienced more respiratory-outbreak infections and mortality due to influenza and other non-SARS-CoV-2 respiratory pathogens. Decreasing crowding in nursing homes is an important patient safety target beyond the COVID-19 pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".