Feasibility of Infection Control Measures in Hemodialysis Units to Prevent Outbreaks of COVID-19: A Descriptive Study from Quebec
Bibliographic record
Abstract
Background: In-center hemodialysis (HD) units pose the perfect conditions for COVID-19 transmission yet limited space and resources are obstacles to infection prevention and control (IPAC) measures. We aimed to describe IPAC measures implemented and document the infection rates within HD units during the first year of the pandemic. Methods: We invited leaders of Quebec's HD units to collect information on IPAC measures from March 1st to June 30th 2020 and HD unit characteristics. Participating units were contacted again in March 2021 to collect information about the total number of cases. The cumulative infection rate of each unit was compared to the regional cumulative infection rate using a standardized infection ratio (SIR). Results: Data was obtained from 38 units, representing 90% of Quebec's HD patients. 30% of units were perceived as crowded, and this was associated with objective distance measures between stations, which was much more likely to be <2m in units considered crowded (83.3% vs 19.2% p<0.001). IPAC measures regarding general prevention, screening procedures, physical distancing, and PPE use were implemented in 50% of units by 3 weeks and the remainder by 6 weeks. Data on cumulative infection rate was obtained in 26 units providing care to 3942 patients. The cumulative infection rate was disproportionally elevated in HD units compared to regional rates (Median SIR:2.68 IQR:1.58; 4.45)(Figure 1). No difference was noted in the SIR related to specific IPAC measures or to the physical characteristics of the units. Conclusions: Hemodialysis units throughout Quebec were able to rapidly implement modified IPAC measures. Despite this, infection rates were disproportionally elevated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".