Infection Control Practices in In-Center Hemodialysis Units During Wave 1 of the COVID-19 Pandemic in Ontario, Canada: Research Letter
Bibliographic record
Abstract
Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a virus that caused coronavirus disease 2019 (COVID-19), the multisystem disease central to the COVID-19 pandemic. As patients receiving in-center maintenance hemodialysis require treatment 3 times weekly, they were unable to fully isolate. It was important for in-center hemodialysis units to implement robust infection control practices to ensure patient safety and minimize risk of transmitting SARS-CoV-2 among patients and staff. There are 27 renal programs within Ontario, Canada, providing care for about 9000 people across about 100 in-center hemodialysis units. These units are funded by the Ontario Renal Network (ORN), which is part of the provincial agency Ontario Health. Objective: The objective was to track infection control practices that were implemented by in-center hemodialysis units and be able to provide a descriptive narrative of the COVID-19 pandemic response of Ontario's hemodialysis units between March and September 2020. Methods: Between May and September 2020, data were collected from Ontario's 27 renal programs on the implementation of key infection control practices, including symptom screening, use of personal protective equipment, testing, practices specifically related to patients from congregate living settings, other prevention practices, and outbreak management. There were 4 data collection cycles, each approximately 1 month apart. The results were compiled and shared across the province, and infection control practices were also discussed at provincial COVID-19 teleconferences hosted by the ORN. Results: By March 2020, all but one renal program had implemented one or more forms of symptom screening, all renal programs had implemented physical distancing in waiting rooms and restricted visitors, and 74% of renal programs had implemented universal masking for all staff. By April 2020, 89% of renal programs had implemented universal masking for all patients, 52% had implemented enhanced contact and droplet precautions for suspected or positive cases, and 59% of renal programs tested all patients from congregate living settings regularly (with a low symptom threshold for testing). Infection control practices became more homogeneous across renal programs over time, and most practices were in place as of the last data collection. Conclusions: The renal system in Ontario was able to respond quickly within the first 2 months of the pandemic to minimize the spread of COVID-19 within in-center hemodialysis units. Through provincial teleconferences, infection control practices were shared across the province as the pandemic and hemodialysis unit responses evolved. This supported renal programs to advocate locally if their hospital was lagging in practices felt to be of value in other hemodialysis units. Although no direct correlation can be made regarding the implementation of infection control practices within in-center hemodialysis units and the number of COVID-19 cases in this population, the limited number of outbreaks in hemodialysis units may have been influenced by the proactive response of renal programs. Practices described in this article may support management and response to subsequent waves of COVID-19 or future similar infectious diseases.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".