Impact of COVID-19 pandemic on Legionella testing and infection rates in Ontario
Bibliographic record
Abstract
BACKGROUND: The disruption of healthcare systems during the COVID-19 pandemic had widespread effects on patient care, including interruption of scheduled visits and diagnostic testing. Many diseases were under-investigated due to the focus on the SARS-CoV-2 virus and the redeployment of resources to the pandemic response. This study aimed to assess Legionella trends in Ontario during the COVID-19 pandemic years, by comparing the demographics of individuals tested for Legionella prior to pandemic (2018 and 2019) to those during the pandemic (2020, 2021 and 2022). Additionally, for individuals who underwent Legionella testing, testing for additional respiratory pathogens was examined in the context of Legionella co-detection. METHODS: Two Poisson regression models were constructed to compare testing rate and positivity rate during the pre-pandemic years with the pandemic years, adjusted for age, sex, year, and Ontario population. RESULTS: Relative to the pre-pandemic years, the testing rates significantly decreased by 8% in 2020, decreased by 8% in 2021 and increased by 14% in 2022. The positivity rate for Legionella decreased by 13% only in 2020 but did not reach significance for the other two years. Individuals older than 50 years of age and males remained the population with highest positivity rate of Legionella infection across all years. Co-detection of Legionella with SARS-CoV-2 or seasonal respiratory viruses was rare but present during the pandemic. CONCLUSIONS: Legionella testing rates decreased by 8% in 2020 and 8% in 2021 and increased by 14% in 2022, which was associated with a decrease in positivity rate only in 2020, at 13%, but not in the other two years. Maintaining vigilance for Legionella testing in future pandemics may support timely diagnosis and treatment, leading to improved patient outcomes. Co-detection of Legionella with SARS-CoV-2 or seasonal respiratory viruses was rare but present during the pandemic. Accordingly, Legionella testing remains essential among high-risk groups, such as the elderly with co-morbidities, critically ill patients, or those with severe or unresponsive pneumonia. Such an approach can aid in differential diagnosis, prompt appropriate treatment, and improve patient outcomes.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".