A ten-year review of healthcare-associated bloodstream infections from forty hospitals in Québec, Canada
Bibliographic record
Abstract
OBJECTIVE: Healthcare-associated bloodstream infections (HABSI) are a significant cause of morbidity and mortality worldwide. In Québec, Canada, HABSI arising from acute-care hospitals have been monitored since April 2007 through the Surveillance des bactériémies nosocomiales panhospitalières (BACTOT) program, but this is the first detailed description of HABSI epidemiology. METHODS: This retrospective, descriptive study was conducted using BACTOT surveillance data from hospitals that participated continuously between April 1, 2007, and March 31, 2017. HABSI cases and rates were stratified by hospital type and/or infection source. Temporal trends of rates were analyzed by fitting generalized estimating equation Poisson models, and they were stratified by infection source. RESULTS: For 40 hospitals, 13,024 HABSI cases and 23,313,959 patient days were recorded, for an overall rate of 5.59 per 10,000 patient days (95% CI, 5.54-5.63). The most common infection sources were catheter-associated BSIs (23.0%), BSIs secondary to a urinary focus (21.5%), and non-catheter-associated primary BSIs (18.1%). Teaching hospitals and nonteaching hospitals with ICUs often had rates higher than nonteaching hospitals without ICUs. Annual HABSI rates did not exhibit statistically significant changes from year to year. Non-catheter-associated primary BSIs were the only HABSI type that exhibited a sustained change across the 10 years, increasing from 0.69 per 10,000 patient days (95% CI, 0.59-0.80) in 2007-2008 to 1.42 per 10,000 patient days (95% CI, 1.27-1.58) in 2016-2017. CONCLUSIONS: Despite ongoing surveillance, overall HABSI rates have not decreased. The effect of BACTOT participation should be more closely investigated, and targeted interventions along alternative surveillance modalities should be considered, prioritizing high-burden and potentially preventable BSI types.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.034 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".