Predicting Methicillin-Resistant Staphylococcus aureus (MRSA) Bloodstream Infection Incidence Rates using Canadian Nosocomial Infection Surveillance Program (CNISP)
Bibliographic record
Abstract
Methicillin-Resistant Staphylococcus aureus (MRSA) is among the most prevalent nosocomial pathogens globally, causing significant morbidity, mortality, and healthcare costs. MRSA bloodstream infection (BSI) incidence rates in Canadian hospitals have significantly risen by almost 60% and have a mortality of over 20% upon Intensive Care Unit admission. MRSA is believed to be spread through healthcare workers; thus, high hand hygiene compliancy in addition to environmental cleaning are the cornerstone countermeasures to disrupting its transmission. The Public Health Agency of Canada (PHAC), in collaboration with the Canadian Nosocomial Infection Surveillance Program (CNISP), conducts national, sentinel surveillance on healthcare-associated infections like MRSA. As a Student Epidemiologist, I developed a research proposal detailing two study objectives: 1) develop a regression model to predict all incident MRSA BSI rates among acute-care hospitals in Canada using CNISP MRSA BSI incident cases from 2000 to 2019, and 2) create a compartmental (Susceptible-Infected-Recovered-Deceased) model to determine the impact of various Infection Prevention and Control (IPC) measures on the risk of healthcare-associated MRSA BSI transmission specifically. This study hopes to demonstrate that proper IPC compliance is associated with lower incident MRSA BSI rates with the goal being to produce a manuscript draft by 2021. MRSA poses a serious threat to patient safety globally and is becoming a growing national public health concern in Canada; determining which IPC strategy is most effective at disrupting MRSA transmission is essential to reducing incidence and mortality rates.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".