Sociodemographic and geospatial associations with community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) infections in a large Canadian city: an 11 year retrospective study
Bibliographic record
Abstract
BACKGROUND: The first Canadian outbreak of community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) was identified in 2004 in Calgary, Alberta. Using a novel model of MRSA population-based surveillance, sociodemographic risk associations, yearly geospatial dissemination and prevalence of CA-MRSA infections over an 11 year period was identified in an urban healthcare jurisdiction of Calgary. METHODS: Positive MRSA case records, patient demographics and laboratory data were obtained from a centralized Laboratory Information System of Calgary Laboratory Services in Calgary, Alberta, Canada between 2004 and 2014. Public census data was obtained from Statistics Canada, which was used to match with laboratory data and mapped using Geographic Information Systems. RESULTS: During the study period, 52.5% of positive MRSA infections in Calgary were CA-MRSA cases. The majority were CMRSA10 (USA300) clones (94.1%; n = 4255), while the remaining case (n = 266) were CMRSA7 (USA400) clones. Period prevalence of CMRSA10 increased from 3.6 cases/100000 population in 2004, to 41.3 cases/100000 population in 2014. Geospatial analysis demonstrated wide dissemination of CMRSA10 annually in the city. Those who are English speaking (RR = 0.05, p < 0.0001), identify as visible minority Chinese (RR = 0.09, p = 0.0023) or visible minority South Asian (RR = 0.25, p = 0.015), and have a high median household income (RR = 0.27, p < 0.0001) have a significantly decreased relative risk of CMRSA10 infections. CONCLUSIONS: CMRSA10 prevalence increased between 2004 and 2007, followed by a stabilization of cases by 2014. Certain sociodemographic factors were protective from CMRSA10 infections. The model of MRSA population-surveillance and geomap outbreak events can be used to track the epidemiology of MRSA in any jurisdiction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".