Spatial and space-time clustering and demographic characteristics of human nontyphoidal Salmonella infections with major serotypes in Toronto, Canada
Bibliographic record
Abstract
Nontyphoidal Salmonella enterica (NTS) causes a substantial health burden to human populations in Canada and worldwide. Exposure sources and demographic factors vary by location and can therefore have a major impact on salmonellosis clustering. We evaluated major NTS serotypes: S. Enteritidis (n = 620), S. Typhimurium (n = 150), S. Thompson (n = 138), and S. Heidelberg (n = 136) reported in the city of Toronto, Canada, between January 1, 2015, and December 31, 2017. Cases were analyzed at the forward sortation area (FSA)-level (an area indicated by the first three characters of the postal code). Serotype-specific global and local clustering of infections were evaluated using the Moran's I method. Spatial and space-time clusters were investigated using Poisson and multinomial scan statistic models. Case-case analyses using a multinomial logistic regression model was conducted to compare seasonal and demographic factors among the different serotypes. High infection rate FSAs clustered in the central region of Toronto for S. Enteritidis, in the south-central region for S. Typhimurium, in north-west region for S. Thompson, and in the south-east region for S. Heidelberg. The relative risk ratio of S. Enteritidis infections was significantly higher in cases who reported travel outside of Ontario. The relative risk ratio of infections was significantly higher in summer for S. Typhimurium, and in fall for S. Thompson. The relative risk ratio of infection was highest for the 0-9 age group for S. Typhimurium, and the 20-39 age group for S. Heidelberg. Our study will aid public health stakeholders in designing serotype-specific geographically targeted disease prevention programs.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".