Evaluating area-level spatial clustering of SalmonellaEnteritidis infections and their socioeconomic determinants in the greater Toronto area, Ontario, Canada (2007 – 2009): a retrospective population-based ecological study
Bibliographic record
Abstract
BACKGROUND: There have been only a few region-level ecological studies conducted in Canada investigating enteric infections in humans. Our study objectives were to 1) assess the spatial clustering of Salmonella enterica serotype Enteritidis (S. Enteritidis) human infections in the Greater Toronto Area, and 2) identify underlying area-level associations between S. Enteritidis infection rates and socioeconomic status (SES) indicators that might explain the clustering of infections. METHODS: Retrospective data on S. Enteritidis infections from 2007 to 2009 were obtained from Ontario's reportable disease surveillance database and were grouped at the forward sortation area (FSA) - level. A spatial scan statistic was employed to identify FSA-level spatial clusters of high infection rates. Negative binomial regression was used to identify FSA-level associations between S. Enteritidis infection rates and SES indicators obtained from the 2006 Census of Canada. Global Moran's I statistic was used to evaluate the final model for residual spatial clustering. RESULTS: A spatial cluster that included nine neighbouring FSAs was identified in downtown Toronto. A significant positive curvilinear relationship was observed between S. Enteritidis infection rates and FSA-level average number of children at home per census family. Areas with high and areas with low average median family income had higher infection rates than FSAs with medium average median family income. Areas with a high proportion of visible minority population had lower infection rates than FSAs with a medium proportion of visible minority population. The Moran's I statistic was not significant, indicating that no residual spatial autocorrelation was present after accounting for the SES variables in the final model. CONCLUSIONS: Our study demonstrated that FSAs with high and low average median family income, medium proportion of visible minority population, and high average number of children at home per census family had the highest S. Enteritidis infection rates. These areas should be targeted when designing disease control and prevention programs. Future studies are needed in areas with high S. Enteritidis infection rates to identify sources of environmental contamination of the local food supply, to assess food safety practices at local food markets, retail stores, and restaurants, and to identify novel individual-level risk factors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.001 | 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".