Socioeconomic Determinants of <i>Campylobacter</i> spp. and Non‐Typhoidal <i>Salmonella</i> spp. Infections in Ontario, Canada, 2015–2017: An Ecological Study
Bibliographic record
Abstract
INTRODUCTION: Campylobacter spp. and non-typhoidal Salmonella spp. (NTS) are major causes of enteric diseases in Ontario, Canada and worldwide. Although low socioeconomic status is generally associated with poor health outcomes, its relationship with enteric diseases in Ontario is not well known. We investigated area-level socioeconomic risk factors for reported enteric infections caused by Campylobacter spp. and NTS, commonly transmitted by food in Ontario, Canada, between 2015 and 2017. METHODS: Using negative binomial regression models, we examined the association between age- and sex-adjusted incidence rates (IRs) of laboratory-confirmed cases of Campylobacter spp. and NTS (aggregated to the forward sortation area [FSA] level), and FSA-level socioeconomic factors (median household income; percent population with bachelor's degree or higher; unemployment rate; and percent visible minorities, Indigenous peoples [as defined by Statistics Canada], total immigrants, recent immigrants and lone-parent families), adjusting for the population of the FSA from the 2016 Census. RESULTS: After controlling for the other variables in the final multivariable models, an increase in the percentage of the population with a bachelor's degree or higher and in the percentage of total immigrants in an FSA significantly increased the IRs of Campylobacter infections, while an increase in the median income and the percentage of total immigrants in an FSA increased the IRs of NTS infections. CONCLUSIONS: Results from our study may inform public health interventions to reduce the rate of infections, for example, via food safety supports relevant to communities with larger numbers of Canadian immigrants. Further individual-level investigations of the socioeconomic factors identified in this study are needed. Also, future studies should assess the mechanisms through which socioeconomic risk factors affect infection rates in different communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".