Incidence, Distribution, and Risk Factors of Five Major Enteric Diseases Commonly Transmitted by Food in Ontario, Canada
Bibliographic record
Abstract
Enteric diseases are a major public health concern in both developing and developed countries, including Canada. Studies examining enteric disease distributions in different parts of Canada show temporal, regional, and socio-demographic differences in disease incidence rates. However, research studies on the distributions and risk factors for Campylobacter, Yersinia, and Listeria infections have not been performed in Ontario. Therefore, the goal of this thesis was to investigate the distributions of major enteric diseases in humans commonly transmitted by food, in Ontario, Canada (2010-2017). The specific objectives were to: estimate the incidence, seasonal, and demographic risk factors of Campylobacter spp., non-typhoidal Salmonella spp., Verotoxin-producing Escherichia coli (VTEC), Yersinia spp., and Listeria monocytogenes reported infections; examine temporal, spatial, and space-time clustering of these reported infections; and identify area-level socioeconomic risk factors for reported infections caused by Campylobacter spp. and non-typhoidal Salmonella spp., the two most commonly reported enteric bacterial infections in Ontario. \n \nDe-identified, laboratory-confirmed disease surveillance data on cases of Campylobacter spp., non-typhoidal Salmonella spp., VTEC, Yersinia spp., and Listeria, reported between 2010 and 2017 inclusive, in Ontario, Canada (population ~13,500,000) were analyzed. Incidence rates were calculated at the public health unit (PHU) level and multivariable Poisson and negative binomial regression models were used to estimate incidence rate ratios (IRRs) for seasonal and demographic risk factors. Campylobacter and Salmonella infections had the highest incidence rates while Listeria infections had the lowest. The rates of infections caused by all five bacteria were highest in the summer. Rates of Campylobacter, Salmonella, Yersinia, and VTEC infections were highest in children 0–4 years old, while Listeria rates peaked in adults 60 years and older. Age-specific rates of Campylobacter, Salmonella, and VTEC infections also varied by sex, e.g., Campylobacter and Salmonella rates in youths (10-19 years) were higher in males than in females. \n \nRetrospective Poisson scan statistic in SaTScan was used to detect high-rate infection clusters in Ontario’s 35 PHUs, and space and space-time clusters were visualized using choropleth maps. Campylobacter, Salmonella, VTEC, and Listeria tended to cluster in the spring/summer, sometimes extending into fall, while Yersinia showed a less clear temporal pattern. Campylobacter, Salmonella, and VTEC infections clustered spatially in the southwestern and central-western regions of Ontario, and Yersinia and Listeria in the central-eastern region. Significant Salmonella, VTEC, and Listeria infection clusters contained high proportions of cases linked to disease outbreaks. \n \nThe number of laboratory-confirmed cases of Campylobacter and Salmonella infections, reported between 2015 and 2017 inclusive, was aggregated at the forward sortation area (FSA) level, and univariable and multivariable negative binomial regression were used to examine the association between the number of cases and FSA-level socioeconomic factors (median household income; percent of the population with bachelor degree or higher; unemployment rate; and the percent visible minorities, Aboriginals (as defined by Statistics Canada), total immigrants, recent immigrants, and lone-parent families), adjusting for the population of the FSA from the 2016 Census. Campylobacter infection rates were significantly lower with an increase in median household income, unemployment rate, percent visible minorities, Aboriginals, and lone-parent families; and significantly higher with an increase in percent population with bachelor degree or higher, and total immigrants. Salmonella infection rates were significantly lower with an increase in percent visible minorities and Aboriginals; and significantly higher with an increase in median household income and percent total immigrants. \n \nThis thesis demonstrates the incidence rates distribution and clustering of major enteric infections, as well as their seasonal, demographic, and socioeconomic risk factors, across Ontario, especially for Campylobacter, Yersinia, and Listeria that have not been previously studied in Ontario. Future research may investigate these risk factors and the mechanisms through which they affect disease rates in different communities.
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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 | 0.001 |
| 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".