Exploring the geographical distribution of human cryptosporidiosis in Southern Ontario from 2011 to 2014
Bibliographic record
Abstract
Cryptosporidium is a protozoan parasite of increasing global public health concern because of its ability to cause disease in both humans and animals through contaminated food and water supplies. In Canada, most human cryptosporidiosis cases are due to Cryptosporidium hominis; however, the presence of zoonotic Cryptosporidium parvum has been observed. Since 2005, the incidence of cryptosporidiosis in Ontario has been consistently higher than the national average; however, it is not understood why, suggesting an incomplete understanding of the pathogen's ecology, epidemiology and transmission pathways. The goal of this study was to explore the spatial distribution of human cryptosporidiosis across the 29 Public Health Unit (PHU) areas of Southern Ontario from 2011 to 2014. Surveillance data on human cryptosporidiosis were obtained from Public Health Ontario. Choropleth and isopleth maps were used to display the distribution of incidence rates of human cryptosporidiosis. High-rate clusters of human cryptosporidiosis were identified. Poisson and spatial Poisson regression models were used to determine the relationship between the incidence of human cryptosporidiosis, cattle density and the smoothed farm-level prevalence of bovine cryptosporidiosis at the PHU level. The annual incidence of reported human cryptosporidiosis in Southern Ontario ranged from 1.62 (95% CI: 1.41-1.86) to 1.82 (95%CI: 1.60-2.06) cases per 100,000 population, with an overall cumulative incidence of 6.91 (95%CI: 6.47-7.39) cases per 100,000 for the 4-year study period. High-risk clusters of human cryptosporidiosis were identified in each year. The relative risk for the clusters ranged from 2.03 (95% CI: 1.63-2.55) to 6.87 (95% CI: 5.07-9.30). A relationship was found between the incidence of cryptosporidiosis and dairy cattle density. Based on this study, the Central West region would be an ideal ecological system to conduct further targeted surveillance to identify factors that may be contributing to the higher burden of cryptosporidiosis in the human and bovine populations in the region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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".