Climate and enteric illness in New Brunswick: Implications for a changing climate
Bibliographic record
Abstract
Annually thousands of Canadians become ill due to infections with enteric pathogens. Several environmental risk factors have been linked to enteric disease incidence; among these are increased temperatures and extreme weather events. An ecological study of enteric illness in New Brunswick was conducted to determine the potential impacts of global climate change on reportable enteric illness in New Brunswick, Canada through changes in temperature and precipitation. The spatial and temporal distribution of enteric illness in New Brunswick was examined. Enteric disease case data from 1992 to 2002 was extracted from the Canadian Institute for Health Information (CIHI) Discharge Abstract Database and New Brunswick's Reportable Disease Surveillance System. Several spatial clusters of disease incidence were identified throughout the province using the spatial scan statistic. Their location and size were dependent on the pathogen being studied and the geographical scale at which the analysis was conducted. The temporal scan statistic and the seasonal-trend LOESS (locally weighted scatterplot smoothing) decomposition (STL) were used to identify seasonal peaks in disease incidence. Peaks in disease incidence for ' Giardia' and 'Salmonella' infections were identified in the spring months. The use of a number of different spatial and temporal methods provided a robust picture of the distribution of enteric disease in New Brunswick. Agricultural and weather variables were evaluated as potential risk factors for enteric disease incidence using negative binomial regression. Mean weekly temperature was associated with increased incidence of 'Campylobacter ', 'Escherichia coli' O157 and 'Giardia' infections; changes in snow depth were associated with 'Giardia' and 'Campylobacter' incidence. Several agricultural variables were also identified as risk factors for enteric illness. This study newly identified or confirmed risk factors that can potentially be used to help guide public health policy. The potential impact of global climate change on enteric illness in New Brunswick was evaluated using negative binomial regression and application of incidence rate difference mapping. Results indicated that increases in temperature due to global climate may provoke relatively large increases in enteric disease incidence in some regions of New Brunswick. The use of incidence rate difference maps provided a method to evaluate the impact of changes in a complex system on disease incidence.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".