Informing source attribution of enteric disease: An analysis of public health inspectors’ opinions on the “most likely source of infection”
Bibliographic record
Abstract
Enteric illness continues to place a significant burden on the health of Canadians. To reduce this burden and establish effective prevention and intervention practices, the sources of these infections need to be understood. Multiple methods have been used to examine source attribution. This study presents a unique method for examining source attribution and enteric disease risk factors within a Canadian community. Open text data from 2006 to 2010 were analyzed on the “most likely source of infection” (MLSI) identified by public health inspectors (PHIs), investigating sporadic endemic cases of enteric illness in the Region of Waterloo, Ontario. The MLSI data were classified under nine categories and analyzed using five disease groups consisting of overall enteric disease, campylobacteriosis, salmonellosis, verotoxigenic Escherichia coli (VTEC) infection, and parasitic disease. Food was the most frequently reported MLSI for overall enteric disease (26.1%), salmonellosis (41.1%), and VTEC infection (31.3%). Animal and water exposure were the most frequently reported MLSI for campylobacteriosis (26.2%) and parasitic disease (45.8%), respectively. Food safety practices were more frequently implicated as the source of infection for salmonellosis (17.7%) and campylobacteriosis (12.6%), compared with verotoxigenic Escherichia coli (VTEC) infection (6.3%) and parasitic disease (1.0%). The category unpasteurized was the third most frequent MLSI for campylobacteriosis (12.6%), along with food safety practices (12.6%). The analysis of PHIs’ opinions on the MLSI of enteric disease is a valuable method to inform source attribution. The enhanced Canada's National Integrated Enteric Pathogen Surveillance Program (C-EnterNet) standardized questionnaires provided an important source of data to complete this analysis. The results from this study can be used to generate hypotheses for future studies and inform public health policy and practice at the local, provincial, and national levels to reduce the burden of enteric illness in Canada.
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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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".