Determining the long-term health burden and risk of sequelae for 14 foodborne infections in British Columbia, Canada: protocol for a retrospective population-based cohort study
Bibliographic record
Abstract
Introduction Over one in eight Canadians is affected by a foodborne infection annually; however, the long-term consequences, including the risks and costs of sequelae, are unclear. We aim to estimate the health burden and direct costs of 14 infections commonly transmitted by food, considering the acute illness and subsequent sequelae and mortality, for the population of British Columbia, Canada (~4.7 million). Methods and analysis We will conduct a population-based retrospective cohort study of the British Columbia provincial population, over a 10-year study period (1 January 2005 to 31 December 2014). Exposure is defined as a provincially reported illness caused by Clostridium botulinum , Campylobacter , Cryptosporidium, Cyclospora, Giardia , hepatitis A virus, Listeria , non-typhoidal Salmonella spp, Salmonella Typhi, Salmonella Paratyphi, Shiga toxin-producing Escherichia coli , Shigella , Vibrio parahaemolyticus or Yersinia (excluding pestis ). We will link individual-level longitudinal data from eight province-wide administrative health and reportable disease databases that include physician visits, hospitalisations and day surgeries, deaths, stillbirths, prescription medications (except those to treat HIV) and reportable foodborne diseases. Using these linked databases, we will investigate the likelihood of various sequelae and death. Hazard models will be used to estimate the risk of outcomes and their association with the type of foodborne infection. Epidemiological analyses will be conducted to determine the progression of illness and the fraction of sequelae attributable to specific foodborne infections. Economic analyses will assess the consequent direct healthcare costs. Ethics and dissemination This study has been approved by a University of Waterloo Research Ethics Committee (no 30645), the University of British Columbia Behavioral Research Ethics Board (no H16-00021) and McGill University’s Institutional Review Board (no A03-M12-19A). Results will be disseminated via presentations to academics, public health practitioners and knowledge users, and publication in peer-reviewed journals. Where such publications are not open access, manuscripts will also be available via the University of Waterloo’s Institutional Repository ( https://uwspace.uwaterloo.ca ).
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".