An economic analysis of «Salmonella» detection in fresh produce, poultry, and eggs using whole genome sequencing technology in Canada
Bibliographic record
Abstract
Foodborne illnesses cause a significant socio-economic burden worldwide. Nontyphoidal Salmonella is one of the major foodborne disease agents in Canada. To date, there is hardly any research on the cost and benefits of the Whole Genome Sequencing (WGS) compared to the traditional technology for the detection of Salmonella from specific food products and the macroeconomic impact of the improved technology in outbreak detection. The current study is an attempt to make a contribution in that direction. The study estimates the annual costs of Salmonella from fresh produce, poultry and eggs in Canada and the economic benefits from the introduction of WGS in the detection of Salmonella clusters and outbreaks. The results from the cost-benefit analysis are then used to measure the impact on industrial output, gross domestic product (GDP) and employment. Cost-of-illness and Health Adjusted Quality Life Years are used to estimate the monetary and non-monetary costs of Salmonella respectively. Probability models are used to account for uncertainty in the cost-of-illness estimates. The input-output framework is used to measure the macroeconomic impact. Four scenarios are exercised to measure the macroeconomic impact: i) productivity improvement, ii) decrease in direct healthcare cost, iii) decrease in federal cost and iv) total net benefits from WGS. The estimated number of cases is 47,082 annually, which represents a cost of $287.78 million from PFGE. The non-monetary estimates from current technology are 529.20 years (Disability Adjusted Life Years) and 289.90 years (Quality Adjusted Life Years), annually. The total net benefits from the introduction of WGS are estimated at $90.25 million (in 2013 CAD). These microeconomic net benefits are then used to measure the macroeconomic impacts of WGS. Positive net benefits from WGS lead to increased industrial output ($15.88 million), GDP ($13.38 million) and labour (116). Overall, WGS will help in reducing the economic burden from Salmonella. The monetary savings from the reduction in direct healthcare cost (medical intervention) and laboratory costs can be invested in further research and development, however, a proper intervention of federal and provincial government is required. A holistic approach to food safety will improve the benefits from WGS in outbreak containment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".