Investigating biosecurity risks associated with the delivery of feed to swine farms
Bibliographic record
Abstract
Introduction Biosecurity protocols reduce the introduction and spread of pathogens among swine farms. For pigs, biosecurity ensures market stability, maintains export opportunities, and controls spread of production and public health diseases. Salmonella, the second most common cause of bacterial foodborne illness, causes gastrointestinal illness. Pigs can be asymptomatic carriers of the bacteria and pork products are a known source of salmonellosis in humans. Salmonella have been isolated from pigs, boots, flies, rodents, bird feces, feed, feed-ingredients, and feed trucks. The objective was to identify management factors to reduce the risk of disease spread among swine farms through feed trucks. Materials and Methods The study was conducted in two phases - the first phase included a series of focus groups and key-informant interviews; discussions held with swine producers and feed company personnel explored the protocols currently in place that reduce the risk of disease spread through the delivery of feed. Participants were asked to identify possible management changes that could further reduce the risk, and to rate these ideas in terms of their effect on disease control, and feasibility based on implementation and economics. This information was used to structure the second phase of the study – a pilot study. The pilot study was conducted over 6 weeks in the winter of 2013, and included 40 feed truck drivers from 3 Ontario feed companies. Truck drivers filled out log sheets as they delivered feed to swine farms, and provided information about the prevalence of the identified biosecurity risks during the day-to-day delivery of feed. Drivers were randomly assigned to be in either the treatment or control groups. Drivers in the control group delivered feed as they normally do. Drivers in the treatment group were: i) asked not to enter the barn unless absolutely necessary, and ii) were provided with re-usable, washable rubber over-shoes and were asked to wear a clean pair at each farm when they felt safe doing so. Chisquared tests were used to compare the usage of clean boots between drivers in the treatment and control groups. In total, 40 drivers from 3 companies, delivering feed to 2202 farms over 6 weeks (in the winter) collected descriptive data on the factors and compared use of different boots. Results The factors followed by the proportion of farms and drivers where the factor was found were found as follows: Most farms were: keeping areas (driveway, feed bin and barnyard) clean of mud and manure (82%), and dead-stock (91%) was appropriately disposed of and not visible to the driver. Some farms had an outbuilding to deliver bagged feed (24%). Other factors that were identified that producers should work on were to provide farm boots and coveralls for the driver if the driver needed to go into the barn, ordering the correct amount of feed to decrease the numbers of deliveries (or frequencies of deliveries) of feed, and notifying feed mill of a disease outbreak on the farm. Feed truck driver factors included wearing a separate pair of clean, disinfected, dried boots (25%) and gloves (50%) at each farm that is visited (even if the driver does not go into the barn, remaining outside the barn (92%); washing the steering wheel (49%), floor mat (77%) and outside of the truck (32%) every 24 hours. From these results, it may be advisable to increase the frequency of washing the truck. Another factor identified was for the driver to know and follow the farm biosecurity protocols. Half of the time, the driver left the feed bill outside barn (54%). Drivers given disinfected rubber boots were more likely to wear these on farms (42%) than plastic disposable boots (4%) (P<0.05). These drivers were also more likely to wear disinfected rubber boots than the control drivers (11%) (P<0.001). Conclusions and Discussion Biosecurity is a responsibility that is shared among all members of the industry. Feed personnel were encouraged to know more about disease transmission. Acknowledgments Canadian Swine Health Board for funding, feed company personnel and producers for participation.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".