Can risk rating tool results be used to predict results of inspection categories of dairy processing plants?
Notice bibliographique
Résumé
Background: Dairy products are consumed by a large portion of the population. The dairy processing plants (DPP) that produce these perishable products may create health hazards (chemical, physical, biological). In order to minimize any health risks from these products, DPP are inspected by regulating authorities. This study examined secondary data derived from the BCCDC dairy program’s semi-quantitative risk ranking tool (RRT) to examine trends over time with DPP inspections, and to assess risk factors within the tool. Methods: RRT based data from individual DPP inspections from 2015 through 2018 were entered into a master spreadsheet. The RRT has two overall risk categories, inherent and measured risk. Inherent risk categories in the tool were sourced from surveys of dairy plants, while measured risks in the tool were sourced from inspection visits (routine and in-depth), environmental and food result submissions from dairy plants and inspectors, and based on compliance and history. In total, 107 items were assessed within the eight categories. Descriptive analyses were conducted, and statistical analyses performed using NCSS 12 software (NCSS, 2018). Results: A total of 128 inspection reports from 30 different DPP were included in this study. From these inspections, 65% were considered low risk, 12% moderate and 23% high risk. DPP that were located on-farm were found to have significantly higher overall inspection risk scores than dairy plants located off-farm (average on-farm inspection risk ranking score = 694; average off-farm inspection risk ranking score = 153; p=0.0003, power=95%). When the microbiological scores category, derived from environmental swabs and food submissions, were compared to the inspection score category, these categories were statistically significantly correlated (p=0.0000, power=100%); when inspection score increases, so too does microbiological score. Higher risk scores were also found in DPP producing more than one category of dairy product (comparing one product versus 6 or 7 products, p=0.009, power=76%). Conclusion: Dairy inspections ensure DPP follow good manufacturing practices and therefore help to protect the population from disease outbreaks or other contaminations. This study demonstrated that there is increased risk of having a dairy processing facility located on-farm, that more complex dairy processing operations that produce more than one type of dairy product have higher risk rating scores and that higher inspection score violations positively correlated to positive microbiological scores. This study further showed that in the absence of microbiological results, a risk score could still be calculated by analyzing the inspection violations alone. The Food Safety Specialists at the BCCDC can use this data to focus their inspection time on higher risk areas and items to maximize time spent out in the field.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».