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Enregistrement W2811491176 · doi:10.5210/ojphi.v10i1.8964

Suitability of Data for the Surveillance of Toxicological Events in Companion Animals

2018· article· en· W2811491176 sur OpenAlexaff
Alexandra Swirski, David L. Pearl, Olaf Berke, Terri L. O’Sullivan, Deborah Stacey

Notice bibliographique

RevueOnline Journal of Public Health Informatics · 2018
Typearticle
Langueen
DomainePharmacology, Toxicology and Pharmaceutics
ThématiqueForensic Toxicology and Drug Analysis
Établissements canadiensUniversity of Guelph
Organismes subventionnairesAmerican Society for the Prevention of Cruelty to Animals
Mots-clésMedicineCompanion animalEnvironmental healthPoison control centerOutbreakAnimal welfareMedical emergencyVeterinary medicinePoison controlInjury preventionPathologyBiology

Résumé

récupéré en direct d'OpenAlex

Objective: Our objective was to assess the suitability of the data collected by the Animal Poison Control Center, run by the American Society for the Prevention of Cruelty to Animals, for the surveillance of toxicological exposures in companion animals in the United States.Introduction: There have been a number of non-infectious intoxication outbreaks reported in North American companion animal populations over the last decade1. The most devastating outbreak to date was the 2007 melamine pet food contamination incident which affected thousands of pet dogs and cats across North America1. Despite these events, there have been limited efforts to conduct real-time surveillance of toxicological exposures in companion animals nationally, and there is no central registry for the reporting of toxicological events in companion animals in the United States. However, there are a number of poison control centers in the US that collect extensive data on toxicological exposures in companion animals, one of which is the Animal Poison Control Center (APCC) operated by the American Society for the Prevention of Cruelty to Animals (ASPCA). Each year the APCC receives thousands of reports of suspected animal poisonings and collects extensive information from each case, including location of caller, exposure history, diagnostic findings, and outcome. The records from each case are subsequently entered and stored in the AnTox database, an electronic medical record database maintained by the APCC. Therefore, the AnTox database represents a novel source of data for real-time surveillance of toxicological events in companion animals, and may be used for surveillance of pet food and environmental contamination events that may negatively impact both veterinary and human health.Methods: Recorded data from calls to the APPC were collected from the AnTox database from January 1, 2005 to December 31, 2014, inclusive. Sociodemographic data were extracted from the American 2010 decennial census and the American Community Surveys. Choropleth maps were used for preliminary analyses to examine the distribution of reporting to the hotline at the county-level and identify any “holes” in surveillance. To further identify if gaps in reporting were randomly distributed or tended to occur in clusters, as well as to look for any predictable spatial clusters of high rates of reporting, spatial scan statistics, based on a Poisson model, were employed. We fitted multilevel logistic regression models, to account for clustering within county and state, to identify factors (e.g., season, human demographic factors) that are related to predictable changes in call volume or reporting, which may bias the results of quantitative methods for aberration/outbreak detection.Results: Throughout the study period, over 40% of counties reported at least one call to the hotline each year, with the majority of calls coming from the Northeast. Conversely, there was a large “hole” in coverage in Midwestern and southeastern states. The location of the most likely high and low call rate clusters were relatively stable throughout the study period and were associated with socioeconomic status (SES), as the most likely high risk clusters were identified in areas of high SES. Similar results were identified using multivariable analysis as indicators of high SES were found to be positively associated with rates of calls to the hotline at the county-level.Conclusions: Socioeconomic status is a major factor impacting the reporting of toxicological events to the APCC, and needs to be accounted for when applying cluster detection methods to identify outbreaks of mass poisoning events. Large spatial gaps in the network of potential callers to the center also need to be recognized when interpreting the spatiotemporal results of analyses involving these data, particularly when statistical methods that are highly influenced by edge effects are used.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,020
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,223
Score d'incertitude au seuil0,688

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0200,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,377
Tête enseignante GPT0,528
Écart entre enseignants0,150 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2018
Routes d'admission1
Résumé présentoui

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