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Record W2804183057 · doi:10.5210/ojphi.v10i1.8858

Epidemiology of Suspected Pesticide Poisoning in Livestock

2018· article· en· W2804183057 on OpenAlexaboutno aff
Judy Akkina, Leah Estberg

Bibliographic record

VenueOnline Journal of Public Health Informatics · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
FundersAmerican Society for the Prevention of Cruelty to Animals
KeywordsLivestockMedicineEnvironmental healthPesticideEpidemiologyPoison control centerAnimal welfarePoison controlInjury preventionVeterinary medicineMedical emergencyGeographyBiologyEcologyForestryPathology

Abstract

fetched live from OpenAlex

ObjectiveThis study characterizes the epidemiology of suspected pesticide poisoning in livestock in the United States (U.S.) and Canada using data from calls to the American Society for the Prevention of Cruelty to Animals (ASPCA) Animal Poison Control Center (APCC).IntroductionPesticides are used in agriculture and in the home to control pests such as insects, weeds, fungi and rodents. Pesticide poisoning in animals is usually due to misuse or accidental exposure1. Information on poisonings in livestock in North America is largely lacking2. Examples of hotlines in the U.S. for animal poisoning consultations include the APCC ($65.00 fee) and the Pet Poison Helpline (PPH) ($59.00 fee). The APCC fields calls 24 hours/day, 7 days/week about animal poisonings from the U.S., its territories and Canada. Using data from almost 4 years of APCC calls we describe the occurrence, category and class of pesticides involved, and outcomes of suspected pesticide exposures in livestock. This information is useful to raise awareness, encourage the proper use of pesticides and identify specific pesticides with negative impact on livestock health.MethodsAPHIS contracts with the APCC to receive de-identified data weekly on livestock calls for the purpose of conducting surveillance. This retrospective study used data from all calls concerning bovine, camelid, caprine, equine, ovine, porcine and poultry species from 10/1/2013 to 9/2/2017, where the caller reported suspected pesticide exposure. There were 1,025 calls regarding 3,028 animals meeting this criteria, representing 52% of all livestock calls with any type of toxic exposure. Caller type was 80% animal owners, 10% veterinarian or veterinary staff, and 10% other types. Most callers (92%) provided their zip code, with 96% of calls from the U.S. and 4% from Canada. Variables used for descriptive analysis were: species; APCC staff assessment that illness was due to pesticide exposure; severity of illness; clinical signs; first, second and third ingredients of the pesticide, and pesticide ingredient class (e.g. pyrethrin). Pesticides were grouped based on the first active ingredient into fungicide, herbicide, insecticide, and rodenticide categories.ResultsThe proportion of calls by species was equine (33%), poultry (26%), bovine (25%), caprine (8%), porcine (6%), ovine (2%), and camelid (0.5%). Some animals were exposed to >1 pesticide product and some pesticide products had >1 ingredient class. The pesticide category with the highest number of exposed animals was insecticides (2,151), followed by herbicides (839), rodenticides (765) and fungicides (286). The treemap below illustrates the number and proportions of animals exposed to the 4 pesticide categories and the top 3 pesticide classes within each category based on the first active ingredient. For all pesticide exposures in all species, no illness was reported in 68% of animals. According to assessment by APCC staff, only 35% (333) of animals showing clinical signs were considered with confidence (medium or high likelihood) to be due to pesticide exposure. For these 333 animals, severity of illness was mild for 80% (266 animals), moderate for 18% (61 animals), major for 1% (3 animals) and caused death in 1% (3 animals). Among animals with confidence that clinical signs were due to pesticide exposure the most frequent syndrome was dermatologic.ConclusionsSuspected pesticide exposure was the most frequent reason a call concerning livestock was made to the APCC. Callers reported that most animals showed no illness, and major illness or death was rare. Livestock were most frequently exposed to the insecticide category, and 46% of the animals with exposure to insecticides were exposed to the pyrethrin class. This is consistent with the phasing out of organophosphate insecticides for residential use since 2000 and the increasing use of pyrethrin insecticides3, which are considered less toxic. Limitations of this study include: 1) data from only one major animal poison control hotline was available for analysis and people may call their veterinarian directly or use the internet 2) calls regarding specific ingredients may be over represented due to corporate client relationships with the APCC 3) illness may have occurred after the call was made, therefore the proportion of animals with illness following suspected exposure may be an underestimate.References1. Wang Y, Kruzik P, Helsber A, Helsberg I, Rausch W. (2006) Pesticide poisoning in domestic animals and livestock in Austria: A 6 year retrospective study. Forensic Science International 169:157-160.2. Gwaltney-Brant SM. (2012). Epidemiology of Animal Poisonings in the United States. In: Gupta RC (Ed.), Veterinary Toxicology: Basic and Clinical Principles. Elsevier, Second ed: 80-87.3. Power LE, Sudakin DL. Pyrethrin and pyrethroid exposures in the United States: A longitudinal analysis of incidents reported to Poison Centers. (2007) J of Medical Toxicology. 3(3):94-99.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.362

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.112
GPT teacher head0.385
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations2
Published2018
Admission routes1
Has abstractyes

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