Evidence map›Paper›PMID 38374423›Full record

ArticleJournal of exposure science & environmental epidemiology2024

Assessing the difference in contamination of retail meat with multidrug-resistant bacteria using for-consumer package label claims that indicate on-farm antibiotic use practices- United States, 2016-2019.

G Sean Stapleton, Gabriel K Innes, Keeve E Nachman, Joan A Casey, Andrew N Patton, Lance B Price, Sara Y Tartof, Meghan F Davis

Abstract read
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In one paragraph

Article in Journal of exposure science & environmental epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Antimicrobial resistance in nontyphoidalFrontiers in public health · 2026
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

G Sean StapletonDepartment of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. gstaple2@alumni.jh.edu.ORCID 0000-0002-1784-6577
Gabriel K InnesYuma Center for Excellence in Desert Agriculture, Yuma, AZ, USA.
Keeve E NachmanDepartment of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Joan A CaseyDepartment of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, WA, USA.
Andrew N PattonGeospatial Analysis Lab, University of San Francisco, Harney Science Center, San Francisco, CA, USA.
Lance B PriceMilken Institute School of Public Health, The George Washington University, Washington, DC, USA.
Sara Y TartofDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA.
Meghan F DavisDepartment of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

Funding

Antimicrobial resistant E. coli before and after California Senate bill 27 (ARES): a natural experimentR01AI130066 · NIAID · KAISER FOUNDATION RESEARCH INSTITUTE · PI TARTOF, SARA · 2017 to 2021
$3.8M
NIAID NIH HHS R01 AI130066
6 · The paper itself

Abstract

backgroundAntibiotic use in food-producing animals can select for antibiotic resistance in bacteria that can be transmitted to people through contamination of food products during meat processing. Contamination resulting in foodborne illness contributes to adverse health outcomes. Some livestock producers have implemented antibiotic use reduction strategies marketed to consumers on regulated retail meat packaging labels ("label claims").

objectiveWe investigated whether retail meat label claims were associated with isolation of multidrug-resistant organisms (MDROs, resistant to ≥3 classes of antibiotics) from U.S. meat samples.

methodsWe utilized retail meat data from the U.S. Food and Drug Administration National Antimicrobial Resistance Monitoring System (NARMS) collected during 2016-2019 for bacterial contamination of chicken breast, ground turkey, ground beef, and pork chops. We used modified Poisson regression models to compare the prevalence of MDRO contamination among meat samples with any antibiotic restriction label claims versus those without such claims (i.e., conventionally produced).

resultsIn NARMS, 62,338 meat samples were evaluated for bacterial growth from 2016-2019. Of these, 24,446 (39%) samples had label claims that indicated antibiotic use was restricted during animal production. MDROs were isolated from 2252 (4%) meat samples, of which 71% (n = 1591) were conventionally produced, and 29% (n = 661) had antibiotic restriction label claims. Compared with conventional samples, meat with antibiotic restriction label claims had a statistically lower prevalence of MDROs (adjusted prevalence ratio: 0.66; 95% CI: 0.61, 0.73). This relationship was consistent for the outcome of any bacterial growth. IMPACT: This repeated cross-sectional analysis of a nationally representative retail meat surveillance database in the United States supports that retail meats labeled with antibiotic restriction claims were less likely to be contaminated with MDROs compared with retail meat without such claims during 2016-2019. These findings indicate the potential for the public to become exposed to bacterial pathogens via retail meat and emphasizes a possibility that consumers could reduce their exposure to environmental reservoirs of foodborne pathogens that are resistant to antibiotics.

Indexed as

Anti-Bacterial AgentsDrug Resistance, Multiple, BacterialMeatAnimalsBacteriaCattleChickensFarmsFood ContaminationFood LabelingFood MicrobiologyHumansMeat ProductsSwineTurkeysUnited StatesAnti-Bacterial AgentsAntimicrobial resistanceFoodborne bacteriaLabel claimsRetail meat

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.