Evidence map›Paper›PMID 35918072›Full record

ArticleBMJ global health2022

The quality of veterinary medicines and their implications for One Health.

Vayouly Vidhamaly, Konnie Bellingham, Paul N Newton, Céline Caillet

Open access · goldAbstract read
In one paragraph

Article in BMJ global health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
3.8field-weighted citation impact, top 7% of its field
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

17 citing papers in PubMed, 21 citations in OpenAlex.

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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

4 authors at 1 institution in 3 countries.

Vayouly VidhamalyLao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Medicine Quality Research Group, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao People's Democratic Republic.ORCID 0000-0003-1941-8944
Konnie BellinghamLao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Medicine Quality Research Group, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao People's Democratic Republic.ORCID 0000-0001-6162-2098
Paul N NewtonLao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Medicine Quality Research Group, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao People's Democratic Republic.ORCID 0000-0003-0734-2022
Céline CailletLao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Medicine Quality Research Group, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao People's Democratic Republic celine.caillet@ndm.ox.ac.uk.ORCID 0000-0002-5554-2603
Mahosot Hospital · LA

Funding

Wellcome Trust
6 · The paper itself

Abstract

objectiveSubstandard and falsified (SF) veterinary medicines affect animal health, agricultural production and food security and will influence antimicrobial resistance (AMR) in both animals and humans. Yet, our understanding of their extent and impact is poor. We assess the available public domain evidence on the epidemiology of SF veterinary medicines, to better understand their prevalence and distribution and their public health impact on animals and humans.

methodsSearches were conducted in Embase, PubMed, MEDLINE, Global Health, Web of Science, CAB Abstracts, Scopus, Google Scholar, Google and websites with interest in veterinary medicines quality up to 28 February 2021. Identified articles in English and French were screened for eligibility. The Medicine Quality Assessment Reporting Guidelines were used to assess the quality of prevalence surveys.

resultsThree hundred and fourteen publications were included with a failure frequency (the percentage of samples that failed at least one quality test) of 6.5% (2335/35 733). The majority of samples were from post-marketing surveillance by medicines regulatory authorities of the Republic of Korea and China. A small proportion (3.5%) of samples, all anti-infectives, were from 20 prevalence surveys, with more than half (53.1%, 662/1246) collected in low-income and lower middle-income countries in Africa and Asia. The prevalence survey sample size ranged from 4 to 310 samples (median (Q1-Q3): 50 (27-80)); 55.0% of surveys used convenience outlet sampling methods. In 20 prevalence surveys more than half of the samples (52.0%, 648/1246) failed at least one quality test. The most common defects reported were out-of-specification active pharmaceutical ingredient(s) (API) content, failure of uniformity of units and disintegration tests. Almost half of samples (49.7%, 239/481) that failed API content tests contained at least one of the stated APIs below pharmacopoeial limits. Fifty-two samples (4.2% of all samples) contained one or more incorrect API. One hundred and twenty-three publications described incidents (recalls/seizures/case reports) of SF veterinary medicines in 29 countries.

conclusionThe data suggest that SF veterinary products are likely to be a serious animal and public health problem that has received limited attention. However, few studies of SF veterinary medicines are available and are geographically restricted. Lower API content and disintegration/dissolution than recommended by pharmacopoeial standards risks treatment failure, animal suffering and contribute to AMR. Our findings highlight the need of more research, with robust methodology, to better inform policy and implement measures to assure the quality of veterinary medicines within supply chains. The mechanism and impact of SF veterinary products on animal and human health, agricultural production, their economy and AMR need more transdisciplinary research.

Indexed as

Counterfeit DrugsOne HealthHumansIncomePovertyPublic HealthCounterfeit Drugsepidemiologypublic healthsystematic review

Identifiers

PMID35918072
PMCPMC9351321
OpenAlexW4289786101

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.