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Multidrug-Resistant Bacteria in Small-Animal Veterinary Healthcare Settings: Mapping the Evidence on Bacterial Contamination, Antimicrobial Resistance, and Detection Sites

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09 October 2026

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10 October 2026

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Abstract
Environmental contamination in veterinary healthcare facilities may contribute to the persistence and potential dissemination of multidrug-resistant (MDR) bacteria, thereby creating challenges for infection control and clinical management. A structured litera-ture search was conducted in PubMed, Scopus, and Google Scholar for studies published between 2010 and 2025 that investigated the bacterial contamination of small-animal veterinary environments, equipment, and/or veterinary personnel. A total of 96 studies met the inclusion criteria. Swabbing was the predominant sampling method, used ex-clusively in 56 studies (58.3%). Resistance-targeted selective and/or chromogenic culture media were used in 41 studies (42.7%). Among the clinical areas, intensive care units showed the highest proportion of studies reporting MDR bacteria (13/20, 65.0%). Re-garding individual sampling sites, MDR bacteria were reported in 27 of 44 studies sam-pling cages (61.4%). High proportions of MDR detection were also reported for scales (8/11, 72.7%), ultrasound equipment (10/15, 66.7%), sinks (13/21, 61.9%), and computer keyboards (19/32, 59.4%). Staphylococcus was the predominant reported genus (58 studies, 60.4%), while MDR S. pseudintermedius and S. aureus were reported in 27 (28.1%) and 25 (26.0%) studies, respectively. Among resistance determinants, mecA was the most fre-quently reported gene. These findings highlight the widespread detection of MDR bac-teria across veterinary clinical environments and support the need for systematic envi-ronmental surveillance and strengthened infection prevention measures.
Keywords: 
evidence map
;  
veterinary hospitals
;  
environmental samples
;  
equipment
;  
veterinary personnel
;  
bacterial contamination
;  
multidrug resistance
Subject: 
Medicine and Pharmacology  -   Veterinary Medicine

1. Introduction

Antimicrobial resistance (AMR) represents a major global health threat [1]. A critical concern is the emergence and spread of antimicrobial-resistant organisms in healthcare-associated infections (HAIs), which constitute a persistent challenge in clinical settings and are associated with treatment failure, prolonged illness, and increased mortality in human medicine [2]. The persistence and dissemination of HAI-associated pathogens are influenced not only by infected patients but also by the healthcare environment, where contaminated surfaces, inanimate fomites, and transiently colonized healthcare personnel may act as interconnected reservoirs and potential transmission routes [3].
In veterinary medicine, multidrug-resistant (MDR) infections are increasingly reported worldwide, involving critical pathogens such as ESKAPE group organisms, methicilin-resistant staphylococci (MRS), and carbapenem-resistant Enterobacterales (CRE) [4,5]. Previous evidence has shown that pathogens associated with healthcare-associated infections and zoonotic transmission can be recovered from clinical cases, environmental surfaces, and equipment in veterinary hospitals [6]. However, available evidence on antimicrobial resistance and environmental contamination in veterinary facilities remains limited and heterogeneous, underscoring the need for improved awareness, early detection, and infection prevention practices among veterinary healthcare personnel [7].
Despite the growing evidence of bacterial contamination in veterinary healthcare environments, important knowledge gaps remain regarding the distribution of MDR bacteria across different clinical areas, environmental surfaces, equipment and veterinary personnel. In routine practice, attention is often focused on the diagnosis and treatment of individual MDR infections, while potential environmental sources of cross-contamination may require greater attention
To address these gaps, this evidence map aims to collect and synthesize the available evidence on bacterial isolation from veterinary healthcare environments, clinical equipment, and veterinary personnel. Specifically, this study aims to characterize the sampling approaches and culture methods used across studies, describe the bacterial taxa and MDR phenotypes identified, examine the distribution of MDR bacteria across functional clinical areas and individual sampling sites, and summarize the reported antimicrobial resistance determinants.

2. Materials and Methods

A structured literature search was conducted to identify studies investigating bacterial contamination and antimicrobial resistance in companion-animal veterinary clinical environments and among veterinary healthcare personnel. The search covered peer-reviewed publications from 2010 to 2025 to capture contemporary trends and relevant evidence. The databases searched were PubMed, Scopus, and Google Scholar, using combinations of terms related to veterinary hospitals and clinics, environmental contamination, veterinary healthcare personnel, bacterial isolation, antimicrobial resistance, and multidrug-resistant (MDR) bacteria.
During the screening process, particular emphasis was placed on studies involving sample collection followed by bacterial culture, isolation, and phenotypic and/or molecular identification from veterinary clinical settings. Eligible sources of bacterial samples included environmental surfaces and areas within veterinary hospitals or clinics, clinical equipment and fomites, and occupational specimens obtained from veterinary personnel, such as nasal or hand swabs and personal items (e.g., shoes, mobile phones, and footwear). Studies were included when bacterial recovery was based on microbiological culture, with subsequent phenotypic and/or molecular identification where reported. Conversely, studies without microbiological sampling or bacterial culture, as well as studies focusing exclusively on animal clinical cases, in vitro disinfection assays without in situ sampling, or narrative reviews, were excluded.
Retrieved records were initially screened at the title and abstract level, followed by detailed full-text assessment according to the eligibility criteria. Extracted information was synthesized into predefined categories: (i) study characteristics, including author(s), publication year, country, and facility type; (ii) sampling characteristics, including target site, sample type, and sampling technique; (iii) microbiological methods, including isolation media and bacterial taxa identified; (iv) antimicrobial resistance characteristics, including antimicrobial susceptibility testing methods, reported resistance, multidrug-resistant strains, and associated sample origins; and (v) resistance determinants, including detected antimicrobial resistance genes (ARGs).
Multidrug-resistant (MDR) status was assigned according to the definitions reported by the individual studies or, where analytical antimicrobial susceptibility profiles were available, evaluated against established criteria [8]. Furthermore, for the purposes of the present evidence synthesis, ESBL-producing organisms, methicillin-resistant staphylococci (MRS), and carbapenem-resistant Enterobacterales (CRE) were included within the MDR-related findings, when reported by the original studies, given their resistance profiles and clinical significance.
Particular attention was given to the origin of the samples, which were categorized according to the clinical area, environmental surface, medical or diagnostic equipment, or veterinary personnel sampled. When reported, the specific sample source associated with MDR bacterial recovery was also recorded. Information was extracted as reported by the original studies, while sampling locations were standardized into functional clinical areas and individual sampling sites to facilitate comparisons across studies. Descriptive frequencies were calculated at the study level; therefore, the percentages represent the proportion of the included studies reporting a given bacterial taxon, resistance phenotype, or sampling location and do not represent the proportion of individual isolates.

3. Results

3.1. Included Studies and Generic Characteristics

The database search across PubMed (180 records), Scopus (1,094 records), and Google Scholar (6,916 records) initially yielded 8,190 records published between 2010 and 2025. Following the removal of duplicate records and preliminary screening of titles and abstracts against the predefined eligibility criteria, 382 records were retained for full-text assessment. Full-text articles were subsequently evaluated for eligibility based on the predefined inclusion criteria, with particular emphasis on studies reporting microbiological sampling and culture-based bacterial isolation from small-animal veterinary clinical environments, equipment, or fomites, and/or veterinary healthcare personnel.
Following the full-text assessment, 96 peer-reviewed studies met the eligibility criteria and were included in the evidence map for data extraction and qualitative evidence synthesis. A study selection flow diagram informed by the PRISMA 2020 framework was used to summarize the identification, screening, eligibility assessment, and inclusion of studies (Figure 1).
The detailed characteristics of each included study, encompassing the year, country, origin of samples, and sampling technique, are summarized in Table 1.
Year Country Sample Origin Sampling Technique Reference
2010 Japan Personnel, Environment/equipment Swabs [10]
2010 UK Environment/equipment Dipslides/contact agar [11]
2010 South Korea Personnel, Environment/equipment Swabs [12]
2010 UK Personnel Swabs [13]
2010 USA Personnel Swabs [14]
2010 Canada Environment/equipment Electrostatic cloths/gloved hands [15]
2011 The Netherlands Personnel, Environment/equipment Swabs/gauze pads [16]
2011 USA Environment/equipment Swabs/electrostatic cloths [17]
2011 South Korea Personnel, Environment/equipment Swabs [18]
2012 Malaysia Personnel, Environment/equipment Swabs [19]
2012 USA Environment/equipment Swabs [20]
2012 USA Environment/equipment Swabs [21]
2012 Canada Personnel Electrostatic cloths [22]
2013 USA Environment/equipment Swabs [23]
2013 USA Environment/equipment Impaction air sampling [24]
2013 USA Environment/equipment Impaction air sampling [25]
2013 Canada Personnel Electrostatic cloths [26]
2013 USA Environment/equipment Swabs/electrostatic cloths [27]
2013 Germany Personnel, Environment/equipment Swabs [28]
2014 South Korea Personnel, Environment/equipment Swabs [29]
2014 Finland Environment/equipment Swabs [30]
2014 USA Environment/equipment Sponges [31]
2014 Zambia Environment/equipment Swabs [32]
2015 Colombia Environment/equipment Swabs [33]
2016 Israel Environment/equipment Swabs [34]
2016 Malaysia Environment/equipment Swabs [35]
2016 UK Environment/equipment Swabs [36]
2016 UK Environment/equipment Boot socks/wipe cloths [37]
2017 Thailand Personnel, Environment/equipment Swabs [38]
2017 Costa Rica Environment/equipment Swabs/electrostatic cloths [39]
2017 Mexico Personnel, Environment/equipment Impaction air sampling/swabs [40]
2018 UK Personnel Contact agar [41]
2018 Germany Personnel, Environment/equipment Swabs [42]
2018 USA Environment/equipment Electrostatic cloths [43]
2018 Japan Personnel, Environment/equipment Swabs [44]
2018 Australia Personnel, Environment/equipment Swabs [45]
2018 South Korea Personnel, Environment/equipment Swabs/contact agar [46]
2019 USA Environment/equipment Swabs [47]
2020 China Personnel, Environment/equipment Swabs/settle plates [48]
2020 USA Environment/equipment Swabs [49]
2020 France Environment/equipment Swabs [50]
2020 USA Environment/equipment Electrostatic cloths [51]
2020 Switzerland Personnel, Environment/equipment Swabs [52]
2020 Brazil Environment/equipment Swabs [53]
2020 Thailand Personnel, Environment/equipment Swabs [54]
2021 Nigeria Environment/equipment Swabs [55]
2021 Thailand Personnel, Environment/equipment Swabs [56]
2021 Italy Environment/equipment Sponges [57]
2021 USA Environment/equipment Electrostatic cloths [58]
2021 Brazil Environment/equipment Swabs/settle plates [59]
2021 Egypt Personnel, Environment/equipment Swabs [60]
2021 Australia Environment/equipment Electrostatic cloths [61]
2021 Hungary Personnel, Environment/equipment Swabs [62]
2021 Thailand Personnel, Environment/equipment Swabs [63]
2021 Slovenia Environment/equipment Swabs [64]
2021 Switzerland Personnel, Environment/equipment Swabs [65]
2021 Brazil Environment/equipment Swabs [66]
2021 Portugal Environment/equipment Swabs [67]
2021 UK Personnel Swabs [68]
2022 Chile Personnel, Environment/equipment Swabs [69]
2022 Brazil Environment/equipment Swabs/membrane filtration [70]
2022 Thailand Environment/equipment Swabs [71]
2022 Brazil Environment/equipment Swabs/settle plates [72]
2022 France Environment/equipment Swabs [73]
2022 Germany Environment/equipment Not specified [74]
2022 China Environment/equipment Not specified [75]
2022 Brazil Personnel, Environment/equipment Swabs/settle plates [76]
2022 The Netherlands Environment/equipment Not specified [77]
2022 USA Environment/equipment Electrostatic cloths [78]
2022 Switzerland Personnel, Environment/equipment Swabs [79]
2022 Thailand Environment/equipment Swabs [80]
2023 Brazil Personnel, Environment/equipment Swabs [81]
2023 Ireland Personnel, Environment/equipment Swabs [82]
2023 UK Environment/equipment Electrostatic cloths [4]
2024 China Personnel, Environment/equipment Swabs [83]
2024 Portugal Personnel, Environment/equipment Swabs/contact agar [84]
2024 China Personnel, Environment/equipment Swabs [85]
2024 USA Environment/equipment Sponges [86]
2024 Portugal Environment/equipment Swabs [87]
2024 Spain Environment/equipment Swabs [88]
2024 Spain Personnel, Environment/equipment Swabs [89]
2024 Spain Personnel, Environment/equipment Swabs/sponges [90]
2024 South Africa Personnel Glove juice technique [91]
2024 Brazil Environment/equipment Swabs [92]
2024 China Environment/equipment Impaction air sampling [93]
2025 Brazil Environment/equipment Swabs [94]
2025 Brazil Environment/equipment Swabs [95]
2025 Portugal Personnel, Environment/equipment Swabs/contact agar [96]
2025 Brazil Personnel, Environment/equipment Swabs [97]
2025 USA Environment/equipment Sponges [98]
2025 UK Environment/equipment Swabs/sponges [99]
2025 USA Environment/equipment Sponges [100]
2025 Peru Environment/equipment Swabs [101]
2025 Italy Environment/equipment Swabs [102]
2025 Bangladesh Environment/equipment Swabs [103]
2025 Italy Personnel, Environment/equipment Swabs/sponges [104]
A total of 96 studies published between 2010 and 2025 were included in the evidence set. Their temporal distribution indicates a progressive increase in relevant research during the later years of the review period. In particular, 51 studies (53.1%) were published between 2021 and 2025. This indicates an increasing research interest in bacterial contamination and antimicrobial resistance within veterinary clinical settings in recent years.
Regarding sample origin, most studies investigated environmental surfaces and/or veterinary clinical equipment. Specifically, 55 studies (57.3%) sampled environmental or equipment-associated sources exclusively, whereas 34 studies (35.4%) included both veterinary personnel and environmental/equipment-associated sources. Only seven studies (7.3%) focused exclusively on veterinary personnel-associated samples.
A wide range of sampling techniques have been reported. Swabbing was by far the most frequently used technique, being reported as the sole sampling approach in 56 studies (58.3%). Other studies combined swabbing with additional sampling approaches, including settle plates (4 studies), electrostatic cloths (3 studies), contact agar (3 studies), sponges (3 studies), impaction air sampling (1 study), and membrane filtration (1 study).
Electrostatic cloths were used as a standalone sampling technique in eight studies (8.3%), whereas sponges were used independently in five studies (5.2%). Active air sampling using impaction-based methods was reported in three studies (3.1%), with one additional study combining impaction air sampling with surface swabbing. Contact-based approaches were also identified, including dipslides and contact agar sampling. Less frequently used approaches included boot socks/wipe cloths, gauze pads, and the glove juice technique, each representing a limited proportion of the included evidence.

3.2. Geographic Distribution of Included Studies

The geographic distribution of the selected studies (n = 96) included 33 countries across six continents, reflecting the global interest in antimicrobial resistance in veterinary clinical settings (Figure 2).
Most studies originated from North America, Europe, and South America. The United States contributed the highest number of publications (n = 17, 17.7%), followed by Brazil (n = 11, 11.5%), the United Kingdom (n = 8, 8.3%), and Thailand (n = 6, 6.3%). Additional contributions were reported from China (n = 5), Portugal (n = 4), and South Korea (n = 4); Canada, Germany, Italy, Spain, and Switzerland each contributed three studies. The remaining 21 countries contributed one or two studies each, indicating a broad but uneven geographical distribution of available evidence.

3.3. Sample Characteristics and Veterinary Facility Settings

The number of samples collected varied substantially across studies. Across articles with available data, the mean number of samples per study was 282.8 (95% CI, 127.6–438.0), with a median of 150 and a range of 4–6,400 samples.
When sample numbers were considered by sample origin, environmental and equipment sampling was more frequently reported than personnel sampling was. Among studies with numerical sample counts, the mean number of environmental samples per study was 160.1 (95% CI, 102.2–218.1), with a median of 95 samples. For personnel-associated sampling, numerical sample counts yielded a mean of 133.2 samples per study (95% CI, 65.5–200.8) and a median of 77.
The included studies were conducted in various veterinary clinical settings. An academic or teaching veterinary facility was identified in 55 of the 96 studies (57.3%). Most studies were conducted within a single veterinary facility (70/96, 72.9%), whereas 23 studies (24.0%) included multiple facilities. These multi-facility studies involved combinations of teaching hospitals, private veterinary hospitals or clinics, referral hospitals, and academic and private facilities.

3.4. Culture Media and Antimicrobial Susceptibility Testing

Substantial heterogeneity was observed in the culture media used for bacterial isolation across the included studies. Selective culture approaches specifically targeting antimicrobial resistance phenotypes or resistance-associated bacterial groups were reported in 41 studies (42.7%). These approaches (Table 2) included selective and/or chromogenic media targeting methicillin-resistant staphylococci, extended-spectrum β-lactamase (ESBL)-producing organisms, carbapenem-resistant organisms etc. In several studies, selective enrichment steps were combined with resistance-specific selective or chromogenic agar, indicating a targeted strategy for the recovery of resistant bacterial populations.
In a group of 39 studies (40.6%), selective and/or differential culture media were used, without an explicit focus on a specific antimicrobial-resistance phenotype. These included media such as MacConkey agar, eosin methylene blue agar, Baird–Parker agar, mannitol salt agar, Slanetz–Bartley agar, and other selective or differential media targeting particular bacterial groups. These approaches were primarily intended to facilitate the recovery and differentiation of specific bacterial taxa rather than directly screen for a defined resistance phenotype.
In contrast, only 11 studies (11.5%) relied solely on general-purpose or non-selective culture media, including blood agar, sheep blood agar, horse blood agar, brain heart infusion agar, Columbia agar, and Plate Count Agar. Finally, in five studies (5.2%), culture media were either not reported or no culture medium was specified.
Overall, the culture strategies ranged from broad bacterial recovery using general-purpose media to highly targeted selective screening for specific antimicrobial resistance phenotypes.
Antimicrobial susceptibility testing (AST) also demonstrated methodological variations. Disk diffusion was the most frequently reported individual approach, being used in 41 studies (42.7%). MIC-based methods were reported in 31 studies (32.3%), including automated or semi-automated systems such as VITEK and MicroScan, Sensititre-based testing, broth microdilution, agar dilution, and other microdilution approaches. A further 11 studies (11.5%) employed combined susceptibility-testing strategies, incorporating disk diffusion together with MIC-based methods or other complementary approaches, such as E-test or resistance-specific screening procedures. Finally, in 13 studies (13.5%), the antimicrobial susceptibility testing method was not specified.
Thus, although disk diffusion is the most common AST approach, MIC-based methods are also widely used, either alone or in combination with other susceptibility-testing techniques.
Overall, the evidence base showed considerable variation in both culture media and AST methodology, reflecting differences in the approaches used to recover, identify, and characterize antimicrobial-resistant bacteria across studies.

3.5. Bacterial Findings and Distribution

A broad range of bacterial taxa were reported in veterinary clinical environments and personnel-associated samples, across the included studies. Detailed data are presented in Table 3. Staphylococcus spp. were the most frequently reported genus, identified in 58 studies (60.4%), followed by Escherichia coli (32, 33.3%), Pseudomonas spp. (20, 20.8%), Klebsiella spp. (18, 18.8%), Acinetobacter spp. (17, 17.7%), Enterococcus spp. (16, 16.7%), and Enterobacter spp. (14, 14.6%). Less frequently reported taxa included Bacillus spp. and Serratia spp. (7 studies each), Citrobacter spp. (6 studies), and Stenotrophomonas, Salmonella, and Micrococcus spp. (5 studies each) (Table 3).
Beyond the predominant taxa, diverse opportunistic and environmental bacteria were recovered across a limited number of studies (n ≤ 3). These included various Gram-negative bacilli, such as Hafnia alvei, Leclercia adecarboxylata, Moraxella spp., Elizabethkingia meningoseptica, Alcaligenes spp., Raoultella spp., and other sporadically reported genera (e.g., Pasteurella, Bordetella, Aeromonas, Achromobacter, and Providencia). Less frequently isolated Gram-positive organisms included Corynebacterium spp., Rothia spp., Streptococcus spp. (including S. canis and S. agalactiae), Paenibacillus spp., Aerococcus spp., and Clostridioides difficile.
As mentioned above, species of the Staphylococcus genus were the most frequently reported. The detailed distribution of staphylococcal species is presented in Table 4.
Among the 58 studies reporting Staphylococcus spp., S. aureus was the most frequently identified species (35 studies, 36.5%), followed by S. pseudintermedius (29 studies, 30.2%) and S. epidermidis (12 studies, 12.5%). Other species were reported less frequently than the above-mentioned species. Overall, the findings demonstrate a marked predominance of staphylococci within the evidence base, with both coagulase-positive and coagulase-negative taxa represented.

3.6. Multidrug-Resistant Bacteria and Antimicrobial Resistance Determinants Identified

The number of MDR-positive samples varied substantially across studies and between environmental and personnel-associated sampling. Among the studies with numerically reported data, the mean number of MDR-positive environmental samples was 23.1 per study (95% CI, 13.3–32.9), based on 64 studies with available numerical counts. For personnel-associated samples, the corresponding mean was 14.0 MDR-positive samples per study (95% CI, 6.1–21.9), based on 31 studies with numerical data. When the MDR-positive environmental and personnel counts were combined within the same study, the mean total number of MDR-positive samples was 28.9 per study (95% CI, 13.9–43.9) among the 26 studies for which numerical counts were available for both sample sources.
Numerous taxa of multidrug-resistant (MDR) bacteria were reported across the included studies. For the study-level synthesis, species nomenclature was standardized and resistance phenotypes were grouped with the corresponding bacterial species when species-level identification was available. S. pseudintermedius was the most frequently reported MDR species, identified in 27 studies (28.1%), followed by S. aureus in 25 studies (26.0%) and E. coli in 16 studies (16.7%). Other frequently reported MDR taxa included S. epidermidis (12 studies, 12.5%), S. haemolyticus (7, 7.3%), E. faecium and K. pneumoniae (5 studies each, 5.2%), S. schleiferi, P. aeruginosa and Acinetobacter baumannii (4 studies each, 4.2%), Stenotrophomonas maltophilia and S. hominis (3 studies each, 3.1%) (Table 5).
Several studies have reported multidrug resistance solely at the genus level (e.g., Enterococcus spp., Acinetobacter spp.) or as broad resistance phenotypes without species determination (e.g., VRE, CRE/CPE, ESBL, or unspecified MRS); consequently, these were excluded from this specific species-level synthesis. This may result in an underestimation of the true occurrence of certain MDR pathogens.
Molecular screening for antimicrobial resistance genes (ARGs) was conducted in 64 of the 96 studies (66.7%). The detailed data regarding the relevant findings are presented in Table 6.
Reflecting the high frequency of methicillin-resistant staphylococci, the mecA gene was the single most prevalent resistance determinant, detected across 37 studies (57.8% of studies with molecular investigation; 38.5% overall). Other Gram-positive resistance determinants included the penicillinase gene blaZ (n = 10), macrolide-lincosamide-streptogramin B determinants (erm genes; n = 6), and vancomycin resistance gene vanA (n = 2).
Among Gram-negative isolates, beta-lactamase genes were widespread. ESBL genes of the blaCTX-M lineage were identified in 14 studies (21.9% of tested studies), followed by blaTEM (n = 11) and blaSHV (n = 7). Plasmid-mediated and chromosomal AmpC determinants (blaCMY, blaDHA, blaEBC, blaCIT, and AmpC) were detected in 10 studies. Acquired carbapenemases or intrinsic/lineage-specific variants of blaOXA gene were identified in 13 studies. Other carbapenemase genes of critical public health concern were identified in multiple facilities, including variants of the metallo-beta-lactamase blaNDM (n = 7), and sporadically blaKPC, blaVIM, and blaIMP (n = 2).
Resistance determinants targeting other key antimicrobials included diverse tet genes conferring tetracycline resistance (n = 12), aminoglycoside-modifying enzyme genes (n = 9), plasmid-mediated quinolone resistance and mutation markers (n = 6), and sulfonamide/trimethoprim resistance determinants (sul, dfr; n = 6). Additionally, four recent investigations utilized sequencing technologies or shotgun metagenomics, demonstrating extensive resistome diversity in veterinary settings.

3.7. Distribution of MDR Bacteria Across Functional Clinical Areas

Hospital areas were grouped into standardized categories (Figure 3) to allow comparison across studies. The analysis focused on areas examined in at least 20 studies. Various terms were merged into primary sectors: hospitalization wards included canine and feline wards, kennels, and recovery rooms; surgery preparation included pre-op rooms and anesthesia induction areas; and examination rooms included consultation rooms and screening offices.
The proportion of studies reporting MDR detection varied across functional clinical areas. MDR bacteria were reported in 13 of 20 studies (65.0%) sampling ICU/critical-care/emergency areas, while none of these studies explicitly reported absence of MDR detection. Surgery preparation rooms (55.0%, 11/20) and hospitalization wards (52.8%, 19/36) also showed high MDR detection rates across studies. These areas may represent settings with frequent patient contact and intensive clinical activity; however, differences in sampling strategies and reporting across studies should also be considered.
In contrast, operating and examination rooms had fewer studies in which MDR was detected (36.4% and 31.4%, respectively) and higher rates of studies with no MDR bacteria reported (24.2% and 22.9%, respectively).

3.8. Distribution of MDR Bacteria Across Environmental Surfaces and Sampling Sites

To allow for comparisons across studies, individual sampling sites were considered separately. The analysis focused on sampling points examined in more than 10 studies. Sampling locations included environmental surfaces, clinical tables and equipment, facility-associated objects, and specimens collected from veterinary healthcare personnel. For each sampling point, studies were categorized according to whether MDR bacteria were detected, whether the MDR outcome was not specified, or whether no MDR bacteria were reported (Figure 4).
The proportion of studies reporting MDR detection varied substantially across sampling sites, with several high-touch surfaces and diagnostic tools exhibiting high frequencies. Among patient-housing facilities, animal cages were the most commonly investigated site, with MDR pathogens detected in 61.4% (27/44) of the studies. High detection rates were also identified on veterinary scales (72.7%, 8/11) and ultrasound equipment/tables (66.7%, 10/15), followed by examination tables (48.6%, 17/35) and surgical tables (47.6%, 10/21). Handheld clinical instruments in direct contact with patients also demonstrated notable MDR detection rates across studies, including clippers (54.5%, 6/11), thermometers (54.5%, 6/11), and stethoscopes (40.0%, 8/20).
Moist clinical environments and handwashing facilities were prominent reservoirs, with MDR bacteria reported in 61.9% (13/21) of studies evaluating sinks and 45.0% (9/20) of studies evaluating faucets/taps. Communal touchpoints and non-clinical fomites also yielded frequent contamination; computer keyboards showed confirmed MDR recovery in 59.4% (19/32) of the studies, followed by door handles (42.9%, 15/35), computer mice (42.9%, 6/14), desk/counter surfaces (38.5%, 10/26), and telephones (38.1%, 8/21). Floors yielded MDR isolates in 54.3% (19/35) of investigations, although an additional 37.1% (13/35) reported facility-level MDR without specifying floor-level counts. Conversely, lower proportions of confirmed MDR detection and higher rates of negative studies were observed for X-ray units (27.3% MDR vs. 63.6% no MDR), light switches (28.6% MDR vs. 57.1% no MDR), and anesthesia machines/rebreathing circuits (40.0% MDR vs. 53.3% no MDR).
Surveillance of veterinary staff revealed frequent reports of MDR isolation in the relevant studies. Nasal carriage among personnel yielded the highest proportion of confirmed MDR detection across all evaluated sites (81.8%, 18/22), primarily driven by methicillin-resistant staphylococci. Similarly, isolation of MDR strains from personnel hands was reported in 52.0% (13/25) of studies, highlighting the dual role of veterinary healthcare workers as both potential reservoirs and mechanical vectors in hospital transmission networks.

4. Discussion

This review synthesizes data from 96 studies published between 2010 and 2025 to map bacterial contamination and resistance patterns across companion-animal veterinary facilities. Despite substantial methodological heterogeneity, particularly in sampling approaches, selective culture protocols, and reporting detail, the synthesized findings provide consistent indications that multidrug-resistant bacteria circulate within veterinary clinical environments. Repeated recovery from both animal-contact surfaces, clinical equipment, and high-touch administrative fomites has been documented, while personnel-associated samples are regularly correlated with MDR isolates. Together, these patterns point to potential transmission pathways linking facilities, equipment, staff, and animal patients, highlighting the need for long-term surveillance protocols and structured risk management in veterinary practice.
Although studies originate from multiple countries and regions, the evidence remains clustered within a few countries and is predominantly derived from veterinary teaching hospitals. While these academic centers provide controlled settings and detailed protocols, they do not necessarily reflect everyday primary care clinics, where variations in infrastructure, patient flow, cleaning routines, and facility management are substantial.
Staphylococci were the most frequently reported bacterial group in both the overall bacterial findings and the MDR findings of the included studies. This predominance is biologically anticipated, given the well-established ability of methicillin-resistant staphylococci (MRS), particularly methicillin-resistant Staphylococcus aureus (MRSA), to persist in healthcare environments and their recognized association with healthcare-associated infections and colonization of healthcare personnel [105]. Furthermore, MRSA isolates recovered from dogs and cats have been reported to be indistinguishable from strains associated with human infections and healthcare settings, highlighting the potential relevance of veterinary environments within the broader epidemiology of methicillin resistance [106]. Other staphylococcal species, particularly S. pseudintermedius and S. schleiferi, are important pathogens in small-animal practice, including dermatological and other clinical infections, while also being recognized as colonizing organisms [107]. Coagulase-negative staphylococci, including S. epidermidis and S. haemolyticus, were also reported among the recovered and MDR bacteria, although less frequently [108].
The frequent reporting of staphylococci was also accompanied by molecular detection of antimicrobial resistance determinants. Among them, mecA was the most frequently identified, consistent with the predominance of methicillin-resistant phenotypes among the MDR staphylococci reported in the included studies. Other resistance determinants were regularly reported, including blaZ and erm genes, reflecting resistance to β-lactam and macrolide-lincosamide-streptogramin antimicrobial classes, respectively.
However, the predominance of staphylococci should be interpreted with caution, considering the sampling and culture methods used in the included studies. A significant proportion of studies specifically targeted staphylococcal species or methicillin-resistant phenotypes using selective or resistance-targeted media [10,11,14,17,22,25,26,27,28,30,32,38,39,42,44,45,48,51,52,68,69,71,78,81,92,95,104]. These targeted approaches may have increased the likelihood of recovering and reporting these species, particularly methicillin-resistant strains, and may have contributed to their apparent predominance.
In addition to staphylococci, several other opportunistic bacteria, including E. coli, Klebsiella, Pseudomonas, Acinetobacter, and Enterococcus species, were detected across facilities, illustrating the broad range of contaminants present. Worryingly, these isolates frequently exhibited high-priority resistance phenotypes, confirmed by the detection of the relevant ARGs, most notably ESBL production [4,36,37,41,65,66,70,73,79,83,85,102], carbapenem resistance [52,66,79,85,98,100,102,103,109], and vancomycin-resistant enterococci (VRE) [46,66]. The clinical relevance of these findings is further highlighted by the increasing recognition of these bacterial groups as causes of difficult-to-treat infections in companion animals, particularly when associated with antimicrobial resistance [110,111,112].
The frequent isolation of MDR pathogens from the ICU and preoperative environments mirrors the findings from human hospitals, where a significant proportion of critically ill patients contract healthcare-associated infections during their ICU stay [113,114]. This issue is particularly critical in veterinary teaching hospitals and referral centers that admit the most severe clinical cases. Together, suppressed patient immunity and heavy antibiotic use make hospital ICUs potential hotspots for the persistence and transmission of resistant bacteria.
Beyond clinical areas, several individual sampling sites showed frequent reporting of MDR bacteria, particularly sites that are repeatedly exposed to direct animal contact, frequent handling by veterinary personnel, or both. Cages, scales, and examination tables represent surfaces with frequent direct contact with animals and may therefore become contaminated through the spread of bacteria from colonized or infected animals. In contrast, frequently touched surfaces such as door handles and computer keyboards are primarily associated with contact by veterinary personnel and may provide opportunities for the transfer of microorganisms between hands and the surrounding environment. Other items, including stethoscopes, thermometers, and selected clinical equipment such as ultrasound devices, may act at the interface between animals and personnel, as they are repeatedly handled by staff while also coming into direct or close contact with animals. The frequent detection of MDR bacteria at these sites highlights the importance of considering both animal-contact and personnel-contact surfaces when assessing potential environmental reservoirs within veterinary healthcare facilities. Similar to human healthcare settings [115], the potential dissemination of MDR bacteria in veterinary facilities is likely to involve interconnected pathways linking animal patients, contaminated environmental surfaces and equipment, and veterinary personnel, although the relative contribution of each pathway remains to be determined.
Sinks and faucets/taps also showed frequent reporting of MDR bacteria in the included studies. This finding is consistent with the recognized challenges that healthcare plumbing systems pose for infection prevention and control (IPC). Wet surfaces within sinks and drains provide a solid–liquid interface that can favor biofilm formation, which has been associated with the persistence and dissemination of healthcare-associated pathogens and, in some settings, with outbreaks [116]. In addition, organic material and waste disposed of into sinks and drains may provide nutrients that support bacterial persistence within the plumbing environment [117]. The connection between clinical plumbing systems and the wider environment may raise broader One Health considerations.
Finally, several studies have reported the isolation of MDR bacteria from samples collected from veterinary personnel. These findings indicate that staff working in clinical environments can harbor resistant strains, raising valid concerns about their potential role as carriers. Beyond the risk of exposure for hospitalized animals, possible public health implications are highlighted, particularly the potential for these bacteria to reach personnel’s household contacts and the broader community. Comparable concerns are frequently discussed in human healthcare, where more extensive data describe potential exposure risks for medical personnel [118,119]. In veterinary medicine, however, the available evidence is much more limited and less conclusive, leaving the actual impact of occupational carriage unclear.
This study has certain limitations that should be considered when interpreting its findings. First, the included studies were predominantly observational and descriptive, limiting the extent to which causal relationships or transmission pathways could be evaluated. Second, substantial methodological heterogeneity was observed across studies, particularly with respect to sampling methods, culture media, antimicrobial susceptibility testing (AST) methods, and the definitions and reporting of multidrug resistance (MDR). In addition, a considerable number of studies used selective or resistance-targeted culture media to investigate specific bacterial taxa and resistance phenotypes. Such targeted approaches may have influenced the spectrum of bacteria recovered and, therefore, may have affected the distribution of bacterial and MDR findings observed in the present evidence map.
Because of this methodological heterogeneity, the analysis was primarily conducted at the study level rather than at the individual sample level. Direct comparison of sample-level frequencies across studies would have been difficult to interpret reliably. Finally, some degree of terminology harmonization and grouping was necessary to allow comparison across studies. Different studies often used different terms for similar clinical areas, equipment, or sampling sites, while in some cases, different parts of the same area or piece of equipment were sampled separately. Although these categories were standardized to facilitate synthesis, this process may have resulted in some loss of site-specific detail and should therefore be considered when interpreting the spatial distribution of MDR detection.
However, this evidence map provides a broad synthesis of the available information on bacterial contamination and antimicrobial resistance in small-animal veterinary healthcare environments, incorporating environmental surfaces, clinical equipment and fomites, and veterinary personnel-associated samples. It also provides an overview of the methodological approaches currently used for bacterial recovery and antimicrobial susceptibility assessment, the main bacterial pathogens reported, and their associated antimicrobial profiles and determinants. Importantly, the mapping of MDR detection across functional clinical areas and individual sampling sites provides an overview of locations that are more frequently associated with the reporting of MDR phenotypes across the available literature. Collectively, these findings provide a useful evidence base for understanding how bacterial contamination and antimicrobial resistance have been investigated in small-animal veterinary healthcare settings and may support the development of more targeted environmental surveillance strategies, infection prevention practices, and standardized approaches for future investigations.
Future research should focus on developing standardized environmental sampling protocols that are practical and compatible with the routine workflows of various veterinary specialties. Greater consistency in the selection of sampling sites, sampling frequency, microbiological methods, antimicrobial susceptibility testing, and reporting would improve comparability between studies and provide a more reliable basis for monitoring MDR bacteria in veterinary healthcare settings. Concurrently, the application of molecular approaches, including whole-genome sequencing, where appropriate, could provide further characterization of resistance determinants and help determine the relatedness of isolates recovered from animals, environmental sites, equipment, and veterinary personnel. These approaches may contribute to a better understanding of potential transmission pathways in veterinary facilities. Longitudinal studies would further help determine the persistence and recurrence of MDR bacteria in specific clinical areas and sampling sites over time.
Future research could move beyond the description of bacterial recovery and resistance profiles and assess their clinical and epidemiological significance. This could include evaluating the frequency and severity of infections associated with the recovered pathogens, the clinical relevance of their resistance determinants, and the potential risk associated with particular sampling sites and exposed populations. Integrating this data could ultimately support more targeted risk assessments and help identify priorities for environmental surveillance and infection prevention in companion animal veterinary facilities.

5. Conclusions

Overall, the available evidence indicates that MDR bacteria are consistently reported across a wide range of small animal veterinary clinical environments. Certain functional areas, high-contact surfaces, frequently used clinical equipment, and veterinary personnel appear more often associated with the detection of MDR organisms, while specific bacterial taxa—particularly staphylococci and certain Gram-negative pathogens—are repeatedly identified across studies. These patterns suggest that MDR contamination is not randomly distributed but may be linked to specific ecological niches within veterinary healthcare settings. However, the current evidence base does not allow reliable estimation of the true distribution of MDR contamination, nor does it permit clear definition of transmission pathways among the environment, animals, and veterinary personnel. This limitation is largely driven by methodological heterogeneity, differences in sampling strategies, and the frequent use of targeted culture approaches. Therefore, there is a clear need for standardized surveillance frameworks, harmonized microbiological methodologies, and integrated One Health-oriented epidemiological studies. Such approaches will clarify MDR occurrence and transmission dynamics and support the development of targeted prevention and control strategies in veterinary clinical practice.

Author Contributions

Conceptualization, M.L., I.C. and G.V.; methodology, M.L., I.C. and G.V.; formal analysis, M.L., I.C. and G.V.; investigation, M.L. and I.C.; writing—original draft preparation, M.L. and I.C.; writing—review and editing, G.V.; supervision, G.V.; All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author(s).

Acknowledgments

During the preparation of this manuscript, the authors used ChatGPT (GPT-5.6 Luna; OpenAI) for the purposes of language refinement and proofreading assistance. The authors have reviewed and edited the output and take full responsibility for the content of this publication.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. PRISMA 2020 flow diagram [9] illustrating the selection process for studies investigating bacterial contamination and antimicrobial resistance in small animal veterinary clinical environments and personnel.
Figure 1. PRISMA 2020 flow diagram [9] illustrating the selection process for studies investigating bacterial contamination and antimicrobial resistance in small animal veterinary clinical environments and personnel.
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Figure 2. Geographic distribution of the included studies. Color intensity corresponds to the number of published studies per country (Map generated using MapChart, https://www.mapchart.net/, accessed on 05 September 2026).
Figure 2. Geographic distribution of the included studies. Color intensity corresponds to the number of published studies per country (Map generated using MapChart, https://www.mapchart.net/, accessed on 05 September 2026).
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Figure 3. Environmental multidrug-resistant (MDR) bacterial detection and reporting patterns across primary functional clinical areas in small animal veterinary facilities. The percentages represent the study-level proportions. Unspecified MDR location represents studies that confirmed the isolation of multidrug-resistant bacteria from a general veterinary facility environment but did not provide disaggregated, room-specific isolation rates.
Figure 3. Environmental multidrug-resistant (MDR) bacterial detection and reporting patterns across primary functional clinical areas in small animal veterinary facilities. The percentages represent the study-level proportions. Unspecified MDR location represents studies that confirmed the isolation of multidrug-resistant bacteria from a general veterinary facility environment but did not provide disaggregated, room-specific isolation rates.
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Figure 4. Multidrug-resistant (MDR) bacterial detection and reporting across samples from environmental surfaces, clinical equipment, and personnel in small animal veterinary facilities. The percentages represent study-level proportions. Unspecified MDR location represents studies that confirmed the isolation of multidrug-resistant bacteria from a general veterinary facility environment but did not provide, site-specific isolation rates.
Figure 4. Multidrug-resistant (MDR) bacterial detection and reporting across samples from environmental surfaces, clinical equipment, and personnel in small animal veterinary facilities. The percentages represent study-level proportions. Unspecified MDR location represents studies that confirmed the isolation of multidrug-resistant bacteria from a general veterinary facility environment but did not provide, site-specific isolation rates.
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Table 2. Distribution of targeted antimicrobial resistance categories across the included studies.
Table 2. Distribution of targeted antimicrobial resistance categories across the included studies.
Targeted Resistance Category Studies, n % of Studies
MRSA1/MRS / Oxacillin resistance 27 28.1%
ESBL/ Extended-spectrum cephalosporin resistance 15 15.6%
Carbapenem resistance 10 10.4%
Other targeted resistance phenotypes (e.g., VRE2, colistin-resistance, and other specific targets) 7 7.3%
1 Methicillin-Resistant Staphylococcus aureus. 2 Vancomycin-resistant enterococci.
Table 3. Bacterial taxa reported across the included studies (taxa reported in ≥ 5 studies included).
Table 3. Bacterial taxa reported across the included studies (taxa reported in ≥ 5 studies included).
Bacterial taxa Studies, n % of total studies
Staphylococcus spp.1 58 60.4%
Escherichia coli 32 33.3%
Pseudomonas spp.2 20 20.8%
Klebsiella spp.3 18 18.8%
Enterococcus spp.4 16 16.7%
Acinetobacter spp.5 17 17.7%
Enterobacter spp.6 14 14.6%
Bacillus spp. 7 7.3%
Serratia spp.7 7 7.3%
Citrobacter spp.8 6 6.2%
Stenotrophomonas maltophilia (incl. spp.) 5 5.2%
Salmonella spp. 5 5.2%
Micrococcus spp. (incl. M. luteus) 5 5.2%
1 Detailed species distribution is presented in Table 4. 2 Includes P. aeruginosa, P. fluorescens, and Pseudomonas spp. 3 Includes K. pneumoniae, K. oxytoca, and Klebsiella spp. 4 Includes E. faecium, E. faecalis, E. hirae, E. gallinarum, E. avium and Enterococcus spp. 5 Includes A. baumannii, A. lwoffii, A. calcoaceticus, A. nosocomialis, A. haemolyticus, A. junii, Acinetobacter spp. 6 Includes En. cloacae, En. (Pantoea) agglomerans, En. aerogenes, En. amnigenus, En, mori, En. Cobei, Enterobacter spp. 7 Includes S. marcescens, S. rubidaea, S. plymuthica and Serratia spp. 8 Includes C. freundii, C. braakii and Citrobacter spp.
Table 4. Distribution of Staphylococcus species reported across the included studies (n = 96).
Table 4. Distribution of Staphylococcus species reported across the included studies (n = 96).
Staphylococcal species Studies, n % of total studies
Staphylococcus aureus 35 36.5%
Staphylococcus pseudintermedius 29 30.2%
Staphylococcus epidermidis 12 12.5%
Staphylococcus schleiferi 6 6.2%
Staphylococcus hominis 6 6.2%
Staphylococcus haemolyticus 5 5.2%
Staphylococcus warneri 4 4.2%
Staphylococcus simulans 4 4.2%
Staphylococcus cohnii 4 4.2%
Staphylococcus intermedius 3 3.1%
Staphylococcus capitis 3 3.1%
Other coagulase-negative staphylococciᵃ 5 5.2%
Mammaliicoccus (Staphylococcus) sciuri 2 2.1%
Unspecified Staphylococcus spp. 21 21.9%
Staphylococcus spp. (overall genus) 58 60.4%
ᵃ Includes S. felis (n = 2 studies), S. succinus (n = 2), S. xylosus (n = 2), S. vitulinus (n = 2), S. equorum (n = 2), S. pettenkoferi (n = 1), S. urealyticus (n = 1), S. saccharolyticus (n = 1), and S. saprophyticus (n = 1).
Table 5. Distribution of MDR species reported in the included studies (n = 96).
Table 5. Distribution of MDR species reported in the included studies (n = 96).
MDR bacteria1 Studies, n % of studies
Staphylococcus pseudintermedius (including MRSP) 27 28.1
Staphylococcus aureus (including MRSA) 25 26.0
Escherichia coli 16 16.7
Staphylococcus epidermidis (including MRS. epidermidis) 12 12.5
Staphylococcus haemolyticus 7 7.3
Enterococcus faecium 5 5.2
Klebsiella pneumoniae 5 5.2
Staphylococcus schleiferi 4 4.2
Pseudomonas aeruginosa 4 4.2
Acinetobacter baumannii 4 4.2
Staphylococcus hominis 3 3.1
Stenotrophomonas maltophilia 3 3.1
Enterococcus faecalis 2 2.1
1 Data are restricted to definitively identified bacterial species with documented multidrug-resistant (MDR) profiles reported in ≥ 2 studies. Categories were not mutually exclusive; individual studies could report multiple MDR species.
Table 6. Distribution of detected antimicrobial resistance genes (ARGs) across veterinary environmental and personnel sampling studies.
Table 6. Distribution of detected antimicrobial resistance genes (ARGs) across veterinary environmental and personnel sampling studies.
Antimicrobial Class / Mechanism Representative ARGs* Studies, n % of Tested Studies (n=64) % of Total Studies (N=96)
Methicillin resistance mecA 37 57.8% 38.5%
ESBLs blaCTX-M variants 14 21.9% 14.6%
Carbapenemases/ Oxacillinases blaOXA variants 13 20.3% 13.5%
Tetracycline resistance tet genes 12 18.8% 12.5%
Broad-spectrum β-lactamases blaTEM variants 11 17.2% 11.5%
AmpC β-lactamases blaCMY, blaDHA, blaEBC, blaCIT, AmpC 10 15.6% 10.4%
Penicillinases blaZ 10 15.6% 10.4%
Aminoglycoside resistance aac, aph, ant, aad, strA/B 9 14.1% 9.4%
Broad-spectrum β-lactamases blaSHV variants 7 10.9% 7.3%
Metallo-β-lactamases (MBLs) blaNDM variants 7 10.9% 7.3%
MLSB1 (Macrolides/Lincosamides) erm genes 6 9.4% 6.2%
Quinolone resistance qnr, oqxAB, norA/C, gyrA 6 9.4% 6.2%
Sulfonamides / Trimethoprim sul and dfr variants 6 9.4% 6.2%
WGS / Metagenomics Multi-class resistomes 4 6.2% 4.2%
Carbapenemases (Other) blaKPC, blaVIM, blaIMP 2 3.1% 2.1%
Glycopeptides vanA 2 3.1% 2.1%
1 MLSB: Macrolide-Lincosamide-Streptogramin B. * Categories were not mutually exclusive; individual studies could report multiple resistance determinants.
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