Previous Article in Journal
Modeling Broiler Discomfort Under Commercial Housing: Seasonal Trends and Predictive Insights for Precision Livestock Farming
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Poultry
Volume 4
Issue 3
10.3390/poultry4030039
poultry-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Potential Risk Factors Related to Antimicrobial Usage and Antimicrobial Resistance in Commercial Poultry Production—A Scoping Review

by
Lena Sonnenschein-Swanson
1,†,
Silvia Baur-Bernhardt
2,†,
Annemarie Käsbohrer
3,
Marcus Georg Doherr
1,*,
Diana Meemken
2 and
Petra Weiermayer
3,4
1
Institute for Veterinary Epidemiology and Biostatistics, School of Veterinary Medicine, Freie Universität, 14163 Berlin, Germany
2
Institute of Food Safety and Food Hygiene, School of Veterinary Medicine, Freie Universität, 14163 Berlin, Germany
3
Clinical Department for Farm Animals and Food System Science, University of Veterinary Medicine, 1210 Vienna, Austria
4
Institute of Integrative Medicine, Department for Medicine, University Witten/Herdecke, 58455 Herdecke, Germany
*
Author to whom correspondence should be addressed.
†
The research in this manuscript is part of Lena Sonnenschein-Swanson and Silvia Baur-Bernhardt’s dissertation.
Poultry 2025, 4(3), 39; https://doi.org/10.3390/poultry4030039
Submission received: 29 June 2025 / Revised: 6 August 2025 / Accepted: 8 August 2025 / Published: 28 August 2025
Browse Figures
Versions Notes

Abstract

Antimicrobial resistance (AMR) constitutes a serious public health issue, and the European Union (EU) requires reduction in the sales of antibiotics in farmed animals of 50% by 2030. A scoping review was conducted in PubMed for the years from 2000 to 2024, limited to the English and German languages, with the aims to (1) provide an overview of factors on commercial poultry farms potentially associated with health-related endpoints such as mortality, disease prevalence, carcass condemnation, performance as well as AMR/antimicrobial usage at different hierarchical levels (animal, flock/batch, stable, farm), and (2) identify inconsistencies with respect to these potential risk factors. Overall, 34 peer-reviewed publications met the inclusion criteria for the review. Significant associations identified in the uni- or multivariable statistical analysis were summarised using graphs and bar charts. The results highlight that risk factor–outcome associations often are complex, inconsistent with regards to the direction of the influence especially for some ordinal or categorical variables. In some associations such as the sex of the animals and performance, contrary directions were reported in different studies—illustrating the multifactorial dynamics of commercial poultry production. This research enhances the understanding of the complexity of commercial poultry production, which is essential when designing future studies and interpreting their results.

1. Introduction

Out of 4.71 million global human deaths associated with bacterial antimicrobial resistance (AMR) in 2021, there were an estimated 1.14 million global deaths directly attributable to AMR [1]. Hence, AMR is recognised as a major problem worldwide, threatening public health, food safety, and veterinary medicine. Inappropriate use of antimicrobial substances substantially contributes to the occurrence and spread of AMR [2]. Between 2014 and 2021, for the European Union/European Economic Area (EU/EEA) region, weighted mean consumption of antimicrobials (AMCs) in food-producing animals decreased by 44%, while in humans, it remained relatively constant. Analyses of the associations between AMC and AMR for selected combinations of antimicrobials, and bacteria exposed to them, demonstrated positive associations between consumption of certain antimicrobials and resistance to those substances in bacteria from both humans and food-producing animals [3]. The spread of resistant strains of bacteria between humans, animals, and the environment may be triggered by multiple factors and pathways, such as antimicrobial exposure, human travel, livestock trade, manure runoff, or water contamination [4,5]. The One Health approach recognises that the health of humans, domestic and wild animals, plants, and the wider environment (including ecosystems) are closely linked and interdependent. Therefore, a better understanding of the risk factors for AMR at the human, animal, and environment interfaces is of high priority to address resistance challenges properly.
The European Green Deal requires or predicts antimicrobial usage (AMU) to be reduced by 50% by 2030 as measured by the overall EU sales of antimicrobials for farmed animals and in aquaculture [6]. Avian pathogens are responsible for major costs to society, both in terms of vast economic losses to the poultry industry and their implications for human health [7]. The poultry sector is well known for the complexity and heterogeneity of its production system, which also includes a quite distinctive hierarchical structure of the production process of farm, flock/pen, production period, and individual animal level [8,9,10,11,12,13,14,15,16,17,18]. Van Limbergen et al. (2020) noted that, although decades of progress in genetic selection and feed formulation have led to high standards of efficient broiler production, a high variability is present between farms and between successive flocks. This implies the need for a multifactorial approach with adaptations involving both improvements in management, housing, health programs, and an increasing level of professionalism of the farmer in order to improve broiler performance and health [8]. These findings were supported by Junghans et al. (2022), who evaluated mortality, stocking density, flock size, season, production week of the parental flock, farm, antibiotic treatment, and even the interaction between antibiotic treatment and season as possible influencing factors on health outcomes, such as mortality, performance, and slaughter results, showing the influence of different risk factors on different endpoints [10].
Given the apparent gaps in the literature regarding potential risk factors related to several endpoints, such as antimicrobial usage (AMU) and antimicrobial resistance (AMR), as well as performance- and production-related endpoints considering the different hierarchical levels in poultry production, a scoping review was conducted to increase the understanding of underlying factors for high antimicrobial use and resistance development in the poultry sector with a special focus on turkey production. In this review, potential risk factors for the following endpoint categories of (1) mortality, (2) disease prevalence, (3) condemnation/pathological/bacterial/viral/parasitological findings, (4) performance, and (5) AMR/AMU in poultry production were identified.
Previous reviews have mostly focused on a specific endpoint [19,20]. The objectives of our scoping review were: (1) to qualitatively describe the available peer-reviewed literature reporting risk factors potentially associated with one or several endpoints and hierarchical levels in poultry production, and (2) to describe possible data gaps in this literature. A priori identification and subsequent inclusion of potential risk factors and confounding variables in future research projects is essential to account for the complex and dynamic processes in commercial poultry and especially in turkey production.

2. Materials and Methods

In this scoping review, the term ‘poultry’ refers to chickens and turkeys only. A risk factor was defined as a measured observation with an investigated potential association or relationship with AMR/AMU as well as with performance and production related endpoints. Hierarchical production levels within the target population were categorised as (a) farm, (b) flock/pen/experimental unit simply labelled as group, (c) production period, and (d) individual animals within each group.
The term endpoint, as used in our review, defines the outcome or dependent variable in uni- or multivariable association models, such as disease incidence, mortality, or indicators of AMU.

2.1. Search

An adapted version of the nominal group technique was used to initially identify potential risk factors [21]. Based on all authors’ knowledge and clinical experience of the second author (SBB), as well as a preliminary literature search, the following potential risk factors were identified: farmers’ general working engagement, their attitudes toward the use of homeopathy and antibiotics, husbandry type, type of production, outdoor husbandry, in-house ventilation technology, housing unit size, stocking rate, litter, watering system, feed origin, feed change, functional phytochemicals, hygiene, governmental measures, season, genetics, gender, raising location, relocation of animals during production period, raising in groups, and vaccination. The first search strategy and the adapted version of the nominal group technique used to identify potential risk factors have been described in the study protocol of a field study where data on these factors were collected [22].
A comprehensive literature search (first search and update) was conducted in PubMed for the publication years from 2000 to 2024 using various search strings to identify as many factors associated with the included outcomes as possible. The search terms ‘male/female/raising/fattening/breeding/husbandry/vaccine*/genetics/ventilation technique/drinking water/food/food processing/housing unit size/stocking rate/stocking density/outdoor husbandry/litter/hygiene/functional phytochemicals/herbs/season/laying week’ AND ‘animal health’ AND ‘poultry’ were used. Each search term for potential risk factors was combined with ‘animal health’ and ‘poultry’ separately. For in depth analysis on farmer related risk factors, ‘farmer’ AND ‘hygiene’ with article type ‘review’, ‘farmer’ AND ‘hygiene’ AND ‘animal health’ AND ‘poultry’ with no article type considered as well as ‘farmer’ AND ‘antibiotic*’ OR ‘antimicrobial*’ with article type ‘review’, farmer’ AND ‘antibiotic*’ OR ‘antimicrobial*’ AND ‘animal health’ AND ‘poultry’ with no article type considered as well as ‘farmer’ AND ‘production intensity’ with article type ‘review’, ‘farmer’ AND ‘production intensity’ AND ‘animal health’ AND ‘poultry’ with no article type considered as well as ‘farmer’ AND ‘homeopath’ AND ‘animal health’ AND ‘poultry’, with no article type considered, were searched for in PubMed.
Search terms added in the update of the literature search were: ‘stocking density’, ‘poultry’ and ‘animal health’ in combination with the term ‘farmer’, with restriction to ‘other animals’. In Supplemental Material S1, search strings with numbers of matches for the first literature search and the update of the literature search for each individual search strategy are presented.

2.2. Eligibility Criteria

The languages searched were German and English, and the geographical areas were restricted to Europe and North America to ensure that production systems were as comparable as possible. Studies concerning poultry other than chickens and turkeys; production other than raising, fattening, and breeding; egg-production in chickens; and other studies in chickens of no clinical relevance for turkeys were excluded. No restriction was applied to the implemented study designs at the screening phase of the literature search. Studies included in reviews/meta-analyses, e.g., in the scoping review on turkeys by Phillips et al. (2022), were additionally screened for relevance [19]. Exclusion criteria applied, in a further step, to focus on risk factor-related studies with relevant information for the intended statistical model development were: (i) review/meta-analysis; (ii) unit of analysis different from farm, flock/pen/experimental unit (in the context of our review termed “group”), production period, or individual animals; (iii) analysed factors (or their categories) not relevant for the field study; (iv) studies not able to identify any risk factors; and (v) descriptive only, without uni- and/or multivariable statistical analysis to assess associations.

2.3. Data Charting

Full-text screening and information extraction was performed by the first (LSS) and the last author (PW). Information on the endpoint(s) addressed in each reference was extracted after selecting articles containing risk factors for poultry production.
The extracted information from each reference included the key outcomes and outcome variables synonymous with endpoints, the type of production, the study size and design including randomisation (yes/no), blinding (yes/no), control groups included (yes/no), the type of statistical analysis, and confounding factors considered (Supplemental Material S2). In-depth analysis of full-text for the development of a summarising assessment (Table 1) was performed by PW, clarifications of specific aspects on poultry production in the studies were cross-checked by the second author (SBB), and clarifications on statistical sections of the studies included were cross-checked by another author (MD).

2.4. Presentation of Results

The PRISMA Extension for Scoping Reviews was followed [23]. Details on the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist are summarised in Supplemental Material S3.
Presentation of potential risk factors for AMU/AMR, for mortality/diseases, and for assessing poultry production and performance/productivity as identified in the included literature was broken down by different aspects, such as the impact of the farmers, the housing conditions and management-related factors, and animal-related factors. The hierarchical levels significant in poultry production were utilised for summarising the published evidence regarding potential risk factors: (a) farm, (b) groups of animals within farms, (c) production period (such as raising, fattening), and (d) individual animal. Individual animals are typically clustered within a production period, several production periods are clustered within a group, and several groups are clustered within a farm. Statistically significant factors identified in uni- or multivariable association models were visualised in directed acyclic graphs (DAGs), broken down by the unit of analysis. Endpoints considered were (1) mortality; (2) disease prevalence; (3) condemnation/pathological/bacterial/viral/parasitological findings; (4) performance; and (5) AMR/AMU. The review and information extraction, in contrast to other published reviews, considered several endpoints to be able to describe the broad range of factors with potential input on AMU and animal health. The potential relevance of specific risk factors (its categories) on one or more endpoints was evaluated by counting the number of studies associating a risk factor (category) with the respective endpoint, and visualised as bar charts. Robust evidence in our approach was defined as more than one statistically significant result per risk factor, in more than one study, per endpoint. Statistical analyses beyond description were not performed as part of this scoping review.

3. Results

3.1. Search Results and Study Selection

The results of the first literature search were assessed based on the clinical experience of the authors for identification of potential risk factors and respective categories. In this process, the first 20 studies covering risk factors were selected by title and abstract screening, with the criterion that each study had to report results on at least one risk factor. The main information from these studies is summarised in Supplemental Material S4.
In addition, all studies identified within the update of the literature search were screened for identification of risk factors relevant for the field study described in Baur-Bernhardt et al. (2024) [22]. Forty-nine studies were included in this interim step. A flow chart reflecting both literature searches can be seen in Figure 1.

3.1.1. Identified Risk Factors

Potential risk factors identified in the 49 studies were grouped at the organisational level. Out of the total, only four studies examined the working engagement of farmers [8,24,25,26], while five assessed the farmers’ attitudes towards antibiotics [9,10,27,28,29]. Concerning housing conditions and management-related factors, there were eight studies identified that examined the husbandry type [11,20,30,31,32,33,34,35], three on outdoor husbandry [12,34,36], one on poultry farms nearby [13], three on the ventilation technique [8,13,31], five on housing unit size [8,10,14,27,34], five on stocking rate/stocking density [10,15,16,37,38], four on litter/bedding [13,15,39,40], two on water [14,27], four on feed origin [8,27,41,42], ten on feed change [14,18,43,44,45,46,47,48,49,50], two on functional phytochemicals [50,51], seven on hygiene [13,19,26,27,31,52,53], and seven on season of the year [10,14,15,17,33,54,55]. Concerning animal-related factors there were four studies identified that examined genetics [33,37,39,56], six on gender [8,14,17,39,56,57], two on the age of the animals [27,56], one on the length of the fattening period [15], six on raising location/relocation of the animals [14,19,27,53,58,59], one on vaccination [14], one on breeding company [15], one on the egg-laying week of parents [10], and four on infection [8,31,34,45]. The references of these 49 studies grouped by risk factors can also be found in Supplemental Material S5.

3.1.2. Risk Factors Considered for Full Information Extraction

Fifteen of the forty-nine studies collected in the final literature search were not further considered as they were not relevant to assist in the risk factor model development for different reasons: (i) review/meta-analysis was performed [19,20,38,47]; (ii) different unit of analysis: country [53], consumer [25], slaughter plant [52], stakeholder of broiler production [24], and turkey product [32]; (iii) analysed risk factor/category not relevant for the field study [29,35,40,42]; (iv) studies not able to identify any risk factors [41]; and (v) no uni- and/or multivariable statistics [26]. Therefore, thirty-five studies were considered in the full information extraction.
Four studies applied univariable statistics only, while the majority applied multivariable approaches. Fourteen studies were performed on turkey production. Out of these fourteen studies, ten studies were considered for final assessment, and seven showed a statistically significant result, with one using a univariable statistical model only. Thirty-five studies were conducted on chicken production. Twenty-four of these studies were considered for final information extraction. Out of these studies, sixteen with multivariable and three with univariable statistics showed at least one statistically significant result.
Four different hierarchical production levels were considered for summarising the published evidence regarding potential risk factors: (a) farm, (b) group, (c) production period, and (d) individual animal. The risk factors identified with impact on at least one of the outcome variables per unit of analysis are summarised in Supplemental Material S6.
Endpoints categorised as (1) mortality; (2) disease prevalence; (3) condemnation/pathological/bacterial/viral/parasitological findings; (4) performance; and (5) AMR/AMU are summarized in Supplemental Material S7.
All endpoints were considered on several units of analysis in the identified studies, as displayed in Figure 2. At the production period level, the endpoint condemnation/pathological/bacterial/viral/parasitological findings was most frequently assessed (ten times), followed by mortality and performance (eight times each), whereas disease prevalence was considered only three times. At the group level, performance was addressed most frequently (11 times), whereas the other endpoints reached lower frequencies, ranging between 5 and 7 studies in which they were included. Only a few studies investigated endpoints at the farm or individual animal level. At the farm level, disease prevalence was considered four times and AMR/AMU once. At the individual animal level, performance and condemnation/pathological/bacterial/viral/parasitological findings were considered in the identified studies once each.

3.2. Risk Factors with Potential Impact Considering the Unit of Analysis

Depending on the direction of the impact of each risk factor (or the specific category under study) on the endpoints included in this review, those resulting in higher mortality, higher disease prevalence, higher condemnation rate or more pathological/bacterial/viral/parasitological findings, lower performance, and higher AMR/AMU were considered as having a negative influence (higher risk for an undesired outcome), while effects in the opposite direction were classified as a positive influence (protective).

3.2.1. Risk Factors with Potential Impact on the Farm Level

At the farm level, four factors with impact were identified, with some contrasting directions on two endpoints considered: organic husbandry type, housing unit size up to 10,000 animals, and infection showed a negative influence on at least one of the endpoints considered, whereas organic husbandry type and application of good hygiene principles showed a positive influence on at least one of the endpoints considered. All relevant factors are displayed in Figure 3.

3.2.2. Risk Factors with Potential Impact on the Group Level

At the group level, typically a flock, experimental group or a pen, a high number of risk factors were assessed, and for each of the five endpoints, positive and negative effects were described. Open housing/natural ventilation type, housing unit size correlated with stocking rate, rotation frequency, age of animals and more enclosure area, compounder feed origin, winter and summer seasons, slow-growing genetics, male gender, raising on the same farm, 6 weeks of age, age of the youngest birds being under 105 days, and infection showed a negative influence on at least one of the endpoints considered. Higher working engagement, negative attitude towards antibiotics, organic husbandry type, smaller housing unit size, water from mains, compounder feed origin, use of feed additives, application of good hygiene principles, summer season, slow-growing genetics, male gender, and being raised on the same farm showed a positive influence on at least one of the endpoints considered. All relevant factors are displayed in Figure 4.

3.2.3. Risk Factors with Potential Impact on the Production Period Level

At the production period level, a high number of risk factors were assessed, and for each of four endpoints, positive and negative effects were described. AMU in interaction with season, smaller housing unit size, use of feed additives, fall and winter seasons, male gender, application of routine vaccination scheme with live vaccines, and longer fattening period with summer season showed a negative influence on at least one of the endpoints considered, whereas a negative attitude towards AMU, smaller housing unit size, higher stocking rate, litter/bedding other than wood shavings in interaction with higher stocking rate or old age of building with cold-humid season with longer fattening period length, application of water hygiene, spring and winter seasons, cold-humid season in interaction with litter/bedding other than wood shavings, slow growing genetics, certain breeding company, and earlier parental production week showed a positive influence on at least one of the endpoints considered. All relevant factors are displayed in Figure 5.

3.2.4. Risk Factors with Potential Impact on the Individual Animal Level

At the individual animal level, two factors with impact were identified on the two endpoints considered: winter season showed a negative influence on at least one of the endpoints considered, while use of feed additives showed a positive influence on at least one of the endpoints considered. All relevant factors are displayed in Figure 6.

3.2.5. Statistical Models Used in the Identified Studies

At the production period level, except for three risk factors (attitude towards AMU, housing unit size, season), all risk factors were assessed by a multivariable statistical model as was the case at the group level. At the farm level, except for one study on organic production type and one on infection, for all risk factors, a univariable statistical model was applied. At the individual animal level, one risk factor was identified by a uni- and one by a multivariable statistical model. More information on the statistical models used in the different studies (as described by the respective authors) are presented in Supplemental Material S2.

3.3. Results from Multivariable Statistical Models Only

When screening for significant results from multivariable statistical models in the included studies, there were 23 risk factors described as having one specific influence on one of the predefined primary endpoints. All other categories of risk factors were described as of positive and negative influence in different correlations, confirming the complexity of potential interaction of various risk factors. There were three categories of risk factors of more positive than negative influence and three categories of risk factors of more negative than positive influence. For the following eight risk factors, no significant results from multivariable statistical models were identified: attitude towards homeopathy, type of production, poultry farm nearby, functional phytochemicals, governmental measures, raising in groups, region, and slaughterhouse.

3.3.1. Synthesis of Influence of Risk Factors on All Specific Endpoints

AMR/AMU
The identified categories of risk factors of importance for low AMU/AMR were negative attitude towards antibiotics, organic husbandry type, smaller housing unit size, use of water from the mains, other than compounder feed origin, application of good hygiene principles, older age of animals, and raising location on another farm. Studies supporting these findings by showing the same direction of influence of negative attitude towards antibiotics, organic husbandry type, smaller housing unit size, application of water hygiene, older age of animals, and raising location on another farm on other endpoints were identified. Studies showing an opposite influence were found for factors other than compounder feed origin and for raising location on another farm [11,27,28].
Mortality
The identified categories of risk factors of importance for low mortality were negative attitude towards antibiotics, other than natural ventilation technique, smaller housing unit size, old age of building in interaction with other than wood shaving litter/bedding, with cold-humid season and with longer fattening period, other than cold-humid seasons in interaction with wood shaving litter/bedding, other than summer season, slow-growing genetics, female gender, shorter fattening period with summer, no infections, a certain breeding company, and earlier production week of parents. Studies supporting these findings by showing the same direction of influence of negative attitude towards antibiotics, other than natural ventilation technique, smaller housing unit size, other than cold-humid seasons in interaction with wood shaving litter/bedding, other than summer season, slow-growing genetics, female gender, no infections, a certain breeding company, earlier production week of parents were identified. Studies showing an opposite influence were found for factors other than summer season, slow-growing genetics, and female gender [8,10,15,17,33,37].
Disease Prevalence
The identified categories of risk factors of importance for low disease prevalence were use of feed supplements, slow-growing genetics, female gender, older age of animals, and raising location on another farm. Studies supporting these findings by showing the same direction of influence of use of feed supplements, slow-growing genetics, female gender, older age of animals, and raising location on another farm were identified. Studies showing opposite influence were found for feed supplements, slow-growing genetics, female gender, and raising location on another farm [9,37,56,57,59].
Condemnation/Pathological/Bacterial/Viral/Parasitological Findings
The identified categories of risk factors of importance for less condemnation/pathological/bacterial/viral/parasitological findings were a negative attitude towards antibiotics, organic husbandry type, no outdoor husbandry in correlation with housing unit size, stocking rate, rotation frequency and age of animals, other than natural ventilation technique, smaller housing unit size, higher stocking rate, application of water hygiene, compounder feed origin, use of feed supplements and no use of feed supplements, other than summer, other than winter, other than fall season, slow-growing and fast-growing genetics, female gender, no vaccination with live vaccines, no infections, and earlier production week of parents. Studies supporting these findings by showing the same direction of influence of a negative attitude towards antibiotics, other than natural ventilation technique, smaller housing unit size, higher stocking rate, use of water from the mains, compounder feed origin, use of feed supplements, other than summer, other than winter season, other than fall season, slow-growing and fast-growing genetics, female gender, no infections, and earlier production week of parents were identified. Studies showing an opposite influence were found for compounder feed origin, use of no/feed supplements, slow-growing and fast-growing genetics, and female gender [8,10,12,14,31,33,34,37,45,54,55]
Performance Parameters
The identified categories of risk factors of importance for higher performance were higher working engagement, negative attitude towards antibiotics in interaction with season, organic husbandry type, smaller housing unit size, higher stocking rate, higher stocking rate or old age of building in interaction with litter/bedding other than wood shavings, with cold-humid season and longer fattening period, other than cold-humid seasons in interaction with wood shaving litter/bedding, compounder feed origin, use of feed supplements, other than summer season for fattening, summer season for raising, other than fall season, other than winter season, fast-growing genetics, female and male gender, raising location on same farm, no infection, and a certain breeding company. Studies supporting these findings by showing the same direction of influence of negative attitude towards antibiotics, organic husbandry type, smaller housing unit size, higher stocking rate, other than cold-humid seasons in interaction with wood shaving litter/bedding, compounder feed origin, use of feed supplements, other than summer season, other than fall season, other than winter season, fast-growing genetics, female gender, older age of animals, no infections, and a certain breeding company were identified. Studies showing an opposite influence were found for compounder feed origin, use of feed supplements, fast-growing genetics, raising location on the same farm, and female/male gender [8,10,15,18,33,48,49,56,58].

3.3.2. Robustness of Evidence of Influence of Risk Factors on Specific Endpoints

Table 1 summarises the findings as regards statistically significant influence of the specific categories of the risk factors on all endpoints considered by providing numbers of studies per category of risk factor and endpoint. Robust evidence is defined as more than one statistically significant result per risk factor, in more than one study, per endpoint.
Figure 7 shows the negative or positive influence of risk factors on specific endpoints with numbers of studies being counted as the numerical value ‘one’ per study with a statistically significant result per risk factor per endpoint, while Table 1 summarises the negative and positive influences of specific categories of risk factors on specific endpoints accordingly. Negative and positive influences of specific categories of specific risk factors acting on the same specific endpoint were identified in four risk factors, i.e., feed change, gender, genetics, and season, while for all other risk factors, it was always different endpoints that were exposed to positive or negative influences of a specific risk factor.
Considering each specific endpoint separately, the number of confirmed categories of risk factors in at least two studies is seven (risk factors: summer and winter seasons, male gender, and infection; protective factors: smaller housing unit size, feed supplements, and slow-growing genetics), i.e., being of robust evidence, which is displayed by Table 1. Summer season was associated with higher mortality [17,33], winter season was associated with higher condemnation/pathological/bacterial/viral/parasitological findings [10,55], and higher mortality was associated with infection [8,31,45]. Male gender was associated with a higher disease prevalence [56,57]. A smaller housing unit size was associated with lower condemnation/pathological/bacterial/viral/parasitological findings [8,10]. The use of feed supplements was associated with a higher performance [18,48,49], and slow-growing genetics was associated with lower mortality [33,37]. Apart from this, when analysed for the specific endpoints, the use of feed supplements, summer season, male gender, and slow-growing genetics show contradicting results in one study each (summer season on performance in raising/in fattening, male gender on performance and use of feed supplements, and slow-growing genetics on condemnation/pathological/bacterial/viral/parasitological findings), while all other categories of risk factors are identified as being of one influence only, which is displayed in Table 1 and graphically by risk factor level in Figure 7.
Table 1. Confirmation of the influence of the specific categories of the risk factors on all endpoints considered. M = mortality, C = condemnation/pathological/bacterial/viral/parasitological findings, P = performance, D = disease prevalence, AMR = AMR/AMU, numbers of studies are given. Numbers of studies are counted as the numerical value ‘one’ per study with a statistically significant result per risk factor (provided in brackets) per endpoint (provided in brackets). Total number of studies with negative or positive influence is provided in the respective column. References can be found in Supplemental Material S6.
Table 1. Confirmation of the influence of the specific categories of the risk factors on all endpoints considered. M = mortality, C = condemnation/pathological/bacterial/viral/parasitological findings, P = performance, D = disease prevalence, AMR = AMR/AMU, numbers of studies are given. Numbers of studies are counted as the numerical value ‘one’ per study with a statistically significant result per risk factor (provided in brackets) per endpoint (provided in brackets). Total number of studies with negative or positive influence is provided in the respective column. References can be found in Supplemental Material S6.
Risk FactorNegative Influence (Higher AMR/AMU, Mortality, Disease Prevalence, Condemnation/Pathological/Bacterial/Viral/Parasitological Findings, Lower Performance)Positive Influence (Lower AMR/AMU, Mortality, Disease Prevalence, Condemnation/Pathological/Bacterial/Viral/Parasitological Findings, Higher Performance)
Factors related to farmers
Working engagement (1)01x higher working engagement (P(1))
Attitude towards antibiotics (4)2x positive attitude towards antibiotic treatment/AMU (M(1)/C(1)), 1x positive attitude towards antibiotic treatment/AMU in interaction with season (P(1))1x negative attitude towards antibiotics/antibiotic-free production (AMR(1))
Housing conditions and management-related factors
Husbandry type (2)02x organic husbandry type (P(1)/AMR(1))
Outdoor husbandry (1)1x outdoor husbandry in correlation with housing unit size, stocking rate, rotation frequency and age of animals (C(1))0
Ventilation technique (1)1x roof or tunnel ventilation (natural ventilation) (M(1)/C(1))0
Housing unit size (3)03x smaller housing unit size (M(1)/C(3)/P(1)/AMR(1))
Stocking rate (3)02x higher stocking rate (C(1)/P(1)), 1x higher stocking rate or old age of building in interaction with other than wood shavings, with cold-humid season and longer fattening period (P(1))
Litter/bedding (2)01x old age of building in interaction with other than wood shaving litter/bedding, with cold-humid season and with longer fattening period (M(1)), 1x other than cold-humid seasons in interaction with wood shaving litter/bedding (M(1)/P(1))
Water (2)01x use of water from mains (AMR(1)), 1x application of water hygiene (C(1))
Feed origin (2)1x compounder feed origin (AMR(1))1x compounder feed origin (C(1)/P(1))
Feed change (5)1x feed supplements (C(1))4x feed supplements (C(1)/P(3)/D(1))
Hygiene (1)01x application of good hygiene principles (AMR(1))
Season (8)2x summer (M(2)/C(1)), 1x summer (fattening) (P(1)), 1x fall (C(1)/P(1)), and 3x winter season (P(1)/C(3))1x summer season (raising) (P(1))
Animal-related factors
Genetics (5)2x slow growing genetics (C(1)/P(2))3x slow-growing genetics (M(2)/C(1)/D(2))
Gender (5)4x male gender (M(1)/C(1)/P(1)/D(2))1x male gender (P(1))
Age of animals (2)1x 6 weeks of age (P(1)/D(1)),
1x age of the youngest birds < 105d (AMR(1))		0
Length of fattening period (1)1x longer fattening period with summer (M (1))0
Raising location (2)1x raising on the same farm (D(1)/AMR(1))1x raising on the same farm (P(1))
Vaccination (1)1x application of routine vaccination scheme with live vaccines (C(1))0
Infection (3)3x infection (M(1)/C(3)/P(1))0
Documentation criteria
Breeding company (1)01x certain breeding company (M(1)/P(1))
Egg-laying week of parents (1)01x earlier production week of parents (M(1)/C(1))
Poultry 04 00039 g007
Figure 7. Negative influence of risk factors on specific endpoints and positive influence of risk factors on specific endpoints. Dark blue: mortality; orange: condemnation/pathological/bacterial/viral/parasitological findings; dark green: performance; light blue: disease prevalence; purple: AMR/AMU. Numbers of studies are counted as the numerical value ‘one’ per study with a statistically significant result per risk factor per endpoint. The endpoint ‘condemnation/pathological/bacterial/viral/parasitological findings’ is abbreviated as ‘condemnation’. Negative influence is represented as negative numbers on the left side of the graph. Positive influence is represented as positive numbers on the right side of the graph. References can be found in Supplemental Material S6.
Figure 7. Negative influence of risk factors on specific endpoints and positive influence of risk factors on specific endpoints. Dark blue: mortality; orange: condemnation/pathological/bacterial/viral/parasitological findings; dark green: performance; light blue: disease prevalence; purple: AMR/AMU. Numbers of studies are counted as the numerical value ‘one’ per study with a statistically significant result per risk factor per endpoint. The endpoint ‘condemnation/pathological/bacterial/viral/parasitological findings’ is abbreviated as ‘condemnation’. Negative influence is represented as negative numbers on the left side of the graph. Positive influence is represented as positive numbers on the right side of the graph. References can be found in Supplemental Material S6.
Poultry 04 00039 g007

4. Discussion

According to a report by the German Federal Institute for Risk Assessment, “Therapy frequency and antibiotic consumption 2018–2021: Trends in cattle, pigs, chickens and turkeys kept for meat production,” antibiotics in category B accounted for a higher proportion of population-wide treatment frequency in turkeys than those in category C. Antibiotics in category D played the largest role overall. According to the report, unauthorised third- and fourth-generation cephalosporins were not used in turkeys. Polypeptide antibiotics accounted for a slightly larger proportion of category B than fluoroquinolones [60]. From the perspective of the Federal Institute for Risk Assessment, efforts to reduce the use of antibiotics must be continued and intensified in order to prevent the spread of resistance and, in the long term, to achieve a decline in resistance rates [60]. This highlights the urgent need for new strategies to reduce AMU in livestock production, including poultry.
In order to improve the understanding of the underlying factors for the high antimicrobial use and the development of resistance in the poultry sector, a scoping review was conducted to identify potential risk factors for the endpoints (1) mortality, (2) disease prevalence, (3) condemnation/pathological/bacterial/viral/parasitological findings, (4) performance, and (5) AMR/AMU in poultry production. To confirm the influence of specific categories of risk factors on all endpoints, only studies with multivariable statistical model and statistically significant results were considered in this part of the assessment of published information. The findings are relevant when developing future field as well as observational studies and related statistical models [22].
The diverging influences of risk factors, depending on the endpoint, emphasise the need to differentiate between the endpoints considered when evaluating the influence of risk factors. The same is valid for different units of analysis; most studies identified in our review focused on one unit of analysis only. For production period and group level, the highest numbers of studies were identified, and data gaps for several risk factors, specific categories of risk factors, certain endpoints, and certain units of analysis were identified.
An extensive search strategy in PubMed was conducted for this scoping review to achieve a balance of recall and precision in the retrieval of studies and to minimise potential selection bias. However, certain limitations may have prevented our search strategy from identifying all relevant publications, including (1) searching in PubMed only, (2) external factors (e.g., lack of standardisation of abstracts, varying terminology, and incorrect indexing) in the published literature, and (3) missing terms (e.g., synonyms, morphological variations, and MeSH terms) in our search. The use of PubMed alone for the literature search is a limitation of the study, as it might not have identified all existing studies over all endpoints and all hierarchy levels. However, it made it possible to include these different facets in a comprehensive approach, which distinguishes it from other scoping reviews such as the scoping review by Phillips et al. (2022) or Costa et al. (2023) that focused on a single endpoint (antibiotic resistance) [19,20]. Based on our experience that the majority of relevant journals are indexed in PubMed, the key findings of our scoping review should be valid based on the identified set of studies.
Considering each specific endpoint separately, the number of risk factor categories identified in at least two studies as significant was seven (risk factors: summer and winter seasons, male gender, infection; protective factors: smaller housing unit size, feed supplements, and slow-growing genetics), which added to the evidence for causality.
Interestingly, two categories of two risk factors (no/use feed supplements [14,45] and slow-/fast-growing genetics [33,37]) were identified that had opposite effects on the endpoint condemnation/pathological/bacterial/viral/parasitological findings and two categories of one risk factor (female/male gender [8,56]) had opposite effects on the endpoint performance. Furthermore, the results show that the influence of specific categories can vary depending on the type of production, as can be seen from the negative influence of the summer season on fattening and the positive influence of the summer season on rearing [33].
Apart from this, we identified risk factors with dependence (correlation/interaction) on other risk factors, such as cold-humid season in interaction with litter/bedding other than wood shavings, or interaction of antibiotic treatment and season. These provide some examples of the complexity of the dynamic processes involved, as at least in part shown by Van Limbergen et al. (2020), Lüning et al. (2023), Junghans et al. (2022) [8,10,13]. Complexity in human and animal lives is high, and the dynamic nature of living systems must be considered [61,62]. This is to a certain extent possible in controlled (experimental) studies where the effect of additional factors such as confounders are typically eliminated by the study design. In observational studies, the control of such additional factors is mostly conducted during the multivariable statistical model; however, this requires substantial a priori knowledge on the complexity of the system, which can be established through extensive reviews, expert elicitation, and visualisation using DAGs.
Since the results show that there is almost no one-to-one relationship between risk factors and endpoints, and there is little robust evidence for the direction of the influence of specific categories of risk factors, and contrary influences have been shown even on the same endpoint, the following summary can be made: The results of the literature search display these complex and dynamic processes in poultry production and need to be taken into account in future research projects. Different hierarchical production levels should be considered by defining in the study protocol which risk factor can be evaluated at which hierarchical production level(s) (farm, group, production period, individual animals). Different endpoints are recommended to be considered by developing multivariable statistical models for each endpoint separately with the evaluation of potential associations. Whenever production periods (raising, fattening, breeding) are not modelled separately, they should be included as an additional variable in the final models to assess potential differences of the effect of other risk factors within the respective categories. The choice of confounding factors for the final multivariable regression model is recommended by not only considering statistical significance, but model validity and biological meaningfulness alongside the literature findings.

5. Conclusions

Development of new strategies to reduce AMU is urgently needed [62]. The importance of observational studies for assessing the suitability of therapies for everyday use has been emphasised [63]. However, many studies investigating the factors that can influence antimicrobial usage in farm animal production do not consider more than one unit of analysis [11,14], more than one endpoint [19,20], nor the dynamic nature of these processes [28,50], represented by two examples from the literature each. Hence, there is a strong need for studies targeting the multivariable aspects of AMU in farm animal production using high-quality data and considering specific random and fixed factors to find robust risk factors. This is the reason for this scoping review performed in advance of the development of a statistical model considering potential risk factors on AMU, performance, and production data in turkey production. To our knowledge, it is the first scoping review considering several units of analysis, thereby displaying the hierarchical structure of poultry production and several endpoints. The results of the literature search illustrate the dynamic structure of the highly complex system of poultry production. For the field study, the results of this scoping review will be considered for statistical model development and discussion of results. In a larger context, the findings of our scoping review will inform priorities and statistical model development for future research on poultry and AMR. This research will help to develop sound recommendations towards sustainable poultry production that takes into account animal health, animal welfare, as well as public health aspects.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/poultry4030039/s1. Supplemental Material S1: Search strings and numbers of matches; Supplemental Material S2: Table of abbreviations of the risk factors, including further specification, reference, key outcome, type of production, study size, study design, statistics, outcome variables/endpoints, number of risk factors, and experimental unit; Supplemental Material S3: Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist; Supplemental Material S4: Preliminary literature search for the identification of potential risk factors; Supplemental Material S5: References for 49 studies considered for the scoping review; Supplemental Material S6: Thirty-four studies included in the scoping review: table of risk factors, species, unit of analysis, outcome variables/endpoints/significance, type of analysis, influence of a specific category of risk factors, and references; Supplemental Material S7: Table of type of production, endpoints, and references. Types of outcome variables/endpoints considered in the studies considered for final statistical model development and details on species.

Author Contributions

Conceptualisation, P.W.; methodology, P.W., L.S.-S., and S.B.-B.; software, P.W., L.S.-S., S.B.-B., and M.G.D.; validation, P.W., D.M., A.K., and S.B.-B.; formal analysis, P.W., L.S.-S., S.B.-B., and M.G.D., investigation, L.S.-S., A.K., M.G.D., S.B.-B., D.M., and P.W.; resources, L.S.-S., M.G.D., A.K., D.M., and P.W.; data curation, P.W., L.S.-S., S.B.-B., and M.G.D.; writing—original draft preparation, P.W.; writing—review and editing, L.S.-S., A.K., M.G.D., S.B.-B., D.M., and P.W., visualisation, L.S.-S., A.K., M.G.D., S.B.-B., D.M., and P.W., supervision, A.K., D.M., M.G.D., and P.W.; project administration, P.W., A.K., D.M., and M.G.D.; funding acquisition, A.K., M.G.D., S.B.-B., D.M., and P.W. All authors have read and agreed to the published version of the manuscript.

Funding

The authors declare that this study received funding from Software AG-Stiftung, Darmstadt, and Sanddorf-Stiftung, Regensburg, both from Germany. The funder was not involved in the study design, collection, analyses, interpretation of data, the writing of this article, or the decision to submit it for publication.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analysed in this study besides extracting and summarizing reported risk factor—outcome observations from already published articles. All publications included in this review are referenced; therefore, further data sharing is not applicable to this article. Further inquiries can be directed to the corresponding author.

Acknowledgments

We want to thank Sara Fox Chapman for checking the English language. We thank Software AG-Stiftung, Darmstadt, and Sanddorf-Stiftung, Regensburg, both in Germany, for supporting this project. We would like to thank the Open Access Fund of the University of Veterinary Medicine (Vetmeduni), Vienna, for supporting open access publication.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Naghavi, M.; Vollset, S.E.; Ikuta, K.S.; Swetschinski, L.R.; Gray, A.P.; Wool, E.E.; Aguilar, G.R.; Mestrovic, T.; Smith, G.; Han, C.; et al. Global burden of bacterial antimicrobial resistance 1990–2021: A systematic analysis with forecasts to 2050. Lancet 2024, 404, 1199–1226. [Google Scholar] [CrossRef] [PubMed]
  2. Smith, D.R.M.; Dolk, F.C.K.; Pouwels, K.B.; Christie, M.; Robotham, J.V.; Smieszek, T. Defining the appropriateness and inappropriateness of antibiotic prescribing in primary care. J. Antimicrob. Chemother. 2018, 73, ii11–ii18. [Google Scholar] [CrossRef] [PubMed]
  3. ECDC. Antimicrobial Consumption and Resistance in Bacteria from Humans and Food-Producing Animals (JIACRA IV–2019−2021). Available online: https://www.ecdc.europa.eu/en/publications-data/antimicrobial-consumption-and-resistance-bacteria-humans-and-food-producing (accessed on 28 May 2024).
  4. Berthe, F.C.J.; Bouley, T.; Karesh, W.; Le Gall, F.G.; Machalaba, C.C.; Plante, C.A.; Seifman, R.M. Operational Framework for Strengthening Human, Animal and Environmental Public Health Systems at Their Interface (English). Available online: http://documents.worldbank.org/curated/en/703711517234402168/Operational-framework-for-strengthening-human-animal-and-environmental-public-health-systems-at-their-interface (accessed on 28 May 2023).
  5. Viñes, J.; Cuscó, A.; Napp, S.; Alvarez, J.; Saez-Llorente, J.L.; Rosàs-Rodoreda, M.; Francino, O.; Migura-Garcia, L. Transmission of Similar Mcr-1 Carrying Plasmids among Different Escherichia coli Lineages Isolated from Livestock and the Farmer. Antibiotics 2021, 10, 313. [Google Scholar] [CrossRef]
  6. European Commission. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions a Farm to Fork Strategy for a Fair, Healthy and Environmentally-Friendly Food System. Available online: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52020DC0381 (accessed on 15 June 2022).
  7. Smith, J.; Gheyas, A.; Burt, D.W. Animal genomics and infectious disease resistance in poultry. Rev. Sci. Tech. 2016, 35, 105–119. [Google Scholar] [CrossRef]
  8. Van Limbergen, T.; Sarrazin, S.; Chantziaras, I.; Dewulf, J.; Ducatelle, R.; Kyriazakis, I.; McMullin, P.; Méndez, J.; Niemi, J.K.; Papasolomontos, S.; et al. Risk factors for poor health and performance in European broiler production systems. BMC Vet. Res. 2020, 16, 287. [Google Scholar] [CrossRef]
  9. Iannetti, L.; Romagnoli, S.; Cotturone, G.; Vulpiani, M.P. Animal Welfare Assessment in Antibiotic-Free and Conventional Broiler Chicken. Animals 2021, 11, 2822. [Google Scholar] [CrossRef]
  10. Junghans, A.; Deseniß, L.; Louton, H. Data evaluation of broiler chicken rearing and slaughter-An exploratory study. Front. Vet. Sci. 2022, 9, 957786. [Google Scholar] [CrossRef]
  11. Mughini-Gras, L.; Di Martino, G.; Moscati, L.; Buniolo, F.; Cibin, V.; Bonfanti, L. Natural immunity in conventionally and organically reared turkeys and its relation with antimicrobial resistance. Poult. Sci. 2020, 99, 763–771. [Google Scholar] [CrossRef]
  12. Cornell, K.A.; Smith, O.M.; Crespo, R.; Jones, M.S.; Crossley, M.S.; Snyder, W.E.; Owen, J.P. Prevalence Patterns for Enteric Parasites of Chickens Managed in Open Environments of the Western United States. Avian Dis. 2022, 66, 60–68. [Google Scholar] [CrossRef]
  13. Lüning, J.; Wunderl, D.; Rautenschlein, S.; Campe, A. Histomonosis in German turkey flocks: Possible ways of pathogen introduction. Avian Pathol. 2023, 52, 199–208. [Google Scholar] [CrossRef]
  14. Smith, R.P.; Lawes, J.; Davies, R.H.; Hutchison, M.L.; Vidal, A.; Gilson, D.; Rodgers, J. UK-wide risk factor study of broiler carcases highly contaminated with Campylobacter. Zoonoses Public Health 2023, 70, 523–541. [Google Scholar] [CrossRef]
  15. Campe, A.; Koesters, S.; Niemeyer, M.; Klose, K.; Ruddat, I.; Baumgarte, J.; Kreienbrock, L. Epidemiology of influences on the performance in broiler flocks--a field study in Germany. Poult. Sci. 2013, 92, 2576–2587. [Google Scholar] [CrossRef]
  16. Jhetam, S.; Buchynski, K.; Shynkaruk, T.; Schwean-Lardner, K. Evaluating the effects of stocking density on the behavior, health, and welfare of turkey hens to 11 weeks of age. Poult. Sci. 2022, 101, 101956. [Google Scholar] [CrossRef]
  17. Lüning, J.; Auerbach, M.; Lindenwald, R.; Campe, A.; Rautenschlein, S. Retrospective Investigations of Recurring Histomonosis on a Turkey Farm. Avian Dis. 2022, 66, 410–417. [Google Scholar] [CrossRef]
  18. Leiber, F.; Amsler, Z.; Bieber, A.; Quander-Stoll, N.; Maurer, V.; Lambertz, C.; Früh, B.; Ayrle, H. Effects of riboflavin supplementation level on health, performance, and fertility of organic broiler parent stock and their chicks. Animal 2022, 16, 100433. [Google Scholar] [CrossRef]
  19. Phillips, C.; Chapman, B.; Agunos, A.; Carson, C.A.; Parmley, E.J.; Reid-Smith, R.J.; Smith, B.A.; Murphy, C.P. A scoping review of factors potentially linked with antimicrobial-resistant bacteria from turkeys (iAM.AMR Project). Epidemiol. Infect. 2022, 150, e153. [Google Scholar] [CrossRef]
  20. Costa, M.M.; Cardo, M.; Ruano, Z.; Alho, A.M.; Dinis-Teixeira, J.; Aguiar, P.; Leite, A. Effectiveness of antimicrobial interventions directed at tackling antimicrobial resistance in animal production: A systematic review and meta-analysis. Prev. Vet. Med. 2023, 218, 106002. [Google Scholar] [CrossRef] [PubMed]
  21. Humphrey-Murto, S.; Varpio, L.; Wood, T.J.; Gonsalves, C.; Ufholz, L.A.; Mascioli, K.; Wang, C.; Foth, T. The Use of the Delphi and Other Consensus Group Methods in Medical Education Research: A Review. Acad. Med. 2017, 92, 1491–1498. [Google Scholar] [CrossRef] [PubMed]
  22. Baur-Bernhardt, S.; Käsbohrer, A.; Doherr, M.G.; Meemken, D.; Sonnenschein-Swanson, L.; Stetina, B.U.; Sommer, M.A.; Weiermayer, P. Assessing the Feasibility of a Two-Cohort Design to Assess the Potential of Homeopathic Medicinal Products to Reduce Antimicrobial Resistance in Turkeys (The HOMAMR Project)-Study Protocol. Homeopathy 2025, 114, 50–57. [Google Scholar] [CrossRef] [PubMed]
  23. Tricco, A.C.; Lillie, E.; Zarin, W.; O’Brien, K.K.; Colquhoun, H.; Levac, D.; Moher, D.; Peters, M.D.J.; Horsley, T.; Weeks, L.; et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann. Intern. Med. 2018, 169, 467–473. [Google Scholar] [CrossRef]
  24. Björkman, I.; Röing, M.; Sternberg Lewerin, S.; Stålsby Lundborg, C.; Eriksen, J. Animal Production With Restrictive Use of Antibiotics to Contain Antimicrobial Resistance in Sweden-A Qualitative Study. Front. Vet. Sci. 2020, 7, 619030. [Google Scholar] [CrossRef]
  25. Henke, K.A.; Alter, T.; Doherr, M.G.; Merle, R. From Stable to Table: Determination of German Consumer Perceptions of the Role of Multiple Aspects of Poultry Production on Meat Quality and Safety. J. Food Prot. 2021, 84, 1400–1410. [Google Scholar] [CrossRef]
  26. Stefania, C.; Alessio, M.; Paolo, M.; Tiziano, D.; Favretto, A.R.; Francesca, Z.; Giulia, M.; Giandomenico, P. The application of biosecurity practices for preventing avian influenza in North-Eastern Italy turkey farms: An analysis of the point of view and perception of farmers. Prev. Vet. Med. 2024, 222, 106084. [Google Scholar] [CrossRef]
  27. Jones, E.M.; Snow, L.C.; Carrique-Mas, J.J.; Gosling, R.J.; Clouting, C.; Davies, R.H. Risk factors for antimicrobial resistance in Escherichia coli found in GB turkey flocks. Vet. Rec. 2013, 173, 422. [Google Scholar] [CrossRef] [PubMed]
  28. Kempf, I.; Le Roux, A.; Perrin-Guyomard, A.; Mourand, G.; Le Devendec, L.; Bougeard, S.; Richez, P.; Le Pottier, G.; Eterradossi, N. Effect of in-feed paromomycin supplementation on antimicrobial resistance of enteric bacteria in turkeys. Vet. J. 2013, 198, 398–403. [Google Scholar] [CrossRef]
  29. Li, G.; Zhao, Y.; Purswell, J.L.; Chesser, G.D.; Lowe, J.W.; Wu, T.L. Effects of antibiotic-free diet and stocking density on male broilers reared to 35 days of age. Part 2: Feeding and drinking behaviours of broilers. J. Appl. Poult. Res. 2020, 29, 391–401. [Google Scholar] [CrossRef]
  30. Luangtongkum, T.; Morishita, T.Y.; Ison, A.J.; Huang, S.; McDermott, P.F.; Zhang, Q. Effect of conventional and organic production practices on the prevalence and antimicrobial resistance of Campylobacter spp. in poultry. Appl. Environ. Microbiol. 2006, 72, 3600–3607. [Google Scholar] [CrossRef] [PubMed]
  31. Van Hoorebeke, S.; Van Immerseel, F.; De Vylder, J.; Ducatelle, R.; Haesebrouck, F.; Pasmans, F.; de Kruif, A.; Dewulf, J. The age of production system and previous Salmonella infections on-farm are risk factors for low-level Salmonella infections in laying hen flocks. Poult. Sci. 2010, 89, 1315–1319. [Google Scholar] [CrossRef] [PubMed]
  32. Davis, G.S.; Waits, K.; Nordstrom, L.; Grande, H.; Weaver, B.; Papp, K.; Horwinski, J.; Koch, B.; Hungate, B.A.; Liu, C.M.; et al. Antibiotic-resistant Escherichia coli from retail poultry meat with different antibiotic use claims. BMC Microbiol. 2018, 18, 174. [Google Scholar] [CrossRef]
  33. Aksoy, T.; Çürek, D.; Narinç, D.; Önenç, A. Effects of season, genotype, and rearing system on broiler chickens raised in different semi-intensive systems: Performance, mortality, and slaughter results. Trop. Anim. Health Prod. 2021, 53, 189. [Google Scholar] [CrossRef]
  34. Andreopoulou, M.; Chaligiannis, I.; Sotiraki, S.; Daugschies, A.; Bangoura, B. Prevalence and molecular detection of Eimeria species in different types of poultry in Greece and associated risk factors. Parasitol. Res. 2022, 121, 2051–2063. [Google Scholar] [CrossRef]
  35. Holt, R.V.; Skånberg, L.; Keeling, L.J.; Estevez, I.; Newberry, R.C. Resource choice during ontogeny enhances both the short- and longer-term welfare of laying hen pullets. Sci. Rep. 2024, 14, 3360. [Google Scholar] [CrossRef] [PubMed]
  36. Berk, J.; Wartemann, S. Influence of a turkey stable with a veranda on performance, behaviour and health of male turkeys. Dtsch. Tierarztl. Wochenschr. 2006, 113, 107–110. [Google Scholar] [PubMed]
  37. Baxter, M.; Richmond, A.; Lavery, U.; O’Connell, N.E. A comparison of fast growing broiler chickens with a slower-growing breed type reared on Higher Welfare commercial farms. PLoS ONE 2021, 16, e0259333. [Google Scholar] [CrossRef] [PubMed]
  38. Krautwald-Junghanns, M.E.; Sirovnik, J. The influence of stocking density on behaviour, health, and production in commercial fattening turkeys-a review. Br. Poult. Sci. 2022, 63, 434–444. [Google Scholar] [CrossRef]
  39. Santos, M.N.; Widowski, T.M.; Kiarie, E.G.; Guerin, M.T.; Edwards, A.M.; Torrey, S. In pursuit of a better broiler: Walking ability and incidence of contact dermatitis in conventional and slower growing strains of broiler chickens. Poult. Sci. 2022, 101, 101768. [Google Scholar] [CrossRef]
  40. Durmuş, M.; Kurşun, K.; Polat Açık, I.; Tufan, M.; Kutay, H.; Benli, H.; Baylan, M.; Kutlu, H.R. Effect of different litter materials on growth performance, the gait score and footpad dermatitis, carcass parameters, meat quality, and microbial load of litter in broiler chickens. Poult. Sci. 2023, 102, 102763. [Google Scholar] [CrossRef]
  41. Kittelsen, K.E.; Vasdal, G.; Moe, R.O.; Steinhoff, F.S.; Tahamtani, F.M. Health effects of feed dilution and roughage in Ross 308 broiler breeder cockerels. Poult. Sci. 2023, 102, 102985. [Google Scholar] [CrossRef]
  42. Avila, L.P.; Sweeney, K.M.; Evans, C.R.; White, D.L.; Kim, W.K.; Regmi, P.; Williams, S.M.; Nicholds, J.; Wilson, J.L. Body composition, gastrointestinal, and reproductive differences between broiler breeders fed using everyday or skip-a-day rearing programs. Poult. Sci. 2023, 102, 102853. [Google Scholar] [CrossRef]
  43. Avila, L.P.; Leiva, S.F.; Abascal-Ponciano, G.A.; Flees, J.J.; Sweeney, K.M.; Wilson, J.L.; Meloche, K.J.; Turner, B.J.; Litta, G.; Waguespack-Levy, A.M.; et al. Effect of combined maternal and post-hatch dietary 25-hydroxycholecalciferol supplementation on broiler chicken Pectoralis major muscle growth characteristics and satellite cell mitotic activity. J. Anim. Sci. 2022, 100, skac192. [Google Scholar] [CrossRef]
  44. Hu, J.Y.; Mohammed, A.A.; Murugesan, G.R.; Cheng, H.W. Effect of a synbiotic supplement as an antibiotic alternative on broiler skeletal, physiological, and oxidative parameters under heat stress. Poult. Sci. 2022, 101, 101769. [Google Scholar] [CrossRef]
  45. Dos Santos, T.S.; Augusto, K.V.Z.; Han, Y.; Sartori, M.M.P.; Batistioli, J.S.; Contin Neto, A.C.; Ferreira Netto, R.G.; Zanetti, L.H.; Pasquali, G.A.M.; Muro, E.M.; et al. Effects of dietary copper and zinc hydroxychloride supplementation on bone development, skin quality and hematological parameters of broilers chickens. J. Anim. Physiol. Anim. Nutr. 2023, 107, 1241–1250. [Google Scholar] [CrossRef]
  46. Freitas, L.F.V.; Dorigam, J.C.P.; Reis, M.P.; Horna, F.; Fernandes, J.B.K.; Sakomura, N.K. Eimeria maxima infection impacts the protein utilisation of broiler chicks from 14 to 28 days of age. Animal 2023, 17, 100807. [Google Scholar] [CrossRef] [PubMed]
  47. Sholikin, M.M.; Sadarman; Irawan, A.; Sofyan, A.; Jayanegara, A.; Rumhayati, B.; Hidayat, C.; Adli, D.N.; Julendra, H.; Herdian, H.; et al. A meta-analysis of the effects of clay mineral supplementation on alkaline phosphatase, broiler health, and performance. Poult. Sci. 2023, 102, 102456. [Google Scholar] [CrossRef] [PubMed]
  48. Al Sulaiman, A.R.; Abudabos, A.M.; Alhotan, R.A. Protective influence of supplementary betaine against heat stress by regulating intestinal oxidative status and microbiota composition in broiler chickens. Int. J. Biometeorol. 2024, 68, 279–288. [Google Scholar] [CrossRef] [PubMed]
  49. Wealleans, A.L.; Desbruslais, A.; Goncalves, R.; Scholey, D.; Gonzalez-Sanchez, D.; Burton, E.; Spaepen, R.; Elliott, A.; Currie, D. Research Note: Comparative effects of liquid and dry applications of a combination of lysolecithin, synthetic emulsifier, and monoglycerides on growth performance, nutrient digestibility, and litter moisture in broilers fed diets of differing energy density. Poult. Sci. 2024, 103, 103345. [Google Scholar] [CrossRef]
  50. Yvon, S.; Beaumont, M.; Dayonnet, A.; Eutamène, H.; Lambert, W.; Tondereau, V.; Chalvon-Demersay, T.; Belloir, P.; Paës, C. Effect of diet supplemented with functional amino acids and polyphenols on gut health in broilers subjected to a corticosterone-induced stress. Sci. Rep. 2024, 14, 1032. [Google Scholar] [CrossRef]
  51. Park, I.; Nam, H.; Wickramasuriya, S.S.; Lee, Y.; Wall, E.H.; Ravichandran, S.; Lillehoj, H.S. Host-mediated beneficial effects of phytochemicals for prevention of avian coccidiosis. Front. Immunol. 2023, 14, 1145367. [Google Scholar] [CrossRef]
  52. Dzieciolowski, T.; Boqvist, S.; Rydén, J.; Hansson, I. Cleaning and disinfection of transport crates for poultry-comparison of four treatments at slaughter plant. Poult. Sci. 2022, 101, 101521. [Google Scholar] [CrossRef]
  53. Schreuder, J.; Simitopoulou, M.; Angastiniotis, K.; Ferrari, P.; Wolthuis-Fillerup, M.; Kefalas, G.; Papasolomontos, S. Development and implementation of a risk assessment tool for broiler farm biosecurity and a health intervention plan in the Netherlands, Greece, and Cyprus. Poult. Sci. 2023, 102, 102394. [Google Scholar] [CrossRef]
  54. Gretarsson, P.; Kittelsen, K.; Oppermann Moe, R.; Toftaker, I. Causes of carcass condemnation in Norwegian aviary housed layers. Acta Vet. Scand. 2023, 65, 18. [Google Scholar] [CrossRef]
  55. Adcock, K.G.; Berghaus, R.D.; Goodwin, C.C.; Ruder, M.G.; Yabsley, M.J.; Mead, D.G.; Nemeth, N.M. Lymphoproliferative Disease Virus and Reticuloendotheliosis Virus Detection and Disease in Wild Turkeys (Meleagris gallopavo). J. Wildl. Dis. 2024, 60, 139–150. [Google Scholar] [CrossRef] [PubMed]
  56. Çapar Akyüz, H.; Onbaşılar, E.E.; Bayraktaroğlu, A.G.; Ceylan, A. Age and sex related changes in fattening performance, dermatitis, intestinal histomorphology, and serum IgG level of slow- and fast-growing broilers under the intensive system. Trop. Anim. Health Prod. 2022, 54, 312. [Google Scholar] [CrossRef] [PubMed]
  57. Ferrante, V.; Lolli, S.; Ferrari, L.; Watanabe, T.T.N.; Tremolada, C.; Marchewka, J.; Estevez, I. Differences in prevalence of welfare indicators in male and female turkey flocks (Meleagris gallopavo). Poult. Sci. 2019, 98, 1568–1574. [Google Scholar] [CrossRef] [PubMed]
  58. Molenaar, R.; Stockhofe-Zurwieden, N.; Giersberg, M.F.; Rodenburg, T.B.; Kemp, B.; van den Brand, H.; de Jong, I.C. Effects of hatching system on chick quality, welfare and health of young breeder flock offspring. Poult. Sci. 2023, 102, 102448. [Google Scholar] [CrossRef]
  59. Montalcini, C.M.; Petelle, M.B.; Toscano, M.J. Commercial hatchery practices have long-lasting effects on laying hens’ spatial behaviour and health. PLoS ONE 2023, 18, e0295560. [Google Scholar] [CrossRef]
  60. Bundesinstitut für Risikobewertung. Tabellen zur Entwicklung der Therapiehäufigkeit und der Antibiotikaverbrauchsmengen 2018–2021. Available online: https://www.bfr.bund.de/cm/343/therapiehaeufigkeit-und-antibiotikaverbrauchsmengen-2018-2021-bericht.pdf (accessed on 4 August 2025).
  61. Wehrens, R.; Oldenhof, L.; Heerings, M.; Petit-Steeghs, V.; Haperen, S.V.; Bal, R.; Greenhalgh, T. Integrating System Dynamics and Action Research: Towards a Consideration of Normative Complexity Comment on “Insights Gained From a Re-analysis of Five Improvement Cases in Healthcare Integrating System Dynamics Into Action Research”. Int. J. Health Policy Manag. 2023, 12, 7582. [Google Scholar] [CrossRef]
  62. Greenhalgh, T.; Papoutsi, C. Studying complexity in health services research: Desperately seeking an overdue paradigm shift. BMC Med. 2018, 16, 95. [Google Scholar] [CrossRef]
  63. Porzsolt, F.; Rocha, N.G.; Toledo-Arruda, A.C.; Thomaz, T.G.; Moraes, C.; Bessa-Guerra, T.R.; Leão, M.; Migowski, A.; Araujo da Silva, A.R.; Weiss, C. Efficacy and effectiveness trials have different goals, use different tools, and generate different messages. Pragmat. Obs. Res. 2015, 6, 47–54. [Google Scholar] [CrossRef]
Poultry 04 00039 g001
Figure 1. Flow chart reflecting both literature searches.
Figure 1. Flow chart reflecting both literature searches.
Poultry 04 00039 g001
Poultry 04 00039 g002
Figure 2. Frequency of consideration of endpoints in identified studies. Dark blue bar: farm level; orange bar: group level; dark green: production period level; light blue bar: individual animal level. Numbers of studies considering certain endpoints are presented. The endpoint ‘condemnation/pathological/bacterial/viral/parasitological findings’ is abbreviated as ‘condemnation’.
Figure 2. Frequency of consideration of endpoints in identified studies. Dark blue bar: farm level; orange bar: group level; dark green: production period level; light blue bar: individual animal level. Numbers of studies considering certain endpoints are presented. The endpoint ‘condemnation/pathological/bacterial/viral/parasitological findings’ is abbreviated as ‘condemnation’.
Poultry 04 00039 g002
Poultry 04 00039 g003
Figure 3. Reported associations between risk factors (squares) and outcomes (oval) at the farm level with direction and type of statistical model and indication of odds ratio (OR) if reported. Green, solid line arrow: positive (protective) effect; red, dashed line arrow: negative effect; number and type of studies on lines; included references listed; H: hygiene; HUS: housing unit size; I: infection; O: organic husbandry type; *: breeding farms.
Figure 3. Reported associations between risk factors (squares) and outcomes (oval) at the farm level with direction and type of statistical model and indication of odds ratio (OR) if reported. Green, solid line arrow: positive (protective) effect; red, dashed line arrow: negative effect; number and type of studies on lines; included references listed; H: hygiene; HUS: housing unit size; I: infection; O: organic husbandry type; *: breeding farms.
Poultry 04 00039 g003
Poultry 04 00039 g004
Figure 4. Reported associations between risk factors (squares) and outcomes (oval) at the group level with direction and type of statistical model and indication of odds ratio (OR) if reported. Green, solid line arrow: positive (protective) effect; red, dashed line arrow: negative effect; number and type of studies on lines; included references listed; AA: age of animals; ATA: attitude towards antibiotics; E: enclosure area; F: feed origin; FA: feed additives; H: hygiene; HUS: housing unit size; I: infection; M: male gender; O: organic husbandry type; OH: outdoor husbandry; RF: rotation frequency; RL: raising location; S: season; SG: slow-growing; SR: stocking rate; V: ventilation technique; WE: working engagement; *: breeding farms.
Figure 4. Reported associations between risk factors (squares) and outcomes (oval) at the group level with direction and type of statistical model and indication of odds ratio (OR) if reported. Green, solid line arrow: positive (protective) effect; red, dashed line arrow: negative effect; number and type of studies on lines; included references listed; AA: age of animals; ATA: attitude towards antibiotics; E: enclosure area; F: feed origin; FA: feed additives; H: hygiene; HUS: housing unit size; I: infection; M: male gender; O: organic husbandry type; OH: outdoor husbandry; RF: rotation frequency; RL: raising location; S: season; SG: slow-growing; SR: stocking rate; V: ventilation technique; WE: working engagement; *: breeding farms.
Poultry 04 00039 g004
Poultry 04 00039 g005
Figure 5. Reported associations between risk factors (squares) and outcomes (oval) at production period level with direction and type of statistical model and indication of odds ratio (OR) if reported. Green, solid line arrow: positive (protective) effect; red, dashed line arrow: negative effect; number and type of studies on lines; included references listed; ATA: attitude towards antibiotics; B: breeding company; FA: feed additives; FP: fattening period; HUS: housing unit size; L: litter; M: male; S: season; SG: slow-growing; SR: stocking rate; PW: production week; V: vaccination; W: water hygiene.
Figure 5. Reported associations between risk factors (squares) and outcomes (oval) at production period level with direction and type of statistical model and indication of odds ratio (OR) if reported. Green, solid line arrow: positive (protective) effect; red, dashed line arrow: negative effect; number and type of studies on lines; included references listed; ATA: attitude towards antibiotics; B: breeding company; FA: feed additives; FP: fattening period; HUS: housing unit size; L: litter; M: male; S: season; SG: slow-growing; SR: stocking rate; PW: production week; V: vaccination; W: water hygiene.
Poultry 04 00039 g005
Poultry 04 00039 g006
Figure 6. Reported associations between risk factors (squares) and outcomes (oval) at the individual animal level with direction and type of statistical model and indication of odds ratio (OR) if reported. Green, solid line arrow: positive (protective) effect; red, dashed line arrow: negative effect; number and type of studies on lines; included references listed; FA: feed additives; W: winter season; *: breeding farms.
Figure 6. Reported associations between risk factors (squares) and outcomes (oval) at the individual animal level with direction and type of statistical model and indication of odds ratio (OR) if reported. Green, solid line arrow: positive (protective) effect; red, dashed line arrow: negative effect; number and type of studies on lines; included references listed; FA: feed additives; W: winter season; *: breeding farms.
Poultry 04 00039 g006
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Sonnenschein-Swanson, L.; Baur-Bernhardt, S.; Käsbohrer, A.; Doherr, M.G.; Meemken, D.; Weiermayer, P. Potential Risk Factors Related to Antimicrobial Usage and Antimicrobial Resistance in Commercial Poultry Production—A Scoping Review. Poultry 2025, 4, 39. https://doi.org/10.3390/poultry4030039

AMA Style

Sonnenschein-Swanson L, Baur-Bernhardt S, Käsbohrer A, Doherr MG, Meemken D, Weiermayer P. Potential Risk Factors Related to Antimicrobial Usage and Antimicrobial Resistance in Commercial Poultry Production—A Scoping Review. Poultry. 2025; 4(3):39. https://doi.org/10.3390/poultry4030039

Chicago/Turabian Style

Sonnenschein-Swanson, Lena, Silvia Baur-Bernhardt, Annemarie Käsbohrer, Marcus Georg Doherr, Diana Meemken, and Petra Weiermayer. 2025. "Potential Risk Factors Related to Antimicrobial Usage and Antimicrobial Resistance in Commercial Poultry Production—A Scoping Review" Poultry 4, no. 3: 39. https://doi.org/10.3390/poultry4030039

APA Style

Sonnenschein-Swanson, L., Baur-Bernhardt, S., Käsbohrer, A., Doherr, M. G., Meemken, D., & Weiermayer, P. (2025). Potential Risk Factors Related to Antimicrobial Usage and Antimicrobial Resistance in Commercial Poultry Production—A Scoping Review. Poultry, 4(3), 39. https://doi.org/10.3390/poultry4030039

Article Metrics

Back to TopTop