# Moving beyond metrics: Capturing the clinical context behind antibiotic prescriptions in French broiler production

Rebecca Hibbard, Lisa Fournune, Matthieu Pinson, Mattias Delpont,
Jean-Pierre Vaillancourt, Céline Faverjon, Mathilde Paul

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Rebecca Hibbard, Lisa Fournune, Matthieu Pinson, Mattias Delpont, Jean-Pierre Vaillancourt, et al. Moving beyond metrics: Capturing the clinical context behind antibiotic prescriptions in French broiler production. Preventive Veterinary Medicine, 2026, 246, pp.106700. 10.1016/j.prevetmed.2025.106700. hal-05348067

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# Moving beyond metrics: Capturing the clinical context behind antibiotic prescriptions in French broiler production

Rebecca Hibbard<sup>a, *, †</sup>, Lisa Fourtune<sup>a</sup>, Matthieu Pinson<sup>b</sup>, Mattias Delpont<sup>a</sup>, Jean-Pierre Vaillancourt<sup>c</sup>, Céline Faverjon<sup>d</sup>, Mathilde C. Paul<sup>a</sup>

## ARTICLE INFO

### Keywords:

Antimicrobial use

Antimicrobial stewardship

Poultry

Indicators

AMU surveillance

Clinical parameters

## ABSTRACT

Significant reductions in antimicrobial use (AMU) in food production animals have been observed over the last 10 years across Europe. We sought to understand recent changes in AMU by characterising antibiotic prescribing patterns in poultry production in the context of associated clinical information. We analysed trends in AMU for conventional broiler chicken production in France based on a dataset of 193,526 sales for 33,831 flocks on 2120 farms for 2015–2023, including 21,218 antibiotic prescriptions. We found the percentage of flocks prescribed antibiotics dropped from 65 % in 2013 to 20 % in 2023, plateauing in 2020–2023 (oscillating between 13 % and 23 %), and observed a reduction in the use of critical antibiotics. A multiple correspondence analysis and hierarchical clustering on principal components of 1112 antibiotic prescriptions and associated clinical data for 2021–2022 produced 1940 prescription events, grouped in five clusters of antibiotic prescribing patterns, each characterised by a combination of clinical indicators related to age at treatment, lesions, syndromes, diagnoses, and isolated bacteria. Two main clusters were associated with bacterial diagnoses, suggesting that use of antibiotics in these clusters was necessary to manage disease. Two clusters were identified as potential targets for further interventions to improve antimicrobial stewardship, focusing on underlying factors driving AMU rather than outright reductions. Our findings raise questions about the sustainability of further reductions in AMU and their implications for animal health and welfare. This calls for a shift to a more sustainable approach to monitoring antimicrobial stewardship, using integrated indicators which consider AMU within its broader context.

## 1. Introduction

Antimicrobial resistance (AMR) has been increasingly recognised as a major challenge for global health and development, with many high-level international and national strategies advocating a One Health approach (European Commission, 2021; United Nations General Assembly, 2024, 2016; World Health Organization, 2015). Antimicrobial use (AMU) in both humans and animals has been highlighted as a key driver of AMR development (Chang et al., 2015; Holmes et al., 2016; Tang et al., 2017). Over the last 20 years, policy responses to AMR have predominantly focused on reducing antimicrobial use (AMU) in animals, such that reducing or optimising AMU in animals has become a central tenant and a constitutive part of the narrative around addressing AMR (Hinchliffe et al., 2018; Kamenshchikova et al., 2021), as can be

observed by its predominance in major AMR global policy documents (O'Neil, 2016; United Nations General Assembly, 2024; World Health Organization, 2015). In the European Union (EU), examples of such policy measures in the animal health sector include the implementation of the European Surveillance of Veterinary Antimicrobial Consumption project (European Medicines Agency and European Surveillance of Veterinary Antimicrobial Consumption, 2023), the categorisation of antimicrobials used in veterinary medicine (European Medicines Agency, 2014), and targets for reduction of colistin sales (European Medicines Agency, 2016). Additional restrictions on AMU in veterinary medicine and reporting requirements for AMU sales data took effect in 2024 (European Parliament and Council, 2019, 2018).

The impact of such policies is often assessed using AMU monitoring systems, which usually evaluate AMU quantitatively using standardised

<footnote><sup>a</sup> IHAP, Université de Toulouse, INRAE, ENVT, Toulouse, France</footnote>

<footnote><sup>b</sup> Labovet Conseil, Réseau Cristal, France</footnote>

<footnote><sup>c</sup> Faculty of Veterinary Medicine, Université de Montréal, Canada</footnote>

<footnote><sup>d</sup> EpiMundi, Lyon, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: rebecca.hibbard@envt.fr (R. Hibbard).</footnote>
indicators such as defined daily doses or quantities of antimicrobial agent relative to animal biomass (Collineau et al., 2017; Sanders et al., 2020). Using these metrics, many European countries have documented significant reductions in AMU in production animals over the last 10 to 20 years (European Medicines Agency and European Surveillance of Veterinary Antimicrobial Consumption, 2023). In France, significant progress has been made in reducing AMU in livestock species within the framework of the Ecoantibio Plans (the French national plans for the reduction of risks of AMR in veterinary medicine) (Ministère de l'Agri-culture and de l'Agroalimentaire et de la Forêt, 2016a; 2017). These plans were run successively over 2012 to 2016 (Ecoantibio1) and 2017 to 2022 (Ecoantibio2) (Ministère de l'Agriculture and de l'Agroalimentaire et de la Forêt, 2016a; 2017). In particular, during the period 2011 to 2022 there was an overall reduction of 52% across all livestock species in the animal level of exposure to antibiotics (ALEA, the indicator used in France to monitor AMU, calculated by the estimated liveweight of animals treated divided by the biomass of the potentially exposed population) (Anses, 2024a; INSEE, 2025). The most marked reductions were seen in the pig and poultry sectors (67% and 72% respectively, compared to a 64% reduction for rabbits, and 23% reduction in cattle, and 3% decrease in cats and dogs) (Anses, 2023; Urban et al., 2020).

The significant reductions in AMU in livestock sectors in recent years are taken as evidence of the success of policies and regulation targeting AMU. However, the scale and extent of these reductions raise key questions about the future for such policies – firstly, whether reductions in AMU come at the cost of compromised animal health and welfare, and secondly, whether further AMU reductions may be feasible or sustainable (Ducrot et al., 2022; Fortané, 2019; Guenin et al., 2022). Both these challenges lend themselves to being addressed by a different, more holistic approach – a change in paradigm to move beyond targeting purely quantitative reductions in AMU, to instead aim for a more appropriate use of antibiotics while maintaining animal health and welfare. This broader approach ties in with recent efforts to improve antimicrobial stewardship, a concept that encompasses the idea that what is “appropriate” or “prudent” AMU will depend on a given context (Charani et al., 2019; Dyar et al., 2017).

One way to approach AMU more holistically is to monitor the quantity of AMU alongside other indicators as a means of interpreting the “appropriateness” of AMU in a given population or situation. These indicators can be included either as composite indicators or as additional information to help inform AMU analysis. Examples include reporting quantitative AMU alongside indicators for frequency of use and route of administration in broiler flocks in Canada (Agunos et al., 2020), disease and treatment outcomes from treatment records on dairy farms in the US (Schrag et al., 2022), and age, disease, antibiotic class, and indication from prescription data for broiler farms in the US (Singer et al., 2023). Other research has demonstrated the potential of multi-variate factorial analyses to investigate the influence of contextual factors on AMU decision-making by farmers, showing that farm health and management characteristics can be associated with different AMU practices (Best et al., 2024). Such contextualised information necessarily implies the use of field-level prescription or sales data, which can provide added value compared to national level sales data in both human and animal health (Sanders et al., 2020). Many studies using these approaches have taken farmers as their starting point for analysis. However, it is also important to study AMU from the perspective of veterinarians, as veterinarians play an important role in the management of antibiotics and as guardians of public health in relation to AMR (Fortané, 2019). Studies have shown that veterinarians’ prescribing practices are influenced by empirical experience (Moya et al., 2024a) and are contingent on changes in clinical context (Moya et al., 2024b), suggesting the importance of studying prescription-level data in relation to veterinarians when investigating AMU practices.

The poultry sector has drawn particular attention in the context of AMU and AMR because it is one of the fastest-growing livestock

production sectors (Organisation for Economic Co-operation and Development and Food and Agriculture Organization of the United Nations, 2022), and one of the sectors with the highest estimated use of antimicrobials per kilogram of meat at a global scale (Van Boeckel et al., 2015). In many high income countries, significant reductions in AMU have been observed in the poultry sector in recent years following strategic plans to tackle AMR (Agunos et al., 2019; Huber et al., 2021; Singer et al., 2023; Anses, 2023; European Medicines Agency and European Surveillance of Veterinary Antimicrobial Consumption, 2023). The poultry sector is therefore of particular interest in understanding how antimicrobials are used and prescribed in practice when AMU reduction plans are in place.

We sought to understand how and why antimicrobials are prescribed by veterinarians in broiler production by interpreting AMU alongside additional contextual indicators. Our objective was to use antimicrobial prescription data from French veterinary clinics to analyse the general trend of AMU reduction in broiler chickens from veterinarians’ perspectives, and to subsequently characterise antimicrobial prescribing patterns in the context of associated clinical information.

## 2. Materials and methods

The dataset consisted of sales records of antibiotics, antiparasitics, disinfectants, feed supplements, farm visits, veterinary acts, and laboratory tests from a large network of veterinary practices in Western France, for four main producer organisations of conventional broiler chickens (each organisation accounting for between 300 and 800 individual producers). As antibiotics can only be sold upon prescription in France, sales of antibiotics were considered equivalent to prescriptions, with verification to ensure they were associated with a given flock and antibiotic for a given time period. In a first step, trends in antibiotic prescriptions for this population were described over the period 2013 to 2023 by examining the number of antibiotic prescriptions per antibiotic class per year, as well as the percentage of flocks treated. In a second step, data on antimicrobial prescriptions for the years 2021 to 2022 were combined with clinical data consisting of results from bacteriologies, necropsies, and antimicrobial sensitivity testing (AST).

For the descriptive analysis of overall trends, two types of information were analysed over the period 2013 to 2023 – the number of antibiotic prescriptions per year (by active ingredient), and the percentage of flocks in the dataset that were prescribed antibiotics per year.

Multidimensional analyses were then used to explore the antibiotic prescriptions in the context of clinical information (necropsies, bacteriology, and AST) using the FactoMineR (Husson et al., 2024) and fac-toextra packages (Kassambara and Mundt, 2020). A multiple correspondence analysis (MCA) was performed followed by a hierarchical clustering on principal components (HCPC) on the dataset of prescription events. Multiple correspondence analysis is a form of exploratory dimensional multivariate analysis that can be used to explore the relationship between categorical variables. It can be used to identify similarities between individuals on the basis of selected variables. In this study, an individual (the epidemiological unit of interest) was an antibiotic “prescription event” – a given antibiotic prescription associated with a given diagnostic result, or the absence of a diagnostic result. The reason for this approach was that a given antibiotic prescription could be associated with multiple diagnostic test results, due to multiple sampling events. The number of prescription events was therefore greater than the number of individual prescriptions. Apparent patterns of prescribing were established on the basis of variables related to the prescriptions themselves and whether clinical testing was performed or not (active variables). The results of clinical testing were then used to aid interpretation of the prescribing patterns observed (supplementary variables). The full list of variables used for the MCA is presented in Table 1.

The patterns of antibiotic prescribing identified in the MCA were confirmed and elaborated using the HCPC. An HCPC is a means of
<table><caption>**Table 1**
List of variables used in the multiple correspondence analysis.</caption><thead><tr><th>Variable type</th><th>Variable name</th><th>Variable description</th></tr></thead><tbody><tr><td>Active</td><td>Age at prescription</td><td>A categorical variable with seven age groups (chick arrival on farm, 0–1, 2–7, 8–14, 15–21, 22–28, and &gt;28 days of age).</td></tr><tr><td>Active</td><td>Active ingredient</td><td>The active ingredient of the prescribed antibiotic, identified from the summary of product characteristics for the registered product. Where two active ingredients were present, a composite name was generated.</td></tr><tr><td>Active</td><td>Necropsy/ bacteriology</td><td>Whether necropsy or bacteriology was performed (yes/no).</td></tr><tr><td>Active</td><td>AST<sup>a</sup></td><td>Whether AST was performed (yes/no).</td></tr><tr><td>Supplementary</td><td>Syndrome</td><td>The syndrome recorded in the necropsy dataset.</td></tr><tr><td>Supplementary</td><td>Lesion</td><td>The lesion recorded in the necropsy dataset.</td></tr><tr><td>Supplementary</td><td>Diagnosis</td><td>The diagnosis recorded in the necropsy dataset.</td></tr><tr><td>Supplementary</td><td>Pathogen</td><td>The main pathogen isolated with bacteriology or for AST (separate variables for each).</td></tr><tr><td>Supplementary</td><td>Farmer</td><td>The anonymised identification code for a given farm.</td></tr><tr><td>Supplementary</td><td>Flock</td><td>The anonymised identification code for a given flock.</td></tr></tbody></table>

constructing a hierarchical tree to visualise the variability and similarities between individuals (between antibiotic prescription events). Hierarchical clustering was performed using Ward's criterion and consolidated using the k-means method. The dendrogram (hierarchical tree) was cut based on the decrease of inertia and the identification of clusters which could be described in a coherent way. Different cut-points were trialled to determine the number of clusters which retained a sufficient level of detail, but allowed interpretation and explanation in light of the variables chosen. The clusters were characterised with regards to three values – the test statistic (v-test), “Cla/Mod”, and “Mod/Cla”. The test statistic (v-test) is a way of converting the p-value into a quantile of a normal distribution (Husson et al., 2011). It indicates the over- or under-representation of a category within a particular cluster – if the absolute value of the v-test is greater than 1.96, then the category

can be considered to characterise the cluster, either over-representing it (if the v-test is positive) or under-representing it (if the v-test is negative). “Cla/Mod” and “Mod/Cla” are ways of describing the frequency of a category in a cluster – across the whole dataset and across the cluster respectively. For example, if the variable was “active ingredient”, and the category “amoxicillin”, “Cla/Mod” would indicate the percentage of all amoxicillin prescription events in the given cluster, while “Mod/Cla” would indicate the percentage of prescription events within a given cluster which are for amoxicillin.

## 3. Results

### 3.1. Descriptive analysis of trends in antibiotic prescriptions from 2013 to 2023

The dataset consisted of 193,526 sales of antibiotics, antiparasitics, disinfectants, feed supplements, farm visits, veterinary acts, and laboratory tests for the period 2015 to 2023, for 33,831 flocks in 2120 farms with a median farm size of 21,230 chickens (interquartile range 12,000–27,000), from four producer organisations. It included 21,218 antibiotic prescriptions. The absolute number of antibiotic prescriptions decreased during the period 2013 to 2023, from 3016 antibiotic prescriptions in 2013 to 460 antibiotic prescriptions in 2022, before rising again to 758 antibiotic prescriptions in 2023 (Fig. 1).

In particular, the greatest decreases were observed for the active ingredients enrofloxacin, colistin, and tylosin. The number of enrofloxacin prescriptions decreased from 867 (28.8 % of all antibiotic prescriptions in 2013) to 66 (8.7 % of all antibiotic prescriptions in 2023), colistin prescriptions decreased from 315 (10.4 % of all antibiotic prescriptions in 2013) to 22 (2.9 % of all antibiotic prescriptions in 2023), and tylosin prescriptions decreased from 437 (14.5 % of all antibiotic prescriptions in 2013) to 15 prescriptions (2 % of all antibiotic prescriptions in 2023). The two active ingredients accounting for the highest proportion of prescriptions since 2021 were trimethoprim-sulfonamide and amoxicillin, although the absolute numbers of prescriptions for these antibiotics remained stable or decreased. By 2023, prescriptions for trimethoprim-sulphonamide (340 prescriptions in 2023) accounted for 44.9 % of all antibiotic prescriptions for that year, and amoxicillin (186 prescriptions in 2023) for 24.5 % of antibiotic

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Number of antibiotic prescriptions per antibiotic active ingredient per year (main active ingredients) and evolution of the proportion of flocks prescribed antibiotics from 2013 to 2023.<sup>a,b</sup></figcaption></figure>

<footnote>a AST = antimicrobial sensitivity testing</footnote>

<footnote>a 95 % confidence intervals for the proportion of flocks prescribed antibiotics are indicated in grey.</footnote>

<footnote>b Displaying the top twelve antibiotic ingredients (corresponding to 99.5 % of all antibiotic prescriptions for the 2013–2023 period).</footnote>
prescriptions for that year. The main exception to the overall reduction in antibiotic prescriptions was the rise in the use of lincomycin and spectinomycin in combination. This antibiotic combination was not used prior to 2015, but has since risen to 102 prescriptions, accounting for 13.5 % of all antibiotic prescriptions in 2023 (the third most prescribed antibiotic for 2023 after amoxicillin and trimethoprim-sulfonamide). The decrease in the absolute number of prescriptions was also reflected in a decrease in the proportion of flocks prescribed antibiotics, which dropped from 65 % in 2013 to 20 % in 2023, plateauing in 2020 where the proportion of flocks prescribed antibiotics oscillated between 13 % and 23 % (Fig. 1).

## 3.2. Characterisation of antibiotic prescriptions in the context of clinical information from 2021 to 2022

We used multidimensional analyses to explore antibiotic prescriptions in relation to contextual clinical information (necropsies, bacteriologies, and AST) for 1112 antibiotic prescriptions by 32 veterinarians for 956 flocks in 325 farms in the years 2021 to 2022, corresponding to 1940 antibiotic prescription events. The three most common syndromes were early mortality, locomotor, and respiratory syndromes. The three most common lesions were airsacculitis/pericarditis, arthritis, and hepato/splenomegaly. The three most common diagnoses were colibacillosis, bacterial arthritis, and omphalitis. The most common pathogens isolated on both bacteriology and AST were *Escherichia coli* and *Enterococcus cecorum*. The first dimension of the MCA was mainly influenced by variables related to whether diagnostic testing was performed or not ("necropsy/bacteriology" and "AST", both with 0.78 contribution), while the second dimension was mainly influenced by the variables "age at prescription" and "active ingredient" (both with 0.89 contribution). In the subsequent HCPC, the dendrogram (hierarchical tree) was cut to establish five clusters (Fig. 2). These five clusters of antibiotic prescriptions were analysed in relation to the first two

dimensions of the MCA (Fig. 3). As a result, some of the clusters overlap (most notably Clusters 3 and 4) where they have similarity on the first two dimensions (specifically, presence/absence of diagnostic testing and age at time of prescription). These overlapping clusters are nonetheless statistically distinct, but the variables that make them different from each other are not visible in a representation of the first two dimensions.

The number and proportion of antibiotic prescription events in each of the five clusters by active ingredient are presented in Table 2. The main characteristics of these clusters are described below, alongside a summary of the categories with the highest v-test values for each cluster, and the associated values for Cla/Mod and Mod/Cla in Tables 3, 4, 5, 6, and 7.

<figure><img src="image_5.png" /><figcaption>Fig. 3. Summary of clusters of antibiotic prescription events from the hierarchical clustering on principal components (HCPC) analysis.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 2. Dendrogram from the hierarchical clustering analysis with indication of the cut-point used to establish five clusters and the diagram demonstrating the incremental gain in inertia associated with each clustering (labelled "inertia gain").</figcaption></figure>
<caption>**Table 2**</caption>

Summary of clusters of antibiotic prescription events from the hierarchical clustering on principal components (HCPC) analysis.

<table><caption>**Table 3**</caption><thead><tr><th>Antibiotic active ingredient</th><th>Cluster 1</th><th>Cluster 2</th><th>Cluster 3</th><th>Cluster 4</th><th>Cluster 5</th><th>Total</th></tr></thead><tbody><tr><td>Amoxicillin</td><td>0 (0 %)</td><td>284 (14.6 %)</td><td>25 (1.3 %)</td><td>252 (13 %)</td><td>0 (0 %)</td><td>561 (28.9 %)</td></tr><tr><td>Ampicillin</td><td>0 (0 %)</td><td>0 (0 %)</td><td>0 (0 %)</td><td>33 (1.7 %)</td><td>0 (0 %)</td><td>33 (1.7 %)</td></tr><tr><td>Colistin</td><td>0 (0 %)</td><td>0 (0 %)</td><td>73 (3.8 %)</td><td>0 (0 %)</td><td>4 (0.2 %)</td><td>77 (4 %)</td></tr><tr><td>Doxycycline</td><td>0 (0 %)</td><td>21 (1.1 %)</td><td>1 (0.1 %)</td><td>6 (0.3 %)</td><td>0 (0 %)</td><td>28 (1.4 %)</td></tr><tr><td>Enrofloxacin</td><td>122 (6.3 %)</td><td>0 (0 %)</td><td>0 (0 %)</td><td>7 (0.4 %)</td><td>1 (0.1 %)</td><td>130 (6.7 %)</td></tr><tr><td>Florfenicol</td><td>0 (0 %)</td><td>0 (0 %)</td><td>0 (0 %)</td><td>0 (0 %)</td><td>2 (0.1 %)</td><td>2 (0.1 %)</td></tr><tr><td>Lincomycin</td><td>0 (0 %)</td><td>0 (0 %)</td><td>0 (0 %)</td><td>12 (0.6 %)</td><td>0 (0 %)</td><td>12 (0.6 %)</td></tr><tr><td>Lincomycin + spectinomycin</td><td>0 (0 %)</td><td>0 (0 %)</td><td>0 (0 %)</td><td>0 (0 %)</td><td>37 (1.9 %)</td><td>37 (1.9 %)</td></tr><tr><td>Oxolinic acid</td><td>5 (0.3 %)</td><td>0 (0 %)</td><td>0 (0 %)</td><td>0 (0 %)</td><td>0 (0 %)</td><td>5 (0.3 %)</td></tr><tr><td>Oxytetracycline</td><td>0 (0 %)</td><td>0 (0 %)</td><td>0 (0 %)</td><td>27 (1.4 %)</td><td>0 (0 %)</td><td>27 (1.4 %)</td></tr><tr><td>Phenoxymethylpenicillin</td><td>0 (0 %)</td><td>0 (0 %)</td><td>0 (0 %)</td><td>6 (0.3 %)</td><td>0 (0 %)</td><td>6 (0.3 %)</td></tr><tr><td>Sulfadiazine + trimethoprim</td><td>430 (22.2 %)</td><td>0 (0 %)</td><td>1 (0.1 %)</td><td>5 (0.3 %)</td><td>3 (0.2 %)</td><td>439 (22.6 %)</td></tr><tr><td>Sulfadimethoxine + trimethoprim</td><td>251 (12.9 %)</td><td>185 (9.5 %)</td><td>3 (0.2 %)</td><td>117 (6 %)</td><td>3 (0.2 %)</td><td>559 (28.8 %)</td></tr><tr><td>Tylosin</td><td>0 (0 %)</td><td>0 (0 %)</td><td>2 (0.1 %)</td><td>20 (1 %)</td><td>2 (0.1 %)</td><td>24 (1.2 %)</td></tr><tr><td>Total</td><td>808 (41.6 %)</td><td>490 (25.3 %)</td><td>105 (5.4 %)</td><td>485 (25 %)</td><td>52 (2.7 %)</td><td>1940 (100 %)</td></tr></tbody></table>

Variable categories for cluster 1 with a v-test greater than 10, ranked by v-test.

<table><thead><tr><th>Variable</th><th>Category</th><th>Cla/Mod<sup>a</sup></th><th>Mod/Cla<sup>b</sup></th><th>v.test</th></tr></thead><tbody><tr><td>Age prescription</td><td>02-07 days</td><td>96.6</td><td>85.1</td><td>Inf</td></tr><tr><td>Active ingredient</td><td>Sulfadiazine + trimethoprim</td><td>97.9</td><td>53.2</td><td>29.1</td></tr><tr><td>Syndrome (necropsy)</td><td>Early mortality</td><td>96.9</td><td>46.2</td><td>26.1</td></tr><tr><td>Lesion (necropsy)</td><td>Hepato/splenomegaly</td><td>99.4</td><td>21.4</td><td>17.6</td></tr><tr><td>Diagnosis (necropsy)</td><td>Colibacillosis</td><td>73.6</td><td>33.4</td><td>13.8</td></tr><tr><td>Active ingredient</td><td>Enrofloxacin</td><td>93.8</td><td>15.1</td><td>13.1</td></tr><tr><td>Lesion (necropsy)</td><td>Amphalitis</td><td>95.6</td><td>13.4</td><td>12.6</td></tr><tr><td>Pathogen (AST)</td><td><i>Escherichia coli</i></td><td>58.3</td><td>52.1</td><td>11.4</td></tr><tr><td>Diagnosis (necropsy)</td><td>Amphalitis</td><td>94.0</td><td>9.8</td><td>10.4</td></tr></tbody></table>

<sup>a</sup> Cla/Mod = The percentage of prescriptions in the dataset with the category of interest in the cluster.

<sup>b</sup> Mod/Cla = The percentage of prescriptions in the cluster with the category of interest.

<caption>**Table 4**</caption>

Variable categories for cluster 2 with a v-test greater than 10, ranked by v-test.

<table><thead><tr><th>Variable</th><th>Category</th><th>Cla/Mod<sup>a</sup></th><th>Mod/Cla<sup>b</sup></th><th>v.test</th></tr></thead><tbody><tr><td>Necropsy/bacteriology</td><td>Yes (necropsy or bacteriology performed)</td><td>44.3</td><td>100.0</td><td>25.8</td></tr><tr><td>AST</td><td>Yes (AST performed)</td><td>46.7</td><td>96.5</td><td>24.9</td></tr><tr><td>Age prescription</td><td>15-21 days</td><td>65.6</td><td>73.3</td><td>24.9</td></tr><tr><td>Pathogen (bacteriology)</td><td><i>Enterococcus cecorum</i></td><td>80.1</td><td>51.0</td><td>22.8</td></tr><tr><td>Pathogen (AST)</td><td><i>Enterococcus cecorum</i></td><td>84.1</td><td>39.8</td><td>20.4</td></tr><tr><td>Syndrome (necropsy)</td><td>Locomotor</td><td>78.9</td><td>43.5</td><td>20.3</td></tr><tr><td>Lesion (necropsy)</td><td>Arthritis</td><td>85.9</td><td>32.2</td><td>18.4</td></tr><tr><td>Active ingredient</td><td>Amoxicillin</td><td>50.6</td><td>58.0</td><td>15.9</td></tr><tr><td>Lesion (necropsy)</td><td>Airsacculitis/pericarditis</td><td>62.7</td><td>34.3</td><td>14.1</td></tr><tr><td>Syndrome (necropsy)</td><td>Respiratory</td><td>61.2</td><td>32.2</td><td>13.3</td></tr></tbody></table>

<sup>a</sup> Cla/Mod = The percentage of prescriptions in the dataset with the category of interest in the cluster.

<sup>b</sup> Mod/Cla = The percentage of prescriptions in the cluster with the category of interest.

### 3.2.1. Cluster 1 "Early mortality", 808 (41.6 %) antibiotic prescription events

Cluster 1 (Table 3) was characterised by antibiotic prescriptions at 2-7 days of age, mostly with trimethoprim-sulfonamide combinations. From diagnostic test results, early mortality and hepato/splenomegaly were most characteristic (over 95 % of prescription events in these categories were found in this cluster), and to a lesser extent, colibacillosis and omphalitis. The pathogens most commonly identified

were *Escherichia coli* and to a lesser extent *Enterococcus faecalis* (not shown). The majority of the prescription events in this cluster (53.2 %) were for sulfadiazine and trimethoprim, followed by a lower number of prescriptions for enrofloxacin (15.1 %) – almost all of the prescription events associated with these two antibiotics across the 2021 to 2022 dataset belonged in this cluster (97.9 % and 93.8 % respectively).

### 3.2.2. Cluster 2 "Mid-production locomotor problems", 490 (25.3 %) antibiotic prescription events

Cluster 2 (Table 4) was characterised by prescriptions at 15-21 days of age (65.6 % of prescription events for this age group were found in this cluster), locomotor syndromes (78.9 % of prescription events for this syndrome were found in this cluster), and arthritic lesions (85.9 % of prescription events for these lesions were found in this cluster), as well as identification of *Enterococcus cecorum* on bacteriology and on AST (over 80 % of identifications of this bacterium by either method across the dataset were in this cluster). All prescription events in this cluster (100 %) had necropsy or bacteriology performed, and almost all

<caption>**Table 5**</caption>

Variable categories for cluster 3 with a v-test greater than 10, ranked by v-test.

<table><thead><tr><th>Variable</th><th>Category</th><th>Cla/Mod<sup>a</sup></th><th>Mod/Cla<sup>b</sup></th><th>v.test</th></tr></thead><tbody><tr><td>Active ingredient</td><td>Colistin</td><td>94.8</td><td>69.5</td><td>21.4</td></tr><tr><td>Age prescription</td><td>> 28 days</td><td>98.1</td><td>50.5</td><td>18.0</td></tr></tbody></table>

<sup>a</sup> Cla/Mod = The percentage of prescription events in the dataset with the category of interest in the cluster.

<sup>b</sup> Mod/Cla = The percentage of prescription events in the cluster with the category of interest.
<table><caption>**Table 6**
Variable categories for cluster 4 with a v-test greater than 10, ranked by v-test.</caption><thead><tr><th>Variable</th><th>Category<sup>a</sup></th><th>Cla/Mod<sup>b</sup></th><th>Mod/Cla<sup>c</sup></th><th>v.test</th></tr></thead><tbody><tr><td>Pathogen (AST)</td><td>NR (AST not performed)</td><td>49.3</td><td>94.2</td><td>25.3</td></tr><tr><td>AST</td><td>No (AST not performed)</td><td>49.3</td><td>94.2</td><td>25.3</td></tr><tr><td>Necropsy/bacteriology</td><td>No (necropsy or bacteriology not performed)</td><td>48.7</td><td>83.9</td><td>21.4</td></tr><tr><td>Syndrome (necropsy)</td><td>NR (necropsy not performed)</td><td>48.7</td><td>83.9</td><td>21.4</td></tr><tr><td>Pathogen (bacteriology)</td><td>NR (bacteriology not performed)</td><td>48.4</td><td>82.7</td><td>20.9</td></tr><tr><td>Lesion (necropsy)</td><td>NR (necropsy not performed)</td><td>46.6</td><td>85.4</td><td>20.8</td></tr><tr><td>Diagnosis (necropsy)</td><td>NR (necropsy not performed)</td><td>40.8</td><td>87.4</td><td>18.2</td></tr><tr><td>Age prescription</td><td>22–28 days</td><td>66.8</td><td>43.9</td><td>17.6</td></tr><tr><td>Active ingredient</td><td>Amoxicillin</td><td>44.9</td><td>52.0</td><td>12.5</td></tr></tbody></table>

<table><caption>**Table 7**
Variable categories for cluster 5 with a v-test greater than 10, ranked by v-test.</caption><thead><tr><th>Variable</th><th>Category</th><th>Cla/Mod<sup>a</sup></th><th>Mod/Cla<sup>b</sup></th><th>v.test</th></tr></thead><tbody><tr><td>Active ingredient</td><td>Lincomycin + spectinomycin</td><td>100.0</td><td>71.2</td><td>17.2</td></tr><tr><td>Age prescription</td><td>Chick arrival on farm</td><td>100.0</td><td>69.2</td><td>16.9</td></tr></tbody></table>

had AST results (96.5 %). This related to almost 50 % of all antibiotic prescription events for 2021 to 2022 where necropsy or bacteriology, or AST was performed. The majority of prescription events in this cluster were for amoxicillin (58.0 %).

### 3.2.3. Cluster 3 “Late-age disease”, 105 (5.4 %) antibiotic prescription events

Cluster 3 (Table 5) was most strongly characterised by prescriptions for late production broilers (>28 days) with colistin, with almost 100 % of all prescription events relating to each of these categories present in the cluster. It was less well-characterised by other categories. Where diagnostic testing was performed, the most over-represented diagnosis was cellulitis (100 % of cellulitis diagnoses were in this cluster, not shown), although the most frequent diagnosis in this cluster was infection with *Enterococcus* spp. (15.2 %, not shown). However, many of the prescription events in this cluster were not associated with diagnostic testing. Cluster 3 overlaps with Cluster 4 in Fig. 2 (the representation of the clusters across the first two dimensions), but these two clusters remain statistically distinct from each other due to differences in other variables such as age at treatment.

### 3.2.4. Cluster 4 “No diagnostic tests”, 485 (25 %) of antibiotic prescription events

Cluster 4 (Table 6) was overwhelmingly characterised by prescribing without laboratory testing being performed, with 83.9 % of prescription events in this cluster not being associated with either necropsy or bacteriology results, and 94.2 % not associated with AST. Three age groups of 8–14, 15–21, and 22–28 days were overrepresented in this cluster, suggesting that this cluster regroups prescription events across these different age groups that were not associated with diagnostic test results. Most of the prescription events in this cluster were for amoxicillin (52.0 %), although the cluster was also characterised by prescribing with ampicillin, oxytetracycline, tylosin, and lincomycin (not shown).

### 3.2.5. Cluster 5 “Treatment at chick arrival”, 52 (2.7 %) antibiotic prescription events

Cluster 5 (Table 7) was highly characterised by prescriptions upon chick arrival on the farm in the absence of diagnostic testing. All prescription events of lincomycin and spectinomycin combinations and florfenicol (not shown) in the 2021 to 2022 dataset belonged to this cluster, with the vast majority (71.2 %) being for lincomycin and

spectinomycin combinations. This cluster was associated to a lesser extent with an absence of laboratory testing for necropsies, bacteriologies, and AST (not shown in Table 7 as the v-test for these categories was lower than 10).

## 4. Discussion

Our findings demonstrate that in our study population of broilers in Western France, AMU has decreased over the last 10 years, both in terms of absolute numbers of antibiotic prescriptions (from 3016 in 2013 to 758 in 2023) and in the proportion of flocks prescribed antibiotics (from 65 % in 2013 to 20 % in 2023) (Fig. 1). This is consistent with the findings of national AMU monitoring in France (Anses, 2023), and with broader trends in AMU across Europe (European Medicines Agency and European Surveillance of Veterinary Antimicrobial Consumption, 2023).

### 4.1. Contextualising changes in AMU within the landscape of AMU initiatives

The trends we observed in overall antibiotic prescriptions as well as the classes of antibiotics prescribed can be contextualised in relation to the landscape of AMU initiatives across France and Europe more broadly. In France, these results can be understood in the context of the Ecoantibio Plans (a combination of regulation, research actions, and veterinary-led initiatives, implemented in 2012 to 2016 and 2017 to 2022, and continuing in 2023 to 2028), as well as the 2013 Law on the future of agriculture, food and forestry (LAAAF). The first Ecoantibio Plan set a target to reduce animal exposure to antibiotics by 25 % in five years from 2011 to 2016 (Ministère de l'Agriculture, de l'Agroalimentaire et de la Forêt, 2016a), which was exceeded with a 37 % reduction by 2016 and a 52 % reduction by 2022 for all livestock species (Anses, 2023). The LAAAF set a target for reducing animal exposure to critically important antimicrobials (defined in French law as certain 3rd and 4th generation cephalosporins and fluoroquinolones) by 25 % in three years from 2013 to 2016 (Ministère de l'Agriculture et de l'Agroalimentaire et de la Forêt, 2017). This target was exceeded with a 81 % reduction in fluoroquinolones and 75 % reduction in 3rd and 4th generation cephalosporins by 2016 (Anses, 2023). Similar trends can be observed at a European level, with antibiotic sales across 25 European countries having declined by 53 % from 161.2 mg/population correction unit (PCU) in 2011 to 75.8 mg/PCU in 2022 (European Medicines Agency

<footnote><sup>a</sup> NR = Not relevant. Testing was not performed for the associated prescription.</footnote>

<footnote><sup>b</sup> Cla/Mod = The percentage of prescription events in the dataset with the category of interest in the cluster.</footnote>

<footnote><sup>c</sup> Mod/Cla = The percentage of prescription events in the cluster with the category of interest.</footnote>

<footnote><sup>a</sup> Cla/Mod = The percentage of prescription events in the dataset with the category of interest in the cluster.</footnote>

<footnote><sup>b</sup> Mod/Cla = The percentage of prescription events in the cluster with the category of interest.</footnote>
## and European Surveillance of Veterinary Antimicrobial Consumption, 2023).

We observed an overall decrease in the quantities of antibiotics prescribed over the last decade in French broilers, as well as a shift in the types of antibiotics prescribed (from antibiotics designated as high risk for AMR development, to those considered comparatively low risk). Since 2018, the majority of antibiotic prescriptions were for amoxicillin and trimethoprim-sulfonamide (rising to 24.5% and 44.8% respectively of all antibiotic prescriptions in 2023). These antibiotics are designated as having the lowest risk for AMR development (category D, “prudence” of the EMA). This change was accompanied by a reduction in prescriptions of antibiotics in more restricted categories, most notably enrofloxacin and colistin, both in absolute numbers and as a proportion of all prescriptions. Enrofloxacin was widely used for the treatment of avian colibacillosis (Temmerman et al., 2021), but its use in veterinary medicine has been increasingly restricted, including bans on its use for chickens in the United States (Center for Veterinary Medicine, 2017), and categorisation as “B-restrict” in Europe (European Medicines Agency, 2019). In France, enrofloxacin is considered a “critically important antibiotic”, with legislation prohibiting its use for preventive purposes and requiring AST before its prescription (Ministère de l'Agriculture, de l'Agroalimentaire et de la Forêt, 2016b). Our temporal analysis showed a decrease in the use of enrofloxacin in broilers over the last 10 years, from 28.7% of all antibiotic prescriptions in 2013 to 8.7% of all antibiotic prescriptions in 2023. Combining AMU data with clinical information, we found enrofloxacin prescription events often corresponded to a specific clinical situation (Cluster 1, early mortality caused by *Escherichia coli*). Colistin is another antibiotic which has been subject to much debate over the last decade. It has been recast as an antimicrobial of last resort following concerns over rising resistance rates in humans (Catry et al., 2015), leading to additional restrictions on its use in veterinary medicine based on advice or recommendations issued by the EMA (Jansen et al., 2022), and its categorisation as a Category B “Restrict” antibiotic (European Medicines Agency, 2019). In France, a target of a 50% decrease in colistin exposure for cattle, pigs, and poultry in five years (by 2020) was set in the second Ecoantibio Plan (Ministère de l'Agriculture, de l'Agroalimentaire et de la Forêt, 2017). Our analysis revealed a decrease in colistin prescriptions from 10.4% of all antibiotic prescriptions in 2013 to 2.9% of all antibiotic prescriptions in 2023. Our multidimensional analysis demonstrated that in 2021 to 2022, colistin prescription events were largely grouped in Cluster 3, related to treatment at a late age (>28 days). Given that most conventional broilers will be slaughtered for consumption at 35 days of age, the use of colistin in this clinical context is likely due to it being one of the few antibiotic agents available in France with a short (one day) withholding period (Anses, 2024b). Overall, the results demonstrate a reduction in the prescription of critical antibiotics (colistin and enrofloxacin), in line with findings from national sales data showing that the Ecoantibio Plan targets for colistin and enrofloxacin were met (with a reduction of 72.5% in colistin exposure for poultry from 2014 to 2022) (Anses, 2023). This supports the idea that France's strategy to reduce AMU through a combination of regulation and voluntary approaches designed in collaboration with veterinarians and industry has been broadly effective.

However, the reduction in AMU in our study population began to plateau in 2020 (oscillating between 13% and 23% of flocks treated). This mirrors recent reports on AMU in Europe showing that after over a decade of consistent decreases in AMU, the rate of reduction is slowing or even ceasing altogether (European Medicines Agency and European Surveillance of Veterinary Antimicrobial Consumption, 2023). Similarly, the national surveillance report of AMU in France for 2023 found animal exposure to antibiotics increased by 6.5% from 2022 to 2023, although it remained below 2021 levels (Anses, 2024a). These findings should be interpreted in the context of significant and consistent reductions in recent years, and suggest that we may have reached a point beyond which further reductions will become increasingly challenging.

## 4.2. Using clinical data to identify potential avenues for further AMU strategies

By combining AMU data with clinical data, we identified two main clinical syndromes around which the majority of antibiotic prescription events in 2021 to 2022 were clustered (Clusters 1 and 2, collectively 67% of antibiotic prescription events). The first syndrome related to early chick mortality, which has long been known as one of the main challenges for broiler production (Heier et al., 2002; Yassin et al., 2009). Cluster 1 (41.6% of all antibiotic prescription events) was characterised by early mortality, the 2–7 days old age group, and diagnoses of *Escherichia coli* and *Enterococcus faecalis* infections. Treatment was mostly with sulfonamide-trimethoprim combinations or enrofloxacin. Early mortality in chickens is a complex multifactorial process influenced by a range of factors which operate throughout different production stages, including hatchery, chick transport, and on-farm management (de Jong and van Riel, 2020). This suggests that strategies seeking to further reduce AMU in broilers would need to include more holistic approaches to mitigate disease risk throughout the production chain. The second clinical syndrome that we identified as a reason for prescribing antibiotics was locomotor problems associated with *Enterococcus* infections (mostly *Enterococcus cecorum*) at mid-production (15–21 days of age) (Cluster 2, 25.3% of antibiotic prescription events, mostly for amoxicillin). This cluster, and the overall increase in amoxicillin use seen over the 2013 to 2023 period, can be viewed in the context of the increasing prevalence of *Enterococcus cecorum* infections in broilers in France over the last 15 years (Souillard et al., 2022), and as a worldwide cause of lameness in fast-growing broilers (Jung et al., 2018). The control of *Enterococcus cecorum* infections remains challenging due to the multifactorial nature of their development, with predisposing factors being related to host genetics, rapid growth, feed composition, husbandry procedures, and animal density, in combination with the bacterium's pathogenic potential (Laurentie et al., 2023). In the absence of available vaccines, antibiotics may be used as soon as *Enterococcus cecorum* infection is diagnosed to prevent further spread, as they may be ineffective at a later stage of disease. Further research addressing risk factors implicated in the pathogenesis of *Enterococcus cecorum* or environmental factors that may predispose farms to repeated infections could therefore help reduce the need for antibiotics in the broiler sector. For both early mortality and locomotor problems, further reductions in AMU would necessitate paying attention to the underlying factors driving these clinical syndromes, and focusing on upstream management solutions in collaboration with field actors.

The cluster analysis also made it possible to identify two situations which could be potential targets for further AMU interventions. The first was lincomycin-spectinomycin combinations prescribed upon chick arrival on farm (Cluster 5). In 2014, French authorities identified antibiotic use at chick arrival as a high-risk practice for AMR emergence, recommending its cessation as soon as possible (Anses, 2014). Our results suggest that in the years 2021 to 2022, prescriptions corresponding to this situation were residual (Cluster 5, 2.68% of antibiotic prescription events). Although made upon chick arrival, prescription of antibiotics at this point in production may be indirectly related to clinical problems such as early mortality and locomotor problems (identified in Clusters 1 and 3), given that lincomycin-spectinomycin combinations have been reported to be used as treatment for *E. cecorum* at an early age in other European countries (Schreier et al., 2022). Informal discussions with field veterinarians suggested that they were prescribing at this early stage as a means of avoiding the need for antibiotics to treat *Enterococcus* spp. infections at a later production stage (which would require higher quantities of antibiotics), a sentiment that has been reported elsewhere in the literature (Hinchliffe et al., 2024). This supports the idea that antibiotic prescriptions of this kind are framed by veterinarians as a means of enabling responsible antibiotic use in the longer term (Moya et al., 2024b). In this case, the decision to prescribe is likely based on what veterinarians know about individual farm history
(whether the farm has a history of recurrent *E. cecorum* infections), and what is known about the infection pressure in the area. We observed that one cluster (Cluster 4, 25 % of antibiotic prescription events) corresponded to prescription events made in the absence of diagnostic tests (necropsy, bacteriology, or AST). It was overrepresented by prescription events mainly for amoxicillin, and three different age groups across 8–28 days of age, in particular 22–28 days of age. However, as the defining characteristic of this cluster was the absence of diagnostic information, our analyses allowed us to identify this cluster but not to explain it fully. The information needed to understand this cluster was not contained within our dataset, and so inferences on the reasons for antibiotic prescriptions in this cluster can only be made on the basis of other information sources, such as knowledge of the regulatory framework around AMU in France, or alternative forms of clinical information. In France, antibiotics can be prescribed by veterinarians without clinical testing only if within the remit of an annual farm health plan, drafted in advance by the farm in collaboration with their veterinarian, and if certain conditions are met (Briand et al., 2015). These prescriptions are likely being made in this context, and could have been related to disease syndromes diagnosed through clinical or faecal examination rather than laboratory testing (such as enteritis, which can present across the 8–28 day age range) (Swayne et al., 2019). Information other than laboratory testing (from routine farm visits or clinical observations) was not included in our dataset – however, it could be possible to collect such information in close collaboration with veterinarians in the field, which could help to understand AMU in this cluster. This would allow a greater understanding of the reasons for which antibiotics continue to be prescribed in these situations (in the absence of diagnostic tests or upon chick arrival on farm) and how they could best be addressed. Acts of reflection by veterinarians on their rationale for prescribing antibiotics are not captured in quantitative data and yet are crucial in interpreting and understanding AMU – suggesting the importance of close collaboration with farmers and veterinarians when developing and implementing AMU interventions. This type of information can also be collected through qualitative research methods – there is a large body of research showing that decision-making around AMU by veterinarians and other actors is influenced by a range of contextual factors, and which places value on the experiential and local knowledge of field actors (Butcher et al., 2021; Moya et al., 2024b; Rees et al., 2021; Skjølstrup et al., 2022; Speksnijder et al., 2015). Such information is often overlooked in studies focusing only on analysing quantities of antibiotics used or prescribed. This suggests the value of working with different types of information and different methodological approaches to give a broader perspective and understanding of AMU. For example, our findings in France function as a complement to recent qualitative research in the French poultry sector, which suggest that French poultry farmers are in a period of adaptation in their AMU practices in response to specific production settings and changes in regulation (Adam et al., 2020, 2017; Bourély et al., 2018; Moya et al., 2024a).

### 4.3. Limitations

This study had a number of limitations. Firstly, the definition of our epidemiological unit for the multidimensional analyses was challenging due to the need to combine multiple individual datasets on prescriptions, necropsies, bacteriologies, and AST. As many individual antibiotic prescriptions had multiple diagnostic test results associated with them, some individual prescriptions were linked to multiple lines of data – typically when multiple bacteria were isolated or where multiple lesions or diagnoses were identified on necropsy. This led to our epidemiological unit being “prescription events”. However, running the multidimensional analyses with a simpler epidemiological unit would have meant excluding relevant diagnostic information. The effect of our choice to include multiple results per prescription would have been to increase the magnitude of certain clusters (most notably Cluster 2, associated with locomotor problems, for which multiple samples were

often taken), but not their existence. This challenge speaks to the nature of databases emanating from antimicrobial prescribers, which are of great value in including information on the clinical context, but are also accompanied by a greater degree of methodological complexity. The complexity of the dataset is also suggestive of the added value of collaborating directly with veterinarians when working with field-level data to aid interpretation. In addition, by focusing on antibiotic prescriptions, we were not able to analyse where diagnostic testing was performed and antibiotics were *not* prescribed. It would be important in the future to analyse diagnostic test results not associated with a prescription to understand the clinical situations where a choice was made to not prescribe antibiotics, and to examine what alternatives (if any) were used – once again, in consultation with veterinarians. Secondly, the use of sales data from veterinary clinics to inform our population at risk meant that there was some uncertainty over this population, as some farms may not have been a regular client of the veterinary clinic. However, we attempted to mitigate this risk by restricting our dataset to the four largest producer organisations, which were known to have long-term relationships with these veterinary clinics. This meant that we excluded smaller producer organisations as well as independent farmers, which could have influenced our results – however, the majority of farms were included, and as the remainder may or may not have been regular clients, their inclusion could have led to greater uncertainty concerning the total population at risk of antibiotic exposure. This exclusion likely did not impact our overall conclusions, as after running an analysis of the general trends for all producer organisations and independent farmers together, we observed that the quantities of antibiotics used, proportion of flocks treated, and changes in the types of antibiotics used over the 2013 to 2023 period remained similar. Further investigation of the trajectories of AMU changes at the level of individual farms (across all organisations) could be a means of further elucidating if AMU practices differ at a more local level between different individual producers, or to understand the impact of different farm management practices on AMU. Thirdly, context was only considered with regards to clinical test results from necropsies, bacteriology, and AST. As a result, Cluster 4 is particularly challenging to interpret in the absence of further information. This underlines the importance of accessing additional data from the field, not only from the farm or veterinary clinic but also from the hatchery or slaughterhouse, as poultry health indicators could be captured from different stages the production cycle. Finally, there are also potentially broader contextual factors that influenced AMU over the study period that were not accounted for. For example, the recent epidemics of highly pathogenic avian influenza in France which led to significant culling of the poultry population (Lambert et al., 2022) could have led to a decrease in the number of antibiotic prescriptions due to the reduced population. However, as the decrease in prescriptions was mirrored by a decrease in the proportion of flocks treated, this does seem to be indicative of a real shift in antibiotic prescribing practices over time.

### 4.4. Perspectives

Overall, our results demonstrate that there have been significant reductions in antibiotic prescriptions in broilers in Western France following strategies targeting AMU, but that we are approaching a point where sustaining reductions at the same rate may be challenging. This may suggest the need for adaptations and changes to systems of production. Intensive broiler production in many countries has characteristics which tend towards an increased use of antibiotics such as shorter production cycles (approximately 30 days), high feed efficiency, fast growth rates, and high stocking densities which have been associated with increased AMU, while higher flock sizes make it impractical to treat animals on an individual basis, necessitating group treatment (Hinchliffe et al., 2024; Slegers et al., 2024). If further reductions in AMU are to be achieved, there may need to be a change in system-wide factors which drive the need for AMU.
Our findings also highlight the importance of using contextual in-
formation when interpreting AMU data. They demonstrate the utility of
using clinical indicators from necropsy, bacteriology, and AST results in
combination with traditional AMU indicators to characterise antibiotic
prescribing patterns. They also suggest the need for further information
on poultry health and welfare throughout the production cycle, as well
as supplementary information from veterinarians on their decision-
making process when prescribing antibiotics. This supports calls for
more holistic AMU metrics, such as multi-indicator metrics that consider
AMU quantities alongside indicators for animal health and welfare
(Ducrot et al., 2022). Integrating different types of information into
AMU surveillance could come as part of a broader strategy to develop
more evidence-based and locally relevant approaches to antimicrobial
stewardship. For example, the two clusters which we identified as
lacking in diagnostic information but potentially linked to clinical syn-
dromes (Clusters 4 and 5) suggest that collecting more refined infor-
mation on health, welfare and presumptive diagnoses (even in the
absence of clinical testing) could help enrich AMU surveillance at farm
level. Such information could be used alongside collaboration with field
stakeholders (farmers, veterinarians) upstream to develop more con-
textualised and targeted strategies to mitigate the risk of animal health
problems and thereby the need for antibiotics from arising in the first
place. This approach would move beyond questions of "how much" to
considerations of "why", "when", and "under what circumstances",
considering what would be appropriate AMU for a given clinical context,
as part of a more sustainable approach to improving antimicrobial
stewardship.

CRediT authorship contribution statement

Jean-Pierre Vaillancourt: Writing – review & editing, Validation.
Céline Faverjon: Writing – review & editing, Supervision, Methodol-
ogy, Conceptualization. Matthieu Pinson: Writing – review & editing,
Validation, Resources, Methodology, Investigation, Data curation,
Conceptualization. Mattias Delmont: Writing – review & editing, Vali-
dation, Methodology, Conceptualization. Rebecca Hibbard: Writing –
review & editing, Writing – original draft, Visualization, Methodology,
Investigation, Formal analysis, Data curation, Conceptualization. Lisa
Fourtune: Writing – review & editing, Visualization, Validation,
Methodology, Investigation, Formal analysis, Data curation, Conceptu-
alization. Mathilde C. Paul: Writing – review & editing, Validation,
Supervision, Project administration, Methodology, Investigation,
Funding acquisition, Conceptualization.

Declaration of Competing Interest

The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.

Acknowledgements

The authors would like to thank the veterinarians who contributed to this research. This work was conducted within the framework of the MOSBA project, as part of the third Ecoantibio Plan (2023–2028), co-ordinated by the Ministry of Agriculture, Agro-Food and Forestry.

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