Introduction

Knowledge of antibiotic use is necessary for containing of antimicrobial resistance. In 1998, the EU invitational conference, "The Microbial Threat", issued recommendations on how to combat the increasing threat of developing antimicrobial resistance [1–3]. In 2000, the WHO emphasized the key role of antibiotic and resistance monitoring system data in controlling antimicrobial resistance in their "Global Principles for the Containment of Antimicrobial Resistance in Animals intended for Food" [4]. Recently, the European Medicines Agency (EMA) and the European Food Safety Authority (EFSA) stressed that collecting antimicrobial resistance and consumption data is key to establishing effective measures to control antimicrobial resistance [5]. The World Health Organization (WHO), the Food and Agriculture Organization of the United Nations (FAO), and the World Organization for Animal Health (OIE) presented a global action plan on antimicrobial resistance considering different integrative measures [6–8].

In recent years, various systems have been developed worldwide to map the quantity and frequency of antibiotic use in veterinary medicine. In Germany, one of these monitoring systems was developed within the VetCAb (Veterinary Consumption of Antibiotics) project, which was the first project to publish representative data on antibiotic use in German livestock husbandry. In 2007 and 2008, the VetCAb feasibility study was conducted to determine whether representative monitoring of antibiotic consumption in Germany is feasible, which data can be used as a basis and how the data collection can be organized. This study’s results were published by Merle et al. [9, 10]. The VetCAb pilot study followed, in which representative data on antibiotic use were collected throughout Germany for 2011 [11]. Thereafter, the VetCAb project was refunded, and the data have been collected from 2013 to present as the longitudinal VetCAb sentinel study.

Currently, the results from various monitoring systems show substantially reduced antibiotic use in German livestock husbandry [12–15], as well as in other European countries (for example MARAN [16], SVARM [17] and DANMAP [18]). Systems in other countries are often based on quantities determined by sales data [19], or the amount prescribed by animal species. In Germany, several monitoring systems present their results based on antibiotic usage frequency, such as the QS monitoring system [20], the nationwide official database implemented by the German Medicinal Products Act (the Federal Office of Consumer Protection and Food Safety; [21]) and the VetCAb project.

To analyze the temporal trend in more detail, the present work focused on the change in antibiotic usage over time in German livestock husbandry, investigating the years 2011, 2013 and 2014. Since some publications have shown that various factors influence both disease incidence [22–24] and antibiotic use magnitude [25], the question arises, weather specific factors associated with antibiotic use can be identified. To develop strategies for further reducing antimicrobial usage and to correct the results of the antimicrobial use frequency for other effects, these factors must be analyzed.

Thus, in the present work, the influences of temporal trends, farm size, farm category, region and the veterinarian supervising the farms on the treatment frequency are considered via regression models.

Materials and methods

Results

Description

Study population.

In total, 51 311 ADFs from participating pig farms were analyzed. For weaners and fattening pigs, the median was 4 antibiotic substance prescriptions per holding biannually (IQR = 1–13 prescriptions per holding for weaners and IQR = 1–10 prescriptions per holding for fattening pigs), while for sows and piglets the median were 6 and 7 prescriptions per holding (IQR = 2–14 prescriptions per holding for sows and IQR = 2–16 prescriptions per holding for piglets), respectively.

The number of participating holdings within the analyses increased over time. In the first half of 2011, 755 holdings were integrated into the evaluation, and 1 102 holdings were analyzed in the second half of 2014. The population composition changed only slightly relative to "Farm size", "Farm category" and "Region". Sow, piglet and weaner holdings comprised approximately 20% of these holdings each, while fattening pig holdings comprised approximately 40%. The increased number of evaluable fattening pig holdings per half-year was slightly more than that of other groups (see Table 1).

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Table 1. Biannual treatment frequency distribution for sows, piglets, weaners and fattening pigs.

https://doi.org/10.1371/journal.pone.0199592.t001

Antibiotic usage and treatment frequency.

To describe the data basis of further statistical analyses, Table 1 shows the distribution of the biannual TF for the four production type groups. Consequently, following statements refer to a mere description of the data. In general, the median semi-annual TF constantly decreased from the first half of 2011 until the second half of 2014. This reduction is most noticeable in the fattening pigs, where the median TF decreased from 5.1 (in the first half of 2011) to 0.7 (in the second half of 2014) and furthermore in the piglets, where the median semi-annual TF decreased from 3.8 in the first half of 2011 to 1.7 in the second half of 2014. This is a TF reduction of more than 55% in piglets and 86% in fattening pigs within four years. The descriptive results of sow and weaner TF deviated. The median of both groups varied only slightly over time. For sows, the median TF remained between 1.0 and 1.2. For weaners, the values vary at higher levels between 8.2 and 12.2.

Fig 1 illustrates descriptive results about the change over time in the proportions of participants without antibiotic usage among the groups. The most obvious change occurred in the piglets. From less than 10% of holdings without antibiotic usage, their proportion increased constantly up to 21.5% in the second half of 2014. In the remaining groups, proportions varied: in 2013 and 2014, weaners and fattening pigs showed higher percentages of holdings without antibiotic use than in 2011. However, in 2013 and 2014 values varied between 18% and 26% for weaners and between 19% and 23% for fattening pigs. The proportion of sow holdings without antibiotic use always remained under 15%.

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Fig 1. Percentage and trend line for participating farms with no antibiotic use by half-year for each production type group.

https://doi.org/10.1371/journal.pone.0199592.g001

Regression models

To improve the statistical significance of the data Tables 2–5 show the regression model results for each production type group. The estimates of fixed effects regression coefficients and random effects covariance parameters are provided in the supplementary material (S1 and S2 Tables). Within the model selection process, for groups with lower TFs, the highest goodness of fit was seen using the logarithm transformation after adding 0.1 (S1–S12 Figs). This occurred in the sow data with medians between 1.0 and 1.2 (Table 1). For distributions with a slightly higher average antibiotic use, the best model fit is obtained using the square root transformation. This applied to the distribution of the residuals of the TF in piglets (median from 1.7 to 4.7), weaners (median from 8.2 to 12.2) and fattening pigs (median from 0.7 to 5.3).

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Table 2. Multi-factorial model results with logarithm transformation for the treatment frequency in sows.

https://doi.org/10.1371/journal.pone.0199592.t002

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Table 3. Multi-factorial model results with square root transformation for the treatment frequency in piglets.

https://doi.org/10.1371/journal.pone.0199592.t003

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Table 4. Multi-factorial model results with square root transformation for the treatment frequency in weaners.

https://doi.org/10.1371/journal.pone.0199592.t004

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Table 5. Multi-factorial model results with square root transformation for the treatment frequency in fattening pigs.

https://doi.org/10.1371/journal.pone.0199592.t005

During the observational period, significant changes occurred in the TF among all groups; however, for sows, the mean TF values estimated via the model remained constant between 0.7 and 1.1. For weaners, the mean values are estimated between 14.7 and 23.1. Although the p-value shows a global statistical significance, no general trend is observed. In contrast, the estimated means are significantly reduced in the piglets and fattening pigs.

"Farm size" has a significant impact on the TF in all models. The estimated TF means increased with increasing size. For all groups in the category, "Upper third", the estimated mean is at least twice as high as for the "Lower third".

The factor "Region" has no significant impact on any group’s TF, except in the weaner model, since the category "East" has a nearly four-fold higher estimated mean (39.0) compared with the "Northwest" and "Middle" regions (11.4).

The factor "Farm category" significantly affects the TF in piglets, weaners and fattening pigs, while the sow model is unaffected. In piglets, the model estimated similar means in the categories, "Breeding" and "Combined", at 2.6 and 2.5. The category "Changer" has a significantly higher estimated mean of 4.2. The estimated means of the weaners and fattening pigs in the category "Fattening" are significantly higher (25.8 and 4.6) than in the categories "Combined" (13.7 and 3.1) and "Changer" (17.7 and 33.1).

In investigating the effect of veterinarians, values below 0.05 were observed for all p-values in the likelihood ratio test for each group model. This indicates that models addressing the veterinarian clusters fit the data better than models omitting the random veterinarian effect.

A sensitivity analysis to investigate the results’ stability was performed by omitting the cases where one veterinarian ID is connected with only one farm ID (data not shown). The analysis showed no changes in interpreting of the p-values that assessed the fixed effects and the veterinarian random effect for all group models.

Discussion

This longitudinal study used data from mandatory ADF documentation in Germany. Veterinarians and farmers participated voluntarily in a panel study and provided data from 2011, 2013, and 2014. Based on the calculated semi-annual TF for each pig holding, the influence of various factors on antibiotic consumption on pig farms was analyzed, using regression models. This study’s main achievement is observing a cohort of pig holdings over several years.

This study is a longitudinal extension of the cross-sectional VetCAb project. Van Rennings et al. [11] showed the successful implementation of a monitoring system for antibiotic use by presenting the first results representative for German pig holdings. To transfer this knowledge, the study design concerning data collection and analysis as well as the general measurement of antibiotic usage was adopted. Furthermore, recruitment focused on maintaining the pilot collective to minimize a possible selection and migration bias.

The data presented here are based on voluntary participation, thus carrying the risk of selection bias, or migration bias in longitudinal studies. The true antimicrobial use may have been higher and the reduction less pronounced than that in the presented data. However, the TF range results suggest that both selection bias [11] and migration bias are unlikely.

Additionally, a bias due to ADF misallocation must be discussed. Using ADFs as data sources risks assigning the ADFs and antibiotic treatments to the incorrect production type groups. The group designations were not standardized, and were often used in colloquial speech for the ADFs, resulting in the allocations being ambiguous. Specifically, the term "weaner" has various meanings in veterinary and farming practice in Germany. Therefore, an exalted misallocation frequency is possible; however, we assume that veterinarians will define production types homogeneously by adhering to the Medicinal Products Act, so that this effect will decrease over the years.

In this study, antibiotic use is measured by the treatment frequency calculated based on the nUDD. Most other studies on antibiotic consumption in veterinary medicine are based on sales data (viz. amounts). Merle et al. [9], van Rennings et al. [27] and Schaekel et al. [29] described the differences between UDD and DDDA in detail. Here, documenting data for calculating used daily doses (UDD) offers two practical advantages. First, in contrast to the theoretical DDDA (defined daily dose for animals), which estimates the use based on the sales data or amounts applied, nUDD gives direct insight into the antibiotic consumption on site. The key differences are that the TF (nUDD) provides information on the actual number of animals treated, and, if the total amount of antimicrobials used is recorded, allows assessing the actual dose of the active ingredient used. If DDDA is applied, standard dosages and standard animal weights are used instead, which will thus provide inaccurate information when animals are treated during different live periods. Consequently, the results are observed with additional variability and therefore may lead to diluted effects when comparing TFs. Thus, calculations based on UDD, especially for running risk factor models, may achieve less biased results. Because the number of treated animals and treatment days are documented on the ADFs in Germany, the nUDD can be calculated directly, without the quantity of active substances or standardized animal weights, so that these possible sources of error remain accurate. Second, the production type of the treated animal (group) is usually indicated on the ADFs in Germany; therefore, using the ADFs facilitates associating the antibiotic consumption with the group treated.

Although TF is similar to amount-based calculations for antibiotic use, its comparison to other studies is restricted due to systematically different approaches in defining standard weights and DDDA. In addition, this comparison is hindered by varying group definitions. In the present VetCAb study, the observed animal holdings are divided into the groups piglet, weaner, fattening pig and sow. In other surveys, production type groups were summarized, such as those of Bos et al. [37] and Jensen et al. [38], who analyzed piglets and sows together in one group. Conversely, some document no group separation at all, such as in Hosoi et al. [39]. Even if production types are separated into groups, the definitions of these groups may differ, such as in the surveys of Callens et al. [40], Trauffler et al. [28] and Sjölund et al. [41]. Hence, it is crucial to differentiate antibiotic use data and standardize definitions when comparing data between studies, regions, and countries.

Overall, internationally and even nationally, no harmonized approach exists for assessing antibiotic consumption; therefore, increased attention should be paid to standardizing definitions and calculation methods in antibiotic monitoring to compare antibiotic use.

Despite the many differences described above, temporal trends within the systems can be compared. In this study, sufficient data were available to assess the temporal development of antibiotic usage in all animal groups. While the median TF for sows remained unchanged and constantly low over the study period, piglets and fattening pigs showed remarkably reduced antimicrobial consumption. In contrast, antibiotic use in weaners fluctuated significantly, which requires further investigation.

In general, the marked differences in the TFs between groups may be explained by animals’ different exposures to infectious hazards. Since fattening animals (piglets, weaners and fattening pigs) receive the most antibiotics in pig farming, this area should be the focus for reduction. The fluctuating TF medians in the weaner collective were striking, which could be explained by the possible misallocation described above.

In total, a statistically significant reduction in antibiotic use over time is evident in this study. This is consistent with reported sales data in Germany, where since 2011, a reduction by 468 tons to approximately 1 238 tons was observed in 2014 [12]. This trend appears to continue in the following years. In the official monitoring system, the median and the upper quartile of the TF decreased constantly. For weaners (up to 30 kg bodyweight (BW)) and fattening pigs (from 30 kg BW), the medians decreased from 4.8 and 1.2, respectively, in the second half of 2014 to 3.4 and 0.4, respectively, in the first half of 2016 [14, 15]. In the QS system, the therapy index medians decreased from 10.71 (weaners, up to 30 kg BW) and 1.76 (fattening pigs, from 30 kg BW), respectively, in the second half of 2014 to 3.53 and 0.37, respectively, in the first half of 2016 [13].

The reduced antibiotic usage can also be seen in other European countries, as shown in Sjölund et al.’s study [42]. The sixth European Surveillance of Veterinary Antimicrobial Consumption (ESVAC) report [19] showed lower sales of veterinary antimicrobial agents for food-producing species per population corrected unit (in mg/PCU) in 2014 for all animals in countries including Belgium, the Netherlands, Sweden and Germany compared to former years. Austria, Denmark, Poland and the UK also reduced their veterinary antimicrobial agent sales for food-producing species per population corrected unit, but in these countries, the reductions began later (in 2012 and 2013, respectively) [19]. In Denmark, the total amounts of antibiotic substances as well as the DAPD (DDDA per 1 000 animals per day), increased from 2011 to 2013 in all groups, until the DAPD decreased in 2014 [43]. However, antibiotic use in Denmark was significantly reduced a few years prior; compared with 2009, the DAPD decreased in 2011 in all groups. This was related to the yellow card initiative, a benchmarking system for pig farms established by the Danish Veterinary and Food Administration (DVFA) in 2010 [44]. In the Netherlands, the reduction in antimicrobial use began in 2007 and has continued since [16].

In summary, nearly all countries with antibiotic use monitoring systems in livestock husbandry document success in reducing antibiotic consumption for many reasons. One of the main reasons is that awareness in both society and the agricultural sector has improved. This increase developed due to implementing monitoring systems, publishing scientific studies from across Europe (e.g., [28, 37, 45–50]) and various measures taken by international and national policy makers [19, 51–53].

International surveys show that antibiotic use in animal husbandry cannot be reduced indefinitely. In 2015, the National Veterinary Institute of Sweden (SVA) published the yearly Swedish Veterinary Antibiotic Resistance Monitoring (SVARM) results, which showed that the temporal trend of veterinary antimicrobial consumption in Sweden has reduced over several years. In contrast, antibiotic sales for pigs remained stagnate over the past five years. However, another change occurred instead. Product sales for individual medications increased, while sales of group medications decreased [17]; thus, it appears that more individual and less metaphylactic treatments have occurred.

Diseases requiring antibiotic treatment can occur in any animal holding type. For this reason, the aim cannot be to ban antibiotic uses, but to change its application. Responsible antibiotic handling does not mean non-use, but prudent use. It must be assumed that in the future, a plateau will be reached, and the reduction will stagnate at the necessary level. Further studies are needed to assess the antimicrobial level needed to treat diseased animals without conflicting with animal welfare through the legal compulsion for further reductions.

Furthermore, all measures must be evaluated regularly and in detail to adjust the monitoring and benchmarking system if necessary. As a first step, multiple linear regression for each defined production type group was performed to investigate weather monitoring antibiotic use should be accompanied by evaluating secondary data.

In this study, the estimated means of TF rose with increasing farm size in all production type groups. In contrast, Vieira et al. [54] identified higher TF on small farms and mentioned worse hygiene management as a possible reason. Van Rennings et al. [11] also obtained different results. The authors determined that farm size did not significantly impact the TF, except for a slight influence in the weaner model. Considering that part of van Rennings et al.’s study population [11] was also analyzed in this study, this influence appears to have changed over time, as the extended study population and more detailed model may have influenced the recent study. Van der Fels-Klerx et al. [25] noted an influence of farm size on antibiotic use in their study population in 2011. They suggested that a rising probability of infection with an increased number of animals could be responsible for this and refer to Österberg et al. [22], Hautekiet et al. [23] und García-Feliz et al. [24]. Regarding the link between herd size and antimicrobial resistance, various patterns are also described [55].

In our study, spatial factors did not significantly impact the TF, except for in weaners. This result is consistent with those of van Rennings et al. [11]. Although a high density of pig holdings prevails in some regions, and the presumption suggests higher infection pressure due to the proximity of neighboring stables [56], high farm density and high antibiotic use were not significantly associated in this study. Even in the weaner model, a statistically significant higher TF was determined in the "East" region, which comprises few but large farms [34].

The factor "Farm category" has a significant effect on the TF in piglets, weaners and fattening pigs, while the sow model was unaffected. Fattening pigs show similar results to those of van der Fels-Klerx et al. [25]; on specialized fattening farms, significantly more antibiotics were used than on combined farms / farrow-to-finish farms. This finding corresponds to the assumption that increased animal movement and pooling of animals from different stables, and thus of different farm-specific germ spectra, results in an increased risk of infection. These results concur with those published by Casal et al. [57] and Moreno [58]. The same applies to the weaners. In sow holdings, the farm category has no significant influence on the TF, whereas in piglet holdings, a significant impact could be seen, but neither "Combined" nor specialized "Breeding" farms used considerably more antibiotics. (In the piglet group, the significance of the factor seems to be due to the high-level antibiotic usage in the "Changers" group. In this group, production system conversion leading to unstable production conditions may have influenced the disease incidence, leading to increased antibiotic use.) These findings support the above hypothesis; sows and piglets were not moved to other stables, regardless of farm category. The increased infection pressure is only apparent after the first regrouping. Van der Fels-Klerx et al. [25] has received other results in this regard; specialized sow farms used fewer antibiotics for piglets than did farrow-to-finish farms. They hypothesized that this result may be due to the term "piglet" is being used longer on farrow-to-finish farms than on specialized farms.

According to van Rennings et al. [11], the influence of veterinarian is taken into account within the adjusted analyses and has again a significant effect on the TF. This could be explained by the veterinarian’s different specializations or typical antimicrobial treatment routines (e.g., using combined products).

Overall, the factors, "Farm size", "Veterinarian" and "Farm category", appears to impact the TF, while the "Region" factor shows no effect when the data are adjusted for confounding. However, it has to be assumed that these three identified factors are not solely influential; thus, to improve existing systems for antibiotic monitoring or establish new intervention measures, additional studies are needed. Evaluating the secondary data may greatly benefit this.

Conclusions

In the longitudinal VetCAb sentinel study, antibiotic administration decreased significantly over time in the study population. The factors, "Farm size", "Veterinarian" and "Farm category", appeared to impact the treatment frequency. Considering these effects, these factors should complement antibiotic use monitoring.

Supporting information

Acknowledgments

The VetCAb project is commissioned and funded by the Federal Institute for Risk Assessment (BfR). We would like to thank the colleagues of the Institute of Pharmacology, Pharmacy and Toxicology of the Faculty of Veterinary Medicine of the University of Leipzig, who supported the part of the study conducted in 2011.