Accepted Manuscript

Relationship between biosecurity and production/antimicrobial treatment char-

acteristics in pig herds

M. Laanen, D. Persoons, S. Ribbens, E. de Jong, B. Callens, M. Strubbe, D.

Maes, J. Dewulf

PII: S1090-0233(13)00404-8

DOI: http://dx.doi.org/10.1016/j.tvjl.2013.08.029

Reference: YTVJL 3848

To appear in: The Veterinary Journal

Accepted Date: 24 August 2013

Please cite this article as: Laanen, M., Persoons, D., Ribbens, S., de Jong, E., Callens, B., Strubbe, M., Maes, D.,

Dewulf, J., Relationship between biosecurity and production/antimicrobial treatment characteristics in pig herds,

The Veterinary Journal (2013), doi: http://dx.doi.org/10.1016/j.tvjl.2013.08.029

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Relationship between biosecurity and production/antimicrobial treatment characteristics in pig herds M. Laanen a, *, D. Persoons a, b, S. Ribbens c, E. de Jong c, B. Callens a, M. Strubbe c, D. Maes a, J. Dewulf a a Unit of Veterinary Epidemiology, Department of Reproduction, Obstetrics and Herd Health, Faculty of Veterinary Medicine, Ghent University, 9820 Merelbeke, Belgium b Pharma.be, Belgian Association for the Pharmaceutical Industry, 1170 Brussels, Belgium c Animal Health Care Flanders, 9000 Drongen, Belgium * Corresponding author: Tel.: +32 9 264 75 48. E-mail address: Maria.Laanen@UGent.be

Abstract

The biosecurity status of 95 breeder-finisher pig herds was quantified using a risk-

based weighted scoring system. Data relating to herd-, farmer- and production- characteristics

and to the prophylactic use of antimicrobials were also collected. The average external

biosecurity score (measures to prevent pathogens from entering a herd) was 65 (range, 45 -89)

and the average internal score (measures to reduce the within-herd spread of pathogens) was

52 (range, 18 - 87).

External scores were positively associated with herd size, while internal scores were

negatively associated with both ‘age of buildings’ and ‘years of experience of the farmer’,

indicating that biosecurity is generally better implemented in larger herds, in more modern

facilities and by younger farmers. External and internal biosecurity scores were positively

associated with daily weight gain and negatively associated with feed conversion ratio of

fattening pigs. Internal scores were negatively associated with disease treatment incidence,

suggesting that improved biosecurity might help in reducing the amount of antimicrobials

used prophylactically. This study demonstrates and quantifies a clear link between biosecurity

and both production- and antimicrobial treatment-related criteria in pig herds.

Keywords: Pig; Biosecurity; Scoring; Daily weight gain; Feed conversion; Antimicrobial

treatment

Introduction

Biosecurity embraces all aspects of the prevention of pathogens entering and spreading

within a group of animals (Amass and Clark, 1999), and can thus be divided into two parts.

External biosecurity relates to the prevention of pathogens entering a herd, and internal

biosecurity is how the spread of pathogens within a herd is reduced. It is assumed that higher

levels of biosecurity lead to improved animal health and productivity, and to a reduction of

the use of antimicrobials (Ribbens et al., 2008), which are important features of sustainable

animal production.

There are documented relationships between the management and incidence of

specific diseases in pig herds (Maes et al., 2008; Fraile et al., 2010; Lambert et al., 2011;

Meyns et al., 2011), and research has resulted in many recommendations with regard to

biosecurity measures to reduce disease (Amass and Clark, 1999; Boklund et al., 2003, 2004;

Ribbens et al., 2008). Given the inherent challenges in measuring an entity such as

biosecurity, previous workers have not attempted to quantify its effect although a risk-based

weighted biosecurity scoring system has now become available (Laanen et al., 2010)1.

Although limited and responsible use of antimicrobials is desirable, recent studies

indicate that over the last 9 years there has been increased prophylactic use of these drugs in

pigs in Belgium, where ‘group level’ use of antimicrobials is the most common way to

administer antimicrobials (Callens et al., 2012). The majority of these treatments are

prophylactic, administered to groups of pigs by farmers at crucial time-points in production

such as at castration and weaning, following veterinary advice (Dunlop et al., 1998).

1 See: http://www.biocheck.ugent.be

The data suggest that guidelines for prudent use of antimicrobials are not currently

being sufficiently implemented in Belgium. However, no official action plan to reduce

antimicrobial use is currently implemented in Belgium as in other European countries

(Nielsen et al., 2007; Cogliani et al., 2011). Studies exploring ways to reduce the amount of

antimicrobials used are therefore warranted. The objectives of this study were to investigate

the relationship between the implementation of biosecurity in pig herds in Belgium, and

various production parameters and the prophylactic use of antimicrobials.

Materials and methods

Herd selection

Herds were selected from the National Identification and Registration Database of

Belgium (Sanitel-Pigs, 2005). The selection criteria were that herds were ‘farrow-to-finish’ in

type, and contained at least 80 sows and 400 fattening pigs. From a total of 2658 herds fitting

these criteria, a random sample of 250 herds, stratified by province, was selected. Random

selection was performed using a computer-generated list (Cameron, 1999), and all selected

herds were invited to participate by telephone. Farmers who declined to take part were asked

to explain their reasons for not participating.

Data collection

All participating herds were visited on one occasion by the same investigator between

September 2009 and January 2011. During this visit, the biosecurity practices on the farm

were observed, and the farmer was interviewed in relation to herd-, farmer- and production-

characteristics, the application of biosecurity, and the prophylactic use of antimicrobials using

a standardised questionnaire. The interviewer ensured no comments were made that might

influence the farmer’s responses and, after the visit, the farmers received a full evaluation and

list of recommendations concerning their biosecurity.

Collection of herd, farmer and production data

The following data were collected: number of sows/fattening pigs, the age of the farm

buildings (taken as the age of the oldest building in which the pigs were housed), and the

years of experience of the farmer (i.e. the number of years a farmer had kept his/her own

herd). Production characteristics collected were: daily weight gain (DWG) and feed

conversion ratio (FCR) of fattening pigs, mortality of fattening pigs, and seroprevalence of

salmonella infection. The FCR is a measure of an animal’s efficiency in converting feed into

body mass and reflects the mass of feed needed to produce 1 kg of bodyweight. A low FCR is

therefore desirable from a financial perspective.

The DWG, FCR and mortality rate (all at herd level) during the year preceding the

visit, were verified by consulting farm production records, and the seroprevalence of

salmonella infection was quantified by the mean sample:positive ratio (S/P)-value obtained

from the official national control programme records2. The mean S/P-values were determined

by indirect ELISA (Herdcheck Swine Salmonella Antibody Test Kit, Idexx laboratories) on

12 blood samples taken from fattening pigs every 4 months. None of the visited herds were

considered salmonella ‘problem herds’ (defined as herds having ≥3 consecutive samplings

with an average S/P-value of >0.6).

Quantification of biosecurity status of herds

2 See: http://www.dgz.be (accessed 22 April 2011).

Quantification was achieved using a previously defined risk-base scoring system of

both external and internal aspects of biosecurity, each of which was divided into six sub-

categories (Table 1)3. Each sub-category contained between 2 and 13 questions resulting in a

total of 109 questions (see Appendix A, Supplementary material). A ‘weight’ was attributed

to each question and sub-category on the basis of published data relating to pathogen

transmission, knowledge of infectious risks, and expert opinion on the risk of infection

through different transmission routes. As direct contact between diseased and susceptible

animals is generally acknowledged to be the most important route of transmission of

infectious diseases, biosecurity measures aimed at preventing such contact between animals

was considered more important than that preventing indirect contact. In addition to the

transmission route per se, the frequency with which a given infection had the opportunity to

transmit via this route also dictates the risk of transmission and was thus factored into our

scoring system.

Based on these principles it was possible to quantify the risks of transmission in

general terms and to determine the importance of the various measures taken to reduce these

risks. The weights given to the sub-categories are detailed in Table 1, and more details,

including the calculations behind the biosecurity scores can be found in Appendix A. For each

of the listed questions, points were obtained when the biosecurity measure was applied

correctly. On occasion, points were earned by not performing a certain action (e.g. not buying

breeding pigs), whereas, where a farmer purchased breeding animals, he/she started with zero

points. The farmer gained points by applying preventive measures such as purchasing from

3 See: http://www.biocheck.ugent.be

only one supplier, or buying from a supplier of known health status (see Appendix A). The

sum of the points relating to each sub-category was ‘weighted’ accordingly.

To calculate the external or internal biosecurity score, the scores for each of the

appropriate sub-categories were totalled to give a score between ‘0’ (total absence of

biosecurity) and ‘100’ (perfect biosecurity). The overall score was calculated as the mean of

the external and internal biosecurity score (Laanen et al., 2010). The scores for the different

sub-categories were then re-calculated to scores between 0 and 100 to facilitate their

interpretation by the farmers. The questionnaire and scoring system are available as a

download4.

Quantification of antimicrobial drug use

As our study design was observational and retrospective, only standard prophylactic

antimicrobial ‘group treatments’, administered from birth until slaughter, were assessed.

Prophylactic group treatments are defined as treatments of all the animals within a group to

prevent clinical disease (Aarestrup, 2005). Because such treatments are standardised and

administered by the farmer, they are less subject to ‘recall bias’. For each group treatment, the

following data were collected: product name, indication for use, duration of therapy (days),

dosage, administration route (in-feed, in-water, or IM), and age of treated animals (days).

Prescriptions and order forms, where available, were used to verify the data.

Quantification was achieved by calculating treatment incidences (TI) defined as the

number of pigs/1000 treated daily with one dose of an antimicrobial drug (Timmerman et al.,

2006). The daily dose was quantified by means of the ‘used daily dose pig’ (UDD pig) which

4 See: http://wwww.biocheck.ugent.be

gives the dose of a drug administered/day/kg. The UDD was calculated by dividing the

amount of antimicrobial administered (mg) by the number of pigs treated, multiplied by the

estimated average weight of the pigs when treated (Timmerman et al., 2006).

Treatment incidences were calculated according to Timmerman et al. (2006). The total

amount of antimicrobial drug used was first calculated/compound. The number of days at risk

(i.e. the time in days that a pig was potentially exposed to ≥ one treatment) was set at 206

days: 70 days for the farrowing and nursery period and 136 days for fattening based on

average for all herds. The ‘kg pig’ was calculated as the number of pigs multiplied by their

average weight (standardised over the different herds) when treated. Using this method, TIs

based on the UDDpig were obtained (TIUDDpig).

Statistical analysis

Difference in herd size between participating and non-participating herds was tested

by means of an independent samples t test. The association between herd/farmer-

characteristics and biosecurity scores were tested using univariable linear regression models.

To evaluate the association between the biosecurity score and the different

production/treatment characteristics, univariable and multivariable linear regression models

were fitted to the data. Firstly, overall biosecurity score was tested univariably vs. the

different ‘outcome’ variables (DWG, FCR, mortality, Salmonella S/P-value and TIUDDpig). If

this association was significant, both parts of the score (external and internal) were also tested

univariably against all outcome variables. Next, to identify the most influential aspects of

biosecurity, all sub-categories of this criterion as scored by the Biocheck scoring system were

tested univariably, and those with P<0.2 in the univariable model were added to the

multivariable model. These variables were assessed for two-way correlations using Pearson’s

correlation coefficients. If the coefficient was >0.6, only the best-fitting variable was added to

the multivariable model. A manual, stepwise backward selection method was used with

P>0.05 as a selection criterion. Finally, all two-way interactions between significant variables

were tested. All analyses were performed using SPSS software (version 19.0).

Results

Study participation rate and descriptive results

Of the 250 herds contacted, 47 were ‘inactive’ and of the remaining 203, 95 were

willing to participate giving a participation rate of 47.8%. Reasons for not participating

included: ‘lack of time’ (44/108), ‘not interested’ (43/108), and other reasons such as

‘personal problems’ or ‘ceasing pig production in near future’ (21/108). The average number

of sows and fattening pigs in non-participant herds (181 and 1046 on average, respectively)

were significantly lower than in those participating in the survey (289 and 1420 on average,

respectively) (P<0.01). The average external and internal biosecurity scores were 65 (range

45 - 89), and 52 (range 18 - 87), respectively. The scores for the different categories of the

Biocheck scoring system are listed in Table 1. Results of herd and farmer information,

production-characteristics of fattening pigs, and the prophylactic use of antimicrobial drugs

are detailed in Table 2.

Data analysis

Farmer and herd characteristics

Numbers of sows and fattening pigs were both positively associated with the external

(P<0.01), but not the internal (P=0.07 and P=0.24, respectively) biosecurity score. Both ‘age

of buildings’ and ‘years of experience’ were negatively associated with internal (P=0.03 and

P=0.04, respectively) but not external biosecurity (P=0.22 and P=0.51, respectively).

Daily weight gain, feed conversion ratio, mortality rate and S/P value

The overall biosecurity score, as well as both external and internal scores were

significantly associated with the DWG of fattening pigs (all P<0.01) in the univariable models

(Table 3). When evaluating associations between sub-categories of biosecurity and DWG,

‘transport of animals, removal of manure and dead animals’ (i.e. transport of animals to

slaughterhouse or other herds, removal of manure, and handling of carcasses) and ‘cleaning

and disinfection’ (i.e. methods used to clean and disinfect animal housing) were positively

associated with DWG in the multivariable model (P=0.03 and P=0.04, respectively; R²=0.14)

(Table 3).

The FCR of fattening pigs was significantly associated with overall, external and

internal biosecurity scores (all P<0.01) (Table 4). When sub-categories of biosecurity were

evaluated together in the multivariable model, the combination of two sub-categories of

external biosecurity, ‘purchase of animals and semen’ (P<0.05) (i.e. purchase and transport of

animals, use of quarantine, and purchase of semen) and ‘vermin/bird control’ (P<0.01), and

one sub-category of the internal biosecurity, ‘farrowing and suckling period’ (P<0.03) (i.e.

washing of sows, cross-fostering and handling of piglets) had the strongest association with

FCR (R²=0.24), indicating that high scores in each of these sub-categories were related to

good (i.e. low) animal FCRs.

No significant associations were found between the mortality rate of fattening pigs and

any of the biosecurity scores and neither the overall, external, or internal scores were

significantly associated with the salmonella S/P-value.

Use of antimicrobial drugs

The mean TIUDDpig was 174.22 (Table 2), indicating that, on average, approximately

17% of the animals in the herd were treated daily with one dose of antimicrobials by means of

prophylactic group treatment. The overall and internal biosecurity scores were negatively

associated (P=0.05 for both, respectively) with TIUDDpig. Using the multivariable model,

‘disease management’ (i.e. disease control, use of hospital pens, handling of diseased animals)

and ‘farrowing/suckling period’ (i.e. washing of sows, cross-fostering, handling of piglets),

were found to be most strongly negatively associated with the TIUDDpig (P=0.04 and P=0.03,

respectively; R²=0.11) (Table 5).

Discussion

This study has attempted to identify and quantify associations between herd

biosecurity and aspects of pig production/treatment. As in every observational study of this

type, the results may be subject to some bias; for example, an analysis of non-participating

farmers indicated that those owning larger herds were more likely to take part. Furthermore,

as the study was cross-sectional in type, it was not possible to allocate causal relationships to

the associations found, and there may have been other confounding factors impacting on our

data.

Although significant associations were identified, the R²-values in the various

regression models were small, suggesting unexplained variation remains. Other factors

relating to herd management, as well as vaccination protocols, animal genetics and nutrition

are likely also to affect DWG, FCR and the frequency of the use of antimicrobial drugs and

therefore contribute to unexplained variation. Moreover, as many biosecurity measures are

considered ‘proxies’ of good management, the observed associations might be partially or

largely influenced by other management factors. In consequence, our data only suggest

possible causal associations that require further assessment by prospective intervention

studies.

The biosecurity in herds in the current study was measured at one point in time and the

information derived was used to identify associations between parameters measured over the

preceding year. In some herds, biosecurity measures may have changed shortly before the

visit, e.g. due to an outbreak of disease, so that the resulting biosecurity score was higher at

the point of data collection than it had been over the preceding year. However, in the authors’

experience, farmers in Belgium are not particularly inclined to change their biosecurity

practices frequently, and the number of herds included in our study minimises the potential

influence of any such changes. Although the weighting attributed to the sub-categories and

questions were based on previous studies and expert opinion, they do remain a somewhat

subjective interpretation of the importance of the various aspects of biosecurity. Our scoring

system can be seen as an attempt to capture and quantify all aspects of biosecurity, facilitating

consistency in the evaluation of herds over time.

Scores of external biosecurity (typically measures implemented by herd owners on

farm visitors/suppliers), were, on average, higher than the scores for internal biosecurity

(measures relating to the husbandry/management strategies of the farmers themselves). The

positive association between herd size and level of biosecurity suggests more attention is paid

to biosecurity on larger farms, a finding previously reported for both pig and cattle herds

(Boklund et al., 2003, 2004; Nöremark et al., 2010). It was also noticeable that internal

biosecurity increased with decreasing age of the buildings, suggesting more attention should

be paid to biosecurity, and that this is more readily implemented within a more modern

infrastructure. The same held true for ‘years of experience’ of farmers, suggesting younger

farmers are more focused on implementing internal biosecurity measures.

The association found between both external and internal biosecurity status and animal

DWG/FCR indicates that, on average, herds with a better biosecurity have more efficient

production. More specifically, ‘transport of animals, removal of manure/dead animals’ and

‘cleaning and disinfection’, measures likely to reduce the risk of pathogens entering or

spreading within a herd, were most strongly associated with DWG, as has been previously

reported (Mannion et al., 2007). Sub-categories of biosecurity most strongly associated with

FCR were ‘purchase of animals and semen’, ‘vermin/bird control’ and ‘farrowing and

suckling period’. The variation in these sub-category scores explains, at least in part, the

variation in FCR between herds (R²=0.24). It would seem logical that herds taking precautions

to prevent diseases being introduced would have more healthy animals with attendant better

FCR. The association between ‘farrowing and suckling period’ and FCR suggests improving a

piglet’s health in its early life will improve its FCR during fattening. Nevertheless, as stated

above, it must be borne in mind that herds with better biosecurity may be better managed

overall, and consequently have better production values.

In contrast to the results of a previous study (Maes et al., 2004), no relationship was

found between biosecurity and the mortality rate of fattening pigs. This might reflect

differences in the methods of measuring biosecurity between these studies, or that biosecurity

is only one factor associated with mortality. There was also no relationship found between

biosecurity and the average salmonella S/P-values. Baptista et al. (2010) and Twomey et al.

(2010) demonstrated a negative association between ‘level of biosecurity’ and prevalence of

salmonella infection. These differing results may be due to variations in the way ‘salmonella

infection’ was assessed in these studies, and the fact that the present study did not include

herds with a high prevalence of salmonella infection.

The negative association between biosecurity score and TIUDDpig indicates that fewer

prophylactic group treatments with antimicrobials are given in herds with higher biosecurity.

Only internal biosecurity was significantly associated with TIUDDpig, and the categories

‘disease management’ and ‘farrowing and suckling period’ were strongly associated with this

parameter. This may reflect the fact that the correct management of diseased animals results

in a lower risk of within-herd spread of infection, which in turn leads to reduced infection

pressure and a reduction in antimicrobial treatments. Given that the majority of strategic

group treatments in pigs are given during the suckling/weaner phase (Callens et al., 2012), the

greatest factor impacting on the frequency of antimicrobial usage is to be expected in the early

part of the production cycle.

The finding of a negative association between biosecurity and the prophylactic use of

antimicrobials could create opportunities in the light of ongoing consumer demand for

reduced antimicrobial usage in animal production systems. Including ‘therapeutic

antimicrobial use’ as a parameter in estimating total antimicrobial use on a herd might be

useful to further clarify the relationship between biosecurity and total antimicrobial

consumption, as this ‘therapeutic use’ category will reflect bacterial diseases occurring

despite the use of prophylactic medication. Given that pig farmers in Belgium are only

required to keep records of antimicrobial treatments for 2 months prior to the slaughter of

animals, it is very difficult to obtain reliable retrospective information on the therapeutic use

of antimicrobials throughout the production cycle. This aspect of our findings merits further

investigation in future prospective studies.

Conclusions

This study demonstrates and quantifies a clear link between biosecurity and both

production- and antimicrobial treatment-related criteria in pig herds, and indicates there are

currently substantial differences in how biosecurity is applied in pig herds in Belgium.

Aspects of both external and internal biosecurity were positively associated with daily weight

gain, and negatively associated with feed conversion ratio, respectively. Internal scores were

negatively associated with disease treatment incidence, suggesting that improved biosecurity

might reduce the prophylactic use of antimicrobials.

Appendix A. Supplementary material

Supplementary data associated with this article can be found, in the online version, at

doi:………………….Setters please insert doi number

Conflict of interest statement

None of the authors of this paper has a financial or personal relationship with other

people or organisations that could inappropriately influence or bias the content of the paper.

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Table 1.

Sub-categories used in the Biocheck scoring system for external and internal biosecurity and overall results for the 95 ‘closed’ or ‘semi-closed’ pig herds studied.

Sub-category Weight 1 Average SD * Min Median MaxExternal biosecurity 65 8 45 66 89 Purchase of animals/semen 24 89 11 58 92 100 Transport of animals, removal of 23 66 12 30 65 96 manure/dead animals Feed, water and equipment supply 15 41 17 17 40 100 Personnel and visitors 17 64 12 24 65 100 Vermin/bird control 11 57 21 0 50 100 Environment and region 10 48 22 10 50 100 Internal biosecurity 52 15 18 53 87 Disease management 10 60 31 20 60 100 Farrowing and suckling period 14 61 20 14 64 93 Nursery unit management 14 56 24 0 57 100 Fattening unit management 14 64 26 0 71 100 Measures between compartments 28 46 18 11 43 100

and use of equipment Cleaning and disinfection 20 38 27 0 35 95 Overall biosecurity score 59 10 36 59 88

1 A ‘weight’ was attributed to each sub-category on the basis of published data relating to pathogen transmission, knowledge of infectious risks, and expert opinion on the risk of infection through different transmission routes. * SD, standard deviation.

Table 2.

Descriptive results for ‘herd’, ‘farmer’, production characteristics, and use of antimicrobials for the 95 ‘closed’ or ‘semi-closed’ pig herds studied.

Factor N Average SD* Min Median Max Number of sows 95 289 161.66 80 220 1000 Number of fattening pigs 95 1420 877.16 400 1250 7000 Years of experience of farmer 95 21 9.90 1 20 42 Age of buildings (years) 95 32 17.01 2 30 129 Daily weight gain (g/day) † 94 681 71.10 486 676 870 Mortality (%) † 89 3.6 2.19 1.0 3.0 11.5 Feed conversion ratio † 73 2.76 0.18 2.28 2.75 3.30 Salmonella S/P value 92 0.28 0.21 0.03 0.21 0.92 TIUDDpig

* 95 174 130.47 0 150 650

* SD, standard deviation; TI, treatment incidence; † Daily weight gain, mortality and feed conversion ratio all relate to fattening pigs at a herd level during the year preceding the study.

Table 3.

Associations between different aspects of external and internal biosecurity and daily weight gain of fattening pigs in 94 ‘closed’ or ‘semi-closed’ pig herds.

R² β-coefficient P-value Univariable model Overall biosecurity 0.125 2.46 <0.01 External biosecurity 0.129 3.15 <0.01 Internal biosecurity 0.090 1.46 <0.01 Biosecurity sub-categories significant in univariable model

Transport of animals, removal of manure/dead Animals (external)

0.102 1.83 <0.01

Vermin/bird control (external) 0.091 1.00 <0.01 Fattening unit management (internal) 0.045 0.59 0.04 Cleaning and disinfection (internal) 0.099 0.84 <0.01 Biosecurity sub-categories significant in multivariable model

Transport of animals, removal of manure/ dead animals (external) 0.143

1.33 0.03

Cleaning and disinfection (internal) 0.60 0.04

Table 4.

Associations between different aspects of external and internal biosecurity and feed conversion ratio of fattening pigs in 73 ‘closed’ or ‘semi-closed’ pig herds.

R² β-coefficient P-value Univariable model Overall biosecurity 0.173 -0.007 <0.01 External biosecurity 0.200 -0.009 <0.01 Internal biosecurity 0.097 -0.004 <0.01 Biosecurity sub-categories significant in the univariable model

Transport of animals, removal of manure/dead animals (external)

0.137 -0.005 <0.01

Vermin/bird control (external) 0.123 -0.004 <0.01 Farrowing and suckling period (internal) 0.081 -0.002 0.03 Biosecurity sub-categories significant in the multivariable model

Purchase of animals/semen (external) 0.240

-0.003 0.05 Vermin/bird control (external) -0.003 <0.01 Farrowing and suckling period (internal) -0.002 0.03

Table 5.

Associations between different aspects of external and internal biosecurity and treatment incidence (TI)UDDpig based on prophylactic group treatments in 95 ‘closed’ or ‘semi-closed’ pig herds. TIUDDpig was expressed as treatment incidence/1000 pigs at risk/day and based on the daily doses used (UDDpig/1000 pigs at risk/day).

R² β-coefficient P-value Univariable model Overall biosecurity 0.037 -2.45 0.05 External biosecurity 0.015 -1.97 0.24 Internal biosecurity 0.040 -1.77 0.05 Biosecurity sub-categories significant in the univariable model

Disease management (internal) 0.061 -1.05 0.02 Farrowing and suckling period (internal) 0.064 -1.66 0.01 Nursery unit management (internal) 0.049 -1.22 0.03 Biosecurity sub-categories significant in the multivariable model

Disease management (internal) 0.108

-0.90 0.04 Farrowing and suckling period (internal) -1.43 0.03