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Development of a biosecurity assessment tool and the assessment of biosecurity levels by this tool on Japanese commercial swine farms
Journal Pre-proof Development of a biosecurity assessment tool and the assessment of biosecurity levels by this tool on Japanese commercial swine farms Yosuke Sasaki, Aina Furutani, Tomohiro Furuichi, Yuiko Hayakawa, Sayoko Ishizeki, Rika Kano, Fumiko Koike, Mali Miyashita, Yoshihiro Mizukami, Yugo Watanabe, Satoshi Otake, on behalf of P-JET
PII:
S0167-5877(18)30890-0
DOI:
https://doi.org/10.1016/j.prevetmed.2019.104848
Reference:
PREVET 104848
To appear in:
Preventive Veterinary Medicine
Received Date:
18 December 2018
Revised Date:
30 September 2019
Accepted Date:
12 November 2019
Please cite this article as: Sasaki Y, Furutani A, Furuichi T, Hayakawa Y, Ishizeki S, Kano R, Koike F, Miyashita M, Mizukami Y, Watanabe Y, Otake S, Development of a biosecurity assessment tool and the assessment of biosecurity levels by this tool on Japanese commercial swine farms, Preventive Veterinary Medicine (2019), doi: https://doi.org/10.1016/j.prevetmed.2019.104848
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
Development of a biosecurity assessment tool and the assessment of biosecurity levels by this tool on Japanese commercial swine farms
Yosuke Sasaki
a,b,1,*
, Aina Furutani c,1, Tomohiro Furuichi d, Yuiko Hayakawa e, Sayoko
Ishizeki f, Rika Kano g, Fumiko Koike h, Mali Miyashita i, Yoshihiro Mizukami j, Yugo
a
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Watanabe f and Satoshi Otake k on behalf of P-JET l
Department of Animal and Grassland Sciences, Faculty of Agriculture, University of
Miyazaki, Miyazaki, Japan
Center for Animal Disease Control, University of Miyazaki, Miyazaki, Japan
c
Course of Animal and Grassland Sciences, Graduate School of Agriculture, University
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of Miyazaki, Miyazaki, Japan
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b
Feed One Co., Ltd., Kanagawa, Japan
e
IDEAS Swine Clinic, Chiba, Japan
f
Summit Veterinary Services, Gunma, Japan
g
Boehringer Ingelheim Animal Health Japan Co. Ltd., Tokyo, Japan
h
SMC Co., Ltd, Kanagawa, Japan
i
Eckstein Swine Service, Tokyo, Japan
j
Akabane Animal Clinic, Aichi, Japan
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d
k
Swine Extension & Consulting, Inc., Niigata, Japan
l
PRRS-Japan Elimination Team, Tokyo, Japan
1
These authors contributed equally to this work.
*Corresponding author 1-1 Gakuen Kibanadai-nishi, Miyazaki 889-2192, Japan Tel.: +81-985-58-7864; Fax: +81-985-58-7864 E-mail addresses: [email protected]
Abstract It is well known that infectious diseases such as porcine reproductive and respiratory
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syndrome (PRRS) and porcine epidemic diarrhea (PED) decrease herd productivity and lead to economic loss. It is believed that biosecurity practices are effective for the prevention and control of such infectious diseases. Therefore, the objective of the present
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study was to investigate whether or not an association between biosecurity level and herd productivity, as well as disease status exists on Japanese commercial swine farms. The
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present study was conducted on 141 farms. Biosecurity in each farm was assessed by a
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biosecurity assessment tool named BioAsseT. BioAsseT has a full score of 100 and consists of three sections (external biosecurity, internal biosecurity and diagnostic monitoring). Production data for number of pigs weaned per sow per year (PWSY) and
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post-weaning mortality per year (PWM) were collected for data analysis. Regarding PRRS status, the farms were categorized into two groups: unknown or unstable and stable
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or negative. In addition, these farms were categorized based on their PED status, either
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positive or negative. The total BioAsseT score was associated with herd productivity: as total score increased by 1, PWSY increased by 0.104 pigs and PWM decreased by 0.051% (P < 0.05). Herd productivity was associated with the score of external and internal biosecurity (P < 0.05), but did not correlate with the score of diagnostic monitoring. Regarding PRRS status, farms with an unknown or unstable status had lower total score than those with stable or negative status (P<0.05). Similarly, PED positive farms had a
lower total score compared to PED negative farms (P < 0.05). In conclusion, the present study provides evidence for the association between high biosecurity levels and increased herd productivity as well as a decreased risk for novel introductions of infectious diseases such as PED.
Keywords: Biosecurity practice; Herd management; Porcine epidemic diarrhea; Porcine
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reproductive and respiratory syndrome; Sow
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1. Introduction
It is well known that infectious diseases decrease herd productivity and lead to
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economic loss. Disease prevention through biosecurity measures is believed to be an
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important factor for improvement of the overall health status in commercial herd production. Biosecurity is the term used in veterinary medicine to describe measures to prevent pathogens from entering farm premises or a group of animals, known as external
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biosecurity, or prevent the spreading of pathogens within farm premises or groups of animals, known as internal biosecurity (Amass and Clark, 1999). To define biosecurity
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levels on commercial farms, it is important to quantify the biosecurity level in both
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external and internal aspects. In US and Europe, a biosecurity assessment tool such as PADRAP (AASV: The American Association of Swine Veterinarians; Bottoms et al., 2013) and Biocheck (Ghent University; Postma et al., 2016) have been developed to measure biosecurity. These systems have risk-based weighted biosecurity scoring that translates questions regarding biosecurity into a score for internal, external and overall biosecurity status, and biosecurity level is assessed by interviewing the farmer regarding
biosecurity practices and collecting data by visual inspection. This score aims at providing an objective, comprehensive and quantitative description of the level of biosecurity and can be used to inform the farmer on possible areas for improvements, and to compare his/her biosecurity level with that of other farms/herds. However, since production systems and geographical conditions vary between countries, development of a biosecurity assessment tool fitting to the situation of each country seemed beneficial.
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At the time of development, no assessment tool existed in Japan. On commercial farms, most producers are aware of the importance to maintain a high biosecurity level in order to prevent production losses. However, there is general
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hesitation in monitoring their biosecurity level because few studies have assessed the association of production parameters and practical biosecurity measures. Assessment of
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the relationship between biosecurity and herd productivity could motivate the
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implementation of biosecurity measures if such measures can be expected to be beneficial for the farm performance (Casal et al., 2007; Valeeva et al., 2011; Laanen et al., 2014), yet to date there is limited quantitative data available to link biosecurity and production
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parameters (Amass and Clark, 1999; Laanen et al., 2013; Postma et al., 2016). In addition, quantitative relationships between biosecurity level and disease status have not been fully
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reported in scientific literature, although a large number of risk factor studies associated
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to preventive measures is available. Recently, many countries experienced an occurrence of infectious diseases such as porcine reproductive and respiratory syndrome (PRRS) and porcine epidemic diarrhea (PED). An outbreak of PRRS decreases the number of piglets born alive, farrowing rate and feed efficiency, and increases pre- and postweaning mortality (Nieuwenhuis et al., 2012; Nathues et al., 2017; Silva et al., 2017; Nathues et al., 2018). Productivity losses in
the United States swine industry are estimated to be $664 million annually (Neumann et al., 2005; Holtkamp et al., 2013). In addition, an outbreak of PED severely increases preweaning mortality and decreases number of pigs weaned (Sasaki et al., 2017a; Furutani et al., 2017; Furutani et al., 2018). Therefore, the objectives of the present study were to develop a biosecurity assessment tool, to assess biosecurity level by using the tool on Japanese commercial
disease status on Japanese commercial swine farms.
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2. Materials and Methods
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farms, and to investigate the associations of biosecurity with herd productivity and
2.1. Development of a biosecurity assessment tool named BioAsseT
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A biosecurity assessment tool was developed by the PRRS-Japan Elimination
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Team (P-JET) that consists of clinical veterinarians and researchers. The tool was named BioAsseT, consisting of 131 questions and a full score of 100 (Appendix A). BioAsseT consists of three sections (external biosecurity, internal biosecurity, and diagnostic
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monitoring). Each individual section also has a full score of 100 and includes several subcategories (Table 1). Total biosecurity score was calculated as the mean of three
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sections. External biosecurity consists of nine subcategories: farm location, replacement
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gilts, semen, personnel, transport vehicles, manure and carcass management, farm equipment, vermin control, and visitors. Internal biosecurity consisted of seven subcategories: barn layouts, pig flow, cleaning and disinfection, personnel, injection needles, farrowing management, and health conditions. Diagnostic monitoring consisted of four subcategories: monitoring tests, pathology appraisals, communication, and information gathering. A number of items in each subcategory are shown in Table 1. All
of the 131 questions were multiple-choice questions, and each question was weighted based on scientific data and knowledge (categorized as high, intermedium or low risk) in order to take account the importance of the different biosecurity aspects. High score items were multiplied by 1.2, intermedium score items were multiplied by 1.0, and low score items were multiplied by 0.8. The results are presented as lists of immediate priority items
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and well-done items together with a numeric score.
2.2. Data collection
The investigation of biosecurity level using BioAsseT was performed by 25
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investigators (range: 1-15 farms per person) on 141 farrow-to-finish farms, equivalent to 3.2% of all herds in Japan. The country had 4,470 herds in February 2018 (Ministry of
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Agriculture, Forestry and Fisheries of Japan, 2018). Average herd size in the study
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population was 757 sows, while national average was 226 sows (Ministry of Agriculture, Forestry and Fisheries of Japan, 2018). Farm inclusion criteria was based on access to farms by investigators and willingness of participation by farms. Data was collected
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between July 2014 and May 2018 through face-to-face interview on farm by a licensed investigator. All investigators received training by a standardized method. The farms were
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visited by the investigators. Of the 141 farms, 7 were assessed twice and 1 was assessed
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5 times during the studied period. 152 records from BioAsseT were used in the present study.
In addition to the biosecurity score, data on herd productivity and herd health
status were collected. Herd productivity was measured by number of pigs weaned per sow per year (PWSY) and post-weaning mortality per year (PWM). PWM was calculated as the total number of post-weaned pigs dead in a given month divided by the total number
of pigs weaned in that same month. Total number of pigs born alive was based on the number of total live-born piglets in a given month. Productivity records over the past year were collected at the investigation, and 117 records for PWSY and 124 records for PWM were available and used in the analysis. Regarding herd health status, PRRS and PED were investigated on the farms. PRRS status was at the time of assessment, while PED status was the history of the infection from 2013 to the time of assessment because there
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was severe PED outbreak in 2013 in Japan (Sasaki et al., 2017b). Each farm was categorized based on their PRRS status as from I to V, based on the P-JET herd
classification that modified the definition reported in a previous study (Holtkamp et al.,
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2011). Briefly, Status I means positive unstable: PRRS virus detected by PCR from
replacement gilts, sows, suckling piglets, and post-weaning pigs. Status II is a transition between positive unstable and positive stable: PRRS virus is detected by PCR
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status
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from suckling piglets and post-weaned pigs, but not from breeding stock, Status III is positive stable: PRRS virus not detected by PCR from breeding stock or suckling piglets but occasionally positive in post-weaned pigs, Status IV is a transition between positive
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stable and PRRS free: all production stages are PCR negative, but breeding stock is ELISA positive, and Status V is PCR and ELISA negative in all stages. For analysis
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purposes in the present study, PRRS status was divided into two groups; unknown or
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unstable (status unknown, I and II) and stable or negative (status III to V). In addition, farms were also categorized based on PED status; positive or negative. In Japan, PED is a notifiable disease, meaning that clinical signs suggestive of PED must be reported to the regional Livestock Hygiene Service Center, upon which confirmation of infection is conducted through laboratory analysis by collected fecal samples. If fecal samples were positive by RT-PCR, farms were confirmed as PED positive. Farms that did not provide
their PED status at the time of data collection were omitted from the analysis regarding association of PED status and biosecurity scores. Farms were also classified into four groups based on sow inventory: ≤200 sows (N=41), 201 to 400 sows (N=35), 401 to 800 sows (N=36), and ≥801 sows (N=40).
2.3. Statistical analysis
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The data was analyzed statistically with SAS software ver. 9.4 (SAS Institute Inc., Cary, NC, USA). The Shapiro-Wilk test of normality was done on total biosecurity scores and biosecurity scores in each subcategory. The null hypothesis was that the data
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follow a normal distribution, and the P value greater than the significance level of 0.05
indicates not to reject the null hypothesis. A linear mixed model using MIXED procedure
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was used for the analysis. In model 1, total biosecurity scores and biosecurity scores in
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each section were compared by sow inventory groups. The dependent variable was biosecurity scores (total, external biosecurity, internal biosecurity, and diagnostic monitoring), and the independent variable was sow inventory groups. In model 2, the
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relationship between herd productivity and biosecurity scores was assessed. The dependent variable was PWSY and PWM, and the independent variable was each
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biosecurity score (total, external biosecurity, internal biosecurity, and diagnostic
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monitoring). In model 3, total biosecurity scores and biosecurity scores in each section were compared by disease status. The dependent variable was biosecurity scores (total, external biosecurity, internal biosecurity, and diagnostic monitoring), and the independent variable was disease status of PRRS and PED. In model 4, herd productivity was compared by disease status. The dependent variable was PWSY and PWM, and the independent variable was disease status of PRRS and PED. In models 2 to 4, farm size
was included as a covariate and farm ID was included as a random effect. Normal probability plot of the residuals in all models showed a nearly straight line, and no transformation was applied to dependent variables. P values less than 0.05 were considered significant, and p values less than 0.10 were considered to indicate tendency of difference.
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3. Results In the 152 records of biosecurity level, mean value (± SD) of total biosecurity score was 62.8 ± 11.2, with range from 35.3 to 88.8, and the total score was normally
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distributed (Fig. 1a: P = 0.43). Mean score of external biosecurity and internal biosecurity were 58.7 ± 13.9 and 66.3 ± 10.1, respectively, and both scores were normally distributed
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(Fig. 1b-c: P = 0.38 and 0.15, respectively). However, diagnostic monitoring score was
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not normally distributed, showing two peak frequencies around 55 and 75 (Fig. 1d: P < 0.05). Table 1 shows biosecurity scores in each subcategory of external biosecurity, internal biosecurity and diagnostic monitoring sections. In the external biosecurity section,
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approximately 40% of farms had more than 80% of full score in subcategories of personnel and manure /carcass, but approximately 80% of farms had less than 40% of full
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score in subcategories of farm equipment (Fig. 2a). In the internal biosecurity section,
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large variation was found in the subcategory of injection needles (Fig. 2b). In the diagnostic monitoring section, farms having more than 60% of full score in subcategory monitoring tests was approximately 40% (Fig. 2c). In addition, biosecurity scores were associated with sow inventory groups. Farms having ≤200 sows had lower scores of total biosecurity, external biosecurity, internal biosecurity and diagnostic monitoring than those having 401 to 800 sows and ≥801 sows (P < 0.05; Fig. 3).
Mean values of PWSY and PWM (± SD) were 23.8 ± 3.1 pigs and 6.4 ± 3.5%, respectively. Figure 4 shows relationships between PWSY or PWM and total biosecurity scores. Total biosecurity score was associated with herd productivity. As total biosecurity score increased by 1, PWSY increased by 0.1043±0.0289 pigs (P < 0.05; Fig. 4a). Farm size was not significant in the model. In addition, PWM tended to decrease by 0.0507±0.0274%, as total biosecurity score increased by 1 (P = 0.09; Fig. 4b). Farm size
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was associated with PWM (P<0.05): PWM increased by 0.1362±0.0408%, as farm size increased by 100 sows. In relationship between the three sections and herd productivity,
internal and external biosecurity scores were associated with PWSY and PWM (P < 0.05),
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but diagnostic monitoring score was not associated with PWSY and PWM.
Table 2 shows comparisons of total biosecurity score, external biosecurity,
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internal biosecurity and diagnostic monitoring sections by disease status of PRRS and
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PED. Regarding PRRS status, farms with an unknown or unstable status had lower total biosecurity scores than those with stable or negative status (P < 0.05). In each section, external biosecurity score was lower in farms with unknown or unstable status than those
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with stable or negative status (P < 0.05), but no differences between PRRS status were found in internal biosecurity score and diagnostic monitoring score. Regarding herd
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productivity by PRRS status, farms with an unknown or unstable status had lower PWSY
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(P < 0.05) and tended to have higher PWM (P = 0.08) than those with stable or negative status. There was no difference of PWSY between PED status, but PED positive farms had a higher PWM than PED negative farms (P < 0.05). Farm size was not significant in the model for herd productivity by PRRS status and by PED status. In each subcategory, PRRS status was associated with biosecurity scores regarding personnel and transport vehicle management in external biosecurity, health
conditions in internal biosecurity, and communications in diagnostic monitoring (P < 0.05; Table 3); farms with unknown or unstable status had lower biosecurity scores in the above subcategories compared to those with stable or negative status. Similarly, PED positive farms had lower total biosecurity and external biosecurity scores than PED negative farms (P < 0.05; Table 2), but no difference between PED status were found in internal biosecurity score and diagnostic monitoring score. In each subcategory, PED
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status was associated with biosecurity scores in semen, transport vehicle management, farm equipment management and visitors in external biosecurity, and communications in diagnostic monitoring (P < 0.05; Table 3); PED positive farms had lower biosecurity
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scores in the above subcategories compared to PED negative farms.
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4. Discussion
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The present study attempted to develop a quantitative risk assessment tool to measure biosecurity on a pig farm level. Although several biosecurity assessment tools such as PADRAP and Biocheck have been developed, this new biosecurity assessment
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tool, BioAsseT, was developed primarily to assess biosecurity under the conditions of Japanese commercial pig farms. Since production systems and geographical conditions
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vary between countries, it is necessary to take in consideration the particulars of that
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individual country. In order to assess biosecurity objectively, it is important to quantify the practices on each farm and provide concrete management suggestions based on the evaluation.
Total biosecurity score in the studied farms was normally distributed, ranging from 35.3 to 88.8, which indicated a large variation among farms and much room for improvement. A low biosecurity level increases the probability of introducing new
infectious diseases to a farm. That in turn could become a source of infection for the surrounding region, especially in high farm density areas (Sasaki et al., 2017b; Alkhamis et al., 2018). The aspects of biosecurity examined, mainly concern the application of measures to prevent the transmission of new infectious diseases to herds, and also to contain the spread of infections already present in different production phases (SimonGrifé et al., 2013). When attempting to improve biosecurity in a low score farm, it is
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important to clearly define the issues, and to make a plan with producers and farm staff that is both cost-effective and practicable. During that process, the evaluator should
consider whether certain biosecurity practices are already prioritized by the farmer, what
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his/her attitude towards disease prevention is, and how much of expectation exists in the implementation of control measures (Richens et al., 2018).
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Our results show that high levels of biosecurity increase herd productivity, as
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measured by PWSY and PWM. This indicates that the economic benefits yielded from implementing biosecurity measures may be positive. This finding could be useful for government officials or clinical veterinarians to motivate pig producers to enhance their
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level of biosecurity. Generally, producers understand that it is crucial to maintain a high level of herd biosecurity, but there is general hesitation to implement because it is a daily
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management behavior that needs to be repeated every day (Zhang et al., 2013).
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Demonstrating a clear relationship between biosecurity and herd productivity could be a source of motivation for the farm. A high biosecurity level can prevent the entry of infectious diseases into the herd, and also prevent the spread of diseases within the farm. At the same time, it may not be realistic to aim extremely high biosecurity score on commercial farm conditions, because farm personnel typically is engaged in a tight labor schedule to manage daily events such as farrowing, weaning, service or shipment. Our
results showing Fig. 4 suggested that 75 to 80 points could be a primary goal for producers to obtain high productivity. In addition to herd productivity, the total biosecurity score was associated with the disease status of PRRS and PED. The present study showed that farms with an unknown or unstable status for PRRS had low PWSY and high PWM, and that PED positive farms had high PWM. Assessment of biosecurity could be useful to determine
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how infectious disease may be introduced in herds and to reduce the frequency of outbreaks (Silva et al., 2018). In particular, external biosecurity was significantly associated with the disease status of PRRS and PED, whereas internal biosecurity and
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diagnostic monitoring were not. These results indicate that it is key to strengthen external
biosecurity for the prevention of infectious disease entry. Among the subcategories of
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external biosecurity, scores for transport vehicle management were associated with both
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PRRS and PED occurrence. Vehicles such as pig loading trucks or feed transport trucks are main risk factors for transmitting diseases between farms (Pileri and Mateu, 2016; Sasaki et al., 2016; Thakur et al., 2017). In addition, score of personnel was related with
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PRRS status. Recently, the risk of disease transmission due to external visitors could be minimized because many feed or transport companies carefully manage the order in
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which farms are visited in the course of a week (VanderWaal et al., 2018). However, our
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results indicate that it is important to also strengthen biosecurity of farm personnel to improve health conditions in the farm. Furthermore, PED status was associated with scores of semen, farm equipment and visitors. These aspects are also crucial for prevention of pathogen introduction. External biosecurity plays an important role to prevent the entry of viruses and bacteria because pathogens can be introduced into farms in different ways (Pileri and
Mateu, 2016). In the present study, large variation among farms was found in external biosecurity scores. Some items of subcategories were associated with disease status, as previously described, and it is important to improve management in these areas to reduce the risk of infectious disease entry. As well as external biosecurity, large variation was found in internal biosecurity. Internal biosecurity generally prevents the spread of diseases within the farm and to serves
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to halt the chain of infection when pathogens are introduced in herds. Recently, herd size is gradually increasing (Ministry of Agriculture, Forestry and Fisheries of Japan, 2018), and the importance of internal biosecurity is increasing as well.
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In contrast to external and internal biosecurity, score of diagnostic monitoring
was not normally distributed. The section diagnostic monitoring includes the item for
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frequency of monitoring tests. This may confuse the interpretation of results because
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farms having disease problems tend to test and monitor the herd health status more frequently, thus increasing the score of diagnostic monitoring. In addition, the present study interestingly found that farms having high communication score (including
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frequency of staff meetings, selection of staff attending those meetings, and efforts to apply diagnostic testing results for improvement of biosecurity practice) had better
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disease status. This finding suggests that communication between farm staff can improve
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herd health status. The success of a biosecurity plan could be influenced by the quality and the quantity of communication among all people involved with the farm. High score of total biosecurity in large farms can simply be explained by the fact
that large farms typically tend to consider an introduction of disease in the herd to cause severe damage and thus perceive that as a risk (Nöremark et al., 2009). Large farms have more connection to other facilities, such as slaughterhouses or feed plants as compared
with small farms, which in turn increases the odds of disease introduction (Sasaki et al., 2016). In addition, large farms can more readily invest their money for biosecurity materials since they usually have enough financial grounds. As a limitation, our results must be interpreted with caution given potential biases associated with our reference population compared with the whole Japanese swine population. In addition, the results obtained in the present study do not allow to identify
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causal relationships. Regarding PED status, there was a time lag between the date of biosecurity assessment and the date the PED outbreak occurred, and that might have
biased the result relating to the PED status, such as no difference of PWSY between PED
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status. Further, the effectiveness of specific biosecurity practices depends on the characteristics of the herd, characteristics of the premises, and surrounding areas and
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connections to other swine premises (Silva et al., 2016).
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In conclusion, the present study evaluated biosecurity practices by using an assessment tool of biosecurity level that fitted to Japanese commercial farms. Large farmto-farm variation was found in biosecurity levels, and farms having high biosecurity
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scores had high herd productivity and less risk for infectious diseases. These findings can be helpful for producers or decision makers to set feasible targets and standards, which
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are necessary to identify problem areas and improve biosecurity. A biosecurity strategy
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should be developed in collaboration with the herd veterinarian, but all stakeholders and visitors should contribute to the practical implementation to prevent economic loss due to a reduction of productivity. Conflict of interest statement None of the authors of this paper has a financial or personal relationship with other people or organizations that could inappropriately influence or bias the content of the paper.
Acknowledgements The authors gratefully thank the cooperative producers and the veterinarians for completing the questionnaires and providing their data for use in the present study, and members of P-Jet (PRRS Japan Elimination Team) for supporting this project.
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strategies to control Porcine Reproductive & Respiratory Syndrome at herd level. Prev. Vet. Med. 152, 89–102. Nieuwenhuis, N., Duinhof, T., van Nes, A., 2012. Economic analysis of outbreaks of porcine reproductive and respiratory syndrome virus in nine sow herds. Vet. Rec. 170, 225. Nöremark, M., Lindberg, A., Vågsholm, I., Sternberg Lewerin, S., 2009. Disease
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Pileri, E., Mateu, E., 2016. Review on the transmission porcine reproductive and
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determinants of farmers' biosecurity attitudes and behaviours. Prev. Vet. Med. 155, 61–74.
Sasaki, Y., Alvarez, J., Sekiguchi, S., Sueyoshi, M., Otake, S., Perez, A., 2016. Epidemiological factors associated to spread of porcine epidemic diarrhea in Japan. Prev. Vet. Med. 123, 161–167. Sasaki, Y., Kawabata, T., Noguchi, M., 2017a. The effect of porcine epidemic diarrhea
(PED) on ovarian function and reproductive performance after weaning in Berkshire sows. Trop. Anim. Health Prod. 49, 879–882. Sasaki, Y., Toyomaki, H., Sekiguchi, S., Sueyoshi, M., Makita, K., Otake, S., Perez, A., Alvarez, J., 2017b. Spatial dynamics of porcine epidemic diarrhea (PED) spread in the southern Kyushu, Japan. Prev. Vet. Med. 144, 81–88. Silva, G.S., Schwartz, M., Morrison, R.B., Linhares, D.C.L., 2017. Monitoring breeding
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vulnerability of swine breeding herds to the introduction of PRRS virus. Prev. Vet. Med. 160, 116–122.
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Thakur, K.K., Revie, C.W., Hurnik, D., Sanchez, J., 2017. Modelling contamination of trucks used in the shipment of pigs infected with porcine reproductive and
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respiratory syndrome virus. J. Swine Health Prod. 25, 183–193.
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Valeeva, N.I., Van Asseldonk, M.A.P.M., Backus, G.B.C., 2011. Perceived risk and strategy efficacy as motivators of risk management strategy adoption to prevent animal diseases in pig farming. Prev. Vet. Med. 102, 284–295.
VanderWaal, K., Perez, A., Torremorrell, M., Morrison, R.M., Craft, M., 2018. Role of animal movement and indirect contact among farms in transmission of porcine epidemic diarrhea virus. Epidemics. 24, 67–75.
Zhang, Y.H., Li, C.S., Liu, C.C., Chen, K.Z., 2013. Prevention of losses for hog farmers in China: Insurance, on-farm biosecurity practices, and vaccination. Res. Vet.
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Sci. 95, 819–824.
Figure Caption Figure 1. Relative frequencies of farms in total biosecurity score (a), external biosecurity score (b), internal biosecurity score (c), and diagnostic monitoring score (d).
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Figure 2. Relative frequencies of farms in each subcategories in external biosecurity score (a), internal biosecurity score (b), and diagnostic monitoring score (c). 0-20% indicates proportions of farms having 0-20% out of full score in each subcategory, and 21-40% was so on.
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Figure 3. Comparisons of biosecurity scores of total biosecurity score (a), external biosecurity score (b), internal biosecurity score (c), and diagnostic monitoring score (d) between sow inventory groups. Different letters (a-c) indicate significant differences (P < 0.05).
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Figure 4. Relationship between number of pigs weaned per sow per year (PWSY) and total biosecurity score (a) and between post-weaning mortality per year (PWM) and total biosecurity score (b).
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Fig 2b
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Table 1. Biosecurity score in each subcategory in external biosecurity, internal biosecurity and diagnostic monitoring section Full score
Mean±SD
CV
6
21.0±6.3
0.30
7
9
29.5±7.5
0.25
3
10.9±3.1
32
6
73
P75
Max.
17
22
26
33
16
24
27.5
37
49
0.29
0
9
11
13
15
21.6±7.5
0.35
5
16
22.5
28
32
14
48.4±11.0
0.23
41
7
26.2±9.1
0.35
9 22 16
2 4 3
2.6±2.8 11.4±4.2 7.9±4.4
1.09 0.37 0.56
26 46
5 8
40
49
58
71
7
18
28
34
41
0 0 0
0 8 4
2 11.5 8.5
5 14 10
8 20 16
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15
0.18 0.27
11 12
18 22
21 28.5
24 33
26 43
48.6±9.9
0.21
20
40
51
56
66
7 5 5 6
20.1±5.9 14.2±7.2 21.7±4.5 18.3±3.0
0.29 0.50 0.21 0.16
6 0 8 12
15 8 18 16
20 14 22 18
23 20 25 20
36 25 29 25
100
21
52.4±16.0
0.32
20
39
49
66
91
10
2
7.5±2.0
0.27
2
6
8
9
10
12 16
3 3
7.3±3.7 12.8±3.6
0.51 0.28
0 0
5 11
9 14.5
10 15
12 16
69
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20.5±3.7 28.1±7.6
12
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Pen layouts Pig flows Cleaning and disinfection Personnel Injection needles Delivery stalls Health condition
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Internal biosecurity
Min. P25
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P50
External biosecurity Farm location 33 Replacement 49 gilts Semen 15 Personnel Transport vehicle Manure and carcass Farm equipment Vermin controls Visitor
Percentile of the value
Number of items
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37 27 29 25
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Diagnostic monitoring Monitoring tests Pathology appraisals Communications Sociability
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PED1 Positive Negative P-value 1
52 100
61.0±1.6 63.8±1.1 P < 0.05
56.5±2.1 60.2±1.8 P < 0.05
54 71
60.8±1.6 65.5±1.3 P < 0.05
53.1±2.0 63.6±1.8 P < 0.05
Diagnostic
Number of pigs
Post-weaning
biosecurity score
monitoring score
weaned per sow per year
mortality per year, %
65.7±1.7 66.8±1.2 NS
64.0±2.4 68.5±1.8 NS
22.4±0.6 24.5±0.3 P < 0.05
7.1±0.6 6.0±0.4 P = 0.08
64.8±1.7 67.5±1.3 NS
63.3±2.3 68.7±2.0 NS
23.9±0.5 23.7±0.4 NS
7.3±0.5 5.7±0.5 P < 0.05
pr
biosecurity score
Internal
e-
biosecurity score
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NS: Not significant
N
Pr
PRRS Unknown or unstable Stable or negative P-value
External
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Disease status
Total
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Table 2. Comparisons of total biosecurity score, external biosecurity, internal biosecurity, diagnostic monitoring section, number of pigs weaned per sow per year and post-weaning mortality per year (Mean±SEM) by disease status of porcine reproductive and respiratory syndrome (PRRS) and porcine epidemic diarrhea (PED)
Scores for total biosecurity, external biosecurity, internal biosecurity, diagnostic monitoring section, number of pigs weaned per sow per year and post-weaning mortality per year in farms that did not provide their PED status at the time of data collection were 59.7±2.0, 52.7±4.7, 65.5±3.5, 68.4±5.6, 22.8±1.0 and 5.7±1.3, respectively.
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Table 3. Comparisons of biosecurity score in each subcategory in external biosecurity, internal biosecurity and diagnostic monitoring section (Mean±SEM) by disease status of porcine reproductive and respiratory syndrome (PRRS) and porcine epidemic diarrhea (PED)1 Unknown
Stable or
of each section
or unstable
negative
1
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NS: Not significant
21.9±1.0 49.1±1.4
Negative
P-value
9.9±0.6
11.4±0.4
44.6±1.7 1.8±0.4
51.9±1.4 3.2±0.4
< 0.05 NS < 0.05 < 0.05
NS
6.2±0.6
9.3±0.6
< 0.05
19.5±0.3
< 0.05
6.1±0.6
7.9±0.4
< 0.05
Only subcategories with significant differences were described.
Positive
NS < 0.05 < 0.05 NS
16.9±0.4
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Visitors Internal biosecurity score Health conditions Diagnostic monitoring score Communications
21.0±1.2 46.8±2.0
Pr
External biosecurity score Semen Personnel Transport vehicle management Farm equipment management
P-value
e-
Subcategories
PED status
pr
PRRS status
NS 6.1±0.6
8.2±0.4
< 0.05
PII:
S0167-5877(18)30890-0
DOI:
https://doi.org/10.1016/j.prevetmed.2019.104848
Reference:
PREVET 104848
To appear in:
Preventive Veterinary Medicine
Received Date:
18 December 2018
Revised Date:
30 September 2019
Accepted Date:
12 November 2019
Please cite this article as: Sasaki Y, Furutani A, Furuichi T, Hayakawa Y, Ishizeki S, Kano R, Koike F, Miyashita M, Mizukami Y, Watanabe Y, Otake S, Development of a biosecurity assessment tool and the assessment of biosecurity levels by this tool on Japanese commercial swine farms, Preventive Veterinary Medicine (2019), doi: https://doi.org/10.1016/j.prevetmed.2019.104848
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
Development of a biosecurity assessment tool and the assessment of biosecurity levels by this tool on Japanese commercial swine farms
Yosuke Sasaki
a,b,1,*
, Aina Furutani c,1, Tomohiro Furuichi d, Yuiko Hayakawa e, Sayoko
Ishizeki f, Rika Kano g, Fumiko Koike h, Mali Miyashita i, Yoshihiro Mizukami j, Yugo
a
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Watanabe f and Satoshi Otake k on behalf of P-JET l
Department of Animal and Grassland Sciences, Faculty of Agriculture, University of
Miyazaki, Miyazaki, Japan
Center for Animal Disease Control, University of Miyazaki, Miyazaki, Japan
c
Course of Animal and Grassland Sciences, Graduate School of Agriculture, University
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of Miyazaki, Miyazaki, Japan
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b
Feed One Co., Ltd., Kanagawa, Japan
e
IDEAS Swine Clinic, Chiba, Japan
f
Summit Veterinary Services, Gunma, Japan
g
Boehringer Ingelheim Animal Health Japan Co. Ltd., Tokyo, Japan
h
SMC Co., Ltd, Kanagawa, Japan
i
Eckstein Swine Service, Tokyo, Japan
j
Akabane Animal Clinic, Aichi, Japan
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d
k
Swine Extension & Consulting, Inc., Niigata, Japan
l
PRRS-Japan Elimination Team, Tokyo, Japan
1
These authors contributed equally to this work.
*Corresponding author 1-1 Gakuen Kibanadai-nishi, Miyazaki 889-2192, Japan Tel.: +81-985-58-7864; Fax: +81-985-58-7864 E-mail addresses: [email protected]
Abstract It is well known that infectious diseases such as porcine reproductive and respiratory
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syndrome (PRRS) and porcine epidemic diarrhea (PED) decrease herd productivity and lead to economic loss. It is believed that biosecurity practices are effective for the prevention and control of such infectious diseases. Therefore, the objective of the present
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study was to investigate whether or not an association between biosecurity level and herd productivity, as well as disease status exists on Japanese commercial swine farms. The
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present study was conducted on 141 farms. Biosecurity in each farm was assessed by a
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biosecurity assessment tool named BioAsseT. BioAsseT has a full score of 100 and consists of three sections (external biosecurity, internal biosecurity and diagnostic monitoring). Production data for number of pigs weaned per sow per year (PWSY) and
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post-weaning mortality per year (PWM) were collected for data analysis. Regarding PRRS status, the farms were categorized into two groups: unknown or unstable and stable
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or negative. In addition, these farms were categorized based on their PED status, either
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positive or negative. The total BioAsseT score was associated with herd productivity: as total score increased by 1, PWSY increased by 0.104 pigs and PWM decreased by 0.051% (P < 0.05). Herd productivity was associated with the score of external and internal biosecurity (P < 0.05), but did not correlate with the score of diagnostic monitoring. Regarding PRRS status, farms with an unknown or unstable status had lower total score than those with stable or negative status (P<0.05). Similarly, PED positive farms had a
lower total score compared to PED negative farms (P < 0.05). In conclusion, the present study provides evidence for the association between high biosecurity levels and increased herd productivity as well as a decreased risk for novel introductions of infectious diseases such as PED.
Keywords: Biosecurity practice; Herd management; Porcine epidemic diarrhea; Porcine
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reproductive and respiratory syndrome; Sow
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1. Introduction
It is well known that infectious diseases decrease herd productivity and lead to
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economic loss. Disease prevention through biosecurity measures is believed to be an
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important factor for improvement of the overall health status in commercial herd production. Biosecurity is the term used in veterinary medicine to describe measures to prevent pathogens from entering farm premises or a group of animals, known as external
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biosecurity, or prevent the spreading of pathogens within farm premises or groups of animals, known as internal biosecurity (Amass and Clark, 1999). To define biosecurity
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levels on commercial farms, it is important to quantify the biosecurity level in both
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external and internal aspects. In US and Europe, a biosecurity assessment tool such as PADRAP (AASV: The American Association of Swine Veterinarians; Bottoms et al., 2013) and Biocheck (Ghent University; Postma et al., 2016) have been developed to measure biosecurity. These systems have risk-based weighted biosecurity scoring that translates questions regarding biosecurity into a score for internal, external and overall biosecurity status, and biosecurity level is assessed by interviewing the farmer regarding
biosecurity practices and collecting data by visual inspection. This score aims at providing an objective, comprehensive and quantitative description of the level of biosecurity and can be used to inform the farmer on possible areas for improvements, and to compare his/her biosecurity level with that of other farms/herds. However, since production systems and geographical conditions vary between countries, development of a biosecurity assessment tool fitting to the situation of each country seemed beneficial.
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At the time of development, no assessment tool existed in Japan. On commercial farms, most producers are aware of the importance to maintain a high biosecurity level in order to prevent production losses. However, there is general
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hesitation in monitoring their biosecurity level because few studies have assessed the association of production parameters and practical biosecurity measures. Assessment of
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the relationship between biosecurity and herd productivity could motivate the
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implementation of biosecurity measures if such measures can be expected to be beneficial for the farm performance (Casal et al., 2007; Valeeva et al., 2011; Laanen et al., 2014), yet to date there is limited quantitative data available to link biosecurity and production
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parameters (Amass and Clark, 1999; Laanen et al., 2013; Postma et al., 2016). In addition, quantitative relationships between biosecurity level and disease status have not been fully
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reported in scientific literature, although a large number of risk factor studies associated
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to preventive measures is available. Recently, many countries experienced an occurrence of infectious diseases such as porcine reproductive and respiratory syndrome (PRRS) and porcine epidemic diarrhea (PED). An outbreak of PRRS decreases the number of piglets born alive, farrowing rate and feed efficiency, and increases pre- and postweaning mortality (Nieuwenhuis et al., 2012; Nathues et al., 2017; Silva et al., 2017; Nathues et al., 2018). Productivity losses in
the United States swine industry are estimated to be $664 million annually (Neumann et al., 2005; Holtkamp et al., 2013). In addition, an outbreak of PED severely increases preweaning mortality and decreases number of pigs weaned (Sasaki et al., 2017a; Furutani et al., 2017; Furutani et al., 2018). Therefore, the objectives of the present study were to develop a biosecurity assessment tool, to assess biosecurity level by using the tool on Japanese commercial
disease status on Japanese commercial swine farms.
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2. Materials and Methods
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farms, and to investigate the associations of biosecurity with herd productivity and
2.1. Development of a biosecurity assessment tool named BioAsseT
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A biosecurity assessment tool was developed by the PRRS-Japan Elimination
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Team (P-JET) that consists of clinical veterinarians and researchers. The tool was named BioAsseT, consisting of 131 questions and a full score of 100 (Appendix A). BioAsseT consists of three sections (external biosecurity, internal biosecurity, and diagnostic
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monitoring). Each individual section also has a full score of 100 and includes several subcategories (Table 1). Total biosecurity score was calculated as the mean of three
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sections. External biosecurity consists of nine subcategories: farm location, replacement
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gilts, semen, personnel, transport vehicles, manure and carcass management, farm equipment, vermin control, and visitors. Internal biosecurity consisted of seven subcategories: barn layouts, pig flow, cleaning and disinfection, personnel, injection needles, farrowing management, and health conditions. Diagnostic monitoring consisted of four subcategories: monitoring tests, pathology appraisals, communication, and information gathering. A number of items in each subcategory are shown in Table 1. All
of the 131 questions were multiple-choice questions, and each question was weighted based on scientific data and knowledge (categorized as high, intermedium or low risk) in order to take account the importance of the different biosecurity aspects. High score items were multiplied by 1.2, intermedium score items were multiplied by 1.0, and low score items were multiplied by 0.8. The results are presented as lists of immediate priority items
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and well-done items together with a numeric score.
2.2. Data collection
The investigation of biosecurity level using BioAsseT was performed by 25
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investigators (range: 1-15 farms per person) on 141 farrow-to-finish farms, equivalent to 3.2% of all herds in Japan. The country had 4,470 herds in February 2018 (Ministry of
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Agriculture, Forestry and Fisheries of Japan, 2018). Average herd size in the study
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population was 757 sows, while national average was 226 sows (Ministry of Agriculture, Forestry and Fisheries of Japan, 2018). Farm inclusion criteria was based on access to farms by investigators and willingness of participation by farms. Data was collected
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between July 2014 and May 2018 through face-to-face interview on farm by a licensed investigator. All investigators received training by a standardized method. The farms were
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visited by the investigators. Of the 141 farms, 7 were assessed twice and 1 was assessed
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5 times during the studied period. 152 records from BioAsseT were used in the present study.
In addition to the biosecurity score, data on herd productivity and herd health
status were collected. Herd productivity was measured by number of pigs weaned per sow per year (PWSY) and post-weaning mortality per year (PWM). PWM was calculated as the total number of post-weaned pigs dead in a given month divided by the total number
of pigs weaned in that same month. Total number of pigs born alive was based on the number of total live-born piglets in a given month. Productivity records over the past year were collected at the investigation, and 117 records for PWSY and 124 records for PWM were available and used in the analysis. Regarding herd health status, PRRS and PED were investigated on the farms. PRRS status was at the time of assessment, while PED status was the history of the infection from 2013 to the time of assessment because there
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was severe PED outbreak in 2013 in Japan (Sasaki et al., 2017b). Each farm was categorized based on their PRRS status as from I to V, based on the P-JET herd
classification that modified the definition reported in a previous study (Holtkamp et al.,
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2011). Briefly, Status I means positive unstable: PRRS virus detected by PCR from
replacement gilts, sows, suckling piglets, and post-weaning pigs. Status II is a transition between positive unstable and positive stable: PRRS virus is detected by PCR
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status
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from suckling piglets and post-weaned pigs, but not from breeding stock, Status III is positive stable: PRRS virus not detected by PCR from breeding stock or suckling piglets but occasionally positive in post-weaned pigs, Status IV is a transition between positive
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stable and PRRS free: all production stages are PCR negative, but breeding stock is ELISA positive, and Status V is PCR and ELISA negative in all stages. For analysis
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purposes in the present study, PRRS status was divided into two groups; unknown or
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unstable (status unknown, I and II) and stable or negative (status III to V). In addition, farms were also categorized based on PED status; positive or negative. In Japan, PED is a notifiable disease, meaning that clinical signs suggestive of PED must be reported to the regional Livestock Hygiene Service Center, upon which confirmation of infection is conducted through laboratory analysis by collected fecal samples. If fecal samples were positive by RT-PCR, farms were confirmed as PED positive. Farms that did not provide
their PED status at the time of data collection were omitted from the analysis regarding association of PED status and biosecurity scores. Farms were also classified into four groups based on sow inventory: ≤200 sows (N=41), 201 to 400 sows (N=35), 401 to 800 sows (N=36), and ≥801 sows (N=40).
2.3. Statistical analysis
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The data was analyzed statistically with SAS software ver. 9.4 (SAS Institute Inc., Cary, NC, USA). The Shapiro-Wilk test of normality was done on total biosecurity scores and biosecurity scores in each subcategory. The null hypothesis was that the data
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follow a normal distribution, and the P value greater than the significance level of 0.05
indicates not to reject the null hypothesis. A linear mixed model using MIXED procedure
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was used for the analysis. In model 1, total biosecurity scores and biosecurity scores in
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each section were compared by sow inventory groups. The dependent variable was biosecurity scores (total, external biosecurity, internal biosecurity, and diagnostic monitoring), and the independent variable was sow inventory groups. In model 2, the
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relationship between herd productivity and biosecurity scores was assessed. The dependent variable was PWSY and PWM, and the independent variable was each
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biosecurity score (total, external biosecurity, internal biosecurity, and diagnostic
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monitoring). In model 3, total biosecurity scores and biosecurity scores in each section were compared by disease status. The dependent variable was biosecurity scores (total, external biosecurity, internal biosecurity, and diagnostic monitoring), and the independent variable was disease status of PRRS and PED. In model 4, herd productivity was compared by disease status. The dependent variable was PWSY and PWM, and the independent variable was disease status of PRRS and PED. In models 2 to 4, farm size
was included as a covariate and farm ID was included as a random effect. Normal probability plot of the residuals in all models showed a nearly straight line, and no transformation was applied to dependent variables. P values less than 0.05 were considered significant, and p values less than 0.10 were considered to indicate tendency of difference.
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3. Results In the 152 records of biosecurity level, mean value (± SD) of total biosecurity score was 62.8 ± 11.2, with range from 35.3 to 88.8, and the total score was normally
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distributed (Fig. 1a: P = 0.43). Mean score of external biosecurity and internal biosecurity were 58.7 ± 13.9 and 66.3 ± 10.1, respectively, and both scores were normally distributed
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(Fig. 1b-c: P = 0.38 and 0.15, respectively). However, diagnostic monitoring score was
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not normally distributed, showing two peak frequencies around 55 and 75 (Fig. 1d: P < 0.05). Table 1 shows biosecurity scores in each subcategory of external biosecurity, internal biosecurity and diagnostic monitoring sections. In the external biosecurity section,
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approximately 40% of farms had more than 80% of full score in subcategories of personnel and manure /carcass, but approximately 80% of farms had less than 40% of full
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score in subcategories of farm equipment (Fig. 2a). In the internal biosecurity section,
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large variation was found in the subcategory of injection needles (Fig. 2b). In the diagnostic monitoring section, farms having more than 60% of full score in subcategory monitoring tests was approximately 40% (Fig. 2c). In addition, biosecurity scores were associated with sow inventory groups. Farms having ≤200 sows had lower scores of total biosecurity, external biosecurity, internal biosecurity and diagnostic monitoring than those having 401 to 800 sows and ≥801 sows (P < 0.05; Fig. 3).
Mean values of PWSY and PWM (± SD) were 23.8 ± 3.1 pigs and 6.4 ± 3.5%, respectively. Figure 4 shows relationships between PWSY or PWM and total biosecurity scores. Total biosecurity score was associated with herd productivity. As total biosecurity score increased by 1, PWSY increased by 0.1043±0.0289 pigs (P < 0.05; Fig. 4a). Farm size was not significant in the model. In addition, PWM tended to decrease by 0.0507±0.0274%, as total biosecurity score increased by 1 (P = 0.09; Fig. 4b). Farm size
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was associated with PWM (P<0.05): PWM increased by 0.1362±0.0408%, as farm size increased by 100 sows. In relationship between the three sections and herd productivity,
internal and external biosecurity scores were associated with PWSY and PWM (P < 0.05),
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but diagnostic monitoring score was not associated with PWSY and PWM.
Table 2 shows comparisons of total biosecurity score, external biosecurity,
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internal biosecurity and diagnostic monitoring sections by disease status of PRRS and
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PED. Regarding PRRS status, farms with an unknown or unstable status had lower total biosecurity scores than those with stable or negative status (P < 0.05). In each section, external biosecurity score was lower in farms with unknown or unstable status than those
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with stable or negative status (P < 0.05), but no differences between PRRS status were found in internal biosecurity score and diagnostic monitoring score. Regarding herd
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productivity by PRRS status, farms with an unknown or unstable status had lower PWSY
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(P < 0.05) and tended to have higher PWM (P = 0.08) than those with stable or negative status. There was no difference of PWSY between PED status, but PED positive farms had a higher PWM than PED negative farms (P < 0.05). Farm size was not significant in the model for herd productivity by PRRS status and by PED status. In each subcategory, PRRS status was associated with biosecurity scores regarding personnel and transport vehicle management in external biosecurity, health
conditions in internal biosecurity, and communications in diagnostic monitoring (P < 0.05; Table 3); farms with unknown or unstable status had lower biosecurity scores in the above subcategories compared to those with stable or negative status. Similarly, PED positive farms had lower total biosecurity and external biosecurity scores than PED negative farms (P < 0.05; Table 2), but no difference between PED status were found in internal biosecurity score and diagnostic monitoring score. In each subcategory, PED
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status was associated with biosecurity scores in semen, transport vehicle management, farm equipment management and visitors in external biosecurity, and communications in diagnostic monitoring (P < 0.05; Table 3); PED positive farms had lower biosecurity
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scores in the above subcategories compared to PED negative farms.
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4. Discussion
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The present study attempted to develop a quantitative risk assessment tool to measure biosecurity on a pig farm level. Although several biosecurity assessment tools such as PADRAP and Biocheck have been developed, this new biosecurity assessment
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tool, BioAsseT, was developed primarily to assess biosecurity under the conditions of Japanese commercial pig farms. Since production systems and geographical conditions
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vary between countries, it is necessary to take in consideration the particulars of that
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individual country. In order to assess biosecurity objectively, it is important to quantify the practices on each farm and provide concrete management suggestions based on the evaluation.
Total biosecurity score in the studied farms was normally distributed, ranging from 35.3 to 88.8, which indicated a large variation among farms and much room for improvement. A low biosecurity level increases the probability of introducing new
infectious diseases to a farm. That in turn could become a source of infection for the surrounding region, especially in high farm density areas (Sasaki et al., 2017b; Alkhamis et al., 2018). The aspects of biosecurity examined, mainly concern the application of measures to prevent the transmission of new infectious diseases to herds, and also to contain the spread of infections already present in different production phases (SimonGrifé et al., 2013). When attempting to improve biosecurity in a low score farm, it is
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important to clearly define the issues, and to make a plan with producers and farm staff that is both cost-effective and practicable. During that process, the evaluator should
consider whether certain biosecurity practices are already prioritized by the farmer, what
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his/her attitude towards disease prevention is, and how much of expectation exists in the implementation of control measures (Richens et al., 2018).
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Our results show that high levels of biosecurity increase herd productivity, as
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measured by PWSY and PWM. This indicates that the economic benefits yielded from implementing biosecurity measures may be positive. This finding could be useful for government officials or clinical veterinarians to motivate pig producers to enhance their
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level of biosecurity. Generally, producers understand that it is crucial to maintain a high level of herd biosecurity, but there is general hesitation to implement because it is a daily
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management behavior that needs to be repeated every day (Zhang et al., 2013).
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Demonstrating a clear relationship between biosecurity and herd productivity could be a source of motivation for the farm. A high biosecurity level can prevent the entry of infectious diseases into the herd, and also prevent the spread of diseases within the farm. At the same time, it may not be realistic to aim extremely high biosecurity score on commercial farm conditions, because farm personnel typically is engaged in a tight labor schedule to manage daily events such as farrowing, weaning, service or shipment. Our
results showing Fig. 4 suggested that 75 to 80 points could be a primary goal for producers to obtain high productivity. In addition to herd productivity, the total biosecurity score was associated with the disease status of PRRS and PED. The present study showed that farms with an unknown or unstable status for PRRS had low PWSY and high PWM, and that PED positive farms had high PWM. Assessment of biosecurity could be useful to determine
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how infectious disease may be introduced in herds and to reduce the frequency of outbreaks (Silva et al., 2018). In particular, external biosecurity was significantly associated with the disease status of PRRS and PED, whereas internal biosecurity and
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diagnostic monitoring were not. These results indicate that it is key to strengthen external
biosecurity for the prevention of infectious disease entry. Among the subcategories of
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external biosecurity, scores for transport vehicle management were associated with both
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PRRS and PED occurrence. Vehicles such as pig loading trucks or feed transport trucks are main risk factors for transmitting diseases between farms (Pileri and Mateu, 2016; Sasaki et al., 2016; Thakur et al., 2017). In addition, score of personnel was related with
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PRRS status. Recently, the risk of disease transmission due to external visitors could be minimized because many feed or transport companies carefully manage the order in
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which farms are visited in the course of a week (VanderWaal et al., 2018). However, our
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results indicate that it is important to also strengthen biosecurity of farm personnel to improve health conditions in the farm. Furthermore, PED status was associated with scores of semen, farm equipment and visitors. These aspects are also crucial for prevention of pathogen introduction. External biosecurity plays an important role to prevent the entry of viruses and bacteria because pathogens can be introduced into farms in different ways (Pileri and
Mateu, 2016). In the present study, large variation among farms was found in external biosecurity scores. Some items of subcategories were associated with disease status, as previously described, and it is important to improve management in these areas to reduce the risk of infectious disease entry. As well as external biosecurity, large variation was found in internal biosecurity. Internal biosecurity generally prevents the spread of diseases within the farm and to serves
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to halt the chain of infection when pathogens are introduced in herds. Recently, herd size is gradually increasing (Ministry of Agriculture, Forestry and Fisheries of Japan, 2018), and the importance of internal biosecurity is increasing as well.
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In contrast to external and internal biosecurity, score of diagnostic monitoring
was not normally distributed. The section diagnostic monitoring includes the item for
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frequency of monitoring tests. This may confuse the interpretation of results because
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farms having disease problems tend to test and monitor the herd health status more frequently, thus increasing the score of diagnostic monitoring. In addition, the present study interestingly found that farms having high communication score (including
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frequency of staff meetings, selection of staff attending those meetings, and efforts to apply diagnostic testing results for improvement of biosecurity practice) had better
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disease status. This finding suggests that communication between farm staff can improve
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herd health status. The success of a biosecurity plan could be influenced by the quality and the quantity of communication among all people involved with the farm. High score of total biosecurity in large farms can simply be explained by the fact
that large farms typically tend to consider an introduction of disease in the herd to cause severe damage and thus perceive that as a risk (Nöremark et al., 2009). Large farms have more connection to other facilities, such as slaughterhouses or feed plants as compared
with small farms, which in turn increases the odds of disease introduction (Sasaki et al., 2016). In addition, large farms can more readily invest their money for biosecurity materials since they usually have enough financial grounds. As a limitation, our results must be interpreted with caution given potential biases associated with our reference population compared with the whole Japanese swine population. In addition, the results obtained in the present study do not allow to identify
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causal relationships. Regarding PED status, there was a time lag between the date of biosecurity assessment and the date the PED outbreak occurred, and that might have
biased the result relating to the PED status, such as no difference of PWSY between PED
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status. Further, the effectiveness of specific biosecurity practices depends on the characteristics of the herd, characteristics of the premises, and surrounding areas and
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connections to other swine premises (Silva et al., 2016).
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In conclusion, the present study evaluated biosecurity practices by using an assessment tool of biosecurity level that fitted to Japanese commercial farms. Large farmto-farm variation was found in biosecurity levels, and farms having high biosecurity
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scores had high herd productivity and less risk for infectious diseases. These findings can be helpful for producers or decision makers to set feasible targets and standards, which
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are necessary to identify problem areas and improve biosecurity. A biosecurity strategy
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should be developed in collaboration with the herd veterinarian, but all stakeholders and visitors should contribute to the practical implementation to prevent economic loss due to a reduction of productivity. Conflict of interest statement None of the authors of this paper has a financial or personal relationship with other people or organizations that could inappropriately influence or bias the content of the paper.
Acknowledgements The authors gratefully thank the cooperative producers and the veterinarians for completing the questionnaires and providing their data for use in the present study, and members of P-Jet (PRRS Japan Elimination Team) for supporting this project.
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Figure Caption Figure 1. Relative frequencies of farms in total biosecurity score (a), external biosecurity score (b), internal biosecurity score (c), and diagnostic monitoring score (d).
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Figure 2. Relative frequencies of farms in each subcategories in external biosecurity score (a), internal biosecurity score (b), and diagnostic monitoring score (c). 0-20% indicates proportions of farms having 0-20% out of full score in each subcategory, and 21-40% was so on.
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Figure 3. Comparisons of biosecurity scores of total biosecurity score (a), external biosecurity score (b), internal biosecurity score (c), and diagnostic monitoring score (d) between sow inventory groups. Different letters (a-c) indicate significant differences (P < 0.05).
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Figure 4. Relationship between number of pigs weaned per sow per year (PWSY) and total biosecurity score (a) and between post-weaning mortality per year (PWM) and total biosecurity score (b).
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Fig 2b
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Table 1. Biosecurity score in each subcategory in external biosecurity, internal biosecurity and diagnostic monitoring section Full score
Mean±SD
CV
6
21.0±6.3
0.30
7
9
29.5±7.5
0.25
3
10.9±3.1
32
6
73
P75
Max.
17
22
26
33
16
24
27.5
37
49
0.29
0
9
11
13
15
21.6±7.5
0.35
5
16
22.5
28
32
14
48.4±11.0
0.23
41
7
26.2±9.1
0.35
9 22 16
2 4 3
2.6±2.8 11.4±4.2 7.9±4.4
1.09 0.37 0.56
26 46
5 8
40
49
58
71
7
18
28
34
41
0 0 0
0 8 4
2 11.5 8.5
5 14 10
8 20 16
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15
0.18 0.27
11 12
18 22
21 28.5
24 33
26 43
48.6±9.9
0.21
20
40
51
56
66
7 5 5 6
20.1±5.9 14.2±7.2 21.7±4.5 18.3±3.0
0.29 0.50 0.21 0.16
6 0 8 12
15 8 18 16
20 14 22 18
23 20 25 20
36 25 29 25
100
21
52.4±16.0
0.32
20
39
49
66
91
10
2
7.5±2.0
0.27
2
6
8
9
10
12 16
3 3
7.3±3.7 12.8±3.6
0.51 0.28
0 0
5 11
9 14.5
10 15
12 16
69
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20.5±3.7 28.1±7.6
12
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Pen layouts Pig flows Cleaning and disinfection Personnel Injection needles Delivery stalls Health condition
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Internal biosecurity
Min. P25
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P50
External biosecurity Farm location 33 Replacement 49 gilts Semen 15 Personnel Transport vehicle Manure and carcass Farm equipment Vermin controls Visitor
Percentile of the value
Number of items
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37 27 29 25
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Diagnostic monitoring Monitoring tests Pathology appraisals Communications Sociability
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PED1 Positive Negative P-value 1
52 100
61.0±1.6 63.8±1.1 P < 0.05
56.5±2.1 60.2±1.8 P < 0.05
54 71
60.8±1.6 65.5±1.3 P < 0.05
53.1±2.0 63.6±1.8 P < 0.05
Diagnostic
Number of pigs
Post-weaning
biosecurity score
monitoring score
weaned per sow per year
mortality per year, %
65.7±1.7 66.8±1.2 NS
64.0±2.4 68.5±1.8 NS
22.4±0.6 24.5±0.3 P < 0.05
7.1±0.6 6.0±0.4 P = 0.08
64.8±1.7 67.5±1.3 NS
63.3±2.3 68.7±2.0 NS
23.9±0.5 23.7±0.4 NS
7.3±0.5 5.7±0.5 P < 0.05
pr
biosecurity score
Internal
e-
biosecurity score
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NS: Not significant
N
Pr
PRRS Unknown or unstable Stable or negative P-value
External
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Disease status
Total
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Table 2. Comparisons of total biosecurity score, external biosecurity, internal biosecurity, diagnostic monitoring section, number of pigs weaned per sow per year and post-weaning mortality per year (Mean±SEM) by disease status of porcine reproductive and respiratory syndrome (PRRS) and porcine epidemic diarrhea (PED)
Scores for total biosecurity, external biosecurity, internal biosecurity, diagnostic monitoring section, number of pigs weaned per sow per year and post-weaning mortality per year in farms that did not provide their PED status at the time of data collection were 59.7±2.0, 52.7±4.7, 65.5±3.5, 68.4±5.6, 22.8±1.0 and 5.7±1.3, respectively.
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Table 3. Comparisons of biosecurity score in each subcategory in external biosecurity, internal biosecurity and diagnostic monitoring section (Mean±SEM) by disease status of porcine reproductive and respiratory syndrome (PRRS) and porcine epidemic diarrhea (PED)1 Unknown
Stable or
of each section
or unstable
negative
1
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NS: Not significant
21.9±1.0 49.1±1.4
Negative
P-value
9.9±0.6
11.4±0.4
44.6±1.7 1.8±0.4
51.9±1.4 3.2±0.4
< 0.05 NS < 0.05 < 0.05
NS
6.2±0.6
9.3±0.6
< 0.05
19.5±0.3
< 0.05
6.1±0.6
7.9±0.4
< 0.05
Only subcategories with significant differences were described.
Positive
NS < 0.05 < 0.05 NS
16.9±0.4
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Visitors Internal biosecurity score Health conditions Diagnostic monitoring score Communications
21.0±1.2 46.8±2.0
Pr
External biosecurity score Semen Personnel Transport vehicle management Farm equipment management
P-value
e-
Subcategories
PED status
pr
PRRS status
NS 6.1±0.6
8.2±0.4
< 0.05