Biosecurity Levels and Risk of Classical Swine Fev

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BIOSECURITY LEVELS AND RISK OF CLASSICAL SWINE FEVER DISEASE ON LARGE PIG FARMS IN CHILE

Pinto Ja and Urcelay Sa.

VEERU, University of Reading, United Kingdom

Pig production requires a safe environment especially with regard to the hygienic conditions in the production lines, which feed high quality products into pork markets. Differences in terms of scale of production, standards of bio-security, production inputs and marketing practices among pig farms would affect the potential risk of transmission, and the spread to other farms in the event of reintroduction of CSF disease in Chile. These variables can be quantified to provide an indicator of risk to introduce diseases on pig farms through different mechanisms, such as contact with other farms, movement of animals or mechanical transmission by feed and staff working in pig houses. A bio-security scoring system can help to assess potential risk of getting or spreading diseases in different pig farms.

Material and Methods A questionnaire survey was applied in 50 large integrated pig farms located in the central region of Chile between October 1997 and June 1998. This included bio-security, general management and history of CSF on each farm. A bio-security scoring system was developed for pig farms adapted from Leslie (1996), this method was originally considered to systematically assess potential poultry bio-security hazards related with infection at farm level.

The factors included in the bio-security score were 74 variables that are related to 12 main production components. Each individual variable was assessed on its, presence or absence, and its effect on recognised good bio-security practices: a) +1 was added if the variable increases bio-security; b) -1 was added if the variable decreases bio-security and c) 0 was added if the variable does not modify bio-security. Consecutive steps or procedures are followed to determine the final farm's susceptibility to CSF infection: a) The farm's bio-security scoring is used as a base line and b) It is adjusted for the presence or absence of factors that were previously implicated in the transmission of the disease such as history of outbreaks, vaccination of pig herd routinely before the withdrawal of vaccination and whether a pig farm is close to small or large farms with history of CSF. Also a literature review looked at the factors most closely related with CSF infection at farm level where a 50% of the outbreaks of CSF are represented by animal movement between farms, 30% by mechanical contacts and 20% due to general poor prevention measures (Stark, 1997; Horst, 1997). The bio-security score obtained is divided into these three components and weighted consecutively in order to obtain the final CSF risk score.

a Faculty of Veterinary and Animal Sciences, University of Chile. Casilla 2, Correo 15, Santiago, Chile.

Proceedings of the 9th International Symposium on Veterinary Epidemiology and Economics, 2000Available at www.sciquest.org.nzProceedings of the 9th International Symposium on Veterinary Epidemiology and Economics, 2000Available at www.sciquest.org.nz

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Results The CSF risk score was computed for the 50 farms (mean=5.33, SE=0.47). Scores indicates that pig holdings with negative values theoretically have a higher potential risk of getting CSF while farms with positive values – especially those with values close to thirteen –would have a lower potential risk of infection. CSF risk score was used as a method to categorise farms into groups of similar potential risk and included in an ordinal logistic regression. Three categories of risk were defined using CSF risk score, high risk, medium risk and low risk category. 17 farms represented the high-risk group while the other two, 15 and 18 farms represented medium and low risk farms respectively.

95% CI

of Odds ratio Predictor Coefficient

SD p value

Odds Ratio

Lower Upper

Constant 1 -10.047 6.771 0.138 * * *

Constant 2 -8.226 6.712 0.221 * * *

Insurance 2.096 0.895 0.019 8.13 1.41 47.04

Replacement -0.069 0.029 0.016 0.93 0.88 0.99

Sows -0.0002 0.0007

0.762 1.00 1.00 1.00

Pre-weaning mortality

0.318 0.1414

0.024 1.37 1.04 1.81

Parturition rates 0.097 0.072 0.181 1.11 0.96 1.27 Table 1 Ordinal logistic regression model

From the Table 1 is observed that sow replacement, insurance against CSF and pre-weaning mortality are significantly associated with high risk of CSF in pig farms. However, variables such as herd size and parturition rates are unrelated with the dependent variable. The odds ratio between those farms with insurance and farms without insurance indicates that producers with insurance have approximately eight times (OR=8.13) higher susceptibility to CSF than those without insurance.

Discussion Distribution of bio-security in pig units indicates that the implementation of disease prevention measures must be encouraged strongly in those farms with low scores for bio-security (Dijkhuizen, 1999). However, in those farms with positive scores of bio-security the chances of infection by diseases such as CSF are minimised. Preventive measures themselves are not a direct determinant of the risk of CSF infection, but classifications based on potential risk must be carried out to evaluate the status of preventive animal health (Meuwissen, et al., 1997). Scoring methods have been widely described, but they have been rarely applied to animal health issues, especially to identify or classify farms according to their susceptibility to diseases. A scoring method can be useful to evaluate risk of CSF where prevention measures are combined with a quantification of main risk factors to CSF (Davies, 1996). CSF risk scoring was based on bio-security scoring, but


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also included the main risk factors for CSF in addition to the history record of CSF in each pig unit which facilitated the classification of pig farms in risk categories. The risk of pig holdings getting CSF holdings is associated with farms with insurance, and with high pre-weaning mortality, both parameters are related to the quality of prevention measures taken by producers. The decreasing associated with an increasing level of replacement in large pig farms could be explained because most pigs are supplied from a few certified breeding units with a known low risk health status, and those not supplied from these sources are provided by on-farm replacements. Insurance policy against CSF in Chile seems not to have relation with an improved levels of bio-security in pig farms, and higher-risks have been shown to be associated with insured pig farms. This may be explained because these farms could tend to have an increased amount of animal movement between pig holdings since large movement of animals has been indicated as a main risk factor, especially in areas of high animal density (Horst, 1997; Stark, 1997). Also hazard awareness could be influencing decisions at farm level because farms that have insured their pig herds, at the same time they could increase their level of acceptable risk because insurance protects potential losses (Howe and Whittaker, 1997). By contrast, Davies in 1996 expected to find better bio-security on those farms, which had insurance. Insurance companies are penalising high-risk farms with a high premium, and reward low-risk farms with low premiums. Relaxation of bio-security measures on farms with insurance can be tackled through epidemiological studies estimating the risk of diseases in pig populations.

Conclusions § Preventive measures to control diseases like CSF were evaluated on pig farms using a

bio-security scoring system that denotes susceptibility to CSF. It incorporates the history of CSF on each farm and specific risk factors associated with CSF.

§ Pig farms were classified as being in high, medium or low risk categories based on CSF risk scoring system. This demonstrated the wide distribution of preventive measures, and specific risk factors present at farm level.

§ High susceptibility to CSF was associated with farms with insurance, high pre-weaning mortality levels and low replacement rates.

§ An estimation of bio-security measures implemented by pig farms could be useful for the same producers, insurance companies and veterinary services to understand the state of disease preparedness in case of potential outbreaks of diseases such as CSF.

References Davies, G. 1996. The role of the public sector in controlling the epidemic disease of

livestock. In Proceedings of the Society of Veterinary Epidemiology and Preventive Veterinary Medicine, pp. 78-83. Glasgow, 27-29 March.

Dijkhuizen, A. A. 1999. The 1997-1998 Outbreak of Classical Swine Fever in the Netherlands: Lessons to be learned. In Proceedings of the Society for Veterinary Epidemiology and Preventive Medicine. Bristol, United Kingdom. p. xi-xx.

Horst, H. S., Huirne, R. B. M. & Dijkhuizen, A. A. 1997. Monte Carlo Simulation of virus introduction into the Netherlands. In Risk and economic consequences of introducing classical swine fever into the Netherlands by feeding swill to swine. Revue Scientifique et Technique. 16, 207-214.


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Howe, K. S. & Whittaker J. M. Guiding. 1997. Guiding Decisions on Methods and Responsibilities for Epidemic Disease Prevention and Control: Perspectives from Environmental and Insurance Economics. In: Proceedings of the Society for Veterinary Epidemiology and Economics, 9-11 April. Chester, United Kingdom, pp. 223-235.

Leslie, J. 1996. Salmonella Infection in Egg-Laying Flocks: A Study of Policy Options and their implications. Ph.D. Thesis. 191 p. Department of Agriculture, University of Reading.

Meuwissen, M. P. M., Horst, H. S., Huirne, R. B. M. & Dijkhuizen, A. A. 1997. Insurance against losses from contagious animal disease. Epidemiologie et Sante Animal, 31-32, 10.13.1-10.13.2.

Stark, K. D. C. 1998. Systems for the Prevention and Control of Infectious Diseases in Pigs. Ph.D. Thesis. University of Massey, New Zealand, pp. 153-168.


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Biosecurity Levels and Risk of Classical Swine Fev