CHAPTER 1

THE IDENTIFICATION OF RISK FACTORS FOR THE PRESENCE OF ENZOOTIC PNEUMONIA-LIKE LESIONS AND PLEURISY IN SLAUGHTERED FINISHING PIGS UTILIZING EXISTING BRITISH PIG INDUSTRY DATA.

Sánchez-Vázquez, MJ(a); Smith, R(b); Gunn, GJ(a); Lewis, F(a); Strachan, WD(c); Edwards, SA.(d)

(a) Scottish Agricultural College, Kings Buildings, West Mains Road, Edinburgh, EH9 3JG.. (b) Veterinary Laboratories Agency, New Haw, Addlestone, Surrey, KT15 3NB. (c) Boehringer Ingelheim Vetmedica, Ellesfield Avenue, Bracknell, Berkshire, RG12 8YS. (d) Newcastle University, Agriculture Building, Newcastle upon Tyne, NE1 7RU.

Paper accepted by the British Pig Journal to be published in Nov-Dec 2009 edition.

1 1. Abstract Abattoir monitoring in slaughtered finishing pigs is carried out as part of established health schemes. Enzootic pneumonia-like (EP-like) lesions and pleurisy are the two more prevalent conditions reported, being associated with reduction in performance traits. This study combined records on EP-like lesion and pleurisy from 129,819 slaughtered pigs with information from the farms of origin in order to identify those production characteristics that may influence the prevalence of these lesions. Five hundred and five farms were recruited using the information available in national quality assurance programmes (QAPs): pig stocking levels; feeding practices; housing systems and geographical location. Relevant epidemiological information common to these databases was extracted. Generalized linear mixed models were used for multivariable analysis, allowing for clustering at batch level. Geographical location of the finishing unit appeared to be a statistically significant risk factor for EP-like lesions and pleurisy. Part-slatted floors also appeared as a potential risk factor for the presence of these two conditions, versus the use of solid floor with bedding which appeared protective. This study demonstrated the potential of combining abattoir and QAPs’ data to perform epidemiological analysis which may lead the British pig industry to a better understanding of how the farm characteristics and geographic location could influence the prevalence of EP- like lesions and pleurisy. 2. Introduction Coordinated industry-wide pig abattoir lesion scoring has been implemented in Great Britain with the development of health schemes; in 2003 when Wholesome Pigs Scotland (WPS) started and in 2005 with the British Pig Health Scheme (BPHS). These schemes report the presence of twelve different conditions detected in the slaughtered pig (in the pluck and on the skin), many of which have been associated with a reduction in performance traits and consequent increases in production costs. The cornerstone of the health scheme’s success has been the frequent feedback of benchmarked results from routine abattoir inspections to the participating producers and their herd veterinarians helping to increase their awareness of the occurrence of these subclinical diseases in their herds. In addition to the more recent implementation of these health schemes the pig industry, supported by the government and food industry, had previously developed quality

2 assurance programmes (QAPs). These initiatives were created in response to an increasing demand from the consumer for warranted animal health and welfare standards. Through regular periodic audits they certify the compliance of their members with the minimum agreed standards of pig production for the scheme (e.g. pig housing and management practices; sources of pigs, feeding, hygiene, medications, etc.). The QAPs also collect and document a large amount of information on farm production descriptors (e.g. type of housing, type of feeding, number of finishing pigs, number of sows, etc.). The two types of lesion that have remained most prevalent since the introduction of the health schemes have been enzootic pneumonia-like (EP-like) lesions and pleurisy. In 2007 the overall prevalence of slaughtered pigs reported with these conditions was 28% and 11% respectively for the WPS and BPHS aggregate annual records. EP-like lesions are reported when the gross pathology observed is consistent with the expected lung lesion caused by Mycoplasma hyopneumoniae (the recognised causative agent of EP); i.e. a confluent consolidation affecting the cranioventral regions of the lungs; with appearance ranging from dark red to greyish pink (Jubb et al., 1993). Although lesions are not pathognomonic for M. hyopneumoniae infection and other organisms have been associated with identical lesions, e.g. Mycoplasma hyorhinis and chronic bacterial infection. Pleurisy (or pleuritis), which denotes inflammation of the pleura, is reported in the health schemes under two pathologies: extensive pleurisy and/or as a pleuropneumonia-like lesion. Extensive pleurisy is commonly manifesting as chronic fibrous, or less commonly more acute fibrinous, pleural adhesions. The lesions are not pathognomonic for any particular pathogen and various infectious agents are reported to be involved in the development of pleuritic lesions including Actinobacilus pleuropneumoniae, Pasteurella spp, Mycoplasma. hyorhinis., swine influenza and Haemophilus parasuis (Enoe et al. 2002; Straw et al., 2006). Discrete pleuropneumonia-like lesions (most frequently involving A. pleuropneumoniae or Pasteurella multocida infection) are reported separately from lesions involving only adhesions of the visceral and/or parietal pleura. The high prevalence of these EP-like lesions and pleurisy at slaughter is a constant reminder of the continued importance of respiratory disorders in pig production and the significant financial losses to the industry incurred by such disorders (Straw et al., 2006). It is has been reported that environmental factors and management practices play an important role in the development of respiratory conditions, together with the presence of the infectious agents (Done, 1991; Jubb et al., 1993; Stärk, 2000). However carrying out large

3 scale epidemiological studies for these conditions necessitates significant budgetary and logistic resources as these lesions cannot easily be detected in the live pig. Previous studies have used abattoir reports to perform epidemiology-based analysis to investigate on-farm risk factors for the presence of respiratory conditions in the slaughtered pigs (Hurnik et al., 1994; Stärk et al., 1998; Clevelance-Nielsen et al., 2002; Enoe et al. 2002; Ostanello et al., 2007), but this approach has not been exploited in the UK. The study described here aimed to optimise the use of the existing pig abattoir scoring reports by linking them to the farm information collected through the QAP in order to investigate which management practices and farm characteristics may be predictors for the prevalence of EP-like lesions and pleurisy.

3. Material and Methods 3.1. Data source Farm information was obtained from the three main farm QAPs: Quality Meat Scotland (QMS) for Scotland and Assured British Pigs (ABP) and Genesis Quality Assurance (QA) for England and Wales. Current and historic data available at the time this study started were used, covering the two year period between October 2005 and September 2007. This information was merged with the herd abattoir records from the health schemes WPS and BPHS. The study population for this project consisted of those pig producers that were members of a health scheme and were also part of any of those three QAPs. Farm was defined as a site that had a specific QA number and a Defra herdmark (Defra, PRIMO rules). This latter is an individual official reference for each holding, consisting of an alphanumeric code, which is also used to identify those pigs sent to the abattoir by its application on each shoulder as a tattoo, otherwise known as a slapmark. A batch was defined as a group of pigs from a single farm submitted to the abattoir on a particular date. The slapmark is used to identify and report the abattoir scoring results for each batch of pigs assessed under the health schemes.

3.2. Data management and data mining. 3.2.1. Farm data Revision of historical information available revealed three main data problems: i) existence of missing values; ii) discrepancies in the date on which the different farm characteristic

4 descriptors were updated; and iii) changes over time in some of the farm characteristics, especially for stocking numbers which commonly presented fluctuations over time. Farm variables that were common to the three different quality assurance schemes, and could potentially be a “proxy” for a risk/protective factor for the occurrence of EP-like lesions or pleurisy in finishing pigs, were identified. The following variables met these criteria: total number of finishing pigs and sows; flooring characteristics in the finishing pigs housing; use of wet-feeding; the presence of a breeding unit on the site; and reported use of outdoors accommodation at any stage of production. A database combining QA information was created with the assured member farm characteristics for a specific herdmark. The information concerning the stocking numbers present in the farms at the time of the inspections and the date at when the information was updated were include in the database. For other variables (e.g. flooring characteristics, feeding system) which were farm structural characteristics and management practices unlikely to change within the two year study period, atemporal farm characteristics were established. This approach allowed our database, and posterior analyses, to account for the potential presence throughout the whole study period of these characteristics reported to be present in some but not all the audits. A summary of all the different variables considered in the analysis can be found in table 1 and 2.

5 Table 1. Categorical variables. Variable Classes Count data Binary data Description

Units N N farms yes (percentage) (percentage) QA Programme QMS Farms 166 (33) QA programme from ABP Farms 149 (29) which the farm Genesis QA Farms 190 (38) characteristics have been obtained Health Scheme WPS Batches 542 (20) Health Scheme where BPHS Batches 2108 (80) the abattoir scoring data have been reported Location North (a) Farms 113 (22) Geographic area where Scotland Farms 166 (33) the farm was located South East (b) Farms 179 (36) South West (c) Farms 47 (9)

Breeding Yes/no 212 (42) Having a breeding herd

Production all Yes/no 459 (91) No records for stages indoors of outdoors production

Use of wet-feeding Yes/no 72 (14) Reported use of wet- feeding

Use of solid floor Yes/no 393 (78) Reported use of solid with bedding floor with bedding

Use of part slatted Yes/no 98 (19) Reported use of partly floor slatted floor

Use of full slatted Yes/no 128 (25) Reported use of fully floor slatted floor (a) North England; York and the Humber. (b) East Midlands; East Anglia and South East. (c) West Midland, Wales and South West.

6 Table 2. Continuous variables. Variable Units/ Mean Median 25th 75th SD Description Classes Percentile Percentile

Number of Count 3536 2500 1350 4296 3599 Number of pigs at the finishers time of farm inspection

Number of Count 223 0 0 366 328 Number of sows at the sows time of farm inspection

Pig farm Farms / 0.033 0.021 0.01 0.044 0.03 Calculated farm density on density in km2 number of farms per km2 the area

Table 3. Continuous variables studied by classes. Variable Classes divisions Number of farms Number of audits (1) (percentage) (percentage)

Number of finishers More that 9000 finishers 23 (5) 28 (4) present in the farms Between 9000 and 2500 finishers 182 (36) 300 (38) Less than 2500 finishers 300 (59) 458 (58)

Number of sows More that 600 sows 28 (6) 53 (7) present in the farms Between 150 and 600 sows 145 (28) 257 (32) Between 15 and 150 sows 31 (6) 46 (6) None sows present 301 (60) 430 (55)

Pig farm density in More that 0.09 farm per Km2 51 (10) - (d) the area Between 0.09 and 0.03 farms per Km2 192 (38) - (d) Less than 0.03 farms per Km2 262 (52) - (d)

(1) Information on the number of animals is updated. (d) Farm density does not vary between the audits.

To investigate area differences in lesion prevalence, Great Britain was divided into four different areas (see figure 1): a) Scotland; b) North England; Yorkshire and the Humber (North); c) East Midlands, East Anglia and South East (South East); and d) West Midlands, Wales and South England (South West). Information from the pig industry concerning location of the abattoirs participating in the scheme and of the farms supplying pigs to those abattoirs was used in deciding this geographical divisions; as representation of a proxy for abattoir farm capture areas. Density of farms per area for each holding, expressed in number

7 of pig farms per km2, was calculated using the map reference for the postcode of 1,100 farms. ESRI ArcGIS 9.2 was used to perform these calculations.

Figure 1. Map presenting the geographical divisions used to study farm location effect.

3.2.2. Abattoir data EP-like lesion scores, representing the approximate percentage of lung area showing consolidation, were recorded on a scale from 0 to 55 in 2.5 steps. Pleuritic lesions were scored in two ways: a) pleural adhesions were recorded using three categories 0, 1 and 2; 1 indicating adhesions between lung lobes only, 2 indicating adhesions involving the visceral pleura and/or the parietal pleura and 0 indicating an absence of adhesions and b) pleuropneumonia-like lesions which are reported as a binary, present (1) or absent (0). For

8 this study we considered presence of pleurisy when any of these two lesions were present (i.e. pleural adhesions or pleuropneumonic-like lesions). The scoring was carried out by swine veterinarians trained in this method of recording, assessing at the abattoir inspection line. Both schemes aimed to obtain a representative sample of the batch of pigs assessing every other pig on the slaughter line. However they differed in one specification of the sampling criteria strategy; WPS sampled pigs up to a maximum of 150 per batch whereas BPHS sampled up to 50 pigs.

3.2.3. Merging of farm and abattoir data The abattoir scoring results reported for a specific slapmark were attributed to the compiled QA farm characteristics with a matching herdmark. A restriction criterion was used to join the abattoir information recorded for the twelve months after the date the farm stock numbers were updated. No historical information was kept in the record for QMS farms, for those records the farm information was related to the lesions reported in the abattoir within one year before and one year after farm inspection. This was done with the aim of providing satisfactory number of batches of pigs assessed per farm to be included in the analysis. The process described above lead to a dataset of QA information from 505 farms, including 786 audits where the stocking information was updated. This dataset included the records from 2650 batches of pigs from those farms assessed through the health schemes. These assessments integrated a total of 129,819 pigs inspected; with a mean of 49 pigs per batch (median 50) and standard deviation of 28 (Q1 40 and Q3 50).

3.3. Statistical analysis; This investigation aimed to study the risk/protective factors associated with the prevalence of EP-like lesions and pleurisy. We investigated the presence of the lesion as a binary response at the pig level; considering the absence as a baseline. Initially the analysis comprised univariate explorations to investigate the associations between the observed prevalence for EP-like lesions and pleurisy and the different farm factors considered. Next the variables were included in a multivariable generalized linear model and were retained in the model if the individual Wald test was considered statistically significant (p<0.05) (Dohoo et al., 2003). Pig farm area density and number of finishing pigs

9 were studied in our model both as continuous and as categorical variables. The number of sows was studied as categorical variable with baseline class being no sows present on the farm. The troughs and the inflection points in the density function were used to establish data-derived cut off points to the create categories for each of these continuous variables (see table 3). Temporal variations were also investigated in the model in three time periods: monthly, quarterly and six monthly. A logarithm transformation for the number of finishing pigs and pig farm area density was used to ensure robust outputs for this continuous variable in the regression analysis. The variables retained in our initial multivariable model were included in a mixed effects binomial logistic regression multivariable model. Random effects at the farm and batch level were investigated. The goodness of fit of the models was evaluated by examining the Akaike's information criterion (AIC) results. Additionally, computed analysis of deviance was used in the comparisons for nested models. In the final model, variables were considered to remain significant on the basis of the Wald test (p<0.05). All the above mentioned analyses were performed with R version 2.7.1 (libraries stasts and lme4) from R Development Core Team (2008). R: A language and environment for statistical computing. R foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07, URL http://www.R-project.org.

4. Results The results from the multivariable mixed models for EP-like lesions and pleurisy that provided the best goodness of fit to the data are presented in table 4 and table 5 respectively. These were mixed models that incorporate clustering at batch level instead of farm level. The three time length periods studied were not significantly associated with the models.

4.1. Results for EP-like lesions. Geographical location has clearly the highest estimated odds ratios of all the variables studied: the odds of a slaughtered pig from South West of England and Wales being reported as having EP-like lesion is 4.22 times higher, 95% confidence intervals (CI) 3.23- 4.84, than the odds of a pig from Scotland. The other two regions investigated also presented higher risk compared with the observed prevalence in Scotland (South East OR 2.25, 95% CI 1.81-2.52; North OR 3.27, 95% CI 2.63-3.65). A weak positive association with presence of EP-like lesions (OR 1.44, 95% CI 1.21-1.57) was estimated for farms with part

10 slatted flooring compared with those not using this type of floor. In contrast those farms using solid floors with bedding seemed to have a lower risk of EP-like lesions (OR 0.79, 95% CI 0.66-0.86) compared to those not having solid floors. Increasing number of finishing pigs in the farm also increases the odds of EP-like lesions. Those farms from areas of low and median pig farm density (i.e. less or equal than 0.09 pig farms per Km2) had a lower risk of EP-like lesions (OR 0.39, 95% CI 0.28-0.45 and OR 0.31, 95% CI 0.23-0.36 respectively) than those from areas of higher density (i.e. more than 0.09 pig farms per Km2).

Table 4. Estimated Odds Ratios in the multivariable mixed model including variables associated with presence of EP-like lesions in finishing pigs and allowing for random effects at batch level. N=505 farms. Farm variable Level Odds ratio 95% Confidence intervals Intercept 0.08 0.04 - 0.12 Part slatted floor 1.44 1.21 - 1.72 Solid floor with bedding 0.79 0.66 - 0.94 Log number of finishers 1.17 1.07 - 1.27 Area (1) North England (a) 3.27 2.63 – 4.07 South East (b) 2.25 1.81 – 2.81 South West (c) 4.23 3.23 – 5.53 Farm density category (2) Median 0.39 0.28 - 0.52 Low 0.31 0.23 - 0.41 (1) Baseline is Scotland. (a) North England; York and the Humber. (b) East Midlands; East Anglia and South East. (c) West Midland, Wales and South West England. (2) Baseline is high farm density area.

4.2. Results for pleurisy. A mild but significant association (OR 1.27, 95% CI 1.08-1.32) with pleurisy was observed for those farms using part slatted floor versus those not using this type of floor. Conversely those farms using solid floor with bedding appear to have a lower risk of pleurisy (OR 0.71, 95% CI 0.64-0.79) than those not using it. Those farms feeding wet-feeding to the finishing pigs had a higher risk of pleurisy (OR 1.48, 95% CI 1.28- 1.59) than those using dry feed. Farms that had a breeding herd had a higher risk of pleurisy (OR 1.33, 95% CI 1.17-1.41) than those farms registered just as finishing units. A smaller risk of pleurisy was detected in those farms where all the production was indoors (OR 0.72, 95% CI 0.59-0.79) compared with those farms which reported production stages outdoors. The producers belonging to BPHS (English and Welsh farms) had a higher risk of pleurisy (OR 1.51, 95% CI 1.3-1.63) that those belonging to WPS (Scotland). Those farms from areas of low and median pig

11 farm density had a lower risk of pleurisy (OR 0.42, 95% CI 0.33-0.47 and OR 0.4, 95% CI 0.31-0.45 respectively) than those from areas of higher density.

Table 5. Estimated Odds Ratios form in the multivariable mixed model including variables associated with presence of pleurisy in finishing pigs and allowing for random effects at batch level. N=505 farms. Farm variable Level Odds ratio 95% Confidence intervals Intercept 0.15 0.11 - 0.22 Part slatted floor 1.27 1.08 - 1.44 Solid floor with bedding 0.71 0.64 - 0.82 Wet feeding 1.48 1.28 - 1.71 Breeding herd on the unit 1.33 1.17 - 1.51 All production Indoors 0.72 0.59 - 0.87 Health Scheme (1) BPHS 1.51 1.3 - 1.76 Farm density category (2) Median 0.4 0.31 - 0.50 low 0.42 0.33 - 0.53 (1) Baseline is WPS. (2) Baseline is high farm density area.

5. Discussion. This was a retrospective study to identify risk/protective factors for two main respiratory conditions reported at the abattoir slaughter inspections (EP-like lesions and pleurisy) which used the existing information available in the databases of farm QA companies and Health Schemes. This was the first time in Great Britain the information collected by those initiatives has been combined for epidemiological analyses adding an extra value to this information initially collected for other purposes. This analysis aimed to provide the British pig industry with a better understanding on the risk factors associated with EP-like lesions and pleurisy.

5.1. Area and density. The location of the farm appears to be an important predictor for the prevalence of EP-like lesions and pleurisy. Slaughtered pigs from South West region (West Midlands, Wales and South West of England) had the highest prevalence of EP-like lesions. The slapmarks assessed through BPHS (i.e. English and Welsh producers) had a higher risk of pleurisy than those reported through WPS (i.e. Scotland). The difference in EP-like lesions and pleurisy prevalence of such large geographical zones may be due to genuine regional differences in health status (i.e. the presence of specific pathogens) and /or differences in husbandry, in

12 particular the type of accommodation used. Controlled ventilation systems may be more likely to be used in Scotland compared to South of England and Wales. There may also be an effect of the presence of some large production enterprises and their respective general health statuses on the overall health status of the region. Herds located in areas of low and medium pig farm density appeared to have lower risk of reported EP-like lesions and pleurisy. Our finding is in line with previous studies where pig geographical density is reported to be a risk factor for pleurisy (Cleveland-Nielsen et al., 2002; Maes et al. 2001). Farms in areas with high pig density could face increased risk of local spread (e.g. air borne; higher likelihood of pig and pig by-product movements, etc.) of some infectious diseases (e.g. M hyopneumoniae and porcine reproductive and respiratory syndrome virus). It was not possible to calculate the true pig farm density due to the presence of farms for which the postcode was not available, and the fact that the study did not include those pig farms that were not registered with any of the three participating QA schemes. It may be assumed, however, that the calculated farm density used in this study is a reliable proxy for the true pig farm area density distribution.

5.2. Farm characteristics Similar results for EP-like lesions and pleurisy were obtained from the investigation of flooring types. Partly slatted floors appear to be a risk factor for higher prevalence of these two conditions, whereas the use of solid floors with bedding seemed to be protective. This has been previously reported (Stärk; 2000) with the explanation that the use of bedding material could be beneficial to insulate the pig from the floor temperatures. However, in this case, it is perhaps more likely that this association is a reflection of the nature of the building characteristics. Partly slatted flooring is more common in 20-30 year old buildings with low ceilings and poorer ventilation which may result in less than optimal environment in which the pigs are growing, increasing the prevalence to respiratory conditions. Also a considerable number of farms using solid floors with bedding may be involved in multisite all-in-all-out productions systems with subsequent benefits on the pathogen transmission control.

Larger number of finishing pigs on site appears to increase the risk of EP-like lesions. Herd size as potential risk factors for respiratory swine diseases has been consistently reported in the literature (Stärk; 2000; Gardner, et al.; 2002). Gardner and others (2002) specifically

13 discussed the role of herd size as a potential risk factor for pig diseases evaluating different explanations such the risk of introducing infectious agent from outside the herd (e.g. with carrier pigs; by airborne routes), and risk of maintaining and transmission of infectious agents within herds. The authors also discussed the presence of management factors that are associated with herd size (e.g. production systems; diseases control practices, labour structure and organization). Those factors correlated with the herd size that were no available in our study could have had a confounder effect on the association of the prevalence of EP-like lesions and number of finishing pigs which needs to be considered for in its interpretation.

Those herds that have reported all phases of production to be indoors had a lower risk of pleurisy compared with those that have some stage of the production outdoors. Extreme external temperature fluctuations could make outdoor pigs more susceptible to respiratory diseases; the potential effect of the meteorological factors on pig respiratory disease has been reviewed by Done (1991). Those units using liquid feeding appeared at higher risk for the presence of pleurisy. This seems to contradict the current view that wet-feeding has an indirect benefit for overall pig health especially by favouring the process involved in digestion (Gill; 2007) and considering the potential collateral benefit of this feeding system for respiratory problems by reducing the presence of aerial dust. Our finding might not necessarily reflect the direct effect of wet- feeding but may be an indicator for other features of production (e.g. more intensive production; specific environmental control). Cleveland-Nielsen and others (2002) also found that dry-feeding had potential a protective effect against pleurisy.

5.3. Breeding animals present in the unit. The presence of breeding animals within the unit appears in this study to be a potential risk factor for pleurisy in finishing pigs. Similar findings have been reported for high health herds by other study based on abattoir lung scoring (Enoe et al. 2002), but contrasting conclusions have been reported by Stärk (2000) and Done (1991); with the latter study more focussed on pneumonic lesions. The latter authors argued that finishing farms are considered at higher risk of respiratory disease than breeding-finishing farms as they rely on other sources for weaner re-stocking. This would subsequently increase the chance of introducing infectious

14 agents into the herd. The association observed here could reflect an increase in the health status in the weaner sources and possible better batch management to reduce mixing of the pigs from different sources compared with historical situations. The increasing preponderance, in the UK industry, of larger integrated multisite operations with perhaps stricter health controls could be reflected in this finding. This would mean that this “long- standing” potential risk factor could now have a lower impact on a finishing farm health status. However information on weaner source was not available in this study and therefore its true role could not be investigated.

5.4. Constraints and further discussion. An interesting output of the statistical analysis is that the models with better goodness of fit were obtained accounting for clustering at batch level rather that at farm level. This indicates the presence of greater variation in the prevalence of EP-like lesions and pleurisy between batches than between farms. This finding suggests that batch reports were not representative of the overall farm prevalence patterns for these two respiratory conditions; perhaps a consequence of variation in health status of the incoming stock on finishing units. Most of the associations reported in these analyses (except for the ones for EP-like lesions related to location) although robust were weak associations (odds ratios close to one) indicating that only marginal differences may exist between exposed and unexposed groups. The sample size included in this study (505 farms) would have helped to detect these weak associations. It is worth noting that for the interpretation of the outputs from this study the observations were recruited from members of pig health schemes and caution needs to be applied when attempting to extrapolate these findings to other populations (e.g. non- members units or other countries) as these may be biased toward the study population of pig producers. One potential drawback in this study was the use of information from existing databases. The datasets may have contained flaws for some of the QAPs records (e.g. the presence of unrecorded changes over time in some of the farm characteristics, inconsistent recording of the farm characteristics) which could have introduced information bias. However it was assumed that these errors were not differential bias, and that these would be equally present in either the exposed or the unexposed group (concerning the farm factors studied) regardless of the prevalence of these conditions. Moreover to minimise this

15 problem, this study did not rely on the information collected on just one farm audit but also has reviewed and contrasted the historical information which was thus included in the combined database used for the analyses. Seasonal variations have been reported for respiratory conditions (Done; 2001) however the temporal divisions investigated were not chosen as a significant predictor in the statistical model used. An important limitation in this study was the lack of information on other variables that might play a role as confounders in predicting of the occurrence of the conditions investigated: e.g. the implementation of health interventions (either vaccines or treatments); the sources of the pigs; the presence of specific infectious agents; and pig handling and husbandry practices. The statistical approach used for the analyses (generalized linear mixed model) would have helped to control for the effect of some of these confounders and other factors occurring at batch level, at the same time allowing to cope with the statistical clustering present at this level for both outcome (EP-like lesions or pleurisy) and predictors (Dohoo, 2008).

6. Conclusion and future actions. This study has demonstrated the potential of using the existing data available through Health Schemes and QAPs in order to provide epidemiological analysis of value to the British pig industry. By exploring these data, this project has contributed to a better understanding of farm characteristics that may influence the prevalence of EP-like lesions and pleurisy within the British pig industry and their geographic distribution. These outputs may serve as a baseline for future studies on pig disease prevention and control in Great Britain. Increasing the reliability and detail of the information concerning farm characteristics and production practices could form a basis for the implementation of risk-based surveillance policies that could help to optimize resources to the benefit of both the industry and governments funds (Stärk et al; 2006). Further planned study involves the investigation of risk factors for other conditions recorded in the health schemes and, by utilising subsets of the data with more detailed descriptors of farm production practice, to investigate other possible risk factors.

16 7. Acknowledgements. This is study is funded by Defra as project OD0215. We would like to acknowledge and thank to the different colleagues and institutions for their contribution to this project: supplying the data; participating in the study design; helping with the data management and GIS, advising through the quarterly meeting arranged during the period this project run; revising this paper and a number of other inputs. The names we would like to highlight are: Malcolm Hall; Donna Clark, Franz Brülisauer, Jill Thompson and Rick D’Eath from SAC; Stan Done and Alex Cook from VLA, Derek Armstrong and Mark Wilson from BPHS- BPEX; Allan Ward from QMS; Jane Johnson; Martin Barker and Michael Hemmings from Genesis QA; Elizabeth Kerrigan from ABP; Jamie Roberson from Livestock Management Services; Zoe Davies from the National Pig Association; Elizabeth Kelly and Alex Morrow from Defra and Ilias Kyriazakis.

17 8. References

Cleveland-Nielsen, A, Nielsen, E.O, Ersboll, A.K., 2002. Chronic pleuritis in Danish slaughter pig herds. Preventive Veterinary Medicine 55, 121-135.

Defra PRIMO movement document; Livestock movements, identification and tracing: pigs. http://www.defra.gov.uk/animalh/id-move/pigs/index.htm

Dohoo, IR; Quantitative epidemiology: Progress and challenges. Preventive Veterinary Medicine, Volume 86, Issues 3-4, 15 September 2008, Pages 260-269

Dohoo, I.; Martin, S., Stryhn, H., 2003. Veterinary Epidemiology Research. AVC Inc., Charlottetown.

Done, S.H., 1991. Environmental factors affecting the severity of pneumonia in pigs. Vet Rec. 128, 582-586.

Enøe, C; Mousing, J; Schirmer, AL; Willeberg, P. Infectious and rearing-system related risk factors for chronic pleuritis in slaughter pigs. Preventive Veterinary Medicine, Volume 54, Issue 4, 30 August 2002, Pages 337-349.

Gardner, IA; Willeberg, P; Mousing, Jan. Empirical and theoretical evidence for herd size as a risk factor for swine diseases. 2002. Animal Health Research Reviews, 3:43-55 Cambridge University Press

Gill P; Liquid feeding: a technology that can deliver benefits to producer profitability, pig health & welfare, environment, food safety and meat quality. Paradigms In Pig Science Proceedings. Nottingham University Press. 2007

Hurnik, D; Dohoo, I and Bate, L. Types of farm management as risk factors for swine respiratory disease, Prev. Vet. Med. 20 (1994), pp. 147–157.

Jubb, KVF; Kennedy, PC; Palmer, N. Pathology of Domestic Animals; Academic Press. Fourth Edition, Volume 2. 1993.

Maes, DG; Deluyker, H; Verdonck, M; de Kruif, A; Ducatelle, R; Castryck, F; Miry, C; and Vrijens, B. Non- infectious factors associated with macroscopic and microscopic lung lesions in slaughter pigs from farrow-to- finish herds. Vet Rec., Jan 2001; 148: 41 - 46.

Ostanello, F; Dottori, M; Gusmara, C; Leotti, G; Sala, V. Pneumonia disease assessment using a slaughterhouse lung-scoring method. Journal of Veterinary Medicine Series A: Physiology Pathology Clinical Medicine 54 (2), March 2007 pp. 70-75

18 Stärk, KDC; Pfeiffer DU; Morris RS. Risk factors for respiratory diseases in New Zealand pig herds. New Zealand Veterinary Journal 46, 3-10, 1998.

Stärk, KDC; Epidemiological Investigation of the Influence of Environmental Risk Factors on Respiratory Diseases in Swine—A Literature Review. The Veterinary Journal, Volume 159, Issue 1, January 2000, Pages 37- 56.

Stärk, KDC; Regula, G, Hernandez, J; Knopf, L; Fuchs, K.; Morris, R; Davies, P; Concepts for risk-based surveillance in the field of veterinary medicine and veterinary public health: Review of current approaches. 2006. BMC Health Services Research 6, 20.

Straw, EB; Zimmerman, JJ; D'Allaire, S; Taylor, DJ;. Diseases of Swine. Ninth Edition. 2006. Blackwell Publishing.

19

APPENDIX 1 TO CHAPTER 1. INVESTIGATING RISK FACTORS FOR THE PRESENCE OF ENZOOTIC PNEUMONIA-LIKE LESIONS AND PLEURISY IN SLAUGHTERED FINISHING PIGS UTILIZING EXISTING BRITISH PIG INDUSTRY DATA RECRUITED FROM ENGLAND AND WALES.

20 1. Producing a dataset including farms from England and Wales.

Following the same methodology as it is mentioned in the material and methods in the paper, a second database was created including those farm characteristics that were common to GQA and ABP. This dataset included housing category-specific information (e.g. for growers, for finishers); and records on the feeding form and on the use of alimentary co- products, not available in the previous dataset. A more detailed summary of all the different variables considered in the analysis is presented in tables 3 and 4. With this, the study population for this second subset consisted of those pig producers that were part of English/Welsh QAPs and whose pigs were assessed by BPHS. Farm location was not available for 35% of these producers consequently it was not included in this second dataset. The process described above led to a dataset of QA information from 338 farms. This dataset included the records from 2,463 batches of pigs assessed by BPHS; which corresponds to a mean of 7 batches per farm (median 6) and  5.53 (Q1 3 and Q3 10). These assessments integrated a total of 105,923 pigs inspected; with a mean of 43 pigs per batch (median 50) and  of 12 (Q1 40 and Q3 50).

21 Table 3. Categorical farm variables for the second subset.

Variable Classes/Level Description Pig assessed

of exposure N yes (percentage) (n)

Having a breeding herd Yes 163 (48) 47,948 No 175 (52) 57,975 Outdoors growing taking place in the Yes 20 (6) 7,845 farm No 318 (94) 98,078 Outdoor finishing taking place in the farm Yes 5 (2) 1,565 No 333 (98) 104,358 Use of solid floor with bedding Yes 251 (74) 71,892 No 87 (26) 34,031 Use of part slatted floor Yes 102 (30) 37,040 No 236 (70) 68,883 Use of full slatted floor Yes 126 (37) 51,114 No 212 (63) 54,809 Use of wet-feeding in finishing pigs Yes 66 (20) 30,792 No 272 (80) 75,131 Use of meal in finishing pigs Yes 99 (30) 27,367 No 239 (70) 78,556 Use of pellets in finishing pigs Yes 210 (62) 60,872 No 128 (38) 45,051 Use of enzymes in finishers Yes 114 (34) 36,063 No 224 (66) 69,860 Use of antibiotics in finishers Yes 94 (28) 29,379 No 244 (72) 76,544 Use of probiotic supplementation Yes 14 (4) 4,562 No 324 (96) 101,361 Use of non-dairy co-products Yes 57 (17) 25,319 No 281 (83) 80,604 Use of dairy co-product Yes 52 (15) 24,951 No 286 (85) 80,972 Use other dry co-products Yes 78 (23) 31,205 No 260 (77) 74,718 Restricted feeding in finishers Yes 34 (10) 9,777 No 304 (90) 96,146

22 Table 4. Continuous variables studied by classes in the second data subset.

Variable Classes divisions Number of farms Pig assessed (n) (percentage)

Number of finishers More that 5000 finishers 22 (7) 13,541 present in the farms Between 5000 and 1500 finishers 157 (46) 62,037 Less than 1500 finishers 159 (47) 30,345 Number of sows present More that 600 sows 21 (6) 9,857 in the farms Between 150 and 600 sows 108 (32) 40,591 Between 1 and 150 sows 29 (9) 5,076 None sows present 180 (53) 50,399

2. Statistical analyses.

The binomial distributions for EP-like lesions and pleurisy were considered to be zero-inflated and the risk factors models for these conditions were investigated assuming a beta binomial distribution to account for extra-clustering. AIC result was used to evaluate the goodness of fit of the models and to choose the best model for each condition (further explanation in the statistical analyses are proved in the chapter 2.

3. Results.

The models that provided better goodness of fit to the data were those using a Beta- binomial distribution. The results from the models are presented in tables 1 and 2 and summarised. The overall prevalence found in the study including farms from the whole Great Britain was 27.8% of pigs affected with EP-like lesions; while the percentage of farms with at least one pig affected with EP-like lesions was 88.3%. For the subset of farms recruited from England and Wales it was 31.8 the percentage of pigs affected; from 98.2% of the farms. The overall prevalence found in the study including farms from the whole Great Britain was 11.9 % of pigs affected with pleurisy; while the percentage of farms with at least one pig affected with pleurisy was 92.7 %. For the subset of farms recruited from England and Wales it was 13.9 the percentage of pigs affected; from 97.3 % of the farms.

23

Table 1. Estimated Odds Ratios in the multivariate beta-binomial model including variables associated with presence of EP-like lesions in finishing pigs. N=338 farms. Farm variable Level Odds ratio 95% CI Outdoor finishing Yes 0.51 0.35 - 0.77 taking place in the farm No 1.00 - Solid floor with Yes 0.84 0.76 - 0.93 bedding No 1.00 - Part slatted floor Yes 1.23 1.12 - 1.35 No 1.00 - Number of finishers Yes 1.00 1.000009 - present in the farms No 1.00 1.00005 Use of antibiotics in Yes 1.19 1.08 - 1.31 finishers No 1.00 - Use of probiotic Yes 1.43 1.16 - 1.75 supplementation No 1.00 - Use of dairy co-product Yes 0.82 0.73 - 0.91 No 1.00 - Restricted feeding in Yes 1.23 1.06 - 1.42 finishers No 1.00 -

Table 2. Estimated Odds Ratios in the multivariate beta-binomial model including variables associated with presence of pleurisy in finishing pigs. N=338 farms. Farm variable Level Odds ratio 95% CI Outdoor finishing Yes 0.39 0.24 - 0.65 taking place in the farm No 1.00 - Outdoors growing Yes 1.26 1.06 - 1.50 taking place in the farm No 1.00 - Solid floor with bedding Yes 0.78 0.71 - 0.86 No 1.00 - Breeding herd on the Yes 1.24 1.13 - 1.35 unit No 1.00 - Use of meal in finishing Yes 1.12 1.02 - 1.24 pigs No 1.00 - Use of antibiotics in Yes 1.15 1.04 - 1.26 finishers No 1.00 - Use of probiotic Yes 1.51 1.25 - 1.84 supplementation No 1.00 - Use of dairy co-product Yes 1.19 1.07 - 1.32 No 1.00 - Restricted feeding in Yes 1.25 1.09 - 1.43 finishers No 1.00 -

24

CHAPTER 2

IDENTIFICATION OF FACTORS INFLUENCING THE OCCURRENCE OF MILK SPOT LIVERS IN SLAUGHTERED PIGS: A NOVEL APPROACH TO UNDERSTANDING ASCARIS SUUM EPIDEMIOLOGY IN BRITISH FARMED PIGS.

Authors: Sanchez-Vazquez, MJ(a)*; Smith, R(b); Kang, S(a); Lewis, F(a); Nielen, M(c); Gunn, GJ(a); Edwards, SA.(d) (a) Scottish Agricultural College, Kings Buildings, West Mains Road, Edinburgh, EH9 3JG, UK. (b) Veterinary Laboratories Agency, New Haw, Addlestone, Surrey, KT15 3NB, UK. (c) Department of Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.

(d) Newcastle University, Agriculture Building, Newcastle upon Tyne, NE1 7RU, UK.

Paper to be submitted to the Journal of Veterinary Parasitology. 1 Abstract.

25 Ascariosis is the most important internal parasitism present worldwide in farmed pigs; being responsible for a number of economic losses. Milk spots are healing lesions occurring when

Ascaris suum larvae migrate through the liver. There is a lack of epidemiological studies to asses the influence of current methods of production (e.g. wet/compound feeding, outdoors/indoors production, use of bedded/slatted floors) on the occurrence of ascariosis.

This is partly due to the complications in detecting the parasitism in the live animal, which increase costs for the execution of large scale study. The use of abattoir reports to perform epidemiological analyses provides a cost effective alternative for performing studies including optimal sample size. This study aimed to identify husbandry practices that influence the prevalence of milk spots in batches of slaughtered pigs, accounting for geographical locations and seasonal patterns. Farm information was accessed through the

British farm quality assurance programmes (QAPs) and information on milk spots was obtained from the pig abattoir based health schemes. Two working datasets were created.

One was a subset of 505 farms recruited from the whole of Great Britain (GB), with information on stocking numbers, housing and feeding characteristics. The other subset combined 338 farms from England and Wales (EW) which had housing and feeding category-specific information (e.g. for growers and finishers separately), which was not fully available in the previous dataset. The variables were studied in multivariable beta-binomial models with presence of milk spots as the response variable. Solid floor with bedding appeared as a risk factor, OR 1.52 (95% confidence intervals (CI) 1.26-1.85) for the GB sample, and OR 1.47 (CI 1.19-1.81) for the EW subset. Those GB herds that had all the stages of production indoors appeared to be at lower risk of milk spots (OR 0.4, CI 0.32-

0.49). Likewise, the EW analyses showed that growing outdoors had higher risk of milk spots OR 1.73 (CI 1.34-2.23). Changes were detected within year, with higher risk of milk

26 spots in the second six months of the year OR 1.17 (CI 1.02-1.35) in the GB sample and

1.21 (95% CI 1.04-1.41) in EW farms. Overall this study suggests that those husbandry practices facilitating optimal levels of hygiene posed lower risk of milk spots, potentially reflecting lower levels of ascariosis. Outdoor production systems and the use of straw bedding, both expanding husbandry practices, require major attention with regard to helminth control.

Key words: Ascaris suum; pig, liver; risk factors.

2 Introduction.

Milk spot liver is a well-established terminology to denote the whitish healing foci, occurring in the liver stroma when Ascaris suum larvae are immobilized by the host inflammatory reaction (Kelly, 1993, pp. 375). Ascariosis is the most important internal macro-parasitism present worldwide in farmed pigs (Stewart and Hoyt, 2006, pp. 904-905). A number of economic losses have been attributed to it, including depression of the growth rate (Stewart et al., 1996) associated with a decrease in the feed conversion rate (Stewart and Hale, 1988).

Ascariosis may also interfere with the pig immune modulation, having a negative effect on post-vaccination immunity levels against Mycoplasma hyopneumoniae (Steenhard et al., 2009). In abattoirs, the presence of milk spots represents considerable losses due to offal condemnations (Barker et al., 1993, pp. 283-285).

It has been reported that housing conditions (i.e. flooring type) and management practices (e.g. cleaning and disinfection procedures; type of feeding) play an important role in the development of Ascaris suum (Roepstorff and Nansen, 1994; Roepstorff and Jorsal,

1990; Petkevicius et al., 1997). Also, seasonal fluctuations in the parasite development and in the subsequent presence of milk spots have been reported (Stevenson, 1979; Goodall et al.,

27 1991; Wagner and Polley; 1999). There have been, however, insufficient recent studies to investigate those factors that influence the prevalence and distribution (both temporal and geographical) of the parasite within the current systems of production used by the British pig industry. Infestations under conditions of good hygiene and husbandry are not usually severe enough to present with clinical signs (Barker et al. 1993, pp. 283-285). Milk spots, if present, tend to occurs asymptomatically being incidental post mortem findings at necropsy or abattoir inspections. Therefore the presence of Ascaris suum – or its lesions - cannot easily be detected in the live pig. The diagnosis is made either directly (e.g. by faecal egg detection or serology) or indirectly (presence of migration lesions in lung or milk spots in liver); consequently carrying out large scale epidemiological studies on ascariosis necessitates significant budgetary and logistic resources.

The potential to perform epidemiological analyses using abattoir monitoring reports from the pig health schemes has been exploited for respiratory conditions (Sanchez-Vazquez et al., in press) and for Salmonella seroprevalence (Smith et al., in press). This is a cost- effective approach allowing for the inclusion of larger and more representative samples.

Bernardo and others (1990) evaluated the detection of milk spots in the abattoir as a surveillance tool, finding good correlation between the herds with a high proportion of pigs affected with milk spots and those where intestinal Ascaris was more prevalent.

The aim of this study was to identify those husbandry practices, geographical locations and seasonal patterns that influence the prevalence of milk spots in batched of slaughtered pigs. Additionally, it optimised the national use of the resource provided through abattoir data reported to participating herds in the British pig health schemes.

3 Material and Methods

28 3.1 Data source.

Abattoir data on milk spots was extracted from the databases of the two existing pig health schemes in Britain: British Pig Health Scheme (BPHS) and Wholesome Pigs (Scotland)

(WPS); which provide services in 17 pig abattoirs. Both schemes aim to obtain a representative sample from each batch of pigs by assessing every other pig on the slaughter line. Milk spot lesions were scored as a binary variable, present or absent, in each assessed pig. The scoring was carried out by swine veterinarians trained in this method of testing on the abattoir inspection line. Different veterinarians carried out assessments in the same abattoir and rotated for duties in more than one abattoir to help maintain the consistency in meeting the diagnostic criteria.

Farm information was obtained from the three main British farm quality assurance programmes (QAPs): Assured British Pigs (ABP) and Genesis Quality Assurance (GQA) in

England and Wales; and Quality Meat Scotland (QMS) in Scotland. The QAPs audit the farms periodically – at least annually – and collect, through questionnaires, and document a large amount of information on farm production descriptors (e.g. type of housing, type of feeding, number of finishing pigs, number of sows, etc.). Current and historic data available at commencement of this study were used, covering the period between September 2005 and

September 2007. Farm was defined as a site that had a specific Defra herdmark (Animal

Health, England, 2007). This latter is a unique official reference identifier for each holding, consisting of an alphanumeric code, which is also used to identify pigs sent to the abattoir by its application as a tattoo on each shoulder, otherwise known as a slapmark. A batch was defined as a group of pigs from a single farm submitted to the abattoir on a particular date.

3.2 Data management.

29 Two datasets were created for this study. Firstly, one including producers belonging to any of the three QAPs, with farms from Scotland, England and Wales (referred to as the GB dataset). English-Welsh QAPs hold category-specific housing characteristics and more comprehensive feeding information not present in the Scottish database. Subsequently, for more detailed analyses, a parallel recruitment for a second dataset was carried out only including farms from English-Welsh QAPs (referred to as the EW dataset).

The records available for two years in the QAPs were reviewed for each individual farm to determine its characteristics. Information concerning the stock numbers present at the time of the farm audits and date when the information was updated were included in the databases. This date of the farm audit was used as a time reference to link husbandry and abattoir data. The other variables (e.g. flooring and housing characteristics, feeding system), which were farm structural characteristics and management practices unlikely to change within the study period, were considered constant over the study period with no specific date attributed to them.

3.2.1 Producing the GB dataset.

For the GB dataset, the study population consisted of those pig producers that had pigs assessed by a health scheme and were also part of any of the three QAPs.

Farm variables that were common to the three QAPs, and potential proxys for a risk/protective factor for the occurrence of milk spots in finishing pigs, were identified. The following variables met those criteria: total number of finishing pigs and sows; flooring characteristics of finishing pigs housing; use of wet-feeding; the presence of a breeding unit on the site; and reported use of outdoors accommodation at any stage of production.

Information on these husbandry characteristics was extracted for the GB dataset together

30 with farm specific abattoir information on milk spots for the 12 month period prior and subsequent to the QAP audits. A summary of all the different variables considered in the analysis can be found in Tables 1 and 2. A more detailed explanation on the methodology followed for this process is described in a previous paper (Sanchez-Vazquez et al.; in press).

Table 1. Categorical farm variables for GB dataset.

Variable Classes/Level of Description Pig positive Pig assessed Pig positive exposure (n) (n) (%) N yes (%)

Health Scheme where the abattoir BPHS 2108 (80) batches 3,212 94,373 3.40 scoring data have been reported WPS 542 (20) batches 2,303 35,446 6.50 Geographic area where the farm was North (a) 113 (22) farms 1,788 38,169 4.68 located Scotland 166 (33) farms 2,303 35,446 6.50 South East (b) 179 (36) farms 640 38,647 1.66 South West (c) 47 (9) farms 784 17,557 4.47 Having a breeding herd Yes 212 (42) 2,291 61,885 3.70 No 293 (58) 3,224 67,934 4.75 All production indoors (without Yes 459 (91) 4,059 114,576 3.54 records for stages of outdoors No 46 (9) 1,456 15,243 9.55 production) Use of wet-feeding Yes 72 (14) 689 29,597 2.33 No 433 (86) 4,826 100,222 4.82 Use of solid floor with bedding Yes 393 (78) 4,723 91,979 5.13 No 112 (22) 792 37,840 2.09 Use of part slatted floor Yes 98 (19) 1,343 36,297 3.70 No 407 (81) 4,172 93,522 4.46 Use of full slatted floor Yes 128 (25) 1,129 51,136 2.21 No 377 (75) 4,386 78,683 5.57 (a) North England; York and the Humber. (b) East Midlands; East Anglia and South East. (c) West Midland, Wales and South West.

Table 2. Continuous farm variables studied by classes for GB dataset.

31 Variable Classes divisions Number of farms Pig positive Pig assessed Pig positive (n) (n) (%) (%)

Number of finishers More that 9000 finishers 23 (5) 954 13,918 6.85 present in the farms Between 2500 and 9000 finishers 182 (36) 2,013 61,221 3.29 Less than 2500 finishers 300 (59) 2,548 54,680 4.66 Number of sows More that 600 sows 28 (6) 927 13,571 6.83 present in the farms Between 150 and 600 sows 145 (28) 1,100 46,883 2.35 Between 15 and 150 sows 31 (6) 168 4,087 4.11 None sows present 301 (60) 3,320 65,278 5.09

To investigate regional differences in lesion prevalence, Great Britain was divided into four areas (see figure 1): a) Scotland; b) North England; Yorkshire and the Humber

(North); c) East Midlands, East Anglia and South East (South East); and d) West Midlands,

Wales and South England (South West). Information from the pig industry concerning location of the abattoirs participating in the scheme and of the farms supplying pigs to those abattoirs dictated geographical divisions; a proxy for abattoir farm capture areas.

32

Figure 1. Map presenting the geographical divisions used to study farm location effect.

3.2.2 Producing the EW dataset.

For the EW dataset, the study population consisted of pig producers from English/Welsh

QAPs and whose pigs were assessed by BPHS. This second database was created, following the same methodology as above, including farm characteristics that were common to GQA and ABP. This included housing category-specific information (e.g. for growers, for finishers) and feeding records (on the feed form and on the use of co-product feeds) not available in the GB datasets. A more detailed summary of all the different variables considered in the analysis is presented in Tables 3 and 4. Farm location was not available for 35% of these producers so it was not included in the EW dataset.

33 Table 3. Categorical farm variables for the EW dataset.

Variable Classes/Level Description Pig positive Pig assessed Pig positive of exposure (n) (n) (%) N yes (%)

Having a breeding herd Yes 163 (48) 1,338 47,948 2.79 No 175 (52) 2,633 57,975 4.54 Outdoors growing taking place in the Yes 20 (6) 652 7,845 8.31 farm No 318 (94) 3,319 98,078 3.38 Outdoor finishing taking place in the Yes 5 (2) 202 1,565 12.91 farm No 333 (98) 3,769 104,358 3.61 Use of solid floor with bedding Yes 251 (74) 3,355 71,892 4.67 No 87 (26) 616 34,031 1.81 Use of part slatted floor Yes 102 (30) 1,296 37,040 3.50 No 236 (70) 2,675 68,883 3.88 Use of full slatted floor Yes 126 (37) 885 51,114 1.73 No 212 (63) 3,086 54,809 5.63 Use of wet-feeding in finishing pigs Yes 66 (20) 420 30,792 1.36 No 272 (80) 3,551 75,131 4.73 Use of meal in finishing pigs Yes 99 (30) 638 27,367 2.33 No 239 (70) 3,333 78,556 4.24 Use of pellets in finishing pigs Yes 210 (62) 2,989 60,872 4.91 No 128 (38) 982 45,051 2.18 Use of enzymes in finishers Yes 114 (34) 1102 36,063 3.06 No 224 (66) 2,869 69,860 4.11 Use of antibiotics in finishers Yes 94 (28) 808 29,379 2.75 No 244 (72) 3,163 76,544 4.13 Use of probiotic supplementation Yes 14 (4) 53 4,562 1.16 No 324 (96) 3,918 101,361 3.87 Use of non-dairy co-products Yes 57 (17) 355 25,319 1.40 No 281 (83) 3,616 80,604 4.49 Use of dairy co-product Yes 52 (15) 316 24,951 1.27 No 286 (85) 3,665 80,972 4.51 Use of other dry co-products Yes 78 (23) 376 31,205 1.20 No 260 (77) 3,595 74,718 4.81 Restricted feeding in finishers Yes 34 (10) 63 9,777 0.64 No 304 (90) 3,908 96,146 4.06

34 Table 4. Continuous variables studied by classes for the EW dataset.

Variable Classes divisions Number of farms Pig positive Pig assessed Pig positive (n) (n) (%) (%)

Number of finishers More that 5000 finishers 22 (7) 647 13,541 4.78 present in the farms Between 1500 and 5000 finishers 157 (46) 1,757 62,037 2.83 Less than 1500 finishers 159 (47) 1,567 30,345 5.16 Number of sows More that 600 sows 21 (6) 495 9,857 5.02 present in the farms Between 150 and 600 sows 108 (32) 697 40,591 1.72 Between 1 and 150 sows 29 (9) 61 5,076 1.20

No sows present 180 (53) 2,718 50,399 5.39

3.3 Statistical analysis

This investigation aimed to study the risk/protective factors associated with the prevalence of milk spots. The response variable in all our analyses was the presence of milk spots.

Initially the analysis comprised univariable explorations to investigate the associations between the observed prevalence for milk spots and the different farm factors considered. The variables were tested for correlation. Number of finishing pigs was studied in our model both as continuous and as categorical variables. The number of sows was studied as categorical variable with baseline class being no sows present on the farm. The troughs and the inflection points in the density function were used to establish data-derived cut off points to create categories for each of these continuous variables. Monthly, quarterly and six-monthly temporal variations in the prevalence of milk spots were also investigated in the model. A logarithm transformation for the number of finishing pigs was used to ensure robust outputs for this continuous variable in the regression analysis.

The variables were included in a multivariable generalized linear model (GLM) and, through a backward elimination process, were retained in the model if the individual Wald

35 test was considered statistically significant (p<0.05). A variance inflation factor was computed to assess collinearity among the predictor variables (Dohoo et al., 2003). The variables retained in our initial multivariable model were included in a mixed effects binomial logistic regression multivariable model. Random effects at the batch, farm and abattoir level were investigated. These initial models assumed a binomial distribution; however due to a high proportion of pigs with absence of lesions zero-inflated binomial and beta-binomial models were also considered. Beta-binomial model incorporates beta-distributed random effects and it is essentially a model for grouped or replicated data (Dohoo et al., 2003). This approach allows accounting for the potential over-dispersion present in our data due to different levels of clustering (e.g. batch, farm or abattoir level). Once the diagnostics criteria of the model were satisfied, the goodness of fit measurement Akaike's information criterion

(AIC) was used to choose the best model among the candidates. Additionally, substantial changes in the estimated coefficients in the models and increases in standard errors during the model building process were investigated. Biologically plausible interactions between the variables present in the final model were investigated using likelihood ratio tests. All the analyses were performed in R (R Development Core Team, 2005) using libraries stasts, lme4 and vgam.

4 Results.

The models that provided best goodness of fit to the data were those using a Beta-binomial distribution. Results are presented in tables 5 and 6 and summarised in the following two sections. The overall prevalence for the GB dataset was 4.4% of pigs affected with milk spots; while the percentage of farms with at least one pig affected with milk spots was 67%.

For the EW subsets, pig prevalence was 3.7% from 71.9% of the farms.

36 4.1 Results from the analyses on the GB dataset.

This dataset held QA information from 505 farms. This included records from 2,650 batches assessed through the health schemes; corresponding to a mean of 5 batches per farm

(median 4) with standard deviation () 4.62 (Quartile (Q)1 2 and Q3 7). These assessments integrated a total of 129,819 pigs inspected; with a mean of 49 pigs per batch (median 50) and  of 28 (Q1 40 and Q3 50).

Geographical location had the highest estimated odds ratios of all the variables studied: the odds of a slaughtered pig from North England being reported as having milk spots was 1.96 times higher than the odds of a pig from the South East (95% confidence intervals (CI) 1.62-

2.37). The other regions investigated also presented higher risk compared with the observed prevalence in the South East (Scotland OR 2.63, 95% CI 2.15-3.21; South England OR 2.62,

95% CI 2.07-3.32).

A mild but significant positive association with milk spots was observed for those farms having finishing buildings constructed with solid floor and using bedding (OR 1.52, 95% CI

1.26-1.85) compared to those not having this type of floor. Those farms with a wet-feeding system for finishing pigs had a lower risk of milk spots (OR 0.52, 95% CI 0.42- 0.64) than those using dry feed. Farms that had a breeding herd had a lower risk of milk spots (OR 0.67,

95% CI 0.57-0.78) than those farms registered just as finishing units. A smaller risk of milk spots was detected in farms where all the production cycle was indoors (OR 0.4, 95% CI

0.32-0.49) compared with those farms which reported production stages outdoors.

The period of the year was a significantly associated factor in our final model; with a slightly higher risk of milk spots present in the second half of the year, (OR 1.17, 95% CI 1.02-1.35) compared to the first half of the year (baseline).

37 Table 5. Estimated Odds Ratios in the Multivariable Beta-Binomial model including variables associated with presence of milk spots in finishing pigs. For GB dataset. N=505 farms. Farm variable Level Odds ratio 95% CI Solid floor with bedding Yes 1.52 1.26 - 1.85 No 1.00 - Wet-feeding Yes 0.52 0.42 - 0.64 No 1.00 - Breeding herd on the unit Yes 0.67 0.57 - 0.78 No 1.00 - All production Indoors Yes 0.40 0.32 - 0.49 No 1.00 - Area North (a) 1.96 1.62 – 2.37 Scotland 2.63 2.15 - 3.21 South West (b) 2.62 2.07 - 3.32 South East (c) 1.00 - Six month period Second six months of the year 1.17 1.02 - 1.35 First six months of the year 1.00 - (a) North England; York and the Humber (b) West Midland, Wales and South West England (c) East Midlands; East Anglia and South East

4.2 Results from the analyses on the EW dataset.

This dataset included QA information from 338 farms. This included records from 2,463 batches of pigs assessed by BPHS; with a mean of 7 batches per farm (median 6) and  5.53

(Q1 3 and Q3 10). These assessments integrated a total of 105,923 pigs inspected; with a mean of 43 pigs per batch (median 50) and  of 12 (Q1 40 and Q3 50).

A higher risk of milk spots was detected in those farms where outdoor growing took place

(OR 1.73, 95% CI 1.34-2.23) compared with those farms with an indoor growing system. A weak but significant positive association with milk spots was observed for those farms having finishing buildings constructed with solid floors and using bedding (OR 1.47, 95% CI

1.19-1.81); whereas slightly reduced risk of milk spots was detected for those farms using fully slatted floors (OR 0.72, 95% CI 0.59-0.87) compared to those not having these types of floor.

38 There was a weak but significant positive association with milk spots (OR 1.33, 95% CI

1.12- 1.57) for those farms feeding finishers with pellets when compared with those farms not employing this feed form, whereas the utilization of probiotics in finishing pigs was associated with a lower risk of milk spots (0.62, 95% CI 0.39-0.99).

Farms that had a breeding herd had a slightly lower risk of milk spots (OR 0.73, 95% CI

0.61-0.87). Slightly lower risk of milk spots was also detected in medium (from 1,500 to

5,000 finishers) and small (less than 1,500 finishers) size farms (OR 0.71, 95% CI 0.55-0.89 and 0.66, 95% CI 0.51-0.86 respectively) compared to the large ones (over 5,000 finishers).

There is an association of the period of the year with the prevalence of milk spots. The risk of milk spots was slightly higher in the second half of the year, (OR 1.21, 95% CI 1.04-1.41) when compared to the first half of the year.

Table 6. Estimated Odds Ratios in the Multivariable Beta-Binomial model including variables associated with presence of milk spots in finishing pigs. For the EW dataset. N=338 farms. Farm variable Level Odds ratio 95% CI Solid floor with bedding Yes 1.47 1.19 - 1.81 No 1.00 - Full slats Yes 0.72 0.59 - 0.87 No 1.00 - Feeding pellets in finishers Yes 1.33 1.12 - 1.57 No 1.00 - Probiotics in finishers Yes 0.62 0.39 - 0.99 No 1.00 - Breeding herd on the unit Yes 0.73 0.61 - 0.87 No 1.00 - Outdoors growing Yes 1.73 1.34 - 2.23 No 1.00 - Farm size Small (less than 1500 pigs) 0.66 0.51 - 0.86 Medium (from1500 to 5000 pigs) 0.71 0.55 - 0.89 Large (more than 5000 pigs) 1.00 - Six month period Second six months of the year 1.21 1.04 - 1.41 First six months of the year 1.00 -

39 5 Discussion.

5.1 Area.

The farm location appears to be an important predictor for the prevalence of milk spots.

Slaughtered pigs from North England, Scotland and South West England seem to be at higher risk of milk spots than those pigs finished in the East of England. Ascaris suum is highly ubiquitous and likely to be present in all the herds. Roepstorff and Nansen (1994) acknowledged the fact that it had proven to be difficult to find herds that could be declared totally free from this ascarid. The regional differences in milk spots prevalence found in this study are therefore perhaps more likely to be due to differences in the husbandry practices focused on controlling within farm Ascaris suum transmission rather than to genuine regional differences in presence/absence of the parasite. These findings may also be an effect of the presence of some large production enterprises within a region and the impact of their respective general health programmes - especially regarding deworming protocols - and cleaning and disinfection practices on the overall health status of the region. It can be argued that the regional differences detected in the risk of milk spots could be attributed to misclassification bias in the abattoir assessments. However the health schemes operate quality control systems to maintain consistency in scoring and the veterinarians rotate among different abattoirs.

5.2 Farm characteristics

Solid floors with bedding appear to be a consistent risk factor for higher prevalence of milk spots; whereas the use of full slats seems to be protective. The use of bedding material may hamper the effectiveness in destruction of the Ascaris suum eggs by routine cleaning and disinfection protocols. Pigs reared in this system have greater contact with faeces, facilitating

40 faecal-oral parasite transmission, than those pigs on slatted floors, particularly fully-slatted. A previous study identified the use of bedding as risk factor for Ascaris suum infection in sows

(Dangolla et al., 1996). Other authors have also reported that those farms where bedding was used had a greater proportion of sows and fatteners affected with Ascaris suum than those farms not using bedding and/or having slatted floors (Roepstorf and Jorsal, 1990).

Dangolla and others (1996) explained that bedding might provide a protective environment for the parasite eggs which help them to survive longer and develop to infective stages.

In the first set of analyses, on the GB dataset, those herds that have reported all phases of production to be indoors had a lower risk of milk spots compared to those that have some stage of the production outdoors. This finding has been discussed previously (Roepstroff and Nansen, 1994) with the conclusion that, although Ascaris suum is capable of completing its life cycle indoors, the higher hygiene standards in housed production result in fewer viable parasites in indoor production than in extensive conditions. Investigated in more detail in the EW dataset, it appeared that there was a higher risk of milk spots for those farms with outdoor growing pigs; the age period that coincides with the peak of Ascaris suum infection (Nasen and Ropestorff; 1999). Outdoor production within the British pig industry is an expanding husbandry practice in response to an increasing demand for free-range pork.

Therefore, our results have notable relevance for the industry indicating that particular attention needs to be paid to helminth control in this production system.

Those units using liquid feeding appeared at lower risk for the presence of milk spots. This could be related to the overall benefits of wet-feeding on the intestinal lumen environment and by favouring the processes involved in digestion (Gill; 2007). Investigating the effect of

41 the feeding more specifically, we found that there was a higher risk of milk spot livers on farms using pelleted feed. This could be related to the level and chemical form of non-starch polysaccharides (NSPs) present in this processed feed form, as pelleting process could have modified NSPs and the gut environment. It has been reported that dietary composition affects the development of intestinal parasites such as Oesophagostomum dentatum, with higher levels of dietary fibre promoting parasite proliferation (Petkevicius et al.; 1997). In the same way, we can speculate that probiotics may modify the intestinal lumen environment, hampering parasite establishment; perhaps explaining the potential protective effect identified. However it is also possible that these findings might not necessarily reflect the direct effect of diet (i.e. liquid feeding, use of pellets, use of probiotics) but may be an indicator for other features of production (e.g. better effort at getting their parasite control, more intensive production; specific environmental control).

Herds with larger number of finishing pigs on site appear to have a higher risk of milk spots as also been reported for Danish sow herds (Dangolla et al., 1996). Our findings may reflect the presence of a more hygienic environment that limits the within-herd transmission; and/or more efficient deworming protocols in small/medium size herds. Gardner and others (2002) - focussing on respiratory problems - specifically discussed the role of herd size as a potential risk factor for pig diseases. Herd size may be related to various risk factors such as introducing infectious agents from outside the herd (e.g. with carrier pigs) and increased transmission of infectious agents within large herds. Those authors also discussed the presence of management factors that are associated with herd size (e.g. production system, disease control practices, labour structure and organization). Thus, herd size may be a proxy for other correlated factors not available in our study.

42 5.3 Breeding animals present in the unit.

The presence of breeding animals within the unit appeared to be a potential protective factor for presence of milk spots in finishing pigs. Those units that produce their own stock may have deworming protocols (e.g. pre-farrowing) and strategies to maintain weaners and finishers away from the areas potentially contaminated with ascarid eggs. Additionally the presence of breeding animals on site indicates farrowing-to-finish rather than a multisite system where there may be more mixing of sources which could lead to increase helminth prevalences (Joachim et al., 2001). Also, breeding herds may have developed immunity levels over the years that help to reduce the prevalence of adults affected with Ascaris suum and the severity of the infection; overall contributing to a lesser egg load in the units.

5.4 Seasonal variation.

There was a significant difference in the risk of milk spots in the first and second half of the year. The combined period for the first six months had lower risk of milk spots compared with the second half of the year. A similar temporal distribution was reported for the detection of milk spots in pigs slaughtered in Northern Irish abattoirs, with lesion peaks detected in summer and autumn (Gooddall et al., 1991). These authors found a good correlation between the temperature in spring and early summer and the prevalence of milk spots. Previous studies have also observed these seasonal variations in the development of the Ascaris suum eggs associated with changes in the temperature of the pig barns (Stevenson,

1979; Warner and Polley, 1999; Joachim et al., 2001).

43 5.5 Constraints and further discussion.

The sample of farms included in this study is considered to be representative of the population of assured (those professional) British pig producers. Most of the associations reported in these analyses (except for the ones related to location and the use of indoor production), although robust, were weak associations (odds ratios close to one) indicating that only marginal differences may exist between exposed and unexposed groups. The large sample size of this study (505 and 338 farms) would have helped to detect such associations.

Our study relied on abattoir findings on milks spots as a proxy for detection of ascariosis.

Bernardo and others (1990) inferred that abattoir liver monitoring could provide satisfactory farm level classification for Ascaris suum parasitism. This finding is in line with the methodology followed in our study which included several batches of pigs per farm; aiming to optimise, with this sample recruitment, the adequate classification of the farms. The lifespan of the milk spot lesions may pose some restrictions in the interpretation of these findings. The lesions tend to disappear within 25 days (Stewart and Hoyt; 2006, pp. 905), so

Ascaris challenges occurring in early stages of production may be missed in the abattoir.

The use of information from existing databases also harbours some potential drawbacks.

The datasets may have contained flaws for some of the QAPs records (e.g. the presence of unrecorded changes over time in some of the farm characteristics, inconsistent recording of the farm characteristics) which could have introduced misclassification bias. It was assumed, however, that these errors had no differential bias, and would be equally present in either the exposed or the unexposed group (concerning the farm factors studied) regardless of the prevalence of these conditions. Moreover, to minimise this problem, this study did not rely

44 on the information collected on just one farm audit but also has reviewed and contrasted the historical information which was thus included in the combined database used for the analyses.

An important limitation in this study was the lack of information on other variables that do play a role in predicting the occurrence of the ascariosis: e.g. the anthelminthic treatment regimes, the protocols for cleaning and disinfection, pig age category management, and other husbandry practices. This situation may have led to a potential clustering of the observations at batch or farm level. The statistical approach used for the analyses (GLM with beta- binomial distribution) would have helped to account for herd specific unknown management factors.

6 Conclusion and future actions.

Overall, this study suggests that those husbandry practices that were more in line with maintaining optimal protocols of hygiene and disinfection (e.g. indoor production, no use of bedding) posed lower risk of milk spots, potentially reflecting lower levels of ascariosis.

Particular attention must be paid to helminth control in outdoor and straw-bedded farming; both production systems expanding in response to an increasing consumer demand on the free-range pork. Also, our findings have shown that, despite the current methods of production, Ascaris suum burdens still show within-year variation. This finding is an important feature to be considered in establishing the optimal timing of deworming intervention. This was the first attempt at combining existing information from the British pig industry to perform large-scale epidemiological studies to investigate the occurrence of

45 milk spots and these outputs may serve as a baseline for future studies on pig disease prevention and control in Great Britain.

7 Acknowledgements.

This study was funded by Defra as project OD0215. We would like to acknowledge and thank the different colleagues and institutions for their contribution to this project: supplying the data, participating in the study design, helping with the data management and

GIS, advising through the quarterly meetings arranged during the period this project ran, revising this paper and a number of other inputs. The names we would like to highlight are:

Malcolm Hall; Donna Clark, Franz Brülisauer, Jill Thompson and Rick D’Eath from SAC;

Stan Done and Alex Cook from VLA, David Strachan from Boehringer Ingelheim

Vetmedica; Derek Armstrong and Mark Wilson from BPHS-BPEX; Allan Ward from QMS;

Jane Johnson now from CMi; Martin Barker and Michael Hemmings from Genesis QA;

Elizabeth Kerrigan from ABP; Jamie Robertson from Livestock Management Services; Zoe

Davies from the National Pig Association; Elizabeth Kelly and Alex Morrow from Defra and Ilias Kyriazakis, now at Newcastle University.

46 8 References

Animal Health, England. The Pigs (Records, Identification and Movement) Order 2007. URL: http://www.opsi.gov.uk/si/si2007/pdf/uksi_20070642_en.pdf

Barker I.K., Dreumel A.A.V., Palmer N. 1993. The Alimetary System. In: Jubb, KVF; Kennedy, PC; Palmer, N. Pathology of Domestic Animals; Academic Press. Fourth Edition, Volume 2, pp. 283-285.

Bernardo, T.M., Dohoo I.R., Ogilvie T. 1990. A critical Assessment of Abattoir Surveillance as a Screening Test for Swine Ascariasis. Canadian Journal of Veterinary Research, 54:274-277.

Dangolla, A., Willeberg, P., Bjørn, H., Roepstorff, A. 1996. Associations of Ascaris suum and Oesophagostomum spp. infections of sows with management factors in 83 Danish sow herds. Preventive Veterinary Medicine, Volume 27, Issues 3-4, Pages 197-209.

Dohoo, I.; Martin, S., Stryhn, H., 2003. Veterinary Epidemiology Research. AVC Inc., Charlottetown.

Gardner, IA; Willeberg, P; Mousing, J. 2002. Empirical and theoretical evidence for herd size as a risk factor for swine diseases. Animal Health Research Reviews, 3:43-55 Cambridge University Press.

Gill, P. 2007. Liquid feeding: a technology that can deliver benefits to producer profitability, pig health & welfare, environment, food safety and meat quality. Paradigms In Pig Science Proceedings. Nottingham University Press.

Goodall, E. A., Mcloughlin, E. M., Menzies, F. D., & Mcilroy, S. G., 1991. Time series analysis of the prevalence of Ascaris suum infections in pigs using abattoir condemnation data. Animal Production. 58. 367- 372.

Joachim, A., Dülmer, N., Daugschies, A., Roepstorff, A. 2001. Occurrence of helminths in pig fattening units with different management systems in Northern Germany. Veterinary Parasitology, Volume 96, Issue 2, 20 March, Pages 135-146.

Kelly W. R. 1993. The Liver and Biliary System. In: Jubb, KVF; Kennedy, PC; Palmer, N. Pathology of Domestic Animals; Academic Press. Fourth Edition, Volume 2, pp. 375.

Petkevicius, S., Bach Knudsen K. E., Nansen, P., Roepstorff, A., Skjøth F., And Jensen K. 1997. Impact of diets varying in carbohydrates resistant to endogenous enzymes and lignin on populations of Ascaris suum and Oesophagostomum dentatum in pigs. Parasitology, Cambridge University Press. 114:555-568.

47 Nansen P.; Roeptorff A. 1999. Parasitic helminths of the pig: factors influencing transmission and the infection levels. International Journal of Parasitology 29 877-891.

R Development Core Team. 2005. R: A language and environment for statistical computing, reference index version 2.2.1. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org

Roepstorff, A., Jorsal, S.E. 1990. Relationship of the prevalence of swine helminths to management practices and anthelmintic treatment in Danish sow herds. Veterinary Parasitology, Volume 36, Issues 3-4, Pages 245- 257.

Roepstorff, A., Nansen P. 1994. Epidemiology and control of helminth infections in pigs under intensive and non-intensive production systems. Veterinary Parasitology, Volume 54, Issues 1-3, Pages 69-85.

Sánchez-Vázquez, M.J., Smith, R., Gunn, G.J., Lewis, F., Strachan, W.D., Edwards, S.A. The Identification of Risk Factors for the Presence of Enzootic Pneumonia-Like Lesions and Pleurisy in Slaughtered Finishing Pigs Utilizing Existing British Pig Industry data. Pig Journal. Vol. Nov-Dec.. In press

Smith, R.P., Sanchez-Vazquez, M.J., Cook, A.J.C., Clough, H.E., Edwards, S.A. An Analysis of Quality Assurance and Zoonoses Action Plan Data from Pig Herds in the United Kingdom. Pig Journal. In press. Pig Journal. Vol. Nov-Dec. In press.

Steenharda, N.R., Jungersenb, G., Kokotovicb, B, Beshahc, E, Dawsonc, H.D., Urban J.F. Jr., Allan Roepstorffa, A, Thamsborga S. M. 2009. Ascaris suum infection negatively affects the response to a Mycoplasma hyopneumoniae vaccination and subsequent challenge infection in pigs. Vaccine. Volume 27, Issue 37.

Stevenson, P. 1979. The influence of environmental temperature on the rate of development of Ascaris suum eggs in Great Britain. Research in Veterinary Science. 27, pp. 193–196.

Stewart T.B., Hale O.M. 1988. Losses to Internal Parasites in Swine Production. Journal of Animal Science. American Society of Animal Science. 66:1548-1554.

Stewart T.B., Fox M.C., Southern J.J. 1996. Economics of Deworming Pigs. Proceedings of the 14th International Pig Veterinary Society. Volume 14, 351.

Stewart T.B., Hoyt P.G. 2006. Internal Parasites. In: Straw, EB; Zimmerman, JJ; D'Allaire, S; Taylor, DJ;. Diseases of Swine. Ninth Edition. Blackwell Publishing, pp. 904-905.

48 Wagner, B., Polley, L. 1999. Ascaris suum: seasonal egg development rates in a Saskatchewan pig barn. Veterinary Parasitology, Volume 85, Issue 1, 16 August, Pages 71-78.

CHAPTER 3

IDENTIFICATION OF FACTORS INFLUENCING THE OCCURRENCE OF HEPATIC SCARRING, PAPULAR DERMATITIS, PERICARDITIS, PERITONITIS, VIRAL-LIKE PENUMONIA, PLEUROPNEUMONUMONIC LESIONS, ABSCESS, PYAEMIA AND TAIL DAMAGE IN SLAUGHTERED PIGS.

Sánchez-Vázquez, MJ(a); Smith, R(b); Kang, SJ(a); Lewis, F(a); Gunn, GJ(a); WD(c); Edwards, SA.(c)

(a) Scottish Agricultural College, Kings Buildings, West Mains Road, Edinburgh, EH9 3JG.. (b) Veterinary Laboratories Agency, New Haw, Addlestone, Surrey, KT15 3NB.

(c) Newcastle University, Agriculture Building, Newcastle upon Tyne, NE1 7RU.

49

1 Material and Methods 1.1 Data source. Abattoir data on the pathologies investigated were recruited from the two existing pig health schemes in Britain: British Pig Health Scheme (BPHS) and Wholesome Pigs (Scotland) (WPS); which included 17 pig abattoirs. Both schemes aim to obtain a representative sample from each batch of pigs by assessing every other pig on the slaughter line. The different lesions were assessed in different ways. EP-like lesion scores, representing the approximate percentage of lung area showing consolidation, are recorded on a scale from 0 to 55 in 2.5 steps. Pleuritic lesions are scored in two ways: a) pleural adhesions are recorded using three categories 0, 1 and 2; 1 indicating adhesions between lung lobes only, 2 indicating adhesions involving the visceral pleura and/or the parietal pleura and 0 indicating an absence of adhesions and b) pleuropneumonia-like lesions which are reported as a binary, present (1) or absent (0). For this study we considered presence of pleurisy when any of these two lesions were present (i.e. pleural adhesions or pleuropneumonic-like lesions). Papular dermatitis is score with three categories: 0, 1 and 2; accounting for severity and distribution of the skin lesions. All the other lesions are scored binary; recording just presence and absence of the lesions. Viral-like pneumonia lesion was not included in this investigation due to presence of known inconsistencies in the scoring of this lesion that could artefactually alter the results of the investigation. The scoring was carried out by swine veterinarians trained in this method of testing at the abattoir inspection line. Different veterinarians assessed in the same abattoir, and rotated duties for more than one abattoir helped to maintain the consistency in the diagnostic criteria. Farm information was obtained from the three main British farm quality assurance programmes (QAPs): Assured British Pigs (ABP) and Genesis Quality Assurance (GQA) for England and Wales; and Quality Meat Scotland (QMS) for Scotland. The QAPs audit the farms periodically – at least annually – and collect, through questionnaires, and document a large amount of information on farm production descriptors (e.g. type of housing, type of feeding, number of finishing pigs, number of sows, etc.). Current and historic data available at commencement of this study were used, covering the period between September 2005 and

50 September 2007. Farm was defined as a site that had a specific Defra herdmark (Defra, PRIMO rules). This latter is an unique official reference for each holding, consisting of an alphanumeric code, which is also used to identify pigs sent to the abattoir by its application as a tattoo on each shoulder, otherwise known as a slapmark. A batch was defined as a group of pigs from a single farm submitted to the abattoir on a particular date.

1.2 Data management. Two datasets were created for this study. Firstly, one including producers belonging to any of the three QAPs, with farms from Scotland, England and Wales (from now on we will refer to this as the GB dataset). English-Welsh QAPs hold category-specific housing characteristics and more comprehensive feeding information not present in the Scottish database. Subsequently, for more detailed analyses, a parallel recruitment for a second dataset was carried out only including farms from English-Welsh QAPs (we will refer to this as the EW dataset). The records available for two years in the QAPs were reviewed for each individual farm to determine its characteristics. Information concerning the stock numbers present at the time of the farm audits and date when the information was updated were included in the databases. This latter was used as a time reference to link husbandry and abattoir data. The other variables (e.g. flooring and housing characteristics, feeding system), which were farm structural characteristics and management practices unlikely to change within the study period, were considered constant over the study period with no specific date attributed to them.

1.2.1 Producing the GB dataset. For the GB dataset, the study population consisted of those pig producers that had pigs assessed by a health scheme and were also part of any of the three QAPs. Farm variables that were common to the three QAPs, and potential proxies for a risk/protective factor for the occurrence of the different lesions in finishing pigs, were indentified. The following variables met those criteria: total number of finishing pigs and sows; flooring characteristics of finishing pigs housing; use of wet-feeding; the presence of a breeding unit on the site; and reported use of outdoors accommodation at any stage of production. These husbandry characteristics were included in the GB dataset together with

51 pathologies farm specific abattoir information for the prior and subsequent years to the QAPs audits. A summary of all the different variables considered in the analysis can be found in Tables 1 and 2. A more detailed explanation on the methodology followed for this process is described in a previous paper, chapter 1 (Sanchez-Vazquez et al; in press).

Table 1. Categorical farm variables for the first subset.

Variable Classes/Level of Description Pig assessed

exposure N yes (percentage) (n) Health Scheme where the abattoir scoring BPHS 2108 (80) batches 94,373 data have been reported WPS 542 (20) batches 35,446 Geographic area where the farm was North (a) 113 (22) farms 38,169 located Scotland 166 (33) farms 35,446 South East (b) 179 (36) farms 38,647 South West (c) 47 (9) farms 17,557 Having a breeding herd Yes 212 (42) 61,885 No 293 (58) 67,934 All production indoors (without records Yes 459 (91) 114,576 for stages of outdoors production) No 46 (9) 15,243 Use of wet-feeding Yes 72 (14) 29,597 No 433 (86) 100,222 Use of solid floor with bedding Yes 393 (78) 91,979 No 112 (22) 37,840 Use of part slatted floor Yes 98 (19) 36,297 No 407 (81) 93,522 Use of full slatted floor Yes 128 (25) 51,136 No 377 (75) 78,683 (a) North England; York and the Humber. (b) East Midlands; East Anglia and South East. (c) West Midland, Wales and South West.

Table 2. Continuous farm variables studied by classes in the first data subset.

Variable Classes divisions Number of farms Pig assessed (n) (percentage)

Number of finishers More that 9000 finishers 23 (5) 13,918 present in the farms Between 9000 and 2500 finishers 182 (36) 61,221 Less than 2500 finishers 300 (59) 54,680 Number of sows present More that 600 sows 28 (6) 13,571

52 in the farms Between 150 and 600 sows 145 (28) 46,883 Between 15 and 150 sows 31 (6) 4,087 None sows present 301 (60) 65,278 To investigate regional differences in lesion prevalence, Great Britain was divided into four different areas (see figure 1): a) Scotland; b) North England; Yorkshire and the Humber

(North); c) East Midlands, East Anglia and South East (South East); and d) West Midlands,

Wales and South England (South West). Information from the pig industry concerning location of the abattoirs participating in the scheme and of the farms supplying pigs to those abattoirs dictated geographical divisions; a proxy for abattoir farm capture areas.

Figure 1. Map presenting the geographical divisions used to study farm location effect.

1.2.2 Producing the EW dataset. For the EW dataset, the study population consisted of pig producers from English/Welsh QAPs and whose pigs were assessed by BPHS. This second database was created, following

53 the same methodology as above, including farm characteristics that were common to GQA and ABP. This included housing category-specific information (e.g. for growers, for finishers) and feeding records (on the feed form and on the use of co-product feeds) not available in the GB datasets. A more detailed summary of all the different variables considered in the analysis is presented in Tables 3 and 4. Farm location was not available for 35% of these producers so it was not included in the EW dataset.

Table 3. Categorical farm variables for the second subset.

Variable Classes/Level Description Pig assessed

of exposure N yes (percentage) (n)

Having a breeding herd Yes 163 (48) 47,948 No 175 (52) 57,975 Outdoors growing taking place in the Yes 20 (6) 7,845 farm No 318 (94) 98,078 Outdoor finishing taking place in the farm Yes 5 (2) 1,565 No 333 (98) 104,358 Use of solid floor with bedding Yes 251 (74) 71,892 No 87 (26) 34,031 Use of part slatted floor Yes 102 (30) 37,040 No 236 (70) 68,883 Use of full slatted floor Yes 126 (37) 51,114 No 212 (63) 54,809 Use of wet-feeding in finishing pigs Yes 66 (20) 30,792 No 272 (80) 75,131 Use of meal in finishing pigs Yes 99 (30) 27,367 No 239 (70) 78,556 Use of pellets in finishing pigs Yes 210 (62) 60,872 No 128 (38) 45,051 Use of enzymes in finishers Yes 114 (34) 36,063 No 224 (66) 69,860 Use of antibiotics in finishers Yes 94 (28) 29,379 No 244 (72) 76,544 Use of probiotic supplementation Yes 14 (4) 4,562 No 324 (96) 101,361 Use of non-dairy co-products Yes 57 (17) 25,319 No 281 (83) 80,604 Use of dairy co-product Yes 52 (15) 24,951 No 286 (85) 80,972

54 Use other dry co-products Yes 78 (23) 31,205 No 260 (77) 74,718 Restricted feeding in finishers Yes 34 (10) 9,777 No 304 (90) 96,146 Table 4. Continuous variables studied by classes in the second data subset.

Variable Classes divisions Number of farms Pig assessed (n) (percentage)

Number of finishers More that 5000 finishers 22 (7) 13,541 present in the farms Between 5000 and 1500 finishers 157 (46) 62,037 Less than 1500 finishers 159 (47) 30,345 Number of sows present More that 600 sows 21 (6) 9,857 in the farms Between 150 and 600 sows 108 (32) 40,591 Between 1 and 150 sows 29 (9) 5,076 None sows present 180 (53) 50,399

1.3 Statistical analysis. This investigation aimed to study the risk/protective factors associated with the prevalence of the different pathologies under study. The response variable in all our analyses was the presence of the lesion. Initially the analysis comprised univariable explorations to investigate the associations between the observed prevalence for the lesion and the different farm factors considered. The variables were tested for correlation. Number of finishing pigs was studied in our model both as continuous and as categorical variables. The number of sows was studied as categorical variable with baseline class being no sows present on the farm. The troughs and the inflection points in the density function were used to establish data-derived cut off points to create categories for each of these continuous variables. Monthly, quarterly and six-monthly temporal variations in the prevalence of the different lesions were also investigated in the model. A logarithm transformation for the number of finishing pigs was used to ensure robust outputs for this continuous variable in the regression analysis. The variables were included in a multivariable generalized linear model (GLM) and, through a backward elimination process, were retained in the model if the individual Wald test was considered statistically significant (p<0.05). A variance inflation factor was computed to assess collinearity among the predictor variables (Dohoo et al., 2003). The variables retained in our initial multivariable model were included in a mixed effects binomial

55 logistic regression multivariable model. Random effects at the batch, farm and abattoir level were investigated. These initial models assumed a binomial distribution; however due to a high proportion of pigs with absence of lesions zero-inflated binomial and beta-binomial models were also considered. Beta-binomial model incorporates beta-distributed random effects and it is essentially a model for grouped or replicated data (Dohoo et al., 2003). This approach allows accounting for the potential over-dispersion present in our data due to different levels of clustering (e.g. batch, farm or abattoir level). Once the diagnostics criteria of the model were satisfied, the goodness of fit measurement Akaike's information criterion (AIC) was used to choose the best model among the candidates. Additionally, substantial changes in the estimated coefficients in the models and increases in standard errors during the model building process were investigated. Biologically plausible interactions between the variables present in the final model were investigated using likelihood ratio tests. All the analyses were performed in R (R Development Core Team, 2005) using libraries stasts, lme4 and vgam.

2 Results. The models that provided better goodness of fit to the data were those using a Beta- binomial distribution.

2.1 Results for papular dermatitis. The overall prevalence found in the study including farms from the whole Great Britain was 3.9 % of pigs affected with papular dermatitis; while the percentage of farms with at least one pig affected with papular dermatitis was 41.4 %. For the subset of farms recruited from England and Wales it was 4.4 the percentage of pigs affected; from 62.1 % of the farms.

56

Table 5. Estimated Odds Ratios in the Multivariable Beta-Binomial model including variables associated with presence of papular dermatitis in finishing pigs. For GB dataset. N=505. Farm variable Level Odds ratio 95% CI Full slatted floor Yes 1.45 1.21 - 1.74 No 1.00 - Number of finishers Yes 1.00 0.99992 - present in the farms No 1.00 0.99998 Farm density category Between 0.09 and 0.03 farms 2.18 1.24 - 3.82 per Km2 Less than 0.03 farms per Km2 2.82 1.61 - 4.91 More that 0.09 farm per Km2 1.00 - Area South East (a) 0.52 0.40 - 0.66 North (b) 0.36 0.28 - 0.45 Scotland 0.38 0.28 - 0.51 South West (c) 1.00 - (a) East Midlands; East Anglia and South East (b) North England; York and the Humber (c) West Midland, Wales and South West England

Table 6. Estimated Odds Ratios in the Multivariable Beta-Binomial model including variables associated with presence of papular dermatitis in finishing pigs. For the EW dataset. N=338 farms. Farm variable Level Odds ratio 95% CI Use of dairy co-product Yes 1.74 1.44 - 2.09 No 1.00 - Solid floor with Yes 1.47 1.22 - 1.78 bedding No 1.00 -

2.2 Results for hepatic scarring. The overall prevalence found in the study including farms from the whole Great Britain was 7.4% of pigs affected with hepatic scarring; while the percentage of farms with at least one pig affected with hepatic scarring was 91.1%. For the subset of farms recruited from England and Wales it was 5.5 the percentage of pigs affected; from 88.2% of the farms.

57

Table 7. Estimated Odds Ratios in the Multivariable Beta-Binomial model including variables associated with presence of hepatic scarring in finishing pigs. For GB dataset. N=505. Farm variable Level Odds ratio 95% CI Full slatted floor Yes 1.44 1.26 - 1.63 No 1.00 - Breeding herd on the Yes 0.84 0.75 - 0.94 unit No 1.00 - Wet feeding Yes 0.88 0.77 - 0.99 No 1.00 - All production Indoors Yes 1.20 1.03 - 1.40 No 1.00 - Solid floor with Yes 1.33 1.17 - 1.51 bedding No 1.00 - Area South East (a) 0.48 0.43 - 0.54 North (b) 0.24 0.21 - 0.28 South West (c) 0.31 0.27 - 0.37 Scotland 1.00 - (a) East Midlands; East Anglia and South East (b) North England; York and the Humber (c) West Midland, Wales and South West England

Table 8. Estimated Odds Ratios in the Multivariable Beta-Binomial model including variables associated with presence of hepatic scarring in finishing pigs. For the EW dataset. N=338 farms. Farm variable Level Odds ratio 95% CI Use of enzymes in Yes 1.38 1.23 - 1.54 finishers No 1.00 - Use of non-dairy co- Yes 1.20 1.05 - 1.37 products No 1.00 - Use of meal in finishing Yes 1.19 1.05 - 1.34 pigs No 1.00 - Solid floor with Yes 1.39 1.22 - 1.59 bedding No 1.00 - Part slatted floor Yes 0.85 0.75 - 0.97 No 1.00 - Having a breeding herd Yes 0.86 0.77 - 0.96 No 1.00 - Six month period Second six months of the year 1.14 1.02 - 1.27 First six months of the year 1.00 -

58

2.3 Results for tail damage.

The overall prevalence found in the study including farms from the whole Great Britain was 0.7 % of pigs affected with tail damage; while the percentage of farms with at least one pig affected with tail damage was 29.7 %. For the subset of farms recruited from England and Wales it was 0.7 the percentage of pigs affected; from 34.6 % of the farms.

Table 9. Estimated Odds Ratios in the Multivariable Beta-Binomial model including variables associated with presence of tail damage in finishing pigs. For GB dataset. N=505. Farm variable Level Odds ratio 95% CI Having a breeding herd Yes 1.35 1.02 - 1.78 No 1.00 - Wet feeding Yes 0.62 0.45 - 0.86 No 1.00 - All production indoors Yes 0.46 0.33 - 0.64 No 1.00 - Solid floor with bedding Yes 0.62 0.47 - 0.83 No 1.00 - Cold-warm season Spring-Summer 1.31 1.02 - 1.68 Autumn-Winter 1.00 -

Table 10. Estimated Odds Ratios in the Multivariable Beta-Binomial model including variables associated with presence of tail damage in finishing pigs. For the EW dataset. N=338 farms. Farm variable Level Odds ratio 95% CI Use of enzymes in Yes 1.76 1.34 - 2.32 finishers No 1.00 - Feeding pellets in Yes 0.67 0.51 - 0.88 finishers No 1.00 - Having a breeding herd Yes 0.60 0.46 - 0.79 No 1.00 - Six month period First six months of the year 1.38 1.04 - 1.83 Second six months of the year 1.00 -

59

2.4 Results for pericarditis.

The overall prevalence found in the study including farms from the whole Great Britain was 3.6 % of pigs affected with pericarditis; while the percentage of farms with at least one pig affected with pericarditis was 88.3 %. For the subset of farms recruited from England and Wales it was 3.4 the percentage of pigs affected; from 92.3 % of the farms.

Table 11. Estimated Odds Ratios in the Multivariable Beta-Binomial model including variables associated with presence of pericarditis in finishing pigs. For GB dataset. N=505. Farm variable Level Odds ratio 95% CI Farm density category Between 0.09 and 0.03 farms 0.72 0.61 - 0.85 per Km2 Less than 0.03 farms per Km2 0.70 0.59 - 0.82 More that 0.09 farm per Km2 1.00 - Area North (a) 0.87 0.78 - 0.97 Scotland 0.79 0.70 - 0.90 South West (b) 0.88 0.76 - 1.02 South East(c) 1.00 - (a) North England; York and the Humber (b) West Midland, Wales and South West England (c) East Midlands; East Anglia and South East

Table 12. Estimated Odds Ratios in the Multivariable Beta-Binomial model including variables associated with presence of pericarditis in finishing pigs. For the EW dataset. N=338 farms. Farm variable Level Odds ratio 95% CI Use of probiotic Yes 1.26 1.03 - 1.55 supplementation No 1.00 - Use of dairy co- Yes 1.18 1.06 - 1.31 product No 1.00 -

60

2.5 Results for peritonitis.

The overall prevalence found in the study including farms from the whole Great Britain was 0.7 % of pigs affected with peritonitis; while the percentage of farms with at least one pig affected with peritonitis was 50.3 %. For the subset of farms recruited from England and Wales it was 0.6 the percentage of pigs affected; from 51.8 % of the farms.

Table 13. Estimated Odds Ratios in the Multivariable Beta-Binomial model including variables associated with presence of peritonitis in finishing pigs. For GB dataset. N=505. Farm variable Level Odds ratio 95% CI Part slatted floor Yes 1.44 1.14 - 1.83 No 1.00 - Number of finishers Yes 1.00 1.00 - 1.00 present in the farms No 1.00 - Solid floor with Yes 1.67 1.31 - 2.14 bedding No 1.00 - Area North (a) 0.28 0.22 - 0.36 Scotland 0.61 0.48 - 0.78 South West (b) 0.34 0.24 - 0.47 South East(c) 1.00 - (a) North England; York and the Humber (b) West Midland, Wales and South West England (c) East Midlands; East Anglia and South East

Table 14. Estimated Odds Ratios in the Multivariable Beta-Binomial model including variables associated with presence of peritonitis in finishing pigs. For the EW dataset. N=338 farms. Farm variable Level Odds ratio 95% CI Use of dairy co- Yes 1.47 1.14 - 1.88 product No 1.00 - Solid floor with Yes 1.62 1.26 - 2.09 bedding No 1.00 - breeding Yes 0.63 0.51 - 0.77 No 1.00 - Six month period Second six months of the year 1.23 1.00 - 1.51 First six months of the year 1.00 -

61

2.6 Results for abscess.

The overall prevalence found in the study including farms from the whole Great Britain was 0.7 % of pigs affected with abscess; while the percentage of farms with at least one pig affected with abscess was 49.1 %. For the subset of farms recruited from England and Wales it was 0.8 the percentage of pigs affected; from 55.3 % of the farms.

Table 15. Estimated Odds Ratios in the Multivariable Beta-Binomial model including variables associated with presence of abscess in finishing pigs. For GB dataset. N=505. Farm variable Level Odds ratio 95% CI Part slatted floor Yes 1.58 1.31 - 1.91 No 1.00 - Breeding herd on the Yes 0.75 0.63 - 0.91 unit No 1.00 - Health Scheme WPS 1.31 1.08 - 1.60 BPHS 1.00 - Solid floor with Yes 0.68 0.56 - 0.82 bedding No 1.00 -

Table 16. Estimated Odds Ratios in the Multivariable Beta-Binomial model including variables associated with presence of abscess in finishing pigs. For the EW dataset. N=338 farms. Farm variable Level Odds ratio 95% CI Use of antibiotics in Yes 1.46 1.18 - 1.80 finishers No 1.00 - Use of non-dairy co- Yes 0.60 0.44 - 0.82 products No 1.00 - Wet feeding Yes 1.39 1.06 - 1.84 No 1.00 - Part slatted floor Yes 1.36 1.12 - 1.67 No 1.00 - Number of finishers Between 5000 and 1500 0.65 0.49 - 0.86 present in the farms finishers Less than 1500 finishers 0.59 0.43 - 0.82 More that 5000 finishers 1.00 -

62

2.7 Results for pyaemia.

The overall prevalence found in the study including farms from the whole Great Britain was 0.3 % of pigs affected with pyaemia; while the percentage of farms with at least one pig affected with pyaemia was 33.7 %. For the subset of farms recruited from England and Wales it was 0.3 the percentage of pigs affected; from 30.2 % of the farms.

Table 17. Estimated Odds Ratios in the Multivariable Beta-Binomial model including variables associated with presence of pyaemia in finishing pigs. For GB dataset. N=505. Farm variable Level Odds ratio 95% CI Health Scheme WPS 2.11 1.67 - 2.68 BPHS 1.00 -

Table 18. Estimated Odds Ratios in the Multivariable Beta-Binomial model including variables associated with presence of pyaemia in finishing pigs. For the EW dataset. N=338 farms. Farm variable Level Odds ratio 95% CI Use of antibiotics in Yes 1.85 1.38 - 2.48 finishers No 1.00 - Use of probiotic Yes 1.82 1.05 - 3.14 supplementation No 1.00 - Feeding pellets in Yes 0.43 0.32 - 0.58 finishers No 1.00 - Solid floor with Yes 1.98 1.41 - 2.80 bedding No 1.00 -

3 Discussion.

This was a retrospective study to identify risk/protective factors for main conditions reported at the abattoir slaughter inspections which used the existing information available in the databases of farm QA companies and Health Schemes. This was the first time in Great Britain the information collected by those initiatives has been combined for epidemiological analyses, adding an extra value to this information initially collected for other purposes. This analysis aimed to provide the British pig industry with a better understanding on the risk factors associated with health scheme abattoir reported.

63

4 References.

Defra Primo Rules. Animal Health, England. The Pigs (Records, Identification and Movement) Order 2007. URL: http://www.opsi.gov.uk/si/si2007/pdf/uksi_20070642_en.pdf

Dohoo, I.; Martin, S., Stryhn, H., 2003. Veterinary Epidemiology Research. AVC Inc., Charlottetown.

Sánchez-Vázquez, M.J., Smith, R., Gunn, G.J., Lewis, F., Strachan, W.D., Edwards, S.A. The Identification of Risk Factors for the Presence of Enzootic Pneumonia-Like Lesions and Pleurisy in Slaughtered Finishing Pigs Utilizing Existing British Pig Industry data. Pig Journal. Vol. Nov-Dec.. In press CHAPTER 5

An Analysis of Quality Assurance and Zoonoses Action Plan Data from Pig Herds in

the United Kingdom

Smith, R.P.1, Sanchez-Vazquez, M.J.2, Cook, A.J.C.1, Clough, H.E.3, Edwards, S.A.4

1Centre of Epidemiology and Risk Analysis, VLA Weybridge, Woodham lane, New Haw,

Addlestone, Surrey, KT15 3NB

2Scottish Agricultural College, Drummond Hill, Stratherrick Road, Inverness, IV2 4JZ

3Epidemiology Group, Department of Veterinary Science, University of Liverpool,

Leahurst, Neston, Wirral, CH64 7TE

4Newcastle University, Agriculture Building, Newcastle upon Tyne, NE1 7RU

Summary

The Zoonoses Action Plan required that meat juice (MJ) samples from slaughtered pigs were monitored for antibodies against Salmonella in a mix-ELISA. These results were linked to data routinely collected by quality assurance schemes to create a dataset,

64 representing over 1,500 pig holdings with an average of 30 MJ ELISA results per farm.

The data were analysed to identify factors associated with Salmonella infection, and to indicate potential analytical approaches. Multivariable analysis showed that samples from farms in Yorkshire and the Humber and those that housed finisher pigs on solid flooring were associated with ELISA results. Univariable analysis showed significant regional differences in pig management, and spatial analysis showed high prevalence holdings in

Yorkshire and the Humber were more clustered in space than low prevalence holdings.

However, poor data quality precluded analysis of many variables and recording errors in identifiers (e.g. slapmark) caused problems in matching samples to holdings. If the quality and range of data collected in the schemes could be improved then the large sample size would ensure sufficient statistical power to identify even weak associations between a broad set of variables and MJ ELISA status, which could help to target on- farm control measures relevant for the UK pig population.

Introduction

Bacteria belonging to the genus Salmonella are gram-negative enterobacteria capable of colonising and infecting a wide range of hosts. In pigs, Salmonella infection can cause a range of clinical signs, from scouring to fever, septicaemia and death, although infection is often sub-clinical. The prevalence of Salmonella in pig caecal samples, collected for a recent large abattoir study in Great Britain, was high (23.4% (19.9-27.3)), when compared to both cattle and sheep (1.4% and 1.1% respectively) (Milnes et al, 2005) and in an EU Baseline survey in 2007, Salmonella was isolated from 21.2% of mediastinal lymph node samples from UK pigs at slaughter (Anon, 2008). Salmonella is also an

65 important foodborne pathogen for the human population, with 13,213 laboratory confirmed cases of salmonellosis in the United Kingdom (UK) identified in 2007 (Defra,

2007). Around 13% of these human cases were infected with the serovar S. Typhimurium, which was also the predominant type detected in samples from pigs (VLA, 2007).

Many studies have tried to ascertain the factors that influence Salmonella prevalence and identify on-farm controls, to reduce the Salmonella burden in pigs. Recent studies in

Great Britain have detected associations with factors such as herd size; outdoor rearing of pigs; flooring type; and farm location (VLA, 2004; Pritchard et al, 2005). These findings have been supported by European, Canadian and US studies (Funk et al, 2001, Nollet et al, 2004; Farzan et al, 2006). However, some of these studies were limited to a small and potentially unrepresentative subset of the pig farm population whilst others may not have had sufficient statistical power to detect modest associations between Salmonella infection and putative risk factors.

The pig industry has been proactive in developing Quality Assurance (QA) schemes to monitor farm practice, and these schemes collect a large amount of data on herd details and management practices and cover a large proportion of the pig farms in the UK. In

June 2002, the British Pig Executive introduced the Zoonoses Action Plan (ZAP)

Salmonella monitoring programme. This programme ran in conjunction with QA schemes to estimate the burden of Salmonella in pigs sent to slaughter by testing meat juice (MJ) samples for antibodies against Group B and C1 Salmonella in a mix-ELISA system (Armstrong, 2003). A positive MJ ELISA test was assumed to represent prior

66 infection and it was recognised that positive pigs were not necessarily infected when slaughtered. Farms that had a prevalence of more than 50% MJ ELISA positive pigs were required to implement an action plan or face eventual loss of their Quality Assured status and it was hoped that this threat would motivate all pig farmers to develop

Salmonella control plans. The scheme was based on a design by the Danish pig industry that had contributed to a reduced Salmonella prevalence (Nielsen et al, 2001). Due to the human health impact of pig Salmonella, the ZAP scheme was linked to the Food

Standards Agency’s initiative to meet the target of reducing Salmonella in pigs by 50% by 2010 (FSA, 2009).

Information from the ZAP and QA schemes were merged and the data assessed for suitability for epidemiological analysis. This paper reports how the data were used to examine relationships between prevalence of MJ ELISA positive pigs and farm characteristics on a large population of pig farms, and the suitability of using updates of this information for further analyses.

Materials and Methods

Creation of a combined QA dataset

Datasets were supplied in 2007 by three pig assurance schemes covering the UK: -

Assured British Pigs (ABP); Genesis Quality Assurance (GQA); and Quality Meat

Scotland (QMS). The datasets differed greatly in format and size, ranging from a single record per holding to a longitudinal dataset held in a series of subtables, with multiple entries per holding. Each dataset was assessed to determine how to consolidate all the

67 data from the QA schemes into a single table and how to combine the records with the

ZAP data.

The criteria for selecting explanatory variables for use in our analysis was:- that the data had to be comparable across all three schemes; the variables had to be biologically plausible risk or protective factors for Salmonella infection; and the variables must not have a high proportion of missing values, as this would reduce the dataset available for multivariable analysis. These criteria omitted a large number of variables such as:- whether teeth were clipped or tails docked; whether antibiotics and enzymes were used; whether feed was restricted or to appetite for the different age categories; and information on the ventilation of the pig houses.

The most recent record for each pig holding was selected from the datasets. Data in the

ABP and GQA schemes were contained in subtables, and not all records could be linked to data corresponding to the same date from all of the subtables, due to missing data. The majority of these holdings could be linked to records from the same date, but 11% of the records were linked to at least one subtable collected a year from the most recent date and

7% had to be linked to a subtable up to two years from the most recent date.

As the data for each scheme were collected and recorded in different ways, data were recoded to allow them to be compared across the schemes. In the ABP and GQA schemes, different categories of sows and pigs were recorded (e.g. in-pig sows, maiden gilts, pigs under 30 kg). These were recoded to match the variables collected in QMS. The number

68 of sows was generated by combining maiden gilts; in-pig gilts; in-pig sows; suckling sows; and other sows. The number of finishers was created by combining the variables

‘feeders <30kg’ and ‘feeders >30kg’. Records with missing values for the number of pigs of a certain type were coded as zero if the number of any other pig type had been recorded for that holding. The total number of pigs was then created based on the number of pigs given in each pig category.

Data collected on the types of feed used were coded into three binary (yes or no) variables for whether:-

1. Wet feeds were fed to finishers;

2. Compound feeds were fed to finishers;

3. Home-mix was used on the farm.

Pellet and meal answers from ABP and GQA were combined as “compound feed” to match that recorded by the QMS scheme. Individual variables on whether any specific floor types, and bedding, were used for finisher pigs on that holding. A variable was also created to record whether or not all the pigs were kept indoors or whether any stage of the production was outdoors.

Postcode was used for each holding to locate a map reference (X & Y coordinates) in the

GIS software ArcGIS 9.1 (ESRI), and these coordinates were used to identify the NUTS

(Nomenclature of Units for Territorial Statistics) region for that holding. NUTS regions are commonly used in the analysis of spatial data as the boundaries are more stable and less subject to change over time. For Northern Irish holdings, as Ireland uses a different

69 coordinate system, the multimap website (www.Multimap.com) was used to collect an

Ordnance Survey reference for each postcode, which was then converted into X & Y coordinates.

The distance between each holding was calculated and the number of other pig holdings within 3 km and 10 km calculated (the standard outbreak protection/ surveillance zone distances used in the UK). Variables for temporal trends and seasonal effects were also designed by adding sine and cosine terms for 3, 6 and 12 month periods to create quarterly, half-yearly and yearly cycles.

ZAP sample testing

Small pieces of skeletal muscle were removed from pigs at the abattoir. The samples were placed in a meat juice tube which was frozen and then thawed to collect the MJ fluid. The MJ sample was tested by a mix-ELISA serological test to detect antibodies to

Salmonella (Nielsen et al, 1998). One sample was collected from every batch of pigs sent to slaughter and additional samples were collected from that batch at a rate of one in fifty pigs thereafter (Armstrong, 2003).

Connection to ZAP data

The holdings from the QA schemes were linked by holding identifiers to all unique samples collected by the ZAP scheme, within a time period of up to a year after the QA record date. Due to errors and inconsistencies in the herdmarks provided in the QA and

ZAP schemes additional identifying information and data checking procedures had to be

70 used to ensure that the linked information was correct. For example, many records had missing herdmarks or the references were recorded with typing errors (e.g. 1C and IC recorded instead of 10) or with spaces in the ZAP herdmark between characters which did not appear in the QA herdmark. The additional identifying information (certifier membership reference and postcode) was not consistently recorded by the three QA schemes or the ZAP programme and so some matching errors occurred when a) holdings had limited identifying information; b) a holding had moved between schemes, or c) a

ZAP sample herdmark and membership reference linked to different holdings. To resolve these problems ZAP samples with duplicate records were matched to the holding with the record date closest to the sampling date. Once the datasets for each individual QA scheme had been verified and checked for duplicate samples, all three schemes were combined into a single dataset (see Table 1 for the final dataset).

Table 1: Explanatory variables in the final combined dataset

Scheme No. of sows Country No. of finishers NUTS region Total no. of pigs Abattoir ID from where sample collected Any finishers housed on full slats X & Y coordinates Any finishers housed on part slats No. of farms within 3 km Any finishers housed on bedding No. of farms within 10 km Any finishers housed on solid floor 3 month temporal trend Any finishers fed wet feed 6 month temporal trend Any finishers fed compound feed 12 month temporal trend Home mix feed used All pigs kept indoors

Data Analysis

A MJ ELISA cut-off value of 0.25 was used to identify positive samples and to create a binary outcome for each sample. A multivariable logistic regression was completed in

71 STATA 10 (Stata corp., college station, Tx) to model associations between exposure factors (management factors, herdsize, region etc) and the binary MJ ELISA outcome. A robust cluster function was used to adjust standard errors for the clustering of samples at the farm level. All factors yielding a univariable p-value of over 0.25 were excluded from further analysis. Once the factors to be included in the model had been selected a backwards stepwise selection was conducted, removing the least significant factor at each step, until only those factors with a p-value under 0.05 were included. Records with missing data for the selected variables were dropped from the model.

Model fit was tested by using the estat gof function in STATA to compare models, and the Wald’s Chi2 test and Akaike Information Criterion (AIC) were also examined. The model was tested for any biologically plausible interactions. Once selection had been completed, dropped factors were added to the model to check whether they could improve the model.

All holdings with available map coordinates were plotted onto a map of the UK using

ArcGIS 9.1 (ESRI, Redlands, Calif., USA) to show the distribution of holdings and the spread of farms for each assurance scheme. For individual NUTS regions, a case-control

K-function analysis, which tests the null hypothesis of an equivalent degree of clustering in high prevalence and low prevalence holdings against an alternative of a differential clustering mechanism in the two groups (Diggle and Chetwynd, 1991) was completed using the splancs library (Rowlingson and Diggle, 1993) in the statistical package R 2.7.1

(R Development Core Team, Vienna, Austria). Data were examined to see whether

72 higher prevalence holdings (those with more than 25% of samples positive) were more clustered in space than the other holdings. A descriptive analysis of the QA information for farms within each individual NUTS region was also completed with univariable logistic regression, used to compare the answers of a single region against all the remaining regions, to investigative whether there were regional differences in farm management.

Results

The final dataset of holdings that linked to at least one ZAP sample contained 1,535 holdings, 767 from ABP, 570 from GQA and 198 from QMS. A total of 45,557 samples were linked to these holdings, with a mean of 30 (1-370) samples per holding. The map

(Figure 1) shows the location of the 1,415 QA scheme holdings that could be matched to map references. ABP farms were spread throughout England and Northern Ireland, GQA farms were more concentrated in Yorkshire and East England, and QMS only had farms from within Scotland.

Figure 1: Map of the UK showing the position of pig farms in relation to their Quality

Assurance scheme.

73

The descriptive analysis of the differences between farm management in different NUTS regions showed that there were distinctions between the regions of East of England, the

South East, Yorkshire and the Humber, Scotland and Northern Ireland (Table 2). The differences between the other regions were less marked (not shown).

The significant results (p-value <0.05) of the analysis of the regional pig farm demographics show that farms from Scotland and Northern Ireland had on average more pigs (total pig number and number of finishers), whereas farms from Yorkshire and the

74 Humber and East of England had the least (Table 2). There was no significant difference

of the number of sows between the regions. Farms in the East of England and Yorkshire

and the Humber had more pig farms within a 10 km radius. East of England and Northern

Irish farms had a high density of farms within 3 km, whereas farms in the South East had

very low densities at either radius.

The differences in the use of flooring, housing and feeding was also found to be different

between the regions (Table 2). In particular, holdings in Northern Ireland were managed

differently, as wet feeding was more practised in Northern Ireland and units were more

likely to have at least one finisher building with fully or partly slatted floor without

bedding, than in the other regions. More farms in Northern Ireland, and also Scotland,

used home-mixed rations (38.8% and 36.9% respectively) than the other regions. All

holdings in Northern Ireland also recorded that all pigs were kept indoors, whereas more

than half (55%) of holdings in South East England had some pigs kept outdoors. The

results also show that finishers in Yorkshire and the Humber, and East and South-East

England were most likely to have been kept in pens with a solid floor and with bedding.

Table 2: Regional differences of the farm density, herd size and meat juice ELISA results

of Pig QA scheme member farms.

Yorkshire and the Variable name Humber East of England South East England Scotland Northern Ireland Mean Range Mean Range Mean Range Mean Range Mean Range Farms within 3 km 2.6 0-12 4.1 0-21 0.2 0-2 1.4 0-11 3.4 0-11 Farms within 10 km 21.3 0-52 27.6 0-73 1.5 0-5 11.4 0-45 6.8 0-16 No. of sows 159 0-1,620 186 0-31,322 273 0-1,423 146 0-1,200 339 0-4,807 No. of finishers 1,950 0-10,000 1,779 0-17,000 2,707 0-11,976 3,910 0-30,000 2,937 0-45,050 Total no. of pigs 2,112 0-10,000 1,968 0-32,738 2,993 100-11,976 4,057 0-30,000 3,280 0-49,864 % of farms with:-

75 Any full slats for finishers 37.6% 8.3% 29.7% 20.7% 80.4% Any part slats for finishers 22.6% 3.0% 37.8% 16.3% 36.2% Any solid floor for finishers 55.7% 85.5% 67.6% 10.3% 3.6% Any bedding for finishers 58.5% 87.5% 67.6% 68.0% 3.6% All indoor production 79.1% 90.9% 45.2% 86.2% 100.0% Use home-mixing 10.2% 9.9% 23.8% 36.9% 38.8% Use wet feed for finishers 4.2% 4.3% 4.8% 8.9% 43.2% No. samples 8,357 14,340 381 13,942 1,118 % positive 42.0 31.0 33.6 9.8 20.1 No. of farms 382 394 42 203 139

The results of the multivariable logistic model indicated that a summarised region

variable (Figure 2) and whether finisher pigs used any solid flooring were the only

variables that entered our final model. The summarised region variable joined

geographically close regions that had similar farm management as shown by the Fig. 64 univariable regional analysis. This indicated that samples taken from farms in Yorkshire

and the Humber and those that used solid flooring were significantly associated with

positive samples (Table 3).

Figure 2: Map to indicate the grouping of neighbouring regions with similar pig farm

management for use in multivariable analysis.

76

Table 3: Results of a multivariate logistic regression of pig quality assurance data and

meat juice ELISA positive/ negative results, adjusted for the clustering of samples from

each holding (1,333 holdings and 40,536 samples)

Variable Class # # Odds ratio (95% P-value farms samples CI) Region Yorkshire and the Humber 359 8,014 1.00 (Baseline) -

East Midlands & North West 115 2,405 0.40 (0.26-0.62) <0.001 England East & South East England 340 12,765 0.55 (0.44-0.68) <0.001 Scotland & North East 224 14,391 0.16 (0.12-0.21) <0.001 England Northern Ireland 144 1,316 0.34 (0.23-0.52) <0.001

77

Wales & South West 151 1,645 0.24 (0.15-0.38) <0.001 England & West Midlands Finisher pigs – any No 650 20,280 1.00 - solid flooring Yes 683 20,256 1.29 (1.06-1.57) 0.01

Figure 3 shows the difference in K-functions between higher (more than 25% of samples

positive) and lower prevalence farms (equating to a measure of the excess clustering in

high- over low-prevalence farms) plotted against the distance between holdings.

“Significant” excess clustering is indicated by the solid line being outside the simulation

envelopes, which are created under a null hypothesis of an equivalent degree of clustering

in both groups. The analysis showed that high prevalence farms were more clustered in

space at just above the 3 km mark, than low prevalence farms, in Yorkshire and the

Humber (Figure 3). However, no clustering was found in the other regions, including the

other region of high pig farm density, East of England (Figure 4).

Figure 3: Plot of the difference in K-function between high and low Salmonella

prevalence pig farms in Yorkshire and the Humber and associated simulation envelopes

(dotted lines).

78

Figure 4: Plot of the difference in K-function between high and low Salmonella prevalence pig farms in the East of England and associated simulation envelopes (dotted lines).

79

Discussion

The created dataset contained a large number of farms, dispersed throughout the pig farming areas of the UK. Although this dataset covered a large population, it only included farms that were QA registered and sending pigs to slaughter and so this may have introduced some bias when extrapolating the findings to the whole UK pig population. However, the authors believe that the population was very close to the total number of producers found in all regions included in this study and so any finding from this study would be relevant for and representative of the UK pig industry.

The housing of finishing pigs on solid floors was associated with an increased risk of MJ

ELISA positive samples in this study. This type of housing has been identified as a risk factor in previous studies (Nollet et al, 2004), as slatted flooring is more effective at removing faeces from the pig’s vicinity, and so assists to control a route of transmission of infection between pigs.

Geographical region was also entered into our final model as a variable associated with

MJ ELISA results, demonstrating that pigs from certain areas are more at risk of infection than others. As indicated by the univariable analysis, region represents differences in housing systems and pig farm management, and the inclusion of region in the model rather than the other factors may indicate that these have been dropped due to collinearity with the region variable and that they did not entirely explain the variation caused by the region variable. Region could also be an indicator of other production practices;

80 producers from the same area being exposed to the same disease control advice; producers using the same veterinary practices and feeding companies; or differences in regional weather conditions over the year. The use of postcode to generate coordinates may also have produced some error, as the postal address of the farm may be different to where the pigs are kept. The grouping of the NUTS regions by similar locations and management may also have removed specific regional differences.

The exploration of the regional management differences shows that the high prevalence regions were more likely to contain fewer pigs per farm; have a greater number of farms within 10 km; use solid flooring rather than slatted floor; and were less likely to use wet feeding and home-mixing than low prevalence regions. However, there are some discrepancies between the results of the individual high prevalence regions and also between the low prevalence regions. The results for whether all pigs were kept indoors may also have been biased by whether a region had a higher percentage of specialist finisher farms in our study population, as breeding herds within the region would not have been present in the ZAP database. This is suggestive that the regional effect cannot be attributed to a single factor but either to a combination of factors or to regional varying factors that were not available in this analysis.

Variables for the spatial clustering of farms did not enter our final multivariable model, but the K-function analysis shows that high prevalence holdings were significantly more clustered in space than low prevalence farms in Yorkshire and the Humber. The significant clustering in only a single region suggests evidence for either a regionally

81 specific contagious mechanism, or for underlying, locally varying, risk factors within the region. Local spreading of Salmonella could occur through animal movements; when sourcing animals from a nearby breeding farm or from the same companies.

Due to the selection criteria, a large number of variables were dropped but the criteria ensured that the dataset contained holdings from all three schemes and that the multivariable model population was not greatly reduced in size by removing those records with missing data for a selected variable. The dataset may also have suffered from some over matching of samples to farms, as some holdings had very little identifying information and so the linkage between holdings and samples could not be validated. In this respect, we would expect that these database problems would have added statistical noise to the analysis, hampering the detection of those factors associated with the prevalence of MJ ELISA positive pigs and reducing the power and the strength of the associations detected.

The use of the MJ ELISA may have introduced some problems for this type of analysis as it records the serological response to an earlier Salmonella infection and does not determine how recent the infection was. This may have explained why no seasonal trends were included in the final multivariable model, where it had been found to be significant in other studies (Christensen & Rudemo, 1998; Placha et al, 2001).

The study reported here shows the types of analysis possible with the data available and that a statistically powerful analysis is possible that would detect even weak associations, due to the large sample size. The utilisation of routinely collected

82 information also provides a cost-effective route to large scale demographical data to be fed into risk assessments and help generate outputs that are representative of the UK pig industry. These types of analyses would provide the scientific evidence to help select factors that could control Salmonella on pig farms and shape national control plans. For example, if risk factors vary by region then control measures could be customised for each region. However, this type of data was not collected for epidemiological analysis and so provided problems due to the quality of the data. The errors in the collection of identifying information made it difficult to match QA scheme holdings to ZAP samples.

The large amount of missing data, up to 70-80% in some instances, and the differences in which variables were collected by the three QA schemes, meant that many variables could not be analysed without compromising the number of holdings present in the dataset.

If increased effort was made to standardise the collection of a larger selection of variables and if stringent validation procedures were used, especially in the recording of holding identifiers, the range and quality of the dataset could be improved. The inspection of the data from the QA schemes also highlighted other variables, known to be significantly associated with Salmonella infection (e.g. PMWS status), that could be additionally collected by the schemes. With these enhancements the schemes would provide vital, updateable information to epidemiologists and risk analysts to allow them to carry out complex analyses with high statistical power and confidence.

Acknowledgements

83 The authors would like to thank the schemes involved for their participation, and BPEx for their support. Defra are also thanked for funding the study under project

FT5088/OD0215.

References

 Milnes, A.S. Stewart, I. Clifton-Hadley, F.A. Davies, R.H. Newell, D.G. Sayers, A.R.

Cheasty, T. Cassar, C. Ridley, A. Cook, A.J. Evans, S.J. (2007) Intestinal carriage of

verocytotoxigenic Escherichia coli O157, Salmonella, thermophilic Campylobacter

and Yersinia enterocolitica, in cattle, sheep and pigs at slaughter in Great Britain

during 2003. Epidemiol Infect. Jun; 136(6):739-51.

 Anon (2008) Report of the Task Force on Zoonoses Data Collection on the analysis

of the baseline survey on the prevalence of Salmonella in slaughter pigs, in the EU,

2006-2007. EFSA journal. 135, 1-111.

 Defra, 2007. Zoonoses Report United Kingdom. 16-17. Last accessed 19/11/08.

http://www.defra.gov.uk/animalh/diseases/zoonoses/zoonoses_reports/zoonoses2007

.pdf

 VLA, 2007. Salmonella in livestock. Page 5. Last accessed 19/11/08.

http://www.defra.gov.uk/vla/reports/docs/rep_salm07_chp2_3.pdf

 VLA, 2004. Risk factors for a positive meat juice ELISA result – an analysis of

routine data. Project FZ2014. Unpublished data.

 Pritchard, G., Dennis, I., Waddilove, J. (2005) Biosecurity: reducing disease risks to

pig breeding herds. In Practice. May(27): 230-237.

 Funk, J.A., Davies, P.R., and Nichols, M.A. (2001) Longitudinal study of Salmonella

84 enterica in growing pigs reared in multiple-site swine production systems. Vet

Microbiol. Oct 22; 83(1):45-60.

 Nollet, N., Maes, D., De Zutter, L., Duchateau, L., Houf, K., Huysmans, K.,

Imberechts, H., Geers, R., de Kruif, A., Van Hoof, J. (2004) Risk factors for the

herd-level bacteriologic prevalence of Salmonella in Belgian slaughter pigs. Prev Vet

Med. Aug 30; 65(1-2):63-75.

 Farzan, A., Friendship, R.M., Dewey, C.E., Warriner, K., Poppe, C., Klotins, K.

(2006) Prevalence of Salmonella spp. on Canadian pig farms using liquid or dry-

feeding. Prev Vet Med. Mar 16; 73(4):241-54.

 Armstrong, D. (2003). Zoonoses action plan Salmonella monitoring programme

update. The Pig Journal. 52166-173.

 Nielsen, B., Alban, L., Stege, H., Sorensen, L.L., Mogelmose, V., Bagger, J., Dahl, J.,

Baggesen, D.L. (2001) A new Salmonella surveillance and control programme in

Danish pig herds and slaughterhouses. Berliner Und Munchener Tierarztliche

Wochenschrift. 114(9-10):323-326; ISSN: 0005-9366.

 Food standards Agency (2009) Salmonella and pig production. Last accessed

11/02/09. http://www.food.gov.uk/foodindustry/farmingfood/salmonellainpigs/

 Nielsen, B., Ekeroth, L., Bager, F., Lind, P. (1998) Use of muscle fluid as a source of

antibodies for serologic detection of Salmonella infection in slaughter pig herds, J

Vet Diagn Invest. 10,158-163.

 Diggle, P.J., Chetwynd, A.G. (1991) 2nd-Order Analysis of Spatial Clustering for

Inhomogeneous Populations. Biometrics 47, 1155-1163.

 Rowlingson, B.S., Diggle, P.J. (1993) Splancs - Spatial Point Pattern-Analysis Code

85 in S-Plus. Computers & Geosciences 19, 627-655.

 Christensen, J. Rudemo, M. (1998) Multiple change-point analysis applied to the

monitoring of Salmonella prevalence in Danish pigs and pork. Prev Vet Med. Aug 7;

36(2):131-43.

 Placha, I., Venglovsky, J., Sasakova, N., Svoboda, I. F. (2001) The effect of summer

and winter seasons on the survival of Salmonella Typhimurium and indicator micro

organisms during the storage of solid fraction of pig slurry. J Appl Microbiol.

91(6):1036-1043; ISSN: 1364-5072.

CHAPTER 6

Analysis of Meat Juice ELISA results and questionnaire data to investigate farm-level risk factors for Salmonella infection in UK pigs Authors: Smith, R.P.* (1); Clough, H.E. (2); Cook, A.J.C. (1) (1) Centre of Epidemiology and Risk Analysis, VLA Weybridge, Addlestone, UK (2) Department of Veterinary Science, University of Liverpool, Leahurst, UK *[email protected]

Impacts o This manuscript explains how routinely collected data from abattoir surveillance, quality assurance schemes, and from a postal questionnaire, could be used for a cost-effective study to detect risk factors for Salmonella infection in pigs. o The study included a large number of serum samples from pigs located on 566 farms, which provided sufficient power to detect even weak associations. o The study identified a number of farm characteristics and management practices, including seasonal cycles; feed types used; frequency of pig deliveries and the density of pig farms within a 10km radius, associated (P<0.01) with Salmonella seroprevalence.

Summary The study set out to explore risk factors for Salmonella infection in pigs, based on seroprevalence amongst slaughtered pigs, using a large study population of holdings and a comprehensive list of farm characteristics and management practises. Farm data were collected from pig quality assurance schemes and supplemented by a postal questionnaire. These data were associated with meat juice serology results from ongoing abattoir Salmonella surveillance, for a multivariable risk factor analysis, modelling the ELISA sample:positive ratio directly.

The study population contained a large number of farms (566), covering a geographically representative spread of farms within the United Kingdom, with a mean average of 224 sample results per holding over a four year period. The model highlighted that temporal factors (quarterly

86 and yearly cycles) and monthly meteorological summaries for rainfall, sunshine and temperature were associated with Salmonella presence (P<0.01). Salmonella seroprevalence was found to be highest in autumn and lowest in spring and summer, whereas yearly averages showed a greater degree of variation than seasonal. Two feed variables (homemix and barley) were found to be protective factors, as was a conventional, rather than organic or freedom foods, farm enterprise type. The number of annual pig deliveries and dead stock collections, and the main cause of pig mortality on the farm were found to be associated with Salmonella infection. Scottish farms had a lower seroprevalence than other regions, and an increased number of pig farms within a 10km radius was associated with a higher seroprevalence.

The study demonstrated that the analysis of routinely collected data from surveillance and quality assurance schemes provide a cost-effective study with sufficient power to detect modest associations between Salmonella and exposure variables. The results of the model can be used to inform on-farm Salmonella control policies and could be used to target specific geographical regions and seasons to assist the efficiency of surveillance.

Keywords Pig; Salmonella; Serology; Risk factor; Epidemiology

1.1 Introduction Salmonella enterica is a zoonosis and different serovars can be carried by livestock raised for food production. Human salmonellosis is characterised by diarrhoea and can be transmitted through foodborne routes (O’Brien, 2005). The importance of pigs as vectors of Salmonella has been shown by a large abattoir study where the prevalence of Salmonella in pig caecal samples, collected in Great Britain, was high (23.4%), when compared to both cattle and sheep (1.4% and 1.1% respectively) (Milnes and others, 2007). In a European Union Baseline survey in 2007, a similar level (21.2%) of Salmonella was isolated from mediastinal lymph node samples from United Kingdom (UK) pigs at slaughter (Anon, 2008). Infection in pigs can cause a range of clinical signs, from scouring to fever and death, but is often sub-clinical and so, is difficult for farmers to monitor and detect. Although it is unknown how many cases of human salmonellosis are attributed to eating pig products, of the 13,213 laboratory confirmed cases in the UK identified in 2007, 13% of these were infected with the serovar S. Typhimurium, which is the predominant type detected in samples from UK pigs (Defra, 2007; VLA, 2007).

Many studies have tried to ascertain the factors that influence Salmonella prevalence, and identify on-farm control measures to reduce the Salmonella burden in pigs. Recent studies in the UK have highlighted associations with factors such as herd size; outdoor rearing of pigs; flooring type; and farm location (VLA, 2004; Pritchard and others, 2005; Smith and others, in press). These findings have been supported by European, Canadian and American studies (Funk, Davies and Gebreyes, 2001; Nollet and others, 2004; Farzan and others, 2006). Seasonal peaks and troughs of Salmonella prevalence have been identified by studies, with a two peaked annual cycle apparent, which may be related to meteorological conditions such as environmental temperature (Funk, Davies and Gebreyes, 2001; Hald and Andersen, 2001). However, a number of the studies above were limited to a small and potentially unrepresentative subset of the pig farm population, which may not have had sufficient statistical power to detect modest associations between Salmonella infection and putative risk factors. Other studies analysed only a small number of variables and so may have missed more important risk factors or not estimated the true affect of a variable by accounting for the affect of other variables.

Schemes are present in the UK that routinely collect data on Salmonella in pigs and farm management characteristics, from a large number of farms. In June 2002, the UK Zoonoses

87 Action Plan (ZAP, now called the Zoonoses National Control Plan) monitoring programme was designed to run in conjunction with Quality Assurance schemes (QAS), to estimate the burden of Salmonella from a sample of slaughtered pigs (Armstrong, 2003). The scheme was based on a design by the Danish pig industry that had contributed to a reduced Salmonella prevalence (Nielsen and others, 2001). The QAS routinely collect details on the structure and management of pig farms to ensure a level of health and welfare standards are met.

This paper reports how data from these sources were used, along with a postal questionnaire, to implement a cross-sectional study to analyse the effect of a large number of explanatory factors (biosecurity, farm demographics, meteorology) on Salmonella seroprevalence for QAS-registered holdings, in Great Britain and Northern Ireland, that submitted finisher pigs to the ZAP scheme. By using a comprehensive list of variables, from a large population of pig holdings, a more detailed picture of the risk factors for Salmonella seroprevalence results would be generated, that would detect factors with even modest (variables with model coefficients close to zero) associations. 1.2 1.3 Materials and Methods Data on explanatory factors were collected from a number of sources and combined into a single dataset, for analysis in the model. Datasets were collected from three QA schemes (Approved British Pigs (ABP); Genesis Quality Assured (GQA); and Quality Meat Scotland (QMS)) and from the ZAP scheme. The data were coded and linked to map reference coordinates according to the previous method (Smith and others, in press). These coordinates were also used to identify the NUTS (Nomenclature of Units for Territorial Statistics) geographical region for that holding. NUTS have four subdivisions and NUTS 1, equivalent to government office regions, were used rather than other sources of clustering, such as county, as they are more stable over time and less subject to boundary changes. It was also believed that the categories represent more biologically sensible categories in terms of the country’s animal species population. Meteorological data of monthly regional summaries, including actual and anomaly (difference from long-term averages) records, were gathered (http:www.metoffice.gov.ukclimateukindex.html) and linked to the dataset by the region of farm and the month of sample collection. A supplementary questionnaire was designed to collect information on a number of covariates previously identified as significantly associated with Salmonella presence and rated as key to Salmonella presence in the UK by a number of experts. These included pig stocking levels (Farzan and others, 2006); feeding practices (Lo Fo Wong and others, 2004; housing systems (Nollet and others, 2004); biosecurity (Beloeil and others, 2004) and geographical location (Benschop and others, 2008), to supplement those routinely collected by the QAS (Table 1). Full details are available on request. The questionnaire was posted, along with a covering letter, to all 2,064 farms listed under the three QAS, asking for the farmer’s voluntarily completion of the questionnaire, which was to be returned in a supplied envelope.

The ZAP data were limited to results collected up to four years prior to the completion date of the postal questionnaire, to allow a comparison of temporal trends over a number of years. Variables for temporal trends and seasonal effects were designed by adding sinusoidal components (sine and cosine terms) for 3, 6 and 12 month periods to create quarterly, half-yearly and yearly cycles (Chatfield, 2003). These cycles may account for seasonal trends or any reduction of ELISA ratio through the years of the study population caused by the control of Salmonella through the ZAP scheme.

For the ZAP scheme, small pieces of skeletal muscle (from diaphragm/ neck) were removed from pigs at the abattoir and placed in meat juice (MJ) tubes which were frozen and then thawed to collect the fluid (Armstrong, 2003; Nielsen and others, 1998). The MJ sample was tested at a single UK laboratory by a mix-ELISA serological test (Guildhay VETSIGN™Kit) for a “host”

88 response of antibodies to Group B and C1 Salmonella (Nielsen and others, 1998). Salmonella infection in pigs produces an immune response, which includes the production of antibodies. These are detected by the ELISA from which a sample:positive ratio (ELISA ratio) is calculated, which is related to the titre of circulating antibodies (Sorensen and others, 2004; Hill and others, 2008). Three samples were randomly collected from every batch of pigs sent to slaughter on any particular date in accordance with the sampling regime agreed on May 2003 (BPEx, personal communication). For routine surveillance, a cut-off point is applied to the ELISA ratio to provide a binary outcome but for this study the ELISA ratio was used directly to allow an analysis of a linear relationship.

Table 1: Variables generated from data collected by quality assurance schemes and a postal questionnaire. Variable category NUTS 1 region Coordinates (X, Y) Pig farm density at 3 km & 10km radii Season of sampling Quality Assurance Scheme Enterprise type Reared on contract Production system (batch/ continuous) Any pig production outdoor Flooring Number of each pig type Other farm animal species present Mixing of pigs Isolation of sick pigs (freq, where) Types of feed fed to weaners, growers, finishers and sows Drinking system and water source Cleaning & disinfection of pig houses and drinking system No. pig deliveries/ collections No. and type of other farm visitors Delivery procedures Bootdip usage Health conditions present Top 3 causes of pig mortality Top 3 causes of pig treatment Regional summaries of meteorological factors Temporal cycles 1.3.1 Data analysis A Boxcox plot was used to verify whether the ELISA ratio results required transformation and what type of transformation was necessary to approximate normality. All negative and zero ELISA ratios were coded to 0.005, which was half of the lowest recorded result, prior to transformation. Relationships between this transformed outcome and explanatory factors were analysed by univariable mixed linear regression (STATA 10, Stata corp. LP,, College Station, TX), with the farm holding identifier selected as a random effect to allow for dependence between observations within the same premises. All continuous variables were plotted on a graph and assessed for normality and whether transformation was necessary. Explanatory factors with more than two levels were also tested to see whether they should be split into multiple

89 dichotomous variables. For example, a variable with levels for each NUTS region was tested at the univariable level, as well as binary variables for each individual region, to see which factor was more significant/ fitted the model better. Explanatory variables for which the association on univariable analysis yielded a P-value of 0.25 or more were omitted from the multivariable model.

Due to the large number of factors under examination, variables were entered into the models manually using a forward stepwise method. The variable with the lowest P-value was entered first into the model, and each subsequent variable was then independently introduced into this model before selecting the next variable with the lowest P-value and repeating the process. Due to the large dataset size, a P-value of 0.01 was set as the significance threshold and this stepwise method continued until no further variables could be identified whose addition generated a P- value of less than 0.01. Records with missing data for the selected variables were dropped from the model. All rejected variables were added separately into the final model to ensure no significant variables had been omitted.

Likelihood ratio tests were used to compare models of the same population size to determine whether the included variable significantly improved the model. The Wald’s Chi2 test and Akaike Information Criterion were also examined to ensure model fit. The standardised residuals were plotted against the fitted values to examine signs of heteroscedasticity and a histogram of model residuals was plotted to evaluate normality, to ensure the standard model assumptions were met. Each variable that entered the final model was compared against the model residuals and a Bartlett test completed to assess homogeneity of variances (R version 2.7.1, R Development Core Team, Vienna, Austria). Explanatory variables, that were perfectly collinear with variables already included in the model, were dropped automatically by the STATA package. All variables in the final models were assessed for biologically plausible interactions, however, due to a small number of positive samples in some strata, this was not possible in all cases.

The farm holding records with map references were plotted as points onto a map of the UK using ArcGIS 9.1 (ESRI, Redlands, Calif., USA).

1.4 Results Between 6th June 2007 and 30th October 2008, a total of 566 questionnaires were returned and successfully linked to the ZAP database. These questionnaires consisted of 305 ABP, 171 GQA and 90 QMS registered holdings. The 554 holdings that provided the necessary information to generate map coordinates were presented on a map (figure 1). This shows the distribution of participating farms around Great Britain and Northern Ireland, and the large difference in farm density between regions such as Yorkshire and the Humber and North West England (Smith and others, in press). A chi-squared comparison between the holdings present in the study population and the total QAS population indicated fewer farms in East England and more in Scotland and the South West (p<0.05). The holdings linked to a total of 119,906 ZAP samples, with a mean average of 224 samples per holding (range 1-1,671). Plots of the ELISA ratio results (figures 2 & 3) indicate that a seasonal average ranged from 0.25 (autumn) to 0.22 (spring & summer) and a comparison of means showed that this was significant (F=29.09, P<0.001), and also the mean ELISA ratio differed greatly (F=12.75, P<0.001) between each year of sampling. The majority of ELISA ratio results were close to zero (60.9% were below 0.10) and a Boxcox plot verified that a logarithmic transformation was required to approximate normality.

90 Table 2: Variables strongly associated (P<0.05) with Salmonella from univariable mixed linear regression of logged meat juice ELISA ratio results collected from slaughtered pigs No. Variable Level Coefficient P-value farms QA scheme ABP Baseline 305 GQA 0.458 <0.001 171 QMS -0.658 <0.001 90 NUTS Region Other Baseline 469 Scotland -0.824 <0.001 93 Pig farm density within 3km radius Continuous 0.085 <0.001 554 Pig farm density within 10km radius Continuous 0.021 <0.001 554 Season that sample was collected from Spring Baseline n/a Summer -0.169 <0.001 n/a Autumn -0.133 <0.001 n/a Winter -0.099 <0.001 n/a Farm enterprise - Conventional no Baseline 51 yes -0.741 <0.001 515 Farm enterprise - Freedom foods no Baseline 480 yes 0.583 <0.001 86 Pigs reared on contract at farm no Baseline 292 yes 0.385 <0.001 254 Cattle present on farm no Baseline 373 yes -0.282 0.001 193 Number of cattle currently present Continuous -0.001 0.019 537 Sheep present on farm no Baseline 419 yes -0.241 0.011 147 Cats present on farm no Baseline 554 yes 0.636 0.025 12 Pigs mixed at weaner group no Baseline 142 yes -0.368 <0.001 390 Pigs mixed at other time no Baseline 376 yes 0.209 0.046 114 Pigs never mixed no Baseline 494 yes 0.477 <0.001 72 Weaners fed fermented feed no Baseline 366 yes -0.692 0.044 8 Weaners fed homemix no Baseline 285 yes -0.623 <0.001 89 Weaners fed concentrates no Baseline 128 yes -0.182 0.031 246 Weaners fed barley no Baseline 190 yes -0.272 0.002 184 Percentage of barley in weaner feed Percentage -0.014 <0.001 533 Growers fed homemix no Baseline 280 yes -0.572 <0.001 126 Growers fed wheat no Baseline 180 yes -0.302 <0.001 226 Percentage of wheat in grower feed Percentage -0.005 0.002 540 Growers fed barley no Baseline 185

91 yes -0.404 <0.001 221 Percentage of barley in grower feed Percentage -0.019 <0.001 545 Finishers fed fermented feed no Baseline 501 yes -0.591 0.003 25 Finishers fed homemix no Baseline 385 yes -0.539 <0.001 141 Finishers fed barley no Baseline 269 yes -0.284 0.001 257 Percentage of barley in finisher feed Percentage -0.014 <0.001 532 Sows fed fermented feed no Baseline 282 yes -0.899 0.009 8 Sows fed homemix no Baseline 187 yes -0.660 <0.001 103 Percentage of wheat in sow feed Percentage -0.006 0.012 542 Sows fed barley no Baseline 134 yes -0.440 <0.001 156 Percentage of barley in sow feed Percentage -0.016 <0.001 541 Pig water source: Mains no Baseline 188 yes 0.218 0.014 366 Pig water source: Borehole no Baseline 376 yes -0.223 0.013 178 Any nipple drinkers used no Baseline 170 yes 0.249 0.006 377 Number of pig deliveries 0-5 Baseline 343 6-11 0.702 <0.001 83 over 11 0.483 <0.001 132 Number of live pig collections 0 Baseline 21 1-5 0.814 0.001 21 6-11 0.626 0.009 74 >11 0.570 0.007 441 Number of dead stock collections 0-5 Baseline 114 >6 0.380 <0.001 442 Number of vermin controller visits 0 Baseline 237 >0 0.193 0.026 295 Number of any other deliveries 0-11 Baseline 564 >11 -1.512 0.026 2 Enzoonotic Pneumonia status (last 12 Negative Baseline 302 months) Positive 0.187 0.026 264 PMWS status (last 12 months) Negative Baseline 250 Positive 0.365 <0.001 316 PRRS status (last 12 months) Negative Baseline 436 Positive 0.435 <0.001 130 Glassers status (last 12 months) Negative Baseline 473 Positive 0.316 0.005 93 Swine dysentery status (last 12 Negative Baseline 535 months) Positive -0.364 0.049 31 Clinical salmonellosis status (last 12 Negative Baseline 528 months) Positive 0.562 0.001 38 No health conditions present (last 12 no Baseline 445

92 months) yes -0.318 0.002 121 Primary cause of pig mortality in the Other Baseline 278 last 12 months Respiratory or wasting 0.510 <0.001 266 Number of sows (log. converted) Continuous 0.036 0.030 438 Any homemix fed no Baseline 334 yes -0.548 <0.001 144 Any wet feeding no Baseline 448 yes -0.434 <0.001 65 Any compound feeding no Baseline 67 yes 0.500 <0.001 446 Any solid flooring in finisher houses no Baseline 275 yes 0.462 <0.001 249 Monthly maximum temperature Continuous 0.023 <0.001 505 anomaly for farm’s region (oC)* Monthly minimum temperature actual Continuous 0.003 0.013 505 for farm’s region (oC) Monthly minimum temperature Continuous 0.032 <0.001 505 anomaly for farm’s region (oC)* Monthly mean temperature anomaly Continuous 0.031 <0.001 505 for farm’s region (oC)* Monthly rainfall actual for farm’s Continuous 0.001 <0.001 505 region (mm) Monthly rainfall anomaly for farm’s Continuous <0.001 0.004 505 region (mm)* Monthly sunshine actual for farm’s Continuous <0.001 <0.001 505 region (hours) Monthly sunshine anomaly for farm’s Continuous 0.001 <0.001 505 region (hours)* Quarterly cycle Cos -0.051 <0.001 566 Sin -0.038 <0.001 566 Yearly cycle Cos -0.070 <0.001 566 Sin 0.060 <0.001 566 *'anomaly’ is the difference from long-term averages.

The results of the linear regression model are presented in Tables 2 & 3, with table 2 presenting the strongly significant variables detected from the univariable screening of the variables and table 3 presenting the final variables that entered the multivariable model. Thirteen variables entered the final model and the model population was reduced to 474 holdings, due to missing data. The model had a significant Wald’s Chi2 result P<0.001 and a likelihood ratio test for the inclusion of the random effect was also significant (P<0.001). The ‘season’ variable was dropped from the model as it was collinear with the temporal cycles, and ‘scheme’ was dropped as it was perfectly collinear with region.

93

Figure 1: Distribution of participating pig holding locations by quality assurance scheme (N=554)

0.35

0.3

0.25 Mean Ratio Mean 0.2

0.15 Spring Summer Autumn Winter Season

94 Figure 2: Mean meat juice ELISA ratio results, with 95% confidence intervals, by season of sampling, for 566 pig holdings. Dotted line indicates mean ELISA ratio.

0.35

0.3

0.25 Mean ratio Mean 0.2

0.15 2003 2004 2005 2006 2007 Year

Figure 3: Mean meat juice ELISA ratio results, with 95% confidence intervals, by year of sampling, for 566 pig holdings. Dotted line indicates mean ELISA ratio.

Table 3: Multivariable mixed linear regression of logged meat juice ELISA ratio results collected from slaughtered pigs (N=109,912 samples (474 holdings)). The standard deviation of the random effect was 0.74 (0.69-0.80 (95% confidence intervals)). Variable Level Coefficient P-value Scotland -0.747 <0.001 NUTS Region Other Baseline Pig farm density within 10km radius 0.017 <0.001 Conventional -0.518 <0.001 Farm enterprise Non-conventional Baseline Primary cause of pig mortality in the Respiratory or wasting 0.290 <0.001 last 12 months Other Baseline Monthly mean temperature anomaly for farm’s region (oC)* 0.024 <0.001 Monthly Rainfall actual for farm’s region (mm) 0.001 <0.001 Monthly Sunshine actual for farm’s region (hours) 0.001 0.001 Yes -0.377 <0.001 Finishers fed homemix No Baseline Percentage of barley in grower feed -0.007 0.003 >11/year 0.289 0.001 Number of pig deliveries 6-11/ year 0.439 <0.001 0-5/year Baseline >6/year 0.245 0.007 Number of dead stock collections 0-6/year Baseline Cos -0.100 <0.001 Yearly cycle Sin 0.042 <0.001

95 Cos -0.046 <0.001 Quarterly cycle Sin -0.041 <0.001 constant -2.866 <0.001 *'anomaly’ is the difference from long-term averages.

1.5 Discussion In total, over a quarter (27%) of the QAS population participated in the study and on average each holding was linked to over two hundred ZAP samples, providing a large dataset for analysis. The geographical spread of the study holdings indicated that the population was generally representational of the quality assured pig farms in the UK, with similar high density clusters in Eastern England (mean average of 28 farms within 10km), Yorkshire and the Humber (21 farms) and in the North East of Scotland (11 farms) (Smith and others, in press).

In the final model both yearly and quarterly cycles were found to be significant and improved the final model, with the highest mean ELISA ratio in autumn and the lowest in spring. Large differences to long term averages in the mean temperature, and high actual rainfall and hours of sunshine were identified as risk factors. These results agree with a previous study which presented increased temperature variability as associated with Salmonella prevalence (Funk, Davies and Gebreyes, 2001). Air temperature has been linked to pig stress, which in turn can increase the shedding of Salmonella and can lower immunity (Hald and Andresen, 2001). The meteorological results came from monthly averages from weather stations within each of the regions, whereas the temporal cycles may represent the influence of specific local or daily weather conditions.

The selected spatial factors showed that pigs in Scotland have a lower logarithmic ELISA ratio and thus farms in Scotland have a lower seroprevalence of Salmonella. This may be because the farms in Scotland are more likely to use certain management procedures (e.g. all indoor production; home-mixing) and, due to their geographical isolation, are more likely to purchase animals from similarly low seroprevalence Scottish farms. The range of neighbouring pig farms within 10 kilometres varied greatly (from 0 to 73) and farms with a higher Salmonella prevalence have been shown to be more clustered in space than low prevalence farms by other studies in the UK and Denmark (Benschop and others, 2008; Clough and others, 2009). In these studies, positive farms were more congregated in space than would be expected, possibly due to local spread and transmission of disease. Location and farm density were identified by a review of UK pig Salmonella, which noted that the “type, number and density of pig holdings in a two kilometre radius is crucial” (Pritchard and others, 2005).

It has been described in other studies that health conditions, especially respiratory and wasting diseases such as Porcine Reproductive & Respiratory Syndrome and Postweaning Multisystemic Wasting Syndrome, may have interacted with Salmonella, possibly by lowering the immune system or increasing transmission by sneezing or shedding Salmonella in larger numbers and for a longer period of time, and this relationship was also identified in the model (Schwartz, 1999; Wills and others, 2000; Beloeil and others, 2004; Beloeil and others, 2007).

A larger number of pig deliveries was also shown to be a risk factor, and the introduction of pigs onto a farm was agreed to be the most likely cause of pig infection by a international expert workshop (Stark and others, 2002). A larger number of pig deliveries may indicate a larger number of suppliers, which has been shown to be a risk factor when farms recruit pigs from more

96 than three herds in comparison to herds that breed their own replacements or recruit from a maximum of three herds (Lo Fo Wong and others, 2004). A higher number of dead stock collections might indicate that the farms have a greater amount of health condition problems, possibly caused by Salmonella or from health conditions associated with Salmonella infection. These factors may also be a risk simply because the increased number of vehicles entering the farm can facilitate the spread of Salmonella. To decrease the risk from deliveries and visitors, biosecurity measures such as wearing farm-specific clothing and footwear; the routine use of bootdips; ensuring deliveries are only made at the farm perimeter, and closing the farm to all but essential external vehicles should be utilised (Pritchard and others, 2005; Beloeil and others, 2007).

Managing a farm as a conventional pig enterprise was found to be protective, and this may be because the other types of enterprise (organic, freedom foods) utilise a higher degree of outdoor production (only 5% of the conventional farms had any outdoor production in comparison with 33%), and these enterprises have been shown to have significantly higher Salmonella seroprevalence in pigs (Gebreyes and others, 2008). Procedures to control Salmonella. transmission which are used in indoor production are harder to implement outdoor and so the pigs may be at an increased risk of infection from wildlife and the environment (Jensen and others, 2006).

Feed has been identified in numerous studies as a factor that influences Salmonella infection. Specific feed types can disrupt the microbial ecosystem in the gut, especially feed with a high level of acid, which can inhibit Salmonella and encourages gram-positive bacteria which favour acidic environments and can out-compete Salmonella (Pappenbrock and others, 2005; Lo Fo Wong and others, 2004). The use of home mix feed was found to be protective, which has been indicated in an earlier British pig study (VLA, 2004) and the use of purchased feed, rather than that mixed on farms, was a significant risk factor for Salmonella in other studies at the multivariable (Benschop and others, 2008) and univariable (Rajic and others, 2007b) levels. A reason for this could be that home mixed feed is usually coarser than purchased feed, with a larger particle size, and these factors influence the growth of competitive gut flora by affecting the acid and starch content in the gut. Purchased feed is also likely to have been pelleted, which has also been indicated as associated with a higher Salmonella prevalence (Lo Fo Wong and others. 2004; Leontides, Grafanakis and Genigeorgis. 2003) However, in a longitudinal study of the use of fermented feed, no significant effect was shown, indicating that Salmonella may be able to bypass the stomach environment via the tonsils (van Winsen and others, 2001; van Winsen and others, 2002). The use of other feed types, such as a higher percentage of barley in the diet fed to growers, was found to be protective, which concurs with the findings from other studies (Jorgensen, 2003; Kelliher, 2002).

2. Collecting information from only one time point for each holding may have introduced error into the analysis as the management of the farm may have changed in the four year period, from which samples were collected. The four year period was decided upon to

97 provide a suitable dataset to analyse the temporal variation in the data, but an improvement to this study design would be to collect data on any changes to the farm over the period. The cross-sectional study design also meant that we were unable to distinguish between risk factors associated with the infection or persistence of Salmonella. The analysis may also have identified risk factors through reverse-causation, with explanatory factors associated with Salmonella which have been instigated as a response to Salmonella presence, rather than contributing towards Salmonella presence. The large sample size and large number of explanatory variables may also have identified factors associated with Salmonella by chance, due to the large amount of statistical power, although the significance level was lowered to account for this. 3. 4. Utilising a study population drawn from the QAS may have provided selection bias to the results, as although the QAS are believed to contain around 50% of all the pig holdings and 90% of the pigs in the UK, it is unknown whether the farms are representational of the remaining farms. Anecdotal evidence suggests that non-assured farms are more likely to be smaller, non-conventional holdings. The non-assured population may utilise different pig management to the QAS holdings and so they may have a different set of factors that are associated with Salmonella. The use of a postal questionnaire may also have provided selection bias, as holdings that responded may be more aware of Salmonella, and Salmonella control, and thus more eager to assist our research. The use of a questionnaire that included questions relating to time periods may also have introduced some recall bias, and it could be theorised that a well-managed and organised farm would have been more likely to be able to use recorded information to answer the questions, whereas a disorganised farm would have been less likely to recall instances over the time period.

The use of serological samples from the ZAP study was a key component of this study, as they provided outcome data from a large number of pig farms, with a large number of samples collected from four years. The MJ ELISA is a useful screening tool for surveillance as the test is cost-effective, quick and does not require more specialised microbiological skills (Bohaychuk and others, 2005). However, the use of serological samples and modelling the ELISA ratio directly, without conversion to a binary positive/negative outcome, may limit the interpretation of the findings when considering the infection status of pigs, as the results represent previous exposure, rather than current infection. As the ELISA ratio is an indicator of previous infection, this may have caused information bias in the temporal results. The ELISA ratio is influenced by the strength of the Salmonella challenge and the time since infection, but immune reactions vary by individuals and are affected by many other factors, such as stress. A high ELISA ratio does not necessarily coincide with a more recent infection and a high mean average ELISA ratio in the autumn does not indicate that pigs were infected in the autumn (Tizard I.R. 2004). The ELISA test benefits from detecting life time infection, even if it is subclinical, but only detects a number of known serovars with potentially differential abilities to detect infection by different serovars (Funk, Harris and Davies, 2005). However, studies have shown a significant correlation between serology results and caecal prevalence, and although farm results can fluctuate between visits/ sampling occasions, the test has been shown to be useful in identifying farms with a Salmonella problem (Sorensen and others. 2004; Rajic and others, 2007a).

The study provided a comprehensive risk factor analysis and examination of the spatial and temporal trends of Salmonella seroprevalence, with a study population large enough to detect even factors with modest associations to the ELISA ratio. Large sample sizes can provide greater statistical power and provide narrower confidence intervals for estimated associations and so are

98 more likely to detect if a significant difference is present in the data. Even though association may be weak, it may still have a significant impact on Salmonella presence in the study population if it is present in a large proportion of the population. Specifically, the model results suggest that measures are needed to control Salmonella infection on farms utilising outdoor production and to protect pigs from the effects of large variations in weather conditions and an intervention study would be required to test this finding. The model also highlighted a region of the UK that may require more intensive surveillance and control to limit the transmission of Salmonella. The utilisation of data collected routinely via the QAS and ZAP schemes, as well as a one-off postal questionnaire provided a cost-effective means to design and analyse a large risk factor study.

Acknowledgments The authors would like to thank the participating farmers and schemes, and BPEx for their support. Colleagues in CERA are thanked for their help in data entry and handling. Defra are also thanked for funding the study under projects OD0215/ OZ0323.

References Anon, 2008: Report of the Task Force on Zoonoses Data Collection on the analysis of the baseline survey on the prevalence of Salmonella in slaughter pigs, in the EU, 2006-2007. EFSA journal 135, 1-111. Armstrong, D., 2003: Zoonoses action plan Salmonella monitoring programme update. Pig Journal. 52166-173. Beloeil, P.A. P. Fravalo, C. Fablet, J. P. Jolly, E. Eveno, Y. Hascoet, C. Chauvin, G. Salvat, and F. Madec, 2004: Risk factors for Salmonella enterica subsp. enterica shedding by market-age pigs in French farrow-to-finish herds. Prev Vet Med. 63,103-20. Beloeil, P.A., C. Chauvin, K. Proux, C. Fablet, F. Madec, and A. Alioum, 2007: Risk factors for Salmonella seroconversion of fattening pigs in farrow-to-finish herds. Vet Res. 38, 835-48. Benschop, J., M. L. Hazelton, M. A. Stevenson, J. Dahl, R. S. Morris, and N. P. French, 2008: Descriptive spatial epidemiology of subclinical Salmonella infection in finisher pig herds: application of a novel method of spatially adaptive smoothing. Vet Res. 39[1], 2. Bohaychuk , V. M, G. E. Gensler, R. K. King, J. T. Wu, and L. M. McMullen, 2005. Evaluation of Detection Methods for Screening Meat and Poultry Products for the Presence of Foodborne Pathogens. J Food Protect, 68 (12) 2637-2647. Chatfield, C. 2003. The analysis of time series: An Introduction, 6th ed. CRC Press. Clough, H. E., S. E. Fenton, N. P. French, A. J. Miller, and A. J. Cook, 2009: Evidence from the UK Zoonoses Action Plan in favour of localised anomalies of Salmonella infection on UK pig farms. Prev vet Med. 89 [1-2], 67-74. Defra, 2007: Zoonoses Report United Kingdom. 16-17. Available at: http://www.defra.gov.uk/animalh/diseases/zoonoses/zoonoses_reports/zoonoses2007.pdf (accessed 24/06/09). Farzan, A., R. M. Friendship, C. E. Dewey, K. Warriner, C. Poppe, and K. Klotins, 2006: Prevalence of Salmonella spp. on Canadian pig farms using liquid or dry-feeding. Prev Vet Med. 73(4):241-54. Funk, J. A., P. R. Davies, and W. Gebreyes, 2001: Risk factors associated with Salmonella enterica prevalence in three-site swine production systems in North Carolina, USA. Berl Munch Tierarztl Wochenschr. 114, 335-8. Funk, J. A., I. T. Harris, and P. R. Davies, 2005. Comparison of fecal culture and Danish mix- ELISA for determination of Salmonella enterica subsp enterica prevalence in growing swine. Vet Microbiol 107(1-2): 115-126. Gebreyes, W. A., P. B. Bahnson, J. A. Funk, J. McKean, and P. Patchanee, 2008: Seroprevalence of Trichinella, Toxoplasma, and Salmonella in antimicrobial-free and conventional swine

99 production systems. Foodborne Pathog Dis. 5, 199-203. Hald T., and J. S. Andersen, 2001: Trends and seasonal variations in the occurrence of Salmonella in pigs, pork and humans in Denmark, 1995-2000. Berl Munch Tierarztl Wochenschr. 114, 346-9. Hill, A. A., E. L. Snary, M. E. Arnold, L. Alban, and A. J. C. Cook, 2008: Dynamics of Salmonella transmission on a British pig grower-finisher farm: a stochastic model. Epidemiol Infect.136, 320-333. Jensen, A. N., A. Dalsgaard, A. Stockmarr, E. M. Nielsen and D. L. Baggesen, 2006. Survival and Transmission of Salmonella enterica Serovar Typhimurium in an Outdoor Organic Pig Farming Environment. Appl Environ Microbiol. 72 (3) 1833-1842. Jorgensen L., 2003. Barley/wheat ratio and wheat bran in pelleted finisher feed – effect on the prevalence of Salmonella, gastro-intestinal health, transit time and productivity. Danish Pig Production Report no. 636. 18th December 2003. Kelliher D., 2002: Responding to the Salmonella control programme. Teagasc Pig Farmers' Conference Proceedings. Leontides, L. S., E. Grafanakis, and C. Genigeorgis, 2003. Factors associated with the serological prevalence of Salmonella enterica in Greek finishing swineherds. Epidemiol Infect. 131(1): 599- 606. Lo Fo Wong, D. M., J. Dahl, H. Stege, P. J. van der Wolf, L. Leontides, A. von Altrock, and B. M. Thorberg, 2004: Herd-level risk factors for subclinical Salmonella infection in European finishing-pig herds. Prev Vet Med. 62, 253-66. Milnes, A. S., I. Stewart, F. A. Clifton-Hadley, R. H. Davies, D. G. Newell, A. R. Sayers, T. Cheasty, C. Cassar, A. Ridley, A. J. Cook, and S. J. Evans, 2007: Intestinal carriage of verocytotoxigenic Escherichia coli O157, Salmonella, thermophilic Campylobacter and Yersinia enterocolitica, in cattle, sheep and pigs at slaughter in Great Britain during 2003. Epidemiol Infect. 136(6), 739-51. Nielsen, B., L. Ekeroth, F. Bager, and P. Lind, 1998: Use of muscle fluid as a source of antibodies for serologic detection of Salmonella infection in slaughter pig herds. J Vet Diagn Investig. 10[2], 158-163. Nielsen, B., L. Alban, H. Stege, L. L. Sorensen, V. Mogelmose, J. Bagger, J. Dahl, and D. L. Baggesen, 2001: A new Salmonella surveillance and control programme in Danish pig herds and slaughterhouses. Berl Munch Tierarztl Wochenschr. 114(9-10):323-326. Nollet, N., D. Maes, L. De Zutter, L. Duchateau, K. Houf, K. Huysmans, H. Imberechts, R. Geers, A. de Kruif, and J. Van Hoof, 2004: Risk factors for the herd-level bacteriologic prevalence of Salmonella in Belgian slaughter pigs. Prev Vet Med. 65(1-2):63-75. O’Brien S., 2005. Foodborne zoonoses. Br Med J 331(7527): 1217-1218. Papenbrock, S., K. Stemme, G. Amtsberg, J. Verspohl, J. Kamphues, 2005: Investigations on prophylactic effects of coarse feed structure and/or potassium diformate on the microflora in the digestive tract of weaned piglets experimentally infected with Salmonella Derby. J Anim Physiol Anim Nutr (Berl). 89, 84-7. Pritchard, G., I. Dennis, and J. Waddilove, 2005: Biosecurity: reducing disease risks to pig breeding herds. In Practise. 27, 230-237. Rajic, A., E. Y. Chow, J. T. Wu, A E. Deckert, R. Reid-Smith, K. Manninen, C. E. Dewey, M. Fleury, and S. A. McEwen, 2007a: Salmonella infections in ninety Alberta swine finishing farms: serological prevalence, correlation between culture and serology, and risk factors for infection. Foodborne Pathog Dis. 4, 169-77. Rajic, A., B. P. O'Connor, A. E. Deckert, J. Keenliside, M. E. McFall, R. J., Reid-Smith, C. E. Dewey, and S. A. McEwen, 2007b: Farm-level risk factors for the presence of Salmonella in 89 Alberta swine-finishing barns. Can J Vet Res. 71, 264-70. Schwartz K.J. 1999: Salmonellosis. Diseases of Swine, Iowa State University Press, Ames, IA. Smith R. S., M. J. Sanchez-Vazquez, A. J. C. Cook, H. E. Clough, and S. E, Edwards, 2010: An

100 Analysis of Quality Assurance and Zoonoses Action Plan Data from Pig Herds in the United Kingdom. Pig Journal. In press. Sorensen, L. L., L. Alban, B. Nielsen, and J. Dahl, 2004: The correlation between Salmonella serology and isolation of Salmonella in Danish pigs at slaughter. Vet Microbiol. 101[2], 131-141. Stark, K. D., A. Wingstrand, J. Dahl, V. Mogelmose, and D.M. Lo Fo Wong, 2002: Differences and similarities among experts' opinions on Salmonella enterica dynamics in swine pre-harvest. Prev Vet Med. 53,7-20. Tizard I. R, 2004. Veterinary immunology; an introduction, 7th ed, Saunders. Van Winsen, R. L., B. A. P. Urlings, L. J. A. Lipman, J. M. A. Snijders, D. Keuzenkamp, J. H. M. Verheijden, and F. Van Knapen, 2001: Effect of fermented feed on the microbial population of the gastrointestinal tracts of pigs. Appl Environ Microb. 67(7), 3071-3076. Van Winsen, R. L., D. Keuzenkamp, B. A. P. Urlings, L. J. A. Lipman, J. A. M. Snijders, J. H. M. Verheijden, and F. Van Knapen, 2002: Effect of fermented feed on shedding of Enterobacteriaceae by fattening pigs. Vet Microbiol. 87(3), 267-276. VLA, 2004: Risk factors for a positive meat juice ELISA result – an analysis of routine data. Report to Defra under project FZ2014. VLA, 2007: Salmonella in livestock. Page 5. Available at: http:www.defra.gov.uk/vla/reports/docs/rep_salm07_chp2_3.pdf (Accessed 24/06/09). Wills, R. W., J. T. Gray, P. J. Fedorka-Cray, K. J. Yoon, S. Ladely, and J. J., Zimmerman, 2000: Synergism between porcine reproductive and respiratory syndrome virus (PRRSV) and Salmonella choleraesuis in swine. Vet Microbiol. 71[3-4], 177-192.

CHAPTER 7

Comparison of health scoring and Zoonoses Action Plan (ZAP) results from pig abattoirs Smith, R.P. BSc1, Sanchez-Vazquez, M.J. MSc MRCVS 2, Cook, A.J.C. MSc BVM&S MRCVS 1, Edwards, S.A. MA PhD 3 1Centre of Epidemiology and Risk Analysis, VLA Weybridge, Woodham lane, New Haw, Addlestone, Surrey, KT15 3NB 2Scottish Agricultural College, Drummond Hill, Stratherrick Road, Inverness, IV2 4JZ 3Newcastle University, Agriculture Building, Newcastle upon Tyne, NE1 7RU

Summary Abattoir data collected for pig health monitoring schemes (ZAP, BPHS, WPS) were combined to investigate associations between Salmonella infection on farms and other health conditions, such as pleurisy, milk spots and tail-biting. Data on samples collected and observations made from farm holdings were uniquely identified and connected by registered slapmarks, although results were not linked at the level of the individual pig. Salmonella infection was determined through the meat juice ELISA (MJE) test and other

101 conditions were assessed by direct observation. The final dataset contained 873 slapmarks with an average of 215 samples tested by MJE per slapmark. Each of the health assessment conditions were individually analysed by regression models against the MJE results, accounting for clustering at the slapmark level and also for seasonality. The results showed positive associations between Salmonella and enzootic pneumonia-like lesions; milk spots; peritonitis; and pericarditis. These health conditions can have an important detrimental impact on pig welfare and productivity, whereas Salmonella infection is often subclinical. These associations indicate that on-farm controls for Salmonella may provide economic benefits, as well as reducing foodborne zoonotic infection in humans.

Introduction Pig health schemes currently assess disease status through abattoir sampling, to monitor health conditions and provide feedback to individual farms. The data gathered by such schemes also provides the potential to investigate epidemiological associations between different health conditions at a population level, which might inform the development of control strategies.

The Zoonoses Action Plan (ZAP, now called the Zoonoses National Control Plan, BPEx 2010a) scheme records the results of a mix-ELISA test to detect antibodies to Salmonella, indicative of previous infection, in meat juice samples collected from slaughtered finisher pigs (Nielsen and others 1998; Armstrong 2003). In a recent European Union Baseline survey, Salmonella was isolated from 21.2% of mediastinal lymph node samples from United Kingdom pigs at slaughter (Anon 2008). The high prevalence of Salmonella in pigs represents a potential risk of infection to humans via foodborne routes. The control of Salmonella on farms is particularly problematic as infection is often subclinical therefore the ZAP scheme has been useful in monitoring and estimating the burden of Salmonella on farms. Through the scheme, farms with more than 50% of samples positive were required to implement an action plan or face eventual loss of their Quality Assured status. The scheme was based on a design by the Danish pig industry that had contributed to a reduced Salmonella prevalence (Nielsen and others 2001). All batches of

102 pigs sent to British Quality Assured abattoirs are sampled and tested by the ELISA to meet Quality Assurance scheme standards.

The Wholesome Pigs Scotland (WPS) scheme, and the British Pig Health Scheme (BPHS) in England and Wales, record data on the presence of enzootic pneumonia (EP)-like lesions, milk spot liver lesions, tail biting lesions, and other lesions in healthy pigs slaughtered at the abattoir (BPEx 2010b; Defra 2009a). The presence of these health conditions is highly suggestive of non-optimal health conditions and these voluntary schemes provide an objective and cost-effective way to assess many economically important health (e.g. EP) and welfare problems (e.g. tail biting). The scoring of pigs at the abattoir inspection line is carried out by swine veterinarians who have completed specific training for this assessment. Both BPHS and WPS aim to obtain a representative sample of a batch of pigs by assessing every other pig on the slaughter line. However, the WPS sampled up to a maximum of 150 pigs per batch whereas BPHS sampled up to 50 pigs per batch.

By combining the data from these schemes, this study aims to identify the associations between the presence of different health conditions and Salmonella status on finisher pig farms. Understanding the associations between the different conditions reported by the health schemes and ZAP Salmonella status could provide evidence to assist the design of common on-farm control strategies to improve pig health and provide added motivation to farmers to control Salmonella.

Materials and Methods Data source A total of 700,582 meat juice samples from 7,629 individual slapmarks were recruited from ZAP, covering the period of time between 2002 and 2007. From BPHS and WPS, 11,501 assessments from 1,748 individual slapmarks were available dating from 2003 to 2007. The number of pigs inspected per assessment ranged from 1 to 397 (mean=47). These assessments include data for 11 different conditions: EP, Actinobacillus PleuroPneumonia, pleurisy, milk spots, hepatic scarring, peritonitis, pericarditis, papular

103 dermatitis, abscess, pyaemia and tail damage (BPEx 2008). Viral-like pneumonia lesion was not included in this study due to inconsistencies in the scoring of this lesion.

Combination of datasets Farm of origin was identified in the data by the herdmark, also called a slapmark when tattooed on a pig, which is a legally required official reference for each pig holding (Defra 2009b). This identifier was used to link records in the different schemes databases from the same holding.

A total of 989 unique health scheme slapmarks were successfully linked to records in the ZAP dataset. Due to errors in recording slapmark in the ZAP data, similar slapmarks (e.g. SL6 and S1G) were also used to link records to the lesion data if they had corresponding information for other identifiers (postcode, Quality Assurance scheme identifier).

Both datasets recorded historical health information and they were merged to enable a comparison of observed pig health conditions during a particular year to MJE samples collected during the same year. The health assessment data were summarised into calendar years so MJE samples could be linked to the most recent lesion assessment within a 365 day period.

Due to the wide variation in the number of pigs assessed for each slapmark during each year in the BPHS and WPS (1-1,045), the data set was limited to years in which 50 or more pigs were assessed per slapmark, to create a more comparable and representative sample from each slapmark.

Data analysis The results from the schemes were coded before analysis. The Salmonella MJE result was derived from optical density results for the sample, and the positive and negative controls, converted to a sample:positive (S:P) ratio (Sorensen and others 2004) and categorised in the ZAP scheme as positive or negative according to a defined cut-off of 0.25. In addition to a binary outcome, the MJE S:P ratio was modelled directly, with the

104 ratios logarithmically converted to improve the normality of the data, and any zero or negative values coded to half of the lowest recorded value (0.005) before logarithmic transformation. The BPHS and WPS assessment results were summarised as the prevalence of each condition for each holding.

Each of the health assessment conditions was analysed by logistic regression against the binary MJE sample outcome, accounting for clustering at the slapmark level. Similarly, linear models were completed using the logged S:P ratio as the outcome. Season and year of MJE sample collection were added to the model to account for any temporal effect, as these had been shown to be significant in a previous analysis of meat juice samples from Danish and GB surveillance (Christensen and Rudemo 1998; Smith and others in press). All analyses were completed using STATA 10 (Stata corp., college station, Tx).

Results Of the 989 slapmarks present in both the ZAP scheme and either the WPS or the BPHS, 873 matched calendar years with 50 or more pigs assessed. From these 873 slapmarks, the health scheme assessments linked to 187,682 MJE sample records, with an average of 215 (3-1,007) samples per slapmark. A total of 21% of MJE records were positive, and the mean S:P ratio from the samples was 0.22, although 30 MJE samples had missing S:P ratio results and were dropped from linear model analysis. A summary of the lesion results is presented in Table 1.

Table 1: Within herd prevalence results from pig health scheme of farms that could be linked to corresponding Salmonella results in the ZAP scheme (873 slapmarks, 309,516 carcasses examined) Health assessment condition Mean % of pigs with condition (range) Enzoonotic pneumonia (EP) like lesions 27.1 (0-100) Pleurisy 10.4 (0-59) Papular dermatitis 3.6 (0-65) Actinobacillus PleuroPneumonia (APP) 1.2 (0-60)

105 Pericarditis 3.2 (0-18) Peritonitis 0.6 (0-13) Milk spot liver 4.6 (0-68) Hepatic scarring 7.9 (0-100) Tail biting lesions 0.5 (0-23) Lung abscesses 0.6 (0-19) Pyaemia 0.3 (0-7)

The logistic model (Table 2) results show that the odds of a meat juice sample from a slaughtered pig being Salmonella positive is 0.4% higher when the percentage of pigs affected with EP-like lesions in the herd increases by 1%. Similar results were found for pericarditis. A higher odds ratio was detected for the association of Salmonella with peritonitis (1.085) and also milk spot liver (1.009). The linear model (Table 3) results indicated the same significant associations with similar magnitudes, for example, for every 1% increase of EP-like lesions, the log S:P result increases by a factor of 1.005, whereas peritonitis increases the S:P result by 1.079.

Table 2: Individual logistic regression models of the associations between binary MJE Salmonella results and the percentage of slaughtered pigs with health assessments from each pig herd, adjusted for the clustering of Salmonella samples from each slapmark and accounting for year and season (873 slapmarks, 187,682 ELISA samples). Health assessment condition Odds 95% confidence P-value ratio interval Enzoonotic pneumonia (EP) like lesions 1.004 1.000-1.006 0.028 Pleurisy 1.000 0.993-1.007 0.998 Papular dermatitis 0.997 0.989-1.005 0.507 Actinobacillus PleuroPneumonia (APP) 1.001 0.985-1.018 0.860 Pericarditis 1.004 1.000-1.007 0.028 Peritonitis 1.085 1.044-1.127 <0.001 Milk spot liver 1.009 1.003-1.016 0.004 Hepatic scarring 1.005 0.996-1.014 0.312 Tail biting lesions 0.996 0.972-1.020 0.717 Lung abscesses 0.995 0.950-1.042 0.837

106 Pyaemia 0.985 0.933-1.040 0.582

Table 3: Individual linear regression models of the associations between logged MJE Salmonella values and the percentage of slaughtered pigs with health assessments from each pig herd, adjusted for the clustering of Salmonella samples from each slapmark and accounting for year and season (873 slapmarks, 187,652 ELISA samples). Health assessment condition Odds 95% confidence P-value ratio interval Enzoonotic pneumonia (EP) like lesions 1.005 1.002-1.008 0.001 Pleurisy 1.002 0.995-1.009 0.644 Papular dermatitis 0.996 0.898-1.004 0.335 Actinobacillus PleuroPneumonia (APP) 1.005 0.991-1.020 0.474 Pericarditis 1.005 1.002-1.008 0.001 Peritonitis 1.079 1.039-1.121 <0.001 Milk spot liver 1.008 1.002-1.014 0.014 Hepatic scarring 1.002 0.944-1.010 0.632 Tail biting lesions 0.998 0.981-1.016 0.862 Lung abscesses 1.010 0.968-1.054 0.638 Pyaemia 0.995 0.957-1.034 0.780

Discussion The model results have indicated positive associations between Salmonella infection and four of the eleven health conditions assessed, with agreement between the linear model, comparing ELISA S:P ratio, and the logistic model, using the ZAP cut-off point to indicate previous Salmonella infection.

Previous studies have presented links between Salmonella choleraesuis and pneumonia, which highlight that co-infection may exist and that there may be a synergistic relationship. An association of S. choleraesuis infection has been shown with pneumonia, and vaccination for S. choleraesuis reduced pneumonia in comparison with a control group, which indicates that Salmonella infection may positively influence the presence of EP (Roof and Doitchinoff 1995; Fedorka-cray, Gray and Wray 2000). However, it should

107 be noted that S. choleraesuis is a primary pathogen and differs from those Salmonella serovars detected by the ZAP scheme.

Mycoplasma hyopneumoniae, which is the causative agent for EP, has been shown to inhibit the defence mechanism of the respiratory tract enhancing the colonisation of other bacteria (Thacker 2006). Although Salmonella are commonly detected in the digestive system, the tonsils and respiratory tract may also be important sites of colonisation and this dampening of the immune system may create a positive relationship between M. hyopneumoniae and Salmonella. Another reason for the association between EP and Salmonella could be that prolonged coughing and sneezing caused by respiratory diseases may help spread Salmonella through aerial transmission of Salmonella aerosols from nasal discharges, previously investigated under controlled condition experiments (Stark 1999; Oliveria, Carvalho and Garcia 2006).

The links between Salmonella and EP may also be explained by the sharing of risk factors. In separate analyses of the risk factors for EP and Salmonella completed on large populations of pig farms, both studies identified the geographical region where the finishing herds were located as a risk factor (Sanchez-Vasquez and others 2009; Smith and others 2009). Herds located in Northern England and Yorkshire and the Humber had a higher risk of developing EP-like lesions and a higher number of pigs testing positive to the Salmonella ELISA test. Previous studies have also shown a higher incidence of respiratory problems in straw-based housing systems rather than full slats (Scott and others 2006), and that all-in/all-out production systems are associated with a lower prevalence of lung lesions and with less severe lesions compared to a continuous flow system (Madec 2003; Maes and others 2008). Similar associations have been found in studies of Salmonella in pigs, with slatted floor systems (rather than solid floor with bedding) and all-in/all-out systems effective at reducing Salmonella infections (Lo Fo Wong and others 2004; Nollett and others 2004; Farzan and others 2006).

Links between Salmonella and peritonitis and pericarditis have been less researched, and these require further investigation. Both pericarditis and peritonitis are symptoms that can

108 be caused by overcrowding and other stressful management factors (transport, mixing of groups etc), and similar stressors have been shown to increase the shedding of Salmonella (Callaway and others 2006). Another plausible mechanism could be the debilitation caused by a pig suffering septicaemia and/or Haemophilus parasuis infections (i.e. Glässer’s disease) that could facilitate the attack from other pathogens.

The association between Salmonella and milk spot liver may indicate a relationship with Ascaris suum, the causative agent of milk spots. Another intestinal helminth (Oesophagostomum) has been shown to cause longer and more intensive S. Typhimurium excretion and a greater number of pigs with Oesophagostomum contained S. Typhimurium in their colon and caecum than pigs infected with S. Typhimurium without Oesophagostomum (Steenhard and others 2002). The proposed reasoning behind the relationship was that these parasites penetrate mucosa and induce intestinal mucosal lesions which can facilitate invasion and persistence of Salmonella infections. However, a later experimental study looking at the effect of Ascaris infection did not detect a significant enhancement in S. Typhimurium infection (Steehnard and others 2006).

As with EP, the results may reflect the effects of farm-level risk factors in common with Salmonella, as milk spots have been shown to be significantly affected by geographical region, flooring (solid floor and bedding) and with the following factors found to be protective:- wet feeding; all production indoors; presence of a breeding herd; spring and winter seasons (Sanchez-Vasquez and others in press). These results concur with Salmonella risk factor studies that found that organic outdoor production herds had significantly higher Salmonella prevalence than indoor herds, and that wet feeding was protective (Gebreyes and others 2006; Farzan and others 2006; Beloeil and others 2004; Van der Wolf and others 2001). However, Salmonella results concerning season have been varied, with winter and spring seasons, increased temperature variability, and below median high temperature on the day of sampling associated with elevated Salmonella prevalence in one study but another found that the stress caused by higher air temperatures caused increased shedding of Salmonella (Funk and others 2001; Hald and Andersen 2001).

109

The limitation of this analysis is that comparisons could only be made on a farm-basis as the results could not be matched from the datasets for individual pigs. The health assessments and the MJE results provide evidence for previous challenges (infection in the case of Salmonella and mainly chronic pathologies for the lesions), and although analysis at the individual pig level would not have given evidence of co-existence at a particular time point, it would have allowed a reflection of lifetime events for each pig. The assessments are also only completed on healthy pigs that have entered the abattoir and the removal of currently ill pigs may have influenced some of the results. The study did not collect data on other conditions that might be important (e.g. swine dysentery, PCV-2) and did not collect risk factor data, such as pig diet or medication.

The BPHS and WPS have not been validated to indicate the sensitivity and specificity of the assessor’s ability to identify and record health assessments. The ability of an assessor to differentiate between some of the assessments is unknown, but the co-dependency between reporting of the lesions is currently being assessed (Sanchez-Vasquez, in prep). However, both schemes use a small number of trained veterinary inspectors to reduce the possible variation between lesion assessments. However, a benefit of using these types of surveillance data is that the results were taken from a large number of pig farms which provides more power for the analyses and increased confidence in the results.

These results provide further motivation for the control of Salmonella on pig farms, as although Salmonella infection in pigs is often asymptomatic, it has been shown to be associated with four health conditions. Although these associations may not be signs of causality, they indicate that improving farm practises to reduce Salmonella may have an effect on the other conditions. These conditions have large detrimental effects on feed conversion and daily live weight gain, and so, on-farm controls of Salmonella may provide economic benefits as well as reducing foodborne zoonotic infection in humans.

Acknowledgements

110 The authors would like to thank the schemes (ZAP/ ZNCP, BPHS, WPS) involved for their participation, and BPEx and the project steering group for their support and comments. Defra are also thanked for funding the study under project FT5088/OD0215.

References o ANON (2008) Report of the Task Force on Zoonoses Data Collection on the analysis of the baseline survey on the prevalence of Salmonella in slaughter pigs, in the EU, 2006-2007. EFSA journal 135, 1-111. o ARMSTRONG, D. (2003) Zoonoses action plan Salmonella monitoring programme update. The Pig Journal, 52166-173. o BELOEIL, P. A., FRAVALO, P., FABLET, C., JOLLY, J. P., EVENO, E., HASCOET, Y., CHAUVIN, C., SALVAT, G. & MADEC, F. (2004) Risk factors for Salmonella Enterica subsp. enterica shedding by market-age pigs in French farrow-to-finish herds. Preventive Veterinary Medicine 63 (1-2), 103-20. o BPEx (2008) BPHS scoring system explain. http://www.bpex.org.uk/PracticalAdvice/ProducerKt/Bphs/. Accessed April 8th, 2010. o BPEx (2010a) Practical advice – producer KT – BPHS. http://www.bpex.org.uk/PracticalAdvice/ProducerKt/Bphs. Accessed April 8th, 2010. o BPEx (2010b) Welcome to ZNCP. http://www.bpex-zncp.org.uk/zncp/ . Accessed April 8th, 2010. o CALLAWAY, T. R., MORROW, J. L., EDRINGTON, T. S., GENOVESE, K. J., DOWD, S., CARROLL, J., DAILEY, J. W., HARVEY, R. B., POOLE, T. L., ANDERSON R. C. & NISBET D. J. (2006) Social stress increases fecal shedding of Salmonella Typhimurium by early weaned piglets. Current Issues in Intestinal Microbiology 7 (2), 65-71. o CHRISTENSEN, J. & RUDEMO, M. (1998) Multiple change-point analysis applied to the monitoring of Salmonella prevalence in Danish pigs and pork. Preventive Veterinary Medicine 36 (2), 131-143.

111 o DEFRA (2009a) Wholesome Pigs (Scotland) Abattoir Monitoring Programme. http://www.defra.gov.uk/foodfarm/farmanimal/diseases/vetsurveillance/bag/docu ments/pres9.pdf. Accessed October 19, 2009. o DEFRA (2009b) Livestock movements, identification and tracing: pigs - identification http://www.defra.gov.uk/foodfarm/farmanimal/movements/pigs/id.htm. Accessed December 1, 2009 o FARZAN, A., FRIENDSHIP, R. M., DEWEY, C. E., WARRINER, K., POPPE, C. & KLOTINS, K. (2006) Prevalence of Salmonella spp. on Canadian pig farms using liquid or dry-feeding. Preventive Veterinary Medicine 73 (4), 241-254. o FEDORKA-CRAY, P. J., GRAY, J. T. & WRAY, C. (2000) Salmonella Infections in pigs. In Salmonella in domestic animals. Eds Wray, C. Cabi publishing, 191-208. o FUNK, J. A., DAVIES, P. R. & GEBREYES, W. (2001) Risk factors associated with Salmonella enterica prevalence in three-site swine production systems in North Carolina, USA. Berliner Und Munchener Tierarztliche Wochenschrift 114, 335-8. o GEBREYES, W. A., THAKUR, S., MORROW, W. E. (2006) Comparison of prevalence, antimicrobial resistance, and occurrence of multidrug-resistant Salmonella in antimicrobial-free and conventional pig production. Journal of Food Protection 69 (4), 743-748. o HALD, T. & ANDERSEN, J. S. (2001) Trends and seasonal variations in the occurrence of Salmonella in pigs, pork and humans in Denmark, 1995-2000. Berliner Und Munchener Tierarztliche Wochenschrift 114, 346-9. o LO FO WONG, D. M. A., DAHL, J., STEGE, H., VAN DER WOLF, P. J., LEONTIDES, L., VON ALTROCK, A. & THORBERG, B. M. (2004) Herd- level risk factors for subclinical Salmonella infection in European finishing-pig herds. Preventive Veterinary Medicine 62 (4), 253-266. o MADEC, F. (2003) Enzootic respiratory diseases in the growing-finishing pig and control: A compound problem and still a challenge. Proceedings of AFSSA

112 (French Agency for Food Safety), Zoopôle “Les Croix”, Ploufragan, France, 2003. 1-13. o MAES, D., SEGALES, J., MEYNS, T., SIBILA, M., PIETERS, M. & HAESEBROUCK F. (2008) Control of Mycoplasma hyopneumoniae infections in pigs. Veterinary Microbiology 126 (4), 297-309. o NIELSEN, B., EKEROTH, L., BAGER, F. & LIND, P. (1998) Use of muscle fluid as a source of antibodies for serologic detection of Salmonella infection in slaughter pig herds. Journal of Veterinary Diagnostic Investigation 10, 158-163. o NIELSEN, B., ALBAN, L., STEGE, H., SORENSEN, L. L., MOGELMOSE, V., BAGGER, J., DAHL, J. & BAGGESEN, D. L. (2001) A new Salmonella surveillance and control programme in Danish pig herds and slaughterhouses. Berliner Und Munchener Tierarztliche Wochenschrift. 114(9-10), 323-326. o NOLLET, N., MAES, D., DE ZUTTER, L., DUCHATEAU, L., HOUF, K., HUYSMANS, K., IMBERECHTS, H., GEERS, R., DE KRUIF, A. & VAN HOOF, J.(2004) Risk factors for the herd-level bacteriologic prevalence of Salmonella in Belgian slaughter pigs. Preventive Veterinary Medicine 65, 63-75. o OLIVEIRA, C. J. B., CARVALHO, L. F. O. S. & GARCIA, T. B. (2006) Experimental airborne transmission of Salmonella Agona and Salmonella Typhimurium in weaned pigs. Epidemiology and Infection 134 (1), 199-209. o ROOF, M. B. & DOITCHINOFF, D. D. (1995) Safety, efficacy, and duration of immunity induced in swine by use of an avirulent live Salmonella choleraesuis- containing vaccine. American journal of veterinary research 56 (1), 39-44. o SÁNCHEZ-VÁZQUEZ, M. J., SMITH, R., GUNN, G. J., LEWIS, F., STRACHAN, W. D. & EDWARDS, S. A. (2009) The Identification of Risk Factors for the Presence of Enzootic Pneumonia-Like Lesions and Pleurisy in Slaughtered Finishing Pigs Utilizing Existing British Pig Industry data. The Pig Journal, in press. o SCOTT, K., CHENNELLS, D. J., CAMPBELL, F. M., HUNT, B., ARMSTRONG, D., TAYLOR, L., GILL, B. P. & EDWARDS, S. A. (2006) The welfare of finishing pigs in two contrasting housing systems: Fully-slatted versus straw-bedded accommodation. Livestock Science 103 (1-2), 104-115.

113 o SMITH, R. P., SANCHEZ-VAZQUEZ, M. J., COOK, A. J. C., CLOUGH, H. E. & EDWARDS, S. A. (2009) An Analysis of Quality Assurance and Zoonoses Action Plan Data from Pig Herds in the United Kingdom. The Pig Journal, in press. o SØRENSEN, L. L., ALBAN, L., NIELSEN, B. & DAHL, J. (2004) The correlation between Salmonella serology and isolation of Salmonella in Danish pigs at slaughter. Veterinary Microbiology 101(2), 131-41. o STARK, K. D. C. (1999) The Role of Infectious Aerosols in Disease Transmission in Pigs. The Veterinary Journal 158 (3), 164-181. o STEENHARD, N. R., JENSEN, T. K., BAGGESEN, D. L., ROEPSTORFF, A. & MØLLER, K. (2002) Excretion in feces and mucosal persistence of Salmonella ser. Typhimurium in pigs subclinically infected with Oesophagostomum spp. American journal of veterinary research 63(1), 130-6. o STEENHARD, N. R., ROEPSTORFF, A., BAGGESEN, D. L., BOES, J; JENSEN, T. K., AASTED, B. & ØRNBJERG, N. (2006) Studies on the interaction between Salmonella Enterica ser. Typhimurium and intestinal helminthes in pigs. Veterinary Parasitology 139, 158-167. o THACKER, E. L. (2006) On Mycoplasmal Disease in: Diseases of Swine. 9th edn. Eds Straw, E. B., Zimmerman, J. J., D'allaire, S., Taylor, D. J. Blackwell Publishing, 703. o VAN DER WOLF, P. J., WOLBERS, W. B., ELBERS, A. R., VAN DER HEIJDEN, H. M., KOPPEN, J. M., HUNNEMAN, W. A., VAN SCHIE, F. W. & TIELEN, M. J. (2001) Herd level husbandry factors associated with the serological Salmonella prevalence in finishing pig herds in the Netherlands. Veterinary Microbiology 78, 205-19.

5. 6. CHAPTER 8 7. 8. Defra project R25 Risk factors for pig disease

114 8.1 Objective 2 - To design, test and implement a web-based Pig Herd Health Plan (PHHP) 9. Final report – phhp project. 10. 11. 12.

Jamie Robertson

Livestock Management Systems Ltd

Pioneer House

79 Waterloo Quay

Aberdeen AB11 5DE 23rd April 2008

lms©

115 13. Defra project R25 Risk factors for pig disease

13.1 Objective 2 - To design, test and implement a web-based Pig Herd Health Plan (PHHP) 13.1.1 Final report – Contents

13.1.2 Section 13.1.3 13.1.4 Page No. 13.1.5 1.0 13.1.6 Project Development 13.1.7 2 13.1.8 2.0 13.1.9 Information Input 13.1.10 3 13.1.11 3.0 13.1.12 Results 13.1.13 4 13.1.14 3.1.1 13.1.15 Link to veterinary 13.1.16 7 databases 13.1.17 3.1.2 13.1.18 Link to production 13.1.19 7 datasets 13.1.20 3.1.3 13.1.21 Link to processor 13.1.22 8 datasets 13.1.23 3.2 13.1.24 Compatibility of 13.1.25 9 datasets 13.1.26 4.0 13.1.27 Knowledge transfer 13.1.28 10 13.1.29 4.1 13.1.30 Future knowledge 13.1.31 11 transfer 13.1.32 4.2 13.1.33 Publications 13.1.34 12 13.1.35 13.1.36 13.1.37 13.1.38 Appendix I 13.1.39 Participant 13.1.40 13 questionnaire results 13.1.41 Appendix 13.1.42 Questionnaire 13.1.43 15 II 13.1.44 Appendix 13.1.45 PVS handout 13.1.46 17 III 13.1.47 Appendix 13.1.48 Phhp – BPEX digital 13.1.49 18 IV capture project

116 14. Defra project R25 Risk factors for pig disease

14.1 Objective 2 - To design, test and implement a web-based Pig Herd Health Plan (PHHP) 14.1.1 Final report – phhp project.

14.1.2 1.0 Project development Initial development of the phhp was driven in part by a need to address weaknesses identified by others in existing herd health plans. Some example comments are shown in table 1. It was agreed by some operators that health plans should not initially be too ambitious, but should allow development over time and be designed with the capability to adapt to changing health status at farm level.

Table 1. Perceived design faults with HHPs – 61 dairy units (Bell, 2005)  7% - time consuming to make  12% - only useful if management is poor  16% - not a real reflection of what is happening  17% - not driving change  24% - bureaucratic  41% - not a working document

There were also clear indications that a significant number of existing farm herd health plans were not actively used or reviewed between quarterly or annual veterinary visits, suggesting that the documents were either not valid or somehow too cumbersome. The design of any upgraded system had to facilitate user access to the information held within that system. There was also consensus that a pilot phhp should contain herd performance indicators and targets, even though some farms may have very little accurate data to utilise.

The review of available research on health planning and discussions with peers provided guidance on the basic design requirements for the PHHP. These were that it needed to be:  Flexible  Easy to use  Grow over time  Allow prioritising of elements  Secure  Provide sharing of data (for/from other documents)  Provide real-time data  Provide data manipulation  Allow some self-assessment

The proposal funded by Defra for the pilot project was to launch a herd health plan as an internet tool, and to link the individual farm records to two new data streams; one from the

117 processors (carcase parameters) and one from the quarterly veterinary visits. An additional aim was to eventually reduce the administration time spent on quarterly veterinary reports (QVRs) for the veterinary practices.

The initial draft design of the phhp was circulated to nominated members of PVS, and their comments incorporated into the pilot design. The design was again updated to accommodate the requests of practitioners after the initial induction meetings, causing minor slippage on the timetable and additional costs to the chosen IT contractor. The current design has been placed onto a web format to give individual producers unique and secure access to their own data, whilst individual veterinary practices will be able to access records for any of their clients who are using the system.

14.1.3 2.0 Information input The phhp contains a range of information that in the first instance is uploaded onto the web via a standard PC. The data is collated and presented in the following areas:  Standard farm details  Source genetics  Biosecurity  Current and previous basic production parameters  Viral, bacterial and physical status  Vaccinations  Routine medications  Treatments  Health assessments  Environmental assessments

A demonstration phhp can be viewed at www.demo.phhpanalysis.com

All the records are date stamped and any updates automatically create a new record and an archive of the previous information. As the records are updated, some detail is automatically brought into the current records to allow instant viewing of, for example, previous treatments and agreed action points in any one area. Most areas of the phhp can be expanded by individual row to add information on animal groups by pen, age or type, and similarly rows can be deleted to maintain the phhp as a valid, current, document.

All the medications fields in the phhp website are linked to a database that contains the suggested route of administration, recommended dose and withdrawal time of available named UK medications. The system uses information from the NOAH website and data collated from commercial sources, and is managed by the system administrator. There are currently 230 medications on the site, and selecting any one of the named products automatically brings the associated data onto the relevant page of the phhp. There is some concern about the ability of the system to remain completely up to date and this remains an issue that needs to be resolved during the next phase. The entry page to an individual farm’s phhp contains a notice board that is linked to any changes in the medications database.

The basic production parameters are placed onto the phhp manually via the keyboard. The possibility of automatically importing data over the internet from existing pig production

118 software is discussed below. Farm specific pig data is also downloaded direct from the processors, which gives a weekly or batch update in the carcase weights, grading and condemnations from the unit. This provides a stream of accurate, relevant data that accrues over time to provide a picture of the trends from the unit. This aim was not met for all farms, and is discussed below.

The phhp has been designed to incorporate data in a systematic fashion from the quarterly vet visits, again with the specific target of being able to assess health and performance over time. A parallel project funded by BPEX has piloted the use of digital pen and paper technology for the collection of data for the phhp during routine, quarterly veterinary visits (Appendix IV).

A number of the areas presented for assessment, such as the physical symptoms of lameness, enteric disorders, or vice, can be scored on a four point scale with provision to add comments or actions points in a way that replicates the comments provided by some vets in their QVRs. Where scoring is used, the current page always shows the last previous result. 14.1.4 3.0 Results

Project farms (24) and veterinary practices (10) submitted copies of existing health information and quarterly veterinary reports (QVRs) to populate a baseline phhp for each farm unit. Initial work was slow, placing a mixture of data from different sources into a generic format, but once the phhp format became more familiar it took approximately 1.0 hour per unit to extract relevant data and upload it. This is relevant to the next stage of the phhp: it will be far more efficient to use secretarial skills or a bureau service for the initial population of the phhp. Thereafter the producer/manager/farm staff become familiar with the system through updating existing information and adding further detail.

The feedback from the practices has covered the full spectrum of responses. There has been strong resistance from some members of PVS since the phhp was first discussed, with a common response during the design stage being that individual practices already have their own health plan systems, and that a generic system such as phhp cannot meet all needs. Two practices who joined the pilot did not, in the end, try the phhp in spite of farm data being uploaded for them and repeated contact to encourage use during quarterly farm visits. Reasons given include client apathy, difficult times, existing good system and vet laziness. At the other end of the scale was ‘a great initiative’.

The timetable of the project was derailed by the presence of FMD in the country, which placed a temporary halt to quality assurance visits (and therefore the routine updating of the pilot phhp). Pig vets were also heavily involved in welfare visits to individual pig units to facilitate pig movements to the processors. As the industry cleared its feet from the impact of FMD the onset of increased feed prices became apparent. The result has been widespread disillusionment in the sector, and interest in the phhp amongst some vets has diminished.

Comment: the role of veterinarians in some applied animal health projects is critical. A significant utilisation of veterinary resources has been accessed over time for projects, without a contractual or financial obligation being involved. This parallels the involvement of farms in applied projects. Recompense from projects is considered to accrue to the industry in general, with successful research leading to improved commercial activity. The experience on this pilot phhp might suggest that a more formal arrangement would benefit project outcomes.

119 Project participants were asked to complete a short questionnaire (Appendix II) before using the phhp, to determine their views on their existing herd health plans. The results are reported in appendix I. The intention was to repeat this exercise after 3 quarterly vet visits using the phhp, but the field work was not completed, for the reasons reported above. A questionnaire was however sent out to ask participants for their specific responses on the strengths and weaknesses of the different components of the phhp.

The various responses to the phhp are summarised below, broken down into the main components of activity. They have also been split into technical and operational comments; most of the technical issues are easy to correct. Technical support was available during the pilot but changes to the phhp design were not implemented after the initial feedback from vet participants.

Table 1. User responses to pilot phhp design Technical Operational Comments Home page Date must be Previous records General farm details/ contact details more must be more definitely useful. Data sharing prominent. clearly accessible. considered a strong plus. Print function Competence and Print function was awkward for some must be training of users. Previous records, dated, are a checked at stockpeople needs definite added value. intro level. to be added. Vet admin page Drop-down list Only available to the vets. for vets must Positive. populate with farm name not contact name Herd Slow to load. No links to some Generally positive. Automated link to Performance processors. Data production data (Agrosoft/Dataporc) sources should be was investigated but was not part of the named. contract. Import of Zap scores would be beneficial Biosecurity - - OK Some desire to Very mixed responses. Too much herd health highlight main information for some. issues Biosecurity OK Few comments! - quarantine Health Status OK Need to explain Generally positive. The concept of - Viral how to use drop- listing diseases and ticking ‘present’, down boxes for ‘absent’, or ‘not tested’ is not appealing medications. Some to some vets. repetition with Ability to remove “standard” diseases treatment section. and then rank in order would be useful Health Status OK - Bacterial Same comment as above Health Status OK Need to explain More useful than initially thought by - Physical how scoring system some. Useful for engaging with farm (1-4) can be used staff. Some vets consider this section of for symptoms no use.

120

Treatments OK; needs Better support Must be able to add off label products - all some information needed by typing in. (This is available in the realignment on to guide the user ‘comments’ area). the screen to improve use of screen space Health OK Need to explain Very mixed responses; the idea of assessments how scoring system quantifying symptoms is not readily (1-4) can be used accepted. for symptoms Environmental OK Need to explain As above. However, many existing vet assessments how scoring system reports describe environmental aspects (1-4) can be used of a unit, but in a random manner for symptoms Medications OK Needs a contracted Only available to the site administrator administrator to for input of dosages, withdrawals etc. check medicines Viewed very positively apart from some databases for concerns about the medicines database updates. being kept up-to-date.

Other Timeout It should not be possible to leave a onscreen needs screen page having made changes a warning without a warning to ‘save’ new data.

There was a split amongst the users on the availability of ‘so much’ data. The design of the phhp allows individuals to only use/access those parts that they require. This message possibly got lost during the pilot, with significant feedback commenting that there was too much to do, and that this was a barrier. Other feedback requested that areas or tabs that were not required at present could be deleted or hidden. This has been relayed back to Innovent but adds significant complexity to the IT requirements.

It is clear that good health planning takes time to evolve. The duration of the pilot has only established for each farm the baseline information, which takes some time. As veterinary visits are repeated the quantity of data that needs changing should decrease dramatically, as only items of significant change should be noted. Any production unit with many changes from one quarter to another in its health planning is probably needing a lot of attention.

There were very few computer hardware issues. The main concerns have been addressed above. The timeout is irritating in a working environment where there are many interruptions. There was some interest from vets, but not producers, to have a software version that can be downloaded onto a laptop, so that any report can be viewed ‘offline’. This is not feasible without substantial financial input.

Technical support must be a one-stop shop. The pilot provided 3 numbers for contact on different issues and was not always successful. Minimal supporting information on screen was generally favoured, but initial training must contain handouts/crib sheets.

121 Confidentiality has been a concern to some vets and producers since the start of the project. The commercial sensitivity of some of the data is high. However, the project has always been promoted as a tool for pig health, and the management of pig health at unit level. Although the potential for sharing data exists because of the technology this should always be approached as an ‘opt-in’, not an opt-out’.

Expectations of the phhp by the users were high, and exposed the risk of developing a system under commercial conditions, as a pilot. Some of the negative responses were understandably influenced by the technical hiccups that occurred, as well as operator unfamiliarity with the IT. Additionally it must be stressed to users that the phhp will cover ‘85%’ of requirements, but not them all. The vets especially want to write quarterly reports that focus on the immediate health problems, which means that the format of their information is constantly shifting, and appears to be unsuited to a generic format. However, if the phhp can routinely address 85% of health management matters, a valuable baseline has been produced.

14.1.5 3.1 Data interaction One of the fundamental design considerations of the phhp was the ability to connect with existing datasets that can help to inform the health management process. The current situation where health information is distributed in different locations, often in different formats, was and still is considered a weakness.

The phhp has approached data sharing or access at a number of levels, with varied success. The technical impediments have been resolved or are resolvable. The human and commercial barriers are of a different magnitude to the technical requirements and are considered by the author to be of major significance to the pig sector health management.

3.1.1 Link to veterinary databases a. It was instructive to discover that there was no ready access to a succinct medicines dataset in a form that would be readily digested at farm level. The National Office for Animal Health database is very informative but is not all inclusive. There was no other apparent source of the information outside of individual company databases. b. It is possible to link to the NOAH organisation on payment of a fee, but there was no obvious advantage for the purposes of farm animal health management c. Access to current medicines information was considered a significant useful factor by vets and producers. This was considered to be a useful ‘added value’ consideration for the phhp and was created from scratch with information from the NOAH website and the Norbrook Compendium of Datasheets 2006-2007. The phhp currently has a drop-down, alphabetical list of medications that is accessed throughout the phhp document. When a product name is highlighted the information relevant to dose, site of application, and withdrawal period is automatically displayed d. The producer support for the veterinary data access is high. The veterinary support is also high, albeit with the condition from some of the practitioners that a water-tight mechanism is in place for updating medicines data as it occurs. The fact that no such mechanism exists at present suggests mixed standards are being applied.

122 e. An addition to the pilot phhp was the introduction of a notice board to the site so that ALL persons entering their own home page is alerted to any changes in the medicines database.

Recommendation: that a contractual obligation is applied to the phhp managers that outlines the frequency and mechanism for updating the medicines database.

3.1.2 Link to production datasets a. Considered from the initial design stage to bring added value to the phhp. Whilst some producers are both willing and able to use production data as an additional source of information to support decision-making relevant to the health process, many do not. b. The project requirement to add data to the phhp manually and for the data to be represented in graphic form has been achieved. c. Discussions were held with the primary supplier of production software, Agrosoft, with the aim of supplying a subset of individual farm production data directly to individual phhps via the internet. This has not advanced, and may be encroaching on commercial sensitivities. d. Contact was made with Dataporc with initial interest being moderated to “will respond at a later date if we feel it would be helpful to take the opportunity further” e. Producer user response has been mixed. The actual production data webpage is slow to load due to the amount of information that is downloaded from the server, and the interruption to the pilot caused by FMD and subsequent events has led to an untimely gap in the dataset

Recommendations: maintain the page but request the IT consultants for advice as to how to increase the speed of operation of the page download. Also, to ask the IT consultants about the mechanism/s required so that users can have a direct link to their own production datasets (eg Dataporc/ Agrosoft). This would mean that the decision to link datsets is in the hands of the users and not the suppliers. The aim is to facilitate the useful links. Note that a summary of production data is frequently used by vets in their existing reporting forms.

3.1.3 Link to processor datasets a. The ability to download/access processor data was considered a further added value aspect of the phhp design, as the processor data is frequently the only accurate descriptive data available on farms. The data also has the value of being equivalent across all farms. b. Where data from pilot farms was made available by the processors, information has been routed from the processors to the individual page of the individual producers. The data is presented in graphical form, and builds over time. c. Some producers export pigs to different processors, and there are concerns about gaps in the datsets as they appear on the phhp. d. The availability of data from the processors relevant to the pilot farms is described in table 1 below. The three possible responses are data available/data given, data available/data not given, and data not available. Data not available refers to the lack of equipment and processes at processor level that create a relevant, electronic

123 data stream on individual pig carcases. Data available but not given is due to commercial sensitivities

Recommendations: request the IT consultants to update the information in table 1. There is a need to review the situations where data is available but access is denied. There may be a requirement for personal intervention at an industry level to resolve any perceived difficulties. It would also be relevant to ask the IT consultants about the mechanisms required to link individual producer’s pages to existing sites that show processor data (eg Dataporc and QBox). For some producers it may be simpler and more robust to link the phhp to existing pages on other web sites rather than import data from the processors to the phhp.

124 Table 1. Processor data in to the phhp Abattoir Receiving Data George Adams Yes Tulip No – Due to meet Dalehead No – Due to discuss with st Dalehead in 1 qtr 08 JH Lambert No – Lambert’s are not releasing their data at the moment. They are also in the process of trying to upgrade their plant, but

nothing will happen until then. Grampian Broxburn No – Broxburn will not release data Grampian Malton Yes 14.1.6 Bowes of Norfolk No – Due to come on stream Jan 14.1.7 08 Blakes No – currently in talks with 14.1.8 Blakes to receive data Unknown. Contact with contact several times but had no 14.1.9 producers ongoing response 14.1.10 14.1.11 3.2 Compatibility of datasets The increasing use of electronic data in the pig sector has not been matched with an increasing compatibility of datasets. This was highlighted to the stakeholders during the design stage of the project. During the pilot there were reports of commercial (non-veterinary practice) versions of pig herd health plan becoming available to suit various needs. Two of these were investigated but health was a minor component only. We have also received two requests to change the scoring system on the health assessment pages to fit in with other, novel systems such as the Intervet ‘Respig’ model. Minor changes to the phhp to increase compatibility with other systems is not problematic.

During 2007 the requirements of the Food Chain Information system became clearer, and the FSA issued guidance. BPEX has worked with the FSA and the Meat Hygiene Service to develop an online system that integrates with other databases such as quality assurance data, ZAP and NOAH with the stated aim of reducing the additional requirement for paperwork at farm level. It is perhaps indicative of the lack of open dialogue within the sector that BPEX did not choose to extract relevant data via the phhp initiative. It remains undeniable that the principle location for information that can effectively impact on improved health and performance of pigs is on farm, as opposed to at the veterinary practice, or the processor, or the QA bodies.

14.1.12 4.0 Knowledge transfer The preparation for the project involved extensive dialogue with members of the Pig Veterinary Society, the National Pig Association, the British Pig Executive and Defra. Meetings were also held with independent vets, and a paper presented to the PVS in 2006.

Meetings were held with potential and eventual project participants in East Anglia and Yorkshire. A project update was presented to PVS in 2007 and a report published in the Pig Journal. A

125 demonstration website was created in summer 2007 and used initially for feedback to project participants www.demo.phhpanalysis.com. The aim is to use the demonstration site as part of the promotion to new users. An abstract was submitted to the Society of European Agricultural Engineers and a full paper has been accepted for publication in summer 2008.

Attended a meeting at Bristol University at the request of Dr David Main to present a summary of the phhp to the steering group of the BPEX project ‘Adding value to quality assurance’. The presentation included demonstration of the live website. A presentation was also given in London at the request of BPEX to contribute to discussions on collaboration/ gaps/ developments in various IT projects.

A CD Rom has been drafted for training and promotional purposes and will be ready by 21st March 2008. This is an outcome for a parallel project that aims to take the phhp and promote it to the pig sector via the producer. A framework programme has been presented that aligns the phhp project with the work of the BPEX Knowledge Transfer team, and involves promotion via producer groups supported by the KT team, short articles and publicity at the main trade show, the Pig & Poultry Fair at Stoneleigh in May 2008.

4.1 Future Knowledge Transfer 14.1.13 The aim of the next phase of the development is to promote the phhp to commercial producers. The phhp will also be used within the R25 Risk Factors for Pig Disease project as part of the framework for bringing together existing datastreams. The principle features of the development phase are:

a. Training in the use of phhp with the BPEX Knowledge Transfer team

b. Use of an interactive CD/DVD for off-line training for farmer groups

c. Introduction of phhp to the established pig producer health groups

d. Promotion of phhp at the Pig & Poultry Fair, 13th & 14th May 2008

e. Promotion to the Scottish sector with QMS 14.1.14 The training for the BPEX team is scheduled for 6th May 2008, with the phhp subsequently promoted to the pig producer health groups throughout the summer. The wider commercial promotion will continue with short articles in the trade press, a presentation to the Pig Veterinary Society on 22nd May 2008, and direct promotion to the following groups:

f. Pig marketing groups

g. Pig processors

h. Major integrators

i. Quality assurance schemes. 14.1.15 The wider promotion of the phhp will include support from the commercial collaborators (Innovent and Longhanddata) who are already working with the

126 livestock production and processing sectors. Phhp promotional material will be added to their existing planned activities with their commercial clients. 14.1.16 4.2 Publications 14.1.17 Robertson J F (2006) Pig Herd Health Plans – An active tool to monitor and manage UK pig herd health. The Pig Journal. 58, 45-54

Jordan J (2007). Pig Health to benefit from the information highway. Pig World. Jan 2007.

Robertson J F (2007). Pig Herd Health Plans – project update. The Pig Journal , Vol 60 pp

Robertson J F (2007) phhp ~ pig herd health plans. Handout for PVS meeting, Nov 2007. Appendix III

Robertson J F, Campbell I W, Colebrook M, and Foubister N (2008). Pig herd health plans; an internet based system to monitor health and performance. AGENG 2008 Conference - Agricultural & Biosystems Engineering for a Sustainable World , 23 - 25 June 2008, Crete. Paper accepted for publication and oral presentation

127

14.1.18 Appendix I Phhp ~ Participant questionnaire responses

All producer members of the phhp project were asked to give their responses to a short questionnaire (appendix II) regarding the usefulness of their original pig unit health plans. The questionnaire responses are summarized below.

All units current used health plans, 57% paper based and 43% on computer. The results show a wide range of use and application. In response to the question “do you use the health plan to inform management?” the answers ranged from ‘daily’ to ‘yearly’. The most frequent response was ‘ quarterly’, probably reflecting the link to the quarterly veterinary visits driven by the QA requirements.

Production data is either accessed weekly (28%), quarterly (57%) or never (14%) to help make health management decisions. This relatively low use of regular, objective and subjective data which can assist decision-making on farm is an area of good potential for the producers. The quarterly use of the production data is probably linked to discussion with the vets on their quarterly visits.

Processor data is significantly underutilised for health matters. The most common response was ‘never’, and only one unit was using the data weekly. The data is available anyway, is sent to each unit as often as pigs are sent to the processor, and can/could provide an accurate dynamic picture of overall health and also specific problems that cause carcass condemnations. It is also an objective source of data.

The health plans are discussed with others from ‘weekly’ to ‘yearly’, with the most common response as ‘quarterly’. This overall lack of dialogue is again a possible source of potential valuable improvement at unit level, as it is highly unlikely that the complex issues of health management cannot be significantly influenced by sharing knowledge and experience. When looking at the existing, paper based health plans from the different units in the project it is possible to see elements of good practice on individual farms that could be usefully passed on to others.

Respondents were asked to rank the effectiveness of their health plans across four areas: As a reminder of compliance issues As a driver for change on the unit As support and guidance for staff on a daily basis As a link between the vet and unit staff Or, no effect. One respondent suggested that there was overall no effectiveness. There was broad concensus that the health plans are useful for support and guidance of staff, and least effective for compliance issues. Providing a link between staff and the vets was overall rated second, and usefulness as a ‘driver of change’ was only marginally behind.

128 Both the health and welfare of the pigs was considered to have been influenced either occasionally or frequently by the use of the health plans over the past 12 months. Only one respondent felt that the health plans had a continuing (‘always’) positive influence of welfare, with one further respondent saying that the health plan was always a cost- effective management tool. For the majority the health plans are seen as ‘sometimes’ a cost-effective management tool. This suggests another area of potential development, as it is essential to increase both the actual and perceived value of the health plans. It may be useful to investigate this area further.

The majority of respondents had implemented one or more of the following activities as a result of reviewing the health plan:  Investigation of a problem  Advice seeking  Veterinary visit requested  Husbandry changes  Action plan put together; This does suggest that the health plans, when they are reviewed, are used to stimulate change. This begs the questions; ‘if health plans were viewed and reviewed more frequently, would they drive more change?’ and ‘what would convince the producers to review the records more frequently?’

The time spent on health and production records varies greatly, from 5 minutes per week for one 800 sow unit to 8 hours a week for 1700sows and 15,000 finishers. The proportion of time spent on either data input or viewing/discussing data also varied greatly, from 3:1 to 1:7. This is another area where the potential benefits of the phhp can be promoted.

Those units with a computer in the farm office were also linked to the internet. Where staff numbers were low, most were competent and comfortable with farm records and with computers. Units with three or more staff tended to have some staff who were less comfortable with records and computers, and who would appreciate computer training.

Comments on the major strengths and weaknesses of current health plans:

STRENGTHS WEAKNESSES Keeps you alert of problems Can be time consuming Helps sort out problems at an early stage Paperwork Management tool Needs discipline to revise the plans Guidance for good practice Time consuming Agreemments and instructions on paper, so Unlikely to impact with owner if done in no (adverse?) discussion about agreements isolation Documentation of issues discussed at visits Involves others associated with the unit

129 14.1.19 Appendix II a. Producer □ or veterinary practice □ ? b. Currently use healthplan/s? Yes □ or No □ If "No" please provide a brief explanation of the reason for this.

c. Current format of health plans. Paper based □ or computer based □ d. Do you use the health plan to inform management … … ……daily/weekly/monthly/quarterly/never?

e. Do you access production data to help make health management decisions ……daily/weekly/monthly/quarterly/never?

f. Do you access processor data to help make health management decisions ……daily/weekly/monthly/quarterly/never?

g. How often do you discuss the health plan with others: ……daily/weekly/monthly/quarterly/never?

h. Please rank the effectiveness of your health plan for the following uses. Rank 1 for most effective; 4 for least effective a) As a reminder of compliance issues □ b) As a driver of change on the unit □ c) As support and guidance for staff on a daily basis □ d) As a link between the vet and the unit staff □ OR, no effect □

i. To what extent has the health of pigs on the unit been positively influenced by use of the health plan in the last 12 months? Not at all/occasionally/ frequently/always

j. To what extent has the welfare of pigs on the unit been positively influenced by use of the health plan in the last 12 months? Not at all/occasionally/ frequently/always

k. Has the health plan been a cost-effective management tool? Not at all/sometimes/ frequently/always

l. What are the major strengths and weaknesses of the current health plans?

STRENGTHS WEAKNESSES

130

m. Have any of the following actions been carried out as a result of reviewing the health plan records? Investigation of a problem Yes/No Advice seeking Yes/No Veterinary visit requested Yes/No Husbandry changes Yes/No Action plan put together Yes/No n. How much time per week is spent in total on health and production records? (to nearest 15 minutes)? ………. o. How much of the above time is spent on data input and how much on viewing and discussing the records?…….

p. How many sows and/or finishers on the unit? q. How many staff on the unit; full-time and part-time ? r. How many staff are comfortable with production records? s. How many are comfortable with using a computer? t. How many would appreciate computer training? u. Does the farm office have access to a computer ? v. Does the farm office currently have access to the internet? Yes/No w. Please add any further comments relevant to the effectiveness of current health plans

131 Many thanks for your time. The responses will be reviewed and a summary report sent to all the pilot project participants. If you would like any further information please do not hesitate to contact me

Jamie Robertson. lms© 07971 564148

132 Appendix III (Handout to PVS Autumn meeting, 2007)

phhp ~ pig herd health plans

 a single location for all your health information  automatic link with your carcase data and condemnations  all recommended dosage details and withdrawals  vaccine records

 create self-assessment records for hygiene management, environmental management, and health assessments  create up-to-date output for quarterly veterinary reports, progress notes, or action lists

 share information with your staff and your vet, in a standard format  records are automatically archived and dated ~ a permanent health history  efficient data handling

Health plans when you want them ~ Health plans as you want them ~ as much or as little as you need

Try the demonstration at www.demo.phhpanlysis.com

Or contact Ian Russell on 07795 335836 for details

iPig # information technology for pigs iPig # designed for the pig industry iPig # using technology to support health management iPig # designed to reduce time at reporting iPig # accessing unit health data from a single point iPig # designed to build from small to complete

A Defra funded, industry run initiative to support pig herd health

133 14.1.20 Appendix IV. Pig Herd Health plans (BPEX Digital Capture project)

Research Partners: Livestock Management Systems Ltd Industrial Partners: Genesis, Longhandata, Innovent Project Duration: Jan 07 to September 07 (extended due to FMD)

Objectives: To design and field trial the use of Anoto digital paper technology and digital pens to automatically capture data created during routine management and veterinary visits

Background The project was designed to complement the pig herd health plan (phhp) project funded by Defra, which has piloted the use of a standard herd health plan format on an internet base, with secure access for the producer and his veterinarian. Whilst aspects of the phhp can be updated on-line when required, there is a need during quarterly visits for new information to be gathered whilst observing the pigs and facilities on farm. The digital capture project has piloted the use of digital pens and paper (Fig1) for health management on commercial units.

The digital phhp form comprises 12 pages and just under 300 database fields. The forms and pens were completed and distributed to participating vets in May 2007 and the technical aspects of the project work well. Information from each completed form is transferred from the pen by Bluetooth to a dedicated mobile phone, which in turn transfers the information to a data ‘wharehouse’. Each phhp form can then be viewed as a complete page-by-page electronic image (pdf) that is an automatic copy. Thereafter the written entries (Figs. 2 & 3) are automatically transferred as a database entry to the farm phhp on-line where it is updated and stored with a new date.

After initial population of the phhp with farm data, the aim for subsequent visits has been to change only those fields that require change, and to eliminate continual rewriting of existing data. The digital phhp form contains numerous fields (seen in yellow in Figs 2&3) that can be overprinted with existing data from the farm phhp prior to a farm vet visit, which provides both a check on previous information (such as action points) and a reduction in form filling (such as addresses and names).

134 Fig 1. Digital pen, docking port, mobile phone and sample forms

Fig 2.Entry type: tick boxes – the user has ticked in the box ‘Genesis’ and on the right is the database entry.

Fig 3. Entry type: free text and tick box – the test text appears on the Longhand web service as an image – and on the right is the database entry – the handwriting having been digitised.

Training Face-to –face training on the use of the pens/paper is considered essential. Longhanddata have developed simple tools for training users, including pangrams – these are test sentences of the sort: THE QUICK BROWN FOX JUMPS OVER THE LAZY DOG (a sentence containing every letter in the alphabet).

A scoring system is available on the Longhand web service containing four pangrams that serve to improve the efficiency of handwriting/text recognition. Longhand experience with other client groups has been to then allow the users to fill in their own data collections forms for a short period, and to then ask them to repeat the pangrams. Their experience is that two trainings sessions of about a hour each – spread out by about a month - is enough to get users up to near 100% accuracy. It is essential that feedback is prompt in order to encourage and support the users.

135

Future The information technology applied to this project has proved competent and suitable for the difficult environment found on pig units. The development will continue. However the reservations about this particular application are highly significant. The feedback from users ranges from ‘never took it out of the box (and don’t intend to)’ to ‘we expect to keep using it; it is part of the future’. The IT contractors are concerned that it is difficult to discern whether users who are negative about the project are concerned about the technology, the phhp, or both.

The phhp was designed to operate independently as an internet-based system. Data collection can involve pen and paper, or the digital technology described above. All information can be checked, updated or corrected in any way at a computer terminal. It is probable that, in phase 2 of the phhp, users will choose the form of data collection and entry that is most appropriate to their needs. Those who choose to input data regularly because they have a number of units, or are involved in project work or quality assurance, should be more likely to adopt the Anoto digital paper technology and digital pens than those who only input data once in three months.

CHAPTER 9

Veterinary validation of the assessment of clinical signs by farmers

Aim To compare the assessment of clinical signs in pigs by veterinary and farm staff, to validate the effectiveness of farm staff to identify and correctly score a number of signs of clinical illness (coughing, sneezing etc).

Method The assessment of six clinical signs was recorded by farmers (or stock-person) at monthly assessments to a number of previously identified pig pens, according to the method described (Annex 1). The pens were assessed by the same member of the farm staff for a period of up to 12 months and at quarterly intervals a veterinary (or external assessor) visited the farm and recorded a separate set of assessments of the pens.

The veterinary assessments were combined to the farmer assessment that was closest in regards to date of assessment (whether before or after the veterinary assessment). Those records where the number of days between a veterinary and farmer assessment were more than 31 days were dropped from analysis, to ensure a comparison of similar animals. The clinical signs assessments were converted to the percentage of positive scores from that group of assessed animals (number of scores/ number of pigs assessed). This percentage was also transformed into a categorical variable with levels for each 10% increase in the number of animals affected. If a score was over 100% of the group population (e.g. 40 coughs by 30 pigs) then the score was categorised into the 91-100% for the grouped analysis.

The data was then transferred to Stata 10 and the veterinary and farmer assessments compared for each clinical sign by kappa test, which was used to measure the agreement between the two percentage scores and also between the two categorical outputs. The data was further analysed by carrying out a separate kappa analysis of each score for only those farm and vet assessments recorded on the same day.

Results There were a total of 64 assessments by veterinary personnel to 24 farms, with 72% of the assessments, from 20 farms, scored within 31 days of a farmer assessment. A further 25% of the assessments, from 8 farms, were scored on the same day.

136

Between 1 and 17 (mean=5, median=5) groups of pigs were assessed at each veterinary visit. At one veterinary assessment two scores for each of scour on pigs, scour on floor, scratching and tail damage were not recorded and so these were dropped from the final analysis.

Comparisons between veterinary and farmer assessments for the six clinical signs, taken within 31 days of each other, are presented in tables 1-6. The results of the kappa agreement tests (tables 7 and 8) show a statistically significant level of agreement for all clinical signs, for both the tests of the categorised and raw scores.

Table 1: Comparison between the percentage of pigs presenting with coughing from farmer and veterinary assessments of the same pig pens, within a 31 days interval. Vet Coughing Farmer coughing assessment assessment 0% 1-10% 11-20% 21-30% 31-40% 41-50% 51-60% 61-70% 81-90% 0% 121 52 3 3 0 0 0 0 0 1-10% 22 73 17 3 2 1 1 0 0 11-20% 0 4 4 0 0 0 0 0 0 21-30% 0 1 0 0 0 0 0 0 2 31-40% 1 0 0 0 0 0 0 0 0

Table 2: Comparison between the percentage of pigs presenting with sneezing from farmer and veterinary assessments of the same pig pens, within a 31 days interval. Vet sneezing Farmer sneezing assessment assessment 0% 1-10% 11-20% 21-30% 31-40% 41-50% 51-60% 91-100% 0% 107 26 8 0 1 1 0 0 1-10% 23 84 10 1 1 2 1 0 11-20% 4 6 7 4 3 0 0 0 21-30% 0 5 5 4 0 0 0 0 31-40% 0 0 1 0 1 0 0 0 51-60% 0 0 0 0 0 0 1 0 71-80% 1 0 0 0 0 0 0 0 91-100% 0 0 0 1 0 0 0 2

Table 3: Comparison between the percentage of pigs with scour visible from farmer and veterinary assessments of the same pig pens, within a 31 days interval. Farmer scour on pigs Vet Scour on pigs assessment assessment 0% 1-10% 0% 286 13 1-10% 4 5

Table 4: Comparison between the percentage of scour present on the floor in comparison to the number of pigs present, from farmer and veterinary assessments of the same pig pens, within a 31 days interval. Farmer Scour on floor Vet Scour on floor assessment assessment 0% 1-10% 11-20% 0% 261 17 0 1-10% 15 13 0 11-20% 0 0 1 31-40% 0 1 0

137

Table 5: Comparison between the percentage of pigs presenting with scratching from farmer and veterinary assessments of the same pig pens, within a 31 days interval. Vet Scratching Farmer Scratching assessment assessment 0% 1-10% 11-20% 0% 210 31 4 1-10% 32 21 1 11-20% 4 3 0 31-40% 1 0 0 61-70% 1 0 0

Table 6: Comparison between the percentage of pigs presenting with tail damage from farmer and veterinary assessments of the same pig pens, within a 31 days interval. Vet Tail Damage Farmer tail damage assessment assessment 0% 1-10% 11-20% 31-40% 0% 274 11 1 2 1-10% 7 12 0 0 11-20% 0 0 1 0

Table 7: Kappa test results to show the agreement between farmer and vet assessments of clinical signs in pigs taken within 31 days of each other, using categorised scores. Observed Expected Clinical sign Agreement Agreement Kappa Std. Err. Z Prob>Z Coughing 42.58% 27.00% 0.2134 0.0196 10.89 <0.001 Sneezing 38.71% 20.24% 0.2316 0.0179 12.95 <0.001 Scour on pig 93.51% 91.41% 0.2437 0.0284 8.59 <0.001 Scour on floor 86.36% 80.89% 0.2863 0.0274 10.44 <0.001 Scratching 69.16% 64.09% 0.141 0.0259 5.45 <0.001 Tail damage 90.91% 85.32% 0.3808 0.0278 13.72 <0.001

Table 8: Kappa test results to show the agreement between farmer and vet assessments of clinical signs in pigs taken within 31 days of each other, directly testing the percentage of signs against the number of pigs present. Observed Expected Clinical sign Agreement Agreement Kappa Std. Err. Z Prob>Z Coughing 63.87% 43.14% 0.3646 0.0456 7.99 <0.001 Sneezing 66.45% 36.39% 0.4726 0.0394 12.01 <0.001 Scour on pig 94.48% 91.58% 0.3448 0.0534 6.45 <0.001 Scour on floor 89.29% 81.80% 0.4114 0.0547 7.52 <0.001 Scratching 75.00% 67.22% 0.2374 0.0515 4.61 <0.001 Tail damage 93.18% 85.77% 0.5208 0.0519 10.04 <0.001

Comparisons between veterinary and farmer assessments for the six clinical signs, taken on the same day, are presented in tables 9-14. The results of the kappa agreement tests (tables 15 and 16) show a statistically significant level of agreement for all clinical signs, for both the tests of the categorised and raw scores.

Table 9: Comparison between the percentage of pigs presenting with coughing from farmer and veterinary assessments of the same pig pens, on the same day. Farmer cough Vet Cough assessment assessment

138 0% 1-10% 0% 52 6 1-10% 5 21 11-20% 0 2 21-30% 0 1

Table 10: Comparison between the percentage of pigs presenting with sneezing from farmer and veterinary assessments of the same pig pens, on the same day. Farmer Sneeze assessment Vet Sneeze assessment 0% 1-10% 11-20% 21-30% 51-60% 91-100% 0% 35 4 0 0 0 0 1-10% 6 29 1 0 0 0 11-20% 0 2 0 1 0 0 21-30% 0 0 1 4 0 0 51-60% 0 0 0 0 1 0 71-80% 1 0 0 0 0 0 91-100% 0 0 0 0 0 2

Table 11: Comparison between the percentage of pigs presenting with scour visible from farmer and veterinary assessments of the same pig pens, on the same day. Farmer scour on pig Vet Scour on pig assessment assessment 0% 1-10% 0% 82 1 1-10% 1 3

Table 12: Comparison between the percentage of scour present on the floor in comparison to the number of pigs present, from farmer and veterinary assessments of the same pig pens, on the same day. Farmer scour on floor Vet Scour on floor assessment assessment 0% 1-10% 11-20% 0% 71 4 0 1-10% 2 9 0 11-20% 0 0 1

Table 13: Comparison between the percentage of pigs presenting with scratching from farmer and veterinary assessments of the same pig pens, on the same day. farmer Scratching Vet Scratching assessment assessment 0% 1-10% 0% 81 4 1-10% 1 1

Table 14: Comparison between the percentage of pigs presenting with tail damage from farmer and veterinary assessments of the same pig pens, on the same day. Vet Tail Damage Farmer tail damage assessment assessment 0% 1-10% 11-20% 0% 68 3 1 1-10% 5 9 0

139 11-20% 0 0 1

Table 15: Kappa test results to show the agreement between farmer and vet assessments of clinical signs in pigs taken on the same day, using categorised scores. Clinical sign Agreement Agreement Kappa Std. Err. Z Prob>Z Coughing 66.67% 43.76% 0.4073 0.0433 9.4 <0.001 Sneezing 52.87% 21.85% 0.397 0.0349 11.37 <0.001 Scour on pig 96.55% 91.07% 0.6139 0.0699 8.78 <0.001 Scour on floor 87.36% 72.40% 0.5419 0.0516 10.5 <0.001 Scratching 94.25% 92.10% 0.2726 0.0541 5.04 <0.001 Tail damage 83.91% 69.51% 0.4723 0.0507 9.31 <0.001

Table 16: Kappa test results to show the agreement between farmer and vet assessments of clinical signs in pigs taken on the same day, directly testing the percentage of signs against the number of pigs present. Clinical sign Agreement Agreement Kappa Std. Err. Z Prob>Z Coughing 83.91% 53.98% 0.6503 0.1009 6.44 <0.001 Sneezing 81.61% 38.76% 0.6997 0.0783 8.93 <0.001 Scour on pig 97.70% 91.23% 0.738 0.1072 6.88 <0.001 Scour on floor 93.10% 74.24% 0.7323 0.1 7.32 <0.001 Scratching 94.25% 92.22% 0.2615 0.0961 2.72 0.003 Tail damage 89.66% 71.69% 0.6346 0.0979 6.49 <0.001

140 Annex 1 Monthly clinical assessment guidance

Pigs to be assessed:

At the first visit, the houses or outdoors areas and the groups of pigs to be assessed will be selected. Once selected, the same scoring locations should always be used.

Assessment and recording of signs:

If possible, the inspection should take place at a time of day when the pig are active and moving, for example after or before feeding time, as it will increase the chance of detecting the presence of any clinical sign.

The inspectors should position themselves so as to observe as many pigs as possible. The three minute sneezing, coughing and scratching assessment can then be completed during one period. Making tally marks on the back of the recording sheet for these three categories might make this easier. After the three minutes, the three scores will be added up and transferred to the correct spaces on the recoding sheet. If the group of pigs that is observed differs from those that can be separately listened to (i.e. in a large building where it is impossible to separately listen to only a group of pigs), then please enter each score on a separate line with the respective number of pigs that was inspected for that clinical sign.

The location, date, the number of pigs observed for each age category and their estimated weight will be noted, along with an indication of the weather on the day of inspection and any other observations that may relate to these clinical signs e.g. faulty ventilation causing increase coughing.

a) Respiratory signs: Sneeze and coughing - Record the number of coughing or sneezing episodes observed in three minutes observation of the pigs allocated in the pen/ field. b) Signs of scratching - Record the number of scratching episodes observed in three minutes observation of the pigs allocated in the pen/ field. c) Signs of scour - The inspector should carefully observe all the pigs within the study group and record each pig with signs of diarrhoea on the pigs’ perineum (skin around anus and tail). Additionally counts of the watery or soft consistency scour present on the floor and wall surfaces of the pens holding the study group of pigs will be also recorded. d) Tail bite or damage - As above, the inspector should carefully observe all the pigs within the study group and count the number of pig observed to have any damage on the tail.

CHAPTER 10

141 Review of the use of digipen technology and the clinical sign inspection 14/01/2010

Opinions from veterinary assessors and farm staff participating in the pilot study

1) Digipen  The digipen had problems turning on when decapped, and to rectify this problem the pen had to be placed on charge for 4-5 minutes. This meant that the heavy charger had to be carried onto farm visits;  Sometimes the pen would buzz in the middle of usage but there was no way of assessing if it was recognising the form properly. This meant that some repetition/overwriting had to be done to ensure that information was recorded;  Accessing data recorded by digipen had problems. The internet system often crashed when the data were being downloaded. it was also found to be difficult to upload data into MS Access due to formatting problems;  The word recognition was poor, with many passages of text being incorrectly recorded, but the handwriting used on the forms was also poor and not in the format that the digipen requires, so this may not be a particular criticism of the technology.  The forms used with the digipen were regarded as overly complicated, with a great deal of wasted time due to unclear headings and formatting plus difficulty with transmission of the forms via the mobile phone (forms had to be sent multiple times and some were never received). A large amount of special paper used in the two forms was wasted as they remained unwritten on. Future studies should use forms specifically designed for the purpose of the study.  It was also strongly felt that the forms were very repetitious – i.e. quarterly forms required the same information such as names, addresses, holding numbers etc;  It was suggested that the digipen could be quite useful if the VLA designed forms around its capability i.e. tick boxes, or funded extensive training for members of staff.

2) Clinical data recording  It was stated that some of the data gathered between Vets on different farms was inconsistent, e.g. the method of recording pig sneezes where a bout of “peffing” being recorded as several sneezes rather than a single coughing episode. Standardisation between assessors was also needed on the status of the pigs (e.g. sleeping or active) when assessed, as this may cause different rates of clinical scores;  It was also mentioned that, as pigs move house and are constantly being shipped on and off farms, coming from and going to different suppliers/processors with different biosecurity measures, the data gathered by monthly assessment cannot be compared; i. It was stated that this form of data gathering would produce peaks and troughs in the results without any viable linkage. It was believed that only a weekly assessment would provide useful data.

142  It was believed that a lot of the data was difficult to record accurately e.g. the ability to see scour in poorly lit sheds or the ability to hear and count sneezes and coughs over fans and animals noises;  It was questioned if some of the required data was relevant i.e. whereas coughing is a key to respiratory health, sneezing does not appear to be;  One Vet believes that some nice patterns and information have been collected;  In general producers paid more attention to the study, or showed more interest, when they could see the graphs from the previous months.  Three producers/stockmen stated that they were very interested in receiving feedback on the study findings.  Another Vet believed that farm data need not be standardized as Vets are doing a good job. He did believe that farmer-collected information could be improved upon.  One of the main problems was being able to identify the denominator (number of pig investigated) and being able to adapt it to any type of housing system and room e.g. Assessing growers in straw yards that kept straw bales piled up in the middle of the yard made the assessment difficult, or identifying scour on floor for pigs kept outdoors.

3) Time consumption It was felt that the time required for the Vets to make their visits was about double that estimated and that the farmers, originally regarded as the most co-operative clients, were not happy about this.  It was judged to be impossible to count all the physical signs required for data entry in the three minutes allocated to it, and the visits often took more than the one hour period initially stated to do this properly;  The time to enter the information gained was judged to be about one more hour, mainly due to the repetition already mentioned in Point 1;  One vet minimised the time spent completing forms by admittedly not being very thorough and by filling in parts of the form back at the office, in his own time.  As the farms do not recognise the trial as being that important they are resentful of the time taken to visit them and, in some cases, have put the visits off;  One Vet states that he feels that information gathered in this format is no better than personalized reports and health plans already in place;  It was found that, on analysing the data, every text field had to be checked for transcription errors.  Vaccines and drugs names were not standardized. For the analyses they have had to be manually checked/ examined to obtain meaningful results.

CHAPTER 11

143 Analysis of the factors influencing the prevalence of clinical signs in pigs: Pilot study

Richard Smith & Manuel Sanchez-Vasquez

Aims To detect farm- and enclosure-level factors associated with the presence of clinical signs in pigs. The pilot was also to trial a method of assessing clinical signs on farms and the use of digipen technology to capture and record data remotely.

Method Enrolment A total of 20 English and 20 Scottish pig farms were to be enrolled into the pilot study.

To identify English farms that might be willing to help, a vet from each of three veterinary practices was enrolled into the study. The identified farms were required to contain finisher pigs, although a breeder farm was included if it supplied growers to an enrolled finisher farm. Each farmer was sent a letter (Appendix 1) to describe the study and request their participation. The farmer was to return a signed slip if they were willing to join the study.

In Scotland, potential participating producers were identified with the help from Scottish Pig Producers and Quality Meat Scotland which provided contacts details. An initial letter was sent to the producers and this was followed up by a phone call to explain the project in more detail, request collaboration and if the farmer was in agreement, to set up a date for the first visit.

Clinical assessment and data collection Each farmer was asked to complete an assessment of his pigs every month. The assessment included the recording of six clinical signs (sneezing, coughing, scratching, tail damage, scour on pigs and scour on the enclosure floor) at a number of pig enclosures or fields identified at the start of the study (Appendix 2&3 (method and recording sheet)). The identified enclosures were selected to be representative of the types of pigs and housing present on the farm, without overburdening the farmer. Each enclosure was assessed for coughing, sneezing and scratching, with tally sheet completed for the three conditions during a three minute period. The pigs, or a subset of pigs, within the enclosure were then visually assessed for scour (either on the floor or on the pig) and for tail damage. Each farmer was provided with a set of assessment scoring sheets, a stopwatch and the assessment instructions. The farmers were also asked to record the weather on the day of assessment along with details of the number, age and weight of pigs in the assessed enclosures.

The enclosures were to be assessed by the same member of the farm staff for a period of up to 12 months. During the twelve months it was assumed that the individual pigs within each enclosure would change but the aim was to analyse the effect of the enclosures and their conditions on pigs rather than following individual groups of pigs. The initial selected enclosures were not to change during the study and so at visits where an enclosure was empty, the farmer did not collect assessments from that enclosure.

At quarterly intervals a veterinary assessor (either the participating private vets in England or staff from the SAC for Scottish farms) visited the farm and recorded a separate set of assessments of the selected enclosures and collected information on the physical structure of the enclosures and any changes to the farm management since the last assessment (Appendix 4 – protocol). Data were also collected on the experience and training of the farm manager. The data were recorded onto the Pig Herd Health Plan and the Genesis Combined Checklist for pigs (Appendix 5 & 6). A Digipen (Longhand Data Ltd) was used to complete the hardcopies of the forms but also to digitally capture the data and send it to a database via a mobile phone. Photocopied sheets of the medicine book could also be collected from farms rather than completing a list of treatments used since the last veterinary visit. The vet also collected the completed monthly assessment sheets from the farmer. In England, the veterinary visit was incorporated into the farms regular quarterly visit, so as not to cause any further disruption to the farmer.

144 The data from the monthly assessment sheets and the digipen were collected and added to a MS Access 2003 database. Both veterinary and farmer assessments were joined together to provide a complete record of assessments for each enclosure, although if a veterinary and farmer assessment were completed on the same day, then only the farmer’s assessment was added to the dataset for analysis. Each assessment was linked to the information collected at the nearest quarterly visit (whether before or after). Data on treatment and vaccination from the Pig Herd Health Plan were summarised for analysis. The treatments and vaccinations were first grouped as binary variables for whether any had been used in the treatment of a certain condition (e.g. Porcine Circovirus 2 (PCV2)) for each category of pigs (sows, gilts, finishers, growers or weaners) in the period covered by that visit. The treatments were also summarised as the number of routine treatments applied to each category of pig. Data collected on the use of non-routine treatment and incidence of health conditions were not analysed as the data quality did not allow for these records to be linked to accurate time points or to specific groups of pigs from which health information would be available.

The clinical signs assessments were recorded as the number of positive scores from that group of assessed animals (number of scores/ number of pigs assessed). If a score was over 100% of the group population (e.g. 40 coughs by 30 pigs) then the score was coded as the group population number to allow investigation by binomial analyses.

Data analysis The distributions of answers to each question were tabulated and the continuous variables were graphically presented. For those variables with more than 25% of the study population with missing data these were analysed and describe at the univariable level but were not analysed at the multivariable model, so as not to greatly reduce the sample size in the final models.

Each of the clinical signs were individually analysed by binominal regression in STATA 10 (Stata corp., college station, Tx) to model associations between the clinical sign and exposure factors. As it was assumed that the clinical sign data would contain a large number of zero values, a zero-inflated negative binomial model was used if a vuong test was found to be significant, indicating the suitability of using the zero-inflated model. The farm and enclosure IDs were combined to create a single variable which was used in the models to adjust the standard errors to account for the clustering of sample results from the same group.

All factors yielding a univariable p-value of over 0.25 were excluded from further analysis. Once the factors to be included in the model had been selected, a forwards stepwise selection was conducted, adding the most significant factor that improved the fit of the model (determined by p-value and Akaike Information Criterion) at each step, until only those factors with a p-value over 0.05 (Z-test) were left. Records with missing data for the selected variables were dropped from the model.

Results Enrolment In England, 57 farms were contacted and of these:- 20 agreed to participate in the study; 34 did not want to join the study; and 3 did not respond to the letter or to follow-up phone calls. A further four farms dropped out of the study before any assessments were completed. Two dropped out as a result of their vet being too busy to complete data collection at quarterly visits, one farmer suffered a staff shortage and the other dropped out but did not state any reason.

Visits and assessments were completed to the remaining 16 farms, however one farm only received one veterinary visit due to the vet being too busy to continue with the study. Not all farms completed 12 months of assessments by the end of the study (10th November 2009) as delays to visits were caused by a change of veterinary staff member delaying the start of the study to eight farms. Other delays were caused by harvest and farmers becoming ill.

In Scotland, out of 23 farms contacted, 6 did not agree to participate in the study. One farmer gave a negative answer without a reason whereas three referred to problems of short staffing and the other two explained that they did not have enough time to be involved in the study. The remaining 17 farms started

145 the study, but one farm dropped out before the first visit as the manager changed and did not want to participate. A further two agreed to participate and started the study but at later quarterly visits these were found to be failing to carry out the clinical signs assessments. Additional visits were carried out by the SAC assessors to try engaging them again in the study, but only in one of the two problematic farms was there success and this was by delegating the responsibility of recording the data to another member of staff on the farm.

Descriptive analysis The dataset held 1,923 group assessments (321 of these were veterinary assessments) from the 32 participating farms, with a mean of 8 assessments per farm (median 9, range 1-15) (Table 1). The assessments covered 418 pig enclosures with a mean of 13 assessed on each farm (median 12, range 2-46).

Table 1: Breakdown of the number of clinical sign assessments and study periods for the 32 participating farms. No. Farm No. Start date Finish date assessments 1 16-Apr-09 09-Sep-09 10 2 27-Mar-09 28-Aug-09 8 3 06-May-09 16-Sep-09 7 4 27-Oct-08 17-Jul-09 12 5 16-Oct-08 20-Aug-09 13 6 20-Mar-09 28-Sep-09 9 7 22-May-09 18-Aug-09 3 8 20-Mar-09 11-Sep-09 9 9 06-May-09 09-Oct-09 6 10 24-Apr-09 03-Oct-09 8 11 01-May-09 31-Aug-09 7 12 28-Apr-09 22-Oct-09 9 13 13-Oct-08 05-Aug-09 11 14 30-Sep-08 21-Aug-09 13 15 28-Jan-09 06-Aug-09 7 16 04-Sep-08 04-Aug-09 15 17 11-Mar-09 11-Mar-09 1 18 04-Dec-08 01-Jul-09 9 19 04-Dec-08 11-Mar-09 5 20 12-Nov-08 22-Oct-09 12 21 09-Jul-08 09-Mar-09 9 22 27-Jun-08 13-May-09 11 23 14-Jan-09 03-Mar-09 3 24 08-Jan-09 06-Mar-09 6 25 18-Nov-08 18-Nov-08 1 26 23-Jun-08 30-May-09 11 27 14-Jan-09 03-Mar-09 3 28 08-Jan-09 06-Mar-09 6 29 20-Jun-08 24-Jun-09 13 30 01-Jul-08 01-Jul-09 11 31 02-Dec-08 26-Jun-09 4 32 14-Jan-09 18-Nov-09 11

146 Table 2 and 3 present the descriptive analysis of all the categorical and continuous variables. The univariable modelling results and which variables were significantly associated with the individual clinical signs at the 0.05 and 0.25 p-values are presented in appendix 7. Tables 4-6 further describe the relationships between selected variables and the clinical signs.

Table 2: Summary statistics for categorical variables from the 32 participating farms (n=1923). No. Variable Level assessments Assessment by Vet No 1602 Yes 321 Dry weather Missing 415 No 807 Yes 701 Hot weather Missing 415 No 967 Yes 541 Dusty weather Missing 415 No 1340 Yes 168 Cold weather Missing 415 No 1363 Yes 145 Wet weather Missing 415 No 1128 Yes 380 Windy weather Missing 415 No 1349 Yes 159 Age (weeks) 12-16 407 16-24 421 4-8 329 8-12 342 Adult breeding 104 Less than 4 320 Manager Type Missing 946 Assistant Manager 59 contracted manager 400 Farm Manager 253 Owner/manager 89 Partner 120 Tenant 56 Formal training of manager Missing 496 No 463 Yes 964 County East Yorkshire 372 Humberside 59 Lincolnshire 85 North Humberside 9 North Yorkshire 158

147 Oxfordshire 76 Scotland 870 Shropshire 56 Staffordshire 147 West Midlands 80 West Yorkshire 11 Other farming species present Missing 106 No 1761 Yes 56 Pig type Dry 103 Farrowing 327 Finisher 427 Grower 623 Other (farrowing, maiden and service) 54 Weaner 389 Outdoors No 1909 Yes 14 Piglet protection in farrowing pen No 85 Yes 819 N/A 976 Flooring Missing 15 Full slat 598 Full and part slat 69 Solid 669 Part slat 529 Bedding None 1117 Sawdust 17 Straw 714 Woodshavings 75 Ventilation Missing 9 Auto 540 Forced 521 Natural 853 Pen score Missing 875 0: ventilation and pen in poor condition 1 1: ventilation in reasonable condition and pen in poor condition or ventilation is in poor condition and pen is in good condition 59 2: ventilation in reasonable condition and pen in good condition 207 3: ventilation in excellent condition and pen in poor condition 286 4: ventilation in excellent condition and pen in good condition 495 Feeding Meal 569 Meal & Pellets 62 Pellets 1263 Wet 29 Feed Rate Appetite 1702

148 Restricted 221 Pig score Missing 881 1: Poor condition 7 2 0 3 25 4 364 5: Good condition 646 Production Type Breeding; Nursery/Grower; Finishing; Indoors 1319 Breeding; Nursery/Grower; Indoors 236 Finishing; Indoors 261 Nursery/Grower; Finishing; Indoors 107 Isolation/ quarantine Missing 1737 Off farm 85 On farm 101 No. weaner treatment drugs* 0 814 1 542 2 374 3 115 4 78 No. grower treatment drugs* 0 961 1 707 2 141 4 114 No. finisher treatment drugs* 0 1732 1 191 No. sow treatment drugs* 0 1604 1 243 2 76 Sows Eryparvo vaccine No 539 Yes 1384 Sows PRRS vaccine No 1381 Yes 542 Sows EP vaccine No 1731 Yes 192 Sows Glassers vaccine No 1723 Yes 200 Sows PMWS vaccine No 1289 Yes 634 Gilts Eryparvo vaccine No 1390 Yes 533 Gilts PRRS vaccine No 1757 Yes 166 Gilts PMWS vaccine No 1624 Yes 299 Gilts EP vaccine No 1796 Yes 127 Gilts other vaccine None 1663 Clostridia 53

149 E. coli 161 Parasuis 46 De-worming None 1252 Sows 543 Sows & growers 128 Weaner PMWS vaccine No 839 Yes 1084 Weaner EP vaccine No 1389 Yes 534 Weaner PRRS vaccine No 1737 Yes 186 Weaner Glassers vaccine No 1731 Yes 192 *Indicates the total number of different routine treatment drugs recorded as give to that type of pig.

Table 3: Summary statistics for continuous variables from the 32 participating farms (n=1923). No. Variable Min. Mean Max. assessments Pig farming experience (years) 15 24.6 53 1514 Farming experience (years) 20 31.0 53 863 Pig weight (kg) 1 48.8 260 932 Min. Farrowing crate length (m) 1.3 2.2 3 179 No. of pigs in enclosure 5 294.4 1680 812 Pen area (m2) 2.8 164.0 840 849 area per pig (m2) 0.14 1.3 28 798 Maiden Gilts 0 21.6 132 1923 In Pig Gilts 0 38.8 413 1923 Productive Sows 0 271.6 702 1923 Breeding Boars 0 3.8 12 1923 Progeny <30 Kg 0 1436.3 4000 1923 Progeny >30 Kg 0 2015.6 5273 1923 Farrowing Rate 65 85.1 92.5 1108 Born Alive 10.3 12.0 13.4 1183 Weaned 9.1 10.7 11.8 1108 Post-Weaning Morality 0.5 5.0 13 1142 Pre-Weaning Morality 5.1 10.5 17.7 1108 Daily Live-Weight Gain 382 628.0 1000 655 Feed Conversion Rate 1.9 2.3 3.21 456 Average Deadweight 72.1 84.7 115 639 Backfat 8.9 10.0 11.5 229 Quarantine Distance From Main Herd (m) 150 102.9 200 359 Duration Of Quarantine (d) 120 64.1 42 245 % of positive ZAP samples 0 16 100 1923

Table 4: Prevalence of clinical signs in relation to the type of flooring used. Solid and Clinical Sign Full Slats Part Slats bedding Cough 2.1% 1.4% 2.7%

150 Sneeze 5.5% 2.4% 3.4% Scour on pigs 0.1% 0.3% 0.1% Scour on floor 0.5% 1.1% 0.3% Scratching 0.2% 0.4% 0.4% Tail damage 0.4% 0.1% 0.1% *removed groups listed as using both full and part slats

Table 5: Prevalence of clinical signs by the age group of the pigs. <4 4-8 9-12 13-16 17-24 Adult Clinical Sign weeks weeks weeks weeks weeks breeding Cough 1.1% 2.1% 2.4% 2.2% 2.9% 1.0% Sneeze 2.7% 8.6% 4.4% 2.2% 2.2% 0.7% Scour on pigs 0.7% 0.1% 0.0% 0.1% 0.1% 0.0% Scour on floor 3.2% 0.3% 0.1% 0.3% 0.1% 0.0% Scratching 0.7% 0.2% 0.3% 0.2% 0.1% 1.2% Tail damage 0.0% 0.1% 0.2% 0.4% 0.4% 0.0%

Table 6: To show the effect of weather conditions on the prevalence of coughing and sneezing in pigs. Weather Coughing Sneezing Dry No 1.6% 3.9% Yes 2.0% 4.1% Hot No 1.9% 4.1% Yes 1.7% 3.8% Dusty No 1.8% 4.0% Yes 2.0% 3.7% Cold No 1.8% 4.2% Yes 1.7% 3.0% Wet No 1.9% 4.0% Yes 1.4% 4.2% Windy No 1.8% 3.8% Yes 1.9% 5.7%

Multivariable models of the six clinical signs were completed and the final models are presented in tables 7-10. No multivariable models were completed for scour on pig and tail damage as the small number of positive assessments meant that many variable levels did not contain any pigs with the clinical sign, and many of the associated variables related only to a single farm, which caused instability in the model.

Table 7: Multivariable zero-inflated binomial model to show the associations between explanatory variables and coughing in pigs (1,723 group assessments from 412 unique groups). Variable Level Odds ratio P-value Region1 North Humberside 0.20 0.006 North Yorkshire 0.34 <0.001 Scotland 0.33 <0.001

151 Shropshire 0.33 <0.001 Staffordshire 0.35 <0.001 West Midlands 0.02 <0.001 West Yorkshire 0.46 0.001 Pig type2 Finisher 4.01 <0.001 Grower 4.46 <0.001 Weaner 2.94 <0.001 Pigs kept outdoors No 19.71 <0.001 Weaner EP* vaccine Yes 1.79 <0.001 Sow EP* vaccine Yes 0.33 <0.001 Assessment by Vet Yes 0.68 0.008 Weaner treatment drugs3 1 1.28 0.045 1Baseline is ‘other region’ (including East Yorkshire, Humberside, Lincolnshire and Oxfordshire).2 Baseline is ‘other type’ (including dry, farrowing, maiden and service). 3 Baseline is 0, 2, 3 or 4 treatments used. *EP = Enzoonotic Pneumonia.

Table 8: Multivariable zero-inflated binomial model to show the associations between explanatory variables and sneezing in pigs (1,714 group assessments from 412 unique groups). Variable Level Odds ratio P-value Feed rate1 Restricted 0.35 0.001 Progeny <30kg No. of pigs 1.0004 <0.001 Bedding2 Sawdust 0.14 <0.001 Woodshavings 0.17 <0.001 Finisher treatment Yes 3.74 <0.001 Ventilation3 Natural 0.52 <0.001 Grower treatment drugs4 4 0.34 <0.001 Pigs kept outdoors Yes 6.05 <0.001 Farm type Specialist finisher 0.61 0.007 Age group5 Adult breeding 0.30 0.005 Assessment by Vet Yes 1.46 0.004 1 Baseline is ‘to appetite’ .2 Baseline is none and straw. 3 Baseline is automatic and forced. 4 Baseline is 0-3 treatments used. 5 Baseline is all other age groups.

Table 9: Multivariable zero-inflated binomial model to show the associations between explanatory variables and the prevalence of scour on the floor of enclosures (1,367 group assessments from 337 unique groups). Variable Level Odds ratio P-value Hot weather Yes 2.72 <0.001 Bedding1 Sawdust 3.32 0.003 Straw 0.42 0.004 Gilts PRRS* vaccine Yes 0.23 0.012 Weaner treatment drugs2 2 2.85 0.020 1 Baseline is none and woodshavings.2Baseline is 0,1,3 and 4. *PRRS=Porcine Respiratory and Reproductive Syndrome.

Table 10: Multivariable binomial model to show the associations between explanatory variables and scratching in pigs (1,146 group assessments from 286 unique groups). Variable Level Odds ratio P-value Pig farm experience No. years 1.05 <0.001

152 Feed type1 Pellets 2.90 0.010 Gilts EP* vaccine Yes 6.27 <0.001 Weaner PRRS# vaccine Yes 4.17 <0.001 Hot weather Yes 0.61 0.010 Ventilation2 Forced 1.57 0.038 1Baseline is meal and wet.2 Baseline is automatic and natural. *EP = Enzoonotic Pneumonia. #PRRS=Porcine Respiratory and Reproductive Syndrome.

Discussion Although the completion of the pilot study was hampered by delays to visits and the dropping out of a number of farmers, still a large number of assessments were completed on the 32 participating farms. The major difficulty, both in the recruitment process and in the follow up, lay in convincing producers to complete the monthly assessments by themselves over a twelve month period. It was noted that on those farms where the stockperson was in charge of the recording rather that the owner, the study progressed more efficiently.

The univariable and multivariable analysis of risk factors of the six clinical signs produced some interesting results. However, a number of variables could not be used for analysis due to a large number of missing values restricting their use in the multivariable. This may have meant that important factors associated with the conditions were not accounted for in the models. An assessment of the missing values showed that variables such as performance indicators contained many missing values as the producers did not record particular performance indicators, whereas variables such as the use of quarantine were not collected (despite being on the data entry forms) as the assessors had not realised that this was required. Variables on the use of non-routine treatments and the incidence of new health conditions on the farm were not used in the analysis as the data was of poor quality and could not be linked to a defined pig group or a defined time. It is believed that the construction of a purpose-built questionnaire would have alleviated many of these issues.

The analysis of coughing showed that whether pigs were kept outdoors was a large protective factor. Only 14 assessments, from a single farm, were completed to groups that were kept outdoors and these groups had an average of 0.2% of pigs coughing compared with an average of 3.4% in other groups. This result concurs with the hypothesis that coughing and pneumonia conditions are more prevalent in enclosures with poor ventilation, high dust levels and high stocking density (pig333.com, thepigsite.com (EP)). The identification of counties as a risk factor may indicate that certain areas are more prone to increased levels of coughing due to the difference in weather conditions or regional farm management differences.

The use of treatment for Enzoonotic Pneumonia (EP) had mixed results depending on which pig group type was treated. This difference may come from whether the treatments were reactive or proactive i.e. the use of EP treatment for weaners was a risk factor because the treatment had been used to treat an increased amount of coughing in those pigs, whereas the treatment of sows may have been precautionary. The use of only one treatment for weaners was found to be a risk factor, in comparison to groups that used either none or 2-4 treatments. The reason for this could be that the use of a treatment indicates the presence of a health problem, whereas those pigs that used more than one treatment were adequately controlling any coughing that was present.

The assessments by vets rather than farm staff, were associated with a lower prevalence of coughing. This finding may indicate a specific combination of other factors at the small number of visits completed by vets, as an assessment of all the vet and farm staff assessments of coughing found an agreement between the scores from both assessors for each farm (chapter 4a). For example, the prevalence of coughing was higher on assessments days when the weather was dry, and 59% of vet assessments had dry weather, in comparison with only 32% of farm staff assessments.

The pig group type was also associated with coughing, with growers and finishers having a relatively high prevalence of coughing, weaners having a medium prevalence, and farrowing and breeding stock having a

153 low prevalence. This factor may have been a proxy for the types of flooring and bedding used and stocking density (area per pig), with finishers and growers more likely to be on straw bedding and with a smaller area per pig than the other pig types. Stocking density, flooring and bedding all showed associations with coughing at the univariable level. However, due to the missing values in stocking density, this variable could not contribute towards the final multivariable model, and flooring and bedding were not found to be significant once other variables were selected in the model.

Pigs that were kept in enclosures with natural ventilation were found to have a lower prevalence of sneezing than when other types of ventilation were used. This is contradictory for reports that poor ventilation is a cause for conditions such as atrophic rhinitis, that causes sneezing (thepigsite.com), but this may because some of these non-natural systems were not effective. Pigs kept outdoors were associated with increased prevalence but as explained above, these results come from a small number of assessments and may be a proxy for some variable specific to the single farm affected. As with coughing, veterinary visits were associated with the condition, although in this case, with increased sneezing. This differences could be explained that veterinary staff assessed the pigs at difference times of the day than farm staff and so the pigs were at different levels of activity.

A number of factors relating to pig type were associated with sneezing, with a higher number of pigs less than 30kg in weight increasing sneezing whereas pigs from specialist finisher farms and adult breeding stock were likely to have less sneezing. This information, along with table 5, may indicate that weaners and growers on farms with a breeding unit are more susceptible to sneezing. This may also account for pigs on a restricted diet, which were breeding stock and some farrowing pigs, having a lower prevalence of sneezing than pigs that were fed to appetite. It is unclear why the use of sawdust and woodshavings as bedding was associated with less sneezing.

The use of a finisher treatment, which included treatments for Glassers and parasites, was associated with increased sneezing, potentially as this may indicate a farm with a low health status. The use of 4 grower treatments (Potencil, Baycox, Aurofac and zinc) was associated with less sneezing than the use of less than 4 treatments, but this result had come from only a single farm and may have related to a unreported factor specific to that farm.

The risk factor analysis of the prevalence of scour on the floor of enclosures showed that only four variables entered the final model. Hot weather and the use of sawdust as bedding were associated with a higher prevalence of scour, which may highlight that increases in temperature can cause increased stress and scouring (ref), and that scour may be easier to detect on sawdust bedding than when other types of bedding or slatted flooring are used. The use of a vaccine for PRRS, administered to gilts, was found to be protective, and although the result of the vaccination may not have a direct effect on scouring, the vaccination may indicate a farm with an improved management and treatment regime.

Finally, the use of two weaner treatments was associated with a higher prevalence. Five farms used two weaner treatments, which included an antibiotic (amoxycillin, Trimediazone or baycox) and a mineral supplement (iron or zinc). As mentioned under coughing, those farms that used fewer treatments may have had a herd with good health and less scouring, whereas those that use more treatments were more adequately treating any scouring.

There are many causes of scour in pigs and so the variables selected as associated with scour prevalence may have been those that are more generally aligned with the causes of scour. At the univariable level, the prevalence of scour on floor was associated with the percentage of positive ZAP samples, indicating a relationship with Salmonella infection.

No multivariable model was completed for the presence of scour on the pigs which may have been because very few assessments detected this clinical sign and 13 of the 32 farms were completely free of scour on pigs. A relatively high prevalence (>3.5%) of scour on pigs was detected on a number of visits to 5 enclosures from two farms. These enclosures and farms were not specifically different from others and so cause of scour on pigs may be explained by some factor that was not captured by this study.

154 Scratching behaviour can be caused by conditions such as sarcoptic mange, and it was interesting to note that an association was found with farmers with more experience of pig farms, which could indicate that older farmers are less concerned with the control and treatment of these skin conditions. Hot weather on the day of assessment was associated with less scratching, which is contrary to reports (http://www.pigprogress.net/health-diseases/m/mange-51.html), however periods of hot and dry weather may also detrimentally effect the survival of mites outside of the pig and so may reduce the chance of new infections. Four other factors were associated with increased scratching prevalence, feeding pelleted feed, the presence of forced ventilation, and EP vaccination of gilts and PRRS vaccination of weaners. The reasons behind these associations are less known and these factors may be proxies for farms with an increased level of pig monitoring and hygiene or they may indicate buildings that easier to keep clear of mites.

Although no multivariable model was completed for the tail damage condition, a number of factors were significant at the univariable level. A number of performance indicators were associated with the condition, and the use of bedding and a reduced number of pigs in an enclosure were linked to a reduction in tail damage, as has been shown by another study (Moinard 2003). Some performance indicators (such as back fat thickness) were associated with an increased prevalence of tail damage, however this may be due to the age and weight of the pigs as higher prevalences were found in pigs aged 16-24 weeks and not in progeny under 30kgs and productive sows. A number of other factors on treatment, pen condition, feed, region and weather were also associated with tail damage, however problems associated with this dataset precluded multivariable analysis and so the true estimates of the effect of variables could not be calculated.

Study limitations Although the dataset from this longitudinal study had a large number of data points, the pilot study only covered 32 farms and any extrapolation of the results to the wider pig farm population should be taken with great care. Also, due to the use of a large dataset of explanatory variables and assessments, some variables may have entered the models by random chance rather than a true association with the clinical signs.

The relatively small presence of the clinical signs meant that multivariable modelling was problematic and overdispersion had to be accounted for. A zero-inflated model was used for most of the models, however, this still meant that certain levels of variables contained zero values when cross-tabulated, which also causes problems in modelling. A larger dataset may remove these problems.

The clinical signs assessments appear to have been completed well and a significant correlation was shown between paired veterinary and farmer assessments (Chapter 4a) indicating that the assessments were valid and there was no significant difference between using farmers or vets to collect this information. However, vet visits were significant for some of the models, although this was a subset of all the veterinary assessments. There were also some concerns around the consistency of the recordings of the assessors e.g. Did assessors count a coughing bout by a single pig as a single or multiple cough? In the main, farm staff only assessed one farm and so this cannot be analysed as any difference could be related to the differences between the farms rather than a difference between assessors.

The quality of the data for some of the questions was insufficient for analysis, with noted illnesses and non- routine treatments being unable to be linked to specific periods and groups of pigs. These issues may have arisen from using two forms that were not designed with this analysis in mind and so the wording of the questions and the formatting caused problems for the assessors to record data in the correct way. Some more detailed information for some of the questions would have been preferable but the study was limited by the forms that could be used along with the digipen technology and the study team were keen not to overburden the farmers and veterinary staff with supplementary forms.

Conclusion This pilot has shown that with improvements to the collection of data and including a statistically appropriate study population, a full investigation could be made into the factors that influence the prevalence of these six clinical signs in pig groups. A larger follow up study could further investigate the variables identified in the models for the 32 farms included in this study.

155 Acknowledgments This pilot study was funded by Defra under project OD0215. The project team would like to thank David Strachan from Boehringer Ingelheim Vetmedic for use of the clinical signs methodology; Martin Barker, Roger Young, Janette Parker and Michael Hemmings from Genesis QA and Longhand Data Ltd for use of the digipen technology and Genesis forms; and Jamie Roberson from Livestock Management Services Ltd and Lorna Paton from Innovent Technology Ltd for the use of the Pig Herd Health Plan forms and database. The team would also like to thank the veterinary and farm staff who participated in the study and the staff at the VLA and SAC who assisted with the running and data handling.

References Moinard, C., Mendl, M., Nicol., C.J., Green, L.E. (2003) A case study of on-farm risk factors for tail biting in pigs. Applied Animal Behaviour Science 81: 333-335.

156 Appendix 1: Participation letter to farms

Dear Sir/ Madam;

Assessing and using herd health information I am writing to request your participation in a study of pig farms in England and Scotland, which is part of a Defra funded project with the participation of the British Pig Executive (BPEX) and Wholesome Pigs Scotland (WPS).

Although there are a number of schemes to monitor pig health in Great Britain, there is a lack of a standardised system to routinely record clinical and on-farm health indicators on pig farms. This study will identify the best way of monitoring and recording the presence of clinical disease in the farm. It will also show ways in which health information can be combined with herd performance indicators and routinely collected information from farm assurance and health schemes (e.g. British Pig Health Schemes, Wholesome Pig Scotland and the Zoonoses Action Plan) to create useful outputs that would be of great value for producers and their vets:- To determinate the onset of clinical signs and estimate prevalences for major disease challenges; To identify how certain changes in management practise can impact on clinical disease and how the presence of one disease can affect the presence of other diseases; To take actions based on the clinical detection; making adjustments to on-farm health strategies, such as vaccination and treatments.

The study would involve a member of your farm staff spending around one hour every month to evaluate clinical signs in pigs, using our simple instructions, for a period of 12 months. Your Quality Assurance Scheme vet will collect some extra details on the farm health status, and note whether any changes have been made to the farm management, during his routine quarterly visits. The collection of this information should only take half an hour. All data collected by the study will be kept in confidence and data protection is ensured.

Each participating farm will receive £100 at the end of the study as an inconvenience payment and each farm will also be enrolled, free of cost, into the Pig Health Herd Plan scheme; an on- line system for recording and monitoring pig farm health and production information. The system allows farmers and their vets to have herd health information and analysis at their fingertips. At the end of the project, we will also release to each farmer our findings for their individual farm and for the study as a whole.

We would appreciate it if you could please complete the slip attached to indicate whether you would like to participate or not and return it to me using the enclosed pre-paid envelope. If you have any queries please do not hesitate to contact me (01932 359465).

Yours sincerely

Richard Smith

157

I, (print name)…..…………………………….………am/ am not (delete as applicable) willing to participate in the study.

14.2 Signature …………………………………………………………………………….. Farm name:

14.2.1

Thank you for your help

158 Appendix 2: Monthly clinical assessment guidance

Pigs to be assessed:

At the first visit, the houses or outdoors areas and the groups of pigs to be assessed will be selected. Once selected, the same scoring locations should always be used.

Assessment and recording of signs:

If possible, the inspection should take place at a time of day when the pig are active and moving, for example after or before feeding time, as it will increase the chance of detecting the presence of any clinical sign.

The inspectors should position themselves so as to observe as many pigs as possible. The three minute sneezing, coughing and scratching assessment can then be completed during one period. Making tally marks on the back of the recording sheet for these three categories might make this easier. After the three minutes, the three scores will be added up and transferred to the correct spaces on the recoding sheet. If the group of pigs that is observed differs from those that can be separately listened to (i.e. in a large building where it is impossible to separately listen to only a group of pigs), then please enter each score on a separate line with the respective number of pigs that was inspected for that clinical sign.

The location, date, the number of pigs observed for each age category and their estimated weight will be noted, along with an indication of the weather on the day of inspection and any other observations that may relate to these clinical signs e.g. faulty ventilation causing increase coughing.

e) Respiratory signs: Sneeze and coughing - Record the number of coughing or sneezing episodes observed in three minutes observation of the pigs allocated in the pen/ field. f) Signs of scratching - Record the number of scratching episodes observed in three minutes observation of the pigs allocated in the pen/ field. g) Signs of scour - The inspector should carefully observe all the pigs within the study group and record each pig with signs of diarrhoea on the pigs’ perineum (skin around anus and tail). Additionally counts of the watery or soft consistency scour present on the floor and wall surfaces of the pens holding the study group of pigs will be also recorded. h) Tail bite or damage - As above, the inspector should carefully observe all the pigs within the study group and count the number of pig observed to have any damage on the tail.

Richard Smith Centre of Epidemiology and Risk Analysis VLA - Weybridge Tel: 01932 359465

Appendix 3: Clinical signs recording sheet PIGS SHEET FARM:

UNIT: HOUSE: DATE:

Approx. Scour Weight No of Scour on Sign of Tail Pen Age (weeks) (kg) pigs Cough Sneeze on pig floor Scratching damage less than 4 less than 4 less than 4 less than 4 less than 4 less than 4 less than 4 Totals 4 to 8 4 to 8 4 to 8 4 to 8 4 to 8 4 to 8 Totals 8 to 12 8 to 12 8 to 12 8 to 12 8 to 12 8 to 12 Totals 12 to 16 12 to 16 12 to 16 12 to 16 12 to 16 12 to 16 Totals 16 to 24 16 to 24 16 to 24 16 to 24 16 to 24 16 to 24 Totals

RECORDED BY :

WEATHER ON DAY OF INSPECTION: (TICK ALL THAT APPLY) DRY HOT DUSTY WET

OTHER OBSERVATIONS:

Appendix 4: Pilot Study: Standardising Protocol

1 Objective of the pilot study.

The first goal of this pilot study is to trial a system for monitoring and recording the presence of clinical phases of diseases on the farm, which should be simple enough to be assimilated by the producers. The second goal is to show the farmer how he can use information on performance indicators, on-farm health, and farm management to improve decision making and to show how money could be saved. The final goal is to show that by collecting this information in a standardised way, the data can be used to perform exploratory epidemiological analyses.

2 Standardising protocol.

Up to 20 finisher or finisher-breeder farms will be enrolled into the study by the VLA. A further 20 are being enrolled and followed up independently in Scotland by the Scottish Agricultural College.

2.1 Main tasks.

The participating farms will each be followed up for 12 months. Each will be visited on a quarterly basis, which can be coincided with the quarterly visits arranged for quality assurance, to minimize the disruption on the farm. Each participating farmer will be given free access to the Pig Herd Health Plan (PHHP) website and will be sent a CD explaining how the PHHP system works. The following steps explain what needs to be completed:-

i. Each farmer will be contacted to agree a date for the first visit and to make sure that the farmer has designated a staff member to be responsible for carrying out the inspections, who will be present at the visit. ii. At the first visit; a. Explain to the designated staff member the background and aims of the project. b. Agree the number of houses included in the study for monthly inspection. c. Explain the case definitions of the different clinical signs and how to record this onto the inspection recording sheet. Four recoding sheets will be left with the staff member, one for each month in the quarter and one spare. Complete the first recoding sheet with the staff member to show in situ the way of identifying and recording the clinical signs (see below guidance for clinical assessment). d. Fill in the appropriate sections of the PHHP questionnaire and Genesis combined checklist for pigs 2008, as indicated by the aide memoir. e. Ask to copy from the farmer’s medicine book all treatments used on the pigs in the last three months. If vaccines are not recorded in the medicine book, please ask them to add these to the medicine books for subsequent visits. iii. At the three follow up visits; a. Speak to the staff member to identify any problem with the clinical signs identification and recording. b. Complete an inspection recording sheet – these quarterly assessments are separate to the monthly inspections by the staff, and these will be used to validate their inspections. c. Collect the completed inspection recording sheets and post these to Richard Smith. Provide the staff member with a new set of 4 recording sheets.

d. Complete the PHHP and Genesis forms to record any changes since the last visit. It will be useful to visit the pigs first, to spot a health condition etc that the farmer is unaware of and may not have identified. e. Copy the medicine book to record treatments used over the last three months.

2.2 Clinical assessment guidance.

2.2.1 Number of houses and number of pigs to be assessed: Indoors production.

A minimum of one house per pig category (finishing; sows and farrowing) is required in the study; however it is preferable to include all the houses present on the farm but this will depend on the willingness of the producer. Once selected, the same scoring locations should always be used.

The number of finisher pigs and pens inspected will vary depending on the house size and design. As a general rule, 4 pens of 25 finisher pigs or at least 100 pigs from different pens in the house should be targeted.

The above calculation on the number of pens and pigs inspected could be adjusted for sows and farrowing units. The inspection should cover as many pigs as possible.

2.2.2 Number of houses and number of pigs to be assessed: Outdoors production.

The above calculation on the number of pens and pigs inspected could be adjusted for outdoors production as the standardized clinical inspection of a group of 25 or more pigs will present difficulties in an extended area. The inspector will visually recruit a group of pigs that can be comfortably observed for three minutes.

2.2.3 Frequency on the inspection.

A minimum compliance of one inspection recording per pig category per month is desirable.

2.2.4 Assessment and recording of signs.

If possible, the inspection should take place at a time of day when the pig are active and moving, for example after or before feeding time; as it will bring up more likely the presence of any clinical sign.

The inspectors should position themselves so as to observe as many pigs as possible. The three minute sneezing, coughing and scratching assessment can then be completed during one period. Making tally marks in a notebook for these three categories will make this easier. After the three minutes, they will be added up and transferred to the recoding sheets. The location, date, the number of pigs observed and their estimated weight will be noted, along with an indication of the weather on the day of inspection and any other observations that may relate to these clinical signs e.g. faulty ventilation causing increase coughing.

i) Respiratory signs: Sneeze and coughing - Record the number of coughing or sneezing episodes observed in three minutes observation of the pigs allocated in the pen/ field. j) Signs of scour - The inspector should carefully observe all the pigs within the study group and record each pig with signs of diarrhoea on the pigs’ perineum (skin around anus and tail). Additionally counts of the watery or soft consistency scour present on the floor and wall surfaces of the pens holding the study group of pigs will be also recorded.

k) Signs of scratching - Record the number of scratching episodes observed in three minutes observation of the pigs allocated in the pen/ field. l) Tail bite or damage - As above, the inspector should carefully observe all the pigs within the study group and count the number of pig observed to have any damage on the tail.

3. Equipment

Stopwatch, inspection scoring sheets, Genesis and PHHP questionnaires, digipen & mobile phone; notebook, pen or pencil

Appendix 7: Univariable model results for the association between explanatory variables and each of the six clinical signs

Variable Name Coughing Sneezing Scour on pig Scour on floor Scratching Tail Damage p- p- p- p- p- p- n OR value n OR value n OR value n OR value n OR value n OR value Assessment by Vet No Yes 1723 0.75 0.018 1723 1.41 0.155 1778 0.35 0.026 1777 0.49 0.035 1773 2.02 0.008 1774 0.26 0.009 Dry weather No Yes 1310 1.46 0.006 1310 1.23 0.340 1368 0.86 0.662 1367 0.46 0.051 1363 2.07 0.003 1365 0.42 0.045 Hot weather No Yes 1310 0.95 0.678 1310 0.94 0.704 1368 1.29 0.451 1367 2.98 <0.001 1363 15.15 0.005 1365 2.30 0.006 Dusty weather No Yes 1310 1.18 0.335 1310 0.96 0.874 1368 0.93 0.901 1367 0.71 0.291 1363 0.88 0.654 1365 0.68 0.270 Cold weather No Yes 1310 1.40 0.050 1310 1.10 0.727 1368 2.41 0.095 1367 0.76 0.552 1363 1.29 0.398 1365 0.35 0.015 Wet weather No Yes 1310 0.62 0.001 1310 0.93 0.746 1368 0.89 0.714 1367 0.52 0.045 1363 0.94 0.816 1365 0.75 0.227 Windy weather No Yes 1310 1.12 0.514 1310 1.54 0.105 1368 0.37 0.044 1367 0.34 0.029 1363 1.31 0.285 1365 0.61 0.353 Age (weeks) 12-16 16-24 1723 1.21 0.393 1723 0.90 0.605 1778 0.67 0.460 1777 0.41 0.133 1773 0.41 0.068 1774 1.05 0.938 4-8 0.89 0.608 3.56 <0.001 0.51 0.158 0.85 0.772 1.02 0.969 0.22 0.027 8-12 0.90 0.598 1.56 0.049 0.30 0.016 0.23 0.009 0.93 0.869 0.43 0.274 Adult breeding 0.23 <0.001 0.16 <0.001 0.00 <0.001 0.00 <0.001 1.74 0.398 0.04 0.001

Less 4 0.38 0.015 0.94 0.803 3.28 0.041 5.57 0.002 1.63 0.350 0.02 <0.001 Manager type Assistant Manager 44560 contracted manager 895 0.76 0.261 895 2.19 0.001 895 35.38 <0.001 895 0.94 0.970 891 1.06 0.847 892 0.03 0.014 68509 Farm Manager 0.05 <0.001 0.17 <0.001 64.48 <0.001 0.56 0.677 0.40 0.023 0.08 0.065 44329 Owner/manager 0.58 0.025 2.33 0.020 68.46 <0.001 1.45 0.798 2.08 0.010 2.29 0.461 15279 Partner 0.45 0.006 0.83 0.486 154.40 <0.001 1.94 0.601 0.73 0.248 0.37 0.370 23706 Tenant 0.39 <0.001 1.03 0.964 38.77 <0.001 0.23 0.134 0.17 <0.001 4.80 0.173 Pig farming experience (years) Continuous data 1314 1.02 0.047 1314 1.01 0.501 1369 1.04 0.337 1368 0.97 0.185 1364 1.15 <0.001 1365 0.99 0.453 Farming experience (years) Continuous data 781 1.03 0.036 781 1.05 <0.001 781 0.99 0.521 781 0.99 0.536 777 1.01 0.565 778 1.06 0.008 Formal training of manager No Yes 1227 1.14 0.507 1227 0.55 0.167 1282 2.88 0.030 1281 1.90 0.124 1277 2.49 0.006 1278 1.28 0.612 County East Yorkshire Humberside 1723 0.99 0.960 1723 1.66 0.189 1778 1.66 0.496 1777 1.96 0.401 1773 2.95 <0.001 1774 15.30 0.005 Lincolnshire 1.78 0.001 1.73 0.025 0.00 <0.001 0.05 0.005 0.78 0.576 0.00 <0.001 North Humberside 0.21 0.001 0.13 <0.001 0.00 <0.001 2.02 0.486 0.00 <0.001 0.00 <0.001 North Yorkshire 0.35 <0.001 0.28 <0.001 5.10 0.002 1.94 0.201 0.91 0.656 4.34 0.132 Oxfordshire 1.56 0.009 1.52 0.140 1.34 0.771 1.75 0.411 0.00 <0.001 0.68 0.728 Scotland 0.62 0.014 0.75 0.472 1.78 0.231 3.94 0.002 0.06 <0.001 7.16 0.035 Shropshire 0.52 <0.001 0.59 0.335 1.24 0.755 0.33 0.190 0.17 <0.001 15.22 0.009 Staffordshire 0.23 <0.001 0.20 <0.001 8.72 0.007 3.08 0.117 1.00 0.992 0.10 0.093 West Midlands 0.04 <0.001 0.09 <0.001 2.32 0.193 0.37 0.081 0.46 0.027 0.30 0.323 West Yorkshire 0.51 0.011 0.37 0.005 3.20 0.191 0.76 0.795 1.61 0.033 0.00 <0.001 Other farming species

present

No Yes 1617 0.70 0.010 1617 0.68 0.492 1672 0.48 0.234 1671 0.12 0.007 1667 0.29 0.005 1668 3.72 0.039 Pig Type Dry 105541 Farrowing 1723 2.31 0.048 1723 5.74 <0.001 1778 53.11 <0.001 1773 0.55 0.080 1774 0.58 0.667 62492 Finisher 6.67 <0.001 6.07 <0.001 0.67 <0.001 0.08 <0.001 28.84 <0.001 33474 Grower 6.36 <0.001 7.29 <0.001 85.42 <0.001 0.23 <0.001 27.45 <0.001 Other 2.95 <0.001 3.01 <0.001 0.56 0.222 0.52 0.021 0.00 <0.001 13595 Weaner 5.64 <0.001 23.48 <0.001 44.13 <0.001 0.36 0.002 6.83 0.044 Pigs kept outdoors Yes 65958 No 1723 41.56 <0.001 1723 8.28 <0.001 n/a 1777 7.43 <0.001 n/a 1774 71.00 <0.001 Piglet protection No Yes 1723 1.93 0.377 1723 3.01 0.074 1778 1.26 0.788 1777 1.69 0.502 n/a 1774 3.53 0.024 N/A 2.56 0.201 3.29 0.021 0.42 0.295 0.19 0.029 1.16 0.770 Flooring Full Full and part 1709 1.37 0.306 1709 0.32 0.001 1764 1.89 0.194 1763 0.59 0.518 1759 0.00 <0.001 1760 1.33 0.686 Solid 0.98 0.916 0.49 0.019 1.41 0.435 0.46 0.269 1.25 0.394 0.30 0.009 Part 0.55 0.007 0.34 0.002 4.09 0.013 1.69 0.346 1.21 0.448 0.20 0.033 Bedding None 1773 Sawdust 1723 0.41 0.084 1723 0.09 <0.001 1778 22.15 <0.001 1777 3.54 0.001 2.41 0.134 1774 0.00 <0.001 Straw 0.98 0.921 0.61 0.061 0.94 0.884 0.43 0.091 27.57 0.316 0.27 0.003 Woodshavings 0.37 <0.001 0.13 <0.001 3.55 0.030 0.81 0.754 2.47 0.012 0.48 0.276 Ventilation Auto

Forced 1714 0.79 0.126 1714 0.71 0.416 1769 1.88 0.183 1768 2.04 0.193 1764 3.17 <0.001 1765 12.69 <0.001 Natural 1.09 0.596 0.53 0.123 2.77 0.082 1.12 0.777 1.58 0.133 2.11 0.159 Pen score 0-1 (poor) 2 966 0.57 0.064 966 2.28 0.150 966 5.03 0.037 966 7.03 0.029 962 5.53 0.001 963 0.01 <0.001 3 1.28 0.174 4.54 0.005 0.92 0.874 2.01 0.369 10.95 <0.001 0.03 <0.001 4 (good) 1.90 <0.001 6.08 <0.001 1.32 0.627 3.34 0.139 10.12 <0.001 0.02 <0.001 Feeding Meal Meal & Pellets 1723 0.55 0.002 1723 0.58 0.317 1778 1.42 0.546 1777 0.56 0.417 1773 0.00 <0.001 1774 16.60 <0.001 Pellets 1.15 0.443 0.95 0.923 1.45 0.469 0.68 0.411 19.81 <0.001 5.36 <0.001 Wet 1.36 0.635 1.03 0.967 0.00 <0.001 0.71 0.515 3.72 0.053 79.98 <0.001 Feed Rate Appetite Restricted 1723 0.25 <0.001 1723 0.20 <0.001 1778 0.41 0.232 1777 0.77 0.641 1773 2.32 0.005 1774 0.04 <0.001 Pig score 1-3 (poor) 4 960 1.56 0.415 960 3.03 0.085 960 0.89 0.676 960 0.52 0.025 956 4.14 0.034 n/a 5 (good) 2.04 0.195 2.94 0.09 0.33 0.145 0.29 0.041 2.18 0.259 Production Type Breeding; Nursery/Grower; Finishing; Indoors Breeding; Nursery/Grower; Indoors 1723 1.23 0.246 1723 1.16 0.650 1778 3.23 0.100 1777 0.84 0.757 1773 1.89 0.047 1774 0.25 0.014 Finishing; Indoors 0.59 <0.001 0.37 0.001 0.69 0.480 0.10 <0.001 0.32 0.001 1.06 0.903 Nursery/Grower; Finishing; Indoors 1.90 0.047 1.36 0.324 4.58 <0.001 0.84 0.677 1.71 0.172 0.46 0.272 Isolation/ quarantine Off farm On farm 186 6.73 <0.001 186 14.77 <0.001 n/a 186 0.02 0.006 186 1.46 0.913 186 1.56 0.303 No. weaner treatment drugs 0

1 1723 1.62 0.002 1723 1.09 0.561 1778 0.40 0.049 1777 1.07 0.818 1773 1.08 0.785 1774 0.78 0.566 2 0.91 0.607 0.81 0.549 2.32 0.009 3.70 0.002 5.18 <0.001 3.47 0.018 3 1.80 0.002 0.99 0.970 0.70 0.578 0.66 0.415 4.28 <0.001 0.10 0.024 4 1.06 0.870 0.63 0.259 0.25 0.072 0.53 0.395 3.95 <0.001 0.17 0.005 No. grower treatment drugs 0 1 1723 2.06 <0.001 1723 1.16 0.680 1778 0.75 0.602 1777 1.45 0.403 1773 0.85 0.536 1774 2.30 0.092 2 1.64 0.001 1.72 0.200 1.78 0.299 0.97 0.931 4.41 <0.001 2.94 0.030 4 0.49 0.001 0.31 0.008 0.30 0.087 0.06 <0.001 1.69 0.093 1.29 0.710 No. finisher treatment drugs 0 1 1723 2.39 <0.001 1723 2.15 0.001 1778 0.22 0.008 1777 0.07 <0.001 1773 0.93 0.839 1774 1.36 0.531 No. sow treatment drugs 0 1 1723 1.44 0.062 1723 1.54 0.149 1778 0.49 0.141 1777 0.58 0.292 1773 1.99 0.060 1774 1.27 0.663 2 2.45 <0.001 2.18 0.020 0.50 0.498 0.60 0.450 0.00 <0.001 0.14 0.011 Sows Eryparvo vaccine No Yes 1723 1.29 0.207 1723 0.52 0.120 1778 0.80 0.450 1777 1.68 0.131 1773 1.18 0.444 1774 1.45 0.367 Sows PRRS vaccine No Yes 1723 1.09 0.544 1723 0.85 0.551 1778 0.50 0.165 1777 0.44 0.080 1773 1.54 0.141 1774 0.44 0.066 Sows EP Vaccine No Yes 1723 0.59 0.044 1723 0.45 0.008 1778 0.27 0.009 1777 0.21 0.023 1773 1.57 0.329 1774 0.40 0.097 Sows Glassers vaccine No Yes 1723 1.15 0.454 1723 0.99 0.970 1778 2.60 0.081 1777 0.83 0.706 1773 1.99 0.050 1774 0.09 0.001 Sows PMWS Vaccine

No Yes 1723 1.51 0.011 1723 0.93 0.808 1778 1.23 0.597 1777 1.60 0.302 1773 1.77 0.012 1774 2.06 0.112 Gilts Eryparvo vaccine No Yes 1723 1.19 0.245 1723 1.14 0.649 1778 1.55 0.266 1777 1.79 0.224 1773 1.75 0.020 1774 2.52 0.041 Gilts PRRS vaccine No Yes 1723 1.95 <0.001 1723 1.74 0.052 1778 0.15 0.054 1777 0.13 <0.001 1773 2.11 0.075 1774 0.36 0.325 Gilts PMWS Vaccine No Yes 1723 1.51 0.011 1723 1.33 0.298 1778 1.56 0.353 1777 0.73 0.465 1773 1.26 0.200 1774 0.52 0.241 Gilts EP Vaccine No Yes 1723 0.87 0.581 1723 0.91 0.820 1778 0.79 0.697 1777 0.39 0.036 1773 3.77 0.001 1774 0.58 0.581 Gilts other vaccine None Clostridia 1723 2.39 <0.001 1723 2.15 0.007 1778 0.00 <0.001 1777 0.03 0.001 1773 1.20 0.655 1774 0.00 <0.001 E. coli 1.29 0.098 1.35 0.335 0.50 0.298 0.43 0.095 1.91 0.019 0.05 0.003 Parasuis 0.22 <0.001 0.57 0.083 1.50 0.623 0.56 0.414 4.61 0.002 0.14 0.105 De-worming none sows 1723 0.54 <0.001 1723 0.50 0.013 1778 0.50 0.165 1777 0.49 0.124 1773 1.10 0.683 1774 1.27 0.614 sows & growers 0.69 0.158 0.43 0.002 0.47 0.224 1.00 0.995 0.00 <0.001 1.87 0.326 Weaner PMWS Vaccine No Yes 1723 1.09 0.591 1723 0.60 0.109 1778 1.11 0.731 1777 0.73 0.487 1773 1.80 0.008 1774 0.23 0.001 Weaner EP Vaccine No Yes 1723 1.64 0.001 1723 0.86 0.592 1778 0.75 0.550 1777 1.19 0.707 1773 1.65 0.076 1774 0.28 0.008 Weaner PRRS Vaccine No

Yes 1723 0.93 0.703 1723 1.03 0.932 1778 0.35 0.040 1777 0.40 0.101 1773 3.79 <0.001 1774 1.30 0.608 Weaner Glassers Vaccine No Yes 1723 0.59 0.044 1723 0.45 0.008 1778 0.27 0.009 1777 0.21 0.023 1773 1.57 0.329 1774 0.40 0.097 Pig weight (kg) Continuous data 874 1.00 0.379 874 0.99 <0.001 874 0.99 0.112 874 0.98 0.005 870 0.99 0.022 871 1.01 0.104 Min. Farrowing crate length (m) Continuous data 166 2.14 0.392 166 1.71 0.534 166 0.07 0.080 166 0.59 0.610 163 1.29 0.418 164 1.92 0.686 No. of pigs in pen Continuous data 766 1.00 0.269 766 1.00 0.666 766 1.00 0.882 766 1.00 0.197 765 1.00 0.004 765 1.00 0.004 Pen area (m2) Continuous data 767 1.00 0.132 767 1.00 0.979 767 1.00 0.191 767 1.00 0.082 763 1.00 0.618 764 1.00 0.088 area per pig (m2) Continuous data 730 0.93 0.013 730 0.68 0.059 730 0.38 0.698 730 0.56 0.072 729 1.00 0.618 729 0.83 0.510 Maiden Gilts Continuous data 1723 1.00 0.198 1723 1.00 0.835 1778 1.01 0.614 1777 1.00 0.926 1773 1.01 0.035 1774 0.99 0.061 In Pig Gilts Continuous data 1723 1.00 0.287 1723 1.00 0.870 1778 1.00 0.404 1777 1.00 0.721 1773 1.02 <0.001 1774 1.00 0.740 Productive Sows Continuous data 1723 1.00 0.019 1723 1.00 0.018 1778 1.00 0.422 1777 1.00 0.030 1773 1.00 0.702 1774 1.00 <0.001 Breeding Boars Continuous data 1723 1.00 0.904 1723 0.96 0.307 1778 1.06 0.789 1777 0.98 0.756 1773 1.08 0.002 1774 0.95 0.207 Progeny <30 Kg Continuous data 1723 1.00 0.147 1723 1.00 0.001 1778 1.00 0.291 1777 1.00 0.345 1773 1.00 0.852 1774 1.00 0.006 Progeny >30 Kg Continuous data 1723 1.00 0.015 1723 1.00 0.008 1778 1.00 0.178 1777 1.00 0.158 1773 1.00 <0.001 1774 1.00 0.835 Farrowing Rate Continuous data 1022 1.01 0.210 1022 1.02 0.141 1021 1.06 0.478 1020 1.04 0.164 1016 0.98 0.062 1017 0.98 0.514 Born Alive Continuous data 1097 1.16 0.163 1097 0.91 0.462 1096 1.18 0.231 1095 1.66 0.041 1091 1.09 0.636 1091 0.95 0.883

Weaned Continuous data 1022 0.93 0.610 1022 0.85 0.401 1021 1.66 0.423 1020 1.17 0.567 1016 1.17 0.479 1017 0.60 0.085 Post-Weaning Mortality Continuous data 1059 1.14 <0.001 1059 1.05 0.264 1059 0.93 0.244 1059 1.07 0.259 1055 0.96 0.253 1055 0.88 0.006 Pre-Weaning Mortality Continuous data 1022 0.99 0.781 1022 1.03 0.464 1021 0.89 0.131 1020 1.14 0.162 1016 1.00 0.984 1017 1.32 0.008 Daily Live-Weight Gain Continuous data 572 1.00 <0.001 572 1.00 0.023 574 1.00 0.131 573 1.00 0.700 570 1.00 0.624 571 1.00 0.720 Feed Conversion Rate Continuous data 376 0.47 0.105 376 0.31 0.007 n/a 377 19.64 0.001 374 0.46 0.090 375 18.93 <0.001 Average Deadweight Continuous data 559 0.98 0.002 559 0.97 <0.001 n/a 559 0.94 0.002 556 1.00 0.702 557 0.95 0.047 Backfat Continuous data 195 1.55 <0.001 195 1.09 0.601 n/a 196 1.56 0.032 193 0.91 0.707 194 5.40 <0.001 Quarantine Distance From Main Herd (m) Continuous data 349 1.00 0.014 349 1.00 0.502 349 0.99 0.013 349 0.98 0.001 348 1.00 0.077 348 1.00 0.942 Duration Of Quarantine Continuous data 235 1.18 <0.001 235 1.02 0.006 234 0.87 0.102 234 0.96 0.010 234 1.00 0.522 234 0.99 0.039 % of positive ZAP samples Continuous data 1094 0.56 0.215 1050 0.56 0.113 1094 0.56 0.215 1093 0.14 0.002 1090 3.57 0/154 1090 0.64 0.603