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Preventive Veterinary Medicine 101 (2011) 182– 191

Contents lists available at ScienceDirect

Preventive Veterinary Medicine

j our na l ho me p age: ww w.elsev ier .com/ locate /prevetmed

arm level risk factors associated with severity of post-weaningulti-systemic wasting syndrome

ablo Alarcon ∗, Martina Velasova, Alexander Mastin, Amanda Nevel, Katharina D.C. Stärk,arbara Wieland

oyal Veterinary College, London, AL9 7TA, United Kingdom

r t i c l e i n f o

rticle history:eceived 7 December 2010eceived in revised form 31 May 2011ccepted 3 June 2011

eywords:MWS severityisk factorsross-sectional study

a b s t r a c t

A cross-sectional study involving 147 pig farms across England was conducted in2008–2009. Farm severity of post-weaning multi-systemic wasting syndrome (PMWS) wasestimated through the use of an algorithm that combined data on post-weaning mortal-ity, PMWS morbidity and proportion of porcine circovirus type 2 PCR positive pigs. Farmswere classified as non/slightly, moderately or highly affected by PMWS. Data on potentialPMWS risk factors were collected through interviews, on-farm assessment and serologicalsampling. Risk factors were identified using multivariable ordinal logistic regression andmultivariable linear regression.

Factors associated with increased PMWS severity were rearing growers indoors(OR = 23.7), requiring a higher number of veterinarian visits per year (OR = 9.6), havingpoorly isolated hospital pens (OR = 6.4), buying replacement boars (OR = 4.8) and seroposi-tivity to Mycoplasma hyopneumoniae (OR = 4.29); factors associated with decreased PMWSseverity were low stocking density for growers (OR = 0.07), adjusting diets at least threetimes between weaning and 14 weeks of age (OR = 0.12), and requiring visitors to be at

least 2 days pig free (OR = 0.14).

This study provides evidence of the association between environmental and managementfactors and PMWS severity, and suggests that other pathogens may be important co-factorsfor the disease. In addition, this study highlights the potential efficacy of biosecurity mea-sures in the reduction/prevention of within-farm PMWS severity.

© 2011 Elsevier B.V. All rights reserved.

. Introduction

Porcine circovirus type 2 (PCV2) was identified in 1996s the causative agent of one of the most economicallyamaging disease syndromes for the pig industry world-ide – post-weaning multi-systemic wasting syndrome

PMWS) (Harding, 1996; Armstrong and Bishop, 2004).MWS typically affects weaned pigs aged 8–15 weeks, withffected pigs presenting with wasting and growth retarda-

∗ Corresponding author. Tel.: +44 01707666024;ax: +44 01707667051.

E-mail address: palarcon@rvc.ac.uk (P. Alarcon).

167-5877/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.prevetmed.2011.06.001

tion, pallor of the skin, respiratory signs and, occasionally,jaundice and intermittent diarrhoea (Harding and Clark,1997). At the histopathological level, the virus has beenshown to produce lymphoid depletion in several organsand tissues, indicating deterioration of the immune system(Nielsen et al., 2003). At the within-farm level, morbidityhas been reported to reach up to 50–60% during the epi-demic phase and decrease to levels of 1–30% in endemicsituations (Quintana et al., 2001; Alarcon et al., 2011). Inaffected farms, post-weaning mortality resulting from dis-

ease is a major problem, where it can rise up to 25% duringoutbreaks, and is an important parameter used in the diag-nosis of PMWS (Madec et al., 2000; Quintana et al., 2001;Segales et al., 2003).

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Despite the considerable impact of PMWS, to date verylittle is known about the mechanism of disease or associ-ated factors. PCV2 has been shown to be highly ubiquitousand has been isolated from both diseased and non-diseasedanimals on PMWS affected farms, as well as from pigs onmost non-affected farms. In addition, most observationalstudies have used presence of disease on a farm as a casedefinition, without taking into account its severity, whichmay have hampered epidemiological investigation.

Some authors have argued that although the high effi-cacy of PCV2 vaccines has demonstrated the essential roleof PCV2 in the aetiology of the disease, it is not yet possibleto exclude the possibility that other agents or factors mayinfluence the development of PMWS (Segales, 2009). Sev-eral studies have found evidence of the importance of otherpathogens, such as Porcine Reproductive and RespiratorySyndrome virus (PRRS), Mycoplasma hyopneumoniae (M.hyo), Porcine Parvovirus (PPV) and Swine Influenza virus(SI) as possible co-factors for triggering PMWS (Krakowkaet al., 2000; Pogranichniy et al., 2002; Opriessnig et al.,2004; Wellenberg et al., 2004; Dorr et al., 2007). However,no pathogen other than PCV2 has been consistently foundto be associated with the disease, and many experimentalstudies have failed to reproduce clinical PMWS through co-infection of PCV2 and any of these pathogens (Allan et al.,2000a,b; Ostanello et al., 2005; Opriessnig et al., 2006b). Inaddition, other factors have been linked with PMWS. Somestudies have hypothesized that procedures for reductionof infection pressure and stress on the farm could protectagainst the disease (Madec et al., 2000; Rose et al., 2003).A sow effect was also identified as an important risk factorfor the development of PMWS at the animal level (Madecet al., 2000; Calsamiglia et al., 2007; Rose et al., 2009). Sev-eral studies have found that early PCV2 infection followedby seroconversion (amongst piglets less than seven weeksof age) significantly increased the risk of PMWS, whichhighlights the importance of effective maternal immunity(Lopez-Soria et al., 2005; Rose et al., 2009). Other studieshave produced evidence that some breeds, such as Lan-drace, are more susceptible to PMWS (Opriessnig et al.,2006a, 2009). Finally, several large scale epidemiologicalstudies have produced evidence of the importance of biose-curity measures in the prevention of PMWS outbreaks(Cottrell et al., 1999; Cook et al., 2001; Woodbine et al.,2007). In summary, the majority of studies conducted sofar have suggested that PMWS is a multi-factorial diseasein which the presence of other pathogens, stress and/orsow/genetic factors is required.

Most studies investigating risk factors for PMWS havebeen based on the comparison of affected and non-affectedfarms, with the majority being conducted during theepidemic stage. Those carried out at a later stage had dif-ficulties in finding enough unaffected farms to achievesuitable analytical power. In the current endemic stage,most farms are affected to some degree by the disease, butconsiderable variation in severity is seen amongst individ-ual farms. This variability was demonstrated in a recent

cross-sectional study involving 147 English pig farms, inwhich correlations between PMWS morbidity in weanersand growers, general post-weaning mortality and the pro-portion of PCV2 PCR positive pigs on a farm were identified

Medicine 101 (2011) 182– 191 183

and used to create an algorithm to quantify PMWS sever-ity at the farm level (Alarcon et al., 2011). This study wasable to differentiate farms that were only slightly affectedor unaffected from those farms that were highly affected bythe disease. An increased understanding of possible factorsassociated with severity, and therefore an increased under-standing of why some farms are more affected than others,is essential for the implementation of appropriate diseasecontrol and preventive measures. Therefore the aim of thisstudy was to identify risk factors associated with the dif-ferent levels of PMWS severity amongst English pig farmsin the current endemic situation.

2. Materials and methods

Using a cross-sectional study design, 147 farms acrossEngland were visited between April 2008 and April 2009.Farms were recruited through a PCV2 vaccination pro-gramme launched by BPEX, the British pig levy payerassociation. In addition, several farms not participating inthe PCV2 vaccination plan were recruited though veteri-nary practitioners, in order to include farms which wereless severely affected by PMWS. In both recruitment pro-cesses, farms were selected for this study if two criteriawere met: (1) PCV2 vaccination, if used, was only imple-mented after blood samples had been collected on thefarm; and (2) all age groups (weaners, growers, finishersand sows) were available for inspection and blood sam-ple collection by the researcher. The total number of farmsrecruited for this study would be expected to detect riskfactors with a minimum odds ratio of 3.3, with a signifi-cance level of 0.05 and a power of 80% (assuming that 25%of the controls are exposed to the risk factor).

2.1. PMWS case definition

For this study, severity of PMWS was estimated foreach farm using the protocol described in Alarcon et al.(2011), which was based on an algorithm obtained from aprincipal component analysis (PCA) of post-weaning mor-tality, proportion of pigs on the farm PCR positive forPCV2 and a factor score representing PMWS morbidityin weaner and grower age groups. The morbidity vari-able was obtained through a factor analysis of quantitativeand qualitative data relating to the farmer’s perception ofPMWS-associated morbidity amongst different age groups.In order to minimize bias resulting from the subjectivenature of morbidity estimation and the ability of farm-ers to correctly identify PMWS, a misclassification processincluding clinical signs reported by the farmer, confirma-tion method used, researcher perception and presence ofPCV2 PCR positive pigs on the farm was used (misclassi-fication trees for this process are shown in Alarcon et al.,2011). The principal component obtained from the PCA wasfound to account for 60% of the variation amongst farms,and was log-transformed to increase the fit to normality ofthe distribution.

From this PMWS severity variable, two PMWS severityoutcomes were considered for the analysis of risk factors:(a) An interval level severity score with values rangingfrom 0 to 10; and (b) PMWS severity categories, in which

184 P. Alarcon et al. / Preventive Veterinary Medicine 101 (2011) 182– 191

Table 1Number of farms in each PMWS severity category.

PMWS severity category Number of farms(excluding DFsa)

Number of farms(including DFsa)

Mean PMWSseverity score(excluding DFsa)

Non-slightly affected 27 39 2.79 (±0.40)Moderately affected 58 66 5.46 (±0.16)Highly affected 25 26 7.52 (±0.28)

Total 110 131 5.28 (±0.35)

PMWS

a idered a

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Missing 37

a Doubtful farms (DF): farms where the post-weaning mortality or thend therefore the PMWS severity, derived from these variables, was cons

arms were classified as belonging to one of the follow-ng: (1) non or slightly affected (severity scores <4); (2)

oderately affected (severity scores between 4 and 6.5);r (3) highly affected farms (severity scores >6.5). For7 farms (‘doubtful farms’, DFs), missing values, doubt-ul or incorrect estimation of PMWS morbidity by the

able 2lassification and description of variables collected in the cross-sectional study in

Variable group Variable subgroup

Farm health Health status and management

Serological results

Environment conditions Farm environment

Housing environment for eachage groupPen environment for each agegroup

Management practices Farmer and stockmen factor

Pig management (for each agegroup)

Genetics Breed composition

Reproduction management

Biosecurity Prevention of pathogenintroduction

Prevention of within farmdissemination

Animal welfare Welfare scores

a To determine the breed composition used by farmers, commercial names useubsequently contacted for information on breed make up for the different commbout the breeds of the parent and grand-parent stock used and from where theyas then calculated.b Data on animal welfare were collected by the researcher through an on-far

n each pen, the percentage of pigs with tail biting, ocular discharge, skin lesioncouring problems was recorded. Each condition was categorized and scores wereith the condition (1 point), and >10% of pigs in the pen with the condition (2 poi

0 or more animals, the 10% cut-off ensures that the higher category represents proup was obtained by summing the pen score for each condition. An overall farmroup.

16

morbidities estimations collected may have been incorrect or imprecise,s doubtful.

farmers or low confidence in the production parameterestimation hampered the calculation of the severity score.

Therefore, the PMWS severity scores obtained for thesefarms were considered to be approximations only, andso were only used to classify farms into one of the threedifferent severity categories (Table 1). These farms were

volving 147 English pig farms.

Variable description

Disease status as perceived by the farmer, vaccinations and medicinesused.Serological samples were tested for the presence of antibodies againstActinobacillus pleuropneumoniae, Mycoplasma hyopneumoniae,Parvovirus, Porcine Reproductive and Respiratory Syndrome virus(PRRS), Swine Influenza virus, and PCV2. In addition, the presence ofPCV2 was assessed by semi-quantitative PCR. Tests and protocols usedare described elsewhere (Wieland et al., 2010).

Number of sites, distribution of age groups amongst sites, size of thefarm and other species present.Indoor/outdoor, ventilation type, temperature control and lightmanagement.Type of bedding, floor type, stocking density, type of feeder and watersystem, feeding and water allowance, floor humidity, and hygienicconditions.

Manager experience and industry activities, number of workers,farmer’s ownership and workers time management with pigs.Mixing and movements, feeding management (number of feedchanges, type of feed, additives and feeding systems), slurrymanagement, and piglet management (tail docking, teeth clipping,iron supplementation and cross-fostering).

Estimation of the percentage of each breed in the final composition ofthe market pig shipment for the farm.a

Gilt and Boar replacement rates, number of parities and amount ofartificial insemination used.

Visitors’ rules (parking, pig free, register and protective clothes),number of visitors by visitor type, number of gilts/boars/semenpurchase, presence of fences and number of neighbouring pig farms.Sick pen type, management and location, all-in all-out system and useof boot dips.

Welfare scores estimated for weaners, growers, finishers and for allage groups overall.b

d for parents and grand-parents were recorded. Genetic companies wereercial pigs. For farms producing their own genetics, farmers were asked

originated. For all farms an estimation of the mean of breed composition

m assessment of a randomly selected weaner, grower and finisher pen.s, skin irritation, pigs with respiratory problems, and diseased pigs and

given as follow: absence of condition (0 points), ≤10% of pigs in the pennts). Considering that most of the farms keep their rearing pigs in pens ofens that have more than 1 pig affected. An animal welfare score for each

welfare score was created by summing the welfare scores for each age

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excluded from the analysis using the severity score asoutcome.

2.2. Data collection

Data were collected through a questionnaire-basedinterview with farmers and by on-farm assessment by theresearcher. The questionnaire was pilot tested on five farmsat the start of the study and questions were reordered,rephrased and redesigned to increase the quality of datacollected and to shorten the interview duration. Closedquestions were used to collect data on potential risk factors.In addition, 20 pig blood samples were collected from eachfarm. To ensure that blood was collected from pigs of allage groups on the farm, the total sample on each farm wasstratified into three levels – six weaners (from weaning to10 weeks old), six growers (from 11 to 14 weeks old) and sixfinishers (≥15 weeks old). In addition, blood samples fromtwo sows were collected on each farm. In order to increasedetection of PMWS pigs on the farm, a second stratifica-tion was done by age, with exception of the sows. In eachage group three suspected PMWS pigs were selected and,if found, were age-matched with three apparently healthypigs. The total number of blood samples collected per farmallowed for the detection of a pathogen in an averagesized herd of 1000 pigs if it was present at a prevalenceof 15% (confidence interval of 95%) and equally distributedamongst different age groups in the herd. Table 2 presentsdefinitions of the variables measured.

2.2.1. Hypothesis testing and statistical analysisPreliminary analysis: variable selection procedures for

model building. For each variable grouping (Table 2) aunivariable analysis was performed in order to iden-tify possible associations with different PMWS severityoutcomes. The following statistical procedure were con-ducted: (1) linear regression for analysis of two continuousvariables, (2) analysis of variance and t-test for differ-ence in means between groups, (3) Kruskal–Wallis test ofdifferences between groups for non-normally distributedvariables, (4) Chi-square test and Chi-square test for trendfor categorical variables, and (5) ordinal logistic regres-sion to assess association with PMWS categories. In thefirst stage, all variables with p-values ≤0.20 were retained.In the second stage, all explanatory variables identifiedin the univariable analysis were checked for high correla-tion with others variables from their corresponding groupas well as potential confounding variables such as farmsize (with number of sows used as a proxy for this) andindoor/outdoor status (defined here as an ordinal scalefrom completely indoor with no access to the external envi-ronment to completely outdoor). When a high correlationwas detected between two variables (defined as a p-value≤0.01), only the variable with the highest strength of asso-ciation with the outcome or considered to have the greatestbiological relevance as a risk factor for PMWS severitywas retained. Variables that were not removed after the

correlation analysis, but where their association with theoutcome was considered to be solely due to confoundingby indoor/outdoor status or farm size also were removedfrom future analysis.

Medicine 101 (2011) 182– 191 185

Multivariable analysis with PMWS severity categoriesas outcome. Two multivariable ordinal logistic regressionmodels were built independently, one of excluding DFsfrom analysis (model 1), and the other including thesefarms (model 2). In both models, all variables retained inthe previous stages were initially placed in the models,and models were then refined using a stepwise backwardvariable selection, until all retained variables met the crite-rion of p < 0.05. Farm size and indoor/outdoor status wereforced into the model in an attempt to control for the possi-ble confounding effect of these variables. In addition, if thepresence of other specific pathogens was found to be asso-ciated with the severity score, the farm vaccination statusagainst this pathogen was also forced into the model. Theproportion of total variation in severity score categoriesaccounted for in the final models (R2) was calculated usingthe following Eq. (1) (Mittlbock and Schemper, 1996):

R2 = 1 − SSE

SST= 1 −

∑i(yi − yi)

2

∑i(yi − �yi)

2(1)

where y and �y are calculated by (Eqs. (2) and (3)):

y = 1 ∗ ps + 2 ∗ pm + 3 ∗ ph (2)

�y =∑

i

yi

N(3)

where ps is the predicted probability of a farm being in thenon/slightly affected category; pm is the predicted proba-bility of a farm being in the moderately affected category;and ph is the predicted probability of a farm being in thehighly affected category. Model fit was also assessed bycomparing the predicted farm severity category (identi-fied as the category with the highest predicted probabilityfor each farm) with the actual farm severity category. Toassess the assumption of proportional odds, the command“omodel” from the Stata statistical package sg79 was used.

Multivariable analysis with PMWS severity scores as out-come. A multivariable linear regression was performedusing the same criteria and variable selection method asdescribed for the multivariable ordinal logistic regressionabove. Doubtful farms (DFs) were excluded from this anal-ysis due to lack of precision of their severity score.

As a post hoc technique, a canonical correlation analysis(CCA) was performed using the variables used to derive thePMWS severity score (post-weaning mortality, proportionof PCV2 PCR positive pigs and PMWS morbidity in weanersand growers) as outcome variables (Y variables), and therisk factors identified in the multivariable models as expo-sure variables (X variables). This was performed in order tobetter understand the relationships between the identifiedrisk factors and the variables underlying the PMWS severityscore. For this analysis all variables were standardized.

All statistical analyses were performed using the statis-tical software Stata 9 (Statacorp, College Station, TX, USA).

3. Results

The geographical distribution of farms included in thestudy reflects the distribution of pigs in England, with agreater number of farms recruited in the higher pig farm

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86 P. Alarcon et al. / Preventive Ve

ensity areas of North Yorkshire and East Anglia. Farm sizeanged widely from small producers, with only 16 sows,o large producers, with up to 2000 sows. The majority ofarms (62%) contained between 180 and 500 sows. Overhree quarters of the farms reared all their pigs in indoornits, while the rest had varying degrees of outdoor accessor pigs. Fifty-seven percent of farms kept their pigs in aingle unit and the remaining farms had two (23%) or moreites (20%).

.1. Outcome: PMWS severity category

Table 3 shows the variables retained after the univari-ble analyses. Similar results were obtained from the twoultivariable ordinal logistic regression models. Most of

he variables retained when DFs were excluded (model) were also retained when DFs were included (model). However, the results of the goodness of fit showedhat the proportion of outcome variation explained wasigher when DFs were excluded (R2 = 0.51) than whenFs were included (R2 = 0.32). Further analysis on modelrediction showed that the PMWS status of most of theoubtful farms (67%) was incorrectly predicted by the ordi-al logistic regression models, compared to non-doubtful

arms, where only 46% and 32% were incorrectly pre-icted by model 2 and model 1 respectively. Therefore,o further consideration was given to the results includingFs.

The results of multivariable ordinal logistic regressionnalysis, model with DFs excluded (model 1), identi-ed that farms which reared growers in indoor facilities,equired more than 3 visits per year by their veterinar-ans, had sick pens draining to other areas holding pigs,ought boars onto the farm and were seropositive to M.yopneumoniae antibodies had higher odds to be in aigher PMWS severity category. In addition, farms thatept their growers in low stocking density pens, adjustedhe diet of pigs between weaning and 14 weeks of age

ore than twice, and required visitors to be at least twoays pig free were found to have higher odds to be in

lower PMWS severity category (Table 4). The test per-ormed to assess the assumption of the proportional oddsas not significant (p = 0.913) and, therefore, proportional

dds may be assumed. Further analysis on the goodness oft showed that, when farms were allocated to ‘expected’everity categories according to predicted probability, 60%f slightly affected, 84% of moderately affected and 57% ofighly affected farms were correctly predicted. None of thelightly affected or highly affected farms were predicted toe in the opposite severity category.

.2. Outcome: PMWS severity score

The results of the multivariable linear regression modeldentified that higher PMWS severity scores were associ-ted with requiring more than 3 visits per year by their

eterinarians, rearing of growers indoors, seropositive to. hyopneumoniae, sick pens draining to other areas hold-

ng pigs and buying replacement boars. Low PMWS severitycores were associated with requiring visitors to be at least

Medicine 101 (2011) 182– 191

2 days pig free before entering the farm. The adjusted R2

was 0.39, indicating a moderate goodness of fit (Table 4).

3.3. Post hoc canonical correlation analysis

Three canonical dimensions (pairs of Y and X canonicalvariates) were obtained, but only the first one was foundto be significant (canonical correlation = 0.64, p < 0.01)(Table 5). The canonical variate (Y1) was positively corre-lated with morbidity in weaners and grower stages andpost-weaning mortality, although there was weak evi-dence (p = 0.07) that the proportion of PCV2 PCR positivepigs also contributed to this canonical variate. All the riskfactors, with the exception of number of diet changes,were positively or negatively correlated with the exposurecanonical variate (X1). The direction of the correlation ofeach risk factor was the same as the one obtained from themultivariable ordinal logistic regression and the multivari-able linear regression.

4. Discussion

The results of this study support the multi-factorialnature of PMWS, in which other pathogens, environmen-tal conditions and management practices have been foundto be associated with disease severity. In addition, thisstudy highlights the importance of adequate biosecuritymeasures in the prevention of PMWS on farms. Despite sev-eral potential biases and limitations, such as the way theoutcome was defined, the high number of variables ana-lyzed and other common factors related to cross-sectionalstudies, the results obtained are compatible with previousstudies and provide further evidence of which variablesmay affect the severity of PMWS on farms.

In this study, the dependent variable was derived froma principal component analysis performed on three vari-ables, and was found to account for 60% of the totalvariance. Therefore, some degree of resolution would beexpected to be lost as 40% of the total variance was notrepresented in the severity score. It is unknown whetherthe categorization process adopted here further reducedthe resolution of this component. PMWS severity cate-gories were intended to represent meaningful constructsin order to facilitate communication of the results andinterpretation of the risk factors by the industry. Althoughsubjectively chosen, the cut-offs for severity categorieswere considered sensible and were identified by plottingthe values of each variable with the PMWS severity com-ponent. This method ensured to some extent that ‘highlyaffected’ farms had higher values in all the three compo-nent variables, while ‘slightly affected’ farms had lowervalues in all variables. Similar results were identified whenusing categorical and interval level scores for PMWS and inthe canonical correlation analysis, which supports the useof the categorization system. Better success in classificationof farms was achieved with the ordinal logistic regression

model when doubtful farms were excluded. This indicatesthat correct disease classification of doubtful farms maynot have been achieved and highlights the need for goodrecord keeping and farmers’ disease awareness in order to

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edicine 101 (2011) 182– 191

187Table 3Variables that showed evidence of association with PMWS severity outcomes in the univariable analyses.

Exposure variables Level Mean PMWS scores (110 farms) PMWS severity categories (%) (110 farms – no DFsa) PMWS severity categories (%) (145 farms – with DFs)

Not/slightly Moderately Highly OR Not/slightly Moderately Highly OR

Farm environmentNumber of sites 1 site 5.48 (4.94–6.03) 15 (56) 25 (43) 19 (76) 1.00 22 (56) 30 (46) 19 (73) 1.00

2 sites 5.22 (4.74–5.69) 4 (18) 19 (32) 5 (20) 0.81 (0.35–1.89) 7 (18) 20 (30) 5 (19) 0.96 (0.44–2.10)>2 sites 4.78 (4.12 –5.44) 7 (26) 14 (24) 1 (4) 0.40 (0.16–1.01)** 10 (26) 16 (24) 2 (8) 0.54 (0.24–1.24)*

Presence of poultry Absence 5.11 (4.74–5.48) 24 (92) 48 (83) 17 (68) 1.00 33 (87) 53 (80) 18 (69) 1.00Presence 6.11 (5.29–6.94) 2 (8) 10 (17) 8 (32) 2.94 (1.14–7.60)*** (13) 13 (20) 8 (31) 2.05 (0.89–4.69)**

House environmentDegree of indoor All outdoor 3.93 (3.14 –4.71) 11 (40) 5 (9) 1 (4) 1.00 14 (36) 5 (8) 1 (4) 1.00

S & W outdoor 5.72 (4.56–6.88) 1 (4) 7 (12) 2 (8) 8.54 (1.81–40.38)*** 1 (3) 8 (12) 2 (8) 9.82 (2.28–42.26)***

All indoor 5.50 (5.11–5.88) 15 (56) 46 (79) 22 (88) 8.37 (2.73–25.65)*** 24 (61) 53 (80) 23 (88) 7.57 (2.65–21.62)***

Pen environmentDrinker type Nipple 5.06 (4.61–5.52) 19 (76) 33 (66) 12 (52) 1.00 29 (80) 39 (67) 13 (54) 1.00

Bowl 6.08 (5.10–7.06) 2 (8) 8 (16) 8 (35) 3.50 (1.25–1.84)*** 2 (6) 9 (16) 8 (33) 4.14 (1.55–11.06)***

Trough 5.06 (4.22–5.91) 4 (16) 9 (18) 3 (13) 1.14 (0.41–3.23) 5 (14) 10 (17) 3 (13) 1.29 (0.49–3.37)

Grower stockingdensity

≤0.50 m2/finisher 5.75 (5.06–6.45) 5 (22) 15 (31) 13 (65) 1.00 10 (28) 17 (32) 13 (65) 1.000.50–1.00 m2/finisher 5.41 (4.89–5.93) 6 (26) 21 (44) 6 (30) 0.46 (0.17–1.20)* 8 (43) 23 (43) 6 (30) 0.69 (0.29–1.63)>1.00 m2/finisher 4.22 (3.95–4.90) 12 (52) 12 (25) 1 (5) 0.12 (0.04–0.37)*** 17 (59) 13 (25) 1 (5) 0.18 (0.07–0.48)***

Feeding allowance infinishers

<4 finishers/feeder 5.06 (4.48 – 5.64) 11 (49) 22 (43) 4 (19) 1.00 12 (34) 26 (45) 4 (19) 1.00≥4 finishers/feeder 5.43 (4.96 – 5.91) 12 (52) 29 (57) 17 (81) 2.17 (0.97–4.85)** 23 (66) 32 (55) 17 (81) 1.28 (0.63–2.61)

Farmer and stockman factorFarmer experience <20 years 4.91 (4.41–5.41) 15 (60) 26 (46) 7 (28) 1.00 21 (57) 27 (42) 8 (31) 1.00

>20 years 5.64 (5.15–6.12) 10 (40) 30 (54) 18 (72) 2.38 (1.12–5.06)*** 16 (43) 37 (58) 18 (69) 2.05 (1.04–4.04)***

Manager owns thefarm

No 5.12 (4.69–5.55) 17 (63) 34 (59) 11 (44) 1.00 21 (54) 39 (59) 12 (46) 1.00Yes 5.47 (4.90–6.02) 10 (37) 24 (41) 14 (56) 1.66 (0.80–3.43)* 18 (46) 27 (41) 14 (54) 1.15 (0.60–2.21)

Feed managementNumber of diets up to

14 weeks.<3 diets 5.78 (4.64–6.61) 2 (7) 5 (9) 6 (24) 1.00 2 (5) 5 (8) 6 (23) 1.003–4 diets 5.45 (4.85–6.06) 11 (41) 20 (34) 12 (48) 0.42 (0.12–1.44)* 14 (36) 23 (35) 13 (50) 0.38 (0.11–1.26)*

>4 diets 5.01 (4.56–5.45) 14 (52) 33 (57) 7 (28) 0.28 (0.08–0.92)*** 23 (59) 38 (57) 7 (37) 0.21 (0.07–0.70)***

Zinc added to feed No 5.08 (4.60–5.57) 17 (65) 34 (60) 10 (40) 1.00 21 (55) 40 (62) 11 (42) 1.00Yes 5.57 (5.09–6.07) 9 (35) 23 (40) 15 (60) 1.98 (0.94–4.18)** 17 (45) 25 (38) 15 (58) 1.30 (0.67–2.53)

About breed compositionMeisham No 5.21 (4.85–5.57) 25 (96) 53 (96) 22 (88) 1.00 36 (95) 60 (95) 23 (88) 1.00

Yes 6.35 (4.44–8.27) 1 (4) 2 (4) 3 (12) 2.98 (0.58–15.42)* 2 (5) 3 (5) 3 (12) 1.93 (0.47–7.89)

Barriers to introductionVisits from

veterinarians<4 visits/year 3.43 (1.18–5.18) 5 (19) 2 (3) 1 (4) 1.00 5 (13) 4 (6) 1 (4) 1.00≥4 visits/year 5.42 (5.10–5.74) 22 (81) 56 (97) 24 (96) 5.22 (1.16 –23.58)*** 34 (87) 62 (94) 25 (96) 2.52 (0.72–8.81)*

Visits from abattoir <2 visits/month 5.69 (5.20–6.18) 5 (21) 28 (51) 15 (63) 1.00 9 (25) 32 (51) 16 (64) 1.00>2 visits/month 4.94 (4.50–5.43) 19 (79) 27 (49) 7 (37) 0.32 (0.15–0.70)*** 27 (75) 31 (49) 9 (36) 0.33 (0.16–0.66)***

Boars bought ontofarm

None 4.86 (4.33–5.40) 12 (46) 23 (40) 5 (20) 1.00 16 (42) 23 (35) 6 (23) 1.00<2/year 5.44 (4.96–5.92) 13 (50) 30 (53) 16 (64) 1.87 (0.86–4.07)* 21 (55) 36 (55) 16 (62) 1.51 (0.75–3.07)>2/year 6.31 (5.13–7.50) 1 (4) 4 (7) 4 (16) 5.23 (1.04–17.56)*** 1 (3) 6 (9) 4 (15) 3.77 (1.08–13.14)***

188P.

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et al.

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Medicine

101 (2011) 182– 191

Days pig cleanrequired

No 5.81 (5.04–6.59) 3 (12) 10 (20) 7 (30) 1.00 5 (14) 13 (22) 7 (29) 1.00Yes 5.07 (4.66–5.49) 23 (88) 40 (80) 16 (70) 0.46 (0.17–1.17)** 32 (86) 45 (78) 17 (71) 0.53 (0.23–1.22)*

Sick pen drain toother areas

No 5.04 (4.61–5.46) 21 (81) 39 (68) 11 (44) 1.00 27 (71) 43 (66) 12 (46) 1.00Yes 5.80 (5.22–6.38) 5 (19) 18 (32) 14 (56) 3.06 (1.38–6.80)*** 11 (29) 22 (34) 14 (54) 1.96 (0.98–3.94)**

Sick pen continuouslyoccupied

No 4.84 (4.26–5.42) 14 (64) 21 (41) 8 (36) 1.00 19 (61) 25 (44) 8 (35) 1.00Yes 5.73 (5.27–6.19) 8 (36) 30 (59) 14 (64) 2.08 (0.94–4.62)** 12 (39) 32 (56) 15 (65) 2.09 (1.01–4.32)***

Serological results from blood samplesMycoplasma

hyopneumoniaeantibodies (nonvaccinated)

Negative 4.66 (3.65–5.67) 8 (73) 9 (53) 3 (33) 1.00 9 (69) 10 (50) 4 (40) 1.00

Positive 5.74 (4.89–6.58) 3 (27) 8 (47) 6 (67) 3.10 (0.87–11.03)** 4 (31) 10 (50) 6 (60) 2.31 (0.73–7.31)*

Actinobacilluspleuropneumoniaeantibodies

Negative 4.96 (4.35–5.72) 8 (30) 18 (31) 3 (12) 1.00 12 (31) 18 (27) 3 (12) 1.00Positive 5.38 (4.97–5.80) 9 (70) 40 (69) 22 (88) 1.75 (0.79–3.88)* 27 (69) 48 (73) 23 (88) 1.83 (0.87–3.84)*

Medicines usedUse of Antibiotics in

waterNo 5.51 (5.07–5.94) 14 (56) 38 (70) 21 (84) 1.00 22 (59) 45 (73) 22 (85) 1.00Yes 4.86 (4.24–5.49) 11 (44) 16 (30) 4 (16) 0.41 (0.18–0.92)*** 15 (41) 17 (27) 4 (15) 0.44 (0.21–0.92)***

a DFs: PMWS doubtful farms.* p-Value <0.20.

** p-Value <0.10.*** p-Value <0.05.

P. Alarcon et al. / Preventive Veterinary Medicine 101 (2011) 182– 191 189

Table 4Risk factors identified in the final multivariable ordinal logistic regression and multivariable linear regression models. Vaccinations against Mycoplasmahyopneumoniae and herd size were forced into the models.

Variables PMWS severity categories (multivariableordinal logistic regression – DFs excluded)

PMWS severity scores (multivariablelinear regression – DFs excluded)

Definition

OR 95% CI p-Value Coef. 95% CI p-Value

>1 m2/grower*,a 0.07 0.02–0.30 <0.001 ProtectiveSick pen drain to other areas holding pigs 6.40 2.29–17.82 <0.001 0.98 0.38–1.58 0.002 Risk>2 diets to the age of 14 weeks old* 0.12 0.02–0.72 0.020 Protective>3 vet visits/year 9.60 1.57–58.62 0.014 2.49 1.36–3.41 <0.001 RiskVisitors pig free 0.14 0.04–0.49 0.002 −0.95 −1.64 to −0.25 0.008 ProtectiveBuying boars 4.81 1.70–13.61 0.003 0.87 0.26–1.49 0.010 RiskHousing growers indoors 23.66 4.09–136.84 <0.001 2.01 1.13–2.89 <0.001 RiskDetection of M. hyopneumoniae antibodies 4.29 1.37–13.49 0.013 1.58 0.86–2.30 <0.001 Risk

R2 = 0.51 Adjusted R2 = 0.39

the mu

Farms in model = 99

a Baseline category is “<0.5 m2/grower”.* These variables were used as continuous variables (no categories) for

avoid the danger of disease misclassification and, there-fore, to avoid the risk of implementing ineffective controlmeasures.

The farm selection criteria, data gathering approach andprotocols adopted during the farm visits aimed to reducebias as much as possible. Further, the number of farms inthe study, the spatial distribution of the farms, and thenumber of farms in each PMWS severity category suggestedthat the sample obtained is representative of the Englishpig farm population. The possibility of recall bias needs tobe considered when interpreting the results. Because of thenature of the cross-sectional study, farms were visited onceand affected farms had been suffering from the disease fora period of time. Thus some of the risk factors identifiedcould have been introduced after an increase or decreaseof PMWS severity on the farm had occurred. In addition,because of the high number of variables that were ana-lyzed, some of the risk factors would be expected to befound associated with the outcome due to random varia-tion alone.

The largest and smallest odds ratios identified in thisstudy corresponded to the rearing of pigs in indoor facili-

ties at least until the age of 14 weeks and to overstocking.Both stocking density and indoor type were correlated,as outdoor farms tended to provide more space to theirpigs. However this correlation was not strong enough to

Table 5Results of the canonical correlation analysis.

Canonical variates Variables in the group

Y1 Morbidity factor 1

Post-weaning mortality

Proportion of PCV2 PCR+

X1 Stocking density in growers pen (decreasing direcSick pen drains to other areas holding pigs

>2 diets to the age of 14 weeks old>3 visits by veterinarian/year

Visitor pig free

Purchasing boarsHousing growers indoors

Detection of M. hyopneumoniae antibodies

* p-Values obtained from the Wald t-test performed on each variable (tests the

Farms in model = 103

ltivariable linear regression.

be eliminated during the correlation analysis. These find-ings support the hypothesis that environmental stressorsand infection pressure are factors that may be linked to thedevelopment of the disease (Madec et al., 2000). In relationto environmental stressors, various studies have demon-strated the negative welfare effects of overstocking (Blacket al., 2001; Geers et al., 2003; Jorgensen, 2003; Broomet al., 2005). In relation to infection pressure, Verreault et al.(2010) confirmed the presence of PCV2 in airborne dustparticles in confined buildings, and found that airbornedust concentration was correlated with airborne PCV2 con-centration. In addition, in several epidemiological studieshigh stocking density has been associated with increasedmorbidity of various enzootic diseases and an increasedrisk of infection (Maes et al., 1999; Broom et al., 2005). It isimportant to note that the present study specifically sug-gests a protective effect of rearing pigs outdoors until atleast the end of the grower period, and of keeping grow-ers in pens of low stocking density. For many pathogens,including PCV2, it is known that maternal derived anti-bodies wane by approximately 10 weeks of age, whichhighlights the importance of providing a protected envi-

ronment for pigs during this delicate transition period.Further research is needed to understand which specificfactors or combinations of factors provide a protectiveeffect in outdoor farms.

Coef. p-Value* Canonicalcorrelation

0.65 <0.001

0.64

0.32 0.0230.24 0.072

tion) −0.46 0.0010.32 0.016−0.21 0.1350.29 0.022−0.35 0.0100.39 0.0030.41 0.0050.34 0.015

null hypothesis that the coefficient is equal to zero).

1 terinary

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frirbiviipttpbotf2

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90 P. Alarcon et al. / Preventive Ve

Farms giving only one or two different diets to their pigsrom weaning to 14 weeks of age were identified as havingigher odds of being in a more severely affected categoryhan farms providing a greater number of different dietsver this period. The extent to which this may have beenaused by an incorrect balance of protein to energy, a lack ofodulation of gut flora or other factors is unknown. Further

nvestigation into this association is advised.Three biosecurity measures were identified as risk

actors for increased PMWS severity. Two of them, notequiring visitors to be at least two days pig free and buy-ng boars onto the farm, are likely to be related to theisk of introduction of the virus. Considering that PCV2 haseen shown to be present on most farms, more research

s needed to understand whether some form of highlyirulent PCV2 could be the cause of this increased sever-ty, or whether repeated reintroduction of virus leading toncreased infection pressure may be responsible for thishenomenon. The other factor, having sick pens drainingo other areas holding pigs, is a lack of internal biosecurityhat can facilitate continued circulation of PCV2 or otherathogens within the farm. These findings are supportedy three epidemiological studies that identified purchasingf replacement gilts, purchasing of semen, close proximityo other affected farms and not requiring visitors to be pigree as risk factors for PMWS (Cook et al., 2001; Rose et al.,003; Woodbine et al., 2007).

Except for M. hyopneumoniae, no significant associationetween exposure to other pathogens and PMWS sever-

ty was found in this study. The ELISA test used to detect. hyopneumoniae antibodies was not able to differenti-

te between those originating from vaccination and thoseue to natural infection. Vaccination against M. hyopneu-oniae would be expected to reduce the clinical signs

nd severity of enzootic pneumonia on the farm, but doesot provide a sterile immunity and therefore does notrevent colonisation or completely eliminate the diseaseHaesebrouck et al., 2004). In the final model, vaccina-ion against M. hyopneumoniae was also forced into the

odel. In this case, farms where no antibodies againsthis pathogen were detected (which were more likelyo be in the non/slightly affected PMWS severity group)ould be considered free from M. hyopneumoniae. Variousxperimental studies have successfully reproduced PMWShrough co-infection of PCV2 with M. hyopneumoniae liveathogen or inoculation of M. hyopneumoniae bacterinsOpriessnig et al., 2004; Krakowka et al., 2007). In addi-ion, another experimental study showed an increase ofhe quantity of PCV2 in serum and increased severity ofymphoid lesions when PCV2 infected pigs were vacci-ated against M. hyopneumoniae (Opriessnig et al., 2006b).he finding that absence of M. hyopneumoniae was linkedith non/slightly PMWS affected farms in our study could

lso indicate a high health farm, where in general a lowevel of any infectious disease is observed. Interestingly,t was also found that farms requiring more veterinaryttention, reflected in increased number of visits, were

ore likely to be severely affected by PMWS. Whether this

s an indication of overall poor health or reverse causal-ty due to the severe PMWS could not be determinedere.

Medicine 101 (2011) 182– 191

The current investigation showed a reduced PMWSseverity amongst farms rearing growers outdoors, thoseproviding sufficient space to growers and being M.hyopneumoniae-free. Farms not adjusting diets accordingto the age of the animals, not requiring visitors to be atleast two days pig-free, not having sick pens properly iso-lated or buying boars were more likely to be highly affectedby PMWS. Communication of these findings to pig farmersis important as it will improve PMWS control in the futureand may reduce the impact of the disease, with or withoutPCV2 vaccines. The impact of these factors on farm pro-ductivity will have to be investigated in more detail witheconomic analyses.

Acknowledgements

The work was funded by a grant (BB/FO18394/1) fromthe BBSRC CEDFAS Initiative, BPEX Ltd., Biobest Laborato-ries Ltd., Pfizer Animal Health Ltd. and Bloomsbury CollegeConsortium. We thank BPEX for the collaboration in thisstudy through their PCV2 vaccination programme, thenumerous veterinarians who helped with the collection ofblood samples and most of all the farmers who participatedin this study. Further we acknowledge Professor Dirk U.Pfeiffer and Professor Dirk Werling for their contributionin setting up this project and for valuable discussions.

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