Paper

Seropositivity to Toxoplasma infection in sheepsamples submitted to Animal and Plant HealthAgency laboratories between 2005 and 2012J. P. Hutchinson, R. P. Smith

Ovine serum samples submitted to Animal and Plant Health Agency (APHA) (formerly theAnimal Health and Veterinary Laboratories Agency) – Weybridge regional laboratories inEngland and Wales for diagnostic and monitoring purposes between 2005 and 2012 wereinvestigated for possible spatial and temporal variations in seropositivity to Toxoplasmagondii infection. Of the 4354 samples tested by latex agglutination, 2361 (54.2 per cent)were seropositive. No correlation between seropositivity and climatic conditions wasidentified by mixed-effects modelling using meteorological data summaries. The proportionof seropositive samples collected during November was found to be significantly lower thanthose collected during other months and samples from the North West England and NorthWales Regions had significantly lower odds of being positive. Spatial cluster analysisidentified a significantly higher proportion of seropositive animals in East Anglia and theSouth, East and Midlands of England. Spatio-temporal cluster analysis detected a singlesignificant cluster of seropositive animals dating from January 2006 to January 2011, whichcovered a large proportion of the farm locations. As well as confirming high overall levels ofinfection within the national flock, these findings also indicate possible temporal andregional variations in exposure of sheep to T. gondii.

Toxoplasmosis, a disease caused by the protozoan parasiteToxoplasma gondii, is considered to be the most common parasiticzoonosis worldwide (EFSA 2007). The parasite has a complexlife cycle, and although virtually all warm blooded animals canbe infected as intermediate hosts, sexual reproduction can onlyoccur in intestinal epithelium cells of the definitive host species,namely domestic cats and other felids. T. gondii has three mainstages, all of which are infective to both definitive and inter-mediate hosts. Infection can result from ingestion of sporozoitesfound in faecal oocysts of infected cats and bradyzoites thatform tissue cysts in intermediate hosts, while congenital infec-tion may occur through transplacental passage of circulatingtachyzoites in an acutely infected pregnant animal (Tenter andothers 2000, Dubey 2004, PHE 2011).

Human postnatal infection can result from ingestion of eitherviable tissue cysts in undercooked meat or sporulated oocysts infood or water contaminated with faeces from infected cats.Although serological tests capable of identifying sporozoite-specific antigen have recently been reported (Hill and others 2011),commonly used diagnostic tests are unable to differentiate

between oocyst and tissue cyst-derived infection and the relativeimportance of each of these sources of infection remains unknown(Cook and others 2000, Dubey 2004, Kijlstra and Jongert 2008).

Infection is usually asymptomatic in immunocompetentpeople and only mild flu-like symptoms are experienced duringthe acute stages in about 10 per cent of cases. However, infectionof a non-immune woman during pregnancy can result in con-genital disease, the clinical severity of which is inversely propor-tional to gestational age. Severe disease, including chorioretinitisand encephalitis, can also occur as a result of primary infectionor reactivation of chronic infection in immunosuppressed orimmunocompromised individuals (Montoya and Liesenfeld2004, PHE 2011, FSA 2012).

Seroprevalence of Toxoplasma infection in people varies con-siderably according to age and geographical location. Antibodiesto Toxoplasma can be detected in 20–40 per cent of UK adults,whereas seroprevalences as high as 77.4 per cent have beenreported in other areas of Europe (FSA 2012). Variation is alsoapparent at the national level, with blood donor sample surveysrevealing higher seroprevalences in western areas of Great Britaincompared to the east. These studies also indicate a positive, non-linear relationship between age and seroprevalence in the popula-tion of the UK, although in the absence of further studies it isnot clear whether this effect is due to falling levels of humaninfection over time or whether susceptibility to infectionincreases with age (EFSA 2007, FSA 2012). Although seropreva-lence data can be used to estimate levels of T. gondii infection inthe population, significant problems have been encounteredwhen trying to quantify the public health burden of toxoplas-mosis, largely as a result of under-detection and under-reportingof clinical, and in particular, congenital toxoplasmosis (B. Bénardand L. R. Salmi, unpublished report, EFSA 2007, FSA 2012).

Veterinary Record (2015) doi: 10.1136/vr.102114

J. P. Hutchinson, BVM&S, MVPH,MRCVS,Animal and Plant Health Agency(APHA), Whitley Road, Newcastle,NE12 9SE, UKR. P. Smith, BSc, PhD,APHA— Weybridge, Woodham Lane,New Haw, Addlestone, Surrey

KT15 3NB, UK

E-mail for correspondence:jpaulhutchinson@hotmail.co.uk

Provenance: not commissioned;externally peer reviewed

Accepted March 20, 2015

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Herbivores acquire T. gondii infection by ingestion of sporu-lated oocysts in contaminated pasture, feed or water, or congeni-tally by transplacental passage of tachyzoites from dam tofoetus. It is estimated that infection in sheep can result fromingestion of as few as 200 sporulated oocysts (McColgan andothers 1988). In a similar manner to human infection, exposureof immunologically naive sheep during early or mid-pregnancycan cause fetal death, stillbirth or the birth of weak lambs, whileclinically normal but infected lambs can occur as a result of con-genital infection during the later stages of gestation (Buxton andothers 2007, Innes and others 2009). Toxoplasmosis is therefore amajor concern to the UK sheep industry, with estimated lossesof £12–24 million resulting from early embryonic death andabortion of approximately 0.5 million lambs annually (Defra n.d.,Innes and others 2009). Veterinary Investigation DiagnosisAnalysis data for 2012 indicated that T. gondii infection wasresponsible for 18.5 per cent of all diagnosed cases of sheep andgoat fetopathy in Great Britain, with only Chlamydophila abortusand Schmallenberg virus accounting for a greater percentage oflosses (Defra 2013).

A recent serological survey of breeding ewes in Great Britainidentified seroprevalences of 74 per cent and 100 per cent at indi-vidual animal and flock level, respectively, while a similar studyof Scottish sheep indicated the same seroprevalence at flock leveland an overall seroprevalence of 56.6 per cent among individuals.The fact that both studies also showed an increasing seropreva-lence with age provides further evidence to suggest that mostinfections occur in the postnatal period (Hutchinson and others2011, Katzer and others 2011). Significant regional variation inwithin-flock seroprevalence to T. gondii infection in sheep hasalso been identified in some studies. In Scotland, for example,median within-flock seroprevalence ranged from 42.3 per cent inthe south of the country to 69.2 per cent in the north (Katzerand others 2011). Similar gradients of seroprevalence have alsobeen reported in sheep in France, in sheep and cervids in Finlandand in roe deer and moose in Norway (Vikøren and others 2004,Halos and others 2010, Jokelainen and others 2010).

Oocysts in freshly voided cat faeces are non-infective untilsporulation occurs, usually one to five days after shedding,depending on environmental conditions. Viability of sporulatedoocysts is in turn influenced by temperature, moisture and aer-ation, and they can remain infective for up to 18 months atoptimal climatic conditions (Lindsay and others 2002, Dubey2004, Buxton and others 2007, EFSA 2007, Innes and others2009, Meerburg and Kijlstra 2009). It has therefore been specu-lated that geographical differences in seroprevalence to T. gondiiinfection in herbivores could be related to climatic variation andits influence over oocyst sporulation, viability and dispersal, andthe ecology of arthropod and annelid transport hosts (Dubey2004, Buxton and others 2007, Innes and others 2009, Meerburgand Kijlstra 2009).

This paper describes the results of a study that used sero-logical data derived from sheep blood samples submitted to theAnimal and Plant Health Agency (APHA) (formerly the AnimalHealth and Veterinary Laboratories Agency) – Weybridge (APHA– Weybridge) laboratories in England and Wales during an eight-year period. The aim of the study was to identify overall levelsof seropositivity in sheep serum samples received for Toxoplasmaserology by APHA – Weybridge between May 2005 andDecember 2012 and to investigate possible temporal and spatialvariation in seropositivity to T. gondii infection. Statistical ana-lysis using meteorological data was also used to explore possibleassociations between climate and seropositivity in sheep.

Materials and methodsStudy population and sample selectionSheep serum samples submitted by veterinary practitioners andfarmers to the 14 APHA – Weybridge regional laboratories inEngland and Wales between May 2005 and December 2012 wereused for the study. All samples submitted for abortion

investigation or disease monitoring purposes where T. gondii ser-ology was subsequently performed were included in the study.

The date of submission was recorded for each submission.Reason for submission, County Parish Holding (CPH) number,farm address and postcode were also to be captured on the sub-mission paperwork. CPH or farm address details were availableto identify an individual farm for 824 of the 1043 submissionsreceived during the study period. This information allowed forthe identification of multiple submissions received from 73 hold-ings. Assuming that there were no multiple submissions fromholdings that submitted samples without CPH details, thedataset included 951 farms (Table 1). Data on age, breed and vac-cination status of the sampled animals were not recorded.

Each submission was linked to 1–47 samples (mean=4.2,median=3), giving a total of 4354 samples tested for Toxoplasma.

Serological testingSerum samples were tested by latex agglutination test (LAT);the standard ISO 17025 (UKAS) accredited test used by APHA –Weybridge to detect T. gondii-specific antibody (both IgM andIgG) in animal serum samples. A series of doubling dilutions(1:16 to 1:2048) was made for each serum sample. Samples exhi-biting significant agglutination at a serum dilution factor (anti-body titre) of 1:64 were defined as positive (Barker and Holliman1992, MAST diagnostics 2004).

Statistical analysis: identification of risk factorsA dataset for statistical analysis was produced with the follow-ing explanatory variables for each record: month, season andyear of submission and the Met Office geographical region(Region) of the sampled farm. Additionally, sinusoidal compo-nents (sine and cosine terms) for 3-, 6- and 12-month periodtemporal trends were added to each record to allow for the indi-vidual modelling of quarterly, half-yearly and yearly cycles(Chatfield, 2003).

Meteorological data of monthly regional summaries, includ-ing actual and ‘anomaly’ (difference from long-term averages)records, were gathered from the Met Office website (http:www.metoffice.gov.ukclimateukindex.html) for minimum, maximumand mean temperature (°C), hours of sunshine, rainfall (mm),days of rain and days of frost. These 14 explanatory variableswere linked to the Toxoplasma dataset by the Region of the farmand the month of sample collection. Records that did notcontain sufficient farm location information (CPH, postcode) tobe linked to a Region were omitted from risk factor analysis.

Univariable logistic modelling was completed on the datasetin Stata V.10 (StataCorp), using the xtmelogit command, toanalyse the spatial, meteorological and temporal variables todetect statistically significant associations with sample-levelToxoplasma presence (positive or negative). To account for mul-tiple samples being collected from the same holding,mixed-effects modelling was used, with the submission referenceincluded as the random effect to account for this non-compliance with the independence assumption. Any variableswith a P value under 0.20 were included in a multivariablemodel that could determine which variables remained signifi-cantly (P

results while accounting for the variance accounted for by theother variables included in the model. A backwards stepwise pro-cedure was used to remove variables from the model utilising alikelihood ratio test to test whether the removal of a variablewould not significantly (P >0.05) reduce the fit of the model. Ateach step, the variable that had the least effect on the model fitwas removed until only those variables remained for whichremoval would cause a significant difference to the model.

Statistical analysis: identification of clusters (spatialand spatio-temporal)The CPH of each record was linked to data recorded in APHA –Weybridge databases to determine the X and Y geographicalcoordinates for each sampled farm location to allow spatial ana-lysis. Where the CPH was missing from a record, the farmaddress and postcode information was used to determine the Xand Y coordinates. Records that did not contain either farm loca-tion or postcode information could not be linked to an X and Ycoordinate and were omitted from the scan statistic analysis.

To examine whether statistically significant spatial andspatio-temporal clusters were present in the results, analyses uti-lising the scan statistic were completed using SaTScan (Kulldorffand Nagarwalla, 1995, Kulldorff, 1997). The analysis identifiesthe position and size of the most likely clusters of high propor-tions of Toxoplasma seropositive samples by comparing the rela-tive risk of being Toxoplasma positive within a circular spatialwindow in comparison with the relative risk outside of the area.The most likely main cluster is identified when the relative riskis above that expected and the cluster has the maximum likeli-hood of representing the study population. Secondary clustersare also detected in this manner but only if they do not overlapthe main cluster. The standard maximum cluster size was used(50 per cent of the population at risk) and Monte Carlo simula-tion using 999 replicates was completed to assess the significance(P value) of the identified clusters (Kulldorff, 1997).

A discrete Poisson-based model, assuming that the number ofseropositive samples was proportional to the population ofsamples at each location, was used to model each sampleToxoplasma result at a farm location rather than classifying thefarms into a binary positive or negative status (Kulldorff, 1997).This model structure was used for both spatial and spatio-temporal analyses. For the spatio-temporal cluster detection,time was aggregated to the level of months. The spatio-temporal

analysis does not require population data to be specified at eachtime point and rather estimates the population using a linearinterpolation based on the population at the time points imme-diately preceding and immediately following to calculate thepopulation at risk at each time point.

ResultsPopulation descriptionOf the 4354 samples tested, 2361 (54.2 per cent) were seroposi-tive and came from 807 (77.4 per cent) of the 1043 submissions(1–18 per submission). Reason of submission was available for3048 samples. Of these, 2558 (84.0 per cent) were classified asdiagnostic and 489 (16.0 per cent) as monitoring. There was noappreciable difference in the proportion of seropositive sampleswithin each group (46.1 per cent for diagnostic samples com-pared with 45.1 per cent for monitoring samples).

Identification of risk factorsA population of 899 submissions could be linked to a Regionwith meteorological summaries (2067 positives from 3705samples). This included 18 samples (0.49 per cent) submitted toEnglish and Welsh APHA – Weybridge laboratories that origi-nated from farms located in Scotland. Univariable mixed-effectsmodelling was completed and three of the explanatory variablesin the dataset had a P value

cycles). However, the results by year did show that there was anon-significant reduction in the percentage of seropositiveresults in 2012 (48.7 per cent), whereas the results from 2005to 2011 ranged from 51.4 to 56.8 per cent. None of themonthly meteorological summaries were found to be signifi-cantly associated with the Toxoplasma serology results,although the ‘anomaly ’ summary of the number of days ofrain was positively associated with a greater odds of being sero-positive, although not below the 0.05 significance level(Tables 2 and 3).

Spatial and spatio-temporal cluster analysisThe Scan statistic was completed on the 884 submissions (2040positives from 3649 samples) with X and Y coordinates. Thespatial cluster analysis identified a significant cluster (P

have occurred through the inclusion of vaccinated animals in thesurvey. The LAT method is unable to differentiate betweenvaccine-induced and naturally occurring antibody and studieshave shown that antibody titres in previously naive sheep peakat 1/64 within two weeks of vaccination and fall to 1/32 orbelow within 12 weeks (Maley and others 1997). However, itwas considered likely that the attending veterinary surgeon willhave considered the probability that the animal was vaccinatedbefore submitting the sample and therefore, although the pro-portion of vaccinated animals was not known, it is likely to belower than the 6.2 per cent of vaccinated animals identified in arecent similar serological survey of British breeding ewes(Hutchinson and others 2011). The use of LATon sheep sera wasfurther complicated by the lack of sensitivity and specificitydata. Although a test sensitivity of 99 per cent and specificity of81 per cent has been described by Barker and Holliman (1992),these data related to the use of human sera and are not applic-able to sheep. It was therefore not possible to estimate the pos-sible effect of false positive or false negative results on theoverall levels of seropositivity. The possibility of false positiveresults arising due to cross-reaction with other coccidian para-sites should also be considered since reactions with unspecifiedIgM have been reported when using LATon human sera (Barkerand Holliman 1992, EFSA 2007). However, notwithstandingthese limitations, the overall proportion of 54.2 per cent positivesamples identified by this study provides further evidence ofhigh levels of exposure to T. gondii within the national flock asdescribed previously (Hutchinson and others 2011, Katzer andothers 2011).

The univariable and multivariable models did not differ forwhich responses of the variables were significantly associatedwith Toxoplasma seropositivity, although the multivariablemodel presented adjusted outcomes once the effect of the othervariables in the model had been accounted for. The sampleresults indicated that a possible seasonal variation in Toxoplasmaseropositivity was present in the results, with samples collectedin spring (March–May) shown to have a low proportion of sero-positives and those collected in winter (December–February)having the highest. However, this was not found to be signifi-cant in the univariable model when multiple samples fromwithin the same submission were taken into consideration. It isinteresting to note that although the multivariable model indi-cated that samples taken during November were significantlyless likely to test positive than the baseline, the percentage ofpositive samples was not consistently the lowest in Novemberof every year. It should also be noted that samples collected inFebruary had a low odds of being positive, which was approach-ing significance (P 0.096). Seasonal variation in the prevalence ofacute toxoplasmosis has previously been reported in people, butthere is little or no evidence to suggest that similar fluctuationsoccur in sheep, and since the LAT test is not capable of distin-guishing between IgM and IgG antibody, it was not possible todistinguish between recently acquired and historic infection inthis study (Meenken and others 1991, Logar and others 2005,Bobić and others 2010).

The ‘anomaly ’ summary of rainfall was the only meteoro-logical summary variable included in the multivariable modeland was positively associated with seropositive results, althoughthe results were only approaching significance (P=0.062). Thismay, however, have been influenced by a lack of resolution as theweather data came from monthly regional summaries ratherthan specific weather conditions at that farm prior to submis-sion. The multivariable model also highlights that North WestEngland and northern Wales had the lowest odds of being sero-positive and that this was significant when compared againstthe baseline of East Anglia. No other Regions had a significantdifference detected, although the low odds in the Midlands wasapproaching significance (P=0.089).

As not all submissions had information provided that couldlink them to an individual farm, it was not possible to use afarm identifier in the model to account for multiple submissions

from the same farm. Although the use of the submission identi-fier improved the fit, it is possible that the addition of a farmidentifier as a random effect may have further improved themodel fit.

The significant spatial cluster of seropositive samples inSouthern and Eastern England appears to reflect geographicalvariation in seroprevalence as previously identified in Scotland,France, Finland and Ghana (Halos and others 2010, Jokelainenand others 2010, Katzer and others 2011, Van der Puije 2000).Although differences in experimental approach, local husbandrypractices and cat population density preclude direct comparisonof previous surveys with the results of this study, it is neverthe-less possible that these findings may in part be related to theinfluence of environmental temperature and precipitation onoocyst sporulation, viability and dispersal (Dubey 2004, EFSA2007, Meerburg and Kijlstra 2009). Although the relative risk forthe cluster was small (1.27), it was related to a large populationof samples and that may explain why this cluster was statistic-ally significant.

The spatio-temporal cluster detected covered over half of thefarm locations in the dataset and ranged across a five-year period.The relative risk of a sample from the cluster being seropositivewas large compared with those submitted outside of it. Thecluster shape and size provides a useful description (especiallyafter graphical presentation) that presents such issues as the reduc-tion in seropositivity in the final year of study and in samplesfrom the geographical locations not covered by the cluster. In viewof the fact that the ‘anomaly’ summary of rainfall results wereapproaching significance, it is tempting to speculate this cluster ofseropositive samples may have resulted from increased exposuredue to favourable climatic conditions for oocyst survival and dis-persal in the five-year period (Dubey 2004).

These spatial and spatio-temporal results highlight thebenefit of using the scan statistic, which can detect significant‘hot spots’ of positive results in time and space without beinglimited by artificial regional boundaries such as those used in themodelling analysis. These analyses could be used in the future todetect clusters that require further investigation, especiallywhere a cluster is large and may not have been detected at alocal level.

It is possible that the interpolation of the population in thespatio-temporal model overestimated the relative risk of thecluster by under-representing the population at risk around thesubmission periods within the study period. However, the ana-lysis was still useful at identifying the most likely cluster.Although reducing the maximum cluster size from 50 to 40 percent did not affect the spatial aspect of the spatio-temporalcluster, it did reduce the scale of the number of months in thecluster. This may indicate that the geographical size of an out-break was more important than the temporal duration. Both thespatial and spatio-temporal analyses may have been limited bythe use of only a circular scanning window as this would havereduced power in detecting long and narrow clusters, and afurther improvement to the cluster detection method wouldhave been the use of other cluster shapes.

The results of the analyses may have been biased by theselection of the population at risk. As only a minority of samplescame from healthy animals tested for monitoring purposes, therisk may have been overestimated by using samples that weremore likely to have Toxoplasma. However, the proportion of sero-positives detected in the diagnostic samples from ewes withabortions was similar to the proportion from the monitoringsamples. The studied population also did not cover many sheepfarms and the Toxoplasma status on these farms may have influ-enced the size and shape of the detected clusters. The lack ofsubmissions from these farms may indicate a lack of abortionsand subsequently a reduced risk of Toxoplasma, and so if these‘missing’ farms were present within the identified cluster, then itmay have reduced the relative risk and significance. The datasetmay also have contravened scan statistic Poisson model assump-tions that the number of seropositives was proportional to the

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size of the population and that multiple submissions from thelocation would be independent. It would be likely that on a farmif one animal is seropositive then it is more likely that otheranimals were also infected. The management and conditions ona farm might also mean that the farm would be more likely tohave multiple occasions of abortion problems and so wouldprovide multiple submissions to the study population. Thesefactors would have over-represented these submissions or farmsin the scan statistic analysis.

Unfortunately, there was insufficient data to examine otherpotentially important variables that might influence levels of T.gondii exposure at a regional level. For example, husbandry prac-tices such as presence of cats on the farm, use of surface drinkingwater sources, housing during lambing and size of farm have pre-viously been identified as risk factors associated with seropositiv-ity to T. gondii (Skjerve and others 1998, Van der Puije and others2000, Vesco and others 2007, Dubey 2009, Katzer and others2011). Despite considerable regional variation in preference forparticular sheep breeds throughout England and Wales, there isno evidence to suggest that certain breeds are more susceptibleto T. gondii infection than others and this was therefore consid-ered unlikely to influence distribution of seropositive samples(Katzer and others 2011).

These findings are particularly relevant in view of theEuropean Food Safety Authority recommendation that all EUMember States should monitor T. gondii infection in animalsentering the food chain (EFSA 2007). While it is not possible todirectly correlate seropositivity in sheep with the presence ofviable tissue cysts in meat, the high levels of exposure to T. gondiiidentified in this study serve to reinforce public health adviceaimed at reducing the risk of infection, particularly in pregnantwomen and other vulnerable groups. Likewise, the scale of thethreat posed to pregnant sheep should not be underestimated,and although control of toxoplasmosis at the level of primaryproduction is difficult, farmers and veterinary surgeons shouldcontinue to promote appropriate husbandry and vaccinationstrategies. In common with previous studies involving sheep andother species, this survey also indicates possible regional varia-tions in levels of infection and provides a basis for future investi-gations, which may further inform our understanding of theepidemiology of T. gondii.

AcknowledgementsThe authors wish to thank colleagues within APHA – Weybridgewho were involved with this project, particularly JonathanDrake for his assistance with the database and data extraction.

Funding This survey was funded by the UK Department for Environment, Food andRural Affairs via project FZ2100.

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Seropositivity to Toxoplasma infection in sheep samples submitted to Animal and Plant Health Agency laboratories between 2005 and 2012SummaryMaterials and methodsStudy population and sample selectionSerological testingStatistical analysis: identification of risk factorsStatistical analysis: identification of clusters (spatial and spatio-temporal)

ResultsPopulation descriptionIdentification of risk factorsSpatial and spatio-temporal cluster analysis

DiscussionReferences