Herd and individual animal risks associated with bovine tuberculosis skin test positivity in cattle in herds in south west England

Preventive Veterinary Medicine 92 (2009) 188–198

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Herd and individual animal risks associated with bovine tuberculosis skin test positivity in cattle in herds in south west England A.M. Ramı´rez-Villaescusa *, G.F. Medley, S. Mason, L.E. Green Department of Biological Sciences, University of Warwick, Coventry CV4 7AL, UK

A R T I C L E I N F O

Keywords: Bovine tuberculosis Cattle movement Cattle to cattle transmission Multilevel analysis

A B S T R A C T

The aim of this study was to investigate the cattle—exposure factors associated with the risk of a bovine animal reacting to a bovine tuberculosis (bTB) skin test at a whole herd test. There were 148 study farms enrolled. These were located in six counties of the south west of England in an area considered endemic for bovine tuberculosis (bTB): 24% were restocked after foot and mouth disease (FMD) in 2001; all farms were located within the Randomised Badger Culling Trial (RBCT) area. Data on cattle on these farms were sourced from the bTB Vetnet database from 1996 to 2004 and from the British Cattle Movement Scheme database. Individual animal records were created that included data on whether or not an animal became a reactor at a full-herd bTB test between 1 June 2001 and 19 August 2004, their prior exposure to cattle with bTB (defined by presence at a bTB test where at least one reactor was detected), whether the animal was homebred, the farm history of bTB and the farm restocking status. Data from 144 farms were used, 4 farms had no data. Cattle were more likely to react to the bTB skin test when they had been present at a previous bTB herd test (or tests) where other cattle had reacted to the skin test. This positively correlated with age and the number of bTB tests an animal had had. Cattle on restocked farms were less likely to react to the skin test compared with cattle on continuously stocked farms. These results highlight the likely importance of exposure to infected cattle at a previous test as a source of infection to cattle that subsequently became reactors and suggest that there was a lower risk of exposure to bTB to cattle in newly formed herds. ß 2009 Elsevier B.V. All rights reserved.

1. Introduction Bovine tuberculosis (bTB) is an infectious disease of cattle caused by Mycobacterium bovis, an important transmissible disease due to its potential public health, socio-economic and animal trade implications. In the UK, bTB is detected through a routine testing regime and through abattoir surveillance. Despite the efforts to control bTB, it remains one of the most problematic diseases for the cattle industry in Britain. The incidence of bTB in cattle

* Corresponding author. Tel.: +44 (0) 24765 23797; fax: +44 (0) 24765 24619. E-mail address: [email protected] (A.M. Ramı´rez-Villaescusa). 0167-5877/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.prevetmed.2009.08.011

has increased over the last 15 years by an average of 14% per year (Defra, 2005a), with an annual incidence of 2.5% new confirmed herd breakdowns (HBD) on unrestricted herds in 2007 (Defra, 2008). This is most obvious in the south west region of England, where the majority of HBD occur. A HBD occurs when a herd that previously tested negative has at least one skin test positive reactor. A herd is restricted after a HBD or after an animal is detected with visible bTB lesions at slaughter, and the herd is then repeatedly tested until there are no further skin test positive cattle for one or two short interval herd tests. At this point movement restrictions are lifted (Defra, 2005b). Routine testing of cattle for bTB is regular in a parish (at 1, 2 or 4 year intervals; reduced intervals are triggered by increased incidence of HBD in a parish) but not random, with up to 80% of cattle in GB not tested in their lifetime

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(Mitchell et al., 2006). In addition, unrestricted herds are targeted for testing where bTB is considered a risk (for example, because a herd is newly formed or the herd has purchased an animal from a herd that later had a HBD or because a herd is contiguous with a herd with a HBD). Consequently, movement of cattle also affects the likelihood of an individual animal being tested, because movement can result in cattle missing tests or being tested more frequently. Cattle movements play an important role in the spread of bTB (Gilbert et al., 2005; Green and Cornell, 2005; Gopal et al., 2006), particularly into areas of the UK where bTB is not endemic. The foot and mouth disease (FMD) outbreak in 2001 (Gibbens et al., 2001; Keeling et al., 2001) led to an unusual depopulation and consequent restocking of cattle herds mainly in the north and, to a lesser extent, in the south west regions of England. Carrique-Mas et al. (2008) reported that herds that restocked after FMD had a 10-fold risk of HBD when they purchased at least one bovine animal from herds that had been tested for bTB at a frequency of more than once every 2 years for the previous 8 years. This was the only associated risk (detected from data stored in the national databases) in farms which had never had a HBD with bTB. Cattle-to-cattle transmission also contributes to the maintenance of bTB throughout the UK (Goodchild and Clifton-Hadley, 2001; Green and Cornell, 2005; CarriqueMas et al., 2008). Most bTB lesions at post-mortem inspection are in the respiratory tract, suggesting that aerosol transmission is the main route of transmission to cattle (Menzies and Neill, 2000; Cassidy, 2006). There is also evidence from experimental studies that cattle to cattle transmission occurs (Pollock et al., 2006) and that just a single particle, containing approximately five bacteria, may be sufficient to cause infection (Dean et al., 2005). Consequently, it is almost certain that some cattle to cattle transmission occurs. This route of transmission was considered sufficient to maintain infection endemically in parts of the UK before the 1960s (Anon., 1965) and might still be according to some theoretical models (Cox et al., 2005). The long inter-test intervals for regular tests were associated with a high risk of HBD and an increased number of cattle detected in Shropshire in the late 1990s (Green and Cornell, 2005) and, this might be sufficient to permit endemic infection through cattle to cattle transmission. In addition, all infectious cattle are not detected, either because they are not tested (Mitchell et al., 2006), or because some truly infected cattle test negative because of the relatively low sensitivity of the skin intradermal comparative cervical tuberculin test (SICCT). The SICCT test is estimated to be more than 99% specific (Wood and Rothel, 1994), but with a sensitivity of approximately 74–95% (Monaghan et al., 1994; Costello et al., 1997). In many areas another source of infection, concurrent with cattle to cattle transmission, is the environment. Carrique-Mas et al. (2008) demonstrated a residual farm effect in restocked herds that had had a history of bTB, indicating a source of infection that persisted after the herd was removed. The environmental sources that have been proposed are contaminated soil (Courtenay et al., 2006), neighbouring stock and badgers (Griffin et al., 2005; Donnelly et al., 2006). The Randomised Badger Culling Trial

189

(RBCT) was initiated in 1998 in the south west England to investigate the contribution of badgers to reactions to the bTB SCITT (Krebs et al., 1997; Bourne et al., 1999). The study involved 30 trial areas, of approximately 100 km2 each, with groups of 3 trial areas (triplets) in 10 geographical areas (identified as A–J). Each trial area within a triplet was randomly allocated to one of three treatments: localised reactive culling of badgers in response to a HBD, widespread proactive culling of badgers throughout the area or surveillance of badgers and no culling. There was a reduction in the incidence of bTB in herds in areas under proactive treatment of 19%, but an increase in the incidence in herds on land bordering to those in the proactive of 29% (Donnelly et al., 2006). There was an increase in the incidence of HBD of 27% associated with reactive culling (Donnelly et al., 2003). Similar results from areas under proactive treatment were also reported from a badger trial carried out from 1997 to 2002 in Ireland (Griffin et al., 2005) suggesting that removal of a stable infected population of badgers may reduce bTB infection in cattle by approximately 20% but that a perturbed group of badgers may increase bTB infection by approximately 30%. The most recent analyses of these areas indicate a continued reduction of HBD in the proactively culled areas and a non-significant effect in the margins of these areas, indicating a time dependent effect on badger culling and HBD (Jenkins et al., 2008; Kelly et al., 2008). The mechanism by which M. bovis is transmitted between badgers and cattle is not well understood (More and Good, 2006) but the results of the RBCT and more recent studies in Ireland investigating the impact of culling badgers suggest that only if a large proportion of badgers are removed for a prolonged period of time will there be a reduction in HBD. This reduction is not 100% and so control of bTB still requires good control of bTB in cattle. These results, and the social unacceptability of culling badgers in GB (Defra, 2007), suggest that control of cattle to cattle transmission of bTB is a necessary and acceptable approach for control. Consequently, elucidating cattle to cattle mechanisms for the spread of bTB continue to be very pertinent to UK science delivering policy. The current study was conducted to investigate herd and individual animal risk factors for reacting to the SICCT, in particular, factors related to cattle to cattle transmission. 2. Materials and methods 2.1. General methods 2.1.1. The reference farms The reference farms included farms wholly located within the trial areas of the Randomised Badger Culling Trial. The RBCT was carried out in high bTB incidence areas in the south west of England (Cornwall, Devon, Somerset and Gloucestershire) and the West Midlands (Herefordshire and Worcestershire). Some farms in these counties had been affected by the FMD epidemic in 2001 and as a consequence, were fully depopulated in 2001; over 80% of these were subsequently restocked by 2003. The Veterinary Laboratory Agency (VLA) provided a list of all farms that had destocked and restocked with cattle, and for each

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restocked farm the VLA randomly identified three continuously stocked farms (i.e., not depopulated in 2001) that were more than 1 km distant from the restocked farm (using a geo-reference, in most instances the distance would be between the buildings associated with each farm). In addition, there were some continuously stocked farms that agreed to participate where the restocked farm had not agreed to participate. These were included in the analysis and so matching was not performed. This gave sufficient power to detect a 2-fold difference in risks assuming a 30% HBD with 80% power and 95% significance. 2.1.2. The study farms Study farms were recruited between November 2002 and October 2003. The farmers on each of the reference farms were contacted by post, at farmer meetings and by telephone, and invited to participate in the study. Each trial area and study farm was identified by codes, but the nature of badger control was not disclosed until the statistical analysis was performed. Of the 471 reference farms, 148 were enrolled into the study. The 148 study farms were located in each of the 6 counties where the RBCT was conducted, but in only 6 (A, B, C, H, I and J) of the 10 RBCT triplets, and 11 of the 30 RBCT trial areas (37.2% were in reactive treatment areas, 27.7% in proactive and 35.1% in survey only). The number of study farms by restocking status and RBCT treatment and trial area is presented in Table 1. A full-herd test was not conducted on 4 of the 148 study herds (2 of these were restocked) during the study period (2 were last tested before June 2001 and 2 had individual animal tests only); these herds were not included in the analysis. 2.1.3. Study animals and study period All cattle that were tested at a full-herd bTB test on at least one occasion between 1 June 2001 and 19 August

Table 1 The number of study farms by restocking status and RBCT treatment and trial area. RBCT Treatment

Restocking status Trial area

Continuously stocked

Total Restocked

Reactive A1 B1 C1 I1

17 9 3 15 44

6 2 1 2 11

23 11 4 17 55

A3 B2 J1

2 19 8 29

1 10 1 12

3 29 9 41

B3 H3 I3 J3

2 15 4 18

1 3 2 7

3 18 6 25

11

39 112

13 36

52 148

Subtotal Proactive

Subtotal

2004 were enrolled into the study. These animals are the ‘study animals’, and each of the tests conducted on a study animal during a full-herd bTB test is an ‘animal test’. 2.2. Data collection and management 2.2.1. Data collection Data were extracted from two sources, the Cattle Tracing System (CTS) of the British Cattle Movement Service (BCMS) and the VetNet databases. The CTS database provides a record of all movements of cattle registered in or imported into GB, including movements on and off farms, to and from markets, and to abattoirs. The CTS data were available from 1 July 1996 to 4 August 2004. The dates of entry to (either birth, for homebred animals, or inward movement) and exit from (either on-farm death or outward movement) the study farms were used to determine the period of residence of each study animal. Study animals were assumed to be on the farm at the end of the study (19 August 2004) unless an earlier exit date was recorded. For each study animal, data were also collected on sex, breed, and any movements, before enrolment into the study. Cattle with missing import data were excluded from the study. The VetNet database provides a record of events relating to national bTB control, including all test results at full-herd tests (coded VE-SI, VE-CON, VE-CON12, VECON6, VE-CT, VE-6M, VE-12M, VE-RHT and VE-WHT; see Table 2 for definitions) and tests that target individual animals (i.e., VE-IR (on inconclusive reactors), VE-TR (on bovines moved from confirmed HBD prior to serving restrictions), VE-PII (on bovines imported from Northern Ireland and Republic of Ireland), VE-PRI (private test), VESLH (pseudo-test type confirmed by culture in a suspect slaughterhouse case) as defined in DEFRA (2005b). At a full-herd test, except VE-SI (short interval) tests, animals under 6 weeks of age are not tested. The VetNet data were available from 1 January 1995 to 19 August 2004. During the study period, full-herd tests conducted on each of the study farms were considered ‘eligible outcome tests’. The VetNet database provides a record of positive, but not negative, animals at each test. So, identifying whether an animal was on a farm and tested was done indirectly by identifying all animals, and their age, that were present on the study farm at the time of the bTB herd test (Mitchell et al., 2006). 2.2.2. Data management The data were managed using a relational database in Access (Microsoft Corporation, Redmond, WA, USA). A dataset was created with a hierarchical structure arranged in three levels: animal-test (level 1), animal (level 2) and herd (level 3). The dataset was imported into the multilevel statistical package MLwiN Version 2.0 (Rasbash et al., 2004).

Survey only

Subtotal Total

2.3. Statistical analysis 2.3.1. The outcome variable The outcome variable was binary: a bovine animal had a positive skin reaction at a bTB test or not. This was not necessarily confirmed by lesions at post-mortem

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Table 2 Distribution of 156,562 animal tests in 697 herd tests and 723 reactors by test type. Test typea Yearly routine test VE-WHT VE-RHT VE-12M VE-CON12 Subtotal

Number of animal tests

% of all animal tests

Number of full-herd tests

Number of reactors

Reactors/1000 animal tests

17,504 1,442 4,378 9,028

11.18 0.92 2.80 5.77

102 8 23 48

56 2 15 24

3.20 1.39 3.43 2.66

32,352

20.67

181

97

3.00

Short-interval tests VE-SI

84,631

54.06

310

392

4.63

Other strategic tests VE-CT VE-6M VE-CON VE-CON6

13,101 18,242 3,000 5,236

8.37 11.65 1.92 3.34

61 85 28 32

109 84 13 28

8.32 4.60 4.33 5.35

39,579 156,562

25.28 100.00

206 697

234 723

5.91 4.62

Subtotal Total

a Test type carried out: VE-WHT routinely and every 12 months in annual parishes; VE-RHT in parishes with a 24, 36 or 48 months interval testing; VE12M 12 months after VE-6M if that test was clear; VE-CON12 12 months after a VE-CON or VE-CON6 (if the latter has been carried out); VE-SI 60 days after removal (or effective isolation) of the last reactor or following confirmation of bTB whilst herd under restrictions; VE-CT outside the normal testing frequency for the herd, to determine its disease status, when there is suspicion of infection on certain situations; VE-6M 6 months after restrictions have been lifted following a clear short interval test; VE-CON on herds contiguous to a confirmed bTB incident outside their regular test frequency; VE-CON6 at the discretion of the DMV 6 months after a VE-CON.

inspection or culture. Although an animal could be tested several times, it could be a reactor only once. 2.3.2. The explanatory variables Thirteen explanatory variables were considered in the analysis, relating to the animal, animal by test, herd and herd by test. Animal variables included sex, homebred or purchased, and the number of movements prior to purchase (none if homebred, one, two and three or more for purchased cattle). These are listed in Table 3. Animal by test variables were the age in years of the animal at the current test, the exposure to reactors at a previous test (for homebred animals this included only bTB tests conducted on the study farm but for purchased animals this also included all tests on the study farm and previous tests on herds where the study animals were prior to purchase), the number of years since an animal had been exposed to a reactor (for homebred animals the study farm, for purchased animals all farms including the study farm and all earlier source farms), with categories including not tested, a positive previous test in the same year as the current test, 1 year before, 2, 3 or 4 or more years since the current test. The tests were used back to 1996. Herd variables included the purpose of the herd (dairy, suckler or both), restocking status (whether the farm was restocked or continuously stocked as a result of the FMD epidemic in 2001), the RBCT treatment (reactive, proactive, survey only), the RBCT triplet location and whether bTB had been diagnosed before June 2001. Herd by test variables included test type (yearly routine test, short-interval or another strategic test conducted more frequently that yearly) and the herd size (calculated from VetNet as the average number of cattle present at all full-herd tests during the period of interest).

Variables that were statistically significant (p  0.05) at the univariable level were investigated in the multivariable analysis. 2.3.3. Multilevel analysis Binomial logistic regression with random effects was carried out using the statistical package MLwiN version 2.0 (Rasbash et al., 2004). The analysis was implemented by using a Generalised Linear Mixed Model (GLMM) with a 3level hierarchical structure and an unstructured covariance structure. In MLwiN, 1st order marginal quasi likelihood (MQL) estimates were derived using Iterative Generalised Least Square (IGLS). The outcome variable was the probability of an animal being a reactor at a test. The model took the form: X X X Logitð pi jk Þ ¼ b0 þ bX k þ bX k j þ bX ik j þ vk þ uk j þ ei jk where pijk = probability that animal j from farm k is a reactor at test i, with b0 as the intercept; bX as a series of vectors of fixed effects varying at level 1 (ijk), 2 (jk) or 3 (k), and vk þ uk j þ ei jk as levels 3, 2 and 1 residual variances, respectively. Once the final model was built, Monte Carlo Markov Chain (MCMC) with Gibbs sampling was used to adjust the conservative estimates of the standard errors (Browne, 2004). The model was run for 70,000 iterations and a burn-in period of 5000. The precision of the parameter estimates was assessed using kernel density plots. The model was re-run with a data point with a large residual value (a herd with 78 reactors at a test) absorbed in the model as a dummy variable. During model development, we checked visually for correlation and confounding by change in coefficient values and investigated this when necessary. The observed and expected

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Table 3 Descriptive analysis of explanatory variables investigated on 156,562 animal tests using 697 herd tests in 144 herds and 48,055 individual animals. Type of explanatory variable

Number of animal tests

Number of reactors

115,067 41,495

650 73

5.6 1.7

Birth location Purchased Homebred

71,463 85,099

384 339

5.4 3.9

Movements prior to purchase None (born on study) Once Twice Three or more

85,099 28,984 14,456 8,230

339 121 72 25

3.9 4.2 4.9 3.0

Animal varying by test Age at test Up to 1 year old >1–2 >2–3 >3–4 >4–5 >5–6 >6

41,754 38,695 19,222 13,381 11,067 8,622 20,616

23 68 102 118 116 81 189

0.5 1.7 5.3 8.8 10.5 9.4 9.2

Exposure to reactors prior to the test Not exposed Exposed

59,439 96,908

181 542

3.0 5.6

Years since farm last bTB positive Never Not previously tested Same year as current test One year since current test Two years since current test Three years since current test Four or more years since current test

53,152 22,760 43,203 13,346 8,499 6,089 9,513

304 163 105 64 33 28 26

5.7 7.2 2.4 4.8 3.8 4.6 2.7

Herd Purpose of herd Suckler Dairy Both

69,070 84,237 3,255

194 510 19

2.8 6.0 5.8

125,249 31,313

666 57

5.3 1.8

RBCT intervention treatment Survey Reactive Proactive

51,560 56,956 48,046

193 270 260

3.7 4.7 5.4

RBCT triplet A B C H I J

29,883 40,764 6,818 19,820 14,880 44,397

129 120 29 96 115 234

4.3 2.9 4.2 4.8 7.7 5.3

47,035 109,527

184 539

3.9 4.9

32,352 84,631 39,579

97 392 234

2.9 4.6 5.9

Animal Sex Female Male

Restocking status Continuously stocked Restocked

bTB diagnosed before June 01 No Yes Herd varying by test Full-herd test type Yearly SI (short interval) Other strategica

Reactors per 1000 animal tests

Selected for multivariable analysis –

U

–

–

U

–

–

U

U

–

–

U

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Table 3 (Continued ) Type of explanatory variable

Herd size <151 151–300 >300 a

Number of animal tests

Number of reactors

Reactors per 1000 animal tests

Selected for multivariable analysis –

28,926 56,117 71,519

109 273 341

3.7 4.8 4.7

Other strategic tests: includes tests other than short interval tests and yearly tests.

values were divided into deciles sorted on the expected values. Model fit was then assessed by calculating the Pearson’s Chi-square (Hosmer and Lemeshow, 2000). 3. Results 3.1. Descriptive results A total of 697 full-herd tests were conducted in the 144 study herds between 25 June 2001 and 3 August 2004 during the study period. The number of herd tests per year varied, 28 (4.0%) in 2001, 203 (29.1%) in 2002, 313 (44.9%) in 2003 and 153 (22.0%) in 2004. Over the study period, 564 (80.9%) and 133 (19.1%) herd tests were conducted in continuously stocked and restocked herds, respectively. In these herds, the median number of tests was 4 and 3, respectively. There were 70 herds that did not breakdown during the course of the study. The number of reactors per test ranged from 1 to 78 with median and IQR of 0 and 0–1, respectively. Of 48,055 study animals, 29,313 (60.9%) were female and 25,469 (52.9%) were purchased. Of those purchased, 12,756 (50.0%) were located on restocked farms and 15,425 (60.5%) were beef cattle. The date of birth was not available for 662 (1.6%) purchased animals. Of the 47,393 study animals with a known date of birth, 10,007 (21.1%) were moved onto study farms when they were less than 1year-old. Approximately 74% (33,206/44,959) animal tests were carried out on homebred cattle 1-year-old and 12.50% (2,579/20,616) were carried out on homebred cattle over 6 years old. In total, 156,562 animal tests were conducted on 48,055 animals (many animals were tested at more than one fullherd test) in these 144 study herds, to disclose 723 reactors (0.46% of all animal tests). The number of animal tests and the rate of reactors varied by full-herd test type (Table 2). During the study a further 53 reactors were disclosed at tests other than full-herd tests and were not included as outcomes, exposure to these cattle was included as the exposure explanatory variable. Out of the 156,562 animal tests, 125,249 (80%) were carried out on continuously stocked herds. The number of animal tests in each of the three treatments of the RBCT was 56,956 (36.37%), 48,046 (30.68%) and 51,560 (32.94%). In Table 3, the descriptive results for the 13 explanatory variables that were tested in the multivariable analysis are presented. The number of reactors per animal test increased with age and number of previous exposures; these were highly correlated. The 723 reactors were disclosed at 204 (29.3%) of the 697 full-herd tests; 650 (89.9%) of the reactors were female

and 510 (70.5%) were dairy cattle. Most reactors (666, 92.1%) were disclosed in continuously stocked herds. Approximately half (384, 53.1%) of the reactors were purchased, including 243 that had been exposed to bTB on the study farm and 141 that had not. Among the 243 purchased reactors exposed on the study farms, 32 (3866 animal tests, 8.3 reactors per 1000 tests) were also exposed to cattle with bTB on source farms, 56 (9792 animal tests, 5.7 reactors per 1000 tests) were not, and for 155 reactors (21,807 animal tests, 7.1 reactors per 1,000 tests) the exposure status from the source farms was unknown because no tests were carried out whilst the animal was on those farms. Among the 141 purchased reactors that were not exposed on the study farm, 21 (4973, 4.2 reactors per 1000 tests) had been exposed to bTB on the source farm(s), 39 (12,033, 3.2 reactors per 1000 tests) had not been exposed to bTB on the source farm(s) and for the other 81 reactors (18,858, 4.3 reactors per 1000 tests) the status was uncertain because no tests were carried out whilst the animals were on the source farms. Most of the reactors that were homebred (278 from a total of 339) had been exposed to reactors disclosed at previous tests. 3.2. Multilevel analyses Explanatory variables presented in Table 3 were first analysed at univariable level. Variables that were associated with an increased risk of an animal becoming reactor were female vs male cattle, purpose of the herd as dairy or mixed vs suckler alone. The risk also increased with an increased number of movements of an animal prior to purchase onto a study farm vs homebred and also increased with the age of animals when tested. These variables were correlated and so only variables that were statistically significant at the univariable level and that could help to elucidate the risk of becoming a reactor further (i.e., excluding age, sex and purpose of herd), were included in the multivariable analysis. The RBCT treatment was forced into the model because it was a farm selection criterion for the study. Multivariable analysis results for the final model are presented in Table 4. The odds ratios are the adjusted risks per animal test. There was a significant increased risk of an animal being a reactor at a test when it had been exposed to a reactor at least one previous test whether purchased or homebred compared with cattle that had not been exposed to any reactors (OR = 1.34, 95% CI = 1.04–1.74). Homebred cattle were at a lower adjusted risk of becoming a reactor at a test compared with purchased cattle (OR = 0.34, CI = 0.25–0.47) when exposure was included in the model and there was a significant interaction between ‘‘home-

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Table 4 Multivariable multilevel logistic regression analysis of 156,562 cattle tests within 48,055 cattle tested at 697 herd tests in 144 herds. Explanatory variable Animal Birth location

Animal by test variables Exposed to at least one reactor at a previous test

Herd by test variables Test type

Herd variables Restocking status

RBCT treatment

Interaction between previous exposure to reactors and born on farm Variance between herds Variance between animals within herds

bred vs purchased’’ and ‘‘exposed to previous reactors vs not exposed’’ that indicated that exposure to previous reactors increased the risk for homebred cattle by 2.2-fold (=1.34  1.65) if exposed and homebred compared with exposed and purchased which had an increased risk of 1.8. Cattle in restocked herds had a reduced adjusted risk of becoming reactor (OR = 0.33, CI = 0.17–0.64) compared with cattle in continuously stocked herds. There were no significant effects of the RBCT treatment although the OR were >1 for both reactive (OR = 1.63, CI = 0.88–3.01) and proactive (OR = 1.60, CI = 0.82–3.10) treatments compared with cattle tested on farms in the survey only triplets. Cattle were less likely to be a reactor at a short interval test (OR = 0.54 and CI = 0.41–0.72) and more likely to be a reactor if tested using ‘‘another strategic tests’’ (OR = 1.40, CI = 1.08–1.83) compared with cattle tested at yearly tests. The Pearson’s Chi-squared tests for the predicted vs observed outcome values for the variance both between herds and between animals within herds, suggested that the model predictions at herd level did not differ significantly from the observed data, whereas at animal level, there was a statistically significant difference between the predicted and observed outcome values. The result for residuals at level 3 (variance between herds) was x2 = 4.35, p = 0.70 and at level 2 (cattle within herds) was x2 68.87, p < 0.01. There was a higher variance between animals within herds compared with the between herd variance (Table 4). 4. Discussion The aim of this study was to investigate risk factors associated with an individual bovine animal becoming a bTB reactor at a full-herd test. The outcome was animal test and this needs to be remembered in the interpretation of the results because cattle could have many tests but only become a reactor at one test and the odds ratios are risk per

Level

Coef.

OR

S.E.

95% CI

Purchased Homebred

Ref. 1.08

0.34

0.16

0.25–0.47

No Yes

Ref. 0.30

1.34

0.13

1.04–1.74

Yearly Short interval Other strategic

Ref 0.61 0.34

0.54 1.40

0.15 0.14

0.41–0.72 1.08–1.83

Continuously stocked Restocked

Ref. 1.11

0.33

0.34

0.17–0.64

Survey only Reactive Proactive

Ref. 0.49 0.47

1.63 1.60

0.31 0.34

0.88–3.01 0.82–3.10

1.65

0.19 0.28 0.45

1.14–2.39

0.50 1.78 9.97

test, not per animal. As a consequence of this outcome variable, the number of previous tests (and therefore possible prior exposure to a reactor) was correlated with age and number of cattle movements since all were temporally bound (Table 3). Age is a classic confounder and there was no evidence that dose of exposure had an important effect (Dean et al., 2005) and also age and movements cannot cause bTB directly since bTB is an infectious disease caused by M. bovis so prior exposure to cattle positive to the skin test was considered the most appropriate of the correlated variables to include in the multivariable model. We have assumed that previous exposure to reactor cattle is associated with an increased risk of exposure to M. bovis. These reactors might have been exposed to, infected with or infectious with M. bovis. The infection status and infectiousness of any one individual reactor is unknown, however, it is reasonable to assume that some reactor cattle were infectious or were a marker for infectiousness in the herd at the time that the reactor was present. If this is not a reasonable assumption then the SICCT is inappropriate for purpose. We have also assumed that lack of prior exposure to reactors is an indication of lack of exposure to M. bovis, but this might also be an inaccurate assumption because of the sensitivity of the test and because many cattle were not in herds that were tested before the test at which they became a reactor. These limitations do not make the analysis invalid, but will influence the strengths of associations and might introduce some misclassification or bias, the impact of bias would be to raise or lower the associations. In the final model, unexposed, homebred cattle had the lowest risk of becoming reactors. Although the farms were situated in areas where bTB was endemic, 50% of herds did not have a HBD in the 4-year study period and 44 (59.5%) of these had not had one since 1996. Homebred cattle on these farms might not have become reactors because there

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was no M. bovis on the farm (i.e., in cattle, wildlife or the farm environs), hence their low risk in the model once exposure to bTB was included as a separate variable. The interaction between homebred and prior exposure, in the current study, highlights that homebred cattle exposed to a reactor at a previous tests (i.e., with infection in their herd and on their farm) had a higher risk of reacting than exposed purchased cattle. Consequently, where there is already M. bovis on the farm, purchased cattle will contribute a smaller proportion of the overall risk for reacting at a herd test. This agrees with Green et al. (2008) who reported a small proportion of extra risk for HBD from purchasing cattle in ‘endemic’ areas using mathematical models. Similar results were also reported by Clegg et al. (2008) from a study in Ireland of the effect of premovement testing on herd restrictions. This does not indicate that this is an unimportant risk, because of the non-linear nature of infectious diseases; purchase of one infected animal will aid persistence (Green, 2007). In addition, with approximately 50% of cattle being purchased, the risk might be small but frequent. This 50% might be slightly higher than typical, because 24% of these herds purchased all their cattle when they restocked, however purchasing cattle is common in GB, especially in suckler herds, where extra calves are frequently purchased to feed from suckler cows. It is possible that this difference between exposed homebred and exposed purchased cattle has occurred because of bias. There is less possibility of misclassifying exposure at a previous bTB test in homebred cattle compared with purchased and so all exposed homebred cattle are probably correctly classified, a downward bias (i.e., likely misclassification to unexposed when in fact exposed) would reduce the magnitude of difference in OR between exposed and non-exposed purchased cattle. This misclassification among purchased cattle might also explain why purchased cattle with no known prior exposure to a bTB reactor were at a higher risk of becoming reactors than homebred cattle with no known prior exposure to reactors. This risk has been reported as a herd risk for HBD by Griffin et al. (1996) and Johnston et al. (2005). It is consistent with the concept that farms where HBD has never occurred or not recently occurred are free from bTB and that purchased cattle introduce infection and they themselves become reactors (Green and Cornell, 2005). Naı¨ve cattle arriving on a naı¨ve farm are not likely to test positive because the test specificity is very high. So, the most likely explanation for apparent lack of exposure is that the history of these purchased cattle is inaccurate because they have not been tested or traced correctly. Mitchell et al. (2006) reported that many cattle are not tested in their life time, and the probability of testing increases with age, and as 21.1% of the purchased cattle were under 1 year of age at purchase there is appreciable opportunity for M. bovis to move undetected between herds. Carrique-Mas et al. (2008) reported that purchased cattle were the only source of infection for restocked herds with no history of bTB, and that this risk was only present when cattle were purchased from high frequency tested parishes. These results concur with the current study: cattle that are moved (i.e., purchased) are at a higher risk of

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becoming reactors on their destination farm if they have been exposed to bTB reactors on their source farm(s). Another explanation that is not mutually exclusive to the above is that when purchased cattle arrive on an infected farm/herd they might be at greater risk of reacting than homebred cattle that are born into this infected situation. They might have been more sensitive to exposure to M. bovis on introduction to an infected herd, e.g., because of the stress of moving or because first exposure was at an older age. Considering introduction of M. bovis and subsequent persistence as a temporal process, we consider that it is thus relatively evidential that moving cattle previously exposed to bTB reactors onto a bTB free farm might introduce bTB and increases the risk of HBD (Griffin et al., 1996; Johnston et al., 2005; Carrique-Mas et al., 2008). In addition, because HBD are a risk for future HBD on a farm, even when destocked (Carrique-Mas et al., 2008), it must be the case that some infected cattle (some of whom will become reactors) are infecting farms (environment, wildlife) and other cattle and are therefore infectious. That is, cattle can move M. bovis into previously uninfected herds but once a herd is infected for some time the clarity of their role is lost. In the final model, cattle on restocked (versus continuously stocked) farms had a lower risk of becoming a reactor after adjusting for their previous exposure. There are three processes that might contribute to this result. First, the removal of all cattle from a farm would have removed any residual infection in cattle that were false negatives at previous bTB skin tests; the sensitivity of approximately 70% makes this likely in some herds. If such cattle (i.e., false negative reactors) are less likely to be present in more recently formed herds than in continuously stocked herds, then the risk of any one animal reacting would be lower in reformed herds. Second, the absence of cattle, and especially infectious cattle, provides a period of during which M. bovis in the environment might reduce. One of the risks for HBD in the first test after restocking was an exponential decay with time since the last bTB breakdown on the farm, indicating a decaying farm, rather than cattle, risk (Carrique-Mas et al., 2008). Third, there was a reduction in the number of SICCT tests in continuously stocked herds during FMD in 2001 (Le Fevre et al., 2005) and so infection had an opportunity to disseminate within continuously stocked herds but not depopulated farms. It could be argued that all cattle detected are primary infections from the farm environment and that they are removed before they become infectious. However, the fact that most cattle have lung lesions and some reactor cattle have open thoracic lesions makes this unlikely. In addition, the evidence from the RBCT and Irish trials indicate that extensive removal of badgers does not prevent all HBD with bTB (Griffin et al., 2005; Donnelly et al., 2006). The presence of badgers on cattle farms (Clifton-Hadley et al., 1995; Martin et al., 1997) and the impact of their removal from farm land (Griffin et al., 2005) have been associated with an increase and reduction of HBD in cattle herds respectively. There was a significant increased risk of HBD in areas in which badgers were reactively culled in the

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RBCT (Donnelly et al., 2003) and a decreased risk of HBD in the centre of areas in which badgers were proactively culled (Donnelly et al., 2006). In our study, there were far fewer farms than in the RBCT and it is not surprising that statistical significance was not achieved. However, it is of interest that cattle in both proactive and reactive triplets had a positive non-significant increased risk of reacting to the skin test. Further work with the most recent results (Jenkins et al., 2008) might provide an interesting insight. The lower risk for a bovine reacting at a short interval (SI) test is consistent with the outcome animal tests. A short interval test occurs approximately 60 days after the detection of at least one reactor at a routine herd test or a previous SI test, and so detects only reactor cattle that are either recently infected or that did not (for some reason) react at the immediately previous herd test. Note that all HBD end with a SI test with no reactors, or two such tests if the HBD was confirmed (Green and Cornell, 2005) so the denominator for the number of tests is large. These facts explain why these tests are a lower risk for disclosing a reactor, although they are likely to be very important for disease control. Animals tested at strategic herd tests were at significantly higher risk of being reactors. These tests are initiated largely on the basis of disclosure of reactors in source or neighbouring farms, again showing that reactors are a risk for other cattle, or act to disclose a shared point source risk. The increase in HBD incidence after the foot and mouth disease epidemic in 2001 when routine testing was halted demonstrates that the current control policy in GB (which does not target wildlife infection) has a significant effect on reducing bTB transmission. The higher rate of reactor disclosure at strategic tests compared with routine tests indicates its importance in bTB control. From the current study results it is possible that the targeting of strategic tests, already done for certain situations, could be improved by considering previous individual animal exposure to reactors to decide when to test herds, e.g., in a 4-year testing areas, herds purchasing cattle with previous exposure might be tested within 1 year of that purchase. Also of interest is that herd size was not significantly related to individual animal risk of becoming a reactor (Table 3). Virtually all previous research (e.g., Griffin et al., 1996; Johnston et al., 2005; Carrique-Mas et al., 2008) has reported that the risk of HBD increases with herd size, which has defied coherent explanation. Since this was a significant univariable association it appears that homebred/purchased cattle and previous exposure to a reactor together with restocking status account for the herd size effect. This study used a large dataset from two national databases from which a great amount of information could be extracted with a large amount of data manipulation. This required a very careful step by step process when creating queries in the relational database. The results of these queries were repeatedly corroborated with the original data. This was essential, given the large amount of data, to assure reproducibility. Nonetheless, a significant challenge remains in terms of developing variables that are revealing in terms of showing groups of cattle with higher risk of reacting, and meaningful in terms of interpretation. In particular, the time and age dependencies of exposure to reactors, source and testing (which is greatly influenced by

disclosure of reactors) and farm history are essentially intractable. The use of the discontinuity caused by the foot and mouth epidemic in 2001 was invaluable in distinguishing between herd and farm effects (Carrique-Mas et al., 2008). The farms for this study included all farmers willing to participate who had depopulated in 2001 and then restocked their farm and a random selection of continuously stocked farms. There were insufficient restocked farms to have a ratio of 1:1, the most efficient when studying an exposure of interest and a ratio of 1:3 was selected as the optimal ratio available with sufficient statistical power for an expected HBD of 30%, in fact the HBD was 50% and so the study is slightly more powerful than anticipated. These were not adjoining farms, to avoid highly complex spatial adjustments in the analysis. The descriptive results presented were very informative and useful given the large dataset. Data were modelled using a hierarchical structure which further helped to understand the risks, given that tests are applied to tests and animals, and animals themselves are not independent observations. The MCMC simulation helps to reduce the bias of conservative standard errors and so reduce the possibility of type 1 errors. The fit to the herd level indicates that there is sufficient data to fit to presence of reactors on a farm (and thus HBD), but cannot determine which individual animals will be reactors. Although not ideal, this is not unexpected since there are many influences on individual animal status that we have not included (e.g., genetic background, immune status, physiological condition, grouping of cattle on farms). The current control policy is based on testing of herds rather than individuals, partly because the relatively low sensitivity of the test at the individual animal level, this also contributes to the relative inaccuracy of individual animal predictions. The matching was not included in the analysis because some non-matched continuously stocked farms were included and because of the hierarchical structure of the data. Consequently the variable restocking was included as a fixed effect. The model had an unstructured covariance. This makes the least assumptions in hierarchical models and maximises the reduction in deviance. One of the key aims of the study was to include a sufficiently large sample of farms to detect farm-level effects, but small enough that detailed information could be collected for individual animals. We believe that this has been successful, in that this is the largest study of individual animal history of exposure to bTB. However, farm-level history of bTB is important for at least 5 years, and individual animal life expectancy is of the same magnitude. Consequently, it is likely that longitudinal studies require at least 10 years of data if current risks related to history are to be completely detected. As data accumulate within BCMS (which became mandatory in 2000 in GB) the value of such studies will increase. 5. Conclusions We conclude from our study that the use of historical individual animal data on location and prior exposure to

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bTB reactor cattle to investigate risk factors associated with bTB in cattle herds in an endemic area has revealed that unexposed homebred cattle were at a low risk of becoming reactors, because some herds and farms are free from bTB. However, exposed homebred cattle were at a higher risk of becoming reactors, suggesting that prior exposure to reactors was associated with an increased risk of reacting to the skin test and that M. bovis was present in the herd or on the farm. Purchased cattle were a greater risk of reacting to the skin test whether previously exposed or not, but known prior exposure increased their risk further. Even in bTB endemic areas, cattle in repopulated herds had a lower risk of reacting, suggesting a farm level risk for M. bovis. We conclude that although the current measures that are in place to control bTB in the UK are targeted to reduce the risk of cattle to cattle transmission, the present study, together with previous epidemiological and pathological research, emphasise that cattle are still an important source of infection to other cattle and that further targeted control of infected cattle would benefit GB industry. Conflict of interest statement None declared. Acknowledgements The study was funded by DEFRA (Project SE3026). The authors are grateful to Andy Mitchell from the VLA for providing the farms and VetNet data; to Alan Aldridge for the BCMS data and to farmers for their collaboration in the study. Thanks to Prof. Simon More for his useful comments in the production of the manuscript. References Anonymous, 1965. Animal health A Century 1865–1965 A Century of Endeavour to Control Diseases of Animals. HMSO, London. Bourne, J., Donnelly, C., Cox, D., Gettinby, G., McInerney, J., Morrison, I., Woodroffe, R., 1999.In: An Epidemiological Investigation into Bovine Tuberculosis. Second Annual Report of the Independent Scientific Group on Cattle TB MAFF Publications, London. Browne, W.J., 2004. MCMC estimation in MLwiN, Version 2.0. Centre for Multilevel Modelling, Institute of Education, University of London, School of Mathematical Sciences, University of Nottingham. Carrique-Mas, J.J., Medley, G.F., Green, L.E., 2008. Risks for bovine tuberculosis in British cattle farms restocked after the foot and mouth disease epidemic of 2001. Prev. Vet. Med. 83, 242–259. Cassidy, J.P., 2006. The pathogenesis and pathology of bovine tuberculosis with insights from studies of tuberculosis in humans and laboratory animal models. Vet. Mircobiol. 112, 151–161. Clegg, T.A., More, S.J., Higgins, I.M., Good, M., Blake, M., Williams, D.H., 2008. Potential infection-control benefit for Ireland from pre-movement testing of cattle for tuberculosis. Prev. Vet. Med. 84, 94–111. Clifton-Hadley, R., Wilesmith, J.W., Richards, M.S., Upton, P., Johnston, S., 1995. The occurrence of Mycobacterium bovis infection in cattle and around an area subject to extensive badger (Meles meles) control. Epidemiol. Infect. 114, 179–193. Costello, E., Egan, J.W., Quigley, F.C., O’Reilly, P.F., 1997. Performance of the single intradermal comparative tuberculin test in identifying cattle with tuberculous lesions in Irish herds. Vet. Rec. 141, 222–224. Courtenay, O., Reilly, L.A., Sweeney, F.P., Hibberd, V., Bryan, S., UI-Hassan, A., Newman, C., McDonald, D.W., Delahay, R.J., Wilson, G.J., Wellington, E.M.H., 2006. Is Mycobacterium bovis in the environment important for the persistence of bovine tuberculosis? Biol. Lett. 2, 460–462.

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