Spatiotemporal analysis of prolonged and recurrent bovine tuberculosis breakdowns in Northern Irish cattle herds reveals a new infection hotspot

Spatial and Spatio-temporal Epidemiology 28 (2019) 33–42

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Spatial and Spatio-temporal Epidemiology journal homepage: www.elsevier.com/locate/sste

Spatiotemporal analysis of prolonged and recurrent bovine tuberculosis breakdowns in Northern Irish cattle herds reveals a new infection hotspot G.M. Milne a,∗, J. Graham a, A. Allen a, A. Lahuerta-Marin a, C. McCormick a, E. Presho a, R. Skuce a, A.W. Byrne a,b a b

Veterinary Sciences Division, Agri-food and Biosciences Institute (AFBI), 12 Stoney Road, Stormont, Belfast BT4 3SD, UK School of Biological Sciences, Queen’s University Belfast, Belfast, UK

a r t i c l e

i n f o

Article history: Received 5 December 2017 Revised 9 October 2018 Accepted 3 November 2018 Available online 14 November 2018 Keywords: Bovine tuberculosis Northern Ireland SaTScan Chronic Cluster Moran s I

a b s t r a c t Despite a state-led eradication programme, bovine tuberculosis (bTB) remains endemic in Northern Ireland (NI). Of particular concern are “chronic” prolonged and recurrent bTB breakdowns, which represent significant financial and administrative burdens. However, little is known regarding the spatiotemporal distribution of chronic breakdowns in NI. We therefore analysed both the spatial and spatiotemporal distributions of chronic bTB breakdowns between 2004 and 2014. Significantly positive values for Moran’s Index of spatial autocorrelation were found, and Local Moran’s I clustering was employed to assess for spatial associations in the number and prevalence of chronic bTB breakdowns across NI. Additional spatio-temporal analysis using SaTScan showed that the burden of chronic bTB infection tends to be found where bTB levels are already high. However, a novel hotspot was revealed wherein the prevalence of chronic breakdowns was higher than expected; this should be the subject of follow-up surveillance. © 2018 Elsevier Ltd. All rights reserved.

1. Introduction There are over 1.6 million cattle in Northern Ireland (NI) (DAERA, 2017a), of which the dairy sector is valued at nearly £500 million, with a further total output value of over £390 million for cattle and calves, including meat production (DAERA, 2015). However, bovine tuberculosis (bTB), caused by members of the Mycobacterium tuberculosis complex (including the M. bovis bacterium) is a chronic infectious disease considered endemic in cattle populations within NI. Infection by M. bovis leads to notable production and economic losses, and places detrimental trading restrictions on farmers (Abernethy et al., 2006). ∗

Corresponding author. E-mail address: [email protected] (G.M. Milne).

https://doi.org/10.1016/j.sste.2018.11.002 1877-5845/© 2018 Elsevier Ltd. All rights reserved.

A state-led bTB eradication programme was established in NI in 1935, (Robinson, 2015), and current eradication procedures are compliant with European Council Directive 64/432/EEC (as amended). The Single Intradermal Comparative Cervical Tuberculin (SICCT) test is used to identify cases of M. bovis infection in live cattle. Herds undergo annual testing and may be declared “Officially Tuberculosis Free” (OTF) if all animals pass the SICCT test. All statutory test-positive animals are removed and sent to slaughter. OTF status is withdrawn in the case of a confirmed bTB breakdown, but can be restored by two subsequent clear herd tests, each at least 60 days apart. Following this, the herd moves back to the annual testing cycle (DAERA, 2017b). Thus, the usual minimum time a herd can remain restricted following a confirmed breakdown is 120 days. Per year, this eradication programme costs over

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£28 m and leads to the active identification and slaughter of over 11,0 0 0 skin-test reactor animals (DAERA, 2016). Despite these ongoing efforts, many herds experience prolonged breakdowns (during which a herd is continually restricted for an extended period of time) and recurrent infections (whereby a herd has OTF status restored, but breaks down again within a short period of time). These herds represent a significant drain of resources and require disproportionate compensation costs (NIAO, 2009; TBSPG, 2016). The phenomenon of prolonged and recurrent breakdowns has been observed elsewhere. In Great Britain (GB), between 2003 and 2005, 30% of new herd breakdowns lasted more than 240 days (Karolemeas et al., 2010) and from 2005 to 2008 inclusive, 23% of herd breakdowns occur within a 12 month window of the last clear herd-test (Karolemeas et al., 2011). In Ireland, herds which have experienced a previous bTB breakdown are at increased risk of further breakdowns (Olea-Popelka et al., 2004; White et al., 2013). Data from 2003 indicate that 10% of Irish herds experienced an additional breakdown six months after clearing infection (Wolfe et al., 2009). Cases of prolonged and/or recurrent bTB infection have also been reported elsewhere, including Spain (Alvarez et al., 2012; Guta et al., 2014), New Zealand (Dawson et al., 2014) and in the African continent (Huang et al., 2014). There is, however, only limited information regarding chronic bTB breakdowns in NI. Between and 2005 and 2010, over 1300 bTB breakdowns were classified as either prolonged or recurrent (Doyle et al., 2016) but nevertheless, little is known regarding the spatio-temporal patterns in such cases. This represents a significant gap in our knowledge, especially if eradication efforts are to be deployed to the greatest effect to target herds with a high burden of infection. The aim of this study was therefore to assess and characterise the spatio-temporal occurrence of chronic episodes in NI between 2004 and 2015, thus identifying clusters which may require further epidemiological investigation. 2. Methods 2.1. Study population and definition of chronic herds and episodes Northern Ireland (NI) is approximately 14,0 0 0 km2 . Within NI, bTB is managed over 123 administrative “patches”, which sit within one of 10 larger administrative Divisional Veterinary Offices (DVOs; Supplementary Material, Fig. 1). The mean patch size is 90 km2 . Data on the cattle population of NI were extracted from the Department for Agriculture, Environment and Rural Affairs (DAERA) Animal and Public Health Information System (APHIS) database (Houston, 2001), and compared to published data. Official figures show that between 2004 and 2014, the mean number of cattle herds in NI was 21,490 (DAERA, 2018). The data extracted from APHIS, however, contained information on 30,204 total registered herds. After the removal of herds with inaccurate or incomplete information, herds which ended before or during the study period, and herds which started during the study period, the dataset contained information on 22,124

herds. This number is approximately 3% larger than the mean herd count for the study period derived from official data (n = 21,490 herds). This discrepancy between the extracted data and published data likely occurs as herd end dates were not always available. However, this small margin of error involved was considered acceptable. Whilst official figures show a general downwards tend of the number of herds year-on-year, (max = 23,776 herds in 2004, min = 20,044 herds in 2014), all years data were within 10% of the number of herds considered in our analysis (n = 22,124). We therefore argue that for the purposes of analysis, it is reasonable to assume that the number of active herds has remained stable throughout the time period. Furthermore, we hypothesise that any temporal changes in the number of herds would be comparative across the spatial extent of NI, and thus unlikely to introduce notable bias into the data. Data on herd bTB breakdowns spanning January 2003 to December 2015 inclusive were also extracted from APHIS (n = 17,142). Breakdowns with incomplete information (e.g., undefined start and end dates, ongoing breakdowns, absent or erroneous data) were omitted, and the final dataset matched to bTB strain data. To mitigate the impact of the 2001 foot-and-mouth crisis, which led to a backlog of bTB testing, data from 2003 were removed. Strain typing (i.e., Multi Locus VNTR Analysis (MLVA) and spoligotyping) is regularly carried out to determine the different genetic bTB strains associated with a breakdowns (Skuce et al., 2005, 2010). In NI, the MLVA-typing loci include MV2163B/QUB11B, MV4052/QUB26A, MV2461/ETRB, MV1955/Mtub21, MV1895/QUB1895, MV2165/ETRA, MV2163/QUB11A and MV3232/QUB3232. Prior to 2009, strain typing was only carried out on the first confirmed reactor or lesion, but post-2009, strain typing was carried out on every reactor animal. The sampling bias between the pre-2009 and post-2009 data was corrected for by computationally selecting a single strain for each breakdown, based on the rates of occurrence of different strains during the breakdown. Strains are named by combining the spoligotype and MLVA data for each breakdown (i.e., MLVA.spoligotype) following Wright et al. (2013). Breakdowns year refers to the date of the first disclosing skin test or identification of a LRS. BTB breakdowns were classified as either “chronic” or “non-chronic”. To ensure that a consistent definition of “chronic” is adhered to within NI, bTB breakdowns were categorised according the convention proposed by Doyle et al (2016), wherein chronic episodes were those either (i) prolonged, i.e., lasting longer than 365, or (ii) recurrent, i.e., lasting less than 365 days but followed by two further breakdowns within the following two years. Breakdowns which commenced in 2015 were also removed to reduce selection bias, as breakdowns which had progressed for than a year at the time of data extraction may be incorrectly classified as non-chronic. As the number of recurrent breakdowns was very small (< 1%), the decision was made to analyse both prolonged and recurrent episodes as a single unit. This is a reasonable epidemiological approach given that a significant process causing the emergence of recurrence is the inability of the diagnostic testing regime to fully clear infection from herds and

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Fig. 1. Map of herds which have experienced at least one chronic breakdown (red) and herds which have not experienced a chronic breakdown (grey).

persistence of bTB on the farm (Green et al., 2012; Doyle et al., 2016). Therefore, we argue that prolonged and recurrent chronic herds represent similar risk units. Chronic herds were defined as those herds which have had at least one prolonged and/or recurrent breakdown during the study period, according to the definition. Two variables of interest were derived from these data; the number of chronic breakdowns per-patch, and the prevalence of breakdowns classified as chronic during the study period. All data were analysed in R (R Core Team 2013) and graphics were rendered in ggplot (Wickham, 2009).

2.2. Assessing spatial and spatio-temporal relationships in chronic herds and chronic breakdowns 2.2.1. Global and local Moran’s I Global Moran’s I (Cliff and Ord, 1981) was generated as a measure of spatial autocorrelation in the occurrence of chronic breakdowns (Bivand et al., 2013; Bivand and Piras, 2015). The resulting value indicates whether the variables of interest are clustered, dispersed or randomly distributed in space. A positive value is associated with positive spatial autocorrelation (i.e., clustered), a negative

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value indicates negative spatial autocorrelation (i.e., dispersed), and a value of zero suggests that the variable has a random distribution. Two types of spatial weights matrices were considered; the Queen’s measure of contiguity (with equal weights applied to each contiguous polygon), and radial distance-based measures. A comparison of the Queen’s measure of contiguity to the distance-based method (counting all polygon centres within a 15 km, 20 km and 25 km search radii respectively) indicated that the Queen’s measure was a better fit (Supplementary Material Fig. 2). To test the null hypothesis that there is no spatial autocorrelation in the occurrence of chronic breakdowns, the resulting Index value was compared against 999 independent permutations of the data generated by Monte-Carlo (MC) simulations of Moran’s I, and significance was established at p ≤ 0.05. Local Moran’s I (Anselin, 1995) values associated with the number of chronic bTB breakdowns was also calculated on a per-patch basis. Statistical significance was established at p ≤ 0.05, and was determined via z-scores and p-values. Bonferroni correction was, however, applied to account for multiple tests. A Moran scatterplot (Anselin, 1996) was then used to categorise patches into clusters and outliers as follows. “High-high” clusters (HH) were defined patches with elevated local variable values, in areas where the variable values are already high (as defined by the lagged variable); “Low-Low” clusters (LL), were defined as patches with low variable values, and located in areas where the variable value is low. “High-low” clusters (HL) were considered outliers, and reveal patches with elevated variable values located in areas with lower values. “Low-High” clusters (LH) also indicated outliers, and are defined as patches with low variable values which are located in areas with higher variable values. 2.2.2. SatScan To identify localised clusters of chronic breakdowns through both space and time, a SaTScan (SaTScanTM version 9.4.4 64 bit) model was created (Kulldorff, 2006). We opted to evaluate the clusters derives via the three different probability models implemented in SaTScan for count data; the Space-Time Permutation (STP) Model, the Bernoulli Model and Discrete Poisson Model. In brief, the STP Model does not require population-at-risk data (Kulldorff et al., 2005). Clusters are those cases in a geographical area, in a given time period, which exhibit a comparatively higher percentage of cases compared to elsewhere. For this model, the case file included pointlocation data for the herd and year of chronic breakdown. Herd size was also included as a covariate, summarised into categories based on enterprise size (small = 1–10 animals, medium = 11–100 animals, large = 101–250 animals, very large = 251–1,412 animals). The Bernoulli space-time model was also employed to detect clustering in pointlocations of chronic breakdowns (i.e., cases), but instead, compared to point-locations of breakdowns which were not categorised as chronic (controls) (Kulldorff, 1997). The case file and control file each contained herd IDs, linked to a co-ordinates file with herd georeferencing data. The year of breakdown was supplied to facilitate the spatio-temporal component of the analysis. The Discrete

Poisson model (Kulldorff, 1997) was used to assess for spatio-temporal clustering in chronic breakdowns when compared to the underlying cattle population. This model assumes that cases are Poisson-distributed, and that the number of cases is proportional to the underlying population. For this model, data were aggregated to the patchlevel. Herd size was included as a covariate, categorised into the same enterprise sizes as with the STP Model. The cases consisted of counts of chronic breakdowns per-patch per-year, whereas the population at-risk file consisted of all herds, aggregated to patch-level on a per-patch per-year basis. Whilst year-on-year herd sizes for non-breakdown herds were not available, we assumed that herd sizes did not differ significantly through time, on a per-patch basis. This assumption was tested using the herd size data for breakdown herds. Thus, individual Poisson GLMs were constructed for each patch, with median herd size as the outcome and year of breakdown (from year 1 = 2004 to year 10 = 2014) as a sole continuous predictor. The results show that there was no significant association between median herd size and year of breakdown in the population of breakdown herds (all 123 patches p > 0.05). In all models, the SaTScan analysis centres moving, variable-diameter cylinders of different sizes on each localities, with cylinder base area representing the geographic area and the height representing the length of the outbreak. The actual and expected number of cases, controls or population information, inside and outside the scanning window can be compared. Statistical significance of clusters were evaluated using Monte Carlo hypothesis testing via creating random spatio-temporal permutations of the data, which were also assessed for clusters. Significance was established at p < 0.05. Time precision was specified yearly, from 2004 to 2014. The maximum spatial cluster size included 50% of the population. The maximum temporal cluster size included 50% of study period, with a minimum temporal span of one year and circular-shaped cluster windows were chosen. 3. Results 3.1. Summary statistics The final cattle population was estimated at 22,124 active herds throughout the study period. There were 8523 breakdown herds within the dataset (i.e., the breakdown population), and from these, 17.2% (n = 1,463) were categorised with at least one episode of chronic infection. Thus, between 2004 and 2014, over 38.5% of all herds in this study in NI experienced a bTB breakdown, and 6.6% of all herds experienced at least one chronic bTB breakdown. The final number of bTB breakdowns totalled 13,009, and 1773 breakdowns (13.5%) were classified as chronic (Fig. 1). Only 150 breakdowns (1.15%) appeared to meet the definition of recurrent, given the criteria. The final chronic breakdown dataset also included 108 breakdowns (0.83%) which were classified as both prolonged and recurrent. At the DVO spatial scale, Newry had the largest number of herds with at least one chronic breakdown (n = 313), the highest number of chronic breakdowns (n = 387), and the highest prevalence of herds with at least one episode of chronic infec-

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Table 1 Chronic breakdowns and chronic herds per-DVO. DVO

Numb. patches

Non-chronic herds

Chronic herds

%age chronic herds

Non-chronic breakdowns

Chronic breakdowns

Armagh Ballymena Coleraine Dungannon Enniskillen Larne Londonderry Newry Newtownards Omagh

17 6 12 14 14 14 6 16 10 14

661 451 863 759 1003 292 230 1023 758 1020

156 44 150 188 125 83 29 313 222 153

19.09 8.89 14.81 19.85 11.08 22.13 11.20 23.43 22.65 13.04

1006 661 1345 1072 1618 416 334 1688 1570 1616

192 60 166 227 136 131 32 387 268 174

16.03 8.32 10.99 17.47 7.75 23.95 8.74 18.65 14.58 9.72

Grand Total

123

7060

1463

17.17

11,326

1773

13.54

tion in the breakdown herd population (23.4%). Larne DVO, however, had the highest prevalence of chronic breakdowns (24.0%); Table 1. At the patch spatial scale, the median number of cattle herds was 169 (IQR: 127, 228). The median number of chronic herds per-patch was 10 (IQR: 6 - 16 herds), whilst the median count of chronic episodes per-patch was 12 (IQR: 7–18) (Fig. 2 A–D). Patch 120 in Newtownards DVO had the largest number of herds with at least one chronic breakdown (n = 55) and also the largest number of chronic breakdowns (n = 71). However, the highest prevalence of chronic bTB breakdowns (48.3%) was recorded in patch 132, in Larne DVO. Supplementary Material Fig. 3A shows the prevalence of herds with at least one chronic breakdown, compared to all herds in NI. The median number of chronic breakdowns per-year between 2004 and 2015 was 162 (IQR: 139–175), and the number of chronic breakdowns was highest in 2005 (n = 222). A negative association between the number of chronic breakdowns and year was observed (Negative Binomial GLM −0.03, p = 0.02, Incidence Rate Ratio (IRR) = 0.97, 95%CI: 0.94, 0.99). The highest percentage of chronic breakdowns was also 2005, where 17.0% of all breakdowns were categorised as chronic. A general decrease in the percentage of breakdowns classified as chronic was observed (Binomial GLM; −0.04, p < 0.01), i.e., a year-on-year IRR of 0.96 (95%CI: 0.95, 0.98). The downwards trend was observed in all DVOs. 3.2. Global and local Moran’s I spatial analysis The results of the Global Moran’s I showed significant positive spatial autocorrelation in both the count of chronic breakdowns (Moran’s I = 0.36, p = 0.001) and the prevalence of chronic breakdowns (Moran’s I = 0.42, p = 0.001). The Local Moran’s I analysis revealed nine HH patches with elevated numbers of chronic breakdowns, compared to the surrounding area (Fig. 3A). These were wholly associated with Newry and Newtownards DVOs. Eight HH patches were identified with elevated percentage of chronic breakdowns which were largely situated within Larne DVO. There were also some LL patches identified in the north and west of NI (Fig. 3B, Table 2). There were no significant outlying patches (HL or LH clusters) identified in this analysis. The results show that the patches which exhibit clustering for elevated numbers of chronic breakdowns are

%age chronic breakdowns

therefore different from those which exhibit elevated percentages of chronic breakdowns, suggesting that recurrent infection may be important in some patches compared to others. Supplementary Material Fig. 3B illustrates Local Moran’s I clusters in herds with at least one chronic breakdown, compared to all herds in NI. 3.3. SaTScan spatio-temporal analysis of chronic bTB breakdowns Fig. 4 illustrates the clusters detected using the three different cluster detection methods implemented in SaTScan. One cluster was identified under the STP Model, controlling for herd size (cluster 1, p = 0.02). It spanned the year 2004 and was situated in the centre of NI (Table 3). The Bernoulli approach, which compared chronic breakdowns to non-chronic breakdowns, also detected a single cluster (cluster 2, p < 0.001), however this cluster was in a different location covering most of the south and south-east of the study area and spanning 20 04–20 08. Two patch-level clusters were detected via the Discrete Poisson method, which also controlled for herd size. Cluster 3 spanned 20 05–20 08 (p = 0.001) and included patches in the north-east of NI, mainly in Larne and Antrim DVOs. Cluster 4 spanned 20 04–20 05 (p = 0.001) and was situated in Newry and Newtownards DVOs. These results suggest moderate levels of agreement in the locations of chronic breakdowns, especially between the Bernoulli and Poisson methods. Clustering in chronic breakdowns was largely focused in the south-east and north of NI, with little evidence of clustering of chronic breakdowns elsewhere. 3.4. The use of MLVA strain data to trace infection The both the SaTScan analysis and cluster analysis detected a cluster of chronically infected herds in the northeast of NI. This area has not been given much attention as a hotspot previously. An investigation revealed that a small number of herds (n = 12) within this area experienced four or more chronic breakdowns during the study period; i.e., were highly recurrent. The bTB MLVA data from these 12 herds were used to determine whether infection was more likely to result from on-farm persistence, or as a result of inwards cattle trade and reintroduction of new strains. Two MLVA strains predominate in Larne DVO; 4.140 (VNTR

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Fig. 2. A–D: The numbers (i.e., counts) and prevalence (i.e., percentage) of chronic herds and chronic breakdowns, per-patch.

Table 2 The number (N) of patches within each cluster category (HH/LL/HL/LH), with significant Local Moran’s I values. Variable

N. HH patches

N. LL patches

N. HL patches

N. LH patches

Count of chronic breakdowns Percentage of chronic breakdowns

9 8

0 3

0 0

0 0

Table 3 Summary statistics associated with the four clusters detected using the three SaTScan clustering methods. Method

Cluster

Start

End

Test statistic

p

Observed

Expected

STPM Bernoulli Poisson Poisson

1 2 3 4

2004 2004 2005 2004

2004 2008 2008 2005

10.28674 64.50318 13.67497 12.13619

0.02 <0.001 0.001 0.001

32 562 254 198

12.76 374.94 337.95 115.06

G.M. Milne, J. Graham and A. Allen et al. / Spatial and Spatio-temporal Epidemiology 28 (2019) 33–42

Fig. 3. A and B: Significant clusters of counts of chronic breakdowns and prevalence of chronic breakdowns per-patch, as determined by Local Moran’s I.

genotype 4, spoligotype 140) and 2.142 (VNTR genotype, spoligotype 142) (Skuce et al., 2010). The investigation revealed that in 82% of breakdowns (45/55), only one strain was isolated, but there was little consistency in the strain isolated from each breakdown through time. Three herds were consistently associated with the recurrence of strain 2.142, suggesting that within-herd persistence is implicated in these breakdowns. The other nine herds exhibit more complex patterns of infection suggesting infection from other sources, possibly associated with cattle movements. 4. Discussion Despite yearly herd-level incidence for bTB remaining relatively low at around 7%, the cumulative impacts of the endemic infection are considerable; indeed, bTB was confirmed in over 38% of herds in this study, and furthermore, over 17% of all breakdowns were classified as chronic. Recent data from NI suggests an elevation in herd-level incidence of bTB to over 9% (DAERA 2017). However, the results presented here suggest a general decrease in the

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levels of chronic infection year-on-year. We are unable to speculate with certainty whether this represents a true decrease, or a censoring of the data. Despite the choice of a long study period (ten years; 2004–2014), some particularly prolonged breakdowns may still be omitted from the study due to the exclusion of breakdowns not-yet complete at the time of data extraction. We also note that the SaTScan clusters mainly included the earlier years of the study (20 04–20 05), which may also be a reflection of this effect. Historic trends in bTB incidence in NI have exhibited variation, with a high of 9.92% in 2002, to a low of 5.35% in 2007, but has recently spiked to over 9% once more, as of 2017 (Abernethy et al., 2013; DAERA, 2017c). We observe that levels chronic bTB infection therefore loosely follow the historical trends for bTB incidence, but whether a similar spike will be observed in the numbers of chronic may be the subject of future analysis. Regional variation in the numbers and percentage of chronic herds and breakdowns at the DVO-level and the patch-level was identified. There was a general pattern whereby chronic bTB breakdowns were clustered in the south-east (i.e., Newtownards and Newry DVOs) and northeast of NI (i.e., Larne DVO), with less evidence of clustering of chronic breakdowns elsewhere. The south-east of NI is a known hotspot for bTB more generally (McGrath et al., 2009; Lahuerta-Marin et al., 2015; Wright et al., 2015; TBSPG, 2016). This area has the largest number of cattle farms in NI, relatively large herd-sizes, and also has high densities of the known bTB wildlife reservoir, the European badger (Meles meles) (Reid et al., 2012) which are all known risk-factors for bTB. Whether the significant clustering of chronic infection in this area simply represent the convergence of known risk factors, or whether there is some additional unquantified epidemiological factor driving chronic infection, thus far remains to be seen. Whilst the analysis did recover clusters which reflect current epidemiological knowledge regarding bTB “hotspots” (i.e., Newry, Armagh and Newtownards DVOs), the analysis revealed clustering in chronic breakdowns in the north-east of NI. This area does not have particularly high cattle densities or numbers of cattle farms, yet clusters of chronic breakdowns were detected using both the Local Moran’s I and SaTScan analysis. The confirmation of a cluster which included Larne DVO by the Discrete Poisson Model is particularly informative, as it suggests that this cluster cannot be explained by herd size alone. Thus, some alternative epidemiological process may be driving infection in this area, such as cattle movements. Many of these breakdowns involve MLVA types which are not usually considered endemic to the immediate vicinity. This is highly suggestive that infection is being carried in from different areas. Critically, the strain data could help inform better control measures, particularly with regard to cattle movements. For example, restrictive trading criteria may be applied to cattle movements to limit the purchase of cattle from high-risk areas to low-risk areas (Enticott, 2016). Furthermore, the MLVA data could facilitate an investigation into the factors associated with within-herd strain persistence, compared to herds where the breakdowns are associated with strain diversity. It should be noted, however, the clusters detected in the

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Fig. 4. Significant clusters of chronic breakdowns as determined by the STP Model, the Bernoulli Model and Discrete Poisson Model.

SaTScan analysis only spanned 20 04–20 08, which may mean that the hotpot was historical, as opposed to contemporary. Nevertheless, these results indicate that in some parts of NI, the risk factors for chronic bTB breakdowns may differ from standard bTB breakdowns; this is being explored in a separate analysis (Doyle et al., 2016; Milne et al, 2018, Milne et al., in review). Future work includes the quantification of factors which differentiate a chronic episode from a standard bTB breakdown (Doyle et al., 2016), but in addition, take into account factors such as variation in bTB genotype, and heterogeneity of wildlife abundance and disease transmission risk. We advocate follow-up surveillance in the hotspot areas identified here, to assess whether the dynamics of the epidemic is changing (e.g., decreasing predominance of chronic breakdowns, but maintenance of infection through time through a network of smaller breakdowns), and whether this is related to changes in herd management practices. The main limitation of this work is how chronic herds were defined. Whilst the convention of Doyle et al. (2016) was followed for comparative reasons, alternative definitions may include, for example, the percentage of time which a herd has been restricted whilst active. Cognisance was also given to the fact that chronic herds could be under restriction for well over a year, whereas the

analyses presented here focused entirely on yearly incidence of disease. The data extraction process also excluded those breakdowns which were not complete by the end of the study date. Given that some chronic breakdowns span many years, some chronic breakdowns may have been selected out of the analysis. We advocate follow-up analysis in future to more fully assess temporal trends.

5. Conclusions These results show that chronic bTB cases are widespread and endemic throughout NI. However, levels of chronic bTB infection differ between DVO’s, and some of this variation reflects known bTB incidence. Larne DVO, however, does not have particularly high bTB incidence yet exhibits a large percentage of chronic bTB cases. This is reflected in the SaTScan analysis, which detected three clusters of chronic bTB cases in NI including Newry DVO, MidUlster and Larne. Newry has already been identified as a hotspot for a bTB incidence, and therefore this cluster of bTB cases is unsurprising, however the hotspot in Larne DVO is previously-unidentified and has received little attention. We advocate for follow up surveillance in hotspot areas confirm whether high levels of prolonged and recurrent bTB breakdowns are still being recorded.

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Acknowledgements This study is a component of a larger project on the evaluation of the role of multiple reactor and chronic breakdown herds in the epidemiology of bovine tuberculosis in Northern Ireland. Funding was provided through the Department of Agriculture, Environment and Rural Affairs (DAERA; www.daera-ni.gov.uk) Evidence and Innovation (E&I) Scheme DAERA under the project entitled“The evaluation of the role of multiple reactor and chronic breakdown herds in the epidemiology of bovine tuberculosis in Northern Ireland (48005 (122035): 15/3/10)” Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.sste.2018. 11.002. References Abernethy DA, Denny GO, Menzies FD, McGuckian P, Honhold N, Roberts AR. The Northern Ireland programme for the control and eradication of Mycobacterium bovis. Vet Microbiol 2006;112:231–7 4th International Conference on Mycobacterium bovis4th International Conference on Mycobacterium bovis. doi:10.1016/j.vetmic.2005.11.023. Abernethy DA, Upton P, Higgins IM, McGrath G, Goodchild AV, Rolfe SJ, Broughan JM, Downs SH, Clifton-Hadley R, Menzies FD, de la RuaDomenech R, Blissitt MJ, Duignan A, More SJ. Bovine tuberculosis trends in the UK and the Republic of Ireland, 1995–2010. Vet Rec 2013;172:20 312312. doi:10.1136/vr.100969. Alvarez J, Perez AM, Bezos J, Casal C, Romero B, Rodriguez-Campos S, Saez-Llorente JL, Diaz R, Carpintero J, de Juan L, Domínguez L. Eradication of bovine tuberculosis at a herd-level in Madrid, Spain: study of within-herd transmission dynamics over a 12 year period. BMC Vet Res 2012;8:100. doi:10.1186/1746-6148-8-100. AERA, 2015. Statistical review of Northern Ireland agriculture, 2015. Policy and Economics Division. Department of Agriculture and Rural Development (now DAERA), Belfast. Anselin L. Local indicators of spatial association—LISA. Geogr Anal 1995;27(2):93–115. doi:10.1111/j.1538-4632.1995.tb00338.x. Anselin L. The Moran scatterplot as an ESDA tool to assess local instability in spatial association. Spatial analytical perspectives on GIS. London: Taylor and Francis; 1996. p. 111–25. Bivand R, Hauke J, Kossowski T. Computing the Jacobian in Gaussian spatial autoregressive models: an illustrated comparison of available methods. Geogr Anal 2013;45(2):150–79. Bivand R, Piras G. Comparing Implementations of Estimation Methods for Spatial Econometrics. J Stat Softw 2015;63(18):1–36. Cliff AD, Ord JK. Spatial processes: models & applications. Pion Limited; 1981. DAERA, 2016. Tuberculosis disease statistics in Northern Ireland 2016. https://www.daera-ni.gov.uk/publications/ tuberculosis- disease- statistics- northern- ireland- 2016. DAERA, 2017a. The agricultural census in Northern Ireland - results for June 2016. CAP Policy, Economics and Statistics Division. Department of Agriculture, Environment and Rural Affairs, Belfast. DAERA, 2017b. Bovine Tuberculosis (TB) testing | Department of Agriculture, Environment and Rural Affairs [WWW Document]. URL https://www.daera-ni.gov.uk/articles/bovine-tuberculosis-tb-testing (accessed 7.19.17). DAERA. Bovine Tuberculosis in Northern Ireland 2016 Annual Report. Service Animal Health Group. Department of Agriculture, Environment and Rural Affairs; 2017c Veterinary, Belfast. DAERA, 2018. Farm enterprise details for Northern Ireland. https://www.daera-ni.gov.uk/publications/ farm- enterprise- details- northern- ireland. Dawson KL, Stevenson MA, Sinclair JA, Bosson MA. Recurrent bovine tuberculosis in New Zealand cattle and deer herds, 2006-2010. Epidemiol Infect 2014;142(10):2065–74. doi:10.1017/s09502688140 0 0910. Doyle LP, Courcier EA, Gordon AW, O’Hagan MJH, Menzies FD. Bovine tuberculosis in Northern Ireland: risk factors associated with du-

41

ration and recurrence of chronic herd breakdowns. Prev Vet Med 2016;131:1–7. doi:10.1016/j.prevetmed.2016.06.016. Enticott G. Market instruments, biosecurity and place-based understandings of animal disease. J Rural Stud 2016;45:312–19. doi:10.1016/j.jrurstud.2016.04.008. Green LE, Carrique-Mas JJ, Mason SA, Medley GF. Patterns of delayed detection and persistence of bovine tuberculosis in confirmed and unconfirmed herd breakdowns in cattle and cattle herds in Great Britain. Prev Vet Med 2012;106:266–74. doi:10.1016/j.prevetmed.2012.04.008. Guta S, Casal J, Garcia-Saenz A, Saez JL, Pacios A, Garcia P, Napp S, Allepuz A. Risk factors for bovine tuberculosis persistence in beef herds of Southern and Central Spain. Prev Vet Med 2014;115:173–80. doi:10.1016/j.prevetmed.2014.04.007. Houston R. A computerised database system for bovine traceability. Rev Sci Tech Off Int Epizoot 2001;20:652. Huang ZYX, Xu C, Van Langevelde F, Prins HHT, Ben Jebara K, De Boer WF. Dilution effect and identity effect by wildlife in the persistence and recurrence of bovine tuberculosis. Parasitology 2014;141:981–7. doi:10.1017/S0031182013002357. Karolemeas K, MKinley TJ, Clifton-Hadley RS, Goodchild AV, Mitchell A, Johnston WT, Conlan AJK, Donnelly CA, Wood JLN. Predicting prolonged bovine tu berculosis breakdowns in Great Britain as an aid to control. Prev Vet Med 2010;97:183–90 Special section: Calvin W. Schwabe Symposium 2009 Methodologies in Epidemiological Research. doi:10.1016/j.prevetmed.2010.09.007. Karolemeas K, MKinley TJ, Clifton-Hadley RS, Goodchild AV, Mitchell A, Johnston WT, Conlan AJK, Donnelly CA, Wood JLN. Recurrence of bovine tuberculosis breakdowns in Great Britain: risk factors and prediction. Prev Vet Med 2011;102:22–9. doi:10.1016/j.prevetmed.2011.06.004. Kulldorff M. A spatial scan statistic. Commun Stat Theory Methods 1997;26(6):1481–96. doi:10.1080/03610929708831995. Kulldorff, M, 2006. SaTScanTM User Guide for version 9.6. SaTScanTM . Available at: http://www.satscan.org/ Kulldorff M, Heffernan R, Hartman J, Assunção R, Mostashari F. A space– time permutation scan statistic for disease outbreak detection. PLOS Med 2005;2:e59. doi:10.1371/journal.pmed.0 020 059. Lahuerta-Marin A, Gallagher M, McBride S, Skuce R, Menzies F, McNair J, McDowell SWJ, Byrne AW. Should they stay, or should they go? Relative future risk of bovine tuberculosis for interferongamma test-positive cattle left on farms. Vet Res 2015;46:90. doi:10.1186/s13567-015-0242-8. McGrath G, Abernethy D, Stringer L, More SJ. An all-island approach to mapping bovine tuberculosis in Ireland. Ir Vet J 2009;62:192. doi:10.1186/2046-0481-62-3-192. Milne MG, Graham J, Allen AR, Lahuerta-Marin A, McCormick C, Presho E, Skuce RA, Byrne AW. Herd characteristics, wildlife risk and bacterial strain genotypes in persistent breakdowns of bovine tuberculosis in Northern Irish cattle herds. Proceedings of the society for veterinary epidemiology and preventive medicine conference, 2018, 2018. Milne M.G., Graham J., Allen A.R., Lahuerta-Marin A., McCormick C., Presho E., Skuce R.A. and Byrne A.W., Cattle herds with a history of chronic bovine tuberculosis infection in Northern Ireland: variation in risk associated with wildlife abundance and Mycobacterium bovis molecular genotypes, Preventative Veterinary Medicine (In Review).. NIAO, 2009. The control of Bovine Tuberculosis in Northern Ireland. (No. NIA 92/08-09). Northern Ireland Audit Office. Olea-Popelka FJ, White PW, Collins JD, O’Keeffe J, Kelton DF, Martin SW. Breakdown severity during a bovine tuberculosis episode as a predictor of future herd breakdowns in Ireland. Prev Vet Med 2004;63:163– 72. doi:10.1016/j.prevetmed.20 04.03.0 01. R Core Team, 2013. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/. Reid N, Etherington TR, Wilson GJ, Montgomery WI, McDonald RA. Monitoring and population estimation of the European badger Meles meles in Northern Ireland. Wildl Biol 2012;18(1):46–57. doi:10.2981/11-016. Robinson PA. A history of bovine tuberculosis eradication policy in Northern Ireland. Epidemiol Infect 2015;143:3182–95. doi:10.1017/S09502688150 0 0291. Skuce RA, Mallon TR, McCormick CM, McBride SH, Clarke G, Thompson A, Couzens C, Gordon AW, McDowell SWJ. Mycobacterium bovis genotypes in Northern Ireland: herd-level surveillance (2003 to 2008). Vet Rec 2010;167:684–9. doi:10.1136/vr.c5108. Skuce RA, McDowell SW, Mallon TR, Luke B, Breadon EL, Lagan PL, McCormick CM, McBride SH, Pollock JM. Discrimination of isolates of Mycobacterium bovis in Northern Ireland on the basis of variable numbers of tandem repeats (VNTRs). Vet Rec 2005;157:501–4. doi:10.1136/vr.157.17.501.

42

G.M. Milne, J. Graham and A. Allen et al. / Spatial and Spatio-temporal Epidemiology 28 (2019) 33–42

TBSPG, 2016. Bovine Tuberculosis eradication strategy for Northern Ireland. The TB Strategic Partnership Group. White PW, Martin SW, De Jong MCM, O’Keeffe JJ, More SJ, Frankena K. The importance of ‘neighbourhood’ in the persistence of bovine tuberculosis in Irish cattle herds. Prev Vet Med 2013;110:346–55. doi:10.1016/j.prevetmed.2013.02.012. Wolfe DM, Berke O, More SJ, Kelton DF, White PW, O’Keeffe JJ, Martin SW. The risk of a positive test for bovine tuberculosis in cattle purchased from herds with and without a recent history of bovine tuberculosis in Ireland. Prev Vet Med 2009;92:99–105. doi:10.1016/j.prevetmed.2009.07.012.

Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer-Verlag; 2009. Wright DM, Allen AR, Mallon TR, McDowell SWJ, Bishop SC, Glass EJ, Bermingham ML, Woolliams JA, Skuce RA. Field-isolated genotypes of Mycobacterium bovis vary in virulence and influence case pathology but do not affect outbreak size. PLOS ONE 2013;8:e74503. doi:10.1371/journal.pone.0074503. Wright DM, Reid N, Montgomery WI, Allen AR, Skuce RA, Kao RR. Herd-level bovine tuberculosis risk factors: assessing the role of low-level badger population disturbance. Sci. Rep. 2015;5:13062. doi:10.1038/srep13062.