Abstract
The incidence of bovine tuberculosis (TB) in Great Britain has generally been increasing in recent decades. Routine ante-mortem testing of cattle herds is required for disease surveillance and control, due to the asymptomatic nature of the infection. The Department for Environment, Food and Rural Affairs (Defra) publishes TB incidence trends as the percentage of officially TB-free (OTF) herds tested per month with OTF status withdrawn due to post-mortem evidence of infection. This method can result in artefactual fluctuations. We have previously demonstrated an alternative method, that distributes incidents equally over the period of risk, provides a more accurate representation of underlying risk. However, this method is complex and it may not be sufficiently straightforward for use in the national statistics. Here we present a simple incidence-based method that adjusts for the time between tests and show it can provide a reasonable representation of the underlying risk without artefactual fluctuations.
Funding Statement
We thank the UK Medical Research Council (MRC) for Centre funding and Department of Environment, Food and Rural Affairs for project funding.Introduction
Bovine tuberculosis (TB) in Great Britain (GB) has generally been increasing in the last thirty years1 and has resulted in a huge strain on the cattle industry. In 2012, 37,735 cattle were compulsorily slaughtered due to detected herd TB incidents,2 resulting in compensation costs of approximately £30 million to farmers in England from the Department for Environment, Food and Rural Affairs (Defra) that year.3
Cattle infected with Mycobacterium bovis (the etiological agent of bovine TB) do not normally display clinical signs unless the infection is well advanced. Routine and targeted ante-mortem screening of herds using the tuberculin skin test is performed in GB for control and surveillance of bovine TB whereby officially TB-free (OTF) cattle herds are tested for evidence of exposure of M. bovis annually or every four years depending on past incidence of TB in a region (until December 2012 two-yearly and three-yearly testing frequencies had also been used)4 . In England, nearly 60% of herds are annually tested for TB (those in the West and Midlands), whereas the remainder are tested every four years, thus reflecting the regional clustered nature of the disease. All cattle herds in Wales have been tested annually since 2010, whereas Scottish herds are routinely tested every four years or are not tested at all (Scotland was declared officially free from bovine TB by the European Commission in September 2009). Unbiased interpretation of the data coming from this surveillance programme is crucial to understand the underlying incidence trends and thereby to inform discussion on the impact of the various control tools, including badger culling5 and vaccination.6
Defra currently publish the national statistics of GB bovine TB incidence as the monthly percentage of OTF herds tested that result in the OTF status being withdrawn (OTFW) since January 19962 (Fig. 1a, red line). We have previously shown that this method can lead to artefactual fluctuations in the reported trends7 when large numbers of herds are moved to more frequent testing, and can therefore be misleading, if the downward part of these fluctuations are interpreted as a decline in the underlying incidence. Recent trends in the national statistics of the GB bovine TB incidence have been discussed in the current debate on the need for badger culling. To inform the debate most usefully, the incidence trends should be quantified using a method which is not liable to artefactual fluctuations.
We have previously presented an alternative method,7 adapted from a method presented by Cox,8 that distributes detected cattle herd incidents across the period between herd tests and provides a more accurate description or the underlying risk over time. However the method is somewhat mathematically complex to compute and it may not be sufficiently straightforward for use in the national statistics and for understanding by a lay audience.
At the beginning of 2014 it is expected that a public consultation will take place to decide how the national incidence trends of bovine TB in GB should be reported from the surveillance data. Here we present and apply a new simple incidence-based method that adjusts the monthly percentage of OTF herds resulting in OTFW breakdowns as a function of the time since the last herd test, which may effectively quantify the underlying incidence trends for the general public. We use simulations from a mathematical stochastic model of herd testing and infection incidence over time7 (re-fitted to include more recent data from 2010-2012 as well) to compare the current Defra reporting method, the previously published adapted Cox method7 and the simple incidence-based method in their ability to represent the model-estimated underlying trends.
Methods
Data on the total number of OTF herds tested per month and the number of these tests that resulted in OTFW status from January 2003 to December 2012 for England, Wales and Scotland were obtained from the publicly reported national statistics published by Defra2 (Data given in Appendix 2).The numbers of tests that resulted in OTFW during May 2011 – December 2012 are still subject to final confirmation and are presented as a range for each month in the national statistics and so for this period the mid-point of the reported range was used. Note the data is not stratified by testing interval.
The stochastic dynamic model of herd testing and incidence of bovine TB in GB was described previously7 and was fitted to the national statistics data. The model contains a monthly risk of developing detectable M. bovis infection per herd. The times that the national incidence risk changed and the magnitude of changes were estimated (assuming a linear spline model for the underlying trend), along with the times that large-scale changes in testing frequencies occurred. The underlying model monthly risk is presented as the mean risk across all herds as a percentage, multiplied by twelve (Fig. 1b) to allow for direct comparison with the current Defra method. Model parameters were estimated using maximum likelihood estimation for the period January 2003 – December 2012. The likelihood of observing the data (number of OTFW breakdowns for a given month) was described previously7 , conditional on the model parameters and the number of observed OTF herds tested. One hundred stochastic simulations were run under the best fit model with a given underlying incidence risk per month (Fig. 1b), recording the number of OTF herds tested per month and the number of these that resulted in OTFW (i.e. representing 100 simulated national surveillance data-sets with a known underlying incidence risk per month).
Three methods were applied to these 100 stochastic simulations of herd testing to evaluate their ability to represent underlying incidence risk: the current Defra method (Fig. 1a, grey lines); the adapted Cox method (Fig. 1c); and the new simple incidence-based method which adjusts the current Defra method for the time spent between routine herd tests (Fig. 1d).
Defra publish the incidence trends over time as the percentage of OTF herds tested that result in an OTFW breakdown per month, smoothed using a 23-term Henderson moving average for seasonally-adjusted data (using the X-11 method from the freely available software http://www.census.gov/srd/www/x12a/ with the windows interface, version 3.0).
The adapted Cox method7 distributes incident events over the period of risk and allows for multiple introductions of infection during this period. The method can either assume all herds in each testing frequency are at equal risk to becoming infected or that there is heterogeneity in risk such that a given proportion of herds in each testing frequency are at risk of becoming infected. We previously found that decreasing the proportion of herds assumed to be at risk of becoming infected resulted in increased estimates of underlying risk but provided a similar temporal trend to assuming no heterogeneity in risk7 . Comparing estimates from assuming no heterogeneity in risk to assuming only1/5 of herds were at risk, provided sufficient bounds of the true underlying risk.
The third method we propose here is incidence-based in that it adjusts the current Defra method for the time period between tests. The incidence-based estimated risk per month (t), , is given as: where Nτ,t corresponds to the number of OTF herds tested in month t on testing frequency τ, and xτ,t denotes the number of these tests that resulted in an OTFW breakdown that were of testing frequencyτ, (whereτ= 1,2,3 or 4 (the number of years between herd tests)). Therefore for a given month annually tested herds contribute more to the month’s incidence compared to less frequently tested herds. The incidence trends were then smoothed using the 23-term Henderson moving average for seasonally-adjusted data and multiplied by 100 to give a percentage.
We did not apply the adapted Cox method or the new incidence-based method to the published GB data as these methods require the number of tests and positive tests to be stratified by testing interval, and this information is not publicly reported in the national statistics.
Results
The smoothed monthly percentage of OTF herds tested that result in the OTF status being withdrawn, as presented by Defra as the national incidence trend, is shown in Fig 1a (red line).
Applying this current Defra method to 100 stochastic simulations from the best-fitting mathematical model of herd testing and infection incidence (Fig 1a grey lines) replicated the observed smoothed national incidence trend presented by Defra, except the model slightly underestimated the peak of the second fluctuation.
As found previously7, the true underlying incidence risk for each of one hundred simulations from the best fitting model was estimated to monotonically increase over time without fluctuations (Fig 1b). In the current fit, a small increase in risk was estimated to occur in the third quarter of 2009. Maximum likelihood estimates of parameters are given in Appendix Table A1 and a comparison of the updated underlying model-estimated incidence risk to our previous fit is shown in Appendix Fig A1.
The current Defra method of using the percentage of tests in OTF herds that result in OTFW breakdowns over time applied to the 100 stochastic simulations (Fig 1a, grey lines) did not correspond well with the estimated underlying risk, producing clear artefactual fluctuations. The incidence trend calculated from this method was also higher than the underlying risk by a factor of 1.8 (mean across time series).
The adapted Cox method7 applied to each of the 100 stochastic simulations provided an accurate representation of the underlying risk, whereby assuming heterogeneity in risk (assuming one fifth of herds in each testing frequency group were at risk of a herd breakdown) provided a closer representation (light blue lines) than assuming no heterogeneity in risk (dark blue lines) (Fig 1c).
The incidence trends inferred by the smoothed new simple incidence-based method applied to each of the 100 stochastic simulations (Fig 1d) also reflected the estimated underlying risk without pronounced fluctuations and no requirement of an assumption regarding heterogeneity of risk, although the timings of the changes in risk were more lagged than with the adapted Cox method.
Discussion
There is an urgent need to communicate clearly the incidence trends of bovine TB in GB. A large debate continues as to whether additional control methods (such as badger culling5 or vaccination6 ) are required to reduce the national incidence of cattle TB. The national reporting system currently uses a method which is liable to artefactual fluctuations. These can lead the public to believe that the incidence is declining when in fact it could instead be an artefact of the methods used.7 i.e. for a given underlying incidence risk which does not contain fluctuations but monotonically increases (as estimated in this work) the national reporting system can produce artefactual fluctuations which do not represent the underlying trend. We have previously shown that an adapted method proposed by Cox7,8 provides an accurate representation of the estimated risk, but this method is mathematically complex and it may not be sufficiently straightforward for use in the national statistics. We therefore developed a new simple incidence-based method to represent the underlying incidence trend from the national targeted surveillance data.
Here we have shown that new proposed method that adjusts the current reporting method for the time period between routine herd tests provides a better representation of the estimated underlying trends compared to the current Defra method. This method weights the test-positive tests with OTF status withdrawn by the time period since the last TB herd test, such that herds on four-yearly testing contribute a quarter compared to annually tested herds to the incidence at a given time.
The incidence trends obtained from both this new incidence-based method and the adapted Cox method are similar in magnitude to the underlying risk, whereas the incidence of bovine TB obtained from the current Defra method is generally higher (by a factor of 1.8) due to the overrepresentation of high risk herds (an inevitable feature of a targeted surveillance program). The expansion of the herds annually tested over time has caused the extent of the bias toward high risk herds to vary over time. The new incidence-based method does not provide as accurate a representation of the underlying risk trends as the adapted Cox method, because the timings of the change in risk are delayed, but importantly the method does not result in pronounced artefactual oscillations. Therefore, this method would be advantageous compared to the current reporting method.
Competing Interests
The authors have declared that no competing interests exist.
Appendix 1
Appendix Table A1
Full details of the model structure and parameter definitions are described previously.7
Model Parameter
Estimate
Proportion of herds with a relatively high risk of developing an incident infection at the start of 2003
0.16
Probability of a herd entering a higher risk category at each time step when time ≥ z1 and < z2
0.015
Probability of a herd entering a higher risk category at each time step when time is ≥ z3 and < z4
0.005
Probability of a herd entering a higher risk category at each time step when time is ≥ z5 and < z6
0.004
z1 Time threshold at which incidence risk increases
September 2003
z2 Time at which incidence risk stops increasing
July 2004
z3 Time at which incidence risk starts increasing again
November 2006
z4 Time at which incidence risk stops increasing
June 2008
z5 Time at which incidence risk starts increasing again
July 2009
z6 Time at which incidence risk stops increasing
November 2009
Threshold number of cumulative incident infections within a parish which when reached, the frequency interval of parish herd testing is changed to two-yearly testing
1 incident infection
Threshold number of cumulative incident infections within a parish which when reached, the frequency interval of parish herd testing is changed to annual testing
1 incident infection
Proportion of high-risk herds at the start of 2003 who are on annual testing
0.18
The first time after the beginning of 2003 when testing frequencies are changed
November 2004
The period between changing of testing frequencies
2 years and 11 months
Appendix Figure A1
Appendix 2
Published statistics on the incidence of tuberculosis (TB) in cattle in Great Britain
(Downloaded from https://www.gov.uk/government/publications/incidence-of-tuberculosis-tb-in-cattle-in-great-britain on 01/09/2013)
Updated on:
14/08/2013
Contact:
[email protected]
Next update:
11/09/2013
Media Enquiries to:
0207 238 6007 (Press Office)
Source:
Animal Health and Veterinary Laboratories Agency (AHVLA) work management IT support system (SAM)
TB in Cattle in Great Britain
Classification:
Public Domain
Units:
Various
TABLE 1: TB INCIDENTS IN GREAT BRITAIN - HERDS
Total tests on herds
Herds not officially TB free (non-OTF herds)
Tests on officially TB free herds (OTF)
Of which: New herd incidents
Of which: officially TB free withdrawn (OTFW)
% of tests on officially TB free herds which resulted in officially TB free status being withdrawn
(1)
(2)
(3)
(4)
(5)
(6)
1996
36,314
1,589
34,812
1,075
490
1.4%
1997
34,065
1,632
32,295
1,195
540
1.7%
1998
37,046
2,077
34,502
1,514
787
2.3%
1999
41,365
2,374
38,338
1,661
967
2.5%
2000
40,669
2,482
37,184
1,738
1,135
3.1%
2001
*
13,187
1,697
11,118
802
571
5.2%
2002
**
49,709
4,167
43,641
3,323
2,042
4.7%
2003
56,208
5,460
47,568
3,214
1,789
3.8%
2004
56,836
5,220
49,027
3,341
1,934
4.0%
2005
55,887
5,669
46,725
3,665
2,308
4.9%
2006
64,457
5,859
56,051
3,530
2,303
4.1%
2007
64,145
6,582
54,856
4,188
2,546
4.7%
2008
66,432
7,935
54,854
5,011
3,093
5.6%
2009
(prov)
72,205
8,386
58,894
4,599
2,847
4.9%
2010
(prov)
74,474
7,964
61,587
4,723
3,013
4.9%
2011
(prov)
76,662
8,252
62,493
4,909
3,109
5.2%
2012
(prov)
88,566
9,067
73,656
5,192
3,465
4.8%
2013
(prov)
42,441
7,533
35,337
2,246
1,404
4.0%
1996
Jan
4,152
638
4,038
120
57
1.4%
Feb
4,968
660
4,843
108
54
1.1%
Mar
5,082
734
4,951
134
54
1.2%
Apr
3,588
780
3,444
114
51
1.5%
May
3,327
789
3,155
90
30
1.0%
Jun
2,022
762
1,881
74
36
1.9%
Jul
1,665
729
1,515
82
30
2.0%
Aug
1,724
668
1,600
52
22
1.4%
Sep
1,639
653
1,522
63
33
2.2%
Oct
2,256
574
2,148
70
30
1.4%
Nov
3,181
499
3,086
91
50
1.6%
Dec
2,710
502
2,629
77
43
1.7%
1997
Jan
3,864
543
3,739
106
51
1.4%
Feb
4,046
565
3,929
95
48
1.2%
Mar
3,794
597
3,679
96
34
0.9%
Apr
3,507
697
3,343
159
69
2.1%
May
3,020
701
2,838
96
36
1.3%
Jun
2,182
729
1,998
107
30
1.5%
Jul
1,866
690
1,710
78
26
1.6%
Aug
1,590
663
1,426
63
31
2.2%
Sep
1,756
661
1,601
106
49
3.1%
Oct
2,541
653
2,377
86
47
2.0%
Nov
2,994
658
2,863
112
60
2.1%
Dec
2,905
651
2,792
91
59
2.1%
1998
Jan
4,133
707
3,934
144
82
2.1%
Feb
4,363
770
4,180
145
76
1.8%
Mar
4,137
861
3,956
176
89
2.3%
Apr
3,692
905
3,445
138
63
1.9%
May
3,011
894
2,779
122
53
1.9%
Jun
2,323
931
2,094
141
61
3.0%
Jul
2,298
912
2,032
107
57
2.9%
Aug
1,664
859
1,467
100
64
4.4%
Sep
1,708
835
1,508
96
51
3.4%
Oct
2,899
813
2,672
112
54
2.0%
Nov
3,521
841
3,298
132
83
2.5%
Dec
3,297
812
3,137
101
54
1.7%
1999
Jan
4,447
852
4,223
139
77
1.8%
Feb
4,794
924
4,569
186
111
2.5%
Mar
4,750
1,052
4,540
220
119
2.6%
Apr
4,884
1,079
4,540
165
89
2.0%
May
3,322
1,093
3,064
147
83
2.7%
Jun
2,318
1,120
2,053
136
71
3.5%
Jul
2,834
1,055
2,467
108
59
2.4%
Aug
1,879
975
1,657
86
52
3.1%
Sep
2,153
918
1,904
87
58
3.1%
Oct
2,713
849
2,452
97
59
2.4%
Nov
3,609
856
3,414
177
113
3.3%
Dec
3,662
839
3,455
113
76
2.2%
2000
Jan
4,304
943
4,037
199
128
3.2%
Feb
4,915
1,017
4,652
200
129
2.8%
Mar
5,656
1,091
5,333
175
110
2.1%
Apr
3,818
1,116
3,497
141
86
2.5%
May
3,515
1,114
3,198
138
93
2.9%
Jun
2,876
1,087
2,531
121
65
2.6%
Jul
2,193
1,023
1,874
104
69
3.7%
Aug
2,104
987
1,828
110
64
3.5%
Sep
2,219
950
1,920
113
77
4.0%
Oct
2,247
935
2,029
132
92
4.6%
Nov
3,657
979
3,340
159
112
3.4%
Dec
3,165
991
2,945
146
110
3.8%
2001
Jan
3,791
1,102
3,496
207
142
4.1%
Feb
*
4,340
1,104
3,997
144
91
2.4%
Mar
*
601
1,007
538
15
13
2.4%
Apr
*
386
1,008
302
35
27
8.9%
May
*
360
1,003
232
28
16
6.9%
Jun
*
332
974
206
32
22
10.7%
Jul
*
358
950
193
35
19
10.1%
Aug
*
355
906
187
19
9
5.1%
Sep
*
364
873
194
42
30
15.5%
Oct
*
398
877
257
65
53
20.6%
Nov
*
841
879
638
76
60
9.5%
Dec
**
1,061
916
878
104
89
10.1%
2002
Jan
**
2,765
1,045
2,515
202
136
5.4%
Feb
**
4,437
1,324
4,185
375
248
5.9%
Mar
**
5,535
1,589
5,253
348
245
4.7%
Apr
**
5,164
1,918
4,649
397
250
5.4%
May
**
5,509
2,144
4,916
320
171
3.5%
Jun
**
2,685
2,190
2,238
194
95
4.3%
Jul
**
3,030
2,304
2,441
235
129
5.3%
Aug
**
2,918
2,298
2,261
182
111
5.0%
Sep
**
2,752
2,298
2,139
206
121
5.7%
Oct
**
3,787
2,339
3,141
249
144
4.7%
Nov
**
6,217
2,406
5,532
329
201
3.6%
Dec
4,910
2,452
4,371
286
191
4.4%
2003
Jan
7,032
2,620
6,227
374
219
3.5%
Feb
6,181
2,678
5,449
337
191
3.5%
Mar
6,213
2,839
5,499
430
238
4.4%
Apr
4,826
2,912
4,066
333
174
4.3%
May
5,332
2,833
4,414
238
130
3.0%
Jun
3,844
2,768
2,999
260
132
4.4%
Jul
3,720
2,612
2,989
206
96
3.2%
Aug
2,991
2,372
2,327
118
58
2.5%
Sep
3,390
2,300
2,713
216
127
4.7%
Oct
4,106
2,188
3,401
201
113
3.4%
Nov
4,499
2,158
3,890
264
171
4.4%
Dec
4,074
2,145
3,594
237
140
3.9%
2004
Jan
6,385
2,216
5,692
337
200
3.5%
Feb
5,973
2,284
5,317
312
183
3.4%
Mar
5,994
2,488
5,416
439
239
4.4%
Apr
6,350
2,564
5,533
344
176
3.2%
May
4,560
2,584
3,877
265
139
3.6%
Jun
3,555
2,583
2,931
272
134
4.6%
Jul
3,949
2,457
3,106
170
95
3.1%
Aug
2,984
2,369
2,394
196
117
4.9%
Sep
3,132
2,226
2,490
159
96
3.9%
Oct
3,832
2,147
3,245
216
135
4.2%
Nov
5,212
2,245
4,641
369
246
5.3%
Dec
4,910
2,223
4,385
262
174
4.0%
2005
Jan
5,793
2,431
5,166
429
270
5.2%
Feb
6,308
2,698
5,561
461
290
5.2%
Mar
5,919
2,929
5,281
423
293
5.6%
Apr
6,357
3,050
5,238
384
227
4.3%
May
4,044
2,983
3,202
257
163
5.1%
Jun
3,344
2,875
2,443
218
134
5.5%
Jul
3,346
2,731
2,504
174
100
4.0%
Aug
2,772
2,602
2,078
194
117
5.6%
Sep
3,591
2,514
2,819
226
132
4.7%
Oct
3,649
2,493
3,059
276
174
5.7%
Nov
5,058
2,573
4,348
340
212
4.9%
Dec
5,706
2,584
5,026
283
196
3.9%
2006
Jan
6,405
2,714
5,638
386
246
4.4%
Feb
6,859
2,721
6,071
315
219
3.6%
Mar
7,821
2,813
7,001
366
229
3.3%
Apr
6,014
2,734
5,207
265
171
3.3%
May
5,146
2,725
4,426
282
183
4.2%
Jun
4,466
2,636
3,713
233
150
4.1%
Jul
3,705
2,549
3,073
222
142
4.6%
Aug
3,277
2,436
2,705
183
109
4.1%
Sep
4,434
2,426
3,709
285
196
5.3%
Oct
4,421
2,462
3,899
345
234
6.0%
Nov
6,178
2,557
5,493
357
236
4.3%
Dec
5,731
2,597
5,116
291
188
3.7%
2007
Jan
6,453
2,850
5,693
458
268
4.7%
Feb
6,982
2,956
6,160
384
211
3.4%
Mar
8,350
3,120
7,421
437
250
3.4%
Apr
6,352
3,246
5,474
416
244
4.5%
May
5,348
3,212
4,514
298
178
4.0%
Jun
4,560
3,066
3,680
278
148
4.0%
Jul
3,826
3,029
3,042
285
155
5.1%
Aug
2,905
2,863
2,297
175
96
4.2%
Sep
3,601
2,851
2,935
270
173
5.9%
Oct
4,298
3,039
3,690
447
303
8.2%
Nov
6,524
3,106
5,662
402
282
5.0%
Dec
4,946
3,165
4,288
338
238
5.6%
2008
Jan
6,593
3,398
5,719
474
313
5.5%
Feb
7,398
3,504
6,353
473
322
5.1%
Mar
5,872
3,602
5,137
427
300
5.8%
Apr
6,909
3,925
5,697
594
366
6.4%
May
5,908
3,901
4,901
390
240
4.9%
Jun
4,319
3,946
3,309
375
194
5.9%
Jul
4,236
3,973
3,171
359
174
5.5%
Aug
3,768
3,947
2,934
305
178
6.1%
Sep
4,105
4,019
3,206
381
220
6.9%
Oct
5,603
4,047
4,582
426
283
6.2%
Nov
6,193
4,116
5,155
448
275
5.3%
Dec
(prov)
5,528
4,136
4,690
359
228
4.9%
2009
Jan
(prov)
7,894
4,320
6,552
533
301
4.6%
Feb
(prov)
7,181
4,339
6,058
438
252
4.2%
Mar
(prov)
7,551
4,542
6,423
572
330
5.2%
Apr
(prov)
7,523
4,563
6,127
440
256
4.2%
May
(prov)
6,019
4,464
4,822
359
207
4.3%
Jun
(prov)
4,609
4,342
3,538
366
212
6.0%
Jul
(prov)
4,889
4,116
3,564
294
193
5.4%
Aug
(prov)
3,953
3,791
3,048
214
139
4.6%
Sep
(prov)
4,413
3,654
3,535
306
209
6.0%
Oct
(prov)
6,270
3,522
5,191
333
235
4.5%
Nov
(prov)
6,139
3,637
5,135
454
308
6.0%
Dec
(prov)
5,764
3,570
4,901
290
205
4.2%
2010
Jan
(prov)
6,852
3,675
5,758
434
284
5.0%
Feb
(prov)
8,114
3,766
6,916
440
277
4.0%
Mar
(prov)
8,121
3,905
7,144
527
328
4.6%
Apr
(prov)
8,834
3,933
7,365
428
259
3.5%
May
(prov)
5,833
3,879
4,764
388
218
4.6%
Jun
(prov)
4,059
3,875
3,104
391
209
6.8%
Jul
(prov)
5,086
3,683
3,733
247
161
4.4%
Aug
(prov)
3,956
3,517
3,032
288
181
6.0%
Sep
(prov)
4,939
3,451
3,937
359
249
6.4%
Oct
(prov)
6,243
3,475
5,231
404
277
5.3%
Nov
(prov)
6,664
3,640
5,687
525
377
6.7%
Dec
(prov)
5,773
3,615
4,916
292
193
3.9%
2011
Jan
(prov)
7,830
3,881
6,531
538
350
5.4%
Feb
(prov)
7,910
4,025
6,653
464
302
4.6%
Mar
(prov)
8,615
4,164
7,486
510
312
4.2%
Apr
(prov)
7,035
4,143
5,694
397
241
4.3%
May
(prov)
6,142
4,199
4,898
467
270 - 285
5.5% - 5.8%
Jun
(prov)
4,675
4,121
3,415
347
202 - 212
5.9% - 6.2%
Jul
(prov)
4,656
4,009
3,388
300
183 - 225
5.4% - 6.6%
Aug
(prov)
4,231
3,864
3,209
292
188 - 248
5.9% - 7.7%
Sep
(prov)
5,900
3,688
4,660
251
166 - 183
3.6% - 3.9%
Oct
(prov)
6,010
3,786
5,026
421
281 - 298
5.6% - 5.9%
Nov
(prov)
6,563
4,013
5,570
489
331 - 356
5.9% - 6.4%
Dec
(prov)
7,095
4,137
5,963
433
283 - 306
4.7% - 5.1%
2012
Jan
(prov)
8,195
4,360
6,996
485
326 - 334
4.7% - 4.8%
Feb
(prov)
9,062
4,498
7,702
465
315 - 326
4.1% - 4.2%
Mar
(prov)
11,717
4,760
10,282
591
398 - 406
3.9% - 3.9%
Apr
(prov)
7,891
4,882
6,536
408
249 - 254
3.8% - 3.9%
May
(prov)
7,306
4,979
5,994
448
259 - 266
4.3% - 4.4%
Jun
(prov)
5,426
4,941
4,177
326
199 - 202
4.8% - 4.8%
Jul
(prov)
5,061
4,922
3,860
315
216 - 218
5.6% - 5.6%
Aug
(prov)
5,422
4,973
4,244
357
245 - 250
5.8% - 5.9%
Sep
(prov)
5,881
5,028
4,742
376
264 - 272
5.6% - 5.7%
Oct
(prov)
6,816
5,214
5,748
452
328 - 333
5.7% - 5.8%
Nov
(prov)
9,060
5,450
7,680
570
393 - 401
5.1% - 5.2%
Dec
(prov)
6,729
5,545
5,695
399
273 - 275
4.8% - 4.8%
2013
Jan
(prov)
8,750
5,795
7,339
508
307 - 319
4.2% - 4.3%
Feb
(prov)
9,004
5,907
7,561
409
278 - 283
3.7% - 3.7%
Mar
(prov)
9,257
6,029
7,990
496
306 - 314
3.8% - 3.9%
Apr
(prov)
8,076
6,103
6,545
429
275 - 282
4.2% - 4.3%
May
(prov)
7,354
6,039
5,902
404
238 - 244
4.0% - 4.1%
Notes:- The data are a snapshot extracted from Sam. Data for 2009 onwards will remain provisional and subject to revision until all culture results are available and final data validation has been carried out. The herd incidence rates for the latest months are given as a range because a number of incidents are still unclassified, so data for these months should be treated as provisional results. TB incidents remain unclassified if at the end of the period covered by this notice they had not been designated OTFW, but were still ongoing and the herd could have its OTF status withdrawn if further testing revealed one or more animals with post-mortem evidence of TB.
(1)
Herds for which tuberculin skin testing is carried out on at least one animal during the period shown.
(2)
Herds that had lost their OTF status at some time during the period shown due to a TB incident.
(3)
Any test carried out in an OTF herd during the period shown.
(4)
Herds which were previously OTF but either had cattle that reacted to a tuberculin test or had a tuberculous animal disclosed by routine meat inspection at slaughter, during the period shown.
(5)
New herd incidents (column 4) where OTF status was withdrawn from the herd.
(6)
Column 5 as a percentage of column 3.
*
Data for 2001 are not comparable with other years. During the outbreak of Foot and Mouth Disease, TB testing was significantly reduced and necessarily targeted to areas of higher risk.
**
Data for 2002 are not comparable with other years. Testing resources were concentrated on herds which were overdue their tests (because of the backlog caused by the Foot and Mouth Disease outbreak).
References
- Krebs JR et al. Bovine tuberculosis in cattle and badgers. (HMSO, London) 1997
Reference Link - Defra. Monthly publication of national statistics on the incidence of tuberculosis (TB) in cattle to end June 2013 for Great Britain (Accessed September 2013)
Reference Link - Defra. Request for information: various bovine TB costs (2008-2013) (Accessed September 2013)
Reference Link - Defra. Changes to bovine TB surveillance: bovine TB information note 04/12 (Accessed September 2013)
Reference Link - Defra. Bovine tuberculosis: The government's approach to tackling the disease and consultation on a badger control policy (Accessed September 2013)
Reference Link - Welsh Government. Badger vaccination underway (Accessed September 2013)
Reference Link - Blake IM, Donnelly CA. Estimating risk over time using data from targeted surveillance systems: application to bovine tuberculosis in Great Britain. Epidemics. 2012 Dec;4(4):179-86. PubMed PMID:23351370.
- Cox DR. An Industrial monitoring problem. Quality Engineering. 2010 22:73-77
Reference Link
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