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Diagnostic sampling strategies for virulent ovine footrot: Simulating detection of Dichelobacter nodosus serogroups for bivalent vaccine formulation
Preventive Veterinary Medicine 95 (2010) 127–136
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Diagnostic sampling strategies for virulent ovine footrot: Simulating detection of Dichelobacter nodosus serogroups for bivalent vaccine formulation Ashley E. Hill a,∗ , Om P. Dhungyel b , Richard J. Whittington b a b
Animal Population Health Institute, Campus Delivery 1644, Colorado State University, Fort Collins, CO 80523-1644, USA Farm Animal and Veterinary Public Health, Faculty of Veterinary Science, University of Sydney, 425 Werombi Road, Camden NSW 2570, Australia
a r t i c l e
i n f o
Article history: Received 31 December 2008 Received in revised form 30 December 2009 Accepted 22 February 2010 Keywords: Dichelobacter nodosus Monte Carlo simulation Sampling Sheep-microbiological diseases Footrot Vaccination
a b s t r a c t Dichelobacter nodosus is a slow-growing anaerobic bacterium that is the causative agent of virulent ovine footrot. Vaccination targeted at up to two specific serogroups can eliminate those serogroups from infected flocks, but requires identification of serogroups present in infected flocks. Serogroups can be identified using slide agglutination or polymerase chain reaction (PCR) methods. The objectives of this project were to use stochastic simulation modeling to estimate the efficacy of sampling strategies encompassing 5–40 sheep per flock and 2–4 colonies per sheep, and to compare efficacies based on slide agglutination or multiplex PCR test results. Foot swabs collected from sheep in 12 flocks were used as the basis for a sampling strategy simulation model. None of the evaluated sampling strategies identified the two most common serogroups in the flock, or all serogroups present in the flock, in 95% of iterations. However, a simulated sample of 22 sheep/flock and 2 colonies/sheep resulted in a simulated vaccine that protected 95% of the sheep that could be protected by a single bivalent vaccine, while a sample of 24 sheep/flock and 2 colonies/sheep resulted in a series of simulated bivalent vaccines that protected 95% of diseased infected sheep. The difference in outcome was due to the distribution and frequency of serogroups within certain flocks where some serogroups were uncommon and others dominant. A sampling strategy (>40 sheep/flock, 4 colonies/sheep) that will identify the two most common serogroups in a flock 95% of the time may not be cost effective. Evaluating efficacy based on the expected effect on the flock may be more useful than one which seeks to determine the most common serogroups. These findings are broadly applicable to diseases where more than one strain or type of pathogen may be present and must be represented in a vaccine. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Dichelobacter nodosus is the essential causative agent of virulent ovine footrot, a severe highly contagious disease which involves painful separation of the hoof from underlying dermis (Roberts and Egerton, 1969). Footrot causes
∗ Corresponding author. Tel.: +1 970 297 4050; fax: +1 970 297 1275. E-mail address: [email protected] (A.E. Hill). 0167-5877/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.prevetmed.2010.02.011
economic losses via lameness: reduced wool growth, loss of body condition and reduced reproductive performance. The disease is a significant international problem, and there are increasing requirements to control it to meet standards in animal welfare (Hektoen et al., 2009). D. nodosus does not survive on pastures for more than 5 days and there are defined environmental circumstances required for transmission so in theory the disease can be eradicated simply by removing all infected sheep from a flock (Whittington, 1995). However, this is usually precluded economically by
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the high prevalence of infection, the difficulty of identification of some infected sheep and the lack of clean pasture. Therefore strategies to reduce prevalence and transmission rates are more common than eradication programs. Successful regional control programs, exemplified by those undertaken in Australia, are complex and have required more than 20 years of highly coordinated effort (Plant and Egerton, 2009). Although these campaigns were not based on improving herd immunity, vaccination against D. nodosus has been used to eradicate the disease from Nepal, Bhutan and some flocks in Australia (Dhungyel et al., 2008, 2001; Egerton et al., 2002; Gurung et al., 2006). Vaccination provides therapeutic and prophylactic benefits to reduce prevalence and reduce transmission, respectively (Egerton, 1979; Egerton and Morgan, 1972; Gurung et al., 2006). However, its application is limited by microbiological and immunological factors: many serogroups of D. nodosus are recognized (Claxton et al., 1983; Claxton, 1986; Day et al., 1986; Gradin et al., 1993; Ghimire et al., 1998) and the immune system of the sheep is incapable of responding adequately if more than two are included in a vaccine formulation (Hunt et al., 1995; Raadsma et al., 1994; Schwartzkoff et al., 1993). Research in Australia, New Zealand, The United States, Europe and the United Kingdom has shown that infections at the flock level may be caused by multiple serogroups (Claxton, 1989; Claxton et al., 1983; Gradin et al., 1993; Kingsley et al., 1986; Thorley and Day, 1986). Therefore a successful vaccination strategy requires accurate identification of the serogroups that are present in an affected flock, and formulation of a series (if necessary) of mono- or bivalent vaccines appropriate for that flock that can be administered sequentially (Dhungyel and Whittington, 2009). Serogroups can be identified using slide agglutination or polymerase chain reaction (PCR) methods (Claxton et al., 1983; Dhungyel et al., 2002b) but the efficacy of these tests has not been compared. Identification of D. nodosus serogroups results from multi-stage sampling (Dohoo et al., 2003): diseased sheep are selected, then affected feet from these sheep, then microbial colonies from cultures of foot swabs. In the most intensive study conducted to date, there was a direct relationship between the number of diseased sheep or feet sampled per flock and the number of serogroups found in that flock (Claxton et al., 1983). However, the optimal sampling strategy for accurate determination of the D. nodosus strains present in a flock is not known; ad hoc protocols therefore tend to be used. As an example, one current protocol for routine diagnosis is to evaluate up to 10 D. nodosus colonies off culture plates derived from swabs taken from a single foot from up to five diseased sheep from a flock (New South Wales Agriculture, 2002). Clearly there is a likelihood that such a strategy will fail to detect all serogroups that may be present. The optimal sampling strategy may differ depending on the number of serogroups present in a diseased flock, the relative prevalence of serogroups within a diseased flock, the potential for clustering of serogroups within sheep or feet, and the diagnostic method used to classify serogroups. The optimal sampling strategy may also differ depending on how success is measured: by maximizing the probabil-
ity of selecting the most common serogroups in the flock, or by the success of treating/protecting the maximum number of sheep in the flock. Traditional methods of sample size calculation (Cannon and Roe, 1982) are not capable of estimating sample sizes for detection of multiple agents, nor do they allow for measures of success other than probability of agent detection. Stochastic simulation modeling has been used previously to evaluate and compare complex multistage sampling strategies for policy development (Regula et al., 2005; Villarroel et al., 2006; Vos et al., 2007). The main objectives of this project were to build a stochastic simulation model to estimate the efficacy of different sampling scenarios for detection of D. nodosus serogroups present in a flock, and to compare efficacies within scenario for two different diagnostic methods (slide agglutination and PCR). Four measures of efficacy were evaluated and compared between tests: (1) the proportion of iterations in which the two most prevalent serogroups could be correctly selected for production of a bivalent vaccine; (2) the proportion of diseased sheep per flock that could be fully protected by the selected bivalent vaccine; (3) the proportion of iterations in which all serogroups in the flock were identified in the sample; and (4) the proportion of diseased sheep per flock that could be fully protected by a long-term vaccination strategy consisting of a series of bivalent vaccines encompassing all serogroups identified in the initial flock sample. The latter two were evaluated to assess whether follow-up studies post vaccination were needed. 2. Materials and methods 2.1. Definitions Throughout the manuscript, “diseased” refers to sheep with clinical lameness, and “infected” refers to sheep that tested positive for D. nodosus using either slide agglutination or PCR methodology. “Protectable” sheep are those theoretically capable of being protected and/or treated by a vaccine or series of vaccines. For evaluation of a series of bivalent vaccines, the number of protectable sheep in a flock equalled the number of infected sheep. For evaluation of a single bivalent vaccine, the number of protectable sheep in a flock equalled the maximum number of sheep identified with any two-serogroup combination in the flock (e.g. in Flock 9, 38 sheep were infected with serogroup A only, 1 with G only, 1 with H only, 3 with A and G, and 10 with A and H; so the maximum number that could be protected by a single vaccine (serogroups AH) was 49). 2.2. Data Foot swabs collected from sheep in 12 flocks from January 1, 2006 through July 30, 2007 were used as the basis for a sampling strategy simulation model. The 12 flocks were from 8 districts in 3 Australian States (South Australia, Tasmania, Victoria) and were a convenience sample of sheep flocks with ongoing clinically apparent virulent D. nodosus infection, where the owners were willing to participate in a larger vaccination trial study. Upon enrollment, each
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flock was visited two to six times prior to vaccination by a veterinarian who was instructed to stratify the flock into mobs defined by the management strategy for the flock (for example into: adult ewes, adult wethers, lambs, rams) and then to randomly sample diseased sheep from each mob, which were identified by obvious lameness and/or clinical signs of footrot in individual feet. Up to 40 diseased sheep from each flock were sampled on each visit. If fewer than 40 sheep were diseased at the time of the visit, all diseased sheep were sampled. All feet of each diseased sheep were visually evaluated and graded (Whittington and Nicholls, 1995), and in most cases the foot with the most severe clinical signs was sampled by inserting a sterile wooden applicator stick into necrotic material or discharge from visible lesions, usually at the active margin of the underrun lesion or most active area of interdigital skin inflammation if no underrun lesion was present. Standard methods of isolation of D. nodosus (Stewart and Claxton, 1993) were followed. Similar numbers of D. nodosus isolates per sheep were obtained from swabs collected from the active margin of underrun lesions (1905 isolates from 296 sheep) or the interdigital skin (2206 isolates 362 sheep). Between 1 and 13 colonies of varying morphology were selected from each swab culture for serogroup classification using slide agglutination (Claxton et al., 1983) and polymerase chain reaction methodology (Dhungyel et al., 2002). The number of colonies selected from each swab culture was based on morphology: if all colonies had homogenous morphology, then only one colony was selected; otherwise, a representative colony from each morphology type was selected. An isolate was classified as virulent if either an elastase test (Stewart, 1979) or a gelatin gel test result (Palmer, 1993) was positive. Data for swabs where primary isolation or sub-culture did not produce colonies of D. nodosus were excluded from analysis. Data obtained from swabs collected from sheep after vaccination were not included in the analysis. Although the number of serogroups identified in a single sheep can potentially range from 0 to 9, between 1 and 4 serogroups were actually identified in a single sheep in the available dataset.
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2.3. Assumptions Any serogroup identified using either slide or PCR methods was considered present in the colony tested. Each sheep was considered infected with all serogroups identified in any of its colonies. Each flock was considered infected with all virulent serogroups identified in any of its sheep. For a sheep to be considered protected by a single vaccine or series of vaccinations, all serogroups present in the sheep must be included in the simulated vaccine(s). The vaccine was assumed to be 100% effective at treating or protecting sheep. 2.4. Model design A Markov Chain Monte Carlo simulation model was constructed to stochastically simulate the detection of D. nodosus in 12 sheep flocks when culturing two, three, or four colonies each from between 5 and 40 diseased sheep within an affected flock. The model was constructed using Excel (Microsoft Corporation, USA) and @Risk software (Palisade Corporation, USA). In each flock, the process of randomly selecting diseased sheep, then randomly selecting colonies from foot cultures of these sheep was simulated (Table 1). For each diagnostic method (slide agglutination and PCR), the results of each flock sample were used to identify all D. nodosus serogroup(s) present in the flock as well as simulate the selection of two serogroup(s) from the sample for inclusion in a vaccine (Table 2). The efficacy of the selected vaccine was evaluated (SAS 9.2, SAS Institute USA) by determining whether the two serogroups present at the highest frequencies in the flock were selected for inclusion in a single bivalent vaccine (yes/no), by determining whether all serogroups in the flock were identified in the sample (yes/no), and by estimating the number of diseased sheep that could be successfully treated by the selected vaccine(s). The model was run for 10,000 iterations. For the yes/no outcomes, the efficacy of a given sampling strategy and diagnostic method was estimated as the
Table 1 Parameters for stochastic model simulating the sampling of one foot from 5 to 40 lame sheep and testing of 2-4 cultured colonies per foot for identification of Dichelobacter nodosus serogroups and selection of D. nodosus serogroups for inclusion in a vaccine(s). Description
Abbreviation
Diseased sheep per flock i j diseased sheep sampled in each flock i
ni bij
Colonies tested per sheep bij k colonies sampled in each sheep bij
dij cijk
Presence/absence of serogroups A–I in each colony cijk identified using slide agglutination (s) Presence/absence of serogroups A–I in each colony cijk identified using multiplex PCR (r) Distribution of serogroups A–I from cijk colonies tested in bij diseased sheep using slide agglutination (s) Distribution of serogroups A–I from cijk colonies tested in bij diseased sheep using multiplex PCR (r) Distribution of serogroups A–I from dij colonies tested in ni diseased sheep in each flock i using both slide agglutination and multiplex PCR
sijk = [Aijks , Bijks , Cijks , Dijks , Eijks , Fijks , Gijks , Hijks , Iijks ]
j draws from Uniform(1, ni ) with replacement k draws from Uniform(1, dij ) with replacement
rijk = [Aijkr , Bijkr , Cijkr , Dijkr , Eijkr , Fijkr , Gijkr , Hijkr , Iijkr ] Si = Ri =
Distribution/formula
sjk rjk
Pi = [Ai , Bi , Ci , Di , Ei , Fi , Gi , Hi , Ii ]
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Table 2 Outputs of stochastic model simulating the sampling of one foot from 5 to 40 lame sheep and testing of 2–4 cultured colonies per foot for identification of Dichelobacter nodosus serogroups and selection of D. nodosus serogroups for inclusion in a vaccine(s). Description
Abbreviation
Distribution/formula
Binary vector of most frequent serogroup(s) in Si
eiS = [AiS , BiS , CiS , DiR , EiS , FiS , GiS , HiS , IiS ]
[Largest element of Si ] = 1 and other elements of eiS are 0. If two elements of Si are equally large, then both elements of eiS = 1. If >2 elements of Si are equally large, then draw from binomial(1, 2/number of equally large elements) for each large element of Si to select 2 elements of eiS = 1. J
Binary vector of most frequent serogroup(s) in Ri
eiR = [AiR , BiR , CiR , DiR , EiR , FiR , GiR , HiR , IiR ]
eiS [z] ≤ 2
z=A
[Largest element of Ri ] = 1 and other elements of eiR are 0. If two elements of Ri are equally large, then both elements of eiR = 1. If >2 elements of Ri are equally large, then draw from binomial(1, 2/number of equally large elements) for each large element of Ri to select 2 elements of eiR = 1. J
eiR [z] ≤ 2
z=A
Binary vector of second-most-frequent serogroup in Si
fiS = [AiS , BiS , CiS , DiS , EiS , FiS , GiS , HiS , IiS ]
J
If
eiS [z] = 2, then
J
z=A
J
fiS [z] = 0. If
z=A
eiS [z] = 1, then the element of fiS
z=A
corresponding to the second-largest element of Si = 1. If multiple elements of Si shared the second-largest frequency, then draw from binomial(1, 1/number of equally large elements) for each large element of Si to select one element of fiS to J
be equal to 1.
Binary vector of second-most-frequent serogroup in Ri
fiR = [AiR , BiR , CiR , DiR , EiR , FiR , GiR , HiR , IiR ]
J
If
eiR [z] = 2, then
z=A
J
fiS [z] ≤ 1
z=A
J
fiR [z] = 0. If
z=A
eiR [z] = 1, then the element of fiR
z=A
corresponding to the second-largest element of Ri = 1. If multiple elements of Ri shared the second-largest frequency, then draw from binomial(1, 1/number of equally large elements) for each large element of Ri to select one element of fiR to J
be equal to 1. Serogroups selected for inclusion in a simulated vaccine based on results obtained using slide agglutination. Serogroups selected for inclusion in a simulated vaccine based on results obtained using multiplex PCR Serogroups selected for inclusion in a series of vaccines based on results obtained using slide agglutination Serogroups selected for inclusion in a series of vaccines based on results obtained using slide agglutination
ViS = [AiS , BiS , CiS , DiS , EiS , FiS , GiS , HiS , IiS ]
eiS + fiS
ViR = [AiR , BiR , CiR , DiR , EiR , FiR , GiR , HiR , IiR ]
eiR + fiR
fiR [z] ≤ 1
z=A
All serogroups with frequency >0 in sjk Si = All serogroups with frequency >0 in rjk Ri =
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proportion of iterations that were successful. For the treatment outcomes, the efficacy of a given sampling strategy and diagnostic method was estimated as the proportion of protectable sheep that were protected/treated by the vaccine(s).
median of 1 serogroup was present per sheep (range: 1–4). A median of 5 serogroups was present per flock (range: 1–7; Table 3).
2.5. Comparison between slide agglutination and PCR results
3.2.1. Simulated selection of two serogroups for inclusion in a bivalent vaccine The simulated efficacy of both slide agglutination and PCR at detecting the most prevalent serogroups in the flock varied according to sampling strategy (Fig. 1). Logistic regression models for two sampling strategies (10 sheep with 2 colonies/sheep and 6 sheep with 4 colonies/sheep) could not converge using 1st order PQL, so estimates for those strategies were obtained using 1st order marginalized quasi-likelihood (MQL). The proportion of iterations correctly including the both of the two most-frequent D. nodosus serogroups present in the flock was significantly higher for PCR vs. slide agglutination for all sampling strategies (p < 1.8 × 10−11 ). Regardless of diagnostic method, none of the sampling strategies correctly identified the two most common serogroups present in the flock on at least 95% of iterations (Fig. 1).
3.2. Model
For each sampling strategy and outcome, diagnostic methods were compared using mixed-effects logistic regression (MlwiN 2.16, Centre for Multilevel Modelling, UK), with flock and iteration as random effects and diagnostic method (slide agglutination vs. PCR) as a fixed effect. A random intercept term was fit, then a fixed effect term for diagnostic method was added and fit using restricted iterative generalized least squares (RIGLS) and 1st order penalized (predictive) quasi-likelihood (PQL) estimation. Statistically significant differences between diagnostic methods were identified using Wald tests of the fixed effect term. To adjust for the 63 comparisons made for each outcome, statistical significance was set at 0.05/63, or p < 0.00079.
3.2.2. Proportion of diseased sheep affected by a single vaccine The maximum number of diseased sheep protectable by a single bivalent vaccine was 28, 39, 31, 52, 33, 17, 18, 16, 49, 15, 16, and 43 for flocks 1–12, respectively, and, ranged from 34.7 to 100% of all diseased sheep in a flock. The proportion of protectable diseased sheep protected by the simulated bivalent vaccine ranged from 0.78 to 0.97, depending on diagnostic method and sampling strategy (Fig. 2). None of the sampling strategies using slide agglutination methods protected more than 95% of protectable sheep, whereas sampling strategies using PCR methods protected at least 95% of protectable sheep when sampling 22, 20, and 20 sheep when evaluating 2, 3, and 4 colonies/sheep, respectively. The proportion of sheep protected was significantly higher (p < 2.2 × 10−18 ) for PCR vs. slide agglutination results for all sampling strategies.
3. Results 3.1. Population The 12 participating flocks were sampled a total of 35 times (median 2; range 2–6 visits/flock). The median time between taking the first and last sample in the same flock was 307.5 days (range: 236–541 days). A total of 608 animals were sampled, with a median of 51 animals sampled per flock (range: 36–64; Table 3). From these 608 animals, 2136 colonies were submitted for D. nodosus serogroup identification (median 3 colonies per animal; range 1–13). One or more D. nodosus serogroups were present in 520 (85.6%) of 608 sheep sampled, and in 1623 (76.0%) of 2136 colonies sampled. Among colonies with D. nodosus present, the median number of serogroups present per colony was 1 (range: 1–3). Among sheep with D. nodosus present, a
Table 3 Frequency of D. nodosus serogroups identified from a sample of 608 sheep in 12 flocks with virulent ovine footrot in 8 districts in 3 Australian states. Flock
Sheep SA* or PCR positive/sampled
Isolates cultured
Frequency of serogroups found in isolates cultured A
1 2 3 4 5 6 7 8 9 10 11 12 a b *
28/44 46/55 49/56 52/54 46/56 49/52 44/47 36/50 53/64 31/36 43/47 43/47
Most prevalent serogroup. Second-most prevalent serogroup. SA, slide agglutination.
83 112 140 227 222 225 119 145 223 101 274 265
a
61 42b 50b 0 84a 28 19 10 155a 0 6 0
B
C
D
E
F
G
H
I
All
0 1 51a 0 0 98a 37a 62a 0 0 95a 0
0 6 13 0 4 0 1 6 0 0 0 0
0 1 19 0 0 15 15 48b 0 47a 78b 0
0 50a 0 214a 57b 0 19 10 0 1 61 0
0 0 0 0 0 0 0 14 0 28b 0 0
0 0 0 0 24 37 34b 0 6 20 8 22a
0 0 0 11b 0 44b 0 2 17b 9 25 0
0 0 9 0 0 0 0 0 0 0 0 156a
61 100 142 225 169 222 125 152 178 105 273 178
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Fig. 1. Proportion of 10,000 iterations in which the two most common D. nodosus serogroups in each of 12 Australian flocks were correctly selected for inclusion in a simulated bivalent vaccine by stochastic simulation of sampling strategies ranging from 5 to 40 sheep/flock and 2–4 colonies/sheep using either slide agglutination or PCR methods for serogroup identification.
3.2.3. Identification of all serogroups present in flock The proportion of iterations in which all serogroups in the flock were identified in the sample ranged from 15 to 75%, depending on sampling strategy and diagnostic method (Fig. 3). For all sampling strategies, the proportion of iterations in which all serogroups in the flock were identified was significantly higher when results were based on PCR compared to slide agglutination (p < 0.0007).
3.2.4. Proportion of diseased sheep affected by a series of vaccines The proportion of diseased sheep in which D. nodosus was isolated that were protected by a series of simulated vaccines ranged from 0.62 to 0.98 (Fig. 4). The logistic regression model for one sampling strategy (26 sheep with 2 colonies/sheep), could not converge using 1st order PQL, so estimates for that strategy were obtained using 1st order marginalized quasi-likelihood (MQL). When results
Fig. 2. Over 10,000 iterations, the proportion of the maximum number of sheep in 12 Australian flocks protectable by a single bivalent D. nodosus vaccine that were protected by a simulated vaccination based on serogroups identified in sampling strategies ranging from 5 to 40 sheep/flock and 2–4 colonies/sheep using either slide agglutination or PCR methods for serogroup identification.
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Fig. 3. Proportion of 10,000 iterations in which all D. nodosus serogroups in each of 12 Australian flocks were identified in a flock sample by stochastic simulation of sampling strategies ranging from 5 to 40 sheep/flock and 2–4 colonies/sheep using either slide agglutination or PCR methods for serogroup identification.
Fig. 4. Over 10,000 iterations, the proportion of diseased sheep diagnosed with D. nodosus in 12 Australian flocks that would be protected by a series of simulated bivalent vaccines encompassing all D. nodosus serogroups identified in a range stochastic sampling strategies ranging from 5 to 40 sheep/flock and 2–4 colonies/sheep executed prior to the start of a vaccination campaign using either slide agglutination or PCR methods for serogroup identification.
were based on slide agglutination, none of the sampling strategies protected >95% of diseased infected sheep. When results were based on PCR, sampling strategies using 24, 20, and 18 sheep and 2, 3, and 4 colonies/sheep, respectively, protected at least 95% of diseased infected sheep (Fig. 4). The proportion of diseased sheep protected by a simulated series of bivalent vaccines was significantly higher (p < 4.5 × 10−16 ) when results were based on PCR vs. slide agglutination for all sampling strategies.
4. Discussion Sampling strategy efficacy varied depending on the sampling strategy, the outcome being evaluated, and the diagnostic method used for serogroup identification. As expected, sampling strategy efficacy improved with both the number of sheep sampled per flock and the number of colonies sampled/sheep, but the degree of improvement varied depending on the outcome assessed and the diagnostic method.
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If a successful strategy is one that is effective 95% of the time, then none of the sampling strategies evaluated were successful at selecting the two most common serogroups present in the flock for inclusion in a single bivalent vaccine, or at identifying all serogroups present in the flock. However, if success is evaluated based on the effect of vaccination on the flock (e.g. the proportion of those animals that could be successfully treated), rather than on serogroup identification, then strategies based on PCR results using about 20 sheep per flock successfully treated the flock using either a single vaccine or a series of vaccines. The difference in results between success measured as serogroup identification and success measured as flock treatment is likely due to the distribution of serogroups within flocks. In a flock like Flock 6, where several serogroups are present at high frequencies, selection of any two of the common serogroups for vaccination will protect a substantial number of sheep, and in some instances, may protect as many sheep as selection of the two most common serogroups in the flock. Likewise, in a flock like Flock 2, where a few serogroups are very common and a few others are very rare, a series of vaccines based on identification of all of the common serogroups in the flock will protect almost all of the flock–so failure to identify all serogroups present in the flock can still result in protection for the majority of animals. Failure to identify all serogroups present in the flock is not ideal if the goal is disease eradication, but may be acceptable if the goal is disease control. Culling of sheep that have not responded to vaccination is an option to overcome a lack of serogroup coverage by the vaccine. The relative cost of sampling additional sheep vs. additional colonies is needed to evaluate the cost-benefits of increasing the number of sheep vs. the number of colonies/sheep. If the cost (beyond laboratory diagnostics) associated with sampling additional sheep is minimal, then strategies employing more sheep and fewer colonies/sheep appear most efficient. For example, when evaluating the proportion of diseased and infected sheep in the flock treated by a series of vaccines, three sampling strategies protect at least 95% of the flock: 24 sheep at 2 colonies/sheep (48 laboratory tests); 20 sheep at 3 colonies/sheep (60 laboratory tests); and 18 sheep at 4 colonies/sheep (72 laboratory tests). For most outcomes, the impact of testing additional colonies/sheep appears to decrease as the number of sheep sampled per flock increases. An exception occurs when evaluating the proportion of iterations in which all serogroups present in the flock were identified in the sample; for this outcome, the impact of testing additional colonies continues to be large, even at the largest sample sizes, and more so for PCR than for slide agglutination. Regardless of the outcome being measured or the sampling strategy, results based on PCR methodology were consistently significantly higher than those based on slide agglutination. It is difficult to confirm that a culture of D. nodosus is pure as colonies may contain several types due to their spreading nature. Polymerase chain reaction may perform significantly better because it can identify multiple serogroups present in a single colony (Zhou and Hickford, 2000), whereas slide agglutination typically identifies the
dominant serogroup in a mixed culture (Whittington, 2008, unpublished data). The effect of the difference in detection of multiple serogroups in a colony was most apparent in Flock 11. Flock 11 was infected with 6 serogroups, and twothirds of isolates cultured were serogroup B or D. Serogroup D was only present as a co-infection; it was never the only serogroup identified in a colony. Serogroup D was not detected by slide agglutination from any colony, whereas it was frequently detected by PCR. As a result, slide agglutination methods never detected the two most common serogroups in the flock, or all serogroups present in the flock. The flocks in the current study are not a representative sample of all sheep flocks infected with virulent D. nodosus. Participating flocks had clinically apparent virulent footrot, and had agreed to participate in a larger vaccination trial study. Participating flocks potentially may have more severe, more persistent, or more complex disease than other flocks because flocks with minimal disease or those able to control disease via culling, vaccination, and pasture management may be less inclined to enroll in a vaccine trial. However, the distribution of serogroups in flocks and sheep in the current study is similar to that reported previously (Claxton et al., 1983) in Australia. Other countries appear to have fewer sheep and flocks infected with multiple serogroups. In Bhutan and Nepal, no clinically affected flocks were infected with more than one serogroup (Ghimire et al., 1996; Gurung et al., 2006). In Canada, 25% and 6% of flocks were infected with >1 and ≥3 serogroups, respectively (Olson et al., 1998); whereas in two studies in Britain, about 60% and 30% of clinically affected flocks were infected with >1 and ≥3 serogroups, respectively, and 12% of sheep were infected with >1 serogroup (Hindmarsh and Fraser, 1985; Moore et al., 2005). In countries where flock co-infections are rare and the prevalence of infection is high in diseased sheep, the efficacy of specific sampling strategies would likely be higher than that reported here. This study was based on samples from a population, not on census data. Therefore not all diseased sheep from the flocks in the current study were used as the basis for the simulation model, nor were all affected feet evaluated from the selected sheep, or all colonies tested from each foot. Between 36 and 64 diseased sheep in each flock were evaluated for D. nodosus serogroup identification, with swabs taken from a single affected foot on each diseased sheep. A sample size of 36 is sufficient to detect serogroups present at a prevalence of 7.7% with 95% confidence (Win Episcope 2.0, Computer-aided Learning in Veterinary Education, UK). Thus additional D. nodosus serogroups may have been present at a lower prevalence. Had more diseased sheep and/or feet been evaluated, and additional D. nodosus serogroups identified in the 12 flocks used as the basis for the current study, the sampling strategy efficacies would likely be lower than that reported. 5. Conclusion Ideally, an effective sampling strategy would provide the optimal results 95% of the time. In the current study,
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none of the evaluated sampling strategies identified the two most common serogroups in the flock, or all serogroups present in the flock, in 95% of iterations. However, the use of PCR methodology and sampling 22 sheep and 2 colonies/sheep resulted in a simulated vaccine that protected 95% of sheep that could be protected by a single bivalent vaccine, and using PCR methodology and sampling 24 sheep and 2 colonies/sheep resulted in a series of simulated vaccines that protected 95% of diseased infected sheep. These are the optimum sampling strategies for these outcomes. Given that sampling up to 40 sheep per flock and 4 colonies/sheep did not consistently identify the two most common D. nodosus serogroups present in a flock, a sampling strategy that will identify the two most common serogroups in a flock 95% of the time may not be cost effective. Evaluating efficacy based on the expected effect on the flock may be more useful. Simulation modeling appears to be a useful method of evaluating the efficacy of a range of sampling strategies for identification of D. nodosus serogroups at the flock level. This methodology may also be useful for other diseases where control programs depend on detection of all serogroups or strains present in a population. Acknowledgement This work was funded by an International Visiting Research Fellowship from the University of Sydney and Australian Wool Innovation. References Cannon, R.M., Roe, R.T., 1982. Livestock disease surveys. In: A Field Manual for Veterinarians. Bureau of Range Science, Department of Primary Industry, Australian Government Publishing Service, Canberra, Australia. Claxton, P.D., 1986. Serogrouping of Bacteroides nodosus isolates. In: Stewart, D.J., Peterson, J.E., McKern, N.M., Emery, D.L. (Eds.), Footrot in Ruminants, Melbourne, 1985. Commonwealth Scientific and Industrial Research Organisation Australia and Australian Wool Corporation, Glebe, pp. 131–134. Claxton, P.D., Ribeiro, L.A., Egerton, J.R., 1983. Classification of Bacteroides nodosus by agglutination tests. Aust. Vet. J. 60, 331– 334. Claxton, P.D., 1989. Antigenic classification of Bacteroides nodosus. In: Egerton, J.R., Yong, W.K., Riffkin, G.G. (Eds.), Footrot and foot abscess of ruminants. CRC Press, Florida, p. 262. Day, S.E.J., Thorley, C.M., Beesley, J.E., 1986. Serotyping of Bacteroides nodosus: proposal for 9 further serotypes (J-R) and a study of the antigenic complexity of B. nodosus pili. In: Stewart, D.J., Peterson, J.E., McKern, N.M., Emery, D.L. (Eds.), Footrot in Ruminants, Melbourne, 1985. Commonwealth Scientific and Industrial Research Organisation Australia and Australian Wool Corporation, Glebe, pp. 147–159. Dhungyel, O.P., Lehmann, D.R., Whittington, R.J., 2008. Pilot trials in Australia on eradication of footrot by flock specific vaccination. Vet. Micro. 132, 364–371. Dhungyel, O.P., Whittington, R.J., Ghimire, S.C., Egerton, J.R., 2001. Pilus ELISA and an anamnestic test for the diagnosis of virulent ovine footrot and its application in a disease control program in Nepal. Vet. Micro. 79, 31–45. Dhungyel, O., Whittington, R., 2009. New approach to vaccination for treatment, control and eradication of footrot in sheep and goats. In: Proceedings of the 7th International Sheep Veterinary Congress, Stavanger, Norway, June 12–16, p. 56. Dhungyel, O.P., Whittington, R.J., Egerton, J.R., 2002. Serogroup specific single and multiplex PCR with pre-enrichment culture and immuno-magnetic bead capture for identifying strains of D-nodosus in sheep with footrot prior to vaccination. Mol. Cell. Probes 16, 285–296.
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Dohoo, I., Martin, W., Stryhn, H., 2003. Veterinary Epidemiologic Research, First Edition. AVC Inc., Charlottetown, Prince Edward Island, 706 p. Egerton, J.R., 1979. Treatment of ovine foot-rot by vaccination with the specific aetiological agent Bacteroides nodosus. Comp. Immunol. Microbiol. Infect. Dis. 2, 61. Egerton, J.R., Ghimire, S.C., Dhungyel, O.P., Shrestha, H.K., Joshi, H.D., Joshi, B.R., Abbott, K.A., Kristo, C., 2002. Eradication of virulent footrot from sheep and goats in an endemic area of Nepal and an evaluation of specific vaccination. Vet. Rec. 151, 290–295. Egerton, J.R., Morgan, I.R., 1972. Treatment and prevention of foot-rot in sheep with Fusiformis nodosus vaccine. Vet. Rec. 91, 453–457. Ghimire, S.C., Egerton, J.R., Dhungyel, O.P., 1996. Characterisation of Dichelobacter nodosus isolated from footrot in sheep and goats in Nepal. Small Rumin. Res. 23, 59–67. Ghimire, S.C., Egerton, J.R., Dhungyel, O.P., Joshi, H.D., 1998. Identification and characterisation of serogroup M among Nepalese isolates of Dichelobacter nodosus, the transmitting agent of footrot in small ruminants. Vet. Microbiol. 62, 217–233. Gradin, J.L., Sonn, A.E., Petrovska, L., 1993. Serogrouping of Bacteroides nodosus isolates from 62 sources in the United States. Am. J. Vet. Res. 54, 1064–1068. Gurung, R.B., Dhungyel, O.P., Tshering, P., Egerton, J.R., 2006. The use of an autogenous Dichelobacter nodosus vaccine to eliminate clinical signs of virulent footrot in a sheep flock in Bhutan. Vet. J. 172, 356–363. Hektoen, L., Hoyland, B., Van Campen, A., 2009. Prevalence of footrot in Norwegian sheep—clinical examination of one million feet. In: Proceedings of 7th International Sheep Veterinary Congress, Stavanger, Norway, pp. 71–72. Hindmarsh, F., Fraser, J., 1985. Serogroups of Bacteroides nodosus isolated from ovine footrot in Britain. Vet. Rec. 116, 187–188. Hunt, J.D., Jackson, D.C., Wood, P.R., Stewart, D.J., Brown, L.E., 1995. Immunological parameters associated with antigenic competition in a multivalent footrot vaccine. Vaccine 13, 1649–1657. Kingsley, D.F., Hindmarsh, F.H., Liardet, D.M., Chetwin, D.H., 1986. Distribution of serogroups of Bacteroides nodosus with particular reference to New Zealand and the United Kingdom. In: Footrot in ruminants, Melbourne, 1985, p. 143. Moore, L.J., Wassink, G.J., Green, L.E., Grogono-Thomas, R., 2005. The detection and characterisation of Dichelobacter nodosus from cases of ovine footrot in England and Wales. Vet. Microbiol. 108, 57–67. New South Wales Agriculture, 2002. Laboratory support for diagnosis of footrot—procedure. New South Wales Agriculture, State Council of Rural Lands Protection Boards. Olson, M.E., Gard, M.S., Gradin, J., Morck, D.W., 1998. Serological classification and virulence determination of Dichelobacter nodosus isolated from Alberta and British Columbia sheep. Can. J. Vet. Res. 62, 33–37. Palmer, M.A., 1993. A gelatin test to detect activity and stability of proteases produced by Dichelobacter (bacteroides) nodosus. Vet. Microbiol. 36, 113–122. Plant, J.W., Egerton, J.R., 2009. The “eradication” of virulent footrot from New South Wales. In: Proceedings of the 7th International Sheep Veterinary Congress, Stavanger, Norway, pp. 101–102. Raadsma, H.W., O’Meara, T.J., Egerton, J.R., Lehrbach, P.R., Schwartzkoff, C.L., 1994. Protective antibody titres and antigenic competition in multivalent Dichelobacter nodosus fimbrial vaccines using characterised rDNA antigens. Vet. Immunol. Immunopathol. 40, 253–274. Regula, G., Lo Fo Wong, D.M.A., Ledergerber, U., Stephan, R., Danuser, J., Bissig-Choisat, B., Stärk, K.D.C., 2005. Evaluation of an antimicrobial resistance monitoring program for campylobacter in poultry by simulation. Prev. Vet. Med. 70, 29. Roberts, D.S., Egerton, J.R., 1969. The aetiology and pathogenesis of ovine foot-rot. II. The pathogenic association of Fusiformis nodosus and F. necrophorus. J. Comp. Pathol. 79, 217–227. Schwartzkoff, C.L., Egerton, J.R., Stewart, D.J., Lehrbach, P.R., Elleman, T.C., Hoyne, P.A., 1993. The effects of antigenic competition on the efficacy of multivalent footrot vaccines. Aust. Vet. J. 70, 123–126. Stewart, D.J., 1979. The role of elastase in the differentiation of Bacteroides nodosus infections in sheep and cattle. Res. Vet. Sci. 27, 99–105. Stewart, D.J., Claxton, P.D., 1993. Ovine footrot: clinical diagnosis and bacteriology. In: Corner, L., Baghust, T. (Eds.), Australian Standard Diagnostic Techniques for Animal Diseases. Thorley, C.M., Day, S.E.J., 1986. Serotyping survey of 1296 strains of Bacteroides nodosus isolated from sheep and cattle in Great Britain and western Europe. In: Footrot in ruminants, Melbourne, 1985, p. 135. Villarroel, A., Morley, P.S., Wittum, T.E., Bolte, D.S., 2006. Use of a simulation model to evaluate sampling strategies for characterization of
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antimicrobial resistance in non-type-specific Escherichia coli isolated from dairy cows. Am. J. Vet. Res. 67, 951. Vos, C.J., Saatkamp, d., Ehlers, H.W.J., 2007. Simulation evaluation of Salmonella monitoring in finishing pigs in Lower Saxony, Germany. Prev. Vet. Med. 82, 123. Whittington, R.J., 1995. Observations on the indirect transmission of virulent ovine footrot in sheep yards and its spread in sheep on unimproved pasture. Aust. Vet. J. 72, 132–134.
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Preventive Veterinary Medicine journal homepage: www.elsevier.com/locate/prevetmed
Diagnostic sampling strategies for virulent ovine footrot: Simulating detection of Dichelobacter nodosus serogroups for bivalent vaccine formulation Ashley E. Hill a,∗ , Om P. Dhungyel b , Richard J. Whittington b a b
Animal Population Health Institute, Campus Delivery 1644, Colorado State University, Fort Collins, CO 80523-1644, USA Farm Animal and Veterinary Public Health, Faculty of Veterinary Science, University of Sydney, 425 Werombi Road, Camden NSW 2570, Australia
a r t i c l e
i n f o
Article history: Received 31 December 2008 Received in revised form 30 December 2009 Accepted 22 February 2010 Keywords: Dichelobacter nodosus Monte Carlo simulation Sampling Sheep-microbiological diseases Footrot Vaccination
a b s t r a c t Dichelobacter nodosus is a slow-growing anaerobic bacterium that is the causative agent of virulent ovine footrot. Vaccination targeted at up to two specific serogroups can eliminate those serogroups from infected flocks, but requires identification of serogroups present in infected flocks. Serogroups can be identified using slide agglutination or polymerase chain reaction (PCR) methods. The objectives of this project were to use stochastic simulation modeling to estimate the efficacy of sampling strategies encompassing 5–40 sheep per flock and 2–4 colonies per sheep, and to compare efficacies based on slide agglutination or multiplex PCR test results. Foot swabs collected from sheep in 12 flocks were used as the basis for a sampling strategy simulation model. None of the evaluated sampling strategies identified the two most common serogroups in the flock, or all serogroups present in the flock, in 95% of iterations. However, a simulated sample of 22 sheep/flock and 2 colonies/sheep resulted in a simulated vaccine that protected 95% of the sheep that could be protected by a single bivalent vaccine, while a sample of 24 sheep/flock and 2 colonies/sheep resulted in a series of simulated bivalent vaccines that protected 95% of diseased infected sheep. The difference in outcome was due to the distribution and frequency of serogroups within certain flocks where some serogroups were uncommon and others dominant. A sampling strategy (>40 sheep/flock, 4 colonies/sheep) that will identify the two most common serogroups in a flock 95% of the time may not be cost effective. Evaluating efficacy based on the expected effect on the flock may be more useful than one which seeks to determine the most common serogroups. These findings are broadly applicable to diseases where more than one strain or type of pathogen may be present and must be represented in a vaccine. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Dichelobacter nodosus is the essential causative agent of virulent ovine footrot, a severe highly contagious disease which involves painful separation of the hoof from underlying dermis (Roberts and Egerton, 1969). Footrot causes
∗ Corresponding author. Tel.: +1 970 297 4050; fax: +1 970 297 1275. E-mail address: [email protected] (A.E. Hill). 0167-5877/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.prevetmed.2010.02.011
economic losses via lameness: reduced wool growth, loss of body condition and reduced reproductive performance. The disease is a significant international problem, and there are increasing requirements to control it to meet standards in animal welfare (Hektoen et al., 2009). D. nodosus does not survive on pastures for more than 5 days and there are defined environmental circumstances required for transmission so in theory the disease can be eradicated simply by removing all infected sheep from a flock (Whittington, 1995). However, this is usually precluded economically by
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the high prevalence of infection, the difficulty of identification of some infected sheep and the lack of clean pasture. Therefore strategies to reduce prevalence and transmission rates are more common than eradication programs. Successful regional control programs, exemplified by those undertaken in Australia, are complex and have required more than 20 years of highly coordinated effort (Plant and Egerton, 2009). Although these campaigns were not based on improving herd immunity, vaccination against D. nodosus has been used to eradicate the disease from Nepal, Bhutan and some flocks in Australia (Dhungyel et al., 2008, 2001; Egerton et al., 2002; Gurung et al., 2006). Vaccination provides therapeutic and prophylactic benefits to reduce prevalence and reduce transmission, respectively (Egerton, 1979; Egerton and Morgan, 1972; Gurung et al., 2006). However, its application is limited by microbiological and immunological factors: many serogroups of D. nodosus are recognized (Claxton et al., 1983; Claxton, 1986; Day et al., 1986; Gradin et al., 1993; Ghimire et al., 1998) and the immune system of the sheep is incapable of responding adequately if more than two are included in a vaccine formulation (Hunt et al., 1995; Raadsma et al., 1994; Schwartzkoff et al., 1993). Research in Australia, New Zealand, The United States, Europe and the United Kingdom has shown that infections at the flock level may be caused by multiple serogroups (Claxton, 1989; Claxton et al., 1983; Gradin et al., 1993; Kingsley et al., 1986; Thorley and Day, 1986). Therefore a successful vaccination strategy requires accurate identification of the serogroups that are present in an affected flock, and formulation of a series (if necessary) of mono- or bivalent vaccines appropriate for that flock that can be administered sequentially (Dhungyel and Whittington, 2009). Serogroups can be identified using slide agglutination or polymerase chain reaction (PCR) methods (Claxton et al., 1983; Dhungyel et al., 2002b) but the efficacy of these tests has not been compared. Identification of D. nodosus serogroups results from multi-stage sampling (Dohoo et al., 2003): diseased sheep are selected, then affected feet from these sheep, then microbial colonies from cultures of foot swabs. In the most intensive study conducted to date, there was a direct relationship between the number of diseased sheep or feet sampled per flock and the number of serogroups found in that flock (Claxton et al., 1983). However, the optimal sampling strategy for accurate determination of the D. nodosus strains present in a flock is not known; ad hoc protocols therefore tend to be used. As an example, one current protocol for routine diagnosis is to evaluate up to 10 D. nodosus colonies off culture plates derived from swabs taken from a single foot from up to five diseased sheep from a flock (New South Wales Agriculture, 2002). Clearly there is a likelihood that such a strategy will fail to detect all serogroups that may be present. The optimal sampling strategy may differ depending on the number of serogroups present in a diseased flock, the relative prevalence of serogroups within a diseased flock, the potential for clustering of serogroups within sheep or feet, and the diagnostic method used to classify serogroups. The optimal sampling strategy may also differ depending on how success is measured: by maximizing the probabil-
ity of selecting the most common serogroups in the flock, or by the success of treating/protecting the maximum number of sheep in the flock. Traditional methods of sample size calculation (Cannon and Roe, 1982) are not capable of estimating sample sizes for detection of multiple agents, nor do they allow for measures of success other than probability of agent detection. Stochastic simulation modeling has been used previously to evaluate and compare complex multistage sampling strategies for policy development (Regula et al., 2005; Villarroel et al., 2006; Vos et al., 2007). The main objectives of this project were to build a stochastic simulation model to estimate the efficacy of different sampling scenarios for detection of D. nodosus serogroups present in a flock, and to compare efficacies within scenario for two different diagnostic methods (slide agglutination and PCR). Four measures of efficacy were evaluated and compared between tests: (1) the proportion of iterations in which the two most prevalent serogroups could be correctly selected for production of a bivalent vaccine; (2) the proportion of diseased sheep per flock that could be fully protected by the selected bivalent vaccine; (3) the proportion of iterations in which all serogroups in the flock were identified in the sample; and (4) the proportion of diseased sheep per flock that could be fully protected by a long-term vaccination strategy consisting of a series of bivalent vaccines encompassing all serogroups identified in the initial flock sample. The latter two were evaluated to assess whether follow-up studies post vaccination were needed. 2. Materials and methods 2.1. Definitions Throughout the manuscript, “diseased” refers to sheep with clinical lameness, and “infected” refers to sheep that tested positive for D. nodosus using either slide agglutination or PCR methodology. “Protectable” sheep are those theoretically capable of being protected and/or treated by a vaccine or series of vaccines. For evaluation of a series of bivalent vaccines, the number of protectable sheep in a flock equalled the number of infected sheep. For evaluation of a single bivalent vaccine, the number of protectable sheep in a flock equalled the maximum number of sheep identified with any two-serogroup combination in the flock (e.g. in Flock 9, 38 sheep were infected with serogroup A only, 1 with G only, 1 with H only, 3 with A and G, and 10 with A and H; so the maximum number that could be protected by a single vaccine (serogroups AH) was 49). 2.2. Data Foot swabs collected from sheep in 12 flocks from January 1, 2006 through July 30, 2007 were used as the basis for a sampling strategy simulation model. The 12 flocks were from 8 districts in 3 Australian States (South Australia, Tasmania, Victoria) and were a convenience sample of sheep flocks with ongoing clinically apparent virulent D. nodosus infection, where the owners were willing to participate in a larger vaccination trial study. Upon enrollment, each
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flock was visited two to six times prior to vaccination by a veterinarian who was instructed to stratify the flock into mobs defined by the management strategy for the flock (for example into: adult ewes, adult wethers, lambs, rams) and then to randomly sample diseased sheep from each mob, which were identified by obvious lameness and/or clinical signs of footrot in individual feet. Up to 40 diseased sheep from each flock were sampled on each visit. If fewer than 40 sheep were diseased at the time of the visit, all diseased sheep were sampled. All feet of each diseased sheep were visually evaluated and graded (Whittington and Nicholls, 1995), and in most cases the foot with the most severe clinical signs was sampled by inserting a sterile wooden applicator stick into necrotic material or discharge from visible lesions, usually at the active margin of the underrun lesion or most active area of interdigital skin inflammation if no underrun lesion was present. Standard methods of isolation of D. nodosus (Stewart and Claxton, 1993) were followed. Similar numbers of D. nodosus isolates per sheep were obtained from swabs collected from the active margin of underrun lesions (1905 isolates from 296 sheep) or the interdigital skin (2206 isolates 362 sheep). Between 1 and 13 colonies of varying morphology were selected from each swab culture for serogroup classification using slide agglutination (Claxton et al., 1983) and polymerase chain reaction methodology (Dhungyel et al., 2002). The number of colonies selected from each swab culture was based on morphology: if all colonies had homogenous morphology, then only one colony was selected; otherwise, a representative colony from each morphology type was selected. An isolate was classified as virulent if either an elastase test (Stewart, 1979) or a gelatin gel test result (Palmer, 1993) was positive. Data for swabs where primary isolation or sub-culture did not produce colonies of D. nodosus were excluded from analysis. Data obtained from swabs collected from sheep after vaccination were not included in the analysis. Although the number of serogroups identified in a single sheep can potentially range from 0 to 9, between 1 and 4 serogroups were actually identified in a single sheep in the available dataset.
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2.3. Assumptions Any serogroup identified using either slide or PCR methods was considered present in the colony tested. Each sheep was considered infected with all serogroups identified in any of its colonies. Each flock was considered infected with all virulent serogroups identified in any of its sheep. For a sheep to be considered protected by a single vaccine or series of vaccinations, all serogroups present in the sheep must be included in the simulated vaccine(s). The vaccine was assumed to be 100% effective at treating or protecting sheep. 2.4. Model design A Markov Chain Monte Carlo simulation model was constructed to stochastically simulate the detection of D. nodosus in 12 sheep flocks when culturing two, three, or four colonies each from between 5 and 40 diseased sheep within an affected flock. The model was constructed using Excel (Microsoft Corporation, USA) and @Risk software (Palisade Corporation, USA). In each flock, the process of randomly selecting diseased sheep, then randomly selecting colonies from foot cultures of these sheep was simulated (Table 1). For each diagnostic method (slide agglutination and PCR), the results of each flock sample were used to identify all D. nodosus serogroup(s) present in the flock as well as simulate the selection of two serogroup(s) from the sample for inclusion in a vaccine (Table 2). The efficacy of the selected vaccine was evaluated (SAS 9.2, SAS Institute USA) by determining whether the two serogroups present at the highest frequencies in the flock were selected for inclusion in a single bivalent vaccine (yes/no), by determining whether all serogroups in the flock were identified in the sample (yes/no), and by estimating the number of diseased sheep that could be successfully treated by the selected vaccine(s). The model was run for 10,000 iterations. For the yes/no outcomes, the efficacy of a given sampling strategy and diagnostic method was estimated as the
Table 1 Parameters for stochastic model simulating the sampling of one foot from 5 to 40 lame sheep and testing of 2-4 cultured colonies per foot for identification of Dichelobacter nodosus serogroups and selection of D. nodosus serogroups for inclusion in a vaccine(s). Description
Abbreviation
Diseased sheep per flock i j diseased sheep sampled in each flock i
ni bij
Colonies tested per sheep bij k colonies sampled in each sheep bij
dij cijk
Presence/absence of serogroups A–I in each colony cijk identified using slide agglutination (s) Presence/absence of serogroups A–I in each colony cijk identified using multiplex PCR (r) Distribution of serogroups A–I from cijk colonies tested in bij diseased sheep using slide agglutination (s) Distribution of serogroups A–I from cijk colonies tested in bij diseased sheep using multiplex PCR (r) Distribution of serogroups A–I from dij colonies tested in ni diseased sheep in each flock i using both slide agglutination and multiplex PCR
sijk = [Aijks , Bijks , Cijks , Dijks , Eijks , Fijks , Gijks , Hijks , Iijks ]
j draws from Uniform(1, ni ) with replacement k draws from Uniform(1, dij ) with replacement
rijk = [Aijkr , Bijkr , Cijkr , Dijkr , Eijkr , Fijkr , Gijkr , Hijkr , Iijkr ] Si = Ri =
Distribution/formula
sjk rjk
Pi = [Ai , Bi , Ci , Di , Ei , Fi , Gi , Hi , Ii ]
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Table 2 Outputs of stochastic model simulating the sampling of one foot from 5 to 40 lame sheep and testing of 2–4 cultured colonies per foot for identification of Dichelobacter nodosus serogroups and selection of D. nodosus serogroups for inclusion in a vaccine(s). Description
Abbreviation
Distribution/formula
Binary vector of most frequent serogroup(s) in Si
eiS = [AiS , BiS , CiS , DiR , EiS , FiS , GiS , HiS , IiS ]
[Largest element of Si ] = 1 and other elements of eiS are 0. If two elements of Si are equally large, then both elements of eiS = 1. If >2 elements of Si are equally large, then draw from binomial(1, 2/number of equally large elements) for each large element of Si to select 2 elements of eiS = 1. J
Binary vector of most frequent serogroup(s) in Ri
eiR = [AiR , BiR , CiR , DiR , EiR , FiR , GiR , HiR , IiR ]
eiS [z] ≤ 2
z=A
[Largest element of Ri ] = 1 and other elements of eiR are 0. If two elements of Ri are equally large, then both elements of eiR = 1. If >2 elements of Ri are equally large, then draw from binomial(1, 2/number of equally large elements) for each large element of Ri to select 2 elements of eiR = 1. J
eiR [z] ≤ 2
z=A
Binary vector of second-most-frequent serogroup in Si
fiS = [AiS , BiS , CiS , DiS , EiS , FiS , GiS , HiS , IiS ]
J
If
eiS [z] = 2, then
J
z=A
J
fiS [z] = 0. If
z=A
eiS [z] = 1, then the element of fiS
z=A
corresponding to the second-largest element of Si = 1. If multiple elements of Si shared the second-largest frequency, then draw from binomial(1, 1/number of equally large elements) for each large element of Si to select one element of fiS to J
be equal to 1.
Binary vector of second-most-frequent serogroup in Ri
fiR = [AiR , BiR , CiR , DiR , EiR , FiR , GiR , HiR , IiR ]
J
If
eiR [z] = 2, then
z=A
J
fiS [z] ≤ 1
z=A
J
fiR [z] = 0. If
z=A
eiR [z] = 1, then the element of fiR
z=A
corresponding to the second-largest element of Ri = 1. If multiple elements of Ri shared the second-largest frequency, then draw from binomial(1, 1/number of equally large elements) for each large element of Ri to select one element of fiR to J
be equal to 1. Serogroups selected for inclusion in a simulated vaccine based on results obtained using slide agglutination. Serogroups selected for inclusion in a simulated vaccine based on results obtained using multiplex PCR Serogroups selected for inclusion in a series of vaccines based on results obtained using slide agglutination Serogroups selected for inclusion in a series of vaccines based on results obtained using slide agglutination
ViS = [AiS , BiS , CiS , DiS , EiS , FiS , GiS , HiS , IiS ]
eiS + fiS
ViR = [AiR , BiR , CiR , DiR , EiR , FiR , GiR , HiR , IiR ]
eiR + fiR
fiR [z] ≤ 1
z=A
All serogroups with frequency >0 in sjk Si = All serogroups with frequency >0 in rjk Ri =
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proportion of iterations that were successful. For the treatment outcomes, the efficacy of a given sampling strategy and diagnostic method was estimated as the proportion of protectable sheep that were protected/treated by the vaccine(s).
median of 1 serogroup was present per sheep (range: 1–4). A median of 5 serogroups was present per flock (range: 1–7; Table 3).
2.5. Comparison between slide agglutination and PCR results
3.2.1. Simulated selection of two serogroups for inclusion in a bivalent vaccine The simulated efficacy of both slide agglutination and PCR at detecting the most prevalent serogroups in the flock varied according to sampling strategy (Fig. 1). Logistic regression models for two sampling strategies (10 sheep with 2 colonies/sheep and 6 sheep with 4 colonies/sheep) could not converge using 1st order PQL, so estimates for those strategies were obtained using 1st order marginalized quasi-likelihood (MQL). The proportion of iterations correctly including the both of the two most-frequent D. nodosus serogroups present in the flock was significantly higher for PCR vs. slide agglutination for all sampling strategies (p < 1.8 × 10−11 ). Regardless of diagnostic method, none of the sampling strategies correctly identified the two most common serogroups present in the flock on at least 95% of iterations (Fig. 1).
3.2. Model
For each sampling strategy and outcome, diagnostic methods were compared using mixed-effects logistic regression (MlwiN 2.16, Centre for Multilevel Modelling, UK), with flock and iteration as random effects and diagnostic method (slide agglutination vs. PCR) as a fixed effect. A random intercept term was fit, then a fixed effect term for diagnostic method was added and fit using restricted iterative generalized least squares (RIGLS) and 1st order penalized (predictive) quasi-likelihood (PQL) estimation. Statistically significant differences between diagnostic methods were identified using Wald tests of the fixed effect term. To adjust for the 63 comparisons made for each outcome, statistical significance was set at 0.05/63, or p < 0.00079.
3.2.2. Proportion of diseased sheep affected by a single vaccine The maximum number of diseased sheep protectable by a single bivalent vaccine was 28, 39, 31, 52, 33, 17, 18, 16, 49, 15, 16, and 43 for flocks 1–12, respectively, and, ranged from 34.7 to 100% of all diseased sheep in a flock. The proportion of protectable diseased sheep protected by the simulated bivalent vaccine ranged from 0.78 to 0.97, depending on diagnostic method and sampling strategy (Fig. 2). None of the sampling strategies using slide agglutination methods protected more than 95% of protectable sheep, whereas sampling strategies using PCR methods protected at least 95% of protectable sheep when sampling 22, 20, and 20 sheep when evaluating 2, 3, and 4 colonies/sheep, respectively. The proportion of sheep protected was significantly higher (p < 2.2 × 10−18 ) for PCR vs. slide agglutination results for all sampling strategies.
3. Results 3.1. Population The 12 participating flocks were sampled a total of 35 times (median 2; range 2–6 visits/flock). The median time between taking the first and last sample in the same flock was 307.5 days (range: 236–541 days). A total of 608 animals were sampled, with a median of 51 animals sampled per flock (range: 36–64; Table 3). From these 608 animals, 2136 colonies were submitted for D. nodosus serogroup identification (median 3 colonies per animal; range 1–13). One or more D. nodosus serogroups were present in 520 (85.6%) of 608 sheep sampled, and in 1623 (76.0%) of 2136 colonies sampled. Among colonies with D. nodosus present, the median number of serogroups present per colony was 1 (range: 1–3). Among sheep with D. nodosus present, a
Table 3 Frequency of D. nodosus serogroups identified from a sample of 608 sheep in 12 flocks with virulent ovine footrot in 8 districts in 3 Australian states. Flock
Sheep SA* or PCR positive/sampled
Isolates cultured
Frequency of serogroups found in isolates cultured A
1 2 3 4 5 6 7 8 9 10 11 12 a b *
28/44 46/55 49/56 52/54 46/56 49/52 44/47 36/50 53/64 31/36 43/47 43/47
Most prevalent serogroup. Second-most prevalent serogroup. SA, slide agglutination.
83 112 140 227 222 225 119 145 223 101 274 265
a
61 42b 50b 0 84a 28 19 10 155a 0 6 0
B
C
D
E
F
G
H
I
All
0 1 51a 0 0 98a 37a 62a 0 0 95a 0
0 6 13 0 4 0 1 6 0 0 0 0
0 1 19 0 0 15 15 48b 0 47a 78b 0
0 50a 0 214a 57b 0 19 10 0 1 61 0
0 0 0 0 0 0 0 14 0 28b 0 0
0 0 0 0 24 37 34b 0 6 20 8 22a
0 0 0 11b 0 44b 0 2 17b 9 25 0
0 0 9 0 0 0 0 0 0 0 0 156a
61 100 142 225 169 222 125 152 178 105 273 178
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Fig. 1. Proportion of 10,000 iterations in which the two most common D. nodosus serogroups in each of 12 Australian flocks were correctly selected for inclusion in a simulated bivalent vaccine by stochastic simulation of sampling strategies ranging from 5 to 40 sheep/flock and 2–4 colonies/sheep using either slide agglutination or PCR methods for serogroup identification.
3.2.3. Identification of all serogroups present in flock The proportion of iterations in which all serogroups in the flock were identified in the sample ranged from 15 to 75%, depending on sampling strategy and diagnostic method (Fig. 3). For all sampling strategies, the proportion of iterations in which all serogroups in the flock were identified was significantly higher when results were based on PCR compared to slide agglutination (p < 0.0007).
3.2.4. Proportion of diseased sheep affected by a series of vaccines The proportion of diseased sheep in which D. nodosus was isolated that were protected by a series of simulated vaccines ranged from 0.62 to 0.98 (Fig. 4). The logistic regression model for one sampling strategy (26 sheep with 2 colonies/sheep), could not converge using 1st order PQL, so estimates for that strategy were obtained using 1st order marginalized quasi-likelihood (MQL). When results
Fig. 2. Over 10,000 iterations, the proportion of the maximum number of sheep in 12 Australian flocks protectable by a single bivalent D. nodosus vaccine that were protected by a simulated vaccination based on serogroups identified in sampling strategies ranging from 5 to 40 sheep/flock and 2–4 colonies/sheep using either slide agglutination or PCR methods for serogroup identification.
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Fig. 3. Proportion of 10,000 iterations in which all D. nodosus serogroups in each of 12 Australian flocks were identified in a flock sample by stochastic simulation of sampling strategies ranging from 5 to 40 sheep/flock and 2–4 colonies/sheep using either slide agglutination or PCR methods for serogroup identification.
Fig. 4. Over 10,000 iterations, the proportion of diseased sheep diagnosed with D. nodosus in 12 Australian flocks that would be protected by a series of simulated bivalent vaccines encompassing all D. nodosus serogroups identified in a range stochastic sampling strategies ranging from 5 to 40 sheep/flock and 2–4 colonies/sheep executed prior to the start of a vaccination campaign using either slide agglutination or PCR methods for serogroup identification.
were based on slide agglutination, none of the sampling strategies protected >95% of diseased infected sheep. When results were based on PCR, sampling strategies using 24, 20, and 18 sheep and 2, 3, and 4 colonies/sheep, respectively, protected at least 95% of diseased infected sheep (Fig. 4). The proportion of diseased sheep protected by a simulated series of bivalent vaccines was significantly higher (p < 4.5 × 10−16 ) when results were based on PCR vs. slide agglutination for all sampling strategies.
4. Discussion Sampling strategy efficacy varied depending on the sampling strategy, the outcome being evaluated, and the diagnostic method used for serogroup identification. As expected, sampling strategy efficacy improved with both the number of sheep sampled per flock and the number of colonies sampled/sheep, but the degree of improvement varied depending on the outcome assessed and the diagnostic method.
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If a successful strategy is one that is effective 95% of the time, then none of the sampling strategies evaluated were successful at selecting the two most common serogroups present in the flock for inclusion in a single bivalent vaccine, or at identifying all serogroups present in the flock. However, if success is evaluated based on the effect of vaccination on the flock (e.g. the proportion of those animals that could be successfully treated), rather than on serogroup identification, then strategies based on PCR results using about 20 sheep per flock successfully treated the flock using either a single vaccine or a series of vaccines. The difference in results between success measured as serogroup identification and success measured as flock treatment is likely due to the distribution of serogroups within flocks. In a flock like Flock 6, where several serogroups are present at high frequencies, selection of any two of the common serogroups for vaccination will protect a substantial number of sheep, and in some instances, may protect as many sheep as selection of the two most common serogroups in the flock. Likewise, in a flock like Flock 2, where a few serogroups are very common and a few others are very rare, a series of vaccines based on identification of all of the common serogroups in the flock will protect almost all of the flock–so failure to identify all serogroups present in the flock can still result in protection for the majority of animals. Failure to identify all serogroups present in the flock is not ideal if the goal is disease eradication, but may be acceptable if the goal is disease control. Culling of sheep that have not responded to vaccination is an option to overcome a lack of serogroup coverage by the vaccine. The relative cost of sampling additional sheep vs. additional colonies is needed to evaluate the cost-benefits of increasing the number of sheep vs. the number of colonies/sheep. If the cost (beyond laboratory diagnostics) associated with sampling additional sheep is minimal, then strategies employing more sheep and fewer colonies/sheep appear most efficient. For example, when evaluating the proportion of diseased and infected sheep in the flock treated by a series of vaccines, three sampling strategies protect at least 95% of the flock: 24 sheep at 2 colonies/sheep (48 laboratory tests); 20 sheep at 3 colonies/sheep (60 laboratory tests); and 18 sheep at 4 colonies/sheep (72 laboratory tests). For most outcomes, the impact of testing additional colonies/sheep appears to decrease as the number of sheep sampled per flock increases. An exception occurs when evaluating the proportion of iterations in which all serogroups present in the flock were identified in the sample; for this outcome, the impact of testing additional colonies continues to be large, even at the largest sample sizes, and more so for PCR than for slide agglutination. Regardless of the outcome being measured or the sampling strategy, results based on PCR methodology were consistently significantly higher than those based on slide agglutination. It is difficult to confirm that a culture of D. nodosus is pure as colonies may contain several types due to their spreading nature. Polymerase chain reaction may perform significantly better because it can identify multiple serogroups present in a single colony (Zhou and Hickford, 2000), whereas slide agglutination typically identifies the
dominant serogroup in a mixed culture (Whittington, 2008, unpublished data). The effect of the difference in detection of multiple serogroups in a colony was most apparent in Flock 11. Flock 11 was infected with 6 serogroups, and twothirds of isolates cultured were serogroup B or D. Serogroup D was only present as a co-infection; it was never the only serogroup identified in a colony. Serogroup D was not detected by slide agglutination from any colony, whereas it was frequently detected by PCR. As a result, slide agglutination methods never detected the two most common serogroups in the flock, or all serogroups present in the flock. The flocks in the current study are not a representative sample of all sheep flocks infected with virulent D. nodosus. Participating flocks had clinically apparent virulent footrot, and had agreed to participate in a larger vaccination trial study. Participating flocks potentially may have more severe, more persistent, or more complex disease than other flocks because flocks with minimal disease or those able to control disease via culling, vaccination, and pasture management may be less inclined to enroll in a vaccine trial. However, the distribution of serogroups in flocks and sheep in the current study is similar to that reported previously (Claxton et al., 1983) in Australia. Other countries appear to have fewer sheep and flocks infected with multiple serogroups. In Bhutan and Nepal, no clinically affected flocks were infected with more than one serogroup (Ghimire et al., 1996; Gurung et al., 2006). In Canada, 25% and 6% of flocks were infected with >1 and ≥3 serogroups, respectively (Olson et al., 1998); whereas in two studies in Britain, about 60% and 30% of clinically affected flocks were infected with >1 and ≥3 serogroups, respectively, and 12% of sheep were infected with >1 serogroup (Hindmarsh and Fraser, 1985; Moore et al., 2005). In countries where flock co-infections are rare and the prevalence of infection is high in diseased sheep, the efficacy of specific sampling strategies would likely be higher than that reported here. This study was based on samples from a population, not on census data. Therefore not all diseased sheep from the flocks in the current study were used as the basis for the simulation model, nor were all affected feet evaluated from the selected sheep, or all colonies tested from each foot. Between 36 and 64 diseased sheep in each flock were evaluated for D. nodosus serogroup identification, with swabs taken from a single affected foot on each diseased sheep. A sample size of 36 is sufficient to detect serogroups present at a prevalence of 7.7% with 95% confidence (Win Episcope 2.0, Computer-aided Learning in Veterinary Education, UK). Thus additional D. nodosus serogroups may have been present at a lower prevalence. Had more diseased sheep and/or feet been evaluated, and additional D. nodosus serogroups identified in the 12 flocks used as the basis for the current study, the sampling strategy efficacies would likely be lower than that reported. 5. Conclusion Ideally, an effective sampling strategy would provide the optimal results 95% of the time. In the current study,
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none of the evaluated sampling strategies identified the two most common serogroups in the flock, or all serogroups present in the flock, in 95% of iterations. However, the use of PCR methodology and sampling 22 sheep and 2 colonies/sheep resulted in a simulated vaccine that protected 95% of sheep that could be protected by a single bivalent vaccine, and using PCR methodology and sampling 24 sheep and 2 colonies/sheep resulted in a series of simulated vaccines that protected 95% of diseased infected sheep. These are the optimum sampling strategies for these outcomes. Given that sampling up to 40 sheep per flock and 4 colonies/sheep did not consistently identify the two most common D. nodosus serogroups present in a flock, a sampling strategy that will identify the two most common serogroups in a flock 95% of the time may not be cost effective. Evaluating efficacy based on the expected effect on the flock may be more useful. Simulation modeling appears to be a useful method of evaluating the efficacy of a range of sampling strategies for identification of D. nodosus serogroups at the flock level. This methodology may also be useful for other diseases where control programs depend on detection of all serogroups or strains present in a population. Acknowledgement This work was funded by an International Visiting Research Fellowship from the University of Sydney and Australian Wool Innovation. References Cannon, R.M., Roe, R.T., 1982. Livestock disease surveys. In: A Field Manual for Veterinarians. Bureau of Range Science, Department of Primary Industry, Australian Government Publishing Service, Canberra, Australia. Claxton, P.D., 1986. Serogrouping of Bacteroides nodosus isolates. In: Stewart, D.J., Peterson, J.E., McKern, N.M., Emery, D.L. (Eds.), Footrot in Ruminants, Melbourne, 1985. Commonwealth Scientific and Industrial Research Organisation Australia and Australian Wool Corporation, Glebe, pp. 131–134. Claxton, P.D., Ribeiro, L.A., Egerton, J.R., 1983. Classification of Bacteroides nodosus by agglutination tests. Aust. Vet. J. 60, 331– 334. Claxton, P.D., 1989. Antigenic classification of Bacteroides nodosus. In: Egerton, J.R., Yong, W.K., Riffkin, G.G. (Eds.), Footrot and foot abscess of ruminants. CRC Press, Florida, p. 262. Day, S.E.J., Thorley, C.M., Beesley, J.E., 1986. Serotyping of Bacteroides nodosus: proposal for 9 further serotypes (J-R) and a study of the antigenic complexity of B. nodosus pili. In: Stewart, D.J., Peterson, J.E., McKern, N.M., Emery, D.L. (Eds.), Footrot in Ruminants, Melbourne, 1985. Commonwealth Scientific and Industrial Research Organisation Australia and Australian Wool Corporation, Glebe, pp. 147–159. Dhungyel, O.P., Lehmann, D.R., Whittington, R.J., 2008. Pilot trials in Australia on eradication of footrot by flock specific vaccination. Vet. Micro. 132, 364–371. Dhungyel, O.P., Whittington, R.J., Ghimire, S.C., Egerton, J.R., 2001. Pilus ELISA and an anamnestic test for the diagnosis of virulent ovine footrot and its application in a disease control program in Nepal. Vet. Micro. 79, 31–45. Dhungyel, O., Whittington, R., 2009. New approach to vaccination for treatment, control and eradication of footrot in sheep and goats. In: Proceedings of the 7th International Sheep Veterinary Congress, Stavanger, Norway, June 12–16, p. 56. Dhungyel, O.P., Whittington, R.J., Egerton, J.R., 2002. Serogroup specific single and multiplex PCR with pre-enrichment culture and immuno-magnetic bead capture for identifying strains of D-nodosus in sheep with footrot prior to vaccination. Mol. Cell. Probes 16, 285–296.
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