Strategies for two-stage sampling designs for estimating herd-level prevalence

We propose a herd-level sample-size formula based on a common adjustment for prevalence estimates when diagnostic tests are imperfect. The formula depends on estimates of herd-level sensitivity and specificity. With Monte Carlo simulations, we explored the effects of different intracluster correlations on herd-level sensitivity and specificity. At low prevalence (e.g. 1% of animals infected), herd-level sensitivity increased with increasing intracluster correlation and many herds were classified as positive based only on false-positive test results. Herd-level sensitivity was less affected at higher prevalence (e.g. 20% of animals infected). A real-life example was developed for estimating ovine progressive pneumonia prevalence in sheep. The approach allows researchers to balance the number of herds and the total number of animals sampled by manipulating herd-level test characteristics (such as the number of animals sampled within a herd).     

Herd-level interpretation of test results for epidemiologic studies of animal diseases

Correct classification of the true status of herds is an important component of epidemiologic studies and animal disease-control programs. We review theoretical aspects of herd-level testing through consideration of test performance (herd-level sensitivity, specificity and predictive values), the factors affecting these estimates, and available software for calculations. We present new aspects and considerations concerning the effect of precision and bias in estimation of individual-test performance on herd-test performance and suggest methods (pooled testing, targeted sampling of subpopulations with higher prevalence, and use of combinations of tests) to improve herd-level sensitivity when the expected within-herd prevalence is low.     

Evaluation of a bulk-milk ELISA test for the classification of herd-level bovine leukemia virus status

The results of a commercial bulk-milk enzyme-linked immunosorbent assay (ELISA) test for herd-level bovine leukemia virus (BLV) status were compared to results obtained from individual agar-gel immunodiffussion (AGID) testing on sampled cattle. A positive herd was defined as a herd having one or more AGID-positive animals. The estimated true herd status was based on the sensitivity and specificity of the AGID test and the number of cattle sampled per herd. Ninety-seven herds were used, with a mean of 13 cows sampled per herd. The AGID test indicated an apparent herd prevalence of 70.1%. After accounting for the number of cows sampled and the sensitivity and specificity of the AGID test, the estimated true herd prevalence of BLV was 52.3%. The ELISA test identified 79.4% of herds as positive for BLV, and had an apparent sensitivity and specificity of 0.97 and 0.62, respectively. However, after accounting for the sensitivity and specificity of the AGID test in individual animals, the specificity of the ELISA test was 0.44. The ELISA test was useful for identifying BLV-negative herds (i.e., ruling out the presence of BLV infection in test negative herds). With the moderately low specificity, herds identified as positive by the ELISA test would require further testing at the individual or herd level to definitively establish their BLV status.     

Simulation model estimates of test accuracy and predictive values for the Danish Salmonella surveillance program in dairy herds

The Danish government and cattle industry instituted a Salmonella surveillance program in October 2002 to help reduce Salmonella enterica subsp. enterica serotype Dublin (S. Dublin) infections. All dairy herds are tested by measuring antibodies in bulk tank milk at 3-month intervals. The program is based on a well-established ELISA, but the overall test program accuracy and misclassification was not previously investigated. We developed a model to simulate repeated bulk tank milk antibody measurements for dairy herds conditional on true infection status. The distributions of bulk tank milk antibody measurements for infected and noninfected herds were determined from field study data. Herd infection was defined as having either >or=1 Salmonella culture-positive fecal sample or >or=5% within-herd prevalence based on antibody measurements in serum or milk from individual animals. No distinction was made between Dublin and other Salmonella serotypes which cross-react in the ELISA. The simulation model was used to estimate the accuracy of herd classification for true herd-level prevalence values ranging from 0.02 to 0.5. Test program sensitivity was 0.95 across the range of prevalence values evaluated. Specificity was inversely related to prevalence and ranged from 0.83 to 0.98. For a true herd-level infection prevalence of 15%, the estimate for specificity (Sp) was 0.96. Also at the 15% herd-level prevalence, approximately 99% of herds classified as negative in the program would be truly noninfected and 80% of herds classified as positive would be infected. The predictive values were consistent with the primary goal of the surveillance program which was to have confidence that herds classified negative would be free of Salmonella infection.     

Prevalence estimates for paratuberculosis adjusted for test variability using Bayesian analysis

The ELISA tests that are available to detect an infection with Mycobacterium avium subsp. paratuberculosis (MAP) have a limited validity expressed as the sensitivity (Se) and specificity (Sp). In many studies, the Se and Sp of the tests are treated as constants and this will result in an underestimation of the variability of the true prevalence (TP). Bayesian inference provided a natural framework for using information on the test variability (i.e., the uncertainty) in the estimates of test Se and Sp when estimating the TP.

Data from two prevalence studies for MAP using an ELISA in several regions in two locations were available for the analyses. In location 1, all cattle of at least 3 years of age were sampled in approximately 90 randomly sampled herds in each of the four regions of the country. In location 2, in 30 randomly sampled herds in each of three regions, approximately 30 randomly selected cows were sampled. Information about the unknown test Se and Sp and MAP prevalence was incorporated into a Bayesian model by joint prior probability distributions. Posterior estimates were obtained by combining the actual likelihood with the prior distributions using Bayes’ formula.

The corrected cow-level TP (proportion of infected cows in a herd) was low, 5.8 and 3.6% in locations 1 and 2, respectively. Certain regions within a location differed significantly in herd-level TP (proportion of infected herds). The herd-level TP was 54.3% in location 1 (95% credible interval (CI) 46.1, 63.3%) and 32.9% in location 2 (95% CI: 14.4, 73.3%). The variation in the herd-level TP estimate for location 2 was more than three times as large as the variation in location 1 mainly because of the relatively small number of investigated herds in location 2. In future prevalence studies for MAP, sample size calculations should be based on a very low cow-level prevalence. Approximately 50 and 90% of the herds in the current study had an estimated cow-level TP below 4 and 10%, respectively.     

Cut-off points for aggreate herd testing in the presence of disease clustering and correlation of test errors

In order to test if disease is present in a large herd, an investigator will often subject only a small sample of animals to a fallible diagnostic test. The herd is declared positive for disease if the number of test-positive animals is greater than or equal to a previously chosen cut-off value. Such a test, called an aggregate test, has a sensitivity and specificity that depends on the sample size, the cut-off point and the sensitivity and specificity of the individual test. It also depends on the distribution of the disease among the herds being tested and on the fact that factors such as herd-level seropositivity may cause some herds to be more prone to testing errors than others. In this paper, we use the beta-binomial distribution to model all these factors and thereby calculate and tabulate aggregate test sensitivities and specificities under a variety of conditions. Receiver operating characteristic (ROC) curve methodology permits the choice of optimum sample sizes and cut-off values. We also investigate the situation in which an investigator may be willing to miss detecting the disease if the prevalence in the herd is low. A compiled FORTRAN program for the calculation of aggregate test cut-off point properties, including positive and negative predictive values, is available from the authors.     

Bayesian modeling of animal- and herd-level prevalences

We reviewed Bayesian approaches for animal-level and herd-level prevalence estimation based on cross-sectional sampling designs and demonstrated fitting of these models using the WinBUGS software. We considered estimation of infection prevalence based on use of a single diagnostic test applied to a single herd with binomial and hypergeometric sampling. We then considered multiple herds under binomial sampling with the primary goal of estimating the prevalence distribution and the proportion of infected herds. A new model is presented that can be used to estimate the herd-level prevalence in a region, including the posterior probability that all herds are non-infected. Using this model, inferences for the distribution of prevalences, mean prevalence in the region, and predicted prevalence of herds in the region (including the predicted probability of zero prevalence) are also available. In the models presented, both animal- and herd-level prevalences are modeled as mixture distributions to allow for zero infection prevalences. (If mixture models for the prevalences were not used, prevalence estimates might be artificially inflated, especially in herds and regions with low or zero prevalence.) Finally, we considered estimation of animal-level prevalence based on pooled samples.     

Bovine viral diarrhoea virus seroprevalence and vaccination usage in dairy and beef herds in the Republic of Ireland

Background

Bovine viral diarrhoea (BVD) is an infectious disease of cattle with a worldwide distribution. Herd-level prevalence varies among European Union (EU) member states, and prevalence information facilitates decision-making and monitoring of progress in control and eradication programmes. The primary objective of the present study was to address significant knowledge gaps regarding herd BVD seroprevalence (based on pooled sera) and control on Irish farms, including vaccine usage.

Methods

Preliminary validation of an indirect BVD antibody ELISA test (Svanova, Biotech AB, Uppsala, Sweden) using pooled sera was a novel and important aspect of the present study. Serum pools were constructed from serum samples of known seropositivity and pools were analysed using the same test in laboratory replicates. The output from this indirect ELISA was expressed as a percentage positivity (PP) value. Results were used to guide selection of a proposed cut-off (PCO) PP. This indirect ELISA was applied to randomly constructed within-herd serum pools, in a cross-sectional study of a stratified random sample of 1,171 Irish dairy and beef cow herds in 2009, for which vaccination status was determined by telephone survey. The herd-level prevalence of BVD in Ireland (percentage positive herds) was estimated in non-vaccinating herds, where herds were classified positive when herd pool result exceeded PCO PP. Vaccinated herds were excluded because of the potential impact of vaccination on herd classification status. Comparison of herd-level classification was conducted in a subset of 111 non-vaccinating dairy herds using the same ELISA on bulk milk tank (BMT) samples. Associations between possible risk factors (herd size (quartiles)) and herd-level prevalence were determined using chi-squared analysis.

Results

Receiver Operating Characteristics Analysis of replicate results in the preliminary validation study yielded an optimal cut-off PP (Proposed Cut-off percentage positivity - PCO PP) of 7.58%. This PCO PP gave a relative sensitivity (Se) and specificity (Sp) of 98.57% and 100% respectively, relative to the use of the ELISA on individual sera, and was chosen as the optimal cut-off since it resulted in maximization of the prevalence independent Youden’s Index.The herd-level BVD prevalence in non-vaccinating herds was 98.7% (95% CI - 98.3-99.5%) in the cross-sectional study with no significant difference between dairy and beef herds (98.3% vs 98.8%, respectively, p = 0.595).An agreement of 95.4% was found on Kappa analysis of herd serological classification when bulk milk and serum pool results were compared in non-vaccinating herds. 19.2 percent of farmers used BVDV vaccine; 81% of vaccinated herds were dairy. A significant association was found between seroprevalence (quartiles) and herd size (quartiles) (p < 0.01), though no association was found between herd size (quartiles) and herd-level classification based on PCO (p = 0.548).

Conclusions

The results from this study indicate that the true herd-level seroprevalence to Bovine Virus Diarrhoea (BVD) virus in Ireland is approaching 100%. The results of the present study will assist with national policy development, particularly with respect to the national BVD eradication programme which commenced recently.     

A retrospective evaluation of a Bovine Herpesvirus-1 (BHV-1) antibody ELISA on bulk-tank milk samples for classification of the BHV-1 status of Danish dairy herds

Bulk-tank milk samples analysed in a Bovine Herpesvirus-1 (BHV-1) blocking ELISA are still in use in the Danish BHV-1 programme as a tool to classify dairy herds as BHV-1 infected or BHV-1 free herds. In this retrospective study, we used data from the Danish BHV-1 eradication campaign to evaluate performance characteristics of the BHV-1 blocking ELISA in 1039 BHV-1-seropositive and 502 repeatedly BHV-1-negative dairy herds using the results of blood testing of the individual animals as the true infection status. At a cut-off value of 30% blocking reaction, the herd-level relative sensitivity and relative specificity were 82 and 100%, respectively. The herd-level relative sensitivity depended on the within-herd prevalence of seropositive cows and the cut-off value in the assay, but not on the time interval (up to 90 days) between the collection of the bulk-tank milk sample and the individual serum samples. The BHV-1 blocking ELISA on bulk-tank milk could detect seropositive herds (few), with prevalence proportions as low as one seropositive cow out of 70 cows.     

Evaluation of an iscom ELISA used for detection of antibodies to Neospora caninum in bulk milk

The intracellular parasite Neospora caninum is increasingly recognized as an important cause of abortion and stillbirth in cattle. Presence of specific antibodies indicates infection, and the immunostimulating complex (iscom) enzyme-linked immunoassay (ELISA) has previously been evaluated for use on individual milk and sera. In the present study, this test is investigated for use on bulk milk. In this study, 124 herds were used to analyse the relationship between within-herd prevalences based on individual sera and bulk milk optical densities. The individual test results were translated into a herd-level result, which enabled comparison of the bulk milk test result to the aggregate of individual serum results. The relative contribution of milk from cows with different milk yield and antibody status to the tank, i.e. its composition, was expected to influence the outcome of the bulk milk test. Therefore, sensitivity and specificity were calculated at different cut-off levels, not only using a standard cross-tabulation technique, but also a logistic regression model. By using the latter method, the sensitivity and specificity could be estimated adjusting for milk yield covariates. Specificity was estimated to be high ( approximately 98%) at the 0.20 cut-off, which can be used as a decision threshold to rule in infection. With more equal emphasis on sensitivity and specificity, a lower cut-off should be used. Although infection cannot be completely ruled out, herds with test results below 0.05 are highly likely to be non-infected. The within-herd prevalence of false negative herds is probably less than 10-15% at this level. From what is known about test performance at the individual level and the prevalence of infection, the estimate of the specificity of the bulk milk test should be quite accurate while the sensitivity is likely to be underestimated. We confirmed that the performance of the bulk milk test depends on the milk tank composition. In particular the milk yield of cows with high antibody levels affects the probability of a positive outcome of the bulk milk test.     

Serological evidence of lack of contact with caprine herpesvirus type 1 and bluetongue virus in goat population in Poland

Investigation into herd-level seroprevalence of caprine herpesvirus type 1 (CpHV-1) and bluetongue virus (BTV) was conducted in 2007 in Poland. It involved the entire population of goats covered by a milk recording program in 2007, which included 49 goat herds. The number of goats examined in each herd was determined statistically in order to detect the presence of at least one seropositive animal in a herd with a 95% probability and simple random method of sampling was applied. No antibodies to CpHV-1 or BTV were detected. Further calculations were carried out to determine the herd-level true seroprevalence, taking into account sensitivity and specificity of the test as well as several other factors. It can be concluded that till the middle of 2007 population of Polish goats covered by the milk recording program remained negative with respect to CpHV-1 and BTV.     

Aggregate testing for the evaluation of Johne's disease herd status

This paper examines methods for evaluating herd Johne's disease status that could be used in a survey of the cattle industry. Emphasis is placed on aggregate testing, a process whereby a random sample of cattle from a herd is assessed using an imperfect test, such as an ELISA for detecting antibody in serum. Important aggregate test parameters discussed include: sample size, herd-level sensitivity, herd-level specificity, the number of reactors used for declaring a positive herd result, and the expected within-herd prevalence of disease. Aggregate testing may be useful for several livestock diseases. However, problems arise when it is applied to Johne's disease because of the poor sensitivity of the available diagnostic tests, the low within herd prevalence of infection, and clustering of false positives within a herd.     

Bulk-tank milk ELISA antibodies for estimating the prevalence of paratuberculosis in Danish dairy herds

Paratuberculosis (Johne's disease) has been widespread in Danish dairy herds for a long time but the herd-level prevalence has never been determined precisely. To evaluate the prevalence of paratuberculosis in Danish dairy herds in various regions, an ELISA based on a commercially available antigen was adapted for testing bulk-tank milk for the presence of antibodies to Mycobacterium avium subsp. paratuberculosis. Bulk-tank milk samples were collected from six milk-collecting centres from six different areas of the country. Samples from 900 herds (about 7.5% of all Danish dairy herds) were examined, and 70% were positive at the statistically optimal cut-off (sensitivity 97.1%; specificity 83.3%). The technical performance of the ELISA was not sufficient to provide a tool for surveillance because even slight changes in optical density for the samples would change the classification of some samples. The infection is more widespread than previous investigations have shown.     

Evaluation of two herd-level diagnostic tests for Streptococcus agalactiae using a latent class approach

Streptococcus agalactiae mastitis persists as a significant economic problem for the dairy industry in many countries. In Denmark, the annual surveillance programme for this mastitis pathogen initially based only on bacteriological culture of bulk tank milk (BTM) samples, has recently incorporated the use of the real-time PathoProof Mastitis PCR assay with the goal of improving detection of infected herds. The objective of our study was to estimate the herd sensitivity (Se) and specificity (Sp) of both tests of BTM samples using latent class models in a Bayesian analysis while evaluating the effect of herd-level covariates on the Se and Sp of the tests. BTM samples were collected from all 4258 Danish dairy herds in 2009 and screened for the presence of S. agalactiae using both tests. The highest Se of PCR was realized at a cycle threshold (Ct) cut-off value of 40. At this cut-off, the Se of the PCR was significantly higher (95.2; 95% posterior credibility interval [PCI] [88.2; 99.8]) than that of bacteriological culture (68.0; 95% PCI [55.1; 90.0]). However, culture had higher Sp (99.7; 95% PCI [99.3; 100.0]) compared to PCR (98.8; 95% PCI [97.2; 99.9]). The accuracy of the tests was unaffected by the herd-level covariates. We propose that screenings of BTM samples for S. agalactiae be based on the PCR assay with Ct readings of <40 considered as positive. However, for higher Ct values, confirmation of PCR test positive herds by bacteriological culture is advisable especially when the between-herd prevalence of S. agalactiae is low.     

Prevalence of paratuberculosis infection in dairy cattle in Northern Italy

Paratuberculosis is a chronic granulomatous infection caused by Mycobacterium avium subsp. paratuberculosis (MAP) that affects multiple ruminant species causing important economic losses. Therefore, control programmes at herd and regional levels have been established worldwide and prevalence estimates are needed for their implementation. Although different herd-level prevalence estimations for paratuberculosis have been reported in Europe, very few studies provided comparable and interpretable values, due to poor study designs and lack of knowledge about the accuracy of the diagnostic tests used. To overcome these problems we applied a latent class analysis to the results of two prevalence studies carried out in two neighbouring Northern Italian regions (Lombardy and Veneto) that account for over 50% of the Italian dairy cattle population. Serum samples from a randomly selected number of farms in the two regions were analyzed by different ELISA tests. The herd-level Apparent Prevalences (AP) were 48% (190/391) for Lombardy and 65% (272/419) for Veneto. Median within-herd APs were 2.6% and 4.0% for Lombardy and Veneto, respectively. Posterior estimates for the herd-level True Prevalences (TP) based on a Bayesian model were very similar between the two regions (70% for Lombardy and 71% for Veneto) and close to previous estimates of infected herds in Europe. The two 95% credibility intervals overlap each other, virtually showing only one distribution of the herd-level true prevalence for both regions. On the contrary, estimates of the within-herd TP distributions differed between the two regions (mean values: 6.7% for Lombardy and 14.3% for Veneto), possibly due to the different age distribution within the herds from the two regions.     

A stochastic-modeling evaluation of the foot-and-mouth-disease survey conducted after the outbreak in Miyazaki, Japan in 2000

When foot-and-mouth-disease (FMD) was identified in Miyazaki prefecture in March 2000, Japan conducted an intensive serological and clinical survey in the areas surrounding the index herd. As a result of the survey during the 21 days of the movement-restriction period, two infected herds were detected and destroyed; there were no other cases in the months that followed. To evaluate the survey used for screening the disease-control area and surveillance area, we estimated the herd-level sensitivity of the survey (HSe) through a spreadsheet model using Monte-Carlo methods. The Reed-Frost model was incorporated to simulate the spread of FMD within an infected herd. In the simulations, 4, 8 and 12 effective-contact scenarios during the 5-day period were examined. The estimated HSes of serological tests (HSeE) were 71.0, 75.3 and 76.3% under the 4, 8 and 12 contact scenarios, respectively. The sensitivity analysis showed that increasing the number of contacts beyond 12 did not improve HSeE, but increasing the number of sampled animals and delaying the dates of sampling did raise HSeEs. Small herd size in the outbreak area (>80% of herds have <20 animals) seems to have helped in maintaining HSeE relatively high, although the serological inspection was carried out before sero-positive animals had a chance to increase in infected herds. The estimated herd-level specificity of serological tests (HSpE) was 98.6%. This HSpE predicted 224 false-positive herds (5th percentile estimate was 200 and 95th percentile was 249), which proved close to the 232 false-positive herds actually observed. The combined-test herd-level sensitivity (serological and clinical inspections combined; CTHSe), averaged 85.5, 87.6 and 88.1% for the 4, 8 and 12 contact scenarios, respectively. Using these CTHSes, the calculated probability that no infected herd was overlooked by the survey was > or =62.5% under the most-conservative, four-contact scenario. The probability that no more than one infected herd was overlooked was > or =89.7%.     

Spatial clustering of swine influenza in Ontario on the basis of herd-level disease status with different misclassification errors

This approach maximizes sensitivity of serology-based monitoring systems by considering spatial clustering of herds classified as false positive by herd testing, allowing outbreaks to be detected in an early phase. The primary objective of this study was to determine whether swine herds infected with influenza viruses cluster in space, and if so, where they cluster. The secondary objective was to investigate the combining of a multivariate spatial scan statistic with herd test results to maximize the sensitivity of the surveillance system for swine influenza. We tested for spatial clustering of swine influenza using the Cuzick–Edwards test as a global test. The location of the most likely spatial clusters of cases for each subtype and strain in a sample of 65 sow and 72 finisher herds in 2001 (Ontario, Canada), and 76 sow herds in 2003 (Ontario, Canada) was determined by a spatial scan statistic in a purely spatial Bernoulli model based on single and multiple datasets.

A case herd was defined by true herd-disease status for sow or finisher herds tested for H1N1, and by apparent herd-disease status for sow herds tested for two H3N2 strains (A/Swine/Colorado/1/77 (Sw/Col/77) and A/Swine/Texas/4199-2/98 (Sw/Tex/98)). In sow herds, there was no statistically significant clustering of H1N1 influenza after adjustment for pig-farm density. Similarly, spatial clustering was not found in finisher herds. In contrast, clustering of H3N2 Sw/Col/77 (prevalence ratio = 12.5) and H3N2 Sw/Tex/98 (prevalence ratio = 15) was identified in an area close to a region with documented isolation of avian influenza isolates from pigs.

For the H1N1 subtype tested by ELISA, we used an approach that minimized overall misclassification at the herd level. This could be more applicable for detecting clusters of positive farms when herd prevalence is moderate to high than when herd prevalence is low. For the H3N2 strains we used an approach that maximized herd-level sensitivity by minimizing the herd cut-off. This is useful in situations where prevalence of the pathogen is low. The results of applying a multivariate spatial scan statistic approach, led us to generate the hypothesis that an unknown variant of influenza of avian origin was circulating in swine herds close to an area where avian strains had previously been isolated from swine. Maximizing herd sensitivity and linking it with the spatial information can be of use for monitoring of pathogens that exhibit the potential for rapid antigenic change, which, consequently, might then lead to diminished cross-reactivity of routinely used assays and lower test sensitivity for the newly emerged variants. Veterinary authorities might incorporate this approach into animal disease surveillance programs that either substantiate freedom from disease, or are aimed at detecting early incursion of a pathogen, such as influenza virus, or both.     

Simulating the sensitivity of pooled-sample herd tests for fecal Salmonella in cattle

Samples from livestock or food items are often submitted to microbiological analysis to determine whether or not the group (herd, flock or consignment) is shedding or is contaminated with a bacterial pathogen. This process is known as 'herd testing' and has traditionally involved subjecting each sample to a test on an individual basis. Alternatively one or more pools can be formed by combining and mixing samples from individuals (animals or items) and then each pool is subjected to a test for the pathogen. I constructed a model to simulate herd-level sensitivity of the individual-sample approach (HSe) and the herd-level sensitivity of the pooled-sample approach (HPSe) of tests for detecting pathogen. The two approaches are compared by calculating the relative sensitivity (RelHSe = HPSe/HSe). An assumption is that microbiological procedures had 100% specificity. The new model accounts for the potential for HPSe and RelHSe to be reduced by the dilution of pathogen that occurs when contaminated samples are blended with pathogen-free samples. Key inputs include a probability distribution describing the concentration of the pathogen of interest in samples, characteristics of the pooled-test protocol, and a 'test-dose-response curve' that quantifies the relationship between concentration of pathogen in the pool and the probability of detecting the target organism. The model also compares the per-herd cost of the pooled-sample and individual-sample approaches to herd testing. When applied to the example of Salmonella spp. in cattle feces it was shown that a reduction in the assumed prevalence of shedding can cause a substantial fall in HPSe and RelHSe. However, these outputs are much less sensitive to changes in prevalence when the number of samples per pool is high, or when the number of pools per herd-test is high, or both. By manipulating the number of pools per herd and the number of samples per pool HPSe can be optimized to suit the range of values of true prevalence of shedding of Salmonella that are likely to be encountered in the field.     

Use of simulation modeling to estimate herd-level sensitivity, specificity, and predictive values of diagnostic tests for detection of tuberculosis in cattle

OBJECTIVE: To estimate herd-level sensitivity (HSe), specificity (HSp), and predictive values for a positive (HPVP) and negative (HPVN) test result for several testing scenarios for detection of tuberculosis in cattle by use of simulation modeling. SAMPLE POPULATION: Empirical distributions of all herds (15,468) and herds in a 10-county area (1,016) in Michigan. PROCEDURE: 5 test scenarios were simulated: scenario 1, serial interpretation of the caudal fold tuberculin (CFT) test and comparative cervical test (CCT); scenario 2, serial interpretation of the CFT test and CCT, microbial culture for mycobacteria, and polymerase chain reaction assay; scenario 3, same as scenario 2 but specificity was fixed at 1.0; and scenario 4, sensitivity was 0.9 (scenario 4a) or 0.95 (scenario 4b), and specificity was fixed at 1.0. RESULTS: Estimates for HSe were reasonably high, ranging between 0.712 and 0.840. Estimates for HSp were low when specificity was not fixed at 1.0. Estimates of HPVP were low for scenarios 1 and 2 (0.042 and 0.143, respectively) but increased to 1.0 when specificity was fixed at 1.0. The HPVN remained high for all 5 scenarios, ranging between 0.995 and 0.997. As herd size increased, HSe increased and HSp and HPVP decreased. However, fixing specificity at 1.0 had only minor effects on HSp and HPVN, but HSe was low when the herd size was small. CONCLUSIONS AND CLINICAL RELEVANCE: Tests used for detecting cattle herds infected with tuberculosis work well on a herd basis. Herds with < approximately 100 cattle should be tested more frequently or for a longer duration than larger herds to ensure that these small herds are free of tuberculosis.     

Management-related risk factors for M. paratuberculosis infection in Michigan, USA, dairy herds

A cross-sectional study was conducted from June through December 1996 to identify management-related risk factors for herd-level M. paratuberculosis infection. Data were collected from 121 participating herds. A two-part questionnaire was administered to gather data on current and previous management practices and herd productivity. A random sample of cows aged ≥24 months was selected from each herd and tested for antibodies to M. paratuberculosis using the IDEXX Antibody ELISA (sensitivity 64%, specificity 96%). A positive herd was one in which ≥2 animals tested positive for antibodies to M. paratuberculosis. A negative herd was one in which no animal tested positive. Herds in which only one animal tested positive were dropped from statistical analysis to reduce the risk of including false-positive herds in the statistical analyses.

There were 80 herds with one or more positive animals and 41 herds with no positive animals in the sample (66% herd-level prevalence). Twenty-six herds (21%) were dropped from further analyses because they had only one positive cow. Twelve herds (10%) were dropped from analysis because of missing data. The resulting sample used for statistical modeling included 46 positive herds and 37 negative herds (55% herd-level prevalence). A multi-variable logistic-regression model was used to evaluate the results. The variable ‘use of an exercise lot for lactating cows' was associated with a three-fold increase in odds of a herd being positive for M. paratuberculosis infection (O.R.=3.01, C.I.=1.03–8.80); ‘cleaning of maternity pens after each use' was associated with a three-fold reduction in odds of a herd being positive for M. paratuberculosis infection (O.R.=0.28, C.I.=0.08–0.89); ‘application of lime to pasture areas in 1993' resulted in a ten-fold decrease in odds of a herd being positive for M. paratuberculosis infection (O.R.=0.10, C.I.=0.02–0.56).