2020
DOI: 10.1016/j.prevetmed.2020.105134
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The interpretation of serial Johne’s disease milk antibody results is affected by test characteristics, pattern of test results and parallel bovine tuberculosis testing

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Cited by 5 publications
(21 citation statements)
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“…The Poisson regression results, however, do suggest that there is a diagnostic benefit in not JD testing too closely to the previous bTB test. This is in line with the observations of May et al and Barden et al [ 22 , 24 ]. The findings of the current study further clarify the effect of bTB–JD testing intervals: for ‘medium’ risk cows, the largest increase in probability of a positive JD test is with testing interval categories of 29–42 days (OR = 2.4) and 43–56 days (OR = 2.6).…”
Section: Discussionsupporting
confidence: 94%
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“…The Poisson regression results, however, do suggest that there is a diagnostic benefit in not JD testing too closely to the previous bTB test. This is in line with the observations of May et al and Barden et al [ 22 , 24 ]. The findings of the current study further clarify the effect of bTB–JD testing intervals: for ‘medium’ risk cows, the largest increase in probability of a positive JD test is with testing interval categories of 29–42 days (OR = 2.4) and 43–56 days (OR = 2.6).…”
Section: Discussionsupporting
confidence: 94%
“…The findings of the current study further clarify the effect of bTB–JD testing intervals: for ‘medium’ risk cows, the largest increase in probability of a positive JD test is with testing interval categories of 29–42 days (OR = 2.4) and 43–56 days (OR = 2.6). This is very similar to the findings of May et al, whilst Barden et al reported an earlier (0–30 days post-bTB test) rise in odds for testing positive for JD [ 22 , 24 ]. The ‘high’ risk cows are more likely to be identified in the bTB–JD testing interval categories 43–56 and 57–70 days (OR = 3.2 and 4.0, respectively).…”
Section: Discussionsupporting
confidence: 90%
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“…Testing and culling was shown to be particularly effective when used prior to the calving season. Acknowledging the key role of testing in planning control strategies, Barden et al (2020) used Bayesian statistics to study milk antibody ELISA tests and calculate the probability of infection under various circumstances. The authors demonstrated a reduction in test specificity associated with parallel bovine tuberculosis testing, highlighting the complex management and health settings which can affect test interpretation, and under which control decisions must be made.…”
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confidence: 99%