Comparative performance and age dependence of tuberculin and defined antigen bovine tuberculosis skin tests assessed with Bayesian latent class analysis

Abstract

Tuberculin skin tests (TST), the primary diagnostic tool for bovine tuberculosis (bTB), cross-react with BCG vaccine. Recently developed defined antigen skin tests (DSTs) aim to differentiate infected amongst vaccinated animals. We evaluated the field performance of different interpretations of the TST and DSTs relative to IGRA and IDEXX M. bovis antibody tests. This panel of tests was assessed in 446 unvaccinated cattle across 22 Ethiopian dairy herds using Bayesian latent class models. We extended the standard Walter-Hui model to include age-related effects to explore evidence of the presence of diagnostic anergy. The latent class models estimate sensitivity and specificity of the DSTs to be between 84-88% and 79-85% respectively. The DSTs perform intermediately between the comparative intradermal test (CIT, sensitivity 77%, specificity 100%) and single intradermal test (SIT, sensitivity 99%, specificity 76%). We observed significant age-related declines in test sensitivity, most notably for CIT (declining from 75 to 52% over 9 years) and DST10 (83% to 68%), while other tests showed more stable sensitivity across age groups. This variable pattern across tests suggests mechanisms beyond simple age-related anergy. Together, these findings demonstrate that DSTs' superior sensitivity to CIT and comparable or better specificity than SIT, combined with their ability to distinguish vaccinated animals, creates a viable pathway for implementing BCG vaccination programs. Given the absence of any gold standard definition of infection with bTB, latent class analyses are essential to assess the relative performance of different diagnostic tests. While our results provide encouraging news for the sensitivity of the new DST tests, the high prevalence of bTB within our study population makes our design underpowered to assess the specificity of the DSTs. Future research, including assessment of the specificity of DSTs in disease-free populations and optimization of test formulation and validation through large-scale field trials is essential to fully establish the case for use in vaccination and surveillance programs.

Keywords: Age-dependent diagnostics; BCG vaccination; Bayesian latent class analysis; Bovine tuberculosis; DIVA; Defined antigen skin test; Disease control; Tuberculin.

Fig. 1

Age dependence of apparent prevalence for each diagnostic test. The apparent prevalence of bTB (proportion of positive tests) was estimated using eight tests; CIT (> 4 mm), CITz (> 0 mm), SIT (> 2 mm), all the three DSTs (DST10, DST30, DSTF ≥ 2 mm), IGRA (≥ 0.1), and IDEXX (≥ 0.3) for 9 discrete annual age groups. Animals older than ten years were excluded from this analysis due to the sparsity of animals surviving to this age. Raw values are presented as points with lines indicated 95% (binomial) confidence intervals. The trend with age for each group is illustrated by a thin plate spline with 4 knots estimated from a (binomial) GAM. Standard errors for the smoothed trend line are plotted as a (shaded) ribbon strip. All plots demonstrate a relatively weak association with age with the highest risk of positivity in middle-aged cattle (in the range of the 4–6 year age groups). Note that the apparent drop in prevalence for animals in age group 7 is a result of both the declining number of animals surviving beyond this age and the uneven distribution of these animals between herds which have very different average levels of prevalence. The relative difference between the peak prevalence and estimated prevalence at the upper age limit (9 years) from the GAM is demonstrates a wide range of variability in the magnitude of the potential “anergy” effect (bottom right inset panel). For the relative difference uncertainty is estimated (and presented as line intervals) using the upper and lower standard errors on the mean of the estimated thin-plate spline from the GAM smoother.

Fig. 2

Sensitivity and specificity of the 8 diagnostic tests as estimated by our selected final model (WH-AC). The respective cut-offs for the eight tests are; DST10, DST30 and DSTF (all at ≥ 2 mm cut-off), CIT (> 4 mm), CITz (CIT > 0 mm), SIT (> 2 mm), IGRA (≥ 0.1), and IDEXX (≥ 0.3). Point (median, red dot) estimates of sensitivity (left) and specificity (right) are presented for each test with lines indicating the 95% credible intervals (thick red line) and posterior range (thinner red line). A shaded density strip (grey scale) for each quantity illustrates the shape of the posterior distribution within intensity of ink proportional to the posterior density.

Fig. 3

Dependence of test sensitivity on age. The estimated sensitivity of the eight tests (interpretations) as a function of age for our final selected model (WH-AC). Posterior predictive distributions for the respective test sensitivity in each discrete age group are presented as point intervals with median estimated value (red dot), 95% intervals (thick red line) and posterior predictive range (thinner red line). A linear smoothed trend line is added (red) to illustrate the relative strength of the estimated age-effect for each test.