Comparative analysis of tuberculin and defined antigen skin tests for the detection of bovine tuberculosis in buffaloes (Bubalus bubalis)

Comparative analysis of tuberculin and defined antigen skin tests for the detection of bovine tuberculosis in buffaloes (Bubalus bubalis) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparative analysis of tuberculin and defined antigen skin tests for the detection of bovine tuberculosis in buffaloes (Bubalus bubalis) Mohit Kumar, Tarun Kumar, Babu Lal Jangir, Mahavir Singh, Devan Arora, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2752899/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Feb, 2024 Read the published version in BMC Veterinary Research → Version 1 posted 4 You are reading this latest preprint version Abstract Background: Bovine tuberculosis (bTB) is a chronic disease that results from infection with any member of the Mycobacterium tuberculosis complex and infected animals are typically diagnosed withtuberculin-based intradermal skin tests per World Organization of Animal Health or similar guidelines. Peptide-based defined skin test (DST) antigens, comprising of ESAT-6, CFP-10 and Rv3615c, are able to differentiate infected from BCG-vaccinated animals and sensitively and specifically identify tuberculin reactor cattle, but their performance in buffaloes remained unknown. To assess the comparative performance of the DST with the tuberculin-based single intradermal test (SIT) and the single intradermal comparative cervical test (SICCT), we screened 543 female buffaloes from 49 organized dairy farms in two districts of Haryana state in India. Results: The results show that 37 (7%), 4 (1%) and 18 (3%) buffaloes were reactors with the SIT, SICCT and DST, respectively. Of the 37 SIT reactors, four were positive with SICCT and 12 were positive with the DST. The results further show that none of the animals tested positive with all three tests, and 6 DST positive animals were SIT negative. Together, a total of 43 animals were reactors with SIT, DST, or both, and the two assays showed moderate agreement (Cohen'sKappa 0.41; 95% CI: 0.23, 0.59). In contrast, only slight agreement (Cohen’s Kappa 0.18; 95% CI: 0.02, 0.34) was observed between SIT and SICCT. Latent class analyses reveal test specificities of 95% for SIT and 99% each for DST and SICCT, but considerably lower sensitivities of 67%, 39%, and 19% for SIT, DST, and SICCT, respectively, albeit with broad and overlapping credible intervals. Conclusion: Taken together, our investigation suggests that DST has a test specificity comparable with SICCT, and sensitivity intermediate between SIT and SICCT for the identification of buffaloes suspected of tuberculosis. Our studies also highlight an urgent need for future well-powered trials with detailed necropsy with immunological and microbiological profiling of reactor and non-reactor animals to better define the underlying drivers for the large observed discrepancies in assay performance, particularly between SIT and SICCT. Bovine tuberculosis buffaloes Haryana India SIT SICCT DST. Figures Figure 1 Figure 2 Figure 3 Introduction Bovine tuberculosis (Bovine TB or bTB) is a chronic disease of cattle caused by members of the Mycobacterium tuberculosis complex (MTBC). It is a multi-host disease that can infect a diverse group of domesticated and wild animals. In cattle, this disease negatively affects milk production (reduces milk yield up to 10–20%) and fertility, thus leading to economic losses [ 1 – 3 ]. Importantly, bTB is also a neglected zoonotic disease that crosses the species barrier and can infect humans either by consumption of unpasteurized milk or undercooked meat [ 4 ]. The tuberculin-based intradermal skin test, recommended by the World Organization for Animal Health (WOAH), is currently used for screening of animals for bTB [ 5 ]. Tuberculin skin testing is based on delayed type hypersensitivity to purified protein derivatives (PPDs) of standard cultures of M. avium (PPD-A) and M. bovis (PPD-B). The single intradermal test (SIT) involves PPD-B alone while in regions with high prevalence of environmental mycobacteria, the single intradermal comparative cervical test (SICCT) with both PPD-B and PPD-A is used to help improve test specificity. Importantly, the presence of cross-reactive antigens between field and vaccine strains causes inability to differentiate infected from Bacille Calmette-Guérin (BCG)–vaccinated animals (DIVA). This limits opportunities for the development and implementation of BCG vaccination-based control programs to help accelerate control of bTB. Here, we tested female buffaloes in organized dairy farms in two districts of Haryana, India. The WOAH-recommended standard tuberculin-based test having PPD was used alongside peptide-based defined skin test (DST) antigens, comprising of ESAT-6, CFP-10 and Rv3615c, that have been recently shown to not only have utility in identifying infection in cattle and buffaloes but also possess DIVA capability [ 6 – 8 ]. Systematic evaluation of the performance of diagnostic tests for bovine tuberculosis is hampered by the lack of a proper gold standard for identification of animals that are truly infected versus those that are merely exposed and may have recovered. The Walter-Hui latent class model provides a theoretical framework to address this problem, allowing the sensitivity and specificity of a set of competing diagnostic tests to be estimated when samples are available from at least two populations with differing prevalence [ 9 , 10 ]. In recent years this approach has been used to evaluate the relative performance of bTB diagnostics using field data from Ireland, Spain, France, Northern Ireland, Brazil, Pakistan and Egypt [ 11 – 20 ]. However, only one of these studies included buffalo (and did not evaluate the WOAH recommended tuberculin test) and no systematic performance of bTB diagnostics has previously been carried out in India [ 18 ]. We use the foundational Walter-Hui latent class model to provide first estimates of the relative sensitivity and specificity of the SIT, SICCT and DST tests in buffaloes in India. Materials And Methods Study population Haryana, a state in Northern India, is located between 27° 37' to 30° 35' latitude and between 74° 28' to 77° 36' longitude. Based on agro-climatic zones in India, Haryana falls in Zone-VI (Trans-Gangetic Plains Region). The state is further subdivided into two zones i.e., Eastern and Western. District A is in western zone while district B is in eastern zone. A total of 49 organized dairy farms in two districts of Haryana viz., A and B were selected to compare performance of PPDs and DST in detecting bTB infection in buffaloes. A total of 543 female buffaloes (326 in A district and 217 in B district) from these organized dairy farms were included in this study, based on a likely prevalence assumption of 15% in female buffaloes at 20% precision, 95% confidence interval. The animals were grouped in three age groups viz. calves (6 months -one year of age), heifers (1–3 years of age) and adults (more than three years of age). At the time of testing, data such as age, breed, lactation stage, milk yield, pregnancy status etc. were collected. Female calves less than 6 months of age and adult buffaloes that were either in an advanced stage of pregnancy or recently calved were excluded from this study. Skin testing The intradermal skin test was performed on both sides of the neck. On the left side of the neck, bovine PPD (PPD-B) and avian PPD (PPD-A) (0.1ml each; Prionics, Switzerland) were administered intradermally using McLintock syringes (Bar Knight McLintock Limited, Scotland). On the right side of the neck, the peptide-based DST was injected. The DST contains overlapping chemically synthesized peptides of ESAT6, CFP10 and Rv3615c (40-mer length with a 20-residue overlap) at > 98% purity at 20 ug/peptide. Before administration, skin thickness was measured in millimeters (0-hour value) using a vernier caliper. Skin thickness was measured again at 72 \(\pm 4\) hours by the same operator. Difference in skin thickness (72 hour – 0 hour) was calculated as per WOAH protocol. An animal with increase in skin thickness of 4mm or more due to bovine PPD (Single intradermal test; SIT) or PPD-B minus PPD-A (Single intradermal comparative cervical tuberculin test; SICCT) was considered as a reactor. Bovine tuberculin PPD consisted of 3000 I.U. /dose while avian tuberculin PPD consisted of 2500 I.U./dose (WOAH, 2009). DST antigen was used at a concentration of 20ug/dose [ 21 ]. An animal with increase in skin thickness by 2mm or more due to DST antigen was considered as a reactor. Statistical analysis The agreement between SIT, SICCT and DST was estimated using Cohen’s Kappa [ 22 ]. We carried out an exploratory analysis to test for associations between measured risk factors and positive status for the three test types. Risk factor model development was carried out in R [ 23 ]. For each test we built a multivariate logistic regression model using purposeful selection [ 24 ]. Firstly, we carried out a univariate screen with a generous cutoff for acceptance of 0.1, followed by a stepwise procedure (forwards and backwards) to select a parsimonious set of explanatory variables. Finally, to adjust for between herd variation in our study population we use a herd level random effect (intercept), estimating our final model using the lme4 R package [ 25 ]. The Hosmer-Lemeshow test as implemented in the Resource Selection R package was used to test for lack of model fit and classification ability of models was assessed through the area under the curve (AUC) of the Receiver-Operator-Characteristic (ROC) curve - calculated by the ROCR R package [ 26 , 27 ]. Statistical significance was considered if p < 0.05. Effect sizes were calculated and reported as odds ratios (OR) with 95% confidence intervals. The Walter-Hui latent class model was implemented in stan, estimated by Hamiltonian MCMC and analyzed in R using the rstan package [ 28 , 29 ]. The key assumption of the Walter-Hui model is conditional independence between tests, i.e., the probability of a test \(k\) being positive for individual ( \(i\) ), \(P\left({T}_{i,k}=1\right)\) only depends on the latent (true) disease status of the individual ( \(D\in \{0,1\}\) ) and not the response of the other tests. Under this assumption the (conditional) probability of a positive test result given that an animal is infected ( \(D=1\) ) or disease free ( \(D=0\) ) can then be modelled by a single parameter for each test: $$P\left({T}_{i,k}=1|D=1\right)={a}_{k}$$ $$P\left({T}_{i,k}=1|D=0\right)={b}_{k}$$ and the sensitivity of test \(k\) will then simply be \({a}_{k}\) and the specificity will be \(1-{b}_{k}\) . Following [ 1 , 30 ], and to allow for an extension to model any conditional dependence between tests, we parameterised the model using a probit ( \(\varPhi\) ) link function: $$P\left({T}_{t,k}=1|D=1\right)=\varPhi \left({a}_{t,1}\right)$$ $$P\left({T}_{t,k}=1|D=0\right)=\varPhi \left({a}_{t,0}\right)$$ To ensure numerical stability we restrict the sensitivity parameters (on the probit scale) \({a}_{t,1}\) to the range \(\left[-8,8\right]\) . To force identifiability of the model (and avoid the label switching problem common with this class of models due to the symmetry of the likelihood) we make the assumption that no tests have a specificity of \(<50\text{\%}\) or sensitivity \(<20\text{\%}\) and thus restrict \({a}_{t,0}\) to the half-range \(\left[-8,0\right]\) and \({a}_{t,1}\) to the range \(\left[-1,0\right]\) . Convergence was assessed through visual inspection of the chains and standard diagnostic statistics ( \(\widehat{R}=1\) for all parameters after \(2,000\) iterations for 8 chains). Estimated parameters are presented as median posterior values with 95% Bayesian credible intervals (CI). Model fit – and the central assumption of conditional dependence – was also assessed through calculating the pairwise probability of agreement between each pair of diagnostic tests ( \(k,k{\prime }\) ): $${\alpha }_{k,k{\prime }}=\frac{\sum _{i=1}^{N}{T}_{i,k}{T}_{i,k{\prime }}-\left(1-{T}_{i,k}\right)\left(1-{T}_{i,k{\prime }}\right)}{N}$$ Any systematic differences between the observed ( \({\alpha }_{k,k{\prime }}\) ) and expected values from the estimated model ( \({\alpha }_{k,k{\prime }}^{\text{*}}\) ) would imply a violation of the assumption of conditional independence. We can use draws from the posterior predictive distribution of \({\alpha }_{k,k{\prime }}^{\text{*}}\) for our fitted model to form a posterior predictive p-value [ 31 ]: $$P\left({\alpha }_{k,k{\prime }}^{\text{*}}>{\alpha }_{k,k{\prime }}\right)$$ If the model fits well, the value of \(P\left({\alpha }_{k,k{\prime }}^{\text{*}}>{\alpha }_{k,k{\prime }}\right)\) is expected to be close to \(0.5\) , with extreme values close to \(0\) or \(1\) indicating a lack of fit (i.e., 0.95). Results Out of 543 female buffaloes screened for bTB in 49 organized dairy farms, 37 (7%) animals in both the districts were found to be reactors by SIT (Fig. 1 ). Only 4 (< 1%) animals were found reactors with the SICCT test; three of which did not show any response to PPD-A. By DST, 18 (3%) buffaloes were found to be reactors as per the cut-off of \(\ge\) 2mm (Fig. 1 ). Considering SIT alone, 30 and 7 buffaloes were reactors in A and B district, respectively. Of the 30 reactor animals identified in district A, 21 were adult animals while eight were heifers and one was a calf. In district B, all seven reactors identified by SIT were adults. Of the 37 reactors identified by SIT, 23 (4%) were milch animals. Of the DST positive animals, 16 (12 adults, 02 heifers, and 02 calves) were in district A while two (both adults) were in district B. Seventeen animals showed higher PPD A response. Of the SICCT positive animals, three were adults and one was heifer and all were from district A. Forty-five animals had a skin thickness difference of 2–3 mm by SIT; these animals were categorized as inconclusive reactors. PPD A response was high in 32 buffaloes (27 adults, 4 heifers, 1 calf) and it varied from 4–20 mm. Of the DST reactors, it was observed that six animals were negative by SIT. Out of the 49 dairy farms whose animals were tested, reactor animals by at least one of the tests used were identified in only 18 dairy farms. None of the tested animals in the remaining 31 dairy farms showed reactivity to tuberculins or DST. The data was also analyzed with respect to the magnitude of skin thickness seen at 72 hours post-administration of antigens. With bovine PPD alone, 27 animals in both the districts had differences in skin thickness between 4-6mm while in the remaining 10 animals the difference was more than 7mm. Using SICCT, all four reactors had 4-6mm difference in skin thickness. With DST, 10 buffaloes were in the range of 2-3mm and 8 buffaloes showed 4-6mm increase in skin thickness (Fig. 1 ). From the present study, we also observed discrepancies in reactions induced by the antigens injected (Fig. 2 ). Twenty-five animals that were reactors by SIT were negative by SICCT and DST (Fig. 2 ). None of the animals tested was found reactor both by SICCT and DST. Interestingly, there were a total of 6 animals that were DST positive but SIT negative. Combining two tests i.e., SIT and DST, 43 animals were found to be reactors. It was observed that SIT and DST showed moderate agreement with Cohen's Kappa of 0.41 (95% CI: 0.23, 0.59) for test positive cases (Table 1 ). Whereas, low Kappa agreement of 0.18 (95% CI: 0.02, 0.34) was found between SIT and SICCT. After variable selection only two putative risk factors – the geographic region where the herd was located and lactation stage were associated with positivity to the SIT and DST tests. For the SICCT response, no variables passed the univariate screen likely due to the sparsity of positive results for this test. The final SIT and DST models including herd level random effect terms showed no evidence for a lack of fit with the Hosmer-Lemeshow test with p-values of 0.23 and 0.99, respectively. The SIT model demonstrates an excellent classification ability with an AUC = 0.88, while the DST model is outstanding with AUC = 0.96. However, this discrimination ability for the DST response appears to be driven purely be the random effects with neither of the selected risk factors being statistically significant. Results for the SICCT model are summarized in Table 2, which suggests that animals in district B have a reduced risk of being test positive to the SIT test (OR = 0.11, 0.02–0.55, 95% CI) while animals in the second lactation stage have a higher risk of testing positive (OR 4.39, 1.41–13.7, 95% CI). The fitted latent class model demonstrates an excellent agreement with the apparent reactor status across all infected and uninfected herds (Supplementary Fig. 1). The entire observed values lie within the 95% posterior predictive intervals of the estimated model (Fig. 3 ). Posterior predictive p-values – based on the pairwise probability of agreement between each pair of diagnostic tests - are all within a 95% interval with no evidence of conditional dependence between the tests based on this data. The latent class model estimates distinct differences in performance between the three diagnostic tests – albeit with relatively large overlaps in the posterior distributions (Fig. 3 ). Our analysis suggests that the DST test has lower diagnostic sensitivity (39%, 23–62 95% CI) compared to SIT (67% (43–96, 95% CI) but comparable specificity (99%, 96–100 95% CI) to the SICCT test (99.7, 98.4–100 95% CI) (Table 3). The DST has an apparent intermediate sensitivity to SIT and SICCT but with broad and overlapping predictive / credible intervals. Discussion The present study was undertaken to compare tuberculin skin test with defined skin antigen in buffaloes. Selection of test(s) used for screening animals is critical for control programs. Hence, it is crucial to validate new diagnostics in buffaloes which can accurately detect the case in order to develop effective control strategies for bTB in buffaloes. Lack of a gold standard test to define positive and negative animals in a herd is a concern in determining the accuracy of any screening test. Here, we tested female buffaloes ( Bubalus bubalis ) in organized dairy farms in two districts of Haryana, India using the WOAH-recommended standard SIT and SICCT skin tests. It is important to note here the crucial differences in these tests in order to understand the limitations while interpreting results. While the SIT offers high sensitivity, PPD-B also elicits an inflammatory reaction in animals sensitized with non-tuberculous mycobacteria (NTM) due to the presence of cross-reactive antigens, resulting in decreased specificity of the test. In order to help improve diagnostic specificity, the SICCT is used wherein both bovine and avian tuberculins are injected simultaneously side-by-side into the skin of the neck. This allows better discrimination than the SIT between animals infected with members of the MTBC and those sensitized to tuberculin due to exposure to members of the M. avium complex or to environmental non-pathogenic mycobacteria. In regions with high prevalence of NTM, the SICCT is recommended; however, increased specificity of SICCT implies a drop in sensitivity. Moreover, these tuberculin antigens are unable to differentiate infection from BCG vaccination due to the presence of cross-reactive antigens. We compared the performance of a peptide-based defined antigen skin test (DST) with that of the tuberculins in a larger cohort of female buffaloes. This test has previously been assessed in both experimental and field trials in cross-bred cattle [ 32 , 33 ]. A proof-of-concept study to evaluate DIVA capability of DST was performed in cross-bred cattle in India [ 34 ]. Recently, a pilot DST dose optimization trial was also conducted in domestic water buffaloes [ 21 ]. In the present study, a total of 543 female buffaloes from organized dairy farms in two districts in the state of Haryana were skin tested for diagnosis of bTB using both tuberculins and DST. A total of 6.81% and 0.73% buffaloes in two districts were found to be reactors by SIT and SICCT, respectively. In the present study, the peptide-based DST detected six additional animals as reactors which were negative by SIT and SICCT. Similarly, 25 animals detected as reactors by SIT were found non-reactors by DST. All SICCT positive animals were also DST non-reactors. These results raise important questions on performance of these tests and the underlying reasons behind these discrepancies. Firstly, the tuberculins themselves are crude reagents that are derived from culture supernatant of M. bovis AN5 strain (PPD-B) and M. avium (PPD-A). A study comparing the potency of PPD-A and -B from various suppliers found that while PPD-A quality was relatively constant, PPD-B quality varied considerably, highlighting a lack of proper standardization [ 34 ]. We would like to mention that a single batch of PPDs was used in the current study. It has also long been recognized that exposure to environmental mycobacteria confounds the accurate interpretation of tuberculin-based skin test results [ 3 , 35 , 36 ]. Prevalence of environmental mycobacteria is particularly high in regions that have tropical weather [ 36 ]; the same is the case with the state where the study was carried out. Our latent class analysis suggests that the DST has a sensitivity that is intermediate between the SICCT and SIT test and specificity comparable to the SICCT test. The uncertainty in these estimates, due to the relatively small sample and group sizes, is reflected in overlapping posterior distributions for diagnostic parameters and wide credible intervals for the bTB infection within each herd. The sample size may also contribute to the lack of evidence for conditional dependence between the diagnostic tests. The absence of any such evidence made exploration of alternative models to estimate such dependence between tests moot for this study, but cannot be ruled out. All three diagnostic tests measure different aspects of the animal’s immune response to M. bovis rather than presence or absence of the organism itself. Indeed, the SIT and SICCT tests are designed to be dependent on each other in the sense that the avian response is used to increase the specificity of SICCT at the expense of sensitivity. The extent to which the sensitivity and specificity of the SIT and SICCT tests trade off against each other within this particular population is difficult to assess in the absence of microbiological or pathological confirmation of infection. The triangulation we carry out here against the DST test provides some insight into this trade-off, but validation of these estimates requires further studies including necropsies of reactor animals and culture of causative pathogens to both directly address this issue and begin to understand the other discrepancies in response between these alternative diagnostic tests. It has been reported that specific antigens such as ESAT-6, CFP-10, and Rv3615c are present in field strains of M. bovis but are either absent or not immunogenic in BCG vaccine strain [ 37 ]. Srinivasan et al . (2019) assessed a peptide cocktail composed of 40-mer peptides covering the sequences of ESAT-6, CFP-10, and Rv3615c with a 20-residue overlap [peptide cocktail–long (PCL)] and a recombinant fusion protein of the same three antigens in animals experimentally infected with M. bovis and naive animals [ 2 ]. The cocktail was administered intradermally in the neck region. The results suggested that PCL performed better than the fusion protein and both were able to accurately detect infected animals and could differentiate them from uninfected animals with high sensitivity and specificity. Defined skin test, peptide-based cocktail of the above-mentioned antigens, has the potential to differentiate infected animals from BCG vaccinated animals i.e., DIVA capability [ 6 ]; the tuberculins lack the said potential. Few animals in this study exhibited higher response to both bovine and avian PPDs and in some animals, PPD-A response was higher than PPD-B. It may be possible to get such a response from environmental mycobacteria. Proano-Perez et al . (2009) also reported that few animals exhibited higher PPD-A response and this response decreased significantly with age [ 38 ]. These authors opined that Mycobacterium avium complex (MAC) is more prevalent in the environment than M. bovis , and young animals are in contact with these environmental mycobacteria early in life. It is to mention that recent studies report the presence of M. orygis rather than M. bovis in cattle and/or buffalo [ 39 – 42 ]. In south-east Asia M. orygis has been isolated from cattle and monkey in Bangladesh [ 43 ]. In India, M. orygis has also been reported from dairy in cattle [ 42 ]. The accuracy of DST for diagnosing infection other than M. bovis has yet not been established. Further studies ae needed to correlate the skin test reactions or outcome with the isolation of pathogen from the animals. Such studies in large cohorts can help to determine the performance of tuberculins or DST. In conclusion, combined with the existing limitations of non-standardized and varying performance characteristics of current diagnostic tests, there is an urgent need for well‐standardized skin tests to enable accurate monitoring of bovine tuberculosis over time. Defined antigen skin tests such as the peptide-based cocktail used in this study are specific and also provide the much-needed DIVA capability of implementation of vaccine-based intervention strategies in LMICs. Abbreviations bTB Bovine Tuberculosis DST Defined Antigen Skin Test PPD Purified Protein Derivative WOAH World Organization of Animal Health SIT Single Intradermal Test SICCT Single Intradermal Comparative Cervical Test MTBC Mycobacterium Tuberculosis Complex LMICs Lower Middle-Income Countries DIVA Differentiating Infected from Vaccinated Animal IAEC Institutional Animal Ethics Committee NTM Non-Tuberculous Mycobacterium BCG Bacille Calmette and Guerin MAC Mycobacterium Avium Complex Declarations Acknowledgements The authors are thankful to the Deputy Directors and Veterinary Surgeons of Department of Animal Husbandry and Dairying, Haryana of both districts for their help in selection of dairy farms and animal testing. Author Contribution NJ, VK, SMB, SS, MV and DBa conceptualized the study. MK, TK, BLJ, DA, MS conducted the testing of animals in field. MK, NJ, YB and AC did the statistical analysis. MK and NJ prepared the first draft. All authors contributed to the article and approved the submitted version. Funding The authors are thankful to the Department of Biotechnology, Government of India (BT/ADV/Bovine tuberculosis/2018 dates 29.09.2018) and Bill & Melinda Gates Foundation (OPP1176950) for providing funds to conduct this study. Data Availability The datasets generated during and/or analyzed during the current study are presented in the manuscript. Ethical Approval The study was approved by the Institutional Animal Ethics Committee (IAEC) vide proceeding no. VCC/IAEC/1630-58 dated 26.07.2018 of the Lala Lajpat Rai Veterinary and Animal Sciences University (LUVAS, Hisar, Haryana, India). All methods were performed in accordance with the relevant guidelines and regulations of IAEC. Conflict of Interest The authors declare that there is no conflict of interest. Consent for publication Not applicable. References Collins JD. Tuberculosis in cattle: strategic planning for the future. 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Educ Psychol Meas. 1960;20:37-46. R Core Team. R: A language and environment for statistical computing. R Foundation for statistical computing, Vienna, Austria. 2021. Bursac Z, Gauss CH, Williams DK, Hosmer DW. Purposeful selection of variables in logistic regression. Biol Med. 2008;3:1-8. Bates D, Maechler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme 4. J Stat Softw. 2015;67(1):1-48. Sing T, Sander O, Beerenwinkel N, Lengauer T. ROCR: visualizing classifier performance in R. Bioinformatics. 2005;2(20):3940-41. Lele SR, Keim JL and Solymos P. Resource selection: resource selection (probability) functions for use-availability data. R package version 0.3-5. 2019 Stan Development Team. RStan: the R interface to Stan. R package version 2.21.3. 2021. Stan Development Team. Stan modelling language users guide and reference manual. 2022; 2(29). Dendukuri N, Hadgu A, Wang L. Modeling conditional dependence between diagnostic tests: A multiple latent variable model. Stat Med. 2009;28(3):441-61. Gelman A, Hwang J, Vehtari A. Understanding predictive information criteria for Bayesian models. Stat Comput. 2014;24:997-1016. Srinivasan S, Jones G, Veerasami M, Steinbach S, Holder T, Zewude A, Fromsa A, Ameni G, Easterling L, Bakker D, Juleff N, Gifford G, Hewinson RG, Vordermeier HM, Kapur V. A defined antigen skin test for the diagnosis of bovine tuberculosis. Sci Adv. 2019;5(7): eaax4899. Srinivasan S, Conlan AJK, Easterling LA, Herrera C, Dandapat P, Veerasami M, Ameni G, Jindal N, Raj GD, Wood J, Juleff N, Bakker D, Vordermeier M, Kapur V. A meta-analysis of the effect of Bacillus Calmette-Guérin vaccination against bovine tuberculosis: is perfect the enemy of good? Front Vet Sci. 2021;8:100495. Bakker D, Eger A, McNair J, Riepema KH, Willemsen PTJ. Comparison of commercially available PPDs: practical considerations for diagnosis and control of bovine tuberculosis. 4th International Conference on Mycobacterium bovis . Dublin, Ireland 22-26th August, 2005. de la Rua-Domenech R, Goodchild AT, Vordermeier HM, Hewinson RG, Christiansen KH, Clifton-Hadley RS. Ante mortem diagnosis of tuberculosis in cattle: a review of the tuberculin tests, gamma-interferon assay and other ancillary diagnostic techniques. Res Vet Sci. 2006;81(2):190-210. Palmer MV, Waters WR, Thacker TC, Greenwald R, Esfandiari J, Lyashchenko KP. Effects of different tuberculin skin-testing regimens on Gamma interferon and antibody responses in cattle experimentally infected with Mycobacterium bovis . Clin Vacc Immunol. 2006;13:387-94. Vordermeier HM, Jones GJ, Buddle BM, Hewinson RG, Villarreal-Ramos B. Bovine tuberculosis in cattle: Vaccines, DIVA tests, and host biomarker discovery. Annu Rev Anim Biosci. 2016;4:87-109. Proano-Perez F, Benitez-Ortiz W, Celi-Erazo M, Ron-Garrido L, Benitez-Capistros R, Portaels F, Rigouts L, Linden A. Comparative intradermal tuberculin test in dairy cattle in the North of Ecuador and risk factors associated with bovine tuberculosis. Am J Trop Med Hyg. 2009;81(6):1103–9. Gey van Pittius NC, Perrett KD, Michel AL, Keet DF, Hlokwe T, Streicher EM, Warren RM, van Helden PD. Infection of African buffalo ( Syncerus caffer ) by oryx bacillus, a rare member of the antelope clade of the Mycobacterium tuberculosis complex. J Wildl Dis. 2012;48(4):849-57. Gey van Pittius NC, van Helden PD, Warren RM. Characterization of Mycobacterium orygis . Emerg Infect Dis. 2012;18(10):1708-9. Dawson KL, Bell A, Kawakami RP, Coley K, Yates G, Collins DM. Transmission of Mycobacterium orygis (M. tuberculosis complex species) from a tuberculosis patient to a dairy cow in New Zealand. J Clin Microbiol. 2012;50(9):3136-38. Refaya AK, Kumar N, Raj D, Veerasamy M, Balaji S, Shanmugam S, Rajendran A, Tripathy SP, Swaminathan S, Peacock SJ, Palaniyandi K. Whole-Genome sequencing of a Mycobacterium orygis strain isolated from cattle in Chennai, India. Microbiol Resour Announc. 2019;8(40):e01080-19. Rahim Z, Thapa J, Fukushima Y, van der Zanden AGM, Gordon SV, Suzuki Y, Nakajima C. Tuberculosis caused by Mycobacterium orygis in dairy cattle and captured monkeys in Bangladesh: a new scenario of tuberculosis in South Asia. Transbound Emerg Dis. 2017;64(6):1965-69. Tables Table 1 Agreement of SIT with SICCT and DST Test SICCT DST Negative Positive Negative Positive SIT negative 506 0 500 06 SIT positive 33 04 25 12 Total 539 04 525 18 Cohen’s Kappa (95% CI) 0.18 (0.02, 0.34), p = 0.001 0.41 (0.23, 0.59), p = 0.001 SIT, Single intradermal test; SICCT, Single intradermal comparative cervical test; DST, Defined skin test Table 2 Comparison of tuberculin’s and defined skin test and associated risk factors for bovine tuberculosis in buffaloes. Characteristic OR 95% CI p-value Region A B 0.11 0.02, 0.55 0.007 Lactation stage 0 1 3.07 0.82, 11.4 0.095 2 4.39 1.41, 13.7 0.011 3 0.84 0.16, 4.49 0.8 4 3.04 0.91, 10.2 0.071 5+ 1.62 0.44, 5.97 0.5 Table 3 Estimated sensitivity and specificity of bTB diagnostics from latent class analysis with 95% Bayesian credible intervals (CI) Test Sensitivity (95% CI) Specificity (95% CI) SIT 67% (43-96) 95.9 (92-99) SICCT 19% (16 -29) 99.7 (98-100) DST 39% (23-62) 99% (96-100) SIT, Single intradermal test; SICCT, Single intradermal comparative cervical test; DST, Defined skin test Additional Declarations No competing interests reported. Supplementary Files SupplemantryFigure1.docx SupplementaryTable1.docx Cite Share Download PDF Status: Published Journal Publication published 23 Feb, 2024 Read the published version in BMC Veterinary Research → Version 1 posted Editorial decision: Major revision 18 May, 2023 Editor assigned by journal 06 Apr, 2023 Submission checks completed at journal 31 Mar, 2023 First submitted to journal 29 Mar, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2752899","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":188163849,"identity":"c3994c3b-00d0-4ba8-869d-0f49952e157c","order_by":0,"name":"Mohit Kumar","email":"","orcid":"","institution":"Department of Veterinary Public Health and Epidemiology, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mohit","middleName":"","lastName":"Kumar","suffix":""},{"id":188163850,"identity":"6fc4a546-60d0-4cb6-b906-3056b1e000dc","order_by":1,"name":"Tarun Kumar","email":"","orcid":"","institution":"Veterinary Clinical Complex, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tarun","middleName":"","lastName":"Kumar","suffix":""},{"id":188163851,"identity":"29431770-7ca5-45f5-ac14-051802812d30","order_by":2,"name":"Babu Lal Jangir","email":"","orcid":"","institution":"Department of Veterinary Pathology, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Babu","middleName":"Lal","lastName":"Jangir","suffix":""},{"id":188163852,"identity":"ee2ec3f2-4d74-43ec-b197-c50c4bd1e01c","order_by":3,"name":"Mahavir Singh","email":"","orcid":"","institution":"College Central Laboratory, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mahavir","middleName":"","lastName":"Singh","suffix":""},{"id":188163853,"identity":"a41a973b-fb76-494c-88b2-c57a079b8c26","order_by":4,"name":"Devan Arora","email":"","orcid":"","institution":"Regional Centre at Karnal, Lala Lajpat Rai University of Veterinary and Animal Sciences, Karnal","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Devan","middleName":"","lastName":"Arora","suffix":""},{"id":188163854,"identity":"a5913889-6775-451d-b534-36f46f78ddfd","order_by":5,"name":"Yogesh Bangar","email":"","orcid":"","institution":"Department of Animal Genetics and Breeding, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yogesh","middleName":"","lastName":"Bangar","suffix":""},{"id":188163855,"identity":"b063cc33-b980-460e-900f-08ff811d00c0","order_by":6,"name":"Andrew Conlan","email":"","orcid":"","institution":"Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge, Cambridge","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Conlan","suffix":""},{"id":188163856,"identity":"430aba8c-bbf7-42f2-877f-c628bd389f9b","order_by":7,"name":"Martin Vordermeier","email":"","orcid":"","institution":"Animal and Plant Health Agency, Surrey, United Kingdom and Centre for Bovine Tuberculosis, Institute for Biological, Environmental and Rural Sciences, University of Aberystwyth, Aberystwyth","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Vordermeier","suffix":""},{"id":188163857,"identity":"805e58eb-099e-4598-8603-de2cfdf23c96","order_by":8,"name":"Douwe Bakker","email":"","orcid":"","institution":"Technical Consultant and Independent Researcher, Lelystad","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Douwe","middleName":"","lastName":"Bakker","suffix":""},{"id":188163858,"identity":"e8b060ef-e12c-42c9-9d9c-1b79d7f8abb4","order_by":9,"name":"S. M. Byregowda","email":"","orcid":"","institution":"Institute of Animal Health and Veterinary Biologicals, Bengaluru","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"S.","middleName":"M.","lastName":"Byregowda","suffix":""},{"id":188163859,"identity":"360edb98-0e39-4ab7-8022-b982b32aac23","order_by":10,"name":"Sreenidhi Srinivasan","email":"","orcid":"","institution":"The Pennsylvania State University, University Park, Pennsylvania","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sreenidhi","middleName":"","lastName":"Srinivasan","suffix":""},{"id":188163860,"identity":"a6e8e5ac-ed44-4e7c-8848-b09fa5e705d4","order_by":11,"name":"Vivek Kapur","email":"","orcid":"","institution":"The Pennsylvania State University, University Park, Pennsylvania","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Vivek","middleName":"","lastName":"Kapur","suffix":""},{"id":188163861,"identity":"18706ce2-5c0f-4d9d-8c89-7cf48586fa50","order_by":12,"name":"Naresh Jindal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYLACxgY2HgaGBMYDCRVAHjNzA9FaGA48OAPSwkiUFhCZwHDwYRuCixOYs599+PDnDj4Zc/bkBwcS59VG87cDtfyo2IZTi2VPurEx7xk2HsueZwYHErcdz51xmLGBsefMbZxaDA6ksUkztrHxGNxIAGk5ltsA1MLM2IZHy/ln7D9/grWkfziQOOdY7nyCWm6ksTHwgrXkAG1pqMndQFjLM2ZpsJYzbwoOJBw7kLsRqOUgXr+cT2P8+LPtmL3B8fSND3/U1OXOO3/44IMfFbi1QMExGOMwmDxASD0Q1MAYdUQoHgWjYBSMgpEGAC3xYwanaQdaAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Veterinary Public Health and Epidemiology, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Naresh","middleName":"","lastName":"Jindal","suffix":""}],"badges":[],"createdAt":"2023-03-29 16:14:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2752899/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2752899/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12917-024-03913-3","type":"published","date":"2024-02-23T15:01:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":35211668,"identity":"f37f75d9-25e0-47a6-9c55-88c49d6d6ed1","added_by":"auto","created_at":"2023-04-03 15:09:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":90498,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of skin thickness amongst the 43 buffaloes that were identified as reactors by different skin tests. SIT, Single intradermal test; SICCT, Single intradermal comparative cervical test; DST, Defined skin test\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2752899/v1/0d34a34341b082f12218a83b.png"},{"id":35211271,"identity":"e0494482-0dea-4af9-9a37-668f9da25de1","added_by":"auto","created_at":"2023-04-03 15:01:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":86823,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of adult buffaloes showing reaction to bovine and avian tuberculins and defined antigen skin test.\u003c/p\u003e\n\u003cp\u003eSIT, Single intradermal test; SICCT, Single intradermal comparative cervical test; DST, Defined skin test, +ve, Positive; -ve, Negative\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2752899/v1/3b4e6f26b43cb7aa0e341677.png"},{"id":35211269,"identity":"69b1e53b-4844-4c1d-92a3-d75e3b10980c","added_by":"auto","created_at":"2023-04-03 15:01:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":161608,"visible":true,"origin":"","legend":"\u003cp\u003eLeft) Posterior estimates of the true within-herd reactor status, points indicate the observed reactor status within each herd for the DST (red), SICCT (green) and SIT (blue) tests. (Right) Posterior distributions for the sensitivity and specificity of the SIT, SICCT and DST diagnostic tests. Table shows Posterior predictive p-values (calculated from pairwise probability of agreement between each test).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2752899/v1/b0c0950c89f11c7102439dad.png"},{"id":51648326,"identity":"225b79c2-3397-475e-9d6a-54974312cce2","added_by":"auto","created_at":"2024-02-26 15:12:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":648842,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2752899/v1/9f56816b-8967-4862-9864-6a700837d1fa.pdf"},{"id":35211272,"identity":"4c6884ab-0756-4fd6-9ad6-f290b3867f02","added_by":"auto","created_at":"2023-04-03 15:01:50","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":209471,"visible":true,"origin":"","legend":"","description":"","filename":"SupplemantryFigure1.docx","url":"https://assets-eu.researchsquare.com/files/rs-2752899/v1/b20d439ce4e5fd1fe6395ff3.docx"},{"id":35211273,"identity":"349b5281-728c-42ad-9d44-90eb663bd93c","added_by":"auto","created_at":"2023-04-03 15:01:50","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":24063,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-2752899/v1/329fd21f796ef0da0aefd544.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative analysis of tuberculin and defined antigen skin tests for the detection of bovine tuberculosis in buffaloes (Bubalus bubalis)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBovine tuberculosis (Bovine TB or bTB) is a chronic disease of cattle caused by members of the \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex (MTBC). It is a multi-host disease that can infect a diverse group of domesticated and wild animals. In cattle, this disease negatively affects milk production (reduces milk yield up to 10\u0026ndash;20%) and fertility, thus leading to economic losses [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Importantly, bTB is also a neglected zoonotic disease that crosses the species barrier and can infect humans either by consumption of unpasteurized milk or undercooked meat [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe tuberculin-based intradermal skin test, recommended by the World Organization for Animal Health (WOAH), is currently used for screening of animals for bTB [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Tuberculin skin testing is based on delayed type hypersensitivity to purified protein derivatives (PPDs) of standard cultures of \u003cem\u003eM. avium\u003c/em\u003e (PPD-A) and \u003cem\u003eM. bovis\u003c/em\u003e (PPD-B). The single intradermal test (SIT) involves PPD-B alone while in regions with high prevalence of environmental mycobacteria, the single intradermal comparative cervical test (SICCT) with both PPD-B and PPD-A is used to help improve test specificity. Importantly, the presence of cross-reactive antigens between field and vaccine strains causes inability to differentiate infected from Bacille Calmette-Gu\u0026eacute;rin (BCG)\u0026ndash;vaccinated animals (DIVA). This limits opportunities for the development and implementation of BCG vaccination-based control programs to help accelerate control of bTB.\u003c/p\u003e \u003cp\u003eHere, we tested female buffaloes in organized dairy farms in two districts of Haryana, India. The WOAH-recommended standard tuberculin-based test having PPD was used alongside peptide-based defined skin test (DST) antigens, comprising of ESAT-6, CFP-10 and Rv3615c, that have been recently shown to not only have utility in identifying infection in cattle and buffaloes but also possess DIVA capability [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSystematic evaluation of the performance of diagnostic tests for bovine tuberculosis is hampered by the lack of a proper gold standard for identification of animals that are truly infected versus those that are merely exposed and may have recovered. The Walter-Hui latent class model provides a theoretical framework to address this problem, allowing the sensitivity and specificity of a set of competing diagnostic tests to be estimated when samples are available from at least two populations with differing prevalence [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In recent years this approach has been used to evaluate the relative performance of bTB diagnostics using field data from Ireland, Spain, France, Northern Ireland, Brazil, Pakistan and Egypt [\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, only one of these studies included buffalo (and did not evaluate the WOAH recommended tuberculin test) and no systematic performance of bTB diagnostics has previously been carried out in India [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. We use the foundational Walter-Hui latent class model to provide first estimates of the relative sensitivity and specificity of the SIT, SICCT and DST tests in buffaloes in India.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eHaryana, a state in Northern India, is located between 27\u0026deg; 37' to 30\u0026deg; 35' latitude and between 74\u0026deg; 28' to 77\u0026deg; 36' longitude. Based on agro-climatic zones in India, Haryana falls in Zone-VI (Trans-Gangetic Plains Region). The state is further subdivided into two zones i.e., Eastern and Western. District A is in western zone while district B is in eastern zone. A total of 49 organized dairy farms in two districts of Haryana viz., A and B were selected to compare performance of PPDs and DST in detecting bTB infection in buffaloes. A total of 543 female buffaloes (326 in A district and 217 in B district) from these organized dairy farms were included in this study, based on a likely prevalence assumption of 15% in female buffaloes at 20% precision, 95% confidence interval. The animals were grouped in three age groups viz. calves (6 months -one year of age), heifers (1\u0026ndash;3 years of age) and adults (more than three years of age). At the time of testing, data such as age, breed, lactation stage, milk yield, pregnancy status etc. were collected. Female calves less than 6 months of age and adult buffaloes that were either in an advanced stage of pregnancy or recently calved were excluded from this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSkin testing\u003c/h2\u003e \u003cp\u003eThe intradermal skin test was performed on both sides of the neck. On the left side of the neck, bovine PPD (PPD-B) and avian PPD (PPD-A) (0.1ml each; Prionics, Switzerland) were administered intradermally using McLintock syringes (Bar Knight McLintock Limited, Scotland). On the right side of the neck, the peptide-based DST was injected. The DST contains overlapping chemically synthesized peptides of ESAT6, CFP10 and Rv3615c (40-mer length with a 20-residue overlap) at \u0026gt;\u0026thinsp;98% purity at 20 ug/peptide. Before administration, skin thickness was measured in millimeters (0-hour value) using a vernier caliper. Skin thickness was measured again at 72\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm 4\\)\u003c/span\u003e\u003c/span\u003e hours by the same operator. Difference in skin thickness (72 hour \u0026ndash; 0 hour) was calculated as per WOAH protocol. An animal with increase in skin thickness of 4mm or more due to bovine PPD (Single intradermal test; SIT) or PPD-B minus PPD-A (Single intradermal comparative cervical tuberculin test; SICCT) was considered as a reactor. Bovine tuberculin PPD consisted of 3000 I.U. /dose while avian tuberculin PPD consisted of 2500 I.U./dose (WOAH, 2009). DST antigen was used at a concentration of 20ug/dose [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. An animal with increase in skin thickness by 2mm or more due to DST antigen was considered as a reactor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe agreement between SIT, SICCT and DST was estimated using Cohen\u0026rsquo;s Kappa [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. We carried out an exploratory analysis to test for associations between measured risk factors and positive status for the three test types. Risk factor model development was carried out in R [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. For each test we built a multivariate logistic regression model using purposeful selection [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Firstly, we carried out a univariate screen with a generous cutoff for acceptance of 0.1, followed by a stepwise procedure (forwards and backwards) to select a parsimonious set of explanatory variables. Finally, to adjust for between herd variation in our study population we use a herd level random effect (intercept), estimating our final model using the lme4 R package [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The Hosmer-Lemeshow test as implemented in the Resource Selection R package was used to test for lack of model fit and classification ability of models was assessed through the area under the curve (AUC) of the Receiver-Operator-Characteristic (ROC) curve - calculated by the ROCR R package [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Statistical significance was considered if p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Effect sizes were calculated and reported as odds ratios (OR) with 95% confidence intervals.\u003c/p\u003e \u003cp\u003eThe Walter-Hui latent class model was implemented in stan, estimated by Hamiltonian MCMC and analyzed in R using the rstan package [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The key assumption of the Walter-Hui model is conditional independence between tests, i.e., the probability of a test \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(k\\)\u003c/span\u003e\u003c/span\u003e being positive for individual (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(P\\left({T}_{i,k}=1\\right)\\)\u003c/span\u003e\u003c/span\u003e only depends on the latent (true) disease status of the individual (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(D\\in \\{0,1\\}\\)\u003c/span\u003e\u003c/span\u003e) and not the response of the other tests. Under this assumption the (conditional) probability of a positive test result given that an animal is infected (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(D=1\\)\u003c/span\u003e\u003c/span\u003e) or disease free (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(D=0\\)\u003c/span\u003e\u003c/span\u003e) can then be modelled by a single parameter for each test:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$P\\left({T}_{i,k}=1|D=1\\right)={a}_{k}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$P\\left({T}_{i,k}=1|D=0\\right)={b}_{k}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eand the sensitivity of test \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(k\\)\u003c/span\u003e\u003c/span\u003e will then simply be \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{k}\\)\u003c/span\u003e\u003c/span\u003e and the specificity will be \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(1-{b}_{k}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFollowing [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and to allow for an extension to model any conditional dependence between tests, we parameterised the model using a probit (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varPhi\\)\u003c/span\u003e\u003c/span\u003e) link function:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$P\\left({T}_{t,k}=1|D=1\\right)=\\varPhi \\left({a}_{t,1}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$P\\left({T}_{t,k}=1|D=0\\right)=\\varPhi \\left({a}_{t,0}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTo ensure numerical stability we restrict the sensitivity parameters (on the probit scale) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{t,1}\\)\u003c/span\u003e\u003c/span\u003e to the range \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left[-8,8\\right]\\)\u003c/span\u003e\u003c/span\u003e. To force identifiability of the model (and avoid the label switching problem common with this class of models due to the symmetry of the likelihood) we make the assumption that no tests have a specificity of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026lt;50\\text{\\%}\\)\u003c/span\u003e\u003c/span\u003e or sensitivity \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026lt;20\\text{\\%}\\)\u003c/span\u003e\u003c/span\u003e and thus restrict \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{t,0}\\)\u003c/span\u003e\u003c/span\u003e to the half-range \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left[-8,0\\right]\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{t,1}\\)\u003c/span\u003e\u003c/span\u003e to the range \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left[-1,0\\right]\\)\u003c/span\u003e\u003c/span\u003e. Convergence was assessed through visual inspection of the chains and standard diagnostic statistics (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\widehat{R}=1\\)\u003c/span\u003e\u003c/span\u003e for all parameters after \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(2,000\\)\u003c/span\u003e\u003c/span\u003e iterations for 8 chains). Estimated parameters are presented as median posterior values with 95% Bayesian credible intervals (CI).\u003c/p\u003e \u003cp\u003eModel fit \u0026ndash; and the central assumption of conditional dependence \u0026ndash; was also assessed through calculating the pairwise probability of agreement between each pair of diagnostic tests (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(k,k{\\prime }\\)\u003c/span\u003e\u003c/span\u003e):\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$${\\alpha }_{k,k{\\prime }}=\\frac{\\sum _{i=1}^{N}{T}_{i,k}{T}_{i,k{\\prime }}-\\left(1-{T}_{i,k}\\right)\\left(1-{T}_{i,k{\\prime }}\\right)}{N}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAny systematic differences between the observed (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\alpha }_{k,k{\\prime }}\\)\u003c/span\u003e\u003c/span\u003e) and expected values from the estimated model (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\alpha }_{k,k{\\prime }}^{\\text{*}}\\)\u003c/span\u003e\u003c/span\u003e) would imply a violation of the assumption of conditional independence. We can use draws from the posterior predictive distribution of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\alpha }_{k,k{\\prime }}^{\\text{*}}\\)\u003c/span\u003e\u003c/span\u003e for our fitted model to form a posterior predictive p-value [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]:\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$P\\left({\\alpha }_{k,k{\\prime }}^{\\text{*}}\u0026gt;{\\alpha }_{k,k{\\prime }}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIf the model fits well, the value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(P\\left({\\alpha }_{k,k{\\prime }}^{\\text{*}}\u0026gt;{\\alpha }_{k,k{\\prime }}\\right)\\)\u003c/span\u003e\u003c/span\u003e is expected to be close to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(0.5\\)\u003c/span\u003e\u003c/span\u003e, with extreme values close to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(0\\)\u003c/span\u003e\u003c/span\u003e or \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(1\\)\u003c/span\u003e\u003c/span\u003e indicating a lack of fit (i.e., \u0026lt;\u0026thinsp;0.05 or \u0026gt;\u0026thinsp;0.95).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOut of 543 female buffaloes screened for bTB in 49 organized dairy farms, 37 (7%) animals in both the districts were found to be reactors by SIT (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Only 4 (\u0026lt;\u0026thinsp;1%) animals were found reactors with the SICCT test; three of which did not show any response to PPD-A. By DST, 18 (3%) buffaloes were found to be reactors as per the cut-off of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e2mm (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Considering SIT alone, 30 and 7 buffaloes were reactors in A and B district, respectively. Of the 30 reactor animals identified in district A, 21 were adult animals while eight were heifers and one was a calf. In district B, all seven reactors identified by SIT were adults. Of the 37 reactors identified by SIT, 23 (4%) were milch animals. Of the DST positive animals, 16 (12 adults, 02 heifers, and 02 calves) were in district A while two (both adults) were in district B. Seventeen animals showed higher PPD A response. Of the SICCT positive animals, three were adults and one was heifer and all were from district A. Forty-five animals had a skin thickness difference of 2\u0026ndash;3 mm by SIT; these animals were categorized as inconclusive reactors. PPD A response was high in 32 buffaloes (27 adults, 4 heifers, 1 calf) and it varied from 4\u0026ndash;20 mm. Of the DST reactors, it was observed that six animals were negative by SIT. Out of the 49 dairy farms whose animals were tested, reactor animals by at least one of the tests used were identified in only 18 dairy farms. None of the tested animals in the remaining 31 dairy farms showed reactivity to tuberculins or DST.\u003c/p\u003e\n\u003cp\u003eThe data was also analyzed with respect to the magnitude of skin thickness seen at 72 hours post-administration of antigens. With bovine PPD alone, 27 animals in both the districts had differences in skin thickness between 4-6mm while in the remaining 10 animals the difference was more than 7mm. Using SICCT, all four reactors had 4-6mm difference in skin thickness. With DST, 10 buffaloes were in the range of 2-3mm and 8 buffaloes showed 4-6mm increase in skin thickness (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFrom the present study, we also observed discrepancies in reactions induced by the antigens injected (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Twenty-five animals that were reactors by SIT were negative by SICCT and DST (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). None of the animals tested was found reactor both by SICCT and DST. Interestingly, there were a total of 6 animals that were DST positive but SIT negative. Combining two tests i.e., SIT and DST, 43 animals were found to be reactors. It was observed that SIT and DST showed moderate agreement with Cohen\u0026apos;s Kappa of 0.41 (95% CI: 0.23, 0.59) for test positive cases (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Whereas, low Kappa agreement of 0.18 (95% CI: 0.02, 0.34) was found between SIT and SICCT.\u003c/p\u003e\n\u003cp\u003eAfter variable selection only two putative risk factors \u0026ndash; the geographic region where the herd was located and lactation stage were associated with positivity to the SIT and DST tests. For the SICCT response, no variables passed the univariate screen likely due to the sparsity of positive results for this test. The final SIT and DST models including herd level random effect terms showed no evidence for a lack of fit with the Hosmer-Lemeshow test with p-values of 0.23 and 0.99, respectively. The SIT model demonstrates an excellent classification ability with an AUC\u0026thinsp;=\u0026thinsp;0.88, while the DST model is outstanding with AUC\u0026thinsp;=\u0026thinsp;0.96. However, this discrimination ability for the DST response appears to be driven purely be the random effects with neither of the selected risk factors being statistically significant. Results for the SICCT model are summarized in Table\u0026nbsp;2, which suggests that animals in district B have a reduced risk of being test positive to the SIT test (OR\u0026thinsp;=\u0026thinsp;0.11, 0.02\u0026ndash;0.55, 95% CI) while animals in the second lactation stage have a higher risk of testing positive (OR 4.39, 1.41\u0026ndash;13.7, 95% CI).\u003c/p\u003e\n\u003cp\u003eThe fitted latent class model demonstrates an excellent agreement with the apparent reactor status across all infected and uninfected herds (Supplementary Fig.\u0026nbsp;1). The entire observed values lie within the 95% posterior predictive intervals of the estimated model (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Posterior predictive p-values \u0026ndash; based on the pairwise probability of agreement between each pair of diagnostic tests - are all within a 95% interval with no evidence of conditional dependence between the tests based on this data.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe latent class model estimates distinct differences in performance between the three diagnostic tests \u0026ndash; albeit with relatively large overlaps in the posterior distributions (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Our analysis suggests that the DST test has lower diagnostic sensitivity (39%, 23\u0026ndash;62 95% CI) compared to SIT (67% (43\u0026ndash;96, 95% CI) but comparable specificity (99%, 96\u0026ndash;100 95% CI) to the SICCT test (99.7, 98.4\u0026ndash;100 95% CI) (Table\u0026nbsp;3). The DST has an apparent intermediate sensitivity to SIT and SICCT but with broad and overlapping predictive / credible intervals.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study was undertaken to compare tuberculin skin test with defined skin antigen in buffaloes. Selection of test(s) used for screening animals is critical for control programs. Hence, it is crucial to validate new diagnostics in buffaloes which can accurately detect the case in order to develop effective control strategies for bTB in buffaloes. Lack of a gold standard test to define positive and negative animals in a herd is a concern in determining the accuracy of any screening test.\u003c/p\u003e \u003cp\u003eHere, we tested female buffaloes (\u003cem\u003eBubalus bubalis\u003c/em\u003e) in organized dairy farms in two districts of Haryana, India using the WOAH-recommended standard SIT and SICCT skin tests. It is important to note here the crucial differences in these tests in order to understand the limitations while interpreting results. While the SIT offers high sensitivity, PPD-B also elicits an inflammatory reaction in animals sensitized with non-tuberculous mycobacteria (NTM) due to the presence of cross-reactive antigens, resulting in decreased specificity of the test. In order to help improve diagnostic specificity, the SICCT is used wherein both bovine and avian tuberculins are injected simultaneously side-by-side into the skin of the neck. This allows better discrimination than the SIT between animals infected with members of the MTBC and those sensitized to tuberculin due to exposure to members of the \u003cem\u003eM. avium\u003c/em\u003e complex or to environmental non-pathogenic mycobacteria. In regions with high prevalence of NTM, the SICCT is recommended; however, increased specificity of SICCT implies a drop in sensitivity. Moreover, these tuberculin antigens are unable to differentiate infection from BCG vaccination due to the presence of cross-reactive antigens. We compared the performance of a peptide-based defined antigen skin test (DST) with that of the tuberculins in a larger cohort of female buffaloes. This test has previously been assessed in both experimental and field trials in cross-bred cattle [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. A proof-of-concept study to evaluate DIVA capability of DST was performed in cross-bred cattle in India [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Recently, a pilot DST dose optimization trial was also conducted in domestic water buffaloes [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the present study, a total of 543 female buffaloes from organized dairy farms in two districts in the state of Haryana were skin tested for diagnosis of bTB using both tuberculins and DST. A total of 6.81% and 0.73% buffaloes in two districts were found to be reactors by SIT and SICCT, respectively. In the present study, the peptide-based DST detected six additional animals as reactors which were negative by SIT and SICCT. Similarly, 25 animals detected as reactors by SIT were found non-reactors by DST. All SICCT positive animals were also DST non-reactors. These results raise important questions on performance of these tests and the underlying reasons behind these discrepancies. Firstly, the tuberculins themselves are crude reagents that are derived from culture supernatant of \u003cem\u003eM. bovis\u003c/em\u003eAN5 strain (PPD-B) and \u003cem\u003eM. avium\u003c/em\u003e (PPD-A). A study comparing the potency of PPD-A and -B from various suppliers found that while PPD-A quality was relatively constant, PPD-B quality varied considerably, highlighting a lack of proper standardization [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. We would like to mention that a single batch of PPDs was used in the current study. It has also long been recognized that exposure to environmental mycobacteria confounds the accurate interpretation of tuberculin-based skin test results [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Prevalence of environmental mycobacteria is particularly high in regions that have tropical weather [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]; the same is the case with the state where the study was carried out.\u003c/p\u003e \u003cp\u003eOur latent class analysis suggests that the DST has a sensitivity that is intermediate between the SICCT and SIT test and specificity comparable to the SICCT test. The uncertainty in these estimates, due to the relatively small sample and group sizes, is reflected in overlapping posterior distributions for diagnostic parameters and wide credible intervals for the bTB infection within each herd. The sample size may also contribute to the lack of evidence for conditional dependence between the diagnostic tests. The absence of any such evidence made exploration of alternative models to estimate such dependence between tests moot for this study, but cannot be ruled out. All three diagnostic tests measure different aspects of the animal\u0026rsquo;s immune response to \u003cem\u003eM. bovis\u003c/em\u003e rather than presence or absence of the organism itself. Indeed, the SIT and SICCT tests are designed to be dependent on each other in the sense that the avian response is used to increase the specificity of SICCT at the expense of sensitivity. The extent to which the sensitivity and specificity of the SIT and SICCT tests trade off against each other within this particular population is difficult to assess in the absence of microbiological or pathological confirmation of infection. The triangulation we carry out here against the DST test provides some insight into this trade-off, but validation of these estimates requires further studies including necropsies of reactor animals and culture of causative pathogens to both directly address this issue and begin to understand the other discrepancies in response between these alternative diagnostic tests.\u003c/p\u003e \u003cp\u003eIt has been reported that specific antigens such as ESAT-6, CFP-10, and Rv3615c are present in field strains of \u003cem\u003eM. bovis\u003c/em\u003e but are either absent or not immunogenic in BCG vaccine strain [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Srinivasan \u003cem\u003eet al\u003c/em\u003e. (2019) assessed a peptide cocktail composed of 40-mer peptides covering the sequences of ESAT-6, CFP-10, and Rv3615c with a 20-residue overlap [peptide cocktail\u0026ndash;long (PCL)] and a recombinant fusion protein of the same three antigens in animals experimentally infected with \u003cem\u003eM. bovis\u003c/em\u003e and naive animals [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The cocktail was administered intradermally in the neck region. The results suggested that PCL performed better than the fusion protein and both were able to accurately detect infected animals and could differentiate them from uninfected animals with high sensitivity and specificity. Defined skin test, peptide-based cocktail of the above-mentioned antigens, has the potential to differentiate infected animals from BCG vaccinated animals i.e., DIVA capability [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]; the tuberculins lack the said potential.\u003c/p\u003e \u003cp\u003eFew animals in this study exhibited higher response to both bovine and avian PPDs and in some animals, PPD-A response was higher than PPD-B. It may be possible to get such a response from environmental mycobacteria. Proano-Perez \u003cem\u003eet al\u003c/em\u003e. (2009) also reported that few animals exhibited higher PPD-A response and this response decreased significantly with age [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These authors opined that \u003cem\u003eMycobacterium avium\u003c/em\u003e complex (MAC) is more prevalent in the environment than \u003cem\u003eM. bovis\u003c/em\u003e, and young animals are in contact with these environmental mycobacteria early in life. It is to mention that recent studies report the presence of \u003cem\u003eM. orygis\u003c/em\u003e rather than \u003cem\u003eM. bovis\u003c/em\u003e in cattle and/or buffalo [\u003cspan additionalcitationids=\"CR40 CR41\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In south-east Asia \u003cem\u003eM. orygis\u003c/em\u003e has been isolated from cattle and monkey in Bangladesh [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In India, \u003cem\u003eM. orygis\u003c/em\u003e has also been reported from dairy in cattle [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The accuracy of DST for diagnosing infection other than \u003cem\u003eM. bovis\u003c/em\u003e has yet not been established. Further studies ae needed to correlate the skin test reactions or outcome with the isolation of pathogen from the animals. Such studies in large cohorts can help to determine the performance of tuberculins or DST.\u003c/p\u003e \u003cp\u003eIn conclusion, combined with the existing limitations of non-standardized and varying performance characteristics of current diagnostic tests, there is an urgent need for well‐standardized skin tests to enable accurate monitoring of bovine tuberculosis over time. Defined antigen skin tests such as the peptide-based cocktail used in this study are specific and also provide the much-needed DIVA capability of implementation of vaccine-based intervention strategies in LMICs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ebTB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Bovine Tuberculosis\u003c/p\u003e\n\u003cp\u003eDST\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Defined Antigen Skin Test\u003c/p\u003e\n\u003cp\u003ePPD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Purified Protein Derivative\u003c/p\u003e\n\u003cp\u003eWOAH\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;World Organization of Animal Health\u003c/p\u003e\n\u003cp\u003eSIT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Single Intradermal Test\u003c/p\u003e\n\u003cp\u003eSICCT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Single Intradermal Comparative Cervical Test\u003c/p\u003e\n\u003cp\u003eMTBC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Mycobacterium Tuberculosis Complex\u003c/p\u003e\n\u003cp\u003eLMICs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Lower Middle-Income Countries\u003c/p\u003e\n\u003cp\u003eDIVA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Differentiating Infected from Vaccinated Animal\u003c/p\u003e\n\u003cp\u003eIAEC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Institutional Animal Ethics Committee\u003c/p\u003e\n\u003cp\u003eNTM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Non-Tuberculous Mycobacterium\u003c/p\u003e\n\u003cp\u003eBCG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Bacille Calmette and Guerin\u003c/p\u003e\n\u003cp\u003eMAC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Mycobacterium Avium Complex\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are thankful to the Deputy Directors and Veterinary Surgeons of Department of Animal Husbandry and Dairying, Haryana of both districts for their help in selection of dairy farms and animal testing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNJ, VK, SMB, SS, MV and DBa conceptualized the study. MK, TK, BLJ, DA, MS conducted the testing of animals in field. MK, NJ, YB and AC did the statistical analysis. MK and NJ prepared the first draft. All authors contributed to the article and approved the submitted version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are thankful to the Department of Biotechnology, Government of India (BT/ADV/Bovine tuberculosis/2018 dates 29.09.2018) and Bill \u0026amp; Melinda Gates Foundation (OPP1176950) for providing funds to conduct this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are presented in the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Institutional Animal Ethics Committee (IAEC) vide proceeding no. VCC/IAEC/1630-58 dated 26.07.2018 of the Lala Lajpat Rai Veterinary and Animal Sciences University (LUVAS, Hisar, Haryana, India). All methods were performed in accordance with the relevant guidelines and regulations of IAEC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCollins JD. Tuberculosis in cattle: strategic planning for the future. Vet Microbiol. 2006;112(2-4):369-81. \u003c/li\u003e\n\u003cli\u003eUne Y, Mori T. Tuberculosis as a zoonosis from a veterinary perspective. Comp Immunol Microbiol Infect Dis. 2007;30(5\u0026ndash;6):415-25. \u003c/li\u003e\n\u003cli\u003eGood M, Duignan A. 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Vet Sci. 2021;8(11):246. \u003c/li\u003e\n\u003cli\u003eCooney R, Kazda J, Quinn J, Cook BR, M\u0026uuml;ller K, Monaghan ML. Environmental mycobacteria in Ireland as a source of non-specific sensitisation to tuberculins. Ir Vet J. 1999;41:363-66.\u003c/li\u003e\n\u003cli\u003eKumar T, Singh M, Jangir BL, Arora D, Srinivasan S, Bidhan D, Yadav DC, Veerasami M, Bakker D, Kapur V, Jindal N. A defined antigen skin test for diagnosis of bovine tuberculosis in domestic water buffaloes (\u003cem\u003eBubalus bubalis\u003c/em\u003e). Front Vet Sci. 2021;8:669898. \u003c/li\u003e\n\u003cli\u003eCohen J. A coefficient of agreement of normal scale. Educ Psychol Meas. 1960;20:37-46. \u003c/li\u003e\n\u003cli\u003eR Core Team. R: A language and environment for statistical computing. R Foundation for statistical computing, Vienna, Austria. 2021. \u003c/li\u003e\n\u003cli\u003eBursac Z, Gauss CH, Williams DK, Hosmer DW. Purposeful selection of variables in logistic regression. 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Bovine tuberculosis in cattle: Vaccines, DIVA tests, and host biomarker discovery. Annu Rev Anim Biosci. 2016;4:87-109. \u003c/li\u003e\n\u003cli\u003eProano-Perez F, Benitez-Ortiz W, Celi-Erazo M, Ron-Garrido L, Benitez-Capistros R, Portaels F, Rigouts L, Linden A. Comparative intradermal tuberculin test in dairy cattle in the North of Ecuador and risk factors associated with bovine tuberculosis. Am J Trop Med Hyg. 2009;81(6):1103\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eGey van Pittius NC, Perrett KD, Michel AL, Keet DF, Hlokwe T, Streicher EM, Warren RM, van Helden PD. Infection of African buffalo (\u003cem\u003eSyncerus caffer\u003c/em\u003e) by oryx bacillus, a rare member of the antelope clade of the Mycobacterium tuberculosis complex. J Wildl Dis. 2012;48(4):849-57. \u003c/li\u003e\n\u003cli\u003eGey van Pittius NC, van Helden PD, Warren RM. Characterization of \u003cem\u003eMycobacterium orygis\u003c/em\u003e. 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Transbound Emerg Dis. 2017;64(6):1965-69. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eAgreement of SIT with SICCT and DST\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"29.032258064516128%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"34.40860215053763%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSICCT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"36.55913978494624%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDST\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"29.032258064516128%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; Negative\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.129032258064516%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Positive \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.204301075268816%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; Negative\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.35483870967742%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"29.032258064516128%\"\u003e\n \u003cp\u003eSIT negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003e506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.129032258064516%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.204301075268816%\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.35483870967742%\"\u003e\n \u003cp\u003e06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"29.032258064516128%\"\u003e\n \u003cp\u003eSIT positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003e\u0026nbsp; 33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.129032258064516%\"\u003e\n \u003cp\u003e04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.204301075268816%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.35483870967742%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"29.032258064516128%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003e539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.129032258064516%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.204301075268816%\"\u003e\n \u003cp\u003e525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.35483870967742%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"29.032258064516128%\"\u003e\n \u003cp\u003eCohen\u0026rsquo;s Kappa\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"34.40860215053763%\"\u003e\n \u003cp\u003e0.18 (0.02, 0.34), p = 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"36.55913978494624%\"\u003e\n \u003cp\u003e0.41 (0.23, 0.59), p = 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSIT, Single intradermal test; SICCT, Single intradermal comparative cervical test; DST, Defined skin test\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eComparison of tuberculin\u0026rsquo;s and defined skin test and associated risk factors for bovine tuberculosis in buffaloes.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.02, 0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eLactation stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.82, 11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e4.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e1.41, 13.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.16, 4.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e3.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.91, 10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e5+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.44, 5.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eEstimated sensitivity and specificity of bTB diagnostics from latent class analysis with 95% Bayesian credible intervals (CI)\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25.84269662921348%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.07865168539326%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.07865168539326%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25.84269662921348%\"\u003e\n \u003cp\u003eSIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.07865168539326%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;67% (43-96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.07865168539326%\"\u003e\n \u003cp\u003e95.9 (92-99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25.84269662921348%\"\u003e\n \u003cp\u003eSICCT\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.07865168539326%\"\u003e\n \u003cp\u003e19% (16 -29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.07865168539326%\"\u003e\n \u003cp\u003e99.7 (98-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25.84269662921348%\"\u003e\n \u003cp\u003eDST \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.07865168539326%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;39% (23-62) \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.07865168539326%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;99% (96-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSIT, Single intradermal test; SICCT, Single intradermal comparative cervical test; DST, Defined skin test\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Bovine tuberculosis, buffaloes, Haryana, India, SIT, SICCT, DST.","lastPublishedDoi":"10.21203/rs.3.rs-2752899/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2752899/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eBovine tuberculosis (bTB) is a chronic disease that results from infection with any member of the \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex and infected animals are typically diagnosed withtuberculin-based intradermal skin tests per World Organization of Animal Health or similar guidelines. Peptide-based defined skin test (DST) antigens, comprising of ESAT-6, CFP-10 and Rv3615c, are able to differentiate infected from BCG-vaccinated animals and sensitively and specifically identify tuberculin reactor cattle, but their performance in buffaloes remained unknown. To assess the comparative performance of the DST with the tuberculin-based single intradermal test (SIT) and the single intradermal comparative cervical test (SICCT), we screened 543 female buffaloes from 49 organized dairy farms in two districts of Haryana state in India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe results show that 37 (7%), 4 (1%) and 18 (3%) buffaloes were reactors with the SIT, SICCT and DST, respectively. Of the 37 SIT reactors, four were positive with SICCT and 12 were positive with the DST. The results further show that none of the animals tested positive with all three tests, and 6 DST positive animals were SIT negative. Together, a total of 43 animals were reactors with SIT, DST, or both, and the two assays showed moderate agreement (Cohen'sKappa 0.41; 95% CI: 0.23, 0.59). In contrast, only slight agreement (Cohen’s Kappa 0.18; 95% CI: 0.02, 0.34) was observed between SIT and SICCT. Latent class analyses reveal test specificities of 95% for SIT and 99% each for DST and SICCT, but considerably lower sensitivities of 67%, 39%, and 19% for SIT, DST, and SICCT, respectively, albeit with broad and overlapping credible intervals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eTaken together, our investigation suggests that DST has a test specificity comparable with SICCT, and sensitivity intermediate between SIT and SICCT for the identification of buffaloes suspected of tuberculosis. Our studies also highlight an urgent need for future well-powered trials with detailed necropsy with immunological and microbiological profiling of reactor and non-reactor animals to better define the underlying drivers for the large observed discrepancies in assay performance, particularly between SIT and SICCT.\u003c/p\u003e","manuscriptTitle":"Comparative analysis of tuberculin and defined antigen skin tests for the detection of bovine tuberculosis in buffaloes (Bubalus bubalis)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-04-03 15:01:45","doi":"10.21203/rs.3.rs-2752899/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-05-18T08:57:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-04-06T09:12:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-03-31T15:00:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Veterinary Research","date":"2023-03-29T16:12:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ceaaaf51-bf18-432a-ace7-9d81e567dd62","owner":[],"postedDate":"April 3rd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-02-26T15:05:26+00:00","versionOfRecord":{"articleIdentity":"rs-2752899","link":"https://doi.org/10.1186/s12917-024-03913-3","journal":{"identity":"bmc-veterinary-research","isVorOnly":false,"title":"BMC Veterinary Research"},"publishedOn":"2024-02-23 15:01:22","publishedOnDateReadable":"February 23rd, 2024"},"versionCreatedAt":"2023-04-03 15:01:45","video":"","vorDoi":"10.1186/s12917-024-03913-3","vorDoiUrl":"https://doi.org/10.1186/s12917-024-03913-3","workflowStages":[]},"version":"v1","identity":"rs-2752899","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2752899","identity":"rs-2752899","version":["v1"]},"buildId":"GqpaHPwrfC8PjnIFayRh5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}