Bayesian Zero-Inflated Predictive Modelling of Herd-Level Salmonella Prevalence for Risk-Based Surveillance

Prospective head-to-head study comparing 2 commercial interferon gamma release assays for the diagnosis of tuberculous uveitis

PURPOSE

To perform a head-to-head comparison of 2 commercially available interferon-gamma release assays, QuantiFERON-TB Gold In-Tube (Cellestis, Chadstone, Victoria, Australia) and T-SPOT.TB (Oxford Immunotech, Abingdon, Oxfordshire, UK), in the diagnosis of tuberculous uveitis.

DESIGN

Prospective cohort to study diagnostic accuracy.

METHODS

We recruited consecutive new patients who presented with uveitis to a tertiary institution over a 2-year period. All patients underwent complete ocular examination and systemic evaluation, including T-SPOT.TB, QuantiFERON-TB Gold In-Tube, and tuberculin skin test. Patients were followed-up for a minimum of 1 year after completion of antituberculous therapy where indicated. The main outcome measures were the sensitivity, specificity and accuracy of each test, estimated using Bayesian latent class analysis (presented with 95% Bayesian credible intervals) (Crl). Prior information was obtained from published meta-analyses for diagnostic tests: QuantiFERON Gold In-Tube sensitivity (0.64, 0.59-0.69) and specificity (0.99, 0.99-1.00); T-SPOT. tuberculosis sensitivity (0.50, 0.33-0.67) and specificity (0.91, 0.88-0.93).

RESULTS

From our study in patients with uveitis, QuantiFERON-TB Gold In-Tube was more specific but slightly less sensitive (sensitivity: 0.64, 0.60-0.69; specificity: 0.995, 0.988-0.999) than T-SPOT.TB (sensitivity: 0.67, 0.60-0.74; specificity: 0.91, 0.88-0.93). However, QuantiFERON Gold In-Tube was significantly more accurate in identifying true-positive tuberculous uveitis cases than was T-SPOT.TB among discordant cases (QuantiFERON Gold In-Tube positive 98% vs T-SPOT.TB positive 76%; ratio 1.28, 95% Crl: 1.11-1.72, ie, 95% Crl >1.0, statistically significant).

CONCLUSION

Based on statistical decision theory, our head-to-head study suggests that QuantiFERON-TB Gold In-Tube is the first-line test that should be performed in preference to T-SPOT.TB (and the tuberculin skin test) for diagnosing tuberculous uveitis.

Zero-inflated models for identifying disease risk factors when case detection is imperfect: application to highly pathogenic avian influenza H5N1 in Thailand

Logistic regression models integrating disease presence/absence data are widely used to identify risk factors for a given disease. However, when data arise from imperfect surveillance systems, the interpretation of results is confusing since explanatory variables can be related either to the occurrence of the disease or to the efficiency of the surveillance system. As an alternative, we present spatial and non-spatial zero-inflated Poisson (ZIP) regressions for modelling the number of highly pathogenic avian influenza (HPAI) H5N1 outbreaks that were reported at subdistrict level in Thailand during the second epidemic wave (July 3rd 2004 to May 5th 2005). The spatial ZIP model fitted the data more effectively than its non-spatial version. This model clarified the role of the different variables: for example, results suggested that human population density was not associated with the disease occurrence but was rather associated with the number of reported outbreaks given disease occurrence. In addition, these models allowed estimating that 902 (95% CI 881-922) subdistricts suffered at least one HPAI H5N1 outbreak in Thailand although only 779 were reported to veterinary authorities, leading to a general surveillance sensitivity of 86.4% (95% CI 84.5-88.4). Finally, the outputs of the spatial ZIP model revealed the spatial distribution of the probability that a subdistrict could have been a false negative. The methodology presented here can easily be adapted to other animal health contexts.