PLoS ONE (Jan 2016)
Prevalence and Diagnosis of Latent Tuberculosis Infection in Young Children in the Absence of a Gold Standard.
Abstract
For adequate disease control the World Health Organization has proposed the diagnosis and treatment of latent tuberculous infection (LTBI) in groups of risk of developing the disease such as children. There is no gold standard (GS) test for the diagnosis of LTBI. The objective of this study was to estimate the prevalence of LTBI in young children in contact with a household case of tuberculosis (TB-HCC) and determine the accuracy and precision of the Tuberculin Skin Test (TST) and QuantiFERON-TB Gold in-tube (QFT) used in the absence of a GS.We conducted a cross-sectional study in children up to 6 years of age in Manaus/Brazil during the years 2009-2010. All the children had been vaccinated with the BCG and were classified into two groups according to the presence of a TB-HCC or no known contact with tuberculosis (TB). The variables studied were: the TST and QFT results and the intensity and length of exposure to the index tuberculosis case. We used the latent class model to determine the prevalence of LTBI and the accuracy of the tests.Fifty percent of the children with TB-HCC had LTBI, with the prevalence depending on the intensity and length of exposure to the index case. The sensitivity and specificity of TST were 73% [95% confidence interval (CI): 53-91] and 97% (95%CI: 89-100), respectively, versus 53% (95%CI: 41-66) and 81% (95%CI:71-90) for QFT. The positive predictive value of TST in children with TB-HCC was 91% (95%CI: 61-99), being 74% for QFT (95%CI: 47-95).This is one of the first studies to estimate the prevalence of LTBI in children and the parameters of the main diagnostic tests using a latent class model. Our results suggest that children in contact with an index case have a high risk of infection. The accuracy and the predictive value of the two tests did not significantly differ. Combined use of the two tests showed scarce improvement in the diagnosis of LTBI.