Risk factors for LTBI among US Army recruits were similar whether measured by the TST or 1 of the 2 commercially available IGRAs. RFQ prediction models were constructed using variables including birth in a country with a high prevalence of TB, close contact with an active TB case, history of living with a family member born outside the United States, and history of a prior positive TB skin test result. Use of these 4 variables resulted in 79% sensitivity, 92% specificity, and an AUC of 0.871 in predicting a positive TB skin test. Targeted testing of only those with a positive response to 1 of these 4 questions would reduce testing by >90%, increasing the efficiency of the testing program. Prediction models for the IGRAs had similar specificities and reductions in testing but had lower sensitivities and AUCs.
This is the first study to compare the effectiveness of targeted testing using either IGRA as an end point in any population, as well as the first to compare targeted testing as a predictive tool using IGRA and TST criterion standards. As in previous studies that used TST result as the outcome, birth in a TB-endemic country was found to be a strong predictor of a positive test result [7
]. Close contact with a TB case, foreign-born family members, and prior positive TST have also been associated with LTBI in previous studies [7
]. Other variables have also sometimes been associated with a positive TST result, including travel [23
], smoking [6
], male sex [5
], health care work [4
], and education [8
], but these were not found to be important predictors of LTBI in this study. Race and ethnic group did not contribute meaningfully as predictors after adjusting for other factors. The only study to assess use of a questionnaire to target testing in a similar heterogeneous adult population was among college students in Virginia [9
]. That study showed that using only the 2 variables of foreign birth and close contact with a patient with TB resulted in a sensitivity of 81.6% and a specificity of 91%. Although our 2-variable model had lower sensitivity than this, we found comparable sensitivity and specificity using a 4-variable model. Two studies in pediatric populations also found that using 4 or 5 questions to identify high-risk patients with LTBI resulted in similar sensitivities and specificities as those seen in this study [7
]. Prediction models of LTBI among contacts of active TB cases have had more modest reductions in testing, because of a higher pretest probability of infection and less concern about false-positive results than about false-negative results [5
This study has several important strengths. Despite other differences (such as age), the population was a good geographic representation of the underlying low prevalence, heterogeneous US source population (data not shown). Also, the 3 forms of TB testing allowed direct comparisons of the effectiveness of targeted testing to predict LTBI as measured by each test, which has not been done previously. There are also several limitations to this study; the most important is the lack of a gold standard in evaluating the presence of LTBI. The potential for false-positive TST results due to receipt of BCG, cross-reactivity to nontuberculous mycobacteria, and other factors is well known [24
]. The IGRAs are also known to have limitations in sensitivity and specificity [11
], and it is uncertain whether the predictive capability of the IGRAs is better than that of the TST. The small number of positive test results also may have led to less power to detect small differences in the groups studied. Misclassification of exposures and outcomes was also possible in association with measurement error, although the outcomes were probably better controlled in this study than they would be in practice. Finally, this study is not expected to be generalizable to higher risk populations, including those with HIV infection or other immunosuppressive conditions, hospital workers, or prison guards.
An important implication of this study is that targeted testing of heterogeneous populations is feasible and effective using the TST or either IGRA. Validation of targeted testing had previously only been performed in a few populations, and it had only been done using the TST. In this study, targeted testing was seen to be less predictive of commercially available IGRAs than the TST but still had effectiveness comparable to universal testing. Although the IGRAs may be more specific tests, their use in low-prevalence populations will still result in predominantly false-positive results if testing is not targeted. Therefore, all testing of low-prevalence populations should be targeted, regardless of the choice of the diagnostic test used.
This study demonstrates that targeted testing using an RFQ is a useful strategy to test for LTBI and can be operationalized with acceptable performance characteristics using any of the commercially-available tests, consistent with CDC recommendations [25
]. Although, the RFQ in this study was better at predicting a positive TST result than for either IGRA, it does not demonstrate that the TST is superior for use in conjunction with targeted testing for several reasons. This study may have been somewhat biased by the use of CDC RSI, because the factors under evaluation were also correlated with a positive result of both the RFQ and the TST. Similarly, the use of a history of a prior positive TST result may bias the prediction model in favor of the TST, although it is noted that the RFQ still had superior sensitivity and specificity in predicting TST, compared with the IGRAs, even when discarding this as a risk factor. This is seen in the 3 variable models in , , and . It is concerning that 56% of positive IGRA results would be missed by the use of the RFQ as compared with 21% for the TST. However, the vast majority of these discordant positive results were positive for only 1 of the 3 tests and had no identifiable risk factors, suggesting that most were false-positive results. In addition, the known risk factors for TB had weaker associations with the IGRAs than with the TST, and no new risk factors were identified using the IGRAs. Finally, the RSI used for the TST increased the specificity of the test, decreasing the number of false-positive results. Because this is not currently done for the IGRAs, this may bias targeted testing against them in this type of evaluation. Therefore, the most likely explanation for the lower predictive ability in a low-prevalence population such as this is false-positive IGRA results. This suggests that risk-stratified interpretation of IGRAs, as is done for the TST, may be useful. It also suggests that IGRAs should not be used to replace targeted testing, because testing in low-prevalence populations will still result in false-positive results, even if the specificity is very high.
As with the TST, testing with IGRAs will result in false-positive results if IGRAs are used in low-prevalence populations. Regardless of the test used, targeted testing is critical in reducing unnecessary testing and treatment and is consistent with CDC guidelines [25
]. Targeted testing in this population would reduce testing by >90%, which would in turn reduce costs of the screening program and adverse events from therapy while still maintaining effectiveness. Some studies suggest that more than 50% of all positive results in low prevalence populations be false-positive results due to nontuberculous mycobacterium and other factors [3
]. Targeted testing should therefore reduce treatment for people with false-positive results who derive no benefit from LTBI therapy but still incur the risk of adverse events.
Future studies suggested by this study include further analysis to improve targeted testing in US and other populations. Analysis to determine the magnitude and relative cost-effectiveness of targeted testing programs for the IGRAs versus the TST is also warranted. Prediction models in other populations may also be considered, including health care workers, prison guards, long-term travelers, and military service members deploying to TB-endemic countries [28
]. Finally, studies comparing the long-term rate of progression to active TB among TST- and IGRA-positive persons will allow a more accurate determination of LTBI status and risk of progression to active TB.