Figures
Abstract
Introduction
The utility of T-cell based interferon-gamma release assays for the diagnosis of latent tuberculosis infection remains unclear in settings with a high burden of tuberculosis.
Objectives
To determine risk factors associated with positive QuantiFERON-TB Gold In-Tube (QFT-GIT) and tuberculin skin test (TST) results and the level of agreement between the tests; to explore the hypotheses that positivity in QFT-GIT is more related to recent infection and less affected by HIV than the TST.
Methods
Adult household contacts of tuberculosis patients were invited to participate in a cross-sectional study across 24 communities in Zambia and South Africa. HIV, QFT-GIT and TST tests were done. A questionnaire was used to assess risk factors.
Results
A total of 2,220 contacts were seen. 1,803 individuals had interpretable results for both tests, 1,147 (63.6%) were QFT-GIT positive while 725 (40.2%) were TST positive. Agreement between the tests was low (kappa = 0.24). QFT-GIT and TST results were associated with increasing age (adjusted OR [aOR] for each 10 year increase for QFT-GIT 1.15; 95% CI: 1.06–1.25, and for TST aOR: 1.10; 95% CI 1.01–1.20). HIV positivity was less common among those with positive results on QFT-GIT (aOR: 0.51; 95% CI: 0.39–0.67) and TST (aOR: 0.61; 95% CI: 0.46–0.82). Smear positivity of the index case was associated with QFT-GIT (aOR: 1.25; 95% CI: 0.90–1.74) and TST (aOR: 1.39; 95% CI: 0.98–1.98) results. We found little evidence in our data to support our hypotheses.
Citation: Shanaube K, Hargreaves J, Fielding K, Schaap A, Lawrence K-A, Hensen B, et al. (2011) Risk Factors Associated with Positive QuantiFERON-TB Gold In-Tube and Tuberculin Skin Tests Results in Zambia and South Africa. PLoS ONE 6(4): e18206. https://doi.org/10.1371/journal.pone.0018206
Editor: Madhukar Pai, McGill University, Canada
Received: October 29, 2010; Accepted: February 23, 2011; Published: April 4, 2011
Copyright: © 2011 Shanaube et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This manuscript/work was supported by the Foundation for Innovative New Diagnostics (FIND). Further support was provided by a subcontract from Johns Hopkins University with funds provided (Grant No. 19790.01) from the Bill and Melinda Gates Foundation via the CREATE (Consortium to Respond Effectively to AIDS and Tuberculosis Epidemic) consortium. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Tuberculosis continues to be a major public health problem in sub-Sahara Africa. The incidence of tuberculosis [1] is being accelerated by high rates of HIV co-infection [2], [3]. Targeted testing and treatment of latent tuberculosis infection (LTBI) especially among HIV positive individuals is an important strategy to reduce the incidence of tuberculosis [4].
Currently, LTBI detection relies on the tuberculin skin test (TST) in most countries with high incidence of tuberculosis although this is not routinely performed and is perceived as a barrier to accessing TB preventive therapy [5]. However, TST has many reported limitations. These include low specificity in populations with high levels of BCG vaccination or significant exposure to non-tuberculosis mycobacteria (NTM), and reduced sensitivity in immunocompromised individuals such as those with HIV infection [6], [7].
T-cell based interferon-gamma release assays (IGRAs) such as the QuantiFERON-TB Gold In-Tube (QFT-GIT) can now also be used to detect LTBI [8]. IGRAs are in-vitro blood tests based on interferon-γ release after T-cell stimulation by antigens more specific to Mycobacterium tuberculosis (Mtb) than the purified protein derivative used in TST. IGRAs are therefore designed to have high specificity that is unaffected by BCG vaccination and cross-reactivity with most NTM [8]. There is also some evidence of greater sensitivity among HIV positive individuals [9], [10] compared to the TST. Current literature suggests that IGRAs detect responses of effector T-cells that have recently encountered antigens in vivo, while TST reflects the mobilization of a wider spectrum of memory T-cells that are long-lived [11].
The use of IGRAs in developed countries is rapidly expanding but their performance in settings with a high prevalence of tuberculosis and HIV still requires further research [12], [13]. There is growing evidence that IGRAs performance varies in different settings [14]. In high-TB burden settings, the results of IGRAs may be influenced by factors that affect the immune response [15] such as HIV co-infection, BCG vaccination, malnutrition, tropical infections and widespread exposure to NTM. Recent studies done in low and middle income settings [16] showed a large reduction in the proportion of positive test results for both QFT-GIT and TSPOT in HIV infected individuals. Another recent study in Bangladesh showed that malnutrition and helminth infections were associated with indeterminate QFT-GIT results in children [17].
Significant challenges exist in directly assessing whether IGRAs are superior to TST in diagnosing LTBI as there remains no gold standard against which to compare either test. In the absence of a practical gold standard for Mtb infection, exposure to an infectious TB index case has been used as a surrogate measure of infection [18], [19], [20], [21]. Studies from low-TB burden countries indicate that the IGRAs correlate better, along a gradient of exposure, than the TST [14]. Nevertheless, in high-TB burden settings, the TST performs reasonably well and correlates as well, or better, with proxy measures of exposure [14]. There are limited data on the comparison of QFT-GIT and TST in relation to Mtb exposure as a surrogate measure of infection and the influence of age [19].
In this study, we describe the prevalence of tuberculous infection among household contacts of recently diagnosed tuberculosis patients as measured by QFT-GIT and TST in 24 communities with a high prevalence of TB and HIV in Zambia and South Africa. We also determine risk factors associated with positive QFT-GIT and TST results and the level of agreement between the tests in each community. We use data from two recent TST surveys [22] to explore the correlation between community TB transmission and infection prevalence in contacts as measured by QFT-GIT and TST. A TST survey, if conducted correctly and technically interpretable, allows an estimation of the extent of Mtb transmission that has occurred in the community [23].
Finally, we formally assess the extent to which our results are compatible with expected findings on the basis of a number of prior hypotheses about the characteristics of each test. Previous studies have given rise to prevailing views about the expected performance of both TST and IGRAs [7], [11], [14], [16]. We therefore explore whether our data support the hypotheses that positivity in QFT-GIT is more related to recent infection and less affected by HIV than the TST.
Methods
Ethics statement
Ethics approval for the study was obtained from the research ethics committees of the University of Zambia, the London School of Hygiene and Tropical Medicine and Stellenbosch University. All individuals involved in the study gave written informed consent.
Study setting
This cross-sectional study was nested within a large community randomized trial of interventions to reduce tuberculosis transmission, the Zambia South Africa TB and AIDS Reduction Study, (ZAMSTAR) in 24 selected communities in Zambia and South Africa [24]. We defined a “community” as the population (minimum size of 25,000) accessing one tuberculosis diagnostic centre and this was the unit of randomization for the ZAMSTAR trial. The communities selected were in five provinces of Zambia (16 communities) and in Western Cape Province of South Africa (8 communities) and included both urban and rural communities. The design of the ZAMSTAR study is described elsewhere [24], [25].
Baseline measurement of tuberculous infection in all ZAMSTAR communities was estimated by means of TST surveys among primary school children [22]. These community-wide surveys served three objectives: to characterize ZAMSTAR communities, with regards to TB infection, in relative terms; to inform the randomization of the communities into intervention arms; and to provide data for one of ZAMSTAR's secondary outcomes.
Zambia and South Africa have among the highest tuberculosis incidence [26] and HIV seroprevalence rates [27] in Africa and globally. The estimated HIV prevalence in new tuberculosis cases is 70% [26].
Participants
From April 2007 to July 2008 we recruited newly notified adult tuberculosis cases from the 24 ZAMSTAR communities, subsequently referred to as index cases, to the study. All tuberculosis cases (pulmonary smear positive, smear negative or extrapulmonary) were eligible if recruited within a month after being notified in the tuberculosis register and started on treatment at a government clinic. We obtained written informed consent from those accepting to take part in the study. In addition, we sought permission from these index cases to visit their households where we invited household members to participate. We made at least three attempts to visit household members who were absent during the first visit to the household.
We defined household contacts as individuals at least 15 years old, who generally slept in the home, ate with the index case and who identified a common household head. We asked all household contacts for individual signed consent before participating in the study. This study focuses on this population of household contacts of newly diagnosed tuberculosis cases.
Measures
Consenting household contacts had blood drawn for HIV antibodies and QFT-GIT testing. Tuberculin skin tests were also performed. A standardized questionnaire was administered to all contacts by trained interviewers, who collected information on risk factors associated with tuberculous infection. Sputum microscopy for index cases was performed as part of the clinic routine services and the results were recorded in the TB registers.
HIV testing was done using the Abbot Murex HIV Ag/Ab combination ELISA (Murex Biotech, Dartford, United Kingdom). All individuals were encouraged to attend counseling and HIV testing at the local health centre. In South Africa, HIV positive individuals were advised to go for TB preventive therapy in accordance with National Tuberculosis Control Program guidelines [28] while in Zambia this is not yet government policy. However, in Zambia, preventive therapy was offered to eligible contacts through collaboration with another study operating in the ZAMSTAR sites.
QFT-GIT procedure.
QFT-GIT test was performed according to the manufacturer's instructions [29]. For four Zambian and all the South African communities, QFT-GIT processing was done centrally at our research laboratories in Lusaka and Stellenbosch University Medical School respectively. However, for twelve Zambian remote communities, QFT-GIT processing was decentralized. In these communities, blood samples were collected, incubated, separated and stored locally. Tubes were incubated for 16–24 hours at 37°C and plasma was harvested and frozen at –20°C. Frozen samples from these sites were transported monthly to the central laboratory in Lusaka where the ELISA was performed manually in batches.
TST procedure.
The skin testing was conducted using 2 TU (Tuberculin Units) of PPD RT23 with Tween, supplied by the Statens Serum Institut (Copenhagen, Denmark). All tests were administered and read by nurses who were trained according to the standard IUATLD protocol [23]. A dose of 0.1 ml was injected intradermally on the left forearm. Skin reactions were read using calipers 72 hours later. A positive TST was defined as an induration of ≥10 mm. Blood for QFT-GIT was drawn before TST was administered usually on the same day.
Statistical analysis
Data were double entered into a “Microsoft SQL Server” database and checked for errors. Analysis was performed using STATA (version 11.0).
The characteristics of the study population were described using frequencies and percentages for categorical variables and the median and interquartile range for quantitative variables. Prevalence of infection was defined as the number of QFT-GIT or TST positive results divided by the total number of individuals with interpretable (positive and negative) results. Individuals having missing TST or indeterminate QFT-GIT results were excluded from the analysis. These did not differ significantly from those that had interpretable results (results not shown). Furthermore, household contacts on TB treatment were excluded from analysis.
The level of agreement between test results was assessed for each community using Cohen's kappa coefficient. By convention, kappa values of less than 0.4 generally indicate poor agreement. Correlation between continuous interferon-γ values (IU/ml) and TST induration (mm) was assessed using Spearman's correlation coefficient for each community.
The distribution of positive reactions to each test in relation to established individual and household level risk factors for LTBI was described. The strength of relationship between risk factors and QFT-GIT/TST positivity was assessed using random effects logistic regression. The random effects approach specified the household of residence as the clustering variable. All models were adjusted for age, sex and community of residence. We present adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for each risk factor.
Finally, we formally assessed four hypotheses related to the expected performance of the tests. We explored whether our data were compatible with expected findings on the basis of a number of prior hypotheses about the characteristics of each test. Ideally, we would have had a gold standard measure of LTBI against which to compare both tests. However, no such test currently exists. Furthermore, the natural history of LTBI remains a source of debate [11] and as in other studies of LTBI we had no direct measure of exposure to Mtb either recently or in the past. However, we did have data on three proxies related to prior exposure to Mtb (age) or recent exposure to Mtb (proximity to and infectivity of the index case).
Thus, we tested: (i) whether HIV infection was associated with a negative TST result, among those with a positive QFT-GIT, since we expect HIV infection to cause more false negative results with TST than QFT-GIT. We restricted the analysis to individuals with a positive QFT-GIT result and used random effects logistic regression models as described previously.
We then explored: (ii) whether age was more strongly associated with a positive TST result than with a positive QFT-GIT result since we expected TST to be more likely to detect evidence of lifetime infection with Mtb while QFT-GIT was more likely to detect recent infections; (iii) whether living with a smear positive index case was more strongly associated with a positive QFT-GIT result than a positive TST result, since we expected that QFT-GIT would be more strongly associated with recent infections than TST; and (iv), for the same reason as (iii), whether sleeping in the same room as the index case was more strongly associated with QFT-GIT than TST positivity.
For these final three hypotheses we conducted matched-pairs analysis using conditional logistic regression in an approach similar to that used by Ewer and others (21), where the outcome is the result of the TST or QFT-GIT test. We used Wald tests to assess the strength of evidence for an interaction between the test used and the factor of interest (age, living with a smear positive index case, sleeping in same room as index case). In these models we specified the individual identifier to indicate the paired data, and calculated confidence intervals using robust standard errors taking account of household level clustering.
Results
Figure 1 shows the flow chart of the study participants. A total of 2220 contacts were recruited across the 24 communities. As shown in table 1, the study population was predominantly women (69.9%), most had attended secondary education (52.1%) and had no history of smoking (81.9%) or alcohol consumption (75.2%). 765/2036 (37.6%) of household contacts were HIV positive, and 766/1507 (50.8%) lived with a smear positive TB index case.
QFT-GIT (QuantiFERON-TB Gold In Tube) not done was due to refusal (18.4%), being absent (18.3%), insufficient blood samples (0.4%) or missing data (1.6%). TST (Tuberculin skin test) not done was due to refusal (5.6%), not returning for reading (3.6%) or missing data (0.8%). Individuals with QFT-GIT/TST not done and those with indeterminate QFT-GIT results were excluded from analysis.
A total of 1803 individuals had interpretable results available for both QFT-GIT and TST (figure 1). Of these, 1147 (63.6%) tested positive with QFT-GIT while 725 (40.2%) tested positive with TST (figure 1). In all but two communities infection prevalence as measured by QFT-GIT was higher than that of TST (table 2). Overall, infection prevalence as measured by TST was higher for South African communities (arithmetic mean 50%, range 24–77%) than for the Zambian communities (arithmetic mean 31%, range 7–73%). Results were similar for QFT-GIT (results not shown).
There were 577 (32%) individuals with concordant positive results and 508 (28.2%) had concordant negative results. QFT+/TST− discordant results were more common than QFT−/TST+ results (31.6% vs 8.2%). There was a low level of agreement overall in the 24 communities (% agreement = 60.2%; kappa = 0.24 (arithmetic mean 0.23, range −0.09–0.49)), and in each of the communities (range of kappas: 0–0.49) (table 2). Using different cut-off values for the TST did not improve overall test agreement in the 24 communities (kappa: 0.26, 0.24 and 0.14 for TST cutoffs of 5, 10 and 15 mm respectively). When results were stratified by HIV status, agreement appeared slightly better in HIV positive (% agreement = 65.1%; kappa 0.322) compared to HIV negative (% agreement = 57.4%; kappa 0.19) contacts. There was a positive correlation (correlation coefficient square, r2 = 0.372) between positive QFT-GIT results in contacts and infection prevalence results in children (figures 2), however, this was weaker for the TST (correlation coefficient square, r2 = 0.155) (figure 3).
Previous TST surveys among children were conducted within the same communities as those of contacts. Infection prevalence in children was defined as TST ≥10 mm.
Previous TST surveys among children were conducted within the same communities as those of contacts. Infection prevalence in contacts and children was defined as TST ≥10 mm.
Tables 3 and 4 show risk factors associated with positive QFT-GIT and TST results respectively. Both QFT-GIT and TST results were associated with increasing age (adjusted OR [aOR] for each 10 year increase for QFT-GIT 1.15; 95% CI: 1.06–1.25; p<0.001 for linear trend, and for TST aOR: 1.10; 95% CI 1.01–1.20; p = 0.025 for linear trend). HIV positivity was less common among those with positive results on QFT-GIT (aOR: 0.51; 95% CI: 0.39–0.67; p<0.001) and TST (aOR: 0.61; 95% CI: 0.46–0.82; p = 0.001).
There was some evidence of an association between smear positivity of the index and QFT-GIT (aOR: 1.25; 95% CI: 0.90–1.74) and TST (aOR: 1.39; 95% CI: 0.98–1.98) results.
Both QFT-GIT (aOR for household size: 1.04; 95% CI: 1.00–1.09, p = 0.65 for linear trend) and TST (aOR: 0.97; 95% CI: 0.93–1.01, p = 0.005 for linear trend) results were not associated with increasing household size.
Finally, we tested the four specific hypotheses described in the methods. As shown in table 5, we found little evidence to support the hypotheses that positivity in QFT-GIT is more related to recent infection and less affected by HIV than the TST.
Discussion
We conducted a large scale evaluation of the prevalence of LTBI as detected by TST and QFT-GIT among household contacts of tuberculosis patients in 24 high HIV and TB prevalence communities in Zambia and South Africa. Our findings suggest a high prevalence of LTBI among this population. QFT-GIT estimates were higher than those of TST in all but two communities. LTBI prevalence was higher in South African communities compared to the Zambian ones, as in previous findings [22].
LTBI was more common among older individuals and those who were HIV negative, similar to previous studies in this setting [9], [10], [30]. HIV positivity was less common among those with positive results on QFT-GIT and TST. We found little evidence to support the hypothesis that HIV infection was associated with TST negativity among QFT-GIT positive individuals as would have been expected if HIV causes more false negatives with TST than QFT-GIT. Both TST and QFT-GIT are prone to false negatives results among different population groups [9], [30]. In a study done in Zambia, low CD4+ counts in HIV positive TB patients were associated with increases in both indeterminate and false-negative QFT-GIT results [9]. Current evidence suggests that IGRAs perform similarly to the TST at identifying HIV-infected individuals with LTBI [16].
For both QFT-GIT and TST, prevalence of infection was higher in contacts exposed to smear positive index cases compared to smear negative ones, consistent with findings of other studies [31]. Sleeping proximity of the contact to the index case was not associated with either QFT-GIT or TST results. In contrast, a study done in Cape town found an association between Mtb contact scores and increasing exposure [19], simliar to findings in the Gambia [21]. Both of these studies had smaller sample sizes compared to our study and were done among HIV negative [19] or few HIV postive contacts [21]. There is growing evidence suggesting a stronger and better defined association between surrogate markers for TB exposure and QFT-GIT results in low TB incidence settings compared to high-TB incidence settings [14], [32], [33], [34] although this is still inconclusive.
Our results suggest that tuberculous infection in adults may often be unrelated to household transmission. It is well recognized that transmission of tuberculosis in high incidence settings occurs not only within households but in the community as well [35], [36] among various social locations [37]. A study in Zimbabwe found that the proportion of ELISpot positive contacts was not different from community controls [31]. In another study done in two communities in Zambia, almost 50% of community controls were QFT-GIT positive [38]. In our study, positive QFT-GIT results in contacts correlated well with infection prevalence results from previous TST surveys, providing further evidence that community transmission seems to play a bigger role in positivity than household exposure. However, in a large study in Colombia, IFN-γ responses to CFP-10 were consistently higher in household contacts of all ages compared to subjects in the source population [34]. Nevertheless, a seven day whole blood culture in-house assay was used, which primarily detects central memory responses. It has been argued that, in settings of high endemicity where a mixture of recent and old infections are commonly found, long term assays are more sensitive than those with shorter culture times [34].
We found little evidence in our matched pair analysis to support the idea that age was more strongly associated with a positive TST result than with a positive QFT-GIT result since we anticipated that TST was more likely to detect evidence of lifetime infection with Mtb while QFT-GIT was more likely to detect recent infections. Our results using conditional logistic regression showed that age was associated with positive QFT-GIT and TST results and there was a trend to increased responses with increasing age for both tests suggesting cumulative exposure to Mtb. In the study done in Colombia [34], exploration of IFN-γ variations by age revealed a trend to increased responses up to adulthood with CFP, but not with CFP-10, similar to observations in Uganda [39]. However, children were included in both of these studies.
We show a low level of agreement between the tests in all communities, consistent with findings of studies done in high-TB burden settings [19], [31], [40]. As IGRAs are designed to be more specific than TST, perfect agreement is not expected [41]. However, better agreement has been shown when the comparison is done within specific risk groups like HIV positives [42]. Although kappa statistics have been widely used as a measure of agreement between IGRAs and TST, alternative statistical approaches have recently been proposed such as latent class analysis [43] but have yet to gain wider acceptance. Similar to other studies reported from poor-resource settings [44], there were particularly high number of QFT-GIT+/TST- discordant results, in contrast to studies done in settings with low TB incidence [45], [46] where TST+/QFT-GIT- discordance is more common.
Our study had both strengths and limitations. Ours is among the first studies to conduct both TST and QFT-GIT tests using a large sample size which illustrates the realistic implementation of QFT-GIT in a setting with a high burden of TB and HIV. However, as for all studies of this nature we had no gold standard measure of LTBI against which to compare our tests. As such, we were unable to comment directly on the accuracy of either test, but rather to compare the findings of each test in relation to prior beliefs about their properties. It is plausible that our failure to prove our hypotheses may have been due to test limitations typical in such high-TB burden settings. In addition, individuals in these settings may have mixed infections due to multiple Mtb exposure.
Our results may have been severely compromised by missing data on some risk factors. Despite efforts to standardize TST training and reading across the two countries, use of different teams may have contributed to inter-reader variability. Although most contacts reported that they had never smoked or taken alcohol, we believe this was due to reporting bias. We had no data on likely exposure to NTM which may provide an alternative reason for false positive results, especially with TST.
Probably the most important characteristic of tests of LTBI is the extent to which they predict subsequent clinical tuberculosis. The data we present here are cross-sectional in nature; however, they come from a larger longitudinal study whose participants have been followed up for later development of active TB.
Conclusion
QFT-GIT may not be more sensitive than the TST to detect risk factors associated with tuberculous infection. Given the lack of strong associations with either TST or QFT-GIT with risk factors generally accepted to be related to household infectivity, these results suggest that tuberculous infection in adults in these communities may often be unrelated to household transmission. We found little evidence to support the hypotheses that positivity in QFT-GIT is more related to recent infection and less affected by HIV than the TST.
Acknowledgments
The authors acknowledge the invaluable contributions of the ZAMSTAR study team which made this study possible. We would also like to thank the Ministry of Health, District Health Management teams and the communities where the study was undertaken for their help and advice.
Author Contributions
Conceived and designed the experiments: HA NB CS PG-F. Performed the experiments: KS AM AS K-AL. Analyzed the data: KS JH KF PG-F. Contributed reagents/materials/analysis tools: AS K-AL CS. Wrote the paper: KS JH KF HA PG-F NB BH. Critical review of the paper: PG-F HA JH KF NB.
References
- 1. Dye C, Watt CJ, Bleed DM, Hosseini SM, Raviglione MC (2005) Evolution of Tuberculosis Control and Prospects for Reducing Tuberculosis Incidence, Prevalence, and Deaths Globally. JAMA 293: 2767–2775.
- 2. Daley CL, Small PM, Schecter GF, Schoolnik GK, McAdam RA, et al. (1992) An Outbreak of Tuberculosis with Accelerated Progression among Persons Infected with the Human Immunodeficiency Virus. New England Journal of Medicine 326: 231–235.
- 3. Guelar A, Gatell JM, Verdejo J, Podzamczer D, Lozano L, et al. (1993) A prospective study of the risk of tuberculosis among HIV-infected patients. Aids 7: 1345–1349.
- 4. Centers for Disease Control and Prevention (2000) Targeted tuberculin testing and treatment of latent tuberculosis infection. MMWR 49(No. RR-6).
- 5.
World Health Organization, Department of HIV/AIDS, Stop TB Department (2011) Guidelines for intensified tuberculosis case finding and isoniazid preventive therapy for people living with HIV in resource constrained settings.
- 6. Fine PE, Bruce J, Ponnighaus JM, Nkhosa P, Harawa A, et al. (1999) Tuberculin sensitivity: conversions and reversions in a rural African population. Int J Tuberc Lung Dis 3: 962–975.
- 7. Pai M, Zwerling A, Menzies D (2008) Systematic Review: T-Cell based Assays for the Diagnosis of Latent Tuberculosis Infection: An Update. Ann Intern Med 149: 177–184.
- 8. Pai M, Kalantri S, Dheda K (2006) New tools and emerging technologies for the diagnosis of tuberculosis: part I. Latent tuberculosis. Expert Rev Mol Diagn 6: 413–422.
- 9. Raby E, Moyo M, Devendra A, Banda J, De Haas P, et al. (2008) The effects of HIV on the sensitivity of a whole blood IFN-γ release assay in Zambian adults with active tuberculosis. PLoS ONE 3: e2489.
- 10. Chapman AL, Munkanta M, Wilkinson KA, Pathan AA, Ewer K, et al. (2002) Rapid detection of active and latent tuberculosis infection in HIV-positive individuals by enumeration of Mycobacterium tuberculosis-specific T cells. Aids 16: 2285–2293.
- 11. Mack U, Migliori GB, Sester M, Reider HL, Ehlers S, et al. (2009) LTBI: latent tuberculosis infection or lasting immune responses to M. tuberculosis? A TBNET consensus statement. Eur Respir J 33: 956–973.
- 12. Canadian Tuberculosis Committee (2007) Interferon gamma release assays for latent tuberculosis infection. An Advisory Committee Statement (ACS). Can Commun Dis Rep 33: 1–18.
- 13. Centers for Disease Control and Prevention (2005) Guidelines for preventing the transmission of Mycobacterium tuberculosis in health-care settings, 2005. MMWR 54(No. RR-17): 1–141.
- 14. Dheda K, Smit RVZ, Badri M, Pai M (2009) T-cell interferon-gamma release assays for the rapid immunodiagnosis of tuberculosis: clinical utility in high-burden vs. low-burden settings. Current Opinion in Pulmonary Medicine 15: 188–200.
- 15. Lee JY, Choi HJ, Park IN, Hong SB, Oh YM, et al. (2006) Comparison of two commercial interferon-gamma assays for diagnosing Mycobacterium tuberculosis infection. Eur Respir J 28: 24–30.
- 16. Cattamanchi A, Smith R, Steingart KR, Metcalfe JZ, Date A, et al. (2011) Interferon-gamma release assays for the diagnosis of latent tuberculosis infection in HIV-infected individuals: A systematic review and meta-analysis. J Acquir Immune Defic Syndr 56: 230–238.
- 17. Thomas TA, Mondal D, Noor Z, Liu L, Alam M, et al. (2010) Malnutrition and Helminth Infection Affect Performance of an Interferon {gamma}-Release Assay. Pediatrics 126: e1522–1529.
- 18. Lalvani A, Pareek M (2010) Interferon gamma release assays: principles and practice. Enfermedades infecciosas y Microbiologia Clinica 28: 245–252.
- 19. Hesseling AC, Mandalakas AM, Kirchner HL, Chegou NN, Marais BJ, et al. (2009) Highly discordant T cell responses in individuals with recent exposure to household tuberculosis. Thorax 64: 840–846.
- 20. Ewer K, Deeks J, Alvarez L, Bryant G, Waller S, et al. (2003) Comparison of T-cell-based assay with tuberculin skin test for diagnosis of Mycobacterium tuberculosis infection in a school tuberculosis outbreak. Lancet 361: 1168–1173.
- 21. Hill PC, Brookes RH, Fox A, Fielding K, Jeffries DJ, et al. (2004) Large-scale evaluation of enzyme-linked immunospot assay and skin test for diagnosis of Mycobacterium tuberculosis infection against a gradient of exposure in The Gambia. Clin Infect Dis 38: 966–973.
- 22. Shanaube K, Sismanidis C, Ayles H, Beyers N, Schaap A, et al. (2009) Annual Risk of Tuberculous Infection Using Different Methods in Communities with a High Prevalence of TB and HIV in Zambia and South Africa. PLoS ONE 4: e7749.
- 23. Rieder HL, Chadha VK, Nagelkerke NJD, Van Leth F, VanderWerf MJ (2011) Guidelines for conducting tuberculin skin test surveys in high-prevalence countries. Int J Tuber Lung Dis 15: S1–S25.
- 24. Ayles H, Sismanidis C, Beyers N, Hayes R, Godfrey-Faussett P (2008) ZAMSTAR, The Zambia South Africa TB and HIV Reduction study: Design of a 2×2 factorial community randomized trial. Trials 9: 63.
- 25. Sismanidis C, Moulton LH, Ayles H, Fielding K, Schaap A, et al. (2008) Restricted randomization of ZAMSTAR: a 2×2 factorial cluster randomized trial. Clinical Trials 5: 316–327.
- 26. World Health Organization (2009) Global tuberculosis control : epidemiology, strategy, financing report. WHO/HTM/TB/2009.411.
- 27.
Central Statistical Office (CSO), Ministry of Health (MOH), Tropical Diseases Research Centre (TDRC), University of Zambia, and Macro International Inc (2009) Zambia Demographic and Health Survey 2007. Calverton, , Maryland, USA: CSO and Macro International Inc.
- 28.
Department of Health Republic of South Africa (2010) Guidelines for tuberculosis preventive therapy among HIV infected individuals in South Africa.
- 29. Cellestis Ltd (2007) QuantiFERON -TB Gold (In-Tube Method) Package insert. Doc. No. 142 05990301B.
- 30. Duncan LE, Elliott AM, Hayes RJ, Hira SK, Tembo G, et al. (1995) Tuberculin sensitivity and HIV-1 status of patients attending a sexually transmitted diseases clinic in Lusaka, Zambia: a cross-sectional study. Transactions of the Royal Society of Tropical Medicine and Hygiene 89: 37–40.
- 31. Mutsvangwa J, Millington KA, Chaka K, Mavhudzi T, Cheung Y-B, et al. (2010) Identifying recent Mycobacterium tuberculosis transmission in the setting of high HIV and TB burden. Thorax 65: 315–320.
- 32. Zellweger JP, Zellweger A, Ansermet S, de Senarclens B, Wrighton-Smith P (2005) Contact tracing using a new T-cell-based test: better correlation with tuberculosis exposure than the tuberculin skin test. Int J Tuberc Lung Dis 9: 1242–1247.
- 33. Pai M, Gokhale K, Joshi R, Dogra S, Kalantri SP, et al. (2005) Mycobacterium tuberculosis infection in health care workers in rural India: comparison of a whole-blood, interferon-γ assay with tuberculin skin testing. JAMA 293: 2746–2755.
- 34. del Corral H, Paris SC, Marin ND, Marin DM, Lopez L, et al. (2009) IFN-gamma responses to Mycobacterium tuberculosis, risk of infection and disease in household contacts of tuberculosis patients in Colombia. PLoS ONE 4: e8257.
- 35. Schaaf HS, Michaelis IA, Richardson M, Booysen CN, Gie RP, et al. (2003) Adult-to-child transmission of tuberculosis: household or community contact? The International Journal of Tuberculosis and Lung Disease 7: 426–431(426).
- 36. Verver S, Warren RM, Munch Z, Richardson M, van der Spuy GD, et al. (2004) Proportion of tuberculosis transmission that takes place in households in a high-incidence area. Lancet 363: 212–214.
- 37. Classen CN, Warren R, Richardson M, Hauman JH, Gie RP, et al. (1999) Impact of social interactions in the community on the transmission of tuberculosis in a high incidence area. Thorax 54: 136–140.
- 38. Boccia D, Hargreaves J, Ayles H, Fielding K, Simwinga M, et al. (2009) Tuberculosis infection in Zambia: the association with relative wealth. Am J Trop Med Hyg 80: 1004–1011.
- 39. Lewinsohn DA, Zalwango S, Stein CM, Mayanja-Kizza H, Okwera A, et al. (2008) Whole Blood Interferon-Gamma Responses to Mycobacterium tuberculosis Antigens in Young Household Contacts of Persons with Tuberculosis in Uganda. PLoS ONE 3: e3407.
- 40. Mahomed H, Hughes EJ, Hawkridge T, Minnies D, Simon E, et al. (2006) Comparison of mantoux skin test with three generations of a whole blood IFN-gamma assay for tuberculosis infection. Int J Tuberc Lung Dis 10: 310–316.
- 41. Davidow AL, Affouf M (2008) Making sense of agreement among interferon-gamma release assays and tuberculosis skin testing. Int J Tuber Lung Dis 12: 152–159.
- 42. Balcells ME, Pérez CM, Chanqueo L, Lasso M, Villanueva M, et al. (2008) A comparative study of two different methods for the detection of latent tuberculosis in HIV-positive individuals in Chile. International journal of infectious diseases 12: 645–652.
- 43. Pai M, Dendukuri N, Wang L, Joshi R, Kalantri SP, et al. (August 2008) Improving the estimation of tuberculosis infection prevalence using T-cell-based assay and mixture models. The International Journal of Tuberculosis and Lung Disease 12: 895–902(898).
- 44. Adetifa IM, Lugos MD, Hammond A, Jeffries D, Donkor S, et al. (2007) Comparison of Two Interferon Gamma Release Assays in the diagnosis of Mycobacterium tuberculosis infection and disease in The Gambia. BMC Infect Dis 7: 122.
- 45. Kang YA, Lee HW, Yoon HI, Cho B, Han SK, et al. (2005) Discrepancy Between the Tuberculin Skin Test and the Whole-Blood Interferon-gamma Assay for the Diagnosis of Latent Tuberculosis Infection in an Intermediate Tuberculosis-Burden Country. JAMA 293: 2756–2761.
- 46. Harada N, Nakajima Y, Higuchi K, Sekiya Y, Rothel J, et al. (2006) Screening for tuberculosis infection using whole-blood interferon-gamma and Mantoux testing among Japanese healthcare workers. Infect Control Hosp Epidemiol 27: 442–448.