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Evaluating Latent Tuberculosis Infection Test Performance Using Latent Class Analysis in a TB and HIV Endemic Setting

Author

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  • Shahieda Adams

    (School of Public Health and Family Medicine, Division of Occupational Medicine, University of Cape Town, Observatory 7925, South Africa)

  • Rodney Ehrlich

    (School of Public Health and Family Medicine, Division of Occupational Medicine, University of Cape Town, Observatory 7925, South Africa)

  • Roslynn Baatjies

    (Department of Environmental and Occupational Studies, Faculty of Applied Sciences, Cape Peninsula University of Technology, Cape Town 8000, South Africa)

  • Nandini Dendukuri

    (Division of Clinical Epidemiology, McGill University Health Centre—Research Institute, Montreal, QC H4A 3J1, Canada)

  • Zhuoyu Wang

    (Division of Clinical Epidemiology, McGill University Health Centre—Research Institute, Montreal, QC H4A 3J1, Canada)

  • Keertan Dheda

    (Centre for Lung Infection and Immunity, Division of Pulmonology, Department of Medicine and UCT Lung Institute & South African MRC/UCT Centre for the Study of Antimicrobial Resistance, University of Cape Town, Observatory, Cape Town 7925, South Africa
    Faculty of Infectious and Tropical Diseases, Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK)

Abstract

Background: Given the lack of a gold standard for latent tuberculosis infection (LTBI) and paucity of performance data from endemic settings, we compared test performance of the tuberculin skin test (TST) and two interferon-gamma-release assays (IGRAs) among health-care workers (HCWs) using latent class analysis. The study was conducted in Cape Town, South Africa, a tuberculosis and human immunodeficiency virus (HIV) endemic setting Methods: 505 HCWs were screened for LTBI using TST, QuantiFERON-gold-in-tube (QFT-GIT) and T-SPOT.TB. A latent class model utilizing prior information on test characteristics was used to estimate test performance. Results: LTBI prevalence (95% credible interval) was 81% (71–88%). TST (10 mm cut-point) had highest sensitivity (93% (90–96%)) but lowest specificity (57%, (43–71%)). QFT-GIT sensitivity was 80% (74–91%) and specificity 96% (94–98%), and for TSPOT.TB, 74% (67–84%) and 96% (89–99%) respectively. Positive predictive values were high for IGRAs (90%) and TST (99%). All tests displayed low negative predictive values (range 47–66%). A composite rule using both TST and QFT-GIT greatly improved negative predictive value to 90% (range 80–97%). Conclusion: In an endemic setting a positive TST or IGRA was highly predictive of LTBI, while a combination of TST and IGRA had high rule-out value. These data inform the utility of LTBI-related immunodiagnostic tests in TB and HIV endemic settings.

Suggested Citation

  • Shahieda Adams & Rodney Ehrlich & Roslynn Baatjies & Nandini Dendukuri & Zhuoyu Wang & Keertan Dheda, 2019. "Evaluating Latent Tuberculosis Infection Test Performance Using Latent Class Analysis in a TB and HIV Endemic Setting," IJERPH, MDPI, vol. 16(16), pages 1-11, August.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:16:p:2912-:d:257549
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    References listed on IDEAS

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    1. Nandini Dendukuri & Lawrence Joseph, 2001. "Bayesian Approaches to Modeling the Conditional Dependence Between Multiple Diagnostic Tests," Biometrics, The International Biometric Society, vol. 57(1), pages 158-167, March.
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