Abstract
Tuberculosis (TB) is the greatest cause of infectious disease deaths worldwide. In highly affected countries, effective TB control requires prompt identification and treatment of individuals with active disease. We examined the performance of TB case-finding in low- and middle-income countries based on a comprehensive analysis of TB diagnosis data reported to the World Health Organization. Using these data we estimated the total number of individuals correctly and incorrectly diagnosed with TB, for 111 countries with a collective 6.8 million TB notifications in 2023. Here we estimate that in 2023, 2.05 (1.83–2.27) million individuals were incorrectly diagnosed with TB (false-positives), and 1.00 (0.71–1.36) million received a false-negative diagnosis, at an assumed 25% disease prevalence among individuals evaluated for TB. As many as three of every ten TB notifications may not have TB, and many individuals with TB receive false-negative diagnoses. Compared to current diagnostic performance, scaling-up new polymerase chain reaction-based diagnostics would substantially reduce under-diagnosis but only produce a small reduction in false-positive diagnoses. Major improvements in TB diagnosis will likely require higher-sensitivity bacteriological tests combined with reduced reliance on clinical diagnosis.
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Data availability
All data used in this study were drawn from publicly available datasets (downloadable from https://www.who.int/teams/global-tuberculosis-programme/data, ‘Case notifications’ and ‘WHO TB burden estimates’ files), as well as published studies listed in Supplementary Table 3.
Code availability
Analytic code used to implement the analysis is available via Zenodo at https://doi.org/10.5281/zenodo.16414104 (ref. 58).
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Acknowledgements
This work was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (award no. U01AI152084 to N.A.M.). The funder had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank N. Arinaminpathy for feedback on an earlier version of this manuscript.
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A.v.L.T., P.J.D., T.C. and N.A.M. conceptualized the research. A.v.L.T., T.C. and N.A.M. developed the methodology used, and P.J.D., N.A.M. and T.C. validated the methodology. The research was supervised by N.A.M., and coordinated by N.A.M. A.v.L.T. curated data, performed the formal analysis, did the investigation and administered the project. A.v.L.T. and N.A.M. made the visualizations. N.A.M. undertook funding acquisition, and resources were provided by N.A.M. The original draft was written by A.v.L.T. and N.A.M., and reviewed, edited and approved by all authors. A.v.L.T. and N.A.M. have full access to all data in the study. All authors read and approved the final version of the article.
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Extended data
Extended Data Fig. 1 Estimates of the probability of false-positive diagnosis and false-negative diagnosis for different values of initial TB prevalence and the percentage of notifications that are laboratory confirmed, for the three alternative specifications.
Panel A: Probability of false-positive diagnosis under an alternative analytic specification assuming an optimistic ROC curve for clinical diagnosis (see Supplementary Fig. 2). Panel B: Probability of false-negative diagnosis under an alternative analytic specification assuming an optimistic ROC curve for clinical diagnosis (see Supplementary Fig. 2). Panel C: Probability of false-positive diagnosis under an alternative analytic specification assuming a pessimistic ROC curve for clinical diagnosis (see Supplementary Fig. 2). Panel D: Probability of false-negative diagnosis under an alternative analytic specification assuming a pessimistic ROC curve for clinical diagnosis (see Supplementary Fig. 2). Panel E: Probability of false-positive diagnosis under an alternative analytic specification assuming that 25% of individuals evaluated for TB are only evaluated clinically. Panel F: Probability of false-negative diagnosis under an alternative analytic specification assuming that 25% of individuals evaluated for TB are only evaluated clinically. Probability of false-positive diagnosis defined as the probability that someone diagnosed with TB does not have TB (1 – PPV). Probability of false-negative diagnosis defined as the probability that someone diagnosed as not having TB does have TB (1 – NPV). Colors indicate different probability levels, indicated by values shown in each panel. All inputs apart from the sensitivity and specificity of clinical diagnosis held at their global average values. Sensitivity and specificity of clinical diagnosis calculated as a function of other values, based on the ROC curve shown in Supplementary Fig. 2. ‘+’ symbol in center of each plot represents mean values from the main analysis.
Extended Data Fig. 2 Schematic of TB diagnosis model.
Figure presents a flow diagram showing how individuals can progress through the diagnostic algorithm. Boxes represent steps in the algorithm. ‘+’ symbols indicate a positive result on an individual step of the algorithm, ‘–‘ symbols indicate a negative result on an individual step of the algorithm. Red lines indicate individuals with TB, blue lines indicate individuals without TB. Dashed lines represent a mechanism explored in sensitivity analysis, but not in the main analysis.
Supplementary information
Supplementary Information
Supplementary Tables 1–3 and Figs. 1 and 2.
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van Lieshout Titan, A., Dodd, P.J., Cohen, T. et al. Estimating the number of incorrect tuberculosis diagnoses in low- and middle-income countries. Nat Med (2026). https://doi.org/10.1038/s41591-025-04097-5
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DOI: https://doi.org/10.1038/s41591-025-04097-5
