An estimated 1.5 million people died from tuberculosis (TB) disease in 2020, and TB is on course to retake its position as the largest cause of death by a single infectious agent (World Health Organisation, 2021). Fuelled by ongoing transmission through exhaled or expectorated Mycobacterium tuberculosis (Mtb) bacteria, TB incidence is declining at a rate of 1–2% per annum, which is too slow given the risk and scale of mortality (World Health Organisation, 2021; Ragonnet et al., 2021), lifelong impairment (Dodd et al., 2021; Alene et al., 2021), poverty (Pedrazzoli et al., 2021), and macroeconomic consequences (Silva et al., 2021). Problematically, most Mtb transmission in high-incidence settings remains unaccounted for (Dodd et al., 2021), with less than 1-in-10 occurrences of TB explained by transmission from a known contact (Glynn et al., 2015).

In recent decades the prevailing paradigm in TB policy held that symptoms and infectiousness commence simultaneously as part of ‘active disease’ (World Health Organization, 2020; World Health Organisation, 1974; Houben et al., 2019). As a consequence, a policy of passive case-finding (Golub et al., 2005), in which individuals are expected to attend a health facility with TB-related symptoms before receiving diagnosis and treatment, was relied upon to prevent deaths from TB, which it has (World Health Organisation, 2021; Mandal et al., 2017), and reduce incidence by interrupting transmission, which it has not (World Health Organisation, 2021).

Over the last decade, this classic paradigm of TB has been increasingly challenged (Barry et al., 2009; Drain et al., 2018; Behr et al., 2018). One important advance has been the finding in population surveys that not all individuals identified with bacteriologically confirmed TB report having symptoms such as cough at the time of screening for TB (Onozaki et al., 2015; Law et al., 2020). As such we can make a distinction between clinical and subclinical TB, where subclinical TB (sometimes referred to as ‘asymptomatic’ [Frascella et al., 2021] or ‘early’ TB [Kendall et al., 2021]) refers to individuals who have detectable Mtb bacteria in their sputum but do not experience, are not aware of, or do not report symptoms (Houben et al., 2019). In contrast, individuals with clinical TB disease report symptoms. We distinguish both disease states from Mtb infection, whereby individuals may test positive on a tuberculin skin test (TST) or interferon-gamma release assay (IGRA) but do not have bacteriologically confirmed disease.

Empirical data have shown that bacteriological state (i.e. whether Mtb is detectable in pulmonary secretions) is a strong predictor of the potential for transmission. For example, molecular epidemiological studies show that sputum smear-positive individuals (i.e. Mtb detected via microscopy) are 3–6 times more likely to be sources for TB disease in contacts compared to smear-negative individuals (Behr et al., 1999; Hernández-Garduño et al., 2004; Tostmann et al., 2008). Surveys of Mtb infection prevalence in household contacts provide similar values (Grzybowski et al., 1975). These studies focussed on passively diagnosed individuals with clinical disease. It is, however, increasingly clear that the presence of respiratory symptoms, such as a persistent cough, is not required for the exhalation of potentially Mtb-containing aerosols (Patterson and Wood, 2019; Asadi et al., 2019; Leung et al., 2020; Dinkele et al., 2022). Indeed, whilst recent empirical studies have suggested that tidal breathing may contribute significantly to Mtb transmission (Dinkele et al., 2022), exhalation of infectious aerosols appears unrelated to the presence of symptoms (Theron et al., 2020) or cough frequency (Williams et al., 2020) in TB patients. This supports the hypothesis that subclinical disease can contribute, potentially substantially, to transmission (Houben et al., 2019; Kendall et al., 2021; Dowdy et al., 2013).

A recent review found that about half of prevalent bacteriologically confirmed pulmonary TB disease is subclinical (Frascella et al., 2021) and it is becoming increasingly apparent that subclinical TB can persist for a long period without progressing to clinical disease (Richards et al., 2021; Ku et al., 2021). As individuals with subclinical TB will not be identified by current passive case-finding strategies, they will continue to contact susceptible individuals and, if infectious, transmit throughout their subclinical phase. It is therefore possible that those with subclinical TB may be a major contributor to ongoing, and unaccounted for, Mtb transmission. If this is the case, and if the ambitious goal to end TB as a global health problem by 2035 is to be met (World Health Organisation, 2022), TB policy needs to shift away from solely focussing on symptom-dependent case-finding (e.g. patient-initiated passive case-finding) towards strategies that are symptom-independent.

To motivate and inform such a shift in research and policy priorities, two key questions that to date remain unanswered must be addressed. Firstly, how infectious are individuals with subclinical TB compared to those with clinical TB per unit time, and, secondly, what is their contribution to overall transmission in the current TB epidemic?

In TB, data sources on the transmission potential from sputum smear-negative individuals relative to smear-positive (e.g. molecular epidemiological studies; Behr et al., 1999; Hernández-Garduño et al., 2004; Tostmann et al., 2008) have often been directly interpreted as relative infectiousness, which is incorrect (Kendall, 2021). Instead of representing the metric of interest, which is the potential for transmission per unit time for a particular group relative to a reference group (i.e. relative infectiousness), these data actually provided a relative estimate of cumulative exposure (as acknowledged by these studies’ authors; Behr et al., 1999; Hernández-Garduño et al., 2004; Tostmann et al., 2008). Cumulative exposure is a composite of relative infectiousness per unit time and disease duration (technically duration of infectiousness), which until now have been unavailable and can be hard to disentangle from each other (Kendall, 2021).

In this work we look to overcome these challenges by harnessing increased understanding of the natural history and prevalence of subclinical TB and re-analysing data from existing population studies.

By fitting a cumulative hazard model of infection to prevalence data amongst household contacts of subclinical and clinical index cases, we were able to provide quantitative estimates for the relative infectiousness per unit time of subclinical TB and its contribution to ongoing Mtb transmission. Despite wide uncertainty intervals, the raw data, as well as the results of our analysis, do not suggest subclinical TB is substantially less infectious than clinical TB. Given the high prevalence of subclinical TB found in surveys (Frascella et al., 2021), it is therefore likely that subclinical TB contributes substantially to ongoing Mtb transmission in high-burden settings.

Our results were relatively robust to the sensitivities that were performed. In two cases, that is, the removal of two studies (sensitivity analysis 1) and the use of a single model to account for all studies (sensitivity analysis 5), our estimates for the relative infectiousness of subclinical TB relative to clinical TB and the proportion of transmission from subclinical TB were lower than in the primary analysis. Our qualitative results and conclusions remain unchanged however. All other sensitivities resulted in higher estimates.

There are no other estimates for the infectiousness of subclinical TB relative to clinical TB in the literature with which to compare our results. Using data from the 2007 Viet Nam prevalence survey, however, Nguyen et al., 2023 find that among children aged 6–10 years, those living with clinical, smear-positive TB, and those living with subclinical, smear-positive TB had similarly increased risks of TST positivity compared with those living without TB. Moreover, a recent small study from Uganda found no evidence of a difference in cumulative infection rates in household contacts of patients who did or did not report symptoms (Baik et al., 2021). Our results are also in keeping with recent results from whole-genome sequencing (Xu et al., 2019) in which 36% of individuals likely transmitted Mtb before symptom onset, assuming a linear SNP mutation rate. We also note that previous hypothetical modelling of subclinical TB used assumed values for relative infectiousness that are in keeping with our estimated range (Dowdy et al., 2013; Arinaminpathy and Dowdy, 2015). More broadly, recent work on SARS-CoV-2 and malaria has similarly shown how ‘asymptomatic’ or ‘subpatent’ infections can be important drivers of transmission (Slater et al., 2019; Emery et al., 2020; Johansson et al., 2021), meaning a role for asymptomatic transmission would not be unique to TB.

Whilst we have presented a novel approach to investigating transmission from individuals with subclinical TB using pre-existing data, limitations in our methodology remain. Identifying relative infectiousness is challenging. Our estimates rely on studies which screened a minimum of 252,000 individuals for bacteriologically confirmed TB disease and 63,000 individuals for Mtb infection. Even at this scale, the small number of studies and diagnosed cases of TB still leads to substantial uncertainty, highlighting the challenge faced by single studies to estimate such values (Marks et al., 2019; Baik et al., 2021). Indeed, the paucity of the data provides an estimate that is consistent with subclinical TB being more infectious than clinical TB. Whilst we consider this to be implausible, we have avoided introducing priors that rule out this possibility. Instead we would emphasise that our results reflect the uncertainty of the data. The lower bound of our estimate precludes subclinical TB being significantly less infectious than clinical TB, while there is no evidence against subclinical TB being as infectious as clinical TB. Despite such uncertainty, this study brings together the best currently available epidemiological data which, combined with appropriate analysis techniques, provides a data-driven estimate for this important question.

Although infection studies in household contacts have provided a novel window into transmission from subclinical individuals, it is not possible to establish a transmission link between presumed index cases and infections amongst household contacts using molecular methods (Kendall, 2021). Such household contact studies are therefore liable to biases and our study necessarily inherits such limitations. For example, whilst our model does use a background rate of infection as a baseline from which to estimate the additional force of infection from presumed index cases within the household, there remains the residual risk that certain household types may systematically contain more or less infections from transmission outside the household than on average.

An important limitation of our cumulative hazard model is the assumption that index cases only ever had the disease type they were diagnosed with during screening (e.g. sputum smear-positive, subclinical). Instead, it is more likely that individuals will fluctuate between being, for example, subclinical and clinical (Richards et al., 2021). The impact such additional dynamics would have on our results remains uncertain since they would depend on the detailed model of tuberculosis natural history assumed. Such a model would require additional data to prevent the need for additional assumptions.

We estimated the contribution of subclinical TB to transmission at the population level, including transmission outside the household, using information on relative infectiousness inferred from household contact studies. A more refined estimate may need to take additional factors into account. For example, it is likely that, whilst inside the household the contact rates for subclinical and clinical individuals are likely to be similar, contact with individuals outside the household may differ (Glynn et al., 2020).

We defined subclinical and clinical TB as being culture and/or NAAT-positive and responding negatively or positively to an initial symptom screen, respectively. In practice subclinical and clinical TB are part of a continuous spectrum and alternative definitions could be defined according to different criteria. Here we have used the definition most closely aligned with the methodology of the majority of prevalence surveys, which is consistent with other studies of subclinical TB (Frascella et al., 2021) and pragmatic for inclusion of future surveys.

Meta-analyses were used to provide ranges for several quantities of interest. Whilst the heterogeneity for the relative infectiousness of subclinical and smear-negative TB were low (I² = 0% and I² = 36%, respectively), the heterogeneity for the proportion of prevalent TB that is subclinical and the proportions of subclinical and clinical TB that are smear-positive were high (I² = 95%, I² = 81% and I² = 89%, respectively). As such, we have used the more conservative prediction interval (as opposed to credible interval) to reflect this heterogeneity in the final results (Riley et al., 2011; Deeks et al., 2021).

Finally, our data are from populations with a low prevalence of HIV co-infection and the HIV status of individuals with TB was mostly unavailable, making a sub-analysis by HIV and antiretroviral (ART) status impossible. Whilst the subclinical TB presentation is likely affected by HIV in terms of duration and prevalence, it is unknown whether or by how much the relative differences in duration and prevalence between subclinical and clinical TB also change (Frascella et al., 2021; National Department of Health - South Africa, 2021). If they exist, any such differences by HIV-coinfection status are likely to be reduced by effective viral suppression, which an estimated two-thirds of people living with HIV have achieved (UNAIDS, 2021). So while it remains highly valuable to accumulate additional relevant data (Baik et al., 2021), we feel our main findings are broadly robust to this limitation.

Our observation that reported symptoms are a poor proxy for infectiousness fits with historical and contemporary observations that symptom-independent TB screening and treatment policies can reduce TB burden at higher rates than usually seen under DOTS (Marks et al., 2019; Krivinka et al., 1974). This is in keeping with increasing data showing that symptoms, in particular the classic TB symptom of cough, are not closely correlated to the amount of Mtb exhaled (Williams et al., 2020) and observations of other pathogens, including SARS-CoV-2 infection (Emery et al., 2020; Johansson et al., 2021; Liu et al., 2020).

Whilst earlier diagnosis (i.e. before symptom onset) will likely bring individual-level benefits in terms of mortality and extent of post-TB sequelae (Allwood et al., 2019), the question of potential population benefits has hampered decisions from policymakers and funders on whether to invest resources in technologies and strategies that can identify subclinical TB. Our results suggest that a non-trivial proportion of all transmission would likely be unaffected by strategies that are insensitive to subclinical TB.

As our results show that subclinical TB likely contributes substantially to transmission, an increased emphasis on symptom agnostic screening in, for example, the TB screening guidelines (World Health Organization, 2021) should be considered, as should the inclusion of subclinical TB in the planned update of WHO case definitions. Target Product Profiles for diagnostic tools should consider all infectious TB, regardless of whether individuals are experiencing or aware of symptoms, and interventions using such tools should be critically evaluated for their impact on Mtb transmission and cost-effectiveness. While symptom-independent tools exist for screening (Williams et al., 2020; Marks et al., 2019; Yoon et al., 2017; Madhani et al., 2020; Scriba et al., 2021; Williams et al., 2014), specificity, costs, and logistics remain an obstacle. In addition, individuals are usually required to produce sputum as part of a confirmatory test, which around half of eligible adults in the general population are unable to do (Marks et al., 2019). Screening or diagnostic technologies that are symptom- as well as sputum-independent, while remaining low-tech and low-cost, remain the goal. Indeed, the advent of bio-aerosol measurements in TB may uncover additional infectious individuals whose sputum-based bacteriological test is negative, although these tools require validation in larger populations (Williams et al., 2020; Williams et al., 2014; Nathavitharana et al., 2022). Any bio-aerosol-positive, sputum-negative individuals are more likely to be subclinical and as such would mean we underestimated the contribution of subclinical TB to global Mtb transmission, even if their relative infectiousness may be lower than sputum-positive TB. As new diagnostic approaches are developed to capture the spectrum of TB disease, policymakers will need to decide on how to treat subclinical TB. In treatment, as in diagnosis, it is key that more tailored approaches are developed and tested so as to prevent over- or undertreatment of individuals with subclinical TB (Esmail et al., 2022).

Replication data and analysis scripts are available at GitHub (copy archived at Emery JC, 2022).

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Author details

1. Jon C Emery

   TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom

   Contribution

   Software, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   Jon.Emery@lshtm.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6644-7604

2. Peter J Dodd

   School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom

   Contribution

   Software, Formal analysis, Methodology, Writing – review and editing

   Competing interests

   has received consultancy fees from WHO (TB burden estimation) and participates as chair of SAB for NIHR grant on TBI screening. The author has no other competing interests to declare

3. Sayera Banu

   International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

4. Beatrice Frascella

   School of Public Health, Vita-Salute San Raffaele University, Milan, Italy

   Contribution

   Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

5. Frances L Garden

   1. South West Sydney Clinical Campuses, University of New South Wales, Sydney, Australia
   2. Ingham Institute of Applied Medical Research, Sydney, Australia

   Contribution

   Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

6. Katherine C Horton

   TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

7. Shahed Hossain

   James P. Grant School of Public Health, BRAC University, Dhaka, Bangladesh

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

8. Irwin Law

   Global Tuberculosis Programme, World Health Organization, Geneva, Switzerland

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

9. Frank van Leth

   1. Department of Health Sciences, VU University, Amsterdam, Netherlands
   2. Amsterdam Public Health Research Institute, Amsterdam, Netherlands

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

10. Guy B Marks

    1. South West Sydney Clinical Campuses, University of New South Wales, Sydney, Australia
    2. Woolcock Institute of Medical Research, Sydney, Australia

    Contribution

    Writing – review and editing

    Competing interests

    acts as President of the International Union Against TB & Lung Disease. The author has no other competing interests to declare

11. Hoa Binh Nguyen

    National Lung Hospital, National Tuberculosis Control Program, Ha Noi, Viet Nam

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

12. Hai Viet Nguyen

    National Lung Hospital, National Tuberculosis Control Program, Ha Noi, Viet Nam

    Contribution

    Formal analysis, Writing – review and editing

    Competing interests

    No competing interests declared

13. Ikushi Onozaki

    Research Institute of Tuberculosis, Japan Anti-Tuberculosis Association, Tokyo, Japan

    Contribution

    Writing – review and editing

    Competing interests

    has received grants from National TB Program Cambodia, WHO and DFAT (Australia) and has received consulting fees from WHO Myanmar Office. The author is on the Board of the Directors, UNION IUATLD and WHO’s Global Task Force on TB Impact Measurement. The author has no other competing interests to declare

14. Maria Imelda D Quelapio

    Tropical Disease Foundation, Makati City, Philippines

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

15. Alexandra S Richards

    TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

16. Nabila Shaikh

    1. TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
    2. Sanofi Pasteur, Reading, United Kingdom

    Contribution

    Visualization, Writing – review and editing

    Competing interests

    received a grant from the Bill and Melinda Gates Foundation and owns stock/stock options in Sanofi Aventis Pharma LTD. The author has no other competing interests to declare

17. Edine W Tiemersma

    KNCV Tuberculosis Foundation, The Hague, Netherlands

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

18. Richard G White

    TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

19. Khalequ Zaman

    International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

20. Frank Cobelens

    Department of Global Health and Amsterdam Institute for Global Health and Development, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands

    Contribution

    Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

21. Rein MGJ Houben

    TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom

    Contribution

    Conceptualization, Formal analysis, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-4132-7467

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