High rifampicin peak plasma concentrations accelerate the slow phase of bacterial decline in tuberculosis patients: Evidence for heteroresistance.

BACKGROUND: Antibiotic treatments are often associated with a late slowdown in bacterial killing. This separates the killing of bacteria into at least two distinct phases: a quick phase followed by a slower phase, the latter of which is linked to treatment success. Current mechanistic explanations for the in vitro slowdown are either antibiotic persistence or heteroresistance. Persistence is defined as the switching back and forth between susceptible and non-susceptible states, while heteroresistance is defined as the coexistence of bacteria with heterogeneous susceptibilities. Both are also thought to cause a slowdown in the decline of bacterial populations in patients and therefore complicate and prolong antibiotic treatments. Reduced bacterial death rates over time are also observed within tuberculosis patients, yet the mechanistic reasons for this are unknown and therefore the strategies to mitigate them are also unknown. METHODS AND FINDINGS: We analyse a dose ranging trial for rifampicin in tuberculosis patients and show that there is a slowdown in the decline of bacteria. We show that the late phase of bacterial killing depends more on the peak drug concentrations than the total drug exposure. We compare these to pharmacokinetic-pharmacodynamic models of rifampicin heteroresistance and persistence. We find that the observation on the slow phase's dependence on pharmacokinetic measures, specifically peak concentrations are only compatible with models of heteroresistance and incompatible with models of persistence. The quantitative agreement between heteroresistance models and observations is very good ([Formula: see text]). To corroborate the importance of the slowdown, we validate our results by estimating the time to sputum culture conversion and compare the results to a different dose ranging trial. CONCLUSIONS: Our findings indicate that higher doses, specifically higher peak concentrations may be used to optimize rifampicin treatments by accelerating bacterial killing in the slow phase. It adds to the growing body of literature supporting higher rifampicin doses for shortening tuberculosis treatments.

Mechanisms of antibiotic action shape the fitness landscapes of resistance mutations.

Antibiotic-resistant pathogens are a major public health threat. A deeper understanding of how an antibiotic's mechanism of action influences the emergence of resistance would aid in the design of new drugs and help to preserve the effectiveness of existing ones. To this end, we developed a model that links bacterial population dynamics with antibiotic-target binding kinetics. Our approach allows us to derive mechanistic insights on drug activity from population-scale experimental data and to quantify the interplay between drug mechanism and resistance selection. We find that both bacteriostatic and bactericidal agents can be equally effective at suppressing the selection of resistant mutants, but that key determinants of resistance selection are the relationships between the number of drug-inactivated targets within a cell and the rates of cellular growth and death. We also show that heterogeneous drug-target binding within a population enables resistant bacteria to evolve fitness-improving secondary mutations even when drug doses remain above the resistant strain's minimum inhibitory concentration. Our work suggests that antibiotic doses beyond this "secondary mutation selection window" could safeguard against the emergence of high-fitness resistant strains during treatment.

vCOMBAT: a novel tool to create and visualize a computational model of bacterial antibiotic target-binding.

BACKGROUND: As antibiotic resistance creates a significant global health threat, we need not only to accelerate the development of novel antibiotics but also to develop better treatment strategies using existing drugs to improve their efficacy and prevent the selection of further resistance. We require new tools to rationally design dosing regimens from data collected in early phases of antibiotic and dosing development. Mathematical models such as mechanistic pharmacodynamic drug-target binding explain mechanistic details of how the given drug concentration affects its targeted bacteria. However, there are no available tools in the literature that allow non-quantitative scientists to develop computational models to simulate antibiotic-target binding and its effects on bacteria. RESULTS: In this work, we have devised an extension of a mechanistic binding-kinetic model to incorporate clinical drug concentration data. Based on the extended model, we develop a novel and interactive web-based tool that allows non-quantitative scientists to create and visualize their own computational models of bacterial antibiotic target-binding based on their considered drugs and bacteria. We also demonstrate how Rifampicin affects bacterial populations of Tuberculosis bacteria using our vCOMBAT tool. CONCLUSIONS: The vCOMBAT online tool is publicly available at https://combat-bacteria.org/ .

Current Approaches of Building Mechanistic Pharmacodynamic Drug-Target Binding Models.

Mechanistic pharmacodynamic models that incorporate the binding kinetics of drug-target interactions have several advantages in understanding target engagement and the efficacy of a drug dose. However, guidelines on how to build and interpret mechanistic pharmacodynamic drug-target binding models considering both biological and computational factors are still missing in the literature. In this chapter, current approaches of building mechanistic PD models and their advantages are discussed. We also present a methodology on how to select a suitable model considering both biological and computational perspectives, as well as summarize the challenges of current mechanistic PD models.

The Infectious Dose Shapes Vibrio cholerae Within-Host Dynamics.

During infection, the rates of pathogen replication, death, and migration affect disease progression, dissemination, transmission, and resistance evolution. Here, we follow the population dynamics of Vibrio cholerae in a mouse model by labeling individual bacteria with one of >500 unique, fitness-neutral genomic tags. Using the changes in tag frequencies and CFU numbers, we inform a mathematical model that describes the within-host spatiotemporal bacterial dynamics. This allows us to disentangle growth, death, forward, and retrograde migration rates continuously during infection. Our model has robust predictive power across various experimental setups. The population dynamics of V. cholerae shows substantial spatiotemporal heterogeneity in replication, death, and migration. Importantly, we find that the niche available to V. cholerae in the host increases with inoculum size, suggesting cooperative effects during infection. Therefore, it is not enough to consider just the likelihood of exposure (50% infectious dose) but rather the magnitude of exposure to predict outbreaks. IMPORTANCE Determining the rates of bacterial migration, replication, and death during infection is important for understanding how infections progress. Separately measuring these rates is often difficult in systems where multiple processes happen simultaneously. Here, we use next-generation sequencing to measure V. cholerae migration, replication, death, and niche size along the mouse gastrointestinal tract. We show that the small intestine of the mouse is a heterogeneous environment, and the population dynamic characteristics change substantially between adjacent gut sections. Our approach also allows us to characterize the effect of inoculum size on these processes. We find that the niche size in mice increases with the infectious dose, hinting at cooperative effects in larger inocula. The dose-response relationship between inoculum size and final pathogen burden is important for the infected individual and is thought to influence the progression of V. cholerae epidemics.

Mathematical Modeling of Remdesivir to Treat COVID-19: Can Dosing Be Optimized?

The antiviral remdesivir has been approved by regulatory bodies such as the European Medicines Agency (EMA) and the US Food and Drug administration (FDA) for the treatment of COVID-19. However, its efficacy is debated and toxicity concerns might limit the therapeutic range of this drug. Computational models that aid in balancing efficacy and toxicity would be of great help. Parametrizing models is difficult because the prodrug remdesivir is metabolized to its active form (RDV-TP) upon cell entry, which complicates dose-activity relationships. Here, we employ a computational model that allows drug efficacy predictions based on the binding affinity of RDV-TP for its target polymerase in SARS-CoV-2. We identify an optimal infusion rate to maximize remdesivir efficacy. We also assess drug efficacy in suppressing both wild-type and resistant strains, and thereby describe a drug regimen that may select for resistance. Our results differ from predictions using prodrug dose-response curves (pseudo-EC50s). We expect that reaching 90% inhibition (EC90) is insufficient to suppress SARS-CoV-2 in the lungs. While standard dosing mildly inhibits viral polymerase and therefore likely reduces morbidity, we also expect selection for resistant mutants for most realistic parameter ranges. To increase efficacy and safeguard against resistance, we recommend more clinical trials with dosing regimens that substantially increase the levels of RDV-TP and/or pair remdesivir with companion antivirals.

Perspectives for systems biology in the management of tuberculosis.

Standardised management of tuberculosis may soon be replaced by individualised, precision medicine-guided therapies informed with knowledge provided by the field of systems biology. Systems biology is a rapidly expanding field of computational and mathematical analysis and modelling of complex biological systems that can provide insights into mechanisms underlying tuberculosis, identify novel biomarkers, and help to optimise prevention, diagnosis and treatment of disease. These advances are critically important in the context of the evolving epidemic of drug-resistant tuberculosis. Here, we review the available evidence on the role of systems biology approaches - human and mycobacterial genomics and transcriptomics, proteomics, lipidomics/metabolomics, immunophenotyping, systems pharmacology and gut microbiomes - in the management of tuberculosis including prediction of risk for disease progression, severity of mycobacterial virulence and drug resistance, adverse events, comorbidities, response to therapy and treatment outcomes. Application of the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach demonstrated that at present most of the studies provide "very low" certainty of evidence for answering clinically relevant questions. Further studies in large prospective cohorts of patients, including randomised clinical trials, are necessary to assess the applicability of the findings in tuberculosis prevention and more efficient clinical management of patients.

RESTAMP - Rate estimates by sequence-tag analysis of microbial populations.

Microbial division rates determine the speed of mutation accumulation and thus the emergence of antimicrobial resistance. Microbial death rates are affected by antibiotic action and the immune system. Therefore, measuring these rates has advanced our understanding of host-pathogen interactions and antibiotic action. Several methods based on marker-loss or few inheritable neutral markers exist that allow estimating microbial division and death rates, each of which has advantages and limitations. Technical bottlenecks, i.e., experimental sampling events, during the experiment can distort the rate estimates and are typically unaccounted for or require additional calibration experiments. In this work, we introduce RESTAMP (Rate Estimates by Sequence Tag Analysis of Microbial Populations) as a method for determining bacterial division and death rates. This method uses hundreds of fitness neutral sequence barcodes to measure the rates and account for experimental bottlenecks at the same time. We experimentally validate RESTAMP and compare it to established plasmid loss methods. We find that RESTAMP has a number of advantages over plasmid loss or previous marker based techniques. (i) It enables to correct the distortion of rate estimates by technical bottlenecks. (ii) Rate estimates are independent of the sequence tag distribution in the starting culture allowing the use of an arbitrary number of tags. (iii) It introduces a bottleneck sensitivity measure that can be used to maximize the accuracy of the experiment. RESTAMP allows studying microbial population dynamics with great resolution over a wide dynamic range and can thus advance our understanding of host-pathogen interactions or the mechanisms of antibiotic action.

Identifying the drivers of multidrug-resistant Klebsiella pneumoniae at a European level.

Beta-lactam- and in particular carbapenem-resistant Enterobacteriaceae represent a major public health threat. Despite strong variation of resistance across geographical settings, there is limited understanding of the underlying drivers. To assess these drivers, we developed a transmission model of cephalosporin- and carbapenem-resistant Klebsiella pneumoniae. The model is parameterized using antibiotic consumption and demographic data from eleven European countries and fitted to the resistance rates for Klebsiella pneumoniae for these settings. The impact of potential drivers of resistance is then assessed in counterfactual analyses. Based on reported consumption data, the model could simultaneously fit the prevalence of extended-spectrum beta-lactamase-producing and carbapenem-resistant Klebsiella pneumoniae (ESBL and CRK) across eleven European countries over eleven years. The fit could explain the large between-country variability of resistance in terms of consumption patterns and fitted differences in hospital transmission rates. Based on this fit, a counterfactual analysis found that reducing nosocomial transmission and antibiotic consumption in the hospital had the strongest impact on ESBL and CRK prevalence. Antibiotic consumption in the community also affected ESBL prevalence but its relative impact was weaker than inpatient consumption. Finally, we used the model to estimate a moderate fitness cost of CRK and ESBL at the population level. This work highlights the disproportionate role of antibiotic consumption in the hospital and of nosocomial transmission for resistance in gram-negative bacteria at a European level. This indicates that infection control and antibiotic stewardship measures should play a major role in limiting resistance even at the national or regional level.

All nonadherence is equal but is some more equal than others? Tuberculosis in the digital era.

Adherence to treatment for tuberculosis (TB) has been a concern for many decades, resulting in the World Health Organization's recommendation of the direct observation of treatment in the 1990s. Recent advances in digital adherence technologies (DATs) have renewed discussion on how to best address nonadherence, as well as offering important information on dose-by-dose adherence patterns and their variability between countries and settings. Previous studies have largely focussed on percentage thresholds to delineate sufficient adherence, but this is misleading and limited, given the complex and dynamic nature of adherence over the treatment course. Instead, we apply a standardised taxonomy - as adopted by the international adherence community - to dose-by-dose medication-taking data, which divides missed doses into 1) late/noninitiation (starting treatment later than expected/not starting), 2) discontinuation (ending treatment early), and 3) suboptimal implementation (intermittent missed doses). Using this taxonomy, we can consider the implications of different forms of nonadherence for intervention and regimen design. For example, can treatment regimens be adapted to increase the "forgiveness" of common patterns of suboptimal implementation to protect against treatment failure and the development of drug resistance? Is it reasonable to treat all missed doses of treatment as equally problematic and equally common when deploying DATs? Can DAT data be used to indicate the patients that need enhanced levels of support during their treatment course? Critically, we pinpoint key areas where knowledge regarding treatment adherence is sparse and impeding scientific progress.

Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones.

Antibiotic resistance is rising and we urgently need to gain a better quantitative understanding of how antibiotics act, which in turn would also speed up the development of new antibiotics. Here, we describe a computational model (COMBAT-COmputational Model of Bacterial Antibiotic Target-binding) that can quantitatively predict antibiotic dose-response relationships. Our goal is dual: We address a fundamental biological question and investigate how drug-target binding shapes antibiotic action. We also create a tool that can predict antibiotic efficacy a priori. COMBAT requires measurable biochemical parameters of drug-target interaction and can be directly fitted to time-kill curves. As a proof-of-concept, we first investigate the utility of COMBAT with antibiotics belonging to the widely used quinolone class. COMBAT can predict antibiotic efficacy in clinical isolates for quinolones from drug affinity (R2>0.9). To further challenge our approach, we also do the reverse: estimate the magnitude of changes in drug-target binding based on antibiotic dose-response curves. We overexpress target molecules to infer changes in antibiotic-target binding from changes in antimicrobial efficacy of ciprofloxacin with 92-94% accuracy. To test the generality of our approach, we use the beta-lactam ampicillin to predict target molecule occupancy at MIC from antimicrobial action with 90% accuracy. Finally, we apply COMBAT to predict antibiotic concentrations that can select for resistance due to novel resistance mutations. Using ciprofloxacin and ampicillin as well defined test cases, our work demonstrates that drug-target binding is a major predictor of bacterial responses to antibiotics. This is surprising because antibiotic action involves many additional effects downstream of drug-target binding. In addition, COMBAT provides a framework to inform optimal antibiotic dose levels that maximize efficacy and minimize the rise of resistant mutants.

Selection or drift: The population biology underlying transposon insertion sequencing experiments.

Transposon insertion sequencing methods such as Tn-seq revolutionized microbiology by allowing the identification of genomic loci that are critical for viability in a specific environment on a genome-wide scale. While powerful, transposon insertion sequencing suffers from limited reproducibility when different analysis methods are compared. From the perspective of population biology, this may be explained by changes in mutant frequency due to chance (drift) rather than differential fitness (selection). Here, we develop a mathematical model of the population biology of transposon insertion sequencing experiments, i.e. the changes in size and composition of the transposon-mutagenized population during the experiment. We use this model to investigate mutagenesis, the growth of the mutant library, and its passage through bottlenecks. Specifically, we study how these processes can lead to extinction of individual mutants depending on their fitness and the distribution of fitness effects (DFE) of the entire mutant population. We find that in typical in vitro experiments few mutants with high fitness go extinct. However, bottlenecks of a size that is common in animal infection models lead to so much random extinction that a large number of viable mutants would be misclassified. While mutants with low fitness are more likely to be lost during the experiment, mutants with intermediate fitness are expected to be much more abundant and can constitute a large proportion of detected hits, i.e. false positives. Thus, incorporating the DFEs of randomly generated mutations in the analysis may improve the reproducibility of transposon insertion experiments, especially when strong bottlenecks are encountered.

Multi-scale modeling of drug binding kinetics to predict drug efficacy.

Optimizing drug therapies for any disease requires a solid understanding of pharmacokinetics (the drug concentration at a given time point in different body compartments) and pharmacodynamics (the effect a drug has at a given concentration). Mathematical models are frequently used to infer drug concentrations over time based on infrequent sampling and/or in inaccessible body compartments. Models are also used to translate drug action from in vitro to in vivo conditions or from animal models to human patients. Recently, mathematical models that incorporate drug-target binding and subsequent downstream responses have been shown to advance our understanding and increase predictive power of drug efficacy predictions. We here discuss current approaches of modeling drug binding kinetics that aim at improving model-based drug development in the future. This in turn might aid in reducing the large number of failed clinical trials.

Quantifying the impact of treatment history on plasmid-mediated resistance evolution in human gut microbiota.

To understand how antibiotic use affects the risk of a resistant infection, we present a computational model of the population dynamics of gut microbiota including antibiotic resistance-conferring plasmids. We then describe how this model is parameterized based on published microbiota data. Finally, we investigate how treatment history affects the prevalence of resistance among opportunistic enterobacterial pathogens. We simulate treatment histories and identify which properties of prior antibiotic exposure are most influential in determining the prevalence of resistance. We find that resistance prevalence can be predicted by 3 properties, namely the total days of drug exposure, the duration of the drug-free period after last treatment, and the center of mass of the treatment pattern. Overall this work provides a framework for capturing the role of the microbiome in the selection of antibiotic resistance and highlights the role of treatment history for the prevalence of resistance.

Transposon-insertion sequencing screens unveil requirements for EHEC growth and intestinal colonization.

Enterohemorrhagic Escherichia coli O157:H7 (EHEC) is an important food-borne pathogen that colonizes the colon. Transposon-insertion sequencing (TIS) was used to identify genes required for EHEC and E. coli K-12 growth in vitro and for EHEC growth in vivo in the infant rabbit colon. Surprisingly, many conserved loci contribute to EHEC's but not to K-12's growth in vitro. There was a restrictive bottleneck for EHEC colonization of the rabbit colon, which complicated identification of EHEC genes facilitating growth in vivo. Both a refined version of an existing analytic framework as well as PCA-based analysis were used to compensate for the effects of the infection bottleneck. These analyses confirmed that the EHEC LEE-encoded type III secretion apparatus is required for growth in vivo and revealed that only a few effectors are critical for in vivo fitness. Over 200 mutants not previously associated with EHEC survival/growth in vivo also appeared attenuated in vivo, and a subset of these putative in vivo fitness factors were validated. Some were found to contribute to efficient type-three secretion while others, including tatABC, oxyR, envC, acrAB, and cvpA, promote EHEC resistance to host-derived stresses. cvpA is also required for intestinal growth of several other enteric pathogens, and proved to be required for EHEC, Vibrio cholerae and Vibrio parahaemolyticus resistance to the bile salt deoxycholate, highlighting the important role of this previously uncharacterized protein in pathogen survival. Collectively, our findings provide a comprehensive framework for understanding EHEC growth in the intestine.

Reaction Kinetic Models of Antibiotic Heteroresistance.

Bacterial heteroresistance (i.e., the co-existence of several subpopulations with different antibiotic susceptibilities) can delay the clearance of bacteria even with long antibiotic exposure. Some proposed mechanisms have been successfully described with mathematical models of drug-target binding where the mechanism's downstream of drug-target binding are not explicitly modeled and subsumed in an empirical function, connecting target occupancy to antibiotic action. However, with current approaches it is difficult to model mechanisms that involve multi-step reactions that lead to bacterial killing. Here, we have a dual aim: first, to establish pharmacodynamic models that include multi-step reaction pathways, and second, to model heteroresistance and investigate which molecular heterogeneities can lead to delayed bacterial killing. We show that simulations based on Gillespie algorithms, which have been employed to model reaction kinetics for decades, can be useful tools to model antibiotic action via multi-step reactions. We highlight the strengths and weaknesses of current models and Gillespie simulations. Finally, we show that in our models, slight normally distributed variances in the rates of any event leading to bacterial death can (depending on parameter choices) lead to delayed bacterial killing (i.e., heteroresistance). This means that a slowly declining residual bacterial population due to heteroresistance is most likely the default scenario and should be taken into account when planning treatment length.

Estimating treatment prolongation for persistent infections.

Treatment of infectious diseases is often long and requires patients to take drugs even after they have seemingly recovered. This is because of a phenomenon called persistence, which allows small fractions of the bacterial population to survive treatment despite being genetically susceptible. The surviving subpopulation is often below detection limit and therefore is empirically inaccessible but can cause treatment failure when treatment is terminated prematurely. Mathematical models could aid in predicting bacterial survival and thereby determine sufficient treatment length. However, the mechanisms of persistence are hotly debated, necessitating the development of multiple mechanistic models. Here we develop a generalized mathematical framework that can accommodate various persistence mechanisms from measurable heterogeneities in pathogen populations. It allows the estimation of the relative increase in treatment length necessary to eradicate persisters compared to the majority population. To simplify and generalize, we separate the model into two parts: the distribution of the molecular mechanism of persistence in the bacterial population (e.g. number of efflux pumps or target molecules, growth rates) and the elimination rate of single bacteria as a function of that phenotype. Thereby, we obtain an estimate of the required treatment length for each phenotypic subpopulation depending on its size and susceptibility.

Deciphering the landscape of host barriers to Listeria monocytogenes infection.

Listeria monocytogenes is a common food-borne pathogen that can disseminate from the intestine and infect multiple organs. Here, we used sequence tag-based analysis of microbial populations (STAMP) to investigate Lmonocytogenes population dynamics during infection. We created a genetically barcoded library of murinized Lmonocytogenes and then used deep sequencing to track the pathogen's dissemination routes and quantify its founding population (Nb) sizes in different organs. We found that the pathogen disseminates from the gastrointestinal tract to distal sites through multiple independent routes and that Nb sizes vary greatly among tissues, indicative of diverse host barriers to infection. Unexpectedly, comparative analyses of sequence tags revealed that fecally excreted organisms are largely derived from the very small number of L. monocytogenes cells that colonize the gallbladder. Immune depletion studies suggest that distinct innate immune cells restrict the pathogen's capacity to establish replicative niches in the spleen and liver. Finally, studies in germ-free mice suggest that the microbiota plays a critical role in the development of the splenic, but not the hepatic, barriers that prevent L. monocytogenes from seeding these organs. Collectively, these observations illustrate the potency of the STAMP approach to decipher the impact of host factors on population dynamics of pathogens during infection.

Using Chemical Reaction Kinetics to Predict Optimal Antibiotic Treatment Strategies.

Identifying optimal dosing of antibiotics has proven challenging-some antibiotics are most effective when they are administered periodically at high doses, while others work best when minimizing concentration fluctuations. Mechanistic explanations for why antibiotics differ in their optimal dosing are lacking, limiting our ability to predict optimal therapy and leading to long and costly experiments. We use mathematical models that describe both bacterial growth and intracellular antibiotic-target binding to investigate the effects of fluctuating antibiotic concentrations on individual bacterial cells and bacterial populations. We show that physicochemical parameters, e.g. the rate of drug transmembrane diffusion and the antibiotic-target complex half-life are sufficient to explain which treatment strategy is most effective. If the drug-target complex dissociates rapidly, the antibiotic must be kept constantly at a concentration that prevents bacterial replication. If antibiotics cross bacterial cell envelopes slowly to reach their target, there is a delay in the onset of action that may be reduced by increasing initial antibiotic concentration. Finally, slow drug-target dissociation and slow diffusion out of cells act to prolong antibiotic effects, thereby allowing for less frequent dosing. Our model can be used as a tool in the rational design of treatment for bacterial infections. It is easily adaptable to other biological systems, e.g. HIV, malaria and cancer, where the effects of physiological fluctuations of drug concentration are also poorly understood.

Smear positivity in paediatric and adult tuberculosis: systematic review and meta-analysis.

BACKGROUND: Tuberculosis (TB) diagnosis continues to rely on sputum smear microscopy in many settings. We conducted a meta-analysis to estimate the percentage of children and adults with tuberculosis that are sputum smear positive. METHODS: We searched PubMed, MEDLINE, Embase, and Global Health databases for studies that included both children and adults with all forms of active TB. The pooled percentages of children and adults with smear positive TB were estimated using the inverse variance heterogeneity model. This review was registered in the PROSPERO database under registration number CRD42015015331. RESULTS: We identified 20 studies meeting our inclusion criteria that reported smear positivity for a total of 18,316 children and 162,574 adults from 14 countries. The pooled percentage of paediatric TB cases that were sputum smear positive was 6.8 % (95 % Confidence Interval (CI) 2.2-12.2 %), compared with 52.0 % (95 % CI 40.0-64.0 %) among adult cases. Eight studies reported data separately for children aged 0-4 and 5-14. The percentage of children aged 0-4 that were smear positive was 0.5 % (95 % CI 0.0-1.9 %), compared with 14.0 % (95 % CI 8.9-19.4 %) among children aged 5-14. CONCLUSIONS: Children, especially those aged 0-4, are much less likely to be sputum smear positive than adults. National TB programs relying on sputum smear for diagnosis are at risk of under-diagnosing and underestimating the burden of TB in children.