Chest Radiograph Screening for Detecting Subclinical Tuberculosis in Asymptomatic Household Contacts, Peru.

The World Health Organization's end TB strategy promotes the use of symptom and chest radiograph screening for tuberculosis (TB) disease. However, asymptomatic early states of TB beyond latent TB infection and active disease can go unrecognized using current screening criteria. We conducted a longitudinal cohort study enrolling household contacts initially free of TB disease and followed them for the occurrence of incident TB over 1 year. Among 1,747 screened contacts, 27 (52%) of the 52 persons in whom TB subsequently developed during follow-up had a baseline abnormal radiograph. Of contacts without TB symptoms, persons with an abnormal radiograph were at higher risk for subsequent TB than persons with an unremarkable radiograph (adjusted hazard ratio 15.62 [95% CI 7.74-31.54]). In young adults, we found a strong linear relationship between radiograph severity and time to TB diagnosis. Our findings suggest chest radiograph screening can extend to detecting early TB states, thereby enabling timely intervention.

COVID-19 vaccination strategies in Africa: A scoping review of the use of mathematical models to inform policy.

OBJECTIVE: Mathematical models are vital tools to understand transmission dynamics and assess the impact of interventions to mitigate COVID-19. However, historically, their use in Africa has been limited. In this scoping review, we assess how mathematical models were used to study COVID-19 vaccination to potentially inform pandemic planning and response in Africa. METHODS: We searched six electronic databases: MEDLINE, Embase, Web of Science, Global Health, MathSciNet and Africa-Wide NiPAD, using keywords to identify articles focused on the use of mathematical modelling studies of COVID-19 vaccination in Africa that were published as of October 2022. We extracted the details on the country, author affiliation, characteristics of models, policy intent and heterogeneity factors. We assessed quality using 21-point scale criteria on model characteristics and content of the studies. RESULTS: The literature search yielded 462 articles, of which 32 were included based on the eligibility criteria. Nineteen (59%) studies had a first author affiliated with an African country. Of the 32 included studies, 30 (94%) were compartmental models. By country, most studies were about or included South Africa (n = 12, 37%), followed by Morocco (n = 6, 19%) and Ethiopia (n = 5, 16%). Most studies (n = 19, 59%) assessed the impact of increasing vaccination coverage on COVID-19 burden. Half (n = 16, 50%) had policy intent: prioritising or selecting interventions, pandemic planning and response, vaccine distribution and optimisation strategies and understanding transmission dynamics of COVID-19. Fourteen studies (44%) were of medium quality and eight (25%) were of high quality. CONCLUSIONS: While decision-makers could draw vital insights from the evidence generated from mathematical modelling to inform policy, we found that there was limited use of such models exploring vaccination impacts for COVID-19 in Africa. The disparity can be addressed by scaling up mathematical modelling training, increasing collaborative opportunities between modellers and policymakers, and increasing access to funding.

Evidence-based Decision Making: Infectious Disease Modeling Training for Policymakers in East Africa.

Background: Mathematical modeling of infectious diseases is an important decision-making tool for outbreak control. However, in Africa, limited expertise reduces the use and impact of these tools on policy. Therefore, there is a need to build capacity in Africa for the use of mathematical modeling to inform policy. Here we describe our experience implementing a mathematical modeling training program for public health professionals in East Africa. Methods: We used a deliverable-driven and learning-by-doing model to introduce trainees to the mathematical modeling of infectious diseases. The training comprised two two-week in-person sessions and a practicum where trainees received intensive mentorship. Trainees evaluated the content and structure of the course at the end of each week, and this feedback informed the strategy for subsequent weeks. Findings: Out of 875 applications from 38 countries, we selected ten trainees from three countries - Rwanda (6), Kenya (2), and Uganda (2) - with guidance from an advisory committee. Nine trainees were based at government institutions and one at an academic organization. Participants gained skills in developing models to answer questions of interest and critically appraising modeling studies. At the end of the training, trainees prepared policy briefs summarizing their modeling study findings. These were presented at a dissemination event to policymakers, researchers, and program managers. All trainees indicated they would recommend the course to colleagues and rated the quality of the training with a median score of 9/10. Conclusions: Mathematical modeling training programs for public health professionals in Africa can be an effective tool for research capacity building and policy support to mitigate infectious disease burden and forecast resources. Overall, the course was successful, owing to a combination of factors, including institutional support, trainees' commitment, intensive mentorship, a diverse trainee pool, and regular evaluations.

Interventions to prevent post-tuberculosis sequelae: a systematic review and meta-analysis.

Background: Tuberculosis (TB) remains a global public health challenge, causing substantial mortality and morbidity. While TB treatment has made significant progress, it often leaves survivors with post-TB sequelae, resulting in long-term health issues. Current healthcare systems and guidelines lack comprehensive strategies to address post-TB sequelae, primarily due to insufficient evidence. This systematic review and meta-analysis aimed to identify effective interventions for preventing post-TB sequelae. Methods: A systematic search was conducted across four databases including PubMed, SCOPUS, Web of Science, and Cochrane Central Register of Controlled Trials from inception to September 22, 2023. Eligible studies reported interventions designed to prevent post-TB sequelae were included. A random effect meta-analysis was conducted where applicable, and heterogeneity between studies was evaluated visually using forest plots and quantitatively using an index of heterogeneity (I2). This study is registered with PROSPERO (CRD42023464392). Findings: From the 2525 unique records screened, 25 studies involving 10,592 participants were included. Different interventions were evaluated for different outcomes. However, only a few interventions were effective in preventing post-TB sequelae. Rehabilitation programs significantly improved lung function (Hedges's g = 0.21; 95% confidence interval (CI): 0.03, 0.39) and prevented neurological sequelae (relative risk (RR) = 0.10; 95% CI: 0.02, 0.42). Comprehensive interventions and cognitive-behavioural therapy significantly reduced the risk of mental health disorders among TB survivors (Hedges's g = -1.89; 95% CI: -3.77, -0.01). In contrast, interventions targeting post-TB liver sequelae, such as vitamin A and vitamin D supplementation and hepatoprotective agents, did not show significant reductions in sequelae (RR = 0.90; 95% CI: 0.52, 1.57). Moreover, adjunctive therapies did not show a significant effect in preventing post-TB neurological sequelae (RR = 0.62, 95% CI: 0.31, 1.24). Interpretation: Rehabilitation programs prevented post-TB lung, neurologic and mental health sequelae, while adjuvant therapies and other interventions require further investigation. Funding: Healy Medical Research Raine Foundation, Curtin School of Population Health and the Australian National Health and Medical Research Council.

Who Transmits Tuberculosis to Whom: A Cross-Sectional Analysis of a Cohort Study in Lima, Peru.

Rationale: The persistent burden of tuberculosis (TB) disease emphasizes the need to identify individuals with TB for treatment and those at a high risk of incident TB for prevention. Targeting interventions toward those at high risk of developing and transmitting TB is a public health priority. Objectives: We aimed to identify characteristics of individuals involved in TB transmission in a community setting, which may guide the prioritization of targeted interventions. Methods: We collected clinical and sociodemographic data from a cohort of patients with TB in Lima, Peru. We used whole-genome sequencing data to assess the genetic distance between all possible pairs of patients; we considered pairs to be the result of a direct transmission event if they differed by three or fewer SNPs, and we assumed that the first diagnosed patient in a pair was the transmitter and the second was the recipient. We used logistic regression to examine the association between host factors and the likelihood of direct TB transmission. Measurements and Main Results: Analyzing data from 2,518 index patients with TB, we identified 1,447 direct transmission pairs. Regardless of recipient attributes, individuals less than 34 years old, males, and those with a history of incarceration had a higher likelihood of being transmitters in direct transmission pairs. Direct transmission was more likely when both patients were drinkers or smokers. Conclusions: This study identifies men, young adults, former prisoners, alcohol consumers, and smokers as priority groups for targeted interventions. Innovative strategies are needed to extend TB screening to social groups such as young adults and prisoners with limited access to routine preventive care.

Utility of Mycobacterium tuberculosis genome sequencing snapshots to assess transmission dynamics over time.

We explored the utility of brief Mycobacterium tuberculosis whole-genome sequencing (WGS) "snapshots" at a sentinel site within Lima, Peru for evaluating local transmission dynamics over time. Within a 17 km2 area, 15/70 (21%) isolates with WGS collected during 2011-2012 and 22/81 (27%) collected during 2020-2021 were clustered (p = 0.414), and additional isolates clustered with those from outside the area. Isolates from the later period were disproportionately related to large historic clusters in Lima from the earlier period. WGS snapshots at a sentinel site may not be useful for monitoring transmission, but monitoring the persistence of large transmission clusters might be.

Spatial scale of tuberculosis transmission in Lima, Peru.

Spatially targeted interventions may be effective alternatives to individual or population-based prevention strategies against tuberculosis (TB). However, their efficacy may depend on the mechanisms that lead to geographically constrained hotspots. Local TB incidence may reflect high levels of local transmission; conversely, they may point to frequent travel of community members to high-risk areas. We used whole-genome sequencing to explore patterns of TB incidence and transmission in Lima, Peru. Between 2009 and 2012, we recruited incident pulmonary TB patients and their household contacts, whom we followed for the occurrence of TB disease. We used whole-genome sequences of 2,712 Mycobacterial tuberculosis isolates from 2,440 patients to estimate pariwise genomic distances and compared these to the spatial distance between patients' residences. Genomic distances increased rapidly as spatial distances increased and remained high beyond 2 km of separation. Next, we divided the study catchment area into 1 × 1 km grid-cell surface units and used household spatial coordinates to locate each TB patient to a specific cell. We estimated cell-specific transmission by calculating the proportion of patients in each cell with a pairwise genomic distance of 10 or fewer single-nucleotide polymorphisms. We found that cell-specific TB incidence and local transmission varied widely but that cell-specific TB incidence did not correlate closely with our estimates of local transmission (Cohen's k = 0.27). These findings indicate that an understanding of the spatial heterogeneity in the relative proportion of TB due to local transmission may help guide the implementation of spatially targeted interventions.

Higher native Peruvian genetic ancestry proportion is associated with tuberculosis progression risk.

We investigated whether ancestry-specific genetic factors affect tuberculosis (TB) progression risk in a cohort of admixed Peruvians. We genotyped 2,105 patients with TB and 1,320 household contacts (HHCs) who were infected with Mycobacterium tuberculosis (M. tb) but did not develop TB and inferred each individual's proportion of native Peruvian genetic ancestry. Our HHC study design and our data on potential confounders allowed us to demonstrate increased risk independent of socioeconomic factors. A 10% increase in individual-level native Peruvian genetic ancestry proportion corresponded to a 25% increased TB progression risk. This corresponds to a 3-fold increased risk for individuals in the highest decile of native Peruvian genetic ancestry versus the lowest decile, making native Peruvian genetic ancestry comparable in effect to clinical factors such as diabetes. Our results suggest that genetic ancestry is a major contributor to TB progression risk and highlight the value of including diverse populations in host genetic studies.

Treatment Outcomes Among Pregnant Patients With Multidrug-Resistant Tuberculosis: A Systematic Review and Meta-analysis.

Importance: The management of multidrug-resistant tuberculosis (MDR-TB) during pregnancy is challenging, yet no systematic synthesis of evidence has accurately measured treatment outcomes. Objective: To systematically synthesize treatment outcomes and adverse events among pregnant patients with MDR-TB. Data Sources: PubMed, Scopus, Web of Science, and ProQuest were searched from the inception of each database through August 31, 2021. Study Selection: Studies containing cohorts of pregnant patients with a defined treatment outcome were eligible. Data Extraction and Synthesis: Independent reviewers screened studies and assessed the risk of bias. The study followed the Preferring Reporting Items for Systematic Review and Meta-analyses reporting guideline. Meta-analysis was performed using random-effects models. The sources of heterogeneity were explored through metaregression. Main Outcomes and Measures: The primary outcome was the proportion of patients with each treatment outcome (including treatment success, death, loss to follow-up, and treatment failure), and the secondary outcomes included the proportion of patients experiencing adverse events during pregnancy. Results: In this systematic review and meta-analysis, 10 studies containing 275 pregnant patients with available data on treatment outcomes were included. The pooled estimate was 72.5% (95% CI, 63.3%-81.0%) for treatment success, 6.8% (95% CI, 2.6%-12.4%) for death, 18.4% (95% CI, 13.1%-24.2%) for loss to follow-up, and 0.6% (95% CI, 0.0%-2.9%) for treatment failure. Treatment success was significantly higher in studies in which the proportion of patients taking linezolid was greater than the median (20.1%) compared with studies in which this proportion was lower than the median (odds ratio, 1.22; 95% CI, 1.05-1.42). More than half of the pregnant patients (54.7%; 95% CI, 43.5%-65.4%) experienced at least 1 type of adverse event, most commonly liver function impairment (30.4%; 95% CI, 17.7%-45.7%), kidney function impairment (14.9%; 95% CI, 6.2%-28.3%), hypokalemia (11.9%; 95% CI, 3.9%-25.6%), hearing loss (11.8%; 95% CI, 5.5%-21.3%), gastrointestinal disorders (11.8%; 95% CI, 5.2%-21.8%), psychiatric disorders (9.1%; 95% CI, 2.5%-21.6%), or anemia (8.9%; 95% CI, 3.6%-17.4%). The pooled proportion of favorable pregnancy outcomes was 73.2% (95% CI, 49.4%-92.1%). The most common types of adverse pregnancy outcomes were preterm birth (9.5%; 95% CI, 0.0%-29.0%), pregnancy loss (6.0%; 95% CI, 1.3%-12.9%), low birth weight (3.9%; 95% CI, 0.0%-18.7%), and stillbirth (1.9%; 95% CI, 0.1%-5.1%). Most of the studies had low-quality (3 studies) or medium-quality (4 studies) scores. Conclusions and Relevance: In this systematic review and meta-analysis, high treatment success and favorable pregnancy outcomes were reported among pregnant patients with MDR-TB. Further research is needed to design shorter, more effective, and safer treatment regimens for pregnant patients with MDR-TB.

Single-cell eQTL models reveal dynamic T cell state dependence of disease loci.

Non-coding genetic variants may cause disease by modulating gene expression. However, identifying these expression quantitative trait loci (eQTLs) is complicated by differences in gene regulation across fluid functional cell states within cell types. These states-for example, neurotransmitter-driven programs in astrocytes or perivascular fibroblast differentiation-are obscured in eQTL studies that aggregate cells1,2. Here we modelled eQTLs at single-cell resolution in one complex cell type: memory T cells. Using more than 500,000 unstimulated memory T cells from 259 Peruvian individuals, we show that around one-third of 6,511 cis-eQTLs had effects that were mediated by continuous multimodally defined cell states, such as cytotoxicity and regulatory capacity. In some loci, independent eQTL variants had opposing cell-state relationships. Autoimmune variants were enriched in cell-state-dependent eQTLs, including risk variants for rheumatoid arthritis near ORMDL3 and CTLA4; this indicates that cell-state context is crucial to understanding potential eQTL pathogenicity. Moreover, continuous cell states explained more variation in eQTLs than did conventional discrete categories, such as CD4+ versus CD8+, suggesting that modelling eQTLs and cell states at single-cell resolution can expand insight into gene regulation in functionally heterogeneous cell types.

The Impact of Vitamin A Deficiency on Tuberculosis Progression.

BACKGROUND: Although previous studies have shown that vitamin A deficiency is associated with incident tuberculosis (TB) disease, the direction of the association has not been established. We investigated the impact of vitamin A deficiency on TB disease progression. METHODS: We conducted a longitudinal cohort study nested within a randomized clinical trial among HIV-infected patients in Haiti. We compared serial vitamin A levels in individuals who developed TB disease to controls matched on age, gender, follow-up time, and time to antiretroviral therapy initiation. We also evaluated histopathology, bacterial load, and immune outcomes in TB infection in a guinea pig model of dietary vitamin A deficiency. RESULTS: Among 773 participants, 96 developed incident TB during follow-up, 62.5% (60) of whom had stored serum samples obtained 90-365 days before TB diagnosis. In age- and sex- adjusted and multivariate analyses, respectively, incident TB cases were 3.99 times (95% confidence interval [CI], 2.41 to 6.60) and 3.59 times (95% CI, 2.05 to 6.29) more likely to have been vitamin A deficient than matched controls. Vitamin A-deficient guinea pigs manifested more extensive pulmonary pathology, atypical granuloma morphology, and increased bacterial growth after experimental TB infection. Reintroduction of dietary vitamin A to deficient guinea pigs after established TB disease successfully abrogated severe disease manifestations and altered cellular immune profiles. CONCLUSIONS: Human and animal studies support the role of baseline vitamin A deficiency as a determinant of future TB disease progression.

Dual TCR-α Expression on Mucosal-Associated Invariant T Cells as a Potential Confounder of TCR Interpretation.

Mucosal-associated invariant T (MAIT) cells are innate-like T cells that are highly abundant in human blood and tissues. Most MAIT cells have an invariant TCRα-chain that uses T cell receptor α-variable 1-2 (TRAV1-2) joined to TRAJ33/20/12 and recognizes metabolites from bacterial riboflavin synthesis bound to the Ag-presenting molecule MHC class I related (MR1). Our attempts to identify alternative MR1-presented Ags led to the discovery of rare MR1-restricted T cells with non-TRAV1-2 TCRs. Because altered Ag specificity likely alters affinity for the most potent known Ag, 5-(2-oxopropylideneamino)-6-d-ribitylaminouracil (5-OP-RU), we performed bulk TCRα- and TCRß-chain sequencing and single-cell-based paired TCR sequencing on T cells that bound the MR1-5-OP-RU tetramer with differing intensities. Bulk sequencing showed that use of V genes other than TRAV1-2 was enriched among MR1-5-OP-RU tetramerlow cells. Although we initially interpreted these as diverse MR1-restricted TCRs, single-cell TCR sequencing revealed that cells expressing atypical TCRα-chains also coexpressed an invariant MAIT TCRα-chain. Transfection of each non-TRAV1-2 TCRα-chain with the TCRß-chain from the same cell demonstrated that the non-TRAV1-2 TCR did not bind the MR1-5-OP-RU tetramer. Thus, dual TCRα-chain expression in human T cells and competition for the endogenous ß-chain explains the existence of some MR1-5-OP-RU tetramerlow T cells. The discovery of simultaneous expression of canonical and noncanonical TCRs on the same T cell means that claims of roles for non-TRAV1-2 TCR in MR1 response must be validated by TCR transfer-based confirmation of Ag specificity.

Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics.

As single-cell datasets grow in sample size, there is a critical need to characterize cell states that vary across samples and associate with sample attributes, such as clinical phenotypes. Current statistical approaches typically map cells to clusters and then assess differences in cluster abundance. Here we present co-varying neighborhood analysis (CNA), an unbiased method to identify associated cell populations with greater flexibility than cluster-based approaches. CNA characterizes dominant axes of variation across samples by identifying groups of small regions in transcriptional space-termed neighborhoods-that co-vary in abundance across samples, suggesting shared function or regulation. CNA performs statistical testing for associations between any sample-level attribute and the abundances of these co-varying neighborhood groups. Simulations show that CNA enables more sensitive and accurate identification of disease-associated cell states than a cluster-based approach. When applied to published datasets, CNA captures a Notch activation signature in rheumatoid arthritis, identifies monocyte populations expanded in sepsis and identifies a novel T cell population associated with progression to active tuberculosis.

Prediction Tool to Identify Children at Highest Risk of Tuberculosis Disease Progression Among Those Exposed at Home.

BACKGROUND: There is a dearth of research to understand which children, among those who are exposed at home to tuberculosis (TB), are at the highest risk of TB disease, to tailor care. We sought to identify predictors of TB progression in children. METHODS: We conducted a prospective cohort study of children living with adults with pulmonary TB in Lima, Peru (2009-2012). We applied classification and regression tree analysis to examine potential predictors of incident TB disease during 12 months in 3 age groups (0-4, 5-9, and 10-14 years). We calculated the relative risk (RR) for top predictors in each age group. RESULTS: Among 4545 children 0-14 years old, 156 (3.4%) were diagnosed with TB within 1 year of household exposure to TB (3.4%, 2.3%, and 4.7% in children 0-4, 5-9, and 10-14 years old, respectively). The most important predictor of TB was having a positive tuberculin skin test (TST) result, with RRs of 6.6 (95% CI, 4.0-10.7), 6.6 (95% CI, 3.2-13.6), and 5.2 (95% CI, 3.0-9.0) in the age groups 0-4, 5-9, and 10-14 years, respectively. In young children with a positive TST, not using isoniazid preventive treatment further increased risk of disease (RR, 12.2 [95% CI, 3.8-39.2]). CONCLUSIONS: We present a tool that identifies child household contacts at high risk of TB disease progression based on data collected during contact tracing. In addition to the use of TB preventive therapy for all children exposed at home to TB, those children at highest risk of progressing to TB disease may benefit from more frequent follow-up.

Integrating Health Systems and Science to Respond to COVID-19 in a Model District of Rural Madagascar.

There are many outstanding questions about how to control the global COVID-19 pandemic. The information void has been especially stark in the World Health Organization Africa Region, which has low per capita reported cases, low testing rates, low access to therapeutic drugs, and has the longest wait for vaccines. As with all disease, the central challenge in responding to COVID-19 is that it requires integrating complex health systems that incorporate prevention, testing, front line health care, and reliable data to inform policies and their implementation within a relevant timeframe. It requires that the population can rely on the health system, and decision-makers can rely on the data. To understand the process and challenges of such an integrated response in an under-resourced rural African setting, we present the COVID-19 strategy in Ifanadiana District, where a partnership between Malagasy Ministry of Public Health (MoPH) and non-governmental organizations integrates prevention, diagnosis, surveillance, and treatment, in the context of a model health system. These efforts touch every level of the health system in the district-community, primary care centers, hospital-including the establishment of the only RT-PCR lab for SARS-CoV-2 testing outside of the capital. Starting in March of 2021, a second wave of COVID-19 occurred in Madagascar, but there remain fewer cases in Ifanadiana than for many other diseases (e.g., malaria). At the Ifanadiana District Hospital, there have been two deaths that are officially attributed to COVID-19. Here, we describe the main components and challenges of this integrated response, the broad epidemiological contours of the epidemic, and how complex data sources can be developed to address many questions of COVID-19 science. Because of data limitations, it still remains unclear how this epidemic will affect rural areas of Madagascar and other developing countries where health system utilization is relatively low and there is limited capacity to diagnose and treat COVID-19 patients. Widespread population based seroprevalence studies are being implemented in Ifanadiana to inform the COVID-19 response strategy as health systems must simultaneously manage perennial and endemic disease threats.

Characterization of Drug-Resistant Lipid-Dependent Differentially Detectable Mycobacterium tuberculosis.

An estimated 15-20% of patients who are treated for pulmonary tuberculosis (TB) are culture-negative at the time of diagnosis. Recent work has focused on the existence of differentially detectable Mycobacterium tuberculosis (Mtb) bacilli that do not grow under routine solid culture conditions without the addition of supplementary stimuli. We identified a cohort of TB patients in Lima, Peru, in whom acid-fast bacilli could be detected by sputum smear microscopy, but from whom Mtb could not be grown in standard solid culture media. When we attempted to re-grow Mtb from the frozen sputum samples of these patients, we found that 10 out of 15 could be grown in a glycerol-poor/lipid-rich medium. These fell into the following two groups: a subset that could be regrown in glycerol after "lipid-resuscitation", and a group that displayed a heritable glycerol-sensitive phenotype that were unable to grow in the presence of this carbon source. Notably, all of the glycerol-sensitive strains were found to be multidrug resistant. Although whole-genome sequencing of the lipid-resuscitated strains identified 20 unique mutations compared to closely related strains, no single genetic lesion could be associated with this phenotype. In summary, we found that lipid-based media effectively fostered the growth of Mtb from a series of sputum smear-positive samples that were not culturable in glycerol-based Lowenstein-Jensen or 7H9 media, which is consistent with Mtb's known preference for non-glycolytic sources during infection. Analysis of the recovered strains demonstrated that both genetic and non-genetic mechanisms contribute to the observed differential capturability, and suggested that this phenotype may be associated with drug resistance.

Multimodally profiling memory T cells from a tuberculosis cohort identifies cell state associations with demographics, environment and disease.

Multimodal T cell profiling can enable more precise characterization of elusive cell states underlying disease. Here, we integrated single-cell RNA and surface protein data from 500,089 memory T cells to define 31 cell states from 259 individuals in a Peruvian tuberculosis (TB) progression cohort. At immune steady state >4 years after infection and disease resolution, we found that, after accounting for significant effects of age, sex, season and genetic ancestry on T cell composition, a polyfunctional type 17 helper T (TH17) cell-like effector state was reduced in abundance and function in individuals who previously progressed from Mycobacterium tuberculosis (M.tb) infection to active TB disease. These cells are capable of responding to M.tb peptides. Deconvoluting this state-uniquely identifiable with multimodal analysis-from public data demonstrated that its depletion may precede and persist beyond active disease. Our study demonstrates the power of integrative multimodal single-cell profiling to define cell states relevant to disease and other traits.

Smoking Cessation in Tuberculosis Patients and the Risk of Tuberculosis Infection in Child Household Contacts.

BACKGROUND: While previous studies have shown that cigarette smoking increases the infectiousness of tuberculosis patients, the impact of smoking cessation on tuberculosis transmissibility has not been evaluated. METHODS: Between 2009 and 2012, we enrolled 4500 tuberculosis patients and followed 14 044 household contacts in Lima, Peru. Tuberculosis patients were classified into 4 categories: never smoked, quit in the distant past (stopped smoking >2 months prior to time of diagnosis), recently quit (stopped smoking ≤2 months prior to time of diagnosis), and active smokers. We used a modified Poisson generalized estimating equation to assess the risk of tuberculosis infection of child contacts at enrollment and by 6 months of follow-up. RESULTS: In total, 1371 (76.8%) child contacts were exposed to patients who had never smoked, 211 (11.8%) were exposed to distant quitters, 155 (8.7%) were exposed to recent quitters, and 49 (2.7%) were exposed to active smokers. Compared with child contacts of index patients who had never smoked, child contacts of recent quitters had a similar risk of tuberculosis infection at enrollment (adjusted risk ratio, 95% confidence intervals [0.81, 0.50-1.32]) and by six months of follow-up (0.76, 0.51-1.13); and by 6 months of follow-up (aRR, 0.76; 95% CI, .51-1.13); child contacts of recent quitters had a significantly reduced risk of tuberculosis infection compared with contacts of active smokers (enrollment 0.45, 0.24-0.87; 6-month follow-up 0.48, 0.29-0.79). CONCLUSIONS: Our results show that the adverse effects of smoking on the transmissibility of tuberculosis are significantly reduced shortly after quitting smoking, reinforcing the importance of smoking cessation interventions in tuberculosis control.