Global biomedical and health research is increasingly relying on genomic and computational approaches, largely driven by the increasing volumes of nucleic acid sequencing. Concurrently, epidemiological studies and clinical records are generating enormous amounts of data amenable to disease modeling, machine learning, and artificial intelligence techniques. Bioinformatics and data science expertise is therefore essential for improved population health. Accordingly, in 2012, the US National Institutes of Health (NIH) in partnership with the Wellcome Trust, and with support from the African Society for Human Genetics, initiated the H3Africa (Human Heredity and Health in Africa) consortium. One of its key goals was to build capacity among African scientists to lead research on genetic and environmental contributors to health and disease across the continent. In 2017, the NIH provided funding to support the establishment of four graduate bioinformatics training programs across five African universities. Over seven years, these programs enrolled multiple trainees (n > 270), with >110 earning Master's degrees and >20 completing PhDs in Bioinformatics. It is thus timely to evaluate the outcomes and impact of these programs, particularly regarding graduation rates, career trajectories, and the institutions and research domains their alumni are serving. We also assess employment outcomes and the nature of the research they are enabling (n > 110 peer-reviewed articles). We additionally include the progress and outputs of the programs' instructors, which were partially enabled by program resources, networks, and trainees. Overall, this review paints valuable insights into the pioneering role of NIH extramural support in shaping Africa's biomedical research landscape.

Understanding the transmission dynamics of infectious diseases is critical for effective public health intervention. Traditional models often rely on simplifying assumptions that overlook the complexity of real-world contact patterns. In this study, we present an extended Bayesian framework that integrates genomic, temporal, and network data to reconstruct transmission networks with greater accuracy. By incorporating network structure as a prior, the model accounts for social and spatial proximity, allowing transmission probabilities to vary with contact or social distance. We further enhance inference sensitivity through a hypothesis testing procedure optimized via constrained likelihood estimation. Simulation results demonstrate that network-informed models outperform non-network-informed models, particularly under limited genetic resolution. Application to a tuberculosis dataset from Kampala, Uganda reveals that the network-informed model resolves transmission ambiguities more effectively than models based solely on genetic and temporal data. Additionally, Exponential Random Graph Model (ERGM) analysis indicates that transmission is more likely to occur through weak social ties than within tightly connected clusters, aligning with sociological theories of information flow. While the framework shows strong performance, limitations such as data sparsity and computational demands remain. Future work will focus on integrating mobility data to further refine transmission inference. This integrative approach offers a robust tool for epidemiological analysis and supports more targeted public health decision-making.

Background:Tuberculosis persists today in many resource-limited countries in the southern hemisphere because unobserved transmission of M. tuberculosis occurs in undefined contact networks of infectious cases. Methods:To study the transmission dynamics of M. tuberculosis in an African city with endemic tuberculosis, we built out a sociocentric network in the Lubaga Division of Kampala, Uganda, using the personal networks of 130 index cases and 123 community controls frequency-matched by age, sex, and parish. Clusters of genetically related strains were identified using whole genome sequencing was from 99 isolates of the cases. The social distance between cases with related pairs was estimated from the sociocentic network. Findings:We found that characteristics of this sociocentric network account, in part, for tuberculosis persistence. These characteristics included highly connected network members, or hubs, where mixing among contacts may occur; predominant transmission among contacts with weak, or distant, ties to the index case; and geographic structural holes in the network that may link cases with these unknown contacts. Interpretation:These findings suggest that active case finding within the social networks of index cases may result in marginal gains in reducing transmission of tuberculosis. To achieve greater gains, transmission in the community may be reduced through population-based strategies that disrupt transmission in geographic hubs of transmission where mixing may occur between infectious cases and community contacts. Funding:This research was conducted with support from the National Institute of Health (AI093856, AI147319, P30 AI 68386, D43TW010045, D43TW012481).

Reconstructing transmission networks is essential for identifying key factors like superspreaders and high-risk locations, which are critical for developing effective pandemic prevention strategies. This study presents a Bayesian transmission model that combines genomic and temporal data to reconstruct transmission networks for infectious diseases. The Bayesian transmission model incorporates the latent period and distinguishes between symptom onset and actual infection time, improving the accuracy of transmission dynamics and epidemiological models. It also assumes a homogeneous effective population size among hosts, ensuring that the coalescent process for within-host evolution remains unchanged, even with missing intermediate hosts. This allows the model to effectively handle incomplete samples. Simulation results demonstrate the model's ability to accurately estimate model parameters and transmission networks. Additionally, our proposed hypothesis test can reliably identify direct transmission events. The Bayesian transmission model was applied to a real dataset of Mycobacterium tuberculosis genomes from 69 tuberculosis cases. The estimated transmission network revealed two major groups, each with a superspreader who transmitted M. tuberculosis, either directly or indirectly, to 28 and 21 individuals, respectively. The hypothesis test identified 16 direct transmissions within the estimated network, demonstrating the Bayesian model’s advantage over a fixed threshold by providing a more flexible criterion for identifying direct transmissions. This Bayesian approach highlights the critical role of genetic data in reconstructing transmission networks and enhancing our understanding of the origins and transmission dynamics of infectious diseases.

Understanding human mobility is essential for applications in public health, transportation, and urban planning. However, mobility data often suffers from sparsity due to limitations in data collection methods, such as infrequent GPS sampling or call detail record (CDR) data that only capture locations during communication events. To address this challenge, we propose BERT4Traj, a transformer based model that reconstructs complete mobility trajectories by predicting hidden visits in sparse movement sequences. Inspired by BERT's masked language modeling objective and self_attention mechanisms, BERT4Traj leverages spatial embeddings, temporal embeddings, and contextual background features such as demographics and anchor points. We evaluate BERT4Traj on real world CDR and GPS datasets collected in Kampala, Uganda, demonstrating that our approach significantly outperforms traditional models such as Markov Chains, KNN, RNNs, and LSTMs. Our results show that BERT4Traj effectively reconstructs detailed and continuous mobility trajectories, enhancing insights into human movement patterns.

Introduction: Wearable smart devices have emerged as effective tools with the potential to support real-time mobility tracking, monitoring, and data collection for public health research. In low-income settings, however, unlocking this potential requires addressing challenges related to implementation, acceptability, and sustained use among research participants. This study explored facilitators and barriers to the effective use of wearable smartwatches in Kampala, Uganda. Methods: We conducted a cross-sectional qualitative study among 36 adults participating in an ongoing prospective cohort. Participants were purposively selected based on adherence patterns: consistent wearers (≥5 days/week) and inconsistent wearers (≤3 days/week). Stratified by sex, we held two focus group discussions (FGDs) with male (n=10) and female (n=10) participants. Additional FGDs were conducted with males (n=8) and females (n=8) who completely failed to wear the smartwatches. Data were inductively coded and analyzed to identify themes reflecting experiences, motivators, and barriers to wearing the devices. Results: Several themes emerged. Prestige and functionality: Some participants perceived the smartwatch as a status symbol, which encouraged regular use. Functional features such as the ability to make or receive calls, check body temperature, and count steps further motivated adherence. Reassurance and social support: Support from researchers, friends, and family reinforced use of the devices. Privacy and community perceptions: Some participants expressed concerns that the smartwatch could reveal intimate details of their daily lives. Others reported being stigmatized or viewed suspiciously by community members who believed the device could be used for surveillance or spying. Conclusion: Social prestige associated with smartwatch use, along with consistent support from researchers and social networks, can facilitate the uptake of wearable digital technologies in public health research. However, addressing individual privacy concerns and community-level misconceptions is essential for unlocking the full potential of these devices in low-income settings.

Tuberculosis (TB) is a leading cause of death from infectious disease, with 1.25 million deaths in 2023. Among the estimated 10.8 million people who developed TB in 2023, only 75

Estimating transmission rates is a challenging yet essential aspect of comprehending and controlling the spread of infectious diseases. Various methods exist for estimating transmission rates, each with distinct assumptions, data needs, and constraints. This study introduces a novel phylogenetic approach called transRate, which integrates genetic information with traditional epidemiological approaches to estimate inter-population transmission rates. The phylogenetic method is statistically consistent as the sample size (i.e. the number of pathogen genomes) approaches infinity under the multi-population susceptible-infected-recovered model. Simulation analyses indicate that transRate can accurately estimate the transmission rate with a sample size of 200 ~ 400 pathogen genomes. Using transRate, we analyzed 40,028 high-quality sequences of SARS-CoV-2 in human hosts during the early pandemic. Our analysis uncovered significant transmission between populations even before widespread travel restrictions were implemented. The development of transRate provides valuable insights for scientists and public health officials to enhance their understanding of the pandemic's progression and aiding in preparedness for future viral outbreaks. As public databases for genomic sequences continue to expand, transRate is increasingly vital for tracking and mitigating the spread of infectious diseases.

The surge in the availability of spatial big data has sparked increased interest in researching human mobility patterns. Despite this, discovering human mobility patterns from such spatial big data and assessing the similarity between patterns remains a formidable challenge. This study introduces two novel methods: the Time-Informed pattern mining (TiPam) method for frequent pattern mining and a Time-Aware Longest Common Subsequence (T-LCS) algorithm for assessing similarity between time-conscious sequences. Leveraging these innovative algorithms, our research introduces an analytical framework for analyzing human mobility patterns at both individual and aggregated levels. As a case study, this proposed workflow is applied to examine the daily mobility patterns of voluntary mobile phone users in Kampala, Uganda. The 135 participants are found in four distinct groups labeled with distinct mobility properties for users in each group: 'stay-at-home', 'unoccupied', 'education-oriented', and 'work-oriented'. The results effectively showcase the efficiency of the framework and the novel techniques employed. The framework's versatility extends to human mobility studies with other forms of data and across various research fields.

Objectives:Public sharing of de-identified biomedical data promotes collaboration between researchers and accelerates the development of disease prevention and treatment strategies. However, open-access data sharing presents challenges to researchers who need to protect the privacy of study participants, ensure that data are used appropriately, and acknowledge the inputs of all involved researchers. This article presents an approach to data sharing which addresses the above challenges by using a publicly available dashboard with de-identified, aggregated participant data from a large HIV surveillance cohort. Materials and Methods:Data in this study originated from the Rakai Community Cohort Study (RCCS), which was integrated into a centralized data mart as part of a larger data management strategy for the Rakai Health Sciences Program in Uganda. These data were used to build a publicly available, protected health information (PHI)-secured visualization dashboard for general research use. Results:Using two unique case studies, we demonstrate the capability of the dashboard to generate the following hypotheses: firstly, that HIV prevention strategies ART and circumcision have differing levels of impact depending on the marital status of investigated communities; secondly, that ART is very successful in comparison to circumcision as an interventional strategy in certain communities. Discussion:The democratization of large-scale anonymized epidemiological data using public-facing dashboards has multiple benefits, including facilitated exploration of research data and increased reproducibility of research findings. Conclusion:By allowing the public to explore data in depth and form new hypotheses, public-facing dashboard platforms have significant potential to generate new relationships and collaborations and further scientific discovery and reproducibility.

BACKGROUND: Current metrics for TB transmission include TB notifications, disease mortality, and prevalence surveys. These metrics are helpful to national TB programs to assess the burden of disease, but they do not directly measure incident infection in the community. METHODS: To estimate incidence of Mycobacterium tuberculosis infection in Kampala, Uganda, we performed a prospective cohort study between 2014 and 2017 which enrolled of 1,275 adult residents without signs of tuberculous infection (tuberculin skin test [TST] ,5 mm and no signs of TB disease) and followed them for conversion of TST at 1 year. RESULTS: During follow-up, 194 participants converted the TST and 158 converted by one year. The incidence density of TST conversion was 13.2 conversions/ 100 person -year (95% CI 11.6-15.1), which corresponds to an annual cumulative incidence of tuberculous infection of 12.4% (95% CI 10.7-14.3). Cumulative incidence was greater among older participants and among men. Among participants who reported prior exposure to TB cases, the cumulative risk was highest among those reporting exposure during follow-up. CONCLUSIONS: The high annual incidence of infection suggests that residents of Kampala have adequate contact for infection with undetected, infectious cases of TB as they go about their daily lives.

Background:The persistence of tuberculosis today and its global disparity send a powerful message that effective tuberculosis control must respond to its regional epidemiology. Active case finding through contact investigation is a standard protocol used for tuberculosis control, but its effectiveness has not been established, especially in endemic areas.Methods:To quantify the potential effectiveness of contact investigation in Kampala, Uganda, we used a cross-sectional design to evaluate the social networks of 123 tuberculosis index cases and 124 controls without tuberculosis.Results:Tuberculous infection was present in 515 of 989 tuberculosis case contacts (52.1%) and 396 of 1026 control contacts (38.6%; adjusted prevalence ratio, 1.4; 95% CI, 1.3-1.6). The proportion of infected participants with known exposure within the social network of the tuberculosis case was 35%. The population-attributable fraction was 11.1% for any known exposure, with 7.3% attributable to household exposure and 3.4% attributable to extrahousehold exposure.Conclusions:This low population-attributable fraction indicates that contact tracing in the social networks of index cases will have only a modest effect in reducing tuberculous infection in a community. New approaches to community-level active case finding are needed.

The transmission rate of COVID-19 varies by location and time. A proper measure of the transmissibility of an infectious disease should be place- and time-specific, which is currently unavailable. This research aims to better understand the spatiotemporally changing transmissibility of COVID-19. It contributes to COVID-19 research in three ways. First, it presents a generally applicable modeling framework to estimate the transmissibility of COVID-19 in a specific place and time based on daily reported case data, called space-time effective reproduction number, denoted as R-st. Then, the developed model is used to create a spatiotemporal data set of R-st values at the county level in the United States. Second, it investigates relationships between R-st and dynamically changing context factors with multiple machine learning and spatial modeling techniques. The research examines the relationships from a cross-sectional perspective and a longitudinal perspective separately. The longitudinal view allows us to understand how local human dynamics and policy factors influence changes in R-st over time in the place, whereas the cross-sectional view sheds light on the demographic, socioeconomic, and environmental factors behind spatial variations of R-st at a specific time slice. Some general trends of the relationships are found, but the level of impact by each context factor varies geographically. Third, the best performing local longitudinal models have promising potential to simulate or forecast future transmissibility. The random forest and the exponential regression models based on time-series data gave the best performances. These models were further evaluated against ground truth data of county-level reported cases. Their good prediction accuracies in the case study prove that these machine learning models are promising in their ability to predict transmissibility in hypothetical or foreseeable scenarios.

Accurate reconstruction of species trees often relies on the quality of input gene trees estimated from molecular sequences. Previous studies suggested that if the sequence length is fixed, the maximum likelihood may produce biased gene trees which subsequently mislead inference of species trees. Two key questions need to be answered in this context: what are the scenarios that may result in consistently biased gene trees? and for those scenarios, are there any remedies that may remove or at least reduce the misleading effects of consistently biased gene trees? In this article, we establish a theoretical framework to address these questions. Considering a scenario where the true gene tree is a 4-taxon star tree T∗=(S1,S2,S3,S4) with two short branches leading to the species S1 and S2, we demonstrate that maximum likelihood significantly favors the wrong bifurcating tree [(S1, S2), S3, S4] grouping the two species S1 and S2 with short branches. We name this inconsistent behavior short branch attraction, which may occur in real-world data involving a 4-taxon bifurcating gene tree with a short internal branch. If no mutation occurs along the internal branch, which is likely if the internal branch is short, the 4-taxon bifurcating tree is equivalent to the 4-taxon star tree and thus will suffer the same misleading effect of short branch attraction. Theoretical and simulation results further demonstrate that short branch attraction may occur in gene trees and species trees of arbitrary size. Moreover, short branch attraction is primarily caused by a lack of phylogenetic information in sequence data, suggesting that converting short internal branches to polytomies in the estimated gene trees can significantly reduce artifacts induced by short branch attraction.

BACKGROUND:The exponential scale and pace of real-time data generated from mobile phones present opportunities for new insights and challenges across multiple sectors, including health care delivery and public health research. However, little attention has been given to the new ethical, social, and legal concerns related to using these mobile technologies and the data they generate in Africa. OBJECTIVE:The objective of this scoping review was to explore the ethical and related concerns that arise from the use of data from call detail records and mobile technology interventions for public health in the context of East Africa. METHODS:We searched the PubMed database for published studies describing ethical challenges while using mobile technologies and related data in public health research between 2000 and 2020. A predefined search strategy was used as inclusion criteria with search terms such as "East Africa," "mHealth," "mobile phone data," "public health," "ethics," or "privacy." We screened studies using prespecified eligibility criteria through a two-stage process by two independent reviewers. Studies were included if they were (1) related to mobile technology use and health, (2) published in English from 2000 to 2020, (3) available in full text, and (4) conducted in the East African region. We excluded articles that (1) were conference proceedings, (2) studies presenting an abstract only, (3) systematic and literature reviews, (4) research protocols, and (5) reports of mobile technology in animal subjects. We followed the five stages of a published framework for scoping reviews recommended by Arksey and O'Malley. Data extracted included title, publication year, target population, geographic region, setting, and relevance to mobile health (mHealth) and ethics. Additionally, we used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Extension for Scoping Reviews checklist to guide the presentation of this scoping review. The rationale for focusing on the five countries in East Africa was their geographic proximity, which lends itself to similarities in technology infrastructure development. RESULTS:Of the 94 studies identified from PubMed, 33 met the review inclusion criteria for the final scoping review. The 33 articles retained in the final scoping review represent studies conducted in three out of five East African countries: 14 (42%) from Uganda, 13 (39%) from Kenya, and 5 (16%) from Tanzania. Three main categories of concerns related to the use of mHealth technologies and mobile phone data can be conceptualized as (1) ethical issues (adequate informed consent, privacy and confidentiality, data security and protection), (2) sociocultural issues, and (3) regulatory/legal issues. CONCLUSIONS:This scoping review identified major cross-cutting ethical, regulatory, and sociocultural concerns related to using data from mobile technologies in the East African region. A comprehensive framework that accounts for the critical concerns raised would be valuable for guiding the safe use of mobile technology data for public health research purposes.

The COVID-19 pandemic has revealed and widened racialized health disparities, underscoring the impact of structural inequities and racial discrimination on COVID-19 vaccination uptake. A sizable proportion of Black American men report that they either do not plan to or are unsure about becoming vaccinated against COVID-19. The present study investigated hypotheses regarding the mechanisms by which experiences of racial discrimination are associated with Black American men’s COVID-19 vaccine hesitancy. Hypotheses were tested using structural equation modeling with 4 waves of data from 242 Black American men (aged ~ 27) living in resource-poor communities in the rural South. Study findings revealed that racial discrimination was indirectly associated with COVID-19 vaccine hesitancy via increased endorsement of COVID-19 conspiratorial beliefs. Findings also demonstrated that increased levels of ethnic identity strengthen the association between experiences of racial discrimination and COVID-19 conspiratorial beliefs. In contrast, increased levels of social support weakened the association between cumulative experiences of racial discrimination and COVID conspiratorial beliefs. Taken together, these results suggest that racial discrimination may promote conspiratorial beliefs which undermine Black American men’s willingness to be vaccinated. Future interventions aimed towards promoting vaccine uptake among Black American men may benefit from the inclusion of targeted efforts to rebuild cultural trust and increase social support.

Abstract Background The two recent simultaneous developments of high-throughput sequencing and increased computational power have brought bioinformatics to the forefront as an important tool for effective and efficient biomedical research. Consequently, there have been multiple approaches to developing bioinformatics skills. In resource rich environments, it has been possible to develop and implement formal fully accredited graduate degree training programs in bioinformatics. In resource limited settings with a paucity of expert bioinformaticians, infrastructure and financial resources, the task has been approached by delivering short courses on bioinformatics—lasting only a few days to a couple of weeks. Alternatively, courses are offered online, usually over a period of a few months. These approaches are limited by both the lack of sustained in-person trainer–trainee interactions, which is a key part of quality mentorships and short durations which constrain the amount of learning that can be achieved. Methods Here, we pioneered and tested a bioinformatics training/mentorship model that effectively uses the available expertise and computational infrastructure to deliver an in-person hands-on skills training experience. This is done through a few physical lecture hours each week, guided personal coursework over the rest of the week, group discussions and continuous close mentorship and assessment of trainees over a period of 1 year. Results This model has now completed its third iteration at Makerere University and has successfully mentored trainees, who have progressed to a variety of viable career paths. Conclusions One-year (intermediate) skills based in-person bioinformatics training and mentorships are viable, effective and particularly appropriate for resource limited settings.

There is little information about the amount of recent tuberculosis transmission in low-income settings. Genetic clustering can help identify ongoing transmission events. A retrospective observational study was performed on Mycobacterium tuberculosis isolates from persons living with HIV (PLHIV) and HIV-seronegative participants who submitted samples to a referral tuberculosis laboratory in Guatemala City, Guatemala from 2010 to 2014. Genotyping results were classified according to the international spoligotyping database, SITVIT2. Spoligotype patterns were categorized as clustered or nonclustered depending on their genotype. The proportion of clustering and the index of recent transmission index (RTIn-1) were estimated. In the RTIn-1 method, clustered cases represent recent transmission, whereas nonclustered cases represent reactivation of older tuberculosis infections. As a secondary aim, the potential risk factors associated with clustering in isolates from the subset of participants living with HIV were explored. From 2010 to 2014, a total of 479 study participants were confirmed as culture-positive tuberculosis cases. Among the 400 available isolates, 71 spoligotype patterns were identified. Overall, the most frequent spoligotyping families were Latin American-Mediterranean (LAM) (39%), followed by T (22%) and Haarlem (14%). Out of the 400 isolates, 365 were grouped in 36 clusters (range of cluster size: 2-92). Thus, the proportion of clustering was 91% and the RTIn-1 was 82%. Among PLHIV, pulmonary tuberculosis was associated with clustering (OR = 4.3, 95% CI 1.0-17.7). Our findings suggest high levels of ongoing transmission of M. tuberculosis in Guatemala as revealed by the high proportion of isolates falling into genomic clusters.

BACKGROUND:BCG vaccines are given to more than 100 million children every year, but there is considerable debate regarding the effectiveness of BCG vaccination in preventing tuberculosis and death, particularly among older children and adults. We therefore aimed to investigate the age-specific impact of infant BCG vaccination on tuberculosis (pulmonary and extrapulmonary) development and mortality. METHODS:In this systematic review and individual participant data meta-analysis, we searched MEDLINE, Web of Science, BIOSIS, and Embase without language restrictions for case-contact cohort studies of tuberculosis contacts published between Jan 1, 1998, and April 7, 2018. Search terms included "mycobacterium tuberculosis", "TB", "tuberculosis", and "contact". We excluded cohort studies that did not provide information on BCG vaccination or were done in countries that did not recommend BCG vaccination at birth. Individual-level participant data for a prespecified list of variables, including the characteristics of the exposed participant (contact), the index case, and the environment, were requested from authors of all eligible studies. Our primary outcome was a composite of prevalent (diagnosed at or within 90 days of baseline) and incident (diagnosed more than 90 days after baseline) tuberculosis in contacts exposed to tuberculosis. Secondary outcomes were pulmonary tuberculosis, extrapulmonary tuberculosis, and mortality. We derived adjusted odds ratios (aORs) using mixed-effects, binary, multivariable logistic regression analyses with study-level random effects, adjusting for the variable of interest, baseline age, sex, previous tuberculosis, and whether data were collected prospectively or retrospectively. We stratified our results by contact age and Mycobacterium tuberculosis infection status. This study is registered with PROSPERO, CRD42020180512. FINDINGS:We identified 14 927 original records from our database searches. We included participant-level data from 26 cohort studies done in 17 countries in our meta-analysis. Among 68 552 participants, 1782 (2·6%) developed tuberculosis (1309 [2·6%] of 49 686 BCG-vaccinated participants vs 473 [2·5%] of 18 866 unvaccinated participants). The overall effectiveness of BCG vaccination against all tuberculosis was 18% (aOR 0·82, 95% CI 0·74-0·91). When stratified by age, BCG vaccination only significantly protected against all tuberculosis in children younger than 5 years (aOR 0·63, 95% CI 0·49-0·81). Among contacts with a positive tuberculin skin test or IFNγ release assay, BCG vaccination significantly protected against tuberculosis among all participants (aOR 0·81, 95% CI 0·69-0·96), participants younger than 5 years (0·68, 0·47-0·97), and participants aged 5-9 years (0·62, 0·38-0·99). There was no protective effect among those with negative tests, unless they were younger than 5 years (0·54, 0·32-0·90). 14 cohorts reported on whether tuberculosis was pulmonary or extrapulmonary (n=57 421). BCG vaccination significantly protected against pulmonary tuberculosis among all participants (916 [2·2%] in 41 119 vaccinated participants vs 334 [2·1%] in 16 161 unvaccinated participants; aOR 0·81, 0·70-0·94) but not against extrapulmonary tuberculosis (106 [0·3%] in 40 318 vaccinated participants vs 38 [0·2%] in 15 865 unvaccinated participants; 0·96, 0·65-1·41). In the four studies with mortality data, BCG vaccination was significantly protective against death (0·25, 0·13-0·49). INTERPRETATION:Our results suggest that BCG vaccination at birth is effective at preventing tuberculosis in young children but is ineffective in adolescents and adults. Immunoprotection therefore needs to be boosted in older populations. FUNDING:National Institutes of Health.

Abstract Background To curb the tuberculosis (TB) epidemic, new cases must be prevented. Preventive therapy is an effective intervention but treating all individuals with latent TB infection (LTBI) based on current diagnostics, tuberculin skin test (TST) and interferon gamma release assay (IGRA), is not feasible. Using proteomic analysis we are developing a peripheral blood marker of LTBI infection that would identify people at high risk for TB progression. Methods Plasma was collected in an ongoing TB household contact study in Kampala, Uganda in which TST-/IGRA- are enrolled and followed for IGRA/TST conversion. Longitudinal sample sets (0,6,12 months) from 81 participants including 24 TST/IGRA converters, 29 resisters (remained TST/IGRA negative), and 28 baseline positives (LTBI) underwent proteomic and multiplex cytokine analysis. Proteomic analysis was performed by multiple reaction monitoring mass spectrometry (MRM-MS) using an MRM-MS assay containing 163 host proteins represented by 392 unique peptides, all of which we have described to significantly change during Mtb infection in our previous study of household contact converters. Results Differential expression of proteins measured by MRM-MS and cytokine assays were assessed. The largest number of significant changes were measured in the LTBI vs Converter comparison. The vast majority of the changes were elevated expression in the converter group compared to the latent group, suggesting that all the biologies affected were induced in the converters (Figure 1). We identified protein panels that were able to predict conversion as in our earlier study. However, the protein panels defined in our earlier study that could distinguish a future converter from a future resister with high fidelity did not have a similar performance in this study. Figure 1Differential expression analysis of latent TB, LTBI vs. converter. Significant changes by both p-value (set at 0.05) and q-value (a correction for multiple comparisons, set at 0.10). Conclusion Peripheral blood protein changes are associated with conversion to LTBI. In this study we found that the protein panels predictive of conversion were different from our previous study. While there are many possible reasons, an important difference between the two studies was the definition of conversion: TST in the older study and QuantiFERON in the current study. We are continuing to investigate this difference and developing time-sensitive protein panels to capture recent conversion. Disclosures Eustache Paramithiotis, PhD, CellCarta Biosciences: Employee.