BACKGROUND:Current efforts to reduce global tuberculosis incidence have proved insufficient, highlighting that urgent action is needed to address underlying modifiable risk factors such as undernutrition. We aimed to estimate the global impact of eliminating undernutrition on tuberculosis incidence among adults, accounting for varying nutritional status by country, sex, and age, in addition to incorporating the continuous, non-linear relationship between BMI and tuberculosis risk. METHODS:For this modelling study, we used a continuous risk framework to consider the population-level implications of BMI distributions for tuberculosis incidence in adults aged 15 years or older in 2023. We generated BMI distributions for each country, sex, and age group, and applied a bilinear model for the logarithmic relative risk of tuberculosis incidence at different BMI values. We assessed the impact of eliminating moderate-to-severe undernutrition (BMI <17 kg/m2) or all undernutrition (BMI <18·5 kg/m2) on tuberculosis incidence by constructing counterfactual BMI distributions that redistributed those with low BMI to high BMI, proportional to the remaining density. FINDINGS:We estimated that eliminating moderate-to-severe undernutrition could avert 1·4 million (95% uncertainty interval 1·1-1·7) tuberculosis episodes globally, representing 14·6% (12·6-16·6) of global adult incidence in 2023, while eliminating all undernutrition could avert 2·3 million (1·8-2·7) episodes, representing a reduction in global tuberculosis incidence of 23·7% (20·9-26·5). The largest proportional reductions in tuberculosis incidence could be achieved by eliminating undernutrition in the WHO African, South-East Asia, and Eastern Mediterranean regions; in females; and in adolescents or older adults. INTERPRETATION:Almost a quarter of global tuberculosis incidence in adults could be averted by eliminating undernutrition, approximately two-and-a-half times higher than current estimates. These findings highlight the urgent need to scale up population-level nutritional interventions, which could have myriad social and health benefits beyond tuberculosis, alongside research to establish optimal implementation strategies and impacts. FUNDING:None.

Introduction Of 1.2 million children and young adolescents (<15 years) developing tuberculosis (TB) yearly, more than 50% are undiagnosed and unreported to national TB programmes (NTPs) and the World Health Organization (WHO). This is mainly due to poor performance of microbiological tests, limited clinical skills and structural barriers for childhood TB diagnosis at decentralised levels of care. Treatment decision algorithms (TDAs) could improve child TB outcomes but require external validation. We aim to evaluate a comprehensive TDA-based approach for childhood TB screening, diagnosis and treatment decision-making at district hospital (DH) and primary health centre (PHC) levels in Mozambique and Zambia.Methods and analysis Decide TB is a pragmatic, hybrid effectiveness-implementation type 2 cluster-randomised trial with a stepped wedge design. The comprehensive TDA-based approach (intervention) will be implemented under programmatic conditions in four districts in each country (each comprising one DH and six PHCs), randomly selected to switch sequentially from the standard of care to the intervention. Evaluations will assess epidemiological, clinical, economic, social sciences, implementation and health policy endpoints. Aggregated and individual data from children with presumptive TB will be extracted from facility registers and individual data will be collected using an electronic medical record (EMR), both data sources will be entered in national Demographic Health Information System 2 databases. Questionnaires and individual/group interviews (among healthcare workers (HCWs), parents/caregivers and key informants), supervision and mentoring reports and quantitative cost tools will be used.Ethics and dissemination Ethics approval was obtained from national ethics committees in Mozambique (Instituto Nacional de Saúde review board and National Committee for Bioethics in Health) and Zambia (University of Zambia ethical review board and National Health Research Authority); this includes a waiver for analysing data collected by NTPs (no identifiable information reported, intervention with minimal risk) without individual consent from children’s parents/caregivers. Informed consent will be obtained from HCWs, parents/caregivers and key informants. Results will be openly shared with the scientific community, WHO and national and international stakeholders for translation into policy and practice. Procedures for requesting further use of Decide TB data will be publicly available.Trial registration number NCT06593080; PACTR202407866544155.

Tuberculosis (TB) is the greatest cause of infectious disease deaths worldwide. In highly affected countries, effective TB control requires prompt identification and treatment of individuals with active disease. We examined the performance of TB case-finding in low- and middle-income countries based on a comprehensive analysis of TB diagnosis data reported to the World Health Organization. Using these data we estimated the total number of individuals correctly and incorrectly diagnosed with TB, for 111 countries with a collective 6.8 million TB notifications in 2023. Here we estimate that in 2023, 2.05 (1.83-2.27) million individuals were incorrectly diagnosed with TB (false-positives), and 1.00 (0.71-1.36) million received a false-negative diagnosis, at an assumed 25% disease prevalence among individuals evaluated for TB. As many as three of every ten TB notifications may not have TB, and many individuals with TB receive false-negative diagnoses. Compared to current diagnostic performance, scaling-up new polymerase chain reaction-based diagnostics would substantially reduce under-diagnosis but only produce a small reduction in false-positive diagnoses. Major improvements in TB diagnosis will likely require higher-sensitivity bacteriological tests combined with reduced reliance on clinical diagnosis.

Background Post–licensure vaccine effectiveness and impact studies provide evidence on how vaccines perform under routine programme conditions in the real world. In sub–Saharan Africa (SSA), vaccine introductions frequently coincide with concurrent public health and social measures that may influence disease risk and transmission. Failure to account for these concurrent interventions may affect the interpretation of vaccine effects. Methods We conducted a systematic review of post–licensure vaccine effectiveness and impact studies conducted in children under five years of age in SSA. Electronic databases were searched for peer–reviewed studies published between January 2000 and December 2019. Eligible studies used observational designs to estimate vaccine effectiveness or population–level impact. Two reviewers independently screened studies, extracted data, and assessed methodological quality using Joanna Briggs Institute tools. We examined study designs, vaccines evaluated, outcomes assessed, and whether public health and social measures (PHSMs) were measured or adjusted for. A narrative synthesis was undertaken. In addition, we conducted a meta–analysis for rotavirus and pneumococcal conjugate vaccines where we explored the heterogeneity in individual–level effectiveness estimates where designs and outcomes were comparable. Results Sixty–four studies met the inclusion criteria, covering eight vaccine–preventable diseases. Rotavirus vaccines were most frequently evaluated, followed by pneumococcal conjugate vaccines. Case-control and ecological designs were most common, while cohort and time–series analyses were less frequently used. None of the included studies collected, reported, or adjusted for PHSMs such as nutrition, WASH, or access to healthcare. The implications of this omission varied by pathogen. Rotavirus vaccine effectiveness estimates from comparable individual–level designs were consistent across settings, with no evidence of between–study heterogeneity. Pneumococcal vaccine effectiveness estimates showed substantial heterogeneity, which appeared to reflect differences in outcome definitions, host risk profiles, and study context. Estimates for other vaccines were generally protective in direction, although the magnitude and precision varied across studies. Conclusions Post–licensure vaccine effectiveness and impact studies in SSA rarely account for concurrent PHSMs. The consequences of this omission are not uniform across vaccines. For some pathogens, effectiveness estimates appear robust to unmeasured contextual change, while for others they are highly sensitive to outcome choice and setting. Future evaluations should prioritise systematic measurement of key PHSMs and consider study designs that better account for time–varying context. Strengthening routine data systems to capture these factors is essential for generating interpretable evidence to inform immunisation policy.

BACKGROUND:Despite known maternal, perinatal, and infant health risks of tuberculosis during pregnancy, global estimates of incidence remain scarce. Existing estimates are outdated, and do not include the postpartum period, HIV co-infection, age, or specific changes in risk, limiting our understanding of the true scale of disease in this understudied population. METHODS:In this rapid review and modelling analysis, we estimated the global tuberculosis incidence in pregnant and postpartum women using a population-based modelling approach. We searched MEDLINE and EMBASE, with no date or language limits, and included studies reporting tuberculosis incidence in pregnancy or postpartum with suitable comparison groups; we also used Feb 6, 2025, interim data from the ongoing ORCHID cohort. We combined WHO age and sex-stratified tuberculosis incidence data with country-specific population and fertility data to estimate baseline tuberculosis incidence, and applied systematic review-based risk ratios to account for elevated increased risk during pregnancy and postpartum. Uncertainty in all inputs was propagated using standard error propagation formulae and summarised as mean tuberculosis incidence rates and mean incidence rate ratios (IRRs), each reported with 95% quantile-based uncertainty intervals (UIs). FINDINGS:We identified 37 studies published between 1996 and 2020, of which three were of sufficient quality to provide data for HIV-negative women. One additional study (ORCHID; Odayar et al, unpublished) provided data for women living with HIV. Compared with non-pregnant women without HIV, tuberculosis IRRs were 1·34 (95% CI 1·17-1·54) during pregnancy and 1·91 (1·53-2·39) during postpartum among HIV-negative women. For women living with HIV, IRRs were 5·73 (95% CI 2·64-10·94) during pregnancy and 3·58 (0·85-9·63) postpartum. We estimated 239 500 pregnant women (95% UI 216 300-262 800) and 97 600 postpartum women (90 100-105 200) developed tuberculosis disease globally in 2023, with HIV contributing to 21·3% (19·8-22·8) and 10·6% (9·9-11·3) of cases, respectively. The WHO African region had the highest incidence (110 600 [95% UI 96 700-124 500] in pregnant women and 40 900 [36 300-45 400] in postpartum women), followed by the South-East Asia region (79 900 [64 100-95 700] in pregnant women and 35 900 [30 800-41 100] in postpartum women). INTERPRETATION:Pregnant and postpartum women face substantial tuberculosis risk, yet remain under-represented in global estimates. Our findings underscore the need for improved surveillance and targeted interventions to reduce tuberculosis incidence in this group. FUNDING:UK Medical Research Council.

Abstract Background Use of oral cholera vaccine (OCV) is globally recommended as a public health response to cholera outbreaks, alongside water, sanitation and hygiene (WASH) interventions. Estimating vaccine effectiveness during emergencies in low- and middle-income countries is challenging because vaccination campaigns are often implemented over short time frames, while individual-level data are frequently incomplete due to constraints in infrastructure, resources and data systems. There is a need for pragmatic approaches that can generate timely, policy-relevant evidence using routinely collected data. Methods We analysed routine surveillance data from a large 2022–2023 cholera outbreak in Blantyre District, Malawi. The EpiEstim framework was used to generate estimates of the time-varying reproduction number (Rₜ) from line-listed case data. We modelled changes in 𝑅 𝑡 as a function of cumulative OCV coverage using a log-linear framework and propagated uncertainty through posterior sampling. Lagged WASH exposure variables were incorporated in the model to generate adjusted vaccine effectiveness estimates and to explore potential interaction effects. Sensitivity analyses assessed robustness to alternative lag structures. Findings The Blantyre outbreak was characterised by an initial period of low-level transmission followed by a sharp increase in cases from late November 2022, after which transmission declined steadily through April 2023. This decline coincided with the implementation of a reactive OCV campaign. The majority of the cases were among middle-aged men living in urban Blantyre. The unadjusted vaccine-associated reduction in transmission was estimated at 53.52% (95% credible interval (CrI):42.5–64.1%). After adjusting for a 7-day rolling average WASH activity, total vaccine effectiveness increased to 62.1% (95% CrI: 49.3–74.9%). Sensitivity analyses using alternative lag structures for WASH exposure produced comparable adjusted estimates. Interpretation Implementation of OCV contributed to a substantial reduction in cholera transmission during the outbreak. This study demonstrates a feasible approach for estimating vaccine-attributable impact whilst accounting for public health and social measures, such as WASH interventions. The methods described will be useful in outbreaks where classical observational designs are not possible, providing actionable evidence to policy makers for outbreak response in resource-limited settings. Funding MRC Discovery Medicine North (DiMeN) Doctoral Training Partnership (UKRI), National Institute for Health and Care Research (NIHR) Global Health Research Group on Gastrointestinal Infections and Wellcome through the core grant to the Malawi-Liverpool-Wellcome Research Programme. Research in context Evidence before this study Before undertaking this analysis, our team and collaborators generated detailed epidemiological and genomic evidence describing the 2022–2023 cholera outbreak in Blantyre, Malawi. Our previous fine-scale spatial work demonstrated marked heterogeneity in cholera burden across urban Blantyre. We found that transmission concentrated in densely populated informal settlements and areas with poor water and sanitation infrastructure. Our genomic analysis of outbreak isolates showed that the epidemic was driven by a recently introduced seventh-pandemic El Tor Vibrio cholerae sub-lineage AFR15 linked to regional and international transmission events, with epidemic expansion occurring during late 2022. These studies characterised where transmission occurred and the evolutionary origin of the outbreak strain. The population-level effect of reactive oral cholera vaccination (OCV) or concurrent and improved water, sanitation and hygiene (WASH) interventions on transmission dynamics during the outbreak was not quantified. In linked work, we conducted a systematic review of post-licensure vaccine impact and effectiveness studies from sub-Saharan Africa (CRD42023436851) to assess the principal study designs and the extent to which they adjusted for concurrent public health and social measures (PHSMs), such as WASH interventions. We searched PubMed, EMBASE, MEDLINE, CINAHL, and Google Scholar for vaccine impact or effectiveness studies conducted in children under five years and screened reference lists of included studies. Across all eligible studies, none measured or adjusted for concurrent PHSMs. Added value of this study Using surveillance, vaccination, and WASH data collected from Blantyre district by the Malawi Ministry of Health, we estimated the vaccine-associated reduction in transmission during a large reactive OCV campaign. By linking time-varying R ₜ estimates to cumulative vaccine coverage within a posterior sampling framework, we provide field-relevant effectiveness estimates from a setting where traditional individual-level designs were not feasible. We further evaluated whether concurrent WASH activity materially altered vaccine effect estimates. Implications of all the available evidence Taken together with prior spatial and genomic analyses of the same outbreak, our findings provide a more complete picture of cholera control in Malawi. The earlier work clarified transmission geography and pathogen introduction. The present analysis quantifies the population-level impact of reactive vaccination under real-world programme conditions. This integrated evidence base supports ministries of health in interpreting vaccine performance during rapidly evolving outbreaks and in strengthening the routine data systems required for timely evaluation of interventions.

Background:The World Health Organization (WHO) recommends 4-month treatment for children with non-severe pulmonary tuberculosis, outlining eligibility criteria for settings with and without chest X-ray (CXR). We evaluated the diagnostic accuracy of the WHO eligibility criteria in settings without CXR (WHO-criteria) and developed clinical scores to support disease classification. Methods:Using data from an individual participant dataset (IPD; Decide TB) of children with confirmed/unconfirmed tuberculosis from four diagnostic studies (RaPaed-TB, Umoya, TB-Speed HIV, TB-Speed Decentralisation), we assessed the diagnostic accuracy of the WHO-criteria (with/without bacteriological testing) using expert CXR interpretation as a reference. We developed two multivariable logistic regression models with (Score 1) and without (Score 2) bacteriological testing, converted coefficients into integer scores with a threshold of >10 corresponding to a sensitivity ≥70%. Results:Of 2,383 children in the Decide TB IPD, 633 (26.6%) met the eligibility criteria for a 4-month regimen, of whom 116 (18.3%) had radiologically severe disease. With and without bacteriological testing, the WHO-criteria had sensitivities of 30.1% (95%CI: 20.3%-40.2%) and 21.7% (95%CI: 10.4%-34.5%), and specificities of 83.4% (95%CI: 80.2%-86.4%) and 81.9% (95%CI: 78.8%-84.9%), respectively. Score 1 and Score 2 had sensitivities of 41.1% (95%CI: 32.4%-49.5%) and 30.9% (95%CI: 22.6%-40.4%), and specificities of 77.3% (95%CI: 73.6%-80.8%) and 83.0% (95%CI: 79.5%-86.3%), respectively. Using WHO-criteria, 91/116 (78.4%) and 105/116 (90.5%) of children were at risk of undertreatment, compared to 68/116 (58.6%) and 80/116 (68.9%) when using developed scores. Conclusions:Developed scores demonstrated better sensitivity than WHO-criteria, however, performance remained suboptimal. Implementing shorter antituberculosis regimens without CXR remains challenging in children.

Introduction: Integrated care is increasingly adopted to address the needs of patients with multimorbidity, but cost-effective configuration of integrated healthcare pathways remains unclear. This study reviewed decision analytic models (DAMs) used in economic evaluations of integrated care interventions for cardiometabolic multimorbidity. Methods: A systematic search of eight electronic databases was conducted to identify peer-reviewed articles published in English until November 2024. Studies using DAMs to evaluate integrated care interventions for patients at risk or having cardiometabolic multimorbidity were included. Data on DAMs characteristics, integrated care models evaluated, and diseases were summarised. The quality of reporting was assessed using Philips (2006) checklist. Results: Sixteen studies met inclusion criteria. Most studies (81%) were cost utility analyses, focused on hypertension and/or diabetes concordant multimorbidity (69%). High-income countries accounted for 69% of the studies. Markov models were used the most (63%), with only three studies employing individual patient simulation (microsimulation) models. Few studies were explicit about data validation and reporting uncertainty. Conclusion: Economic evaluations of integrated care cardiometabolic multimorbidity should adopt microsimulation to better capture patient-level interactions and health outcomes. Better reporting of validation and uncertainty is needed. There is limited application of DAM-based economic evaluations of integrated care in low- and middle-income countries.

We assessed how evolving global tuberculosis (TB) trends might influence Mycobacterium tuberculosis immunoreactivity and TB risk among persons immigrating to low-incidence countries. We projected annual risk for infection (ARI) in 168 countries for 2024-2050, focusing on China, India, the Philippines, and Vietnam. We applied projections to the age profile of immigrants to 4 low-incidence countries to estimate changes in M. tuberculosis immunoreactivity prevalence and TB risk under status quo and accelerated ARI decline scenarios. In the status quo 2024 estimate, M. tuberculosis immunoreactivity prevalence ranged from 14.7% in China to 40.1% in the Philippines, declining to 5.8% in China and 23.0% in the Philippines by 2050; TB risk also declined. Accelerated ARI reductions yielded greater relative decreases in disease risk than immunoreactivity prevalence. Declining global TB incidence could reduce M. tuberculosis immunoreactivity and disease risk among immigrant populations, which could inform cost-benefit analyses for future TB screening strategies in low-incidence settings.

Background Reversing the rising trend in the burden of non-communicable diseases (NCDs) in Kenya requires the implementation and scaleup of prevention and control interventions. Health economics can help to inform priority setting processes by comparing the costs and outcomes from alternative interventions. However, there is limited research regarding the role played by health economic evidence in NCD priority setting in Kenya. This study explored the perspectives of Kenyan stakeholders regarding the use of, and barriers affecting the uptake of health economic evidence in NCD priority setting in Kenya. Methods We conducted sixteen comprehensive interviews with Kenyan stakeholders engaged in NCD policy, management and research. The study participants comprised officials from the Ministry of Health at national and county levels, representatives from civil society organisations, the private sector, health economists, and researchers. We applied an inductive thematic approach in coding and data analysis. Results The study found a limited and inconsistent uptake of health economic evidence for informing NCD decision making, which was described as fragmented, ad hoc, and peripheral. Investment cases and cost analyses were the most commonly applied forms of economic evidence. Key barriers to increased uptake included the low prioritisation of health economic evidence within decision-making processes, misalignment between health economic research outputs and policy priorities, and limited capacity to conduct and interpret economic analyses. The scarcity of locally relevant, high-quality economic data also emerged as a major impediment to the reliability and credibility of health economic evidence. Conclusion Despite growing recognition of its value, health economic evidence remains inconsistently integrated into NCD decision-making in Kenya. Addressing gaps in prioritisation, capacity, data availability, and alignment between research and policy needs may strengthen the systematic and sustained use of health economic evidence to support effective NCD policy and resource allocation. Analysts should involve the relevant stakeholders while designing and generating health economic evidence to improve uptake.

Tuberculosis (TB) is a major public health concern and the leading infectious cause of mortality globally. The disease exhibits strong prevalence patterns by age and sex, but the implications of these patterns for likely TB exposure and transmission have not previously been systematically assessed. We combined estimates of social mixing patterns and TB prevalence for 177 countries to estimate the proportion of TB exposure to and transmission from age groups and sexes. We found that a majority of TB transmission, in both sexes, and for both children and adults, is attributable to contact with adult men. Across age groups, TB exposure typically peaked in adolescence, whereas contributions to TB transmission was flatter or increasing with age, and more variable across regions. Our analysis highlights an important and under-appreciated contribution to transmission in some settings from older adults, who may face particular barriers to healthcare access. More systematic analyses focusing on understanding the epidemiology of TB transmission should be used to inform context-specific prioritization of interventions.

Accurate dengue forecasting is vital for public health preparedness. Despite a surge in forecasting approaches, a quantitative ranking of the relative performance and practical utility of dengue forecasting is lacking. A systematic review and Network Meta-Analysis (NMA) of studies comparing dengue forecasting methods (2014–2024) was conducted. Models were categorised into five groups: Time Series, Deep Learning (DL), Machine Learning (excluding DL), Hybrid, and Ensembles. NMA was applied to the logarithm of the most common forecast error metric to rank relative performance—an “Implementability Score” quantified analyst and data requirements, and computational costs. 59 studies were included. NMA of Root Mean Squared Error identified k-Nearest Neighbour (k-NN) models as achieving the highest predictive accuracy, followed closely by Vector Autoregression, Kalman Filtering, Generalised Linear Model and Autoregressive Neural Network (ARNN). While DL models showed high potential, they scored lowest in implementability due to poor interpretability and high data requirements. Most studies utilised meteorological covariates, with significant gaps in the use of socio-economic and entomological predictors. Although there was some trade-off between accuracy and implementability, traditional statistical models were often comparable in accuracy to machine learning approaches, with advantages in interpretability and data needs. Under-explored areas for future research include the use of ensemble models and the use of socio-economic and entomological data. PROSPERO CRD420251016662. Dengue is a critical global health threat affecting the world’s population. While many forecasting models exist to help officials prepare for outbreaks, there has been no standardised way to compare their performance. This leaves health experts in resource-limited areas uncertain about which tools are truly reliable or easy to use under their specific local conditions. We conducted a network meta-analysis of studies comparing dengue forecasting methods’ accuracy, grouping them into five categories: Machine Learning, Deep Learning, Time Series, Ensemble, and Hybrid. Beyond ranking their accuracy, we developed an “Implementability Score” to evaluate the practical feasibility of each model, accounting for technical complexity, data requirements, and software accessibility. Our analysis identified the top-performing models. Notably, traditional statistical models often performed as well as complex Deep Learning algorithms. While advanced models show potential, they are often difficult to implement or explain to decision-makers. There is no “one-size-fits-all” solution; the best model depends on capacity and data in each setting. This study provides a roadmap for public health officials to select tools that are both accurate and feasible.

Objective:To develop a new tuberculosis transmission model, addressing the limitations of and building on the TB Impact Model and Estimates software tool, to enable decision-makers to assess the impact of various tuberculosis interventions and allocate resources more effectively. Methods:We designed a model incorporating diagnosis and treatment pathways across public and private sectors, stratified across age groups, drug susceptibility, human immunodeficiency virus status and vaccination status. We calibrated our model using country-specific data from 29 high-burden countries and determined calibration target indicators according to national epidemic profiles. We performed the model calibration using a Bayesian adaptive Markov chain Monte Carlo process. We compare modelled and actual data for Indonesia and Nigeria. Findings:Our model calibration results showed good agreement with historical tuberculosis data. In Indonesia, we demonstrate that comprehensive implementation of the Stop TB Partnership Global plan to end TB interventions, including a public-private partnership, modern diagnostics, improved treatment for drug-resistant tuberculosis and a post-exposure vaccine, could enable the country to achieve the targets of the World Health Organization (WHO) End TB Strategy by 2035. In Nigeria, implementing the National strategic plan for tuberculosis control 2021-2026 could reduce tuberculosis incidence by 27% and mortality by 37% by 2030, even without a vaccine. Conclusion:Our model provides a robust analytical foundation from which to assess the epidemiological impact of diverse interventions, prioritize investments and guide policy. The model's open-source design and alignment with WHO recommendations make it a valuable tool for guiding evidence-based investment.

Abstract Background There is no recommended guidance on appropriate sample sizes for pilot cluster-randomised controlled trials (cRCTs). Pilot trials should not aim to demonstrate efficacy, and achieving power should not be used to justify the sample size. However, the CONSORT extension for pilot trials states that some justification for their sample size should be given. We conducted a review to understand the choices and justifications of pilot cRCT sample sizes and their trends over time, and to explore apparent changes following the publication of CONSORT extensions for cluster trials and for pilot and feasibility trials. Methods We searched PubMed and Web of Science for pilot cRCTs. The search took place on 01/10/2020 and was restricted to papers published on or after 01/01/2010. Identification of papers was based around a search for the terms ‘pilot’ or ‘feasibility’ in the title and abstract/topic. The primary interest in the review was in the planned sample size in terms of clusters per-arm. We also examined participants per-arm and enrolled sample size. Analyses were descriptive or displayed graphically. Results Our search returned 3090 records. After removing exact duplicates, aggregating records into unique studies and excluding ineligible studies, we identified 170 pilot or feasibility cRCTs. The median sample size was four clusters per arm. Stratification showed this to be fairly consistent, regardless of the justification given, whether a formal analysis was planned, whether the intention was to estimate the Intra-Cluster Correlation, cluster type, general medical area, funding type, and over time. Conclusion Average sample sizes for cRCTs have remained strikingly constant over the period 2010–2020 and across several key features of studies; they do not appear to be meaningfully impacted by the stated study aims or sample size justifications. This is despite the fact that the reported main aims for pilot cRCTs, and justifications for their sample sizes, do appear to have changed during this time. Given that aims and justifications appear to have changed, but typical sample sizes have not, it is possible that some researchers choose sample size for pilot cRCTs primarily out of convenience or practical reasons, whilst stating other justifications.

BACKGROUND:Estimating the proportion of individuals currently infected with Mycobacterium tuberculosis (Mtb) is key for informing global health policies. Although a substantial portion of the global population exhibit tuberculous immunoreactivity, not all have a viable Mtb infection. Moreover, individuals with recent infections are at a higher risk of developing tuberculosis (TB). Here, we present estimates of the global burden of viable Mtb infection, using new insights into the natural history of TB. METHODS AND FINDINGS:We constructed country-specific trends in annual risk of infection considering estimates of TB burden, immunoreactivity reversion, and age-specific mixing. We applied these trends to a deterministic mathematical model incorporating reinfection and self-clearance to estimate recent (within 2 years) and total viable Mtb infections. Empirical data on self-clearance are limited, so rates were informed by modelling estimates. In 2022, we estimated that 133.7 million people (95% uncertainty interval [UI]: 104.0, 171.1) had a recent Mtb infection, representing 1.7% (95% UI: 1.3, 2.2) of the global population. In total, 288.9 million people (95% UI: 242.2, 342.7)-or 3.7% (95% UI: 3.1, 4.3) globally-were estimated to harbour a viable Mtb infection. Among those recently infected, 12.0% (95% UI: 11.4, 12.7) were children under 15 years of age. Most recent infections were found in the World Health Organization regions of South-East Asia (49.0%; 95% UI: 37.2, 62.4), the Western Pacific (19.7%; 95% UI: 12.6, 30.5), and Africa (17.9%; 95% UI: 12.9, 24.1). India, Indonesia, and China had the highest burden, with 39.1 million (95% UI: 18.0, 73.6), 12.0 million (95% UI: 5.8, 22.9), and 11.2 million (95% UI: 5.0, 25.5) people, respectively, recently infected with Mtb. Sensitivity analyses of varying self-clearance scenarios showed significant changes in global estimates of viable Mtb infection, particularly in total burden, with lower self-clearance rates. Overall uncertainty in the estimates was considerable, reflecting limitations in the underlying data informing key model parameters. CONCLUSIONS:Our findings offer global burden estimates of viable Mtb infection and reveal a sizable population recently infected with Mtb and at high risk of progression to disease. New diagnostic tools that can detect individuals with viable Mtb-particularly those who would benefit from TB preventive therapy-are urgently needed.

INTRODUCTION:During adolescence, tuberculosis incidence rises, with a greater increase in males compared with females. Tuberculosis notifications and estimates infrequently disaggregate adolescent age groups. Moreover, the factors that drive the increases in overall incidence and the male-to-female (MF) ratio remain unclear. METHODS:We constructed a mechanistic model to estimate cumulative Mycobacterium tuberculosis infection and tuberculosis disease incidence in the WHO's 30 high-tuberculosis burden countries (HBCs), which represent 86%-90% of global tuberculosis incidence. We derived infection risk from tuberculosis prevalence and assortative social mixing based on sex and age (10-14 years vs 15-19 years old). We adjusted age subgroup-specific risks of disease progression by age- and sex-specific risks of low body mass index (BMI), pregnancy and postpartum period (PPP) and HIV coinfection. We calculated population attributable fractions (PAFs) to these factors. RESULTS:In 2019, 91.2 million (95% uncertainty interval (UI) 83.9 to 99.3 million) adolescents in the 30 HBCs had been infected with M. tuberculosis, and an estimated 1.0 million (95% UI 0.8 to 1.2 million) developed tuberculosis disease. The median PAF of tuberculosis disease to HIV, modified by antiretroviral therapy, was 1% and highest in Southern Africa. The median PAF for PPP among older adolescents of both sexes was 2.6%. The median PAF to low BMI was 16% and highest in South Asia. The MF risk ratio of tuberculosis disease was 1.2-fold higher among older adolescents, relative to young adolescents. The widening MF risk ratio was attributable mostly to low BMI, with a smaller contribution from sex-assortative social mixing. CONCLUSION:Globally, large numbers of adolescents have been infected by M. tuberculosis and develop tuberculosis disease. Low BMI is the most important contributor to the overall incidence of tuberculosis disease, as well as to the sex difference that widens with age.

Delays in identifying and treating individuals with infectious tuberculosis (TB) contribute to poor health outcomes and allow ongoing community transmission of M. tuberculosis (Mtb). Current recommendations for screening for tuberculosis specify community characteristics (e.g., areas with high local tuberculosis prevalence) that can be used to target screening within the general population. However, areas of higher tuberculosis burden are not necessarily areas with higher rates of transmission. We investigated the transmission of Mtb using high-resolution surveillance data in Blantyre, Malawi. We extracted and performed whole genome sequencing on mycobacterial DNA from cultured M. tuberculosis isolates obtained from culture-positive tuberculosis cases at the time of tuberculosis (TB) notification in Blantyre, Malawi between 2015-2019. We constructed putative transmission networks identified using TransPhylo and investigated individual and pair-wise demographic, clinical, and spatial factors associated with person-to-person transmission. We found that 56% of individuals with sequenced isolates had a probable transmission link to at least one other individual in the study. We identified thirteen putative transmission networks that included five or more individuals. Five of these networks had a single spatial focus of transmission in the city, and each focus centered in a distinct neighborhood in the city. We also found that approximately two-thirds of inferred transmission links occurred between individuals residing in different geographic zones of the city. While the majority of detected tuberculosis transmission events in Blantyre occurred between people living in different zones, there was evidence of distinct geographical concentration for five transmission networks. These findings suggest that targeted interventions in areas with evidence of localized transmission may be an effective local tactic, but will likely need to be augmented by city-wide interventions to improve case finding to have sustained impact.

Immortal time bias is a spurious or exaggerated protective association that commonly arises in naive analyses of observational data. It occurs when people receive the intervention because they survive, rather than survive because they received the intervention. Studies in conditions with substantial early mortality, such as acute severe infections, are particularly vulnerable. We developed IMMORTOOL, an R package accessible via a user-friendly web interface (https://petedodd.github.io/IMMORTOOL-live/). This tool will estimate the potential for immortal time bias using empiric or assumed data on the distributions of time to intervention and time to event. Assumptions are that no other biases are present and that the intervention does not affect the outcome. The tool was benchmarked using studies presenting both naive analyses and analyses with the intervention fit as a time-varying exposure. We applied IMMORTOOL to a set of influential observational studies that used naive analyses when estimating the impact of polyclonal intravenous immunoglobulin (IVIG) on survival in streptococcal toxic shock syndrome (STSS). IMMORTOOL demonstrated that published estimates suggesting a survival advantage from giving IVIG in STSS are explained, at least in part, by immortal time bias. IMMORTOOL can quantify the potential for immortal time bias in observational analyses. Importantly, it simulates only bias resulting from misallocation of person-time, not other related selection biases. The tool may help readers interrogate published studies. We do not advocate IMMORTOOL being used to justify naive analyses where robust analyses are possible. To what extent giving IVIG in STSS improves survival remains uncertain.

Bacille Calmette-Guérin (BCG) is the most commonly-used vaccination globally, but multi-country cost-effectiveness analyses are outdated and have not considered sequelae in tuberculosis survivors. A rationale is lacking to guide the size of safety stocks. For the 110 countries using universal neonatal BCG vaccination, we used a decision tree model to compare the costs and health benefits of status-quo BCG vaccination for children aged 0-4 years in 2023 to a counterfactual where no BCG was used, accounting for post-tuberculosis sequelae. We assessed cost-effectiveness against a threshold of 30% per capita gross domestic product from a health system perspective. We determined the safety stock that maximized expected net benefit. BCG vaccination prevented 742,000 (95% uncertainty interval[UI]: 541,000 to 991,000) tuberculosis episodes and 192,000 (95%UI:138,000 to 264,000) tuberculosis deaths globally. Of these, 49,000 (95%UI: 32,300 to 72,100) episodes and 30,000 (95%UI: 19,600 to 45,100) deaths prevented were from tuberculous meningitis. Universal neonatal BCG vaccination was cost-effective in the majority (75/110) of countries where it is used and in all countries with estimated tuberculosis incidence over 42 per 100,000 per year, with a median incremental cost-effectiveness ratio of $276 (interquartile range: $84 to $1514) per disability-adjusted life-year averted. Median optimal safety stock for a 10% uncertainty in demand was 11% (IQR: 7% to 17%) of expected demand. BCG vaccination continues to prevent substantial morbidity and mortality in children globally, and cost-effectiveness considerations support continued universal vaccination in most countries with this policy currently. UK EPSRC & DHSC