Factors associated with tuberculosis treatment initiation among bacteriologically negative individuals evaluated for tuberculosis: an individual patient data meta-analysis.

Background: Globally, over one-third of pulmonary tuberculosis (TB) disease diagnoses are made based on clinical criteria after a negative diagnostic test result. Understanding factors associated with clinicians' decisions to initiate treatment for individuals with negative test results is critical for predicting the potential impact of new diagnostics. Methods: We performed a systematic review and individual patient data meta-analysis using studies conducted between January/2010 and December/2022 (PROSPERO: CRD42022287613). We included trials or cohort studies that enrolled individuals evaluated for TB in routine settings. In these studies participants were evaluated based on clinical examination and routinely-used diagnostics, and were followed for ≥1 week after the initial test result. We used hierarchical Bayesian logistic regression to identify factors associated with treatment initiation following a negative result on an initial bacteriological test (e.g., sputum smear microscopy, Xpert MTB/RIF). Findings: Multiple factors were positively associated with treatment initiation: male sex [adjusted Odds Ratio (aOR) 1.61 (1.31-1.95)], history of prior TB [aOR 1.36 (1.06-1.73)], reported cough [aOR 4.62 (3.42-6.27)], reported night sweats [aOR 1.50 (1.21-1.90)], and having HIV infection but not on ART [aOR 1.68 (1.23-2.32)]. Treatment initiation was substantially less likely for individuals testing negative with Xpert [aOR 0.77 (0.62-0.96)] compared to smear microscopy and declined in more recent years. Interpretation: Multiple factors influenced decisions to initiate TB treatment despite negative test results. Clinicians were substantially less likely to treat in the absence of a positive test result when using more sensitive, PCR-based diagnostics. Funding: National Institutes of Health. Research in context: Evidence before this study: In countries with a high burden of tuberculosis, over one-third of notified cases for pulmonary TB are diagnosed based on clinical criteria, without bacteriological confirmation of disease ('clinical diagnosis'). For these individuals with negative bacteriological test results, there is limited evidence on the factors associated with higher or lower rates of clinical diagnosis. In the context of individual clinical trials, some analyses have reported lower rates of treatment initiation for individuals testing negative on new cartridge-based PCR tests (e.g., Xpert MTB-RIF), as compared to individuals testing negative in sputum smear microscopy.Added value of this study: This study conducted a systematic review of studies that collected data on patient characteristics and treatment initiation decisions for individuals receiving a negative bacteriological test result as part of initial evaluation for TB. Patient-level data from 13 countries across 12 studies (n=15121) were analyzed in an individual patient data meta-analysis, to describe factors associated with clinicians' decisions to treat for TB disease. We identified significant associations between multiple clinical factors and the probability that a patient would be initiated on TB treatment, including sex, history of prior TB, reported symptoms (cough and night sweats), and HIV status. Controlling for other factors, patients testing negative on PCR-based diagnostics (e.g., Xpert MTB/RIF) were less likely to be initiated on treatment than those testing negative with smear microscopy.Implications of all the available evidence: Rates of clinical diagnosis for TB differ systematically as a function of multiple clinical factors and are lower for patients who test negative with new PCR-based diagnostics compared to earlier smear-based methods. This evidence can be used to refine diagnostic algorithms and better understand the implications of introducing new diagnostic tests for TB.

The recent rapid expansion of multidrug resistant Ural lineage Mycobacterium tuberculosis in Moldova.

The projected trajectory of multidrug resistant tuberculosis (MDR-TB) epidemics depends on the reproductive fitness of circulating strains of MDR M. tuberculosis (Mtb). Previous efforts to characterize the fitness of MDR Mtb have found that Mtb strains of the Beijing sublineage (Lineage 2.2.1) may be more prone to develop resistance and retain fitness in the presence of resistance-conferring mutations than other lineages. Using Mtb genome sequences from all culture-positive cases collected over two years in Moldova, we estimate the fitness of Ural (Lineage 4.2) and Beijing strains, the two lineages in which MDR is concentrated in the country. We estimate that the fitness of MDR Ural strains substantially exceeds that of other susceptible and MDR strains, and we identify several mutations specific to these MDR Ural strains. Our findings suggest that MDR Ural Mtb has been transmitting efficiently in Moldova and poses a substantial risk of spreading further in the region.

Disparities in Tuberculosis Incidence by Race and Ethnicity Among the U.S.-Born Population in the United States, 2011 to 2021 : An Analysis of National Disease Registry Data.

BACKGROUND: Elevated tuberculosis (TB) incidence rates have recently been reported for racial/ethnic minority populations in the United States. Tracking such disparities is important for assessing progress toward national health equity goals and implementing change. OBJECTIVE: To quantify trends in racial/ethnic disparities in TB incidence among U.S.-born persons. DESIGN: Time-series analysis of national TB registry data for 2011 to 2021. SETTING: United States. PARTICIPANTS: U.S.-born persons stratified by race/ethnicity. MEASUREMENTS: TB incidence rates, incidence rate differences, and incidence rate ratios compared with non-Hispanic White persons; excess TB cases (calculated from incidence rate differences); and the index of disparity. Analyses were stratified by sex and by attribution of TB disease to recent transmission and were adjusted for age, year, and state of residence. RESULTS: In analyses of TB incidence rates for each racial/ethnic population compared with non-Hispanic White persons, incidence rate ratios were as high as 14.2 (95% CI, 13.0 to 15.5) among American Indian or Alaska Native (AI/AN) females. Relative disparities were greater for females, younger persons, and TB attributed to recent transmission. Absolute disparities were greater for males. Excess TB cases in 2011 to 2021 represented 69% (CI, 66% to 71%) and 62% (CI, 60% to 64%) of total cases for females and males, respectively. No evidence was found to indicate that incidence rate ratios decreased over time, and most relative disparity measures showed small, statistically nonsignificant increases. LIMITATION: Analyses assumed complete TB case diagnosis and self-report of race/ethnicity and were not adjusted for medical comorbidities or social determinants of health. CONCLUSION: There are persistent disparities in TB incidence by race/ethnicity. Relative disparities were greater for AI/AN persons, females, and younger persons, and absolute disparities were greater for males. Eliminating these disparities could reduce overall TB incidence by more than 60% among the U.S.-born population. PRIMARY FUNDING SOURCE: Centers for Disease Control and Prevention.

Quantifying gaps in the tuberculosis care cascade in Brazil: A mathematical model study using national program data.

BACKGROUND: In Brazil, many individuals with tuberculosis (TB) do not receive appropriate care due to delayed or missed diagnosis, ineffective treatment regimens, or loss-to-follow-up. This study aimed to estimate the health losses and TB program costs attributable to each gap in the care cascade for TB disease in Brazil. METHODS AND FINDINGS: We constructed a Markov model simulating the TB care cascade and lifetime health outcomes (e.g., death, cure, postinfectious sequelae) for individuals developing TB disease in Brazil. We stratified the model by age, human immunodeficiency virus (HIV) status, drug resistance, state of residence, and disease severity, and developed a parallel model for individuals without TB that receive a false-positive TB diagnosis. Models were fit to data (adult and pediatric) from Brazil's Notifiable Diseases Information System (SINAN) and Mortality Information System (SIM) for 2018. Using these models, we assessed current program performance and simulated hypothetical scenarios that eliminated specific gaps in the care cascade, in order to quantify incremental health losses and TB diagnosis and treatment costs along the care cascade. TB-attributable disability-adjusted life years (DALYs) were calculated by comparing changes in survival and nonfatal disability to a no-TB counterfactual scenario. We estimated that 90.0% (95% uncertainty interval [UI]: 85.2 to 93.4) of individuals with TB disease initiated treatment and 10.0% (95% UI: 7.6 to 12.5) died with TB. The average number of TB-attributable DALYs per incident TB case varied across Brazil, ranging from 2.9 (95% UI: 2.3 to 3.6) DALYs in Acre to 4.0 (95% UI: 3.3 to 4.7) DALYs in Rio Grande do Sul (national average 3.5 [95% UI: 2.8 to 4.1]). Delayed diagnosis contributed the largest health losses along the care cascade, followed by post-TB sequelae and loss to follow up from TB treatment, with TB DALYs reduced by 71% (95% UI: 65 to 76), 41% (95% UI: 36 to 49), and 10% (95% UI: 7 to 16), respectively, when these factors were eliminated. Total health system costs were largely unaffected by improvements in the care cascade, with elimination of treatment failure reducing attributable costs by 3.1% (95% UI: 1.5 to 5.4). TB diagnosis and treatment of false-positive individuals accounted for 10.2% (95% UI: 3.9 to 21.7) of total programmatic costs but contributed minimally to health losses. Several assumptions were required to interpret programmatic data for the analysis, and we were unable to estimate the contribution of social factors to care cascade outcomes. CONCLUSIONS: In this study, we observed that delays to diagnosis, post-disease sequelae and treatment loss to follow-up were primary contributors to the TB burden of disease in Brazil. Reducing delays to diagnosis, improving healthcare after TB cure, and reducing treatment loss to follow-up should be prioritized to improve the burden of TB disease in Brazil.

Identifying local foci of tuberculosis transmission in Moldova using a spatial multinomial logistic regression model.

BACKGROUND: Multidrug resistant tuberculosis (MDR-TB) represents a major public health concern in the Republic of Moldova, with an estimated 31% of new and 56% of previously treated TB cases having MDR disease in 2022. A recent genomic epidemiology study of incident TB occurring in 2018 and 2019 found that 92% of MDR-TB was the result of transmission. The MDR phenotype was concentrated among two M. tuberculosis (Mtb) lineages: L2.2.1 (Beijing) and L4.2.1 (Ural). METHODS: We developed and applied a hierarchical Bayesian multinominal logistic regression model to Mtb genomic, spatial, and epidemiological data collected from all individuals with diagnosed TB in Moldova in 2018 and 2019 to identify locations in which specific Mtb strains are being transmitted. We then used a logistic regression model to estimate locality-level factors associated with local transmission. FINDINGS: We found differences in the spatial distribution and degree of local concentration of disease due to specific strains of Beijing and Ural lineage Mtb. Foci of transmission for four strains of Beijing lineage Mtb, predominantly of the MDR-TB phenotype, were located in several regions, but largely concentrated in Transnistria. In contrast, transmission of Ural lineage Mtb had less marked patterns of spatial aggregation, with a single strain (also of the MDR phenotype) spatially clustered in southern Transnistria. We found a 30% (95% credible interval 2%-80%) increase in odds of a locality being a transmission cluster for each increase of 100 persons per square kilometer, while higher local tuberculosis incidence and poverty were not associated with a locality being a transmission focus. INTERPRETATION: Our results identified localities where specific Mtb transmission networks were concentrated and quantified the association between locality-level factors and focal transmission. This analysis revealed Transnistria as the primary area where specific Mtb strains (predominantly of the MDR-TB phenotype) were locally transmitted and suggests that targeted intensified case finding in this region may be an attractive policy option. FUNDING: Funding for this work was provided by the National Institute of Allergy and Infectious Diseases at the US National Institutes of Health.

Estimated rates of progression to tuberculosis disease for persons infected with Mycobacterium tuberculosis in the United States.

BACKGROUND: In the United States, over 80% of tuberculosis (TB) disease cases are estimated to result from reactivation of latent TB infection (LTBI) acquired more than 2 years previously ("reactivation TB"). We estimated reactivation TB rates for the US population with LTBI, overall, by age, sex, race-ethnicity, and US-born status, and for selected comorbidities (diabetes, end-stage renal disease, and HIV). METHODS: We collated nationally representative data for 2011-2012. Reactivation TB incidence was based on TB cases reported to the National TB Surveillance System that were attributed to LTBI reactivation. Person-years at risk of reactivation TB were calculated using interferon-gamma release assay (IGRA) positivity from the National Health and Nutrition Examination Survey, published values for interferon-gamma release assay sensitivity and specificity, and population estimates from the American Community Survey. RESULTS: For persons aged ≥6 years with LTBI, the overall reactivation rate was estimated as 0.072 (95% uncertainty interval: 0.047, 0.12) per 100 person-years. Estimated reactivation rates declined with age. Compared to the overall population, estimated reactivation rates were higher for persons with diabetes (adjusted rate ratio [aRR] = 1.6 [1.5, 1.7]), end-stage renal disease (aRR = 9.8 [5.4, 19]), and HIV (aRR = 12 [10, 13]). CONCLUSIONS: In our study, individuals with LTBI faced small, non-negligible risks of reactivation TB. Risks were elevated for individuals with medical comorbidities that weaken immune function.

Racial and ethnic disparities in diagnosis and treatment outcomes among US-born people diagnosed with tuberculosis, 2003-19: an analysis of national surveillance data.

BACKGROUND: Persistent racial and ethnic disparities in tuberculosis incidence exist in the USA, however, less is known about disparities along the tuberculosis continuum of care. This study aimed to describe how race and ethnicity are associated with tuberculosis diagnosis and treatment outcomes. METHODS: In this analysis of national surveillance data, we extracted data from the US National Tuberculosis Surveillance System on US-born patients with tuberculosis during 2003-19. To estimate the association between race and ethnicity and tuberculosis diagnosis (diagnosis after death, cavitation, and sputum smear positivity) and treatment outcomes (treatment for more than 12 months, treatment discontinuation, and death during treatment), we fitted log-binomial regression models adjusting for calendar year, sex, age category, and regional division. Race and ethnicity were defined based on US Census Bureau classification as White, Black, Hispanic, Asian, American Indian or Alaska Native, Native Hawaiian or Pacific Islander, and people of other ethnicities. We quantified racial and ethnic disparities as adjusted relative risks (aRRs) using non-Hispanic White people as the reference group. We also calculated the Index of Disparity as a summary measure that quantifies the dispersion in a given outcome across all racial and ethnic groups, relative to the population mean. We estimated time trends in each outcome to evaluate whether disparities were closing or widening. FINDINGS: From 2003 to 2019, there were 72 809 US-born individuals diagnosed with tuberculosis disease of whom 72 369 (35·7% women and 64·3% men) could be included in analyses. We observed an overall higher risk of any adverse outcome (defined as diagnosis after death, treatment discontinuation, or death during treatment) for non-Hispanic Black people (aRR 1·27, 95% CI 1·22-1·32), Hispanic people (1·20, 1·14-1·27), and American Indian or Alaska Native people (1·24, 1·12-1·37), relative to non-Hispanic White people. The Index of Disparity for this summary outcome remained unchanged over the study period. INTERPRETATION: This study, based on national surveillance data, indicates racial and ethnic disparaties among US-born tuberculosis patients along the tuberculosis continuum of care. Initiatives are needed to reduce diagnostic delays and improve treatment outcomes for US-born racially marginalised people in the USA. FUNDING: US Centers for Disease Control and Prevention.

Predictive power of wastewater for nowcasting infectious disease transmission: A retrospective case study of five sewershed areas in Louisville, Kentucky.

BACKGROUND: Epidemiological nowcasting traditionally relies on count surveillance data. The availability and quality of such count data may vary over time, limiting representation of true infections. Wastewater data correlates with traditional surveillance data and may provide additional value for nowcasting disease trends. METHODS: We obtained SARS-CoV-2 case, death, wastewater, and serosurvey data for Jefferson County, Kentucky (USA), between August 2020 and March 2021, and parameterized an existing nowcasting model using combinations of these data. We assessed the predictive performance and variability at the sewershed level and compared the effects of adding or replacing wastewater data to case and death reports. FINDINGS: Adding wastewater data minimally improved the predictive performance of nowcasts compared to a model fitted to case and death data (Weighted Interval Score (WIS) 0.208 versus 0.223), and reduced the predictive performance compared to a model fitted to deaths data (WIS 0.517 versus 0.500). Adding wastewater data to deaths data improved the nowcasts agreement to estimates from models using cases and deaths data. These findings were consistent across individual sewersheds as well as for models fit to the aggregated total data of 5 sewersheds. Retrospective reconstructions of epidemiological dynamics created using different combinations of data were in general agreement (coverage >75%). INTERPRETATION: These findings show wastewater data may be valuable for infectious disease nowcasting when clinical surveillance data are absent, such as early in a pandemic or in low-resource settings where systematic collection of epidemiologic data is difficult.

Spatially-targeted tuberculosis screening has limited impact beyond household contact tracing in Lima, Peru: A model-based analysis.

Mathematical models have suggested that spatially-targeted screening interventions for tuberculosis may efficiently accelerate disease control, but empirical data supporting these findings are limited. Previous models demonstrating substantial impacts of these interventions have typically simulated large-scale screening efforts and have not attempted to capture the spatial distribution of tuberculosis in households and communities at a high resolution. Here, we calibrate an individual-based model to the locations of case notifications in one district of Lima, Peru. We estimate the incremental efficiency and impact of a spatially-targeted interventions used in combination with household contact tracing (HHCT). Our analysis reveals that HHCT is relatively efficient with a median of 40 (Interquartile Range: 31.7 to 49.9) household contacts required to be screened to detect a single case of active tuberculosis. However, HHCT has limited population impact, producing a median incidence reduction of only 3.7% (Interquartile Range: 5.8% to 1.9%) over 5 years. In comparison, spatially targeted screening (which we modeled as active case finding within high tuberculosis prevalence areas 100 m2 grid cell) is far less efficient, requiring evaluation of ≈12 times the number of individuals as HHCT to find a single individual with active tuberculosis. Furthermore, the addition of the spatially targeted screening effort produced only modest additional reductions in tuberculosis incidence over the 5 year period (≈1.3%) in tuberculosis incidence. In summary, we found that HHCT is an efficient approach for tuberculosis case finding, but has limited population impact. Other screening approaches which target areas of high tuberculosis prevalence are less efficient, and may have limited impact unless very large numbers of individuals can be screened.

Global burden of disease due to rifampicin-resistant tuberculosis: a mathematical modeling analysis.

In 2020, almost half a million individuals developed rifampicin-resistant tuberculosis (RR-TB). We estimated the global burden of RR-TB over the lifetime of affected individuals. We synthesized data on incidence, case detection, and treatment outcomes in 192 countries (99.99% of global tuberculosis). Using a mathematical model, we projected disability-adjusted life years (DALYs) over the lifetime for individuals developing tuberculosis in 2020 stratified by country, age, sex, HIV, and rifampicin resistance. Here we show that incident RR-TB in 2020 was responsible for an estimated 6.9 (95% uncertainty interval: 5.5, 8.5) million DALYs, 44% (31, 54) of which accrued among TB survivors. We estimated an average of 17 (14, 21) DALYs per person developing RR-TB, 34% (12, 56) greater than for rifampicin-susceptible tuberculosis. RR-TB burden per 100,000 was highest in former Soviet Union countries and southern African countries. While RR-TB causes substantial short-term morbidity and mortality, nearly half of the overall disease burden of RR-TB accrues among tuberculosis survivors. The substantial long-term health impacts among those surviving RR-TB disease suggest the need for improved post-treatment care and further justify increased health expenditures to prevent RR-TB transmission.

Tabby2: a user-friendly web tool for forecasting state-level TB outcomes in the United States.

BACKGROUND: In the United States, the tuberculosis (TB) disease burden and associated factors vary substantially across states. While public health agencies must choose how to deploy resources to combat TB and latent tuberculosis infection (LTBI), state-level modeling analyses to inform policy decisions have not been widely available. METHODS: We developed a mathematical model of TB epidemiology linked to a web-based user interface - Tabby2. The model is calibrated to epidemiological and demographic data for the United States, each U.S. state, and the District of Columbia. Users can simulate pre-defined scenarios describing approaches to TB prevention and treatment or create their own intervention scenarios. Location-specific results for epidemiological outcomes, service utilization, costs, and cost-effectiveness are reported as downloadable tables and customizable visualizations. To demonstrate the tool's functionality, we projected trends in TB outcomes without additional intervention for all 50 states and the District of Columbia. We further undertook a case study of expanded treatment of LTBI among non-U.S.-born individuals in Massachusetts, covering 10% of the target population annually over 2025-2029. RESULTS: Between 2022 and 2050, TB incidence rates were projected to decline in all states and the District of Columbia. Incidence projections for the year 2050 ranged from 0.03 to 3.8 cases (median 0.95) per 100,000 persons. By 2050, we project that majority (> 50%) of TB will be diagnosed among non-U.S.-born persons in 46 states and the District of Columbia; per state percentages range from 17.4% to 96.7% (median 83.0%). In Massachusetts, expanded testing and treatment for LTBI in this population was projected to reduce cumulative TB cases between 2025 and 2050 by 6.3% and TB-related deaths by 8.4%, relative to base case projections. This intervention had an incremental cost-effectiveness ratio of $180,951 (2020 USD) per quality-adjusted life year gained from the societal perspective. CONCLUSIONS: Tabby2 allows users to estimate the costs, impact, and cost-effectiveness of different TB prevention approaches for multiple geographic areas in the United States. Expanded testing and treatment for LTBI could accelerate declines in TB incidence in the United States, as demonstrated in the Massachusetts case study.

Global, regional, and national estimates of tuberculosis incidence and case detection among incarcerated individuals from 2000 to 2019: a systematic analysis.

BACKGROUND: People who are incarcerated are at high risk of developing tuberculosis. We aimed to estimate the annual global, regional, and national incidence of tuberculosis among incarcerated populations from 2000 to 2019. METHODS: We collected and aggregated data for tuberculosis incidence and prevalence estimates among incarcerated individuals in published and unpublished literature, annual tuberculosis notifications among incarcerated individuals at the country level, and the annual number of incarcerated individuals at the country level. We developed a joint hierarchical Bayesian meta-regression framework to simultaneously model tuberculosis incidence, notifications, and prevalence from 2000 to 2019. Using this model, we estimated trends in absolute tuberculosis incidence and notifications, the incidence and notification rates, and the case detection ratio by year, country, region, and globally. FINDINGS: In 2019, we estimated a total of 125 105 (95% credible interval [CrI] 93 736-165 318) incident tuberculosis cases among incarcerated individuals globally. The estimated incidence rate per 100 000 person-years overall was 1148 (95% CrI 860-1517) but varied greatly by WHO region, from 793 (95% CrI 430-1342) in the Eastern Mediterranean region to 2242 (1515-3216) in the African region. Global incidence per 100 000 person-years between 2000 and 2012 among incarcerated individuals decreased from 1884 (95% CrI 1394-2616) to 1205 (910-1615); however, from 2013 onwards, tuberculosis incidence per 100 000 person-years was stable, from 1183 (95% CrI 876-1596) in 2013 to 1148 (860-1517) in 2019. In 2019, the global case detection ratio was estimated to be 53% (95% CrI 42-64), the lowest over the study period. INTERPRETATION: Our estimates suggest a high tuberculosis incidence rate among incarcerated individuals globally with large gaps in tuberculosis case detection. Tuberculosis in incarcerated populations must be addressed with interventions specifically tailored to improve diagnoses and prevent transmission as a part of the broader global tuberculosis control effort. FUNDING: National Institutes of Health.

Changes in Population Immunity Against Infection and Severe Disease From Severe Acute Respiratory Syndrome Coronavirus 2 Omicron Variants in the United States Between December 2021 and November 2022.

BACKGROUND: Although a substantial fraction of the US population was infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during December 2021-February 2022, the subsequent evolution of population immunity reflects the competing influences of waning protection over time and acquisition or restoration of immunity through additional infections and vaccinations. METHODS: Using a Bayesian evidence synthesis model of reported coronavirus disease 2019 (COVID-19) data (diagnoses, hospitalizations), vaccinations, and waning patterns for vaccine- and infection-acquired immunity, we estimate population immunity against infection and severe disease from SARS-CoV-2 Omicron variants in the United States, by location (national, state, county) and week. RESULTS: By 9 November 2022, 97% (95%-99%) of the US population were estimated to have prior immunological exposure to SARS-CoV-2. Between 1 December 2021 and 9 November 2022, protection against a new Omicron infection rose from 22% (21%-23%) to 63% (51%-75%) nationally, and protection against an Omicron infection leading to severe disease increased from 61% (59%-64%) to 89% (83%-92%). Increasing first booster uptake to 55% in all states (current US coverage: 34%) and second booster uptake to 22% (current US coverage: 11%) would increase protection against infection by 4.5 percentage points (2.4-7.2) and protection against severe disease by 1.1 percentage points (1.0-1.5). CONCLUSIONS: Effective protection against SARS-CoV-2 infection and severe disease in November 2022 was substantially higher than in December 2021. Despite this high level of protection, a more transmissible or immune evading (sub)variant, changes in behavior, or ongoing waning of immunity could lead to a new SARS-CoV-2 wave.

Development of treatment-decision algorithms for children evaluated for pulmonary tuberculosis: an individual participant data meta-analysis.

BACKGROUND: Many children with pulmonary tuberculosis remain undiagnosed and untreated with related high morbidity and mortality. Recent advances in childhood tuberculosis algorithm development have incorporated prediction modelling, but studies so far have been small and localised, with limited generalisability. We aimed to evaluate the performance of currently used diagnostic algorithms and to use prediction modelling to develop evidence-based algorithms to assist in tuberculosis treatment decision making for children presenting to primary health-care centres. METHODS: For this meta-analysis, we identified individual participant data from a WHO public call for data on the management of tuberculosis in children and adolescents and referral from childhood tuberculosis experts. We included studies that prospectively recruited consecutive participants younger than 10 years attending health-care centres in countries with a high tuberculosis incidence for clinical evaluation of pulmonary tuberculosis. We collated individual participant data including clinical, bacteriological, and radiological information and a standardised reference classification of pulmonary tuberculosis. Using this dataset, we first retrospectively evaluated the performance of several existing treatment-decision algorithms. We then used the data to develop two multivariable prediction models that included features used in clinical evaluation of pulmonary tuberculosis-one with chest x-ray features and one without-and we investigated each model's generalisability using internal-external cross-validation. The parameter coefficient estimates of the two models were scaled into two scoring systems to classify tuberculosis with a prespecified sensitivity target. The two scoring systems were used to develop two pragmatic, treatment-decision algorithms for use in primary health-care settings. FINDINGS: Of 4718 children from 13 studies from 12 countries, 1811 (38·4%) were classified as having pulmonary tuberculosis: 541 (29·9%) bacteriologically confirmed and 1270 (70·1%) unconfirmed. Existing treatment-decision algorithms had highly variable diagnostic performance. The scoring system derived from the prediction model that included clinical features and features from chest x-ray had a combined sensitivity of 0·86 [95% CI 0·68-0·94] and specificity of 0·37 [0·15-0·66] against a composite reference standard. The scoring system derived from the model that included only clinical features had a combined sensitivity of 0·84 [95% CI 0·66-0·93] and specificity of 0·30 [0·13-0·56] against a composite reference standard. The scoring system from each model was placed after triage steps, including assessment of illness acuity and risk of poor tuberculosis-related outcomes, to develop treatment-decision algorithms. INTERPRETATION: We adopted an evidence-based approach to develop pragmatic algorithms to guide tuberculosis treatment decisions in children, irrespective of the resources locally available. This approach will empower health workers in primary health-care settings with high tuberculosis incidence and limited resources to initiate tuberculosis treatment in children to improve access to care and reduce tuberculosis-related mortality. These algorithms have been included in the operational handbook accompanying the latest WHO guidelines on the management of tuberculosis in children and adolescents. Future prospective evaluation of algorithms, including those developed in this work, is necessary to investigate clinical performance. FUNDING: WHO, US National Institutes of Health.

Transmission modeling to infer tuberculosis incidence prevalence and mortality in settings with generalized HIV epidemics.

Tuberculosis (TB) killed more people globally than any other single pathogen over the past decade. Where surveillance is weak, estimating TB burden estimates uses modeling. In many African countries, increases in HIV prevalence and antiretroviral therapy have driven dynamic TB epidemics, complicating estimation of burden, trends, and potential intervention impact. We therefore develop a novel age-structured TB transmission model incorporating evolving demographic, HIV and antiretroviral therapy effects, and calibrate to TB prevalence and notification data from 12 African countries. We use Bayesian methods to include uncertainty for all TB model parameters, and estimate age-specific annual risks of TB infection, finding up to 16.0%/year in adults, and the proportion of TB incidence from recent (re)infection, finding a mean across countries of 34%. Rapid reduction of the unacceptably high burden of TB in high HIV prevalence settings will require interventions addressing progression as well as transmission.

Spatial modeling of Mycobacterium tuberculosis transmission with dyadic genetic relatedness data.

Understanding factors that contribute to the increased likelihood of pathogen transmission between two individuals is important for infection control. However, analyzing measures of pathogen relatedness to estimate these associations is complicated due to correlation arising from the presence of the same individual across multiple dyadic outcomes, potential spatial correlation caused by unmeasured transmission dynamics, and the distinctive distributional characteristics of some of the outcomes. We develop two novel hierarchical Bayesian spatial methods for analyzing dyadic pathogen genetic relatedness data, in the form of patristic distances and transmission probabilities, that simultaneously address each of these complications. Using individual-level spatially correlated random effect parameters, we account for multiple sources of correlation between the outcomes as well as other important features of their distribution. Through simulation, we show the limitations of existing approaches in terms of estimating key associations of interest, and the ability of the new methodology to correct for these issues across datasets with different levels of correlation. All methods are applied to Mycobacterium tuberculosis data from the Republic of Moldova, where we identify previously unknown factors associated with disease transmission and, through analysis of the random effect parameters, key individuals, and areas with increased transmission activity. Model comparisons show the importance of the new methodology in this setting. The methods are implemented in the R package GenePair.

Population Immunity to Pre-Omicron and Omicron Severe Acute Respiratory Syndrome Coronavirus 2 Variants in US States and Counties Through 1 December 2021.

BACKGROUND: Both severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and coronavirus disease 2019 (COVID-19) vaccination contribute to population-level immunity against SARS-CoV-2. This study estimated the immunological exposure and effective protection against future SARS-CoV-2 infection in each US state and county over 2020-2021 and how this changed with the introduction of the Omicron variant. METHODS: We used a Bayesian model to synthesize estimates of daily SARS-CoV-2 infections, vaccination data and estimates of the relative rates of vaccination conditional on infection status to estimate the fraction of the population with (1) immunological exposure to SARS-CoV-2 (ever infected with SARS-CoV-2 and/or received ≥1 doses of a COVID-19 vaccine), (2) effective protection against infection, and (3) effective protection against severe disease, for each US state and county from 1 January 2020 to 1 December 2021. RESULTS: The estimated percentage of the US population with a history of SARS-CoV-2 infection or vaccination as of 1 December 2021 was 88.2% (95% credible interval [CrI], 83.6%-93.5%). Accounting for waning and immune escape, effective protection against the Omicron variant on 1 December 2021 was 21.8% (95% CrI, 20.7%-23.4%) nationally and ranged between 14.4% (13.2%-15.8%; West Virginia) and 26.4% (25.3%-27.8%; Colorado). Effective protection against severe disease from Omicron was 61.2% (95% CrI, 59.1%-64.0%) nationally and ranged between 53.0% (47.3%-60.0%; Vermont) and 65.8% (64.9%-66.7%; Colorado). CONCLUSIONS: While more than four-fifths of the US population had prior immunological exposure to SARS-CoV-2 via vaccination or infection on 1 December 2021, only a fifth of the population was estimated to have effective protection against infection with the immune-evading Omicron variant.

Quantifying Mycobacterium tuberculosis Transmission Dynamics Across Global Settings: A Systematic Analysis.

The degree to which individual heterogeneity in the production of secondary cases ("superspreading") affects tuberculosis (TB) transmission has not been systematically studied. We searched for population-based or surveillance studies in which whole genome sequencing was used to estimate TB transmission and in which the size distributions of putative TB transmission clusters were enumerated. We fitted cluster-size-distribution data to a negative binomial branching process model to jointly infer the transmission parameters $R$ (the reproduction number) and the dispersion parameter, $k$, which quantifies the propensity of superspreading in a population (generally, lower values of $k$ ($<1.0$) suggest increased heterogeneity). Of 4,796 citations identified in our initial search, 9 studies from 8 global settings met the inclusion criteria (n = 5 studies of all TB; n = 4 studies of drug-resistant TB). Estimated $R$ values (range, 0.10-0.73) were below 1.0, consistent with declining epidemics in the included settings; estimated $k$ values were well below 1.0 (range, 0.02-0.48), indicating the presence of substantial individual-level heterogeneity in transmission across all settings. We estimated that a minority of cases (range, 2%-31%) drive the majority (80%) of ongoing TB transmission at the population level. Identifying sources of heterogeneity and accounting for them in TB control may have a considerable impact on mitigating TB transmission.

Signatures of transmission in within-host M. tuberculosis variation.

Background: Because M. tuberculosis evolves slowly, transmission clusters often contain multiple individuals with identical consensus genomes, making it difficult to reconstruct transmission chains. Finding additional sources of shared M. tuberculosis variation could help overcome this problem. Previous studies have reported M. tuberculosis diversity within infected individuals; however, whether within-host variation improves transmission inferences remains unclear. Methods: To evaluate the transmission information present in within-host M. tuberculosis variation, we re-analyzed publicly available sequence data from three household transmission studies, using household membership as a proxy for transmission linkage between donor-recipient pairs. Findings: We found moderate levels of minority variation present in M. tuberculosis sequence data from cultured isolates that varied significantly across studies (mean: 6, 7, and 170 minority variants above a 1% minor allele frequency threshold, outside of PE/PPE genes). Isolates from household members shared more minority variants than did isolates from unlinked individuals in the three studies (mean 98 shared minority variants vs. 10; 0.8 vs. 0.2, and 0.7 vs. 0.2, respectively). Shared within-host variation was significantly associated with household membership (OR: 1.51 [1.30,1.71], for one standard deviation increase in shared minority variants). Models that included shared within-host variation improved the accuracy of predicting household membership in all three studies as compared to models without within-host variation (AUC: 0.95 versus 0.92, 0.99 versus 0.95, and 0.93 versus 0.91). Interpretation: Within-host M. tuberculosis variation persists through culture and could enhance the resolution of transmission inferences. The substantial differences in minority variation recovered across studies highlights the need to optimize approaches to recover and incorporate within-host variation into automated phylogenetic and transmission inference. Funding: NIAID: 5K01AI173385.

Changes in population immunity against infection and severe disease from SARS-CoV-2 Omicron variants in the United States between December 2021 and November 2022.

Importance: While a substantial fraction of the US population was infected with SARS-CoV-2 during December 2021 - February 2022, the subsequent evolution of population immunity against SARS-CoV-2 Omicron variants reflects the competing influences of waning protection over time and acquisition or restoration of immunity through additional infections and vaccinations. Objective: To estimate changes in population immunity against infection and severe disease due to circulating SARS-CoV-2 Omicron variants in the United States from December 2021 to November 2022, and to quantify the protection against a potential 2022-2023 winter SARS-CoV-2 wave. Design setting participants: Bayesian evidence synthesis of reported COVID-19 data (diagnoses, hospitalizations), vaccinations, and waning patterns for vaccine- and infection-acquired immunity, using a mathematical model of COVID-19 natural history. Main Outcomes and Measures: Population immunity against infection and severe disease from SARS-CoV-2 Omicron variants in the United States, by location (national, state, county) and week. Results: By November 9, 2022, 94% (95% CrI, 79%-99%) of the US population were estimated to have been infected by SARS-CoV-2 at least once. Combined with vaccination, 97% (95%-99%) were estimated to have some prior immunological exposure to SARS-CoV-2. Between December 1, 2021 and November 9, 2022, protection against a new Omicron infection rose from 22% (21%-23%) to 63% (51%-75%) nationally, and protection against an Omicron infection leading to severe disease increased from 61% (59%-64%) to 89% (83%-92%). Increasing first booster uptake to 55% in all states (current US coverage: 34%) and second booster uptake to 22% (current US coverage: 11%) would increase protection against infection by 4.5 percentage points (2.4-7.2) and protection against severe disease by 1.1 percentage points (1.0-1.5). Conclusions and Relevance: Effective protection against SARS-CoV-2 infection and severe disease in November 2022 was substantially higher than in December 2021. Despite this high level of protection, a more transmissible or immune evading (sub)variant, changes in behavior, or ongoing waning of immunity could lead to a new SARS-CoV-2 wave. Key points: Question: How did population immunity against SARS-CoV-2 infection and subsequent severe disease change between December 2021, and November 2022?Findings: On November 9, 2022, the protection against a SARS-CoV-2 infection with the Omicron variant was estimated to be 63% (51%-75%) in the US, and the protection against severe disease was 89% (83%-92%).Meaning: As most of the newly acquired immunity has been accumulated in the December 2021-February 2022 Omicron wave, risk of reinfection and subsequent severe disease remains present at the beginning of the 2022-2023 winter, despite high levels of protection.