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Reexamining the prevalence of persons infected with tuberculosis (TB) is important to determine trends over time. In 2011-2012 a TB component was included in the National Health and Nutrition Examination Survey (NHANES) to estimate the reservoir of persons infected with TB. Civilian, noninstitutionalized U.S. population survey participants aged 6 years and older were interviewed regarding their TB history and eligibility for the tuberculin skin test (TST) and interferon gamma release assay (IGRA) blood test. Once eligibility was confirmed, both tests were conducted. Prevalence and numbers of TST positive (10 mm or greater), IGRA positive, and both TST and IGRA positive were calculated by adjusting for the complex survey design after applying corrections for item nonresponse and digit preference in TST induration measurements. To examine TST positivity over time, data from NHANES 1999-2000 were reanalyzed using the same statistical methods. The TST was performed using Tubersol, a commercially available purified protein derivative (PPD), rather than PPD-S, which was the antigen used in NHANES 1999-2000. Prior patient history of TB vaccination was not collected in this study nor were patients examined for the presence of a Bacillus of Calmette and Guerin (BCG) vaccine scar. For NHANES 2011-2012, TST and IGRA results were available for 6,128 (78.4%) and 7,107 (90.9%) eligible participants, respectively. There was no significant difference between the percentage of the U.S. population that was TST positive in 2011-2012 (4.7% [95% CI 3.4-6.3]; 13,276,000 persons) compared with 1999-2000 (4.3%; 3.5-5.3). In 2011-2012 the percentage that was IGRA positive was 5.0% (4.2-5.8) and double TST and IGRA positivity was 2.1% (1.5-2.8). The point estimate of IGRA positivity prevalence in foreign-born persons (15.9%; 13.5-18.7) was lower than for TST (20.5%; 16.1-25.8) in 2011-2012. The point estimate of IGRA positivity prevalence in U.S.-born persons (2.8%; 2.0-3.8) was higher than for TST (1.5%; 0.9-2.6). No statistically significant decline in the overall estimated prevalence of TST positivity was detected from 1999-2000 to 2011-2012. The prevalence of TB infection, whether measured by TST or IGRA, remains lower among persons born in the United States compared with foreign-born persons.

We analyzed a pharmacy dataset to assess the 20% decline in tuberculosis (TB) cases reported to the US National Tuberculosis Surveillance System (NTSS) during the coronavirus disease pandemic in 2020 compared with the 2016-2019 average. We examined the correlation between TB medication dispensing data to TB case counts in NTSS and used a seasonal autoregressive integrated moving average model to predict expected 2020 counts. Trends in the TB medication data were correlated with trends in NTSS data during 2006-2019. There were fewer prescriptions and cases in 2020 than would be expected on the basis of previous trends. This decrease was particularly large during April-May 2020. These data are consistent with NTSS data, suggesting that underreporting is not occurring but not ruling out underdiagnosis or actual decline. Understanding the mechanisms behind the 2020 decline in reported TB cases will help TB programs better prepare for postpandemic cases.

We expanded a published mathematical model of SARS-CoV-2 transmission with complex, age-structured transmission and with laboratory-derived source and wearer protection efficacy estimates for a variety of face masks to estimate their impact on COVID-19 incidence and related mortality in the United States. The model was also improved to allow realistic age-structured transmission with a pre-specified R0 of transmission, and to include more compartments and parameters, e.g. for groups such as detected and undetected asymptomatic infectious cases who mask up at different rates. When masks are used at typically-observed population rates of 80% for those ≥ 65 years and 60% for those < 65 years, face masks are associated with 69% (cloth) to 78% (medical procedure mask) reductions in cumulative COVID-19 infections and 82% (cloth) to 87% (medical procedure mask) reductions in related deaths over a 6-month timeline in the model, assuming a basic reproductive number of 2.5. If cloth or medical procedure masks’ source control and wearer protection efficacies are boosted about 30% each to 84% and 60% by cloth over medical procedure masking, fitters, or braces, the COVID-19 basic reproductive number of 2.5 could be reduced to an effective reproductive number ≤ 1.0, and from 6.0 to 2.3 for a variant of concern similar to delta (B.1.617.2). For variants of concern similar to omicron (B.1.1.529) or the sub-lineage BA.2, modeled reductions in effective reproduction number due to similar high quality, high prevalence mask wearing is more modest (to 3.9 and 5.0 from an R 0  = 10.0 and 13.0, respectively). None-the-less, the ratio of incident risk for masked vs. non-masked populations still shows a benefit of wearing masks even with the higher R0 variants.

Preventing tuberculosis (TB) disease requires treatment of latent TB infection (LTBI) as well as prevention of person-to-person transmission. We estimated the LTBI prevalence for the entire United States and for each state by medical risk factors, age, and race/ethnicity, both in the total population and stratified by nativity. We created a mathematical model using all incident TB disease cases during 2013-2017 reported to the National Tuberculosis Surveillance System that were classified using genotype-based methods or imputation as not attributed to recent TB transmission. Using the annual average number of TB cases among US-born and non-US-born persons by medical risk factor, age group, and race/ethnicity, we applied population-specific reactivation rates (and corresponding 95% confidence intervals [CI]) to back-calculate the estimated prevalence of untreated LTBI in each population for the United States and for each of the 50 states and the District of Columbia in 2015. We estimated that 2.7% (CI: 2.6%-2.8%) of the U.S. population, or 8.6 (CI: 8.3-8.8) million people, were living with LTBI in 2015. Estimated LTBI prevalence among US-born persons was 1.0% (CI: 1.0%-1.1%) and among non-US-born persons was 13.9% (CI: 13.5%-14.3%). Among US-born persons, the highest LTBI prevalence was in persons aged ≥65 years (2.1%) and in persons of non-Hispanic Black race/ethnicity (3.1%). Among non-US-born persons, the highest LTBI prevalence was estimated in persons aged 45-64 years (16.3%) and persons of Asian and other racial/ethnic groups (19.1%). Our estimations of the prevalence of LTBI by medical risk factors and demographic characteristics for each state could facilitate planning for testing and treatment interventions to eliminate TB in the United States. Our back-calculation method feasibly estimates untreated LTBI prevalence and can be updated using future TB disease case counts at the state or national level.

Mycobacterium tuberculosis transmission dynamics in high-burden settings are poorly understood. Growing evidence suggests transmission may be characterized by extensive individual heterogeneity in secondary cases (i.e., superspreading), yet the degree and influence of such heterogeneity is largely unknown and unmeasured in high burden-settings. We conducted a prospective, population-based molecular epidemiology study of TB transmission in both an urban and rural setting of Botswana, one of the highest TB burden countries in the world. We used these empirical data to fit two mathematical models (urban and rural) that jointly quantified both the effective reproductive number, R , and the propensity for superspreading in each population. We found both urban and rural populations were characterized by a high degree of individual heterogeneity, however such heterogeneity disproportionately impacted the rural population: 99% of secondary transmission was attributed to only 19% of infectious cases in the rural population compared to 60% in the urban population and the median number of incident cases until the first outbreak of 30 cases was only 32 for the rural model compared to 791 in the urban model. These findings suggest individual heterogeneity plays a critical role shaping local TB epidemiology within subpopulations.

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.

Recently, the Cochrane Library released its anticipated update on physical interventions to control the spread of respiratory viruses, including masks to contain the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).1 The update was widely read and cited, becoming a point of controversy in the public debate about the efficacy of face masks, as it appeared to contradict both public health guidance2 and research.3 The appearance of controversy was in part owing to the methodological approach of Cochrane reviews, which allows inclusion of only randomized controlled trials (RCTs).The authors added 11 new RCTs and cluster RCTs, of which six were conducted during the COVID-19 pandemic and evaluated various interventions for hygiene, including face masks and hand washing. Only two of the six studies compared use of face masks with no use of masks: one from Denmark, the DAN MASK-19 RCT,4 and one from Bangladesh.5 But even with these limited, additional data, the appearance of disagreement between the Cochrane review results and public health guidance disappears if infectious disease models are applied, because the models calibrate quite well to the new Cochrane data and, when extrapolated, show that masks can reduce respiratory infections significantly.

For the two largest studies (each at least an order of magnitude larger than the other seven that Kivelä reanalyzes), the effect sizes for masks either were very small and corroborated by our modeling results lntheAbalucketal4study, as we discussed,1 or were expected to be small given the comparatively much lower mask use (24%) In the treatment arm of theAlfelall et al.5 study. [...]the absence of a statistically significant result was not unexpected given the barely observable effect In the largest studies. [...]Cochrane's metaanalyses use fixed and random effects methods of evaluation; these are inappropriate tools for masks because masks do not have a fixed effect size. A time-to-infection analysis would likely produce a more robust evaluation of the effect of masks.

Mathematical modelling is commonly used to evaluate infectious disease control policy and is influential in shaping policy and budgets. Mathematical models necessarily make assumptions about disease natural history and, if these assumptions are not valid, the results of these studies can be biased. We did a systematic review of published tuberculosis transmission models to assess the validity of assumptions about progression to active disease after initial infection (PROSPERO ID CRD42016030009). We searched PubMed, Web of Science, Embase, Biosis, and Cochrane Library, and included studies from the earliest available date (Jan 1, 1962) to Aug 31, 2017. We identified 312 studies that met inclusion criteria. Predicted tuberculosis incidence varied widely across studies for each risk factor investigated. For population groups with no individual risk factors, annual incidence varied by several orders of magnitude, and 20-year cumulative incidence ranged from close to 0% to 100%. A substantial proportion of modelled results were inconsistent with empirical evidence: for 10-year cumulative incidence, 40% of modelled results were more than double or less than half the empirical estimates. These results demonstrate substantial disagreement between modelling studies on a central feature of tuberculosis natural history. Greater attention to reproducing known features of epidemiology would strengthen future tuberculosis modelling studies, and readers of modelling studies are recommended to assess how well those studies demonstrate their validity.

Mixing among sub-populations, as well as heterogeneity in characteristics affecting their reproduction numbers, must be considered when evaluating public health interventions to prevent or control infectious disease outbreaks. In this overview, we apply a linear algebraic approach to re-derive some well-known results pertaining to preferential within- and proportionate among-group contacts in compartmental models of pathogen transmission. We give results for the meta-population effective reproduction number ([Formula: see text]) assuming different levels of vaccination in the sub-populations. Specifically, we unpack the dependency of [Formula: see text] on the fractions of contacts reserved for individuals within one's own subgroup and, by obtaining implicit expressions for the partial derivatives of [Formula: see text], we show that these increase as this preferential-mixing fraction increases in any sub-population.