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.

Factors Associated with Development of Tuberculosis Disease Among Refugees, Massachusetts, 2008-2018.

Refugees and immigrants undergo tuberculosis screening prior to arrival in the United States. CDC Technical Instructions for screening changed in 2007. Our goal was to quantify TB disease in refugees after 2007 and identify risks for disease. Massachusetts refugee and tuberculosis databases were matched to identify refugees who arrived 2008-2017 and were diagnosed with tuberculosis infection or disease 2008-2018. Factors associated with disease were analyzed in SAS. Of 19,583 refugees, 4706 were diagnosed with infection at arrival and 60 with disease during the observation period. Lack of treatment for infection was strongly associated (OR = 26.5, p = 0.0001) with diagnosis of disease; in a multivariate logistic regression model, positive screening test (AOR = 12.5, p = 0.0001), class B1 status (AOR = 4.0, p = 0.0004), and < 2 years since arrival (AOR = 60.0, p = 0.0001) were associated with disease. Providers should continue screening new arrivals, providing accessible services, and treating infection to further reduce tuberculosis morbidity and mortality.

What Can Genetic Relatedness Tell Us About Risk Factors for Tuberculosis Transmission?

BACKGROUND: To stop tuberculosis (TB), the leading infectious cause of death globally, we need to better understand transmission risk factors. Although many studies have identified associations between individual-level covariates and pathogen genetic relatedness, few have identified characteristics of transmission pairs or explored how closely covariates associated with genetic relatedness mirror those associated with transmission. METHODS: We simulated a TB-like outbreak with pathogen genetic data and estimated odds ratios (ORs) to correlate each covariate and genetic relatedness. We used a naive Bayes approach to modify the genetic links and nonlinks to resemble the true links and nonlinks more closely and estimated modified ORs with this approach. We compared these two sets of ORs with the true ORs for transmission. Finally, we applied this method to TB data in Hamburg, Germany, and Massachusetts, USA, to find pair-level covariates associated with transmission. RESULTS: Using simulations, we found that associations between covariates and genetic relatedness had the same relative magnitudes and directions as the true associations with transmission, but biased absolute magnitudes. Modifying the genetic links and nonlinks reduced the bias and increased the confidence interval widths, more accurately capturing error. In Hamburg and Massachusetts, pairs were more likely to be probable transmission links if they lived in closer proximity, had a shorter time between observations, or had shared ethnicity, social risk factors, drug resistance, or genotypes. CONCLUSIONS: We developed a method to improve the use of genetic relatedness as a proxy for transmission, and aid in understanding TB transmission dynamics in low-burden settings.

Estimation of the generation interval using pairwise relative transmission probabilities.

The generation interval (the time between infection of primary and secondary cases) and its often used proxy, the serial interval (the time between symptom onset of primary and secondary cases) are critical parameters in understanding infectious disease dynamics. Because it is difficult to determine who infected whom, these important outbreak characteristics are not well understood for many diseases. We present a novel method for estimating transmission intervals using surveillance or outbreak investigation data that, unlike existing methods, does not require a contact tracing data or pathogen whole genome sequence data on all cases. We start with an expectation maximization algorithm and incorporate relative transmission probabilities with noise reduction. We use simulations to show that our method can accurately estimate the generation interval distribution for diseases with different reproductive numbers, generation intervals, and mutation rates. We then apply our method to routinely collected surveillance data from Massachusetts (2010-2016) to estimate the serial interval of tuberculosis in this setting.

Increasing Drug Resistance Among Persons With Tuberculosis in Massachusetts, 2009-2018.

We examined Massachusetts tuberculosis surveillance data from to 2009 to 2018. Of 1533 culture-confirmed cases, 190 (12.4%) demonstrated resistance to isoniazid including 32 (2.1%) with rifampin resistance. In multivariable analysis, isoniazid resistance increased significantly over time (per-year odds ratio = 1.07, 95% confidence interval = 1.01-1.13, P = .018) and was associated with younger age, foreign birth, and prior tuberculosis treatment.

Civil Surgeon Tuberculosis Evaluations for Foreign-Born Persons Seeking Permanent U.S. Residence.

Foreign-born persons in the United States seeking to adjust their status to permanent resident must undergo screening for tuberculosis (TB) disease. Screening is performed by civil surgeons (CS) following technical instructions by the Centers for Disease Control and Prevention. From 2011 to 2012, 1,369 practicing CS in California, Texas, and New England were surveyed to investigate adherence to the instructions. A descriptive analysis was conducted on 907 (66%) respondents. Of 907 respondents, 739 (83%) had read the instructions and 565 (63%) understood that a chest radiograph is required for status adjustors with TB symptoms; however, only 326 (36%) knew that a chest radiograph is required for immunosuppressed status adjustors. When suspecting TB disease, 105 (12%) would neither report nor refer status adjustors to the health department; 91 (10%) would neither start treatment nor refer for TB infection. Most CS followed aspects of the technical instructions; however, educational opportunities are warranted to ensure positive patient outcomes.