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

Abstract

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.

Keywords: Epidemiology; Infectious disease; Mathematical modeling; Tuberculosis; Web application.

Fig. 1

Schematic of the structure of the transmission-dynamic TB model, showing model compartments and transitions Legend: *The TB progression risk dimension represents differences in LTBI reactivation rates within the modeled population. **The mortality risk dimension represents differences in non-TB mortality rates within the modeled population. ***The socio-economic disadvantage dimension represents poor and marginalized individuals operationalized as elevated TB contact rates, elevated mortality rates, higher LTBI screening rates, and higher TB treatment default rates. We modeled TB transmission assuming assortative mixing within U.S.–born and non-U.S.–born groups, and within levels of the socio-economic disadvantage dimension (additional details described in [14])

Fig. 2

2019 Reported TB data for each U.S. state and the District of Columbia. Legend: These data were used for calibrating the underlying TB model. TB case data from National Tuberculosis Surveillance System for 2019 [6]. Deaths with TB from CDC Multiple Cause of Death for 2019 [20]. NA represents death counts under 10 which are suppressed. Population fractions from the 2019 American Community Survey [19]. Percent completing TLTBI are the 2017 values reported in the 2019 City and State Indicators Report [23]

Fig. 3

The targeted testing and treatment intervention scenario builder. Legend: Using this interface, users can design and simulate custom interventions that modify the levels of targeted testing and treatment for specific populations

Fig. 4

Projected trends in TB outcomes for each modeled geography, 2022 to 2050. Legend: Trends are shown on the log scale. Highlighted results represent the U.S. overall, and the four states representing over 50% of reported TB in 2019 (California, Texas, Florida, and New York)

Fig. 5

Tabby2 results pages for TB incidence trends and cost-effectiveness estimates. Legend: A The “Time trends” panel from Tabby2, displaying the results of the base case and expanded LTBI testing and treatment scenarios in Massachusetts. B The “Cost-effectiveness comparison” panel from Tabby2 displaying cost-effectiveness results for the expanded LTBI testing and treatment scenario, as compared to the base case scenario