About Tabby2

This product is currently under development.

Tabby2 is a web application that provides projections of future tuberculosis (TB) epidemiology in the United States under a range of TB and LTBI testing and treatment scenarios that can be chosen by the user. The application includes several predefined scenarios, which explore changes in the care cascades for TB disease and latent TB infection (LTBI). The application also allows users to define new scenarios, which can project the impact of greater targeted testing and treatment of high-risk populations, or changes to the performance of current TB control programs. After specifying scenarios, users can select a health outcome and subpopulation of interest, and then view results of one or more of the scenarios they have specified. Results are displayed graphically, and these graphs can be adjusted based on user input to include additional results or focus on particular population subgroups. Visualizations can be downloaded in various formats, and the estimates underlying the graphs can also be downloaded in tabular format.

The estimates shown by Tabby2 are based on a mathematical model of TB epidemiology in the United States, which incorporates TB transmission and natural history; prior and future TB prevention and control; heterogeneity in TB risks among U.S.-born and non-U.S.-born populations; and age-based differences in disease mechanisms and risk factor prevalence. This model has been fit to local data from each of the following high TB incidence states: California, Florida, Georgia, Illinois, Massachusetts, New Jersey, New York, Pennsylvania, Texas, Virginia, and Washington. The model has also been fit to aggregate data for the United States to allow national-level analyses. These analyses expand on an earlier study on future TB epidemiology in the United States (Menzies et al 2018. “Prospects for tuberculosis elimination in the United States: results of a transmission dynamic model” Am J Epid 187(9):2011-2020) (https://academic.oup.com/aje/article/187/9/2011/4995883)).

This research was funded by the CDC, National Center for HIV, Viral Hepatitis, STD, and TB Prevention Epidemiologic and Economic Modeling Agreement (NEEMA, # 5U38PS004644-01).

Select a Location

After specifying a location, Tabby2 will load historical data and model parameters calibrated to that location.

Predefined Scenarios

Tabby2 provides estimates of future TB outcomes for a small number of predefined scenarios, in addition to a base case scenario that assumes continuation of current TB policy and services. The tool's predefined scenarios include 5 hypothetical scenarios that reflect a range of changes to latent TB and TB disease testing and treatment, described below.

  • LTBI treatment for new migrants: Provision of LTBI testing and treatment for all new legal migrants entering the United States.

  • Improved LTBI treatment in the United States: Intensification of the current LTBI targeted testing and treatment efforts for high-risk populations, doubling treatment uptake within each risk group compared to current levels, and increasing the percentage cured among individuals initiating LTBI treatment, via a 3-month Isoniazid-Rifapentine drug regimen.

  • Enhanced case detection: Improved detection of TB disease cases, such that the duration of untreated disease (time from TB incidence to the initiation of treatment) is reduced by 50% from current levels.

  • Enhanced TB treatment: Improved treatment quality for TB disease, such that treatment default, failure rates, and the percentage of individuals receiving an incorrect drug regimen are reduced by 50% from current levels.

  • All improvements: The combination of all changes described in other scenarios described in the four scenarios shown above.

Each of these scenarios is automatically available when the user chooses scenarios to plot in each of the Modelled Outcomes pages.

After the user reviews the descriptions of the predefined scenarios, they can proceed to define new scenarios by navigating to the Build Custom Scenarios page, or they can proceed to one of the Modelled Outcomes pages to view the results corresponding to these predefined scenarios.

Build Custom Model Scenarios



Use the Targeted Testing and Treatment input to create scenarios that simulate additional screening of specific risk groups over a period of specified years. Targeted groups can be specified by their risk, age, and nativity status. Custom risk groups can be defined by specifying rate ratios of LTBI prevalence, progression, and mortality.


Define a Targeted Testing and Treatment Intervention

Population Summary Statistics in 2018


Targeted Group

Incidence per 100,000:

LTBI Prevalence: %

Population:


Age-Nativity Group

Population Size:


Define a Targeted Testing and Treatment Intervention

Population Summary Statistics in 2018


Targeted Group

Incidence per 100,000:

LTBI Prevalence: %

Population:


Age-Nativity Group

Population Size:


Define a Targeted Testing and Treatment Intervention

Population Summary Statistics in 2018


Targeted Group

Incidence per 100,000:

LTBI Prevalence: %

Population:


Age-Nativity Group

Population Size:


Care Cascade Changes allow users to change model parameters related to the LTBI and TB testing and treatment care cascades.


Combination Scenarios allow users to simulate combinations of Targeted Testing and Treatment interventions and Care Cascade Changes.

Define a Combination Scenario

Define a Combination Scenario

Define a Combination Scenario

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New TB infections, in the total US population, all age groups

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LTBI prevalence (per hundred thousand), in the total US population, for 2018

Comparison to Recent Data

Definitions and Abbreviations

Base Case

The base case is the default scenario, assuming no change in current TB prevention and control activities. This scenario is automatically included in all visualizations, and other scenarios are defined and analyzed with reference to this scenario.

Dynamic Transmission Model

Dynamic transmission models are systems of mathematical equations designed to reproduce the epidemiology of communicable diseases. These analyses assume that improvements in disease control (such as more rapid diagnosis and treatment of infectious individuals) will reduce the risk that uninfected individuals will be exposed to infection. In this manner, individuals not directly reached by an intervention may still benefit by experiencing a lower risk of infection.

IGRA – Interferon-Gamma Release Assays

IGRAs are blood tests that can aid in the diagnosis of tuberculosis infection.

Incident Cases

Incident cases are new disease cases. Incidence refers to the number of new cases that develop in a particular population in a given period of time.

Isoniazid (INH)

A medicine used to prevent TB disease in people who have latent TB infection. INH is also one of the four medicines often used to treat TB disease.

LTBI – Latent tuberculosis infection

A condition in which individuals are infected with TB bacteria, but this infection is controlled by the individual's immune system. People with latent TB infection have no symptoms, don't feel sick, and can't spread TB to others. Individuals with LTBI usually have a positive TB skin test or positive TB blood test reaction. Individuals with LTBI may develop TB disease in the future if they do not receive treatment.

Prevalence

The number of cases of a disease present in a population at a given time.

Rifapentine (RPT)

A medication used to treat latent TB infection.

TST – Tuberculin Skin Test

TSTs determine if someone has developed an immune response to the bacterium that causes tuberculosis, Mycobacterium tuberculosis.

TB – Tuberculosis

A disease caused by bacteria that are spread from person to person through the air. TB usually affects the lungs, but it can also affect other parts of the body, such as the brain, the kidneys, or the spine. In most cases, TB is treatable and curable; however, people with TB disease can die if they do not get proper treatment.

Organization of the Tool

Users of Tabby2 progress through a sequence of pages that provide a brief introduction to the tool, allow them to specify scenarios and choose outcomes of interest, and to view and download graphs of their chosen outcomes. The tool's sidebar (Figure 1) serves as the primary navigational aid for the user.

  • Introduction
  • Scenarios
    • Predefined Scenarios
    • Build Custom Scenarios
      • Targeted Testing and Treatment
      • Care Cascade Changes
      • Combination Scenarios
  • Modelled Outcomes
    • Estimates
    • Time Trends
    • Age Groups
    • Comparison to Recent Data
  • Further Description
  • Feedback
Figure 1: Sidebar Showing the Sections of the Tabby2 Application
Figure 1: Sidebar Showing the Sections of the Tabby2 Application

Introduction

On the introduction page of Tabby2 (Figure 2), the user is shown the About Tabby2 text and is prompted to select a location. After specifying a location, Tabby2 will load figures showing historical data and model estimates calibrated to that location.

Figure 2: The Introduction Page of the Tabby2 Web Application
Figure 2: The Introduction Page of the Tabby2 Web Application

Scenarios

Predefined Scenarios

Tabby2 provides estimates of future TB outcomes for a small number of predefined scenarios, in addition to a base case scenario that assumes no change in current TB prevention and control activities. The tool's predefined scenarios include five hypothetical scenarios that reflect a range of changes to latent TB and TB disease testing and treatment, described below.

  • LTBI treatment for new migrants: Provision of LTBI testing and treatment for all new legal migrants entering the United States.

  • Improved LTBI treatment in the United States: Intensification of the current LTBI targeted testing and treatment efforts for high-risk populations, doubling treatment uptake within each risk group compared to current levels, and increasing the percentage cured among individuals initiating LTBI treatment, via a 3-month Isoniazid-Rifapentine drug regimen.

  • Enhanced case detection: Improved detection of TB disease cases, such that the duration of untreated disease (time from TB incidence to the initiation of treatment) is reduced by 50% from current levels.

  • Enhanced TB treatment: Improved treatment quality for TB disease, such that treatment default, failure rates, and the percentage of individuals receiving an incorrect drug regimen are reduced by 50% from current levels.

  • All improvements: The combination of all changes described in other scenarios described in the four scenarios shown above.

Each of these scenarios is automatically available when the user chooses scenarios to plot in each of the Modelled Outcomes pages.

After the user reviews the descriptions of the predefined scenarios, they can proceed to define new scenarios by navigating to the Build Custom Scenarios page, or they can proceed to one of the Modelled Outcomes pages to view the results corresponding to these predefined scenarios.

Custom Scenarios

Custom Model Scenarios allow users to generate a new scenario by selecting different options for Targeted Testing and Treatment of LTBI (“Targeted Testing and Treatment Interventions”) or TB disease and latent infection treatment (“Care Cascade Changes”). Users can also create scenarios as a combination of changes in both of these areas, specified on the “Combination Scenarios” page.

After specifying a Targeted Testing and Treatment scenario, Care Cascade Change scenario, or Combination scenario, the user clicks the “Run Model” button to simulate the scenario they have specified. Upon navigating to one of the Modelled Outcomes pages, their scenario will appear as an option for visualization or download in the Estimates, Time Trends, and Age Groups pages of the application.

Custom Scenarios – Targeted Testing and Treatment Interventions

Figure 3: User Interface for Building Targeted Testing and Treatment Interventions
Figure 3: User Interface for Building Targeted Testing and Treatment Interventions

The Targeted Testing and Treatment (TTT) Interventions input page (Figure 3) is used to create scenarios that simulate additional screening of specific risk groups over a specified number of years. Within the TTT scenario builder, a user can either select from a list of high-risk groups (such as people living with HIV) or choose to define a custom risk group. To do so, the user must define the new group in terms of their rate ratios of LTBI prevalence, progression, and mortality, as compared to the general population in the same age and nativity group. Additionally, a user must provide an age range, nativity group, and total targeted population size.

Custom Scenarios – Care Cascade Changes

Figure 4: User Interface for Building Program Change Scenarios
Figure 4: User Interface for Building Care Cascade Change Scenarios

The Care Cascade Changes page (Figure 4) allows users to change assumptions related to the LTBI treatment and TB treatment care cascades. These changes do not change any historical projections the model has made and will only be active in the years following the user-inputted start year.

Custom Scenarios – Combination Scenarios

The Combination Scenarios page (Figure 5) allows users to simulate combinations of targeted testing and treatment for LTBI, and changes to the care cascade.

Figure 5: User Interface for Building Combination Scenarios
Figure 5: User Interface for Building Combination Scenarios

Modelled Outcomes

Figure 6: The Time Trends page of Tabby2 Depicting the Incident M. tuberculosis Infections Outcome for the 5 Predefined Scenarios
Figure 6: The Time Trends page of Tabby2 Depicting the Incident M. tuberculosis Infections Outcome for the 5 Predefined Scenarios

Model outcomes are presented as four interactive pages with visualizations: Estimates, Time Trends, Age Groups, and Comparison to Recent Data.

The Estimates page provides graphs of modelled results at five major time points: 2018, 2020, 2025, 2035, and 2050.

The Time Trends page (Figure 6) provides graphs of modelled results for each individual year from 2018 to 2050.

The Age Groups page provides graphs of modelled results for a specific year chosen by the user, subdivided into 11 age groups.

The Comparison to Recent Data page shows model results compared to recent empirical data and estimates.

A detailed description of each of these pages is provided below.

Modelled Outcomes – Estimates

User options are shown in a column on the left. The user specifies:

Comparison: results can be shown as absolute values for each outcome in each year, as a percentage of the base case scenario in the same year, or as a percentage of the base case scenario in 2018.

Subgroup: results can be shown for the total population, or for a subgroup described by nativity (U.S.-born, non-U.S.-born), and broad age groups (0-24 years, 25-64 years, 65+ years).

Outcome: results can be shown for five different outcomes:

  • Incident TB infections representing the annual number of incident M. tuberculosis (Mycobacterium tuberculosis) infections per 100,000 due to transmission within the United States (includes reinfection of individuals with prior LTBI, excludes migrants entering the United States with established LTBI);

  • LTBI Prevalence representing the percentage of individuals with latent TB infection in a given year;

  • TB Incidence representing the annual number of notified TB disease cases per 100,000, including those after death;

  • TB-Related Deaths representing annual TB-attributable mortality per 100,000.

Scenarios: results can be shown for up to five scenarios selected by the user, describing hypothetical changes to current TB prevention and control activities (“Modelled Scenarios”).

Download: clicking on a button initiates download of the visualization itself (.png, .pdf, .pptx) or the estimates underlying the visualization (.csv, .xlsx).

Modelled Outcomes – Time Trends

User options are shown in a column on the left. The user specifies:

Comparison: results can be shown as absolute values for each outcome in each year, as a percentage of the base case scenario in the same year, or as a percentage of the base case scenario in 2018.

Subgroup: results can be shown for the total population, or for a subgroup described by nativity (U.S.-born, non-U.S.-born), and broad age groups (0-24 years, 25-64 years, 65+ years).

Outcome: results can be shown for five different outcomes:

  • Incident TB infections representing the annual number of incident M. tuberculosis (Mycobacterium tuberculosis) infections per 100,000 due to transmission within the United States (includes reinfection of individuals with prior LTBI, excludes migrants entering the United States with established LTBI);

  • LTBI Prevalence representing the percentage of individuals with latent TB infection in a given year;

  • TB Incidence representing the annual number of notified TB disease cases per 100,000, including those after death;

  • TB-Related Deaths representing annual TB-attributable mortality per 100,000.

Scenarios: results can be shown for up to five scenarios selected by the user, describing hypothetical changes to current TB prevention and control activities (“Modelled Scenarios”).

Download: clicking on a button initiates download of the visualization itself (.png, .pdf, .pptx) or the estimates underlying the visualization (.csv, .xlsx).

Modelled Outcomes – Age Groups

This page matches the format of the first two pages with the following exceptions:

Comparison: results are only shown as absolute values for each outcome in each year.

Subgroup: results can be shown for the total population, or for U.S.-born and non-U.S.-born alone.

Outcomes: results can be shown for three major outcomes (LTBI prevalence, TB incidence, and TB-related deaths), either as a prevalence or incidence rate with each age group (first three selections), or in absolute numbers (last three selections).

Modelled Outcomes – Comparison to Recent Data

The Comparison to Recent Data Page

Figure 7: The selected plot depicts model outcomes compared to the
reported Total Population in 2016 in the US by Age and Nativity.
Figure 7: The selected plot depicts model outcomes compared to the reported Total Population in 2016 in the US by Age and Nativity.

In the Comparison to Recent Data page (Figure 7), users can compare the model's output to reported data on the demography and TB epidemiology for their selected geography.

Further Description

In this Further Description page of the Tabby2 web application, documentation detailing the Organization of the tool, Definitions and Abbreviations, Frequently Asked Questions, and a notice about the 508 Accessibility of This Product are provided.

Feedback

The Feedback page in the Tabby2 web application prompts users of the application with feedback to either email ppml@hsph.harvard.edu with their questions, comments, or feedback, or to submit it directly through the web application.

Frequently Asked Questions

How do I export data from Tabby2?

Tabby2 users are able to download the data visualizations that they have created using the “Estimates”, “Time Trends”, or “Age Groups” tabs and the underlying estimates that were used to generate their visualizations.

To download a data visualization:

  1. Navigate to the last heading on the “Estimates,” “Time Trends,” or “Age Groups” tab, which reads “Download” (this can be found on the bottom left-hand corner of a typical web browser)

  2. Select PNG or PDF or PPTX depending on desired format

To download underlying data estimates:

  1. Navigate to the last heading on the “Estimates,” “Time Trends,” or “Age Groups” tab, which reads “Download” (this can be found on the bottom left-hand corner of a typical web browser)

  2. Select CSV or XLSX depending on desired format

Downloads should begin immediately after selection. If not, contact ppml@hsph.harvard.edu for assistance.

Where can I find more information about TB / TB modelling?

General information and resources on tuberculosis can be found on the Centers for Disease Control and Prevention's Tuberculosis webpage: https://www.cdc.gov/tb/default.htm

508 Accessibility of This Product

Section 508 requires Federal agencies and grantees receiving Federal funds to ensure that individuals with disabilities who are members of the public or Federal employees have access to and use of electronic and information technology (EIT) that is comparable to that provided to individuals without disabilities, unless an undue burden would be imposed on the agency.

If you need assistance with this web application, please contact ppml@hsph.harvard.edu.

Have feedback?

If you have any questions, comments, or feedback that you'd like to share with us, please send it through the below form or email it to ppml@hsph.harvard.edu