About Tabby

Tabby is a web application that predicts future tuberculosis (TB) epidemiology in the United States, based on hypothetical scenarios chosen by the user. Users can select a health outcome and population group of interest, then select one or multiple scenarios to compare. Results are displayed graphically, and following inputs from the user, the graphs update automatically to reflect the new selections. Visualizations can be downloaded in various formats, and the estimates underlying the graphs can also be downloaded in tabular format.

For the definitions of terms and abbreviations used in this application, see the Definitions page of the application. Additionally, common questions about Tabby are answered in the Frequently Asked Questions (FAQ) page. Any further questions may be directed to ppml@hsph.harvard.edu.

The estimates shown by Tabby are based on a mathematical model of TB epidemiology in the United States. The model can be used to investigate how changes in the drivers of TB epidemiology could lead to changes in long term epidemiology. For further details on the epidemiological factors included in the model, detailed methods, and main results see the paper “Prospects for tuberculosis elimination in the United States: results of a transmission dynamic model” Menzies et al, American Journal of Epidemiology 2018 (forthcoming). While the model itself includes a large number of factors determining TB epidemiology, this web tool is restricted to the scenarios included in the published journal article, and does not allow user control of individual parameters.

The findings and conclusions described in this web application and linked journal article are those of the author(s) and do not necessarily represent the views of the U.S. Centers for Disease Control and Prevention. This web tool was funded by the CDC, National Center for HIV, Viral Hepatitis, STD, and TB Prevention Epidemiologic and Economic Modeling Agreement (NEEMA, # 5U38PS004644-01).

Organization of the tool

Tabby has three pages with interactive visualizations: Estimates, Time Trends, and Age Groups. Each page can be accessed from the menu bar at the top of the page.

The Estimates page visualizes predicted TB outcomes at five major time points: 2016, 2025, 2050, 2075, and 2100.

The Time Trends page depicts predicted TB outcomes for each individual year from 2016 to 2100.

The Age Groups page visualizes predicted TB outcomes for a specified year subdivided into 11 age groups.

Estimates page

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 2016.

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. tb (Mycobacterium tuberculosis) infections per million due to transmission within the United States (includes reinfection of individuals with 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;
  • Active TB Incidence representing the annual number of notified TB cases per million, including TB cases identified after death;
  • MDR-TB in Incident TB Cases representing the percentage of all incident TB cases with multidrug-resistant TB (MDR-TB); and
  • TB-Related Deaths representing annual TB-attributable mortality per million.

Scenarios: results can be shown for up to five scenarios selected by the user, describing hypothetical changes to current TB prevention and control activities (‘Modeled Scenarios'). Descriptions for each scenario are provided below.

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

Time Trends page

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 2016.

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. tb (Mycobacterium tuberculosis) infections per million due to transmission within the United States (includes reinfection of individuals with prior infection, excludes migrants entering the United States with established LTBI);
  • LTBI Prevalence representing the percentage of individuals with latent TB infection in a given year;
  • Active TB Incidence representing the annual number of notified TB cases per million, including TB cases identified after death;
  • MDR-TB in incident TB cases representing the percentage of all incident TB cases with MDR-TB; and
  • TB-Related Deaths representing annual TB-attributable mortality per million.

Scenarios: results can be shown for up to five scenarios selected by the user, describing different assumptions about future TB prevention and control policy (‘Modeled Scenarios'). Descriptions for each scenario are provided below.

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

Age Groups page

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).

The following are descriptions of the intervention scenarios and outcomes available for visualization in Tabby.

Scenarios

Base case scenario

The base case scenario projects TB health outcomes assuming steady coverage and effectiveness of current TB prevention and treatment activities.

Each of the other scenarios that the user can select modify this base case scenario in some way, as described below.

Modeled Scenarios

  • TLTBI for New Immigrants: Provision of LTBI testing and treatment for all new legal immigrants entering the United States.
  • Improved TLTBI in the United States: Intensification of the current LTBI targeted testing and treatment policy for high-risk populations, doubling treatment uptake within each risk group compared to current levels, and increasing the fraction cured among individuals initiating LTBI treatment, via a 3-month Isoniazid-Rifapentine drug regimen.
  • Better Case Detection: Improved detection of active TB cases, such that the duration of untreated active disease (time from TB incidence to the initiation of treatment) is reduced by 50%.
  • Better TB Treatment: Improved treatment quality for active TB, such that treatment default, failure rates, and the fraction of individuals receiving an incorrect drug regimen are reduced by 50% from current levels.
  • All Improvements: The combination of all intervention scenarios described above.

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.

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.

Uncertainty Intervals

Uncertainty intervals are used to express the degree of uncertainty associated with a statistic. Where the intervals are wider, this means that there is greater uncertainty about the true value of the statistic. For the uncertainty intervals shown in the visualizations, there is a 2.5% probability (a one-in-forty chance) that the true value is above the upper end of the interval, and a 2.5% probability that the true value is below the lower end of the interval.

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.

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 TB bacteria are alive, but inactive in the body. People with latent TB infection have no symptoms, don’t feel sick, can’t spread TB to others, and usually have a positive TB skin test or positive TB blood test reaction. However, they may develop TB disease if they do not receive treatment for latent TB infection.

MDR-TB – Multidrug-resistant tuberculosis

Multidrug-resistant tuberculosis includes strains of TB that are resistant to isoniazid and rifampin, two common and potent TB drugs.

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.

TLTBI

Treatment and testing for latent tuberculosis infection

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 can die if they do not get proper treatment.

Frequently Asked Questions (FAQ)

How do I export data from Tabby? 

Tabby 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*

  3. The data will include mean values and 95% coincidence intervals (labeled ci_high and ci_low)

*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’s Tuberculosis webpage: https://www.cdc.gov/tb/default.htm

For detailed information on the dynamic transmission model used to generate Tabby estimates, see PAPER CITATION and link

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

LTBI prevalence (per million), in the total US population, for 2016