Summary Description of the Tool

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About this Tool

This tool is a web application that shows the projected impact of increased targeted testing and treatment on tuberculosis incidence among different high-risk populations in the four states: California, Florida, New York and Texas. Tuberculosis (TB) is 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.

Targeted testing and treatment (TTT) is an important TB prevention and control strategy that is used to identify, evaluate and treat people who are at high risk for latent TB infection (LTBI) or at high risk for active TB disease once infected. In this study, the impact of TTT is shown as the percent reduction in the projected baseline TB incidence during the 10-year period after the intervention (2016-2025), due to TTT compared to the absence of any additional intervention. Users can observe the impact of TTT among five high-risk groups individually, and compare these values to the total impact of TTT if implemented across all five high-risk groups together in each state.

For the definitions of terms and abbreviations used in this application, see the GLOSSARY section of this summary. Any further questions may be directed to

The charts shown in this tool are based on calculations from reported TB incidence data in the United States. They can be used to understand differences in the impact of TTT across high-risk groups in each of four states.

Depending on the requirements of TB controllers in each state, this tool can:

  1. Illustrate the potential impact of increased targeted testing and treatment on TB incidence among key populations;
  2. Highlight projected between-state differences in TB reduction under TTT to inform TB elimination programs.

For further details on the epidemiological factors included in the model, detailed methods, and main results see the paper, “Impact and Effectiveness of State-level Tuberculosis interventions in California, Florida, New York and Texas: A model-based analysis”; Shrestha S, Cherng S, Hill AN, Reynolds S, Flood J, Barry PM, Readhead A, Oxtoby M, Lauzardo M, Privett T, Marks SM, Dowdy, DW. American Journal of Epidemiology. 2019, doi: 10.1093/aje/kwz147

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/AIDS, Viral Hepatitis, STD, and TB Prevention Epidemiologic and Economic Modeling Agreement (NEEMA, # 5 NU38PS004646-05-00)

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Organization of the Tool

This tool has a graph and a data table: the Impact Plot and Impact Table.

The Impact Plot shows the projected 10-year reduction in TB incidence from 2016 to 2025 under increased targeted testing and treatment (TTT) of different risk groups in each of four U.S. states.

The Impact Table shows estimates of the projected 10-year percent reduction in TB incidence across risk groups in four U.S. states.

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Impact Plot

The user can examine the projected impact of TTT on TB incidence in each of five high-risk groups, as follows:

  • Non-US–Born: Persons who were born in countries outside the United States and live in the United States;
  • Diabetic: Persons with diabetes living in the United States.
  • HIV-Positive: Persons living with HIV and/or AIDS in the United States.
  • Homeless: Persons experiencing homelessness while living in the United States.
  • Incarcerated: Persons experiencing incarceration while living in the United States.

Human Immunodeficiency Virus, or HIV, is a virus that is spread through certain bodily fluids. HIV infects the human immune system, the system in the body in charge of fighting off illness. In most cases, HIV is preventable and treatable; however, people with HIV can progress to AIDS and die if they do not get proper treatment.

The resulting bar graph depicts the projected 10-year reductions in TB incidence, spanning 2016 and 2025, under increased TTT of different key populations in California, Florida, New York, and Texas, using state-level TB transmission models.

Reductions are shown as the percentage of the projected baseline TB incidence modeled in the absence of any additional intervention. For each state, the model assumes 50% TTT coverage among all non-US-born adults and 80% TTT coverage among all people living with diabetes; and includes the entire HIV+, incarcerated, and homeless populations. These coverage assumptions apply to both intervention scenarios: the impact of TTT among five high-risk groups individually and total impact of TTT across all five high-risk groups together. The reduction in TB incidence from the total impact intervention does not equal the sum of reduction for the five individual populations largely because individuals with more than one risk factor only receive the intervention once.

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Impact Table

In this Table, users can view the underlying data used to generate the Impact Plot. The values in this table correspond to the projected achievable 10-year reductions in TB incidence (and 95% range) across different high risk groups in four U.S. states.

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Further Information on the Tool

While many state health departments may be aware of which populations in their specific state are at highest risk for TB disease, showing the projected impact of TB interventions within these key populations across all states provides a basis for measurable improvement.

Importantly, differences in TTT impact between states may mean that different states have different needs and priorities regarding the reduction of TB incidence by risk group.

User-friendly online tools can thus aid state and local jurisdictions in decision-making about resource allocation in TB control and prioritization of TB elimination activities, and are complementary to the state TB epidemiology reports.

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Sources of Data

Non-U.S.–born persons

The sizes of non-U.S.–born populations by region of birth were obtained from the American Community Survey (ACS) factfinder website using the dataset containing 5-year estimates from 2015 for the years 2011-2015 by state (tables S0503, S0504, S0505 and S0506).

Persons living with diabetes

Populations with diabetes were estimated from the Behavioral Risk Factor Surveillance System (BRFSS) using the U.S. Diabetes Surveillance System website from 2010-2014. Respondents were considered to have diagnosed diabetes if they responded "yes" to the question, "Has a doctor, nurse, or other health professional ever told you that you have diabetes?" Diabetes prevalence from each state was multiplied by the total estimated population in that state using U.S. census data.

Persons experiencing homelessness

Homeless populations were estimated for years 2012-2015 from Part 2 of the U.S. Department of Housing and Urban Development's (HUD's) annual homeless assessment report (AHAR) to the U.S. Congress. Based on the AHAR, one-year national estimate of sheltered homelessness cases and the estimated proportion of total sheltered homeless populations by state, we calculated the total cases of homeless people occurring in every state. Sheltered homelessness is defined as people who "used an emergency shelter or transitional housing program at any time from October 1 through September 30 of the following year." These data are associated with the calendar year corresponding to October 1st in our analyses, and exclude people served by victim service providers and individuals experiencing homelessness who never accessed a shelter program during the 12-month period. HUD estimates that approximately 35% of homelessness can be attributed to individuals experiencing homelessness outside of a sheltered setting. Assuming our homelessness denominators were to increase proportionally across all states, we would expect our estimates of incidence differences and percentage of heterogeneity attributable cases to decrease slightly both in risk-group specific estimates, and across the United States.

Persons experiencing incarceration

Incarcerated populations were estimated from the U.S. Department of Justice Corrections Statistical Analysis for the years 2011-2015. This population includes all correctional inmates in both prison and jail settings under the jurisdiction of state or federal correctional authorities, as reported by central respondents in each of the 50 state departments of corrections. These data through the 31st of December are reported annually.

Persons living with HIV

HIV-positive populations were estimated from the CDC National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention AtlasPlus database from 2010-2014. We used HIV-positive case counts by state for individuals aged 13 years and older from the online tool.

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Glossary of Terms

Risk factor

Any characteristic or exposure of an individual that increases their likelihood of being infected and/or developing a disease. For example, risk factors include diabetes, homelessness, and incarceration.

Risk Group

The population with a specified risk factor. For example, risk groups include diabetics, persons experiencing homelessness, and persons experiencing incarceration.

Incidence (or 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.


The projected percent reduction in TB incidence for a given population that receives a particular TB intervention compared to a scenario in which the same population receives no additional intervention.

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Where can I find more information about TB?

General information and resources on tuberculosis can be found on the Centers for Disease Control’s Tuberculosis webpage.

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To return to the data, click here