Impact and Effectiveness of State-Level Tuberculosis Interventions in California, Florida, New York, and Texas: A Model-Based Analysis

Abstract

The incidence of tuberculosis (TB) in the United States has stabilized, and additional interventions are needed to make progress toward TB elimination. However, the impact of such interventions depends on local demography and the heterogeneity of populations at risk. Using state-level individual-based TB transmission models calibrated to California, Florida, New York, and Texas, we modeled 2 TB interventions: 1) increased targeted testing and treatment (TTT) of high-risk populations, including people who are non-US-born, diabetic, human immunodeficiency virus (HIV)-positive, homeless, or incarcerated; and 2) enhanced contact investigation (ECI) for contacts of TB patients, including higher completion of preventive therapy. For each intervention, we projected reductions in active TB incidence over 10 years (2016-2026) and numbers needed to screen and treat in order to avert 1 case. We estimated that TTT delivered to half of the non-US-born adult population could lower TB incidence by 19.8%-26.7% over a 10-year period. TTT delivered to smaller populations with higher TB risk (e.g., HIV-positive persons, homeless persons) and ECI were generally more efficient but had less overall impact on incidence. TTT targeted to smaller, highest-risk populations and ECI can be highly efficient; however, major reductions in incidence will only be achieved by also targeting larger, moderate-risk populations. Ultimately, to eliminate TB in the United States, a combination of these approaches will be necessary.

Keywords: mathematical modeling; preventative therapy; tuberculosis; tuberculosis prevention.

Figure 1.

Percentage of incident tuberculosis (TB) cases occurring among populations with selected risk factors in California (CA), Florida (FL), New York (NY), and Texas (TX), 2010–2014. The graph shows percentages of reported TB cases in 5 risk groups: the non–US-born (A), persons with diabetes (B), persons positive for human immunodeficiency virus (C), homeless persons (D), and incarcerated persons (E). Solid columns show reported data (averaged over 5 years from 2010 to 2014); hatched columns show model simulations (2016–2026). Note the different scales of the y-axes for the non–US-born and diabetic populations, indicating their larger relative sizes. Error bars, 95% ranges.

Figure 2.

Projected comparative efficiency of tuberculosis (TB) interventions in California (CA), Florida (FL), New York (NY), and Texas (TX) (model simulations). A) Estimated number of persons needed to screen (NNS) in order to avert 1 case of active TB for targeted testing and treatment (TTT) of latent TB infection (LTBI) among persons with selected risk factors (those who are non–US-born (purple), diabetic (turquoise), human immunodeficiency virus (HIV)-positive (red), homeless (dark blue), and incarcerated (gold)), as well as for enhanced contact investigation (ECI; maroon); B) estimated number of persons needed to treat (NNT) in order to avert a single case of TB. Columns show median estimates; error bars, 95% ranges. Columns with asterisks above them denote risk groups for which meaningful point estimates (**) or 95% ranges (*) could not be estimated because of small population sizes. Note that the NNS is not applicable for ECI, since it was modeled as increased evaluation rates and improved LTBI treatment completion rates among already-identified contacts, with no additional screening.