Menzies Lab

Optimal Targeting for Individual and Population-Level TB Prevention

Within the same community, TB risks can differ by several orders of magnitude due to differences in infectious exposure and immune competence, and TB control depends heavily on targeting services to those most at risk. Priority groups described by the CDC and other agencies capture major TB risk factors, but these broad categories include many individuals with low TB risk, and exclude others who would benefit from screening. Our long-term objective is to provide individually- and locally-tailored evidence on TB risks and intervention effects, to optimize TB prevention services.

Project Summary: This project aims to provide locally and individually tailored evidence on TB risks to optimize TB prevention services in the United States and internationally. In prior work, we have demonstrated the feasibility of estimating TB risks for small population groups.

1. In Aim 1, we will create granular estimates of TB risk for the US population via a Bayesian evidence synthesis combining time series data on TB cases and population size, prevalence of latent infection (LTBI), and the fraction of cases due to recent infection. This analysis will allow us to produce individually tailored risk predictions to better target preventive services and provide patients with quantitative information on the risks they face.
2. Aim 2 will directly address these questions, creating highly disaggregated estimates of the costs, harms, and benefits of LTBI testing and treatment. We will develop a user-friendly web tool to provide patients and clinicians prompt, validated, and individually tailored information on possible treatment outcomes. We will also conduct analyses and develop a companion tool that will report the impact and cost-effectiveness of LTBI screening for user-defined target groups for the purpose of guiding program decision-making.
3. In Aim 3, we will develop a transmission-dynamic simulation model to predict long-term outcomes for a broad set of TB control options (including but not limited to LTBI treatment) and risk factor trends.

Public Health Impact: The evidence gathered from this project will be used to develop web-based interfaces predicting the costs and benefits of different TB treatment options. The user-friendly web-based tools aim to assist with clinical decision-making as well as TB policy-making at the city, state, and national levels.