Optimal Targeting for Individual and Population Level TB Prevention

Project Title Optimal Targeting for Individual and Population Level TB Prevention
Principle Investigator Nicolas Menzies
Project Period 2019 – 2024
Collaborators/Institutions Boston University, Stanford University, Yale University

Project Summary: This project aims to provide locally and individually tailored evidence on TB risks to optimize TB preventions services in the United States and internationally. In prior work we have demonstrated the feasibility of estimating TB risks for small population groups, and 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. The number of patients to whom this applies is substantial, approximately half of all US residents have been tested for LTBI, and of those testing positive only half initiate treatment. This represents a large number of people facing decisions about LTBI testing and treatment.

Aim 2 will directly address these questions, creating highly-disaggregated estimates of the costs, harms, and benefits of LTBI testing and treatment. To do so we will construct a Markov microsimulation model of LTBI screening and treatment. Using this model we will estimate long-term patient-level outcomes, including changes in TB risk, survival, costs, and adverse events. Based on these analyses 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. To increase the reach and impact of these tools we will adapt them for other countries with TB incidence below 20 per 100,000.

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. The model will be calibrated for multiple jurisdictions, and a web-based interface will allow users to specify scenarios and visualize outcomes. By identifying how current and novel interventions can be most effectively deployed to improve health, this research addresses the NIH’s highest priority area of health economics research, and responds directly to the need for computational tools and models to better understand and respond to infectious disease risks.

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 TB policy-making at the city, state, and national levels.