# A New Approach to an Old Problem: Redesigning Latent Tuberculosis Screening and Treatment

> **NIH NIH R01** · KAISER FOUNDATION RESEARCH INSTITUTE · 2024 · $760,514

## Abstract

SUMMARY/ABSTRACT
In 2015, tuberculosis (TB) surpassed HIV as the number one cause of infectious disease deaths worldwide. In
the U.S., California has the highest incidence and largest number of TB cases in the nation, comprising nearly
one-quarter of all new active TB cases in 2017. More than 80% of active TB disease in the U.S. is due to
reactivation, which could be prevented via screening and treatment of LTBI. Yet, adoption of the latent TB
screening and treatment guidelines has been extremely poor. The screening guidelines are inefficient and rely
on data that are almost never available to clinicians, and the treatment guidelines are confusing. Further,
treatment initiation and completion rates are low for patients with LTBI and the barriers to successful treatment
are poorly understood. To address these missed opportunities, we will use expansive electronic health record
data across 2 of the largest healthcare institutions in California as well as qualitative data from LTBI
stakeholders to conduct three specific aims. For Aim 1, we will look at current gaps in screening practices for
LTBI, as well as gaps in the guidelines themselves, by simulating what it would look like if screening was being
perfectly implemented. To do this, we will collect laboratory testing data for LTBI stratified by characteristics of
interest and will use modeling to estimate the number of TB cases prevented by current screening as a
measure of effectiveness. We will use simulation models to estimate effectiveness of optimal screening. For
Aim 2, we will develop and validate new screening and treatment guidelines for LTBI based on variables widely
available in electronic health records. First, we will use machine learning and traditional regression to identify
risk factors for positive LTBI tests and reactivation TB. Next, we will assign risk scores to each risk factor, and
will use joint probability analyses to identify populations at greatest need for screening and treatment. To
estimate performance of the newly proposed strategy, we will use simulation modeling. For Aim 3, we will
develop and pilot a culturally-tailored educational video intervention to improve LTBI treatment initiation and
completion. We will first identify barriers to treatment adherence through qualitative interviews with patients
and providers and will subsequently develop a short video based on findings from the interview. We will
perform an individually randomized efficacy trial to assess impact of the intervention on initiation and treatment
completion rates. The approach is innovative because we propose a complete re-framing of the current U.S.
LTBI control strategy in a way that dramatically enhances ease-of-use for frontline providers. Results of this
work will make a significant contribution to public health by providing low-cost and easily expandable solutions
to address ongoing and substantial gaps in the current LTBI care continuum.

## Key facts

- **NIH application ID:** 10798335
- **Project number:** 5R01AI151072-05
- **Recipient organization:** KAISER FOUNDATION RESEARCH INSTITUTE
- **Principal Investigator:** Sara Tartof
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $760,514
- **Award type:** 5
- **Project period:** 2020-03-17 → 2026-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10798335

## Citation

> US National Institutes of Health, RePORTER application 10798335, A New Approach to an Old Problem: Redesigning Latent Tuberculosis Screening and Treatment (5R01AI151072-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10798335. Licensed CC0.

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