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.
California has the highest incidence and largest number of tuberculosis (TB) cases in the contiguous U.S., of which 80% are due to reactivation. These TB cases can be prevented via screening and treatment of latent tuberculosis infection (LTBI), yet, current adoption of the latent TB screening and treatment guidelines has been extremely poor. We will leverage expansive electronic health record data from two of the largest healthcare organizations in California to create and validate a new LTBI screening and treatment approach that can vastly improve identification and successful treatment of LTBI.
