Author ORCID Identifier

0000-0001-9781-5748

Date of Award

1-5-2024

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Public Health

First Advisor

Dr. Christine Stauber

Second Advisor

Dr. Laura Salazar

Third Advisor

Dr. Kathryn Winglee

Abstract

The current rate of decline in the tuberculosis (TB) disease case incidence rate will not achieve the United States (U.S.) TB elimination goals. Over 80% of reported U.S. TB cases are attributed to reactivation of latent TB infection (LTBI); to achieve TB elimination LTBI prevalence must be reduced. This doctoral dissertation utilized novel methodological approaches combined with an electronic health record (EHR) data cohort from the largest network of safety net clinics in the U.S. to measure LTBI among populations in the U.S.

In study 1 a systematic review and meta-analysis determined that mean LTBI prevalence estimate in the U.S. general population was 4.04% (95% CI: 3.35%, 4.87%) and 16.49% (95% CI: 14.70%, 18.50%) in the non-U.S.–born population. Current LTBI prevalence estimates in the U.S. were found to have methodological limitations.

Study 2 utilized the EHR data cohort to measure the LTBI care cascade. While 43.5% of the cohort met LTBI screening criteria, only 21.4% were tested, less than half with the recommended test. Among patients diagnosed with LTBI, 29.1% were prescribed LTBI treatment; only 33.6% were prescribed a recommended rifamycin-based regimen.

Study 3 utilized the EHR data cohort to create a novel algorithm to classify individuals into hierarchical definitions for LTBI, TB disease, patients with no TB infection, and patients not evaluated for TB. Using the novel algorithm definitions, the TB incidence rate was 11.8/100,000 persons in the patient population; LTBI prevalence was 14.2% among patients evaluated for TB infection.

This dissertation provides a mean estimate of LTBI prevalence for the U.S. general population and the non-U.S.–born population that can be used to determine resources needed to improve targeted testing and treatment to reduce the burden of TB infection in the U.S. However, updated LTBI prevalence estimates using robust methodology for determining infection are needed. EHR data is a novel data source that can be used to estimate LTBI prevalence in clinical networks among patients at higher risk, as well as to identify gaps in LTBI testing and treatment. Updating national LTBI prevalence estimates, especially for subpopulations at higher risk, and addressing identified gaps in testing and treatment may have a direct impact on improving TB prevention and accelerate progress towards elimination.

DOI

https://doi.org/10.57709/36275860

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