b008 Unlocking our understanding of latent TB | Doherty Website

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20 Apr 2021

Unlocking our understanding of latent TB

As global efforts ramp up to eliminate tuberculosis (TB), researchers have revealed significant gaps in our understanding of reactivation rates years after infection.

TB is an infectious disease caused by Mycobacterium tuberculosis (Mtb) that generally affects the lungs and other parts of the body, causing serious illness and sometimes death. While the risk of developing TB is greatest in the early months following infection, it can remain dormant in a latent state without symptoms and reactivate to cause disease later.

In the first systematic literature review investigating how often TB disease occurs in the years following infection, published today in the Lancet Infectious Diseases journal, a research team led by the Doherty Institute investigated studies to shed light on TB reactivation rates more than two years after infection, known as ‘late reactivation’.

Lead author Katie Dale, Epidemiologist with the Victorian Tuberculosis Program and University of Melbourne PhD Candidate, said the data revealed just how little is known about the risk that latent tuberculosis poses for future TB disease.

“It’s commonly understood that patients with latent tuberculosis have a five to 10 percent chance of developing active tuberculosis over the course of their lifetime, however the review found that there was little evidence for how often TB disease occurs beyond five years from infection,” said Ms Dale.

“The uncertainty around the magnitude of late reactivation risk is problematic - accurate estimates are crucial for predicting the long-term benefits of preventive therapy."

In recent years, there’s been an increased emphasis on the detection and treatment of latent TB infection to prevent future TB disease, particularly given global efforts to eradicate the disease.

Modelling approaches often estimate public health impact and cost effectiveness when evaluating programmatic strategies, and parameter values used to simulate late reactivation can have a significant impact on model outputs, and resultant recommendations.

“While our study reveals how poorly understood late reactivation is, it also provides an opportunity for researchers to redress this.

“As we focus our efforts globally on eliminating tuberculosis, we must shine a light on what we do not know in order to improve our efforts.”

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