Tuberculosis is an infectious disease that primarily affects developing countries, and the various strains are rapidly becoming increasingly resistant to currently available drugs. A recent study uses a mathematical model to predict the future burden of drug-resistant tuberculosis, which will help with the development of strategies to combat its rise.
Tuberculosis (TB) is an infectious disease caused by a particular strain of bacteria called Mycobacterium tuberculosis. TB mainly affects the lungs, but the disease is latent in most individuals, which means that there are no real symptoms and the disease is not spread. However, a small proportion of those who have the latent disease can progress into the active disease state, which has symptoms such as: chronic cough, fever, bloody saliva and mucus. Additionally, smoking and HIV infection are risk factors for developing active TB. TB infections are common in developing countries and the only way to treat the disease is through the use of antibiotics. If patients are left untreated, many of them will end up passing away due to the disease.
Many worldwide initiatives that aim to provide infected individuals with successful treatment and stop the spread of TB have shown success in recent years. However, one major problem with TB is that the strains of bacteria are constantly evolving to become drug-resistant. In 2015, almost half a million individuals worldwide were infected with TB strains that were resistant to some of the most commonly used antibiotics for TB treatment. There are also emerging strains of TB that gain resistance to other less commonly used antibiotics. Drug-resistant strains may have emerged due to the use of antibiotics to treat TB, or just happened by chance. Furthermore, mathematical models have predicted that more drug-resistant strains will arise from a lack of detection and appropriate treatment of TB. The authors of a study recently published in The Lancet Infectious Diseases aim to determine the future burden of drug-resistant TB using mathematical models.
The study focused on four different countries (Philippines, Russia, India, and South Africa) known to have many cases of drug-resistant TB and differing capabilities to deal with the disease. One global initiative taken to prevent the rise of drug-resistant TB was called the Green Light Committee (GLC). They aimed to increase access to TB drugs in various countries, and provided support to deal with drug-resistant strains of TB. The four countries being studied have different amounts of GLC support. Philippines and Russia had GLC-approved programs, India had a pilot phase of the program, and South Africa did not have any GLC support. Looking at countries with different exposure to this GLC program allows the authors to determine its effect. This study’s mathematical model took into account several factors: the condition of TB in patient populations, progression from latent to active TB, treatment outcomes, transmission of TB and HIV, drug resistance of TB strains, and each country’s future population growth.
The model intricately predicts how individuals can become infected with TB or drug-resistant TB, and progress through the disease with different possible outcomes, which depends on whether or not they get proper treatment and if the treatment is effective. The model also considers how patients can transmit the disease to others and whether patients relapse and get TB again. In addition, part of the model considered HIV infections, since it negatively impacts TB progression. The model predicts an increase in the proportion of TB cases that will be drug-resistant by 2040 in the four countries. In most countries, a crucial factor affecting the rise in drug resistance was whether patients received appropriate treatment. In South Africa, treatment outcomes among individuals with HIV and the transmission of TB among them were predicted to play a role in increasing TB drug resistance. The transmission of TB was a major component that increased drug resistance in India and Philippines. Lastly, they observed that treating patients under GLC-approved conditions would decrease the likelihood of drug-resistant TB.
This study highlights the severity drug-resistant TB, which is on the rise worldwide, as predicted by the research groups model. However, the model assumes no significant changes in the way patients are currently being treated. Their extensive analysis gives insight to specific countries on what steps they should focus on to prevent the spread of TB and increase in drug resistance. They also provide evidence that GLC-approved programs could help slow down the rise of drug resistance. With this additional knowledge, health organizations can take more rigorous steps to slow or prevent the rise in TB drug resistance, in order to help prevent the 1.8 million deaths caused by TB worldwide every year.
Written By: Branson Chen, BHSc