World TB Day is marked on March 24 to reflect the date in 1882 when Dr. Robert Koch announced his discovery of Mycobacterium tuberculosis, the bacterium that causes the disease tuberculosis (TB). World TB Day is aimed at educating the public about the impact of TB around the world and it is one of eight official global public health campaigns marked by the World Health Organization (WHO).
The theme of World TB Day 2019 is “It’s TIME”. The emphasis for this year’s event is to pressure world leaders to act on their commitments.
TB remains the world’s deadliest infectious killer and it is estimated that, each day, around 4,500 people lose their lives to TB and some 30,000 people fall ill. Yet TB is a preventable and curable disease. So far, global efforts to combat TB have saved an estimated 54 million lives since the year 2000 and reduced the TB mortality rate by 42 percent – but more can be done through political action.
Tuberculosis
Tuberculosis is an infectious airborne disease normally caused by the bacterium M. tuberculosis. While the disease typically affect the lungs, other parts of the body can become infected. Most infections do not lead to any symptoms (‘latent tuberculosis’). However, around 10 percent of infections turn into an ‘active disease’, and this can lead to a high fatality rate. As with other bacterial infections, there is a growing trend towards antimicrobial resistance.
With the 2019 theme, WHO is calling on world leaders to:
Scale up access to prevention and treatment;
Build accountability;
Ensure sufficient and sustainable financing including for research;
Promote an end to stigma and discrimination, and
Promote an equitable, rights-based and people-centered TB response.
In other words, action in scale up, research, funding, human rights and accountability.
Latest research
Scientists around the world are working hard to develop more effective treatments and prevention measures. To mark World TB Day, three examples of the type of research being conducted are highlighted.
Improving screening for latent cases
Screening and treatment of people within key populations, such as health care workers, recent contacts of actively infected individuals, immunocompromised individuals, children, and immigrants from countries where tuberculosis is endemic, is regarded as an essential step in the eradication of the disease. To achieve this requires better and fast methods for detecting latent cases (where there are no symptoms). Researchers from the Texas A&M University School of Medicine, writing in Clinical Microbiology Newsletter, outline a new method for such detection called an ‘interferon gamma release assay’. This method can test whether a patient has had tuberculosis exposure with improved sensitivity and specificity over conventional skin tests. See: “Diagnosis of Latent Mycobacterium tuberculosis Infection in the Era of Interferon Gamma Release Assays.”
Replacing conventional test methods
In many low-income countries, the assessment of tuberculosis is by sputum microscopy. This method has a imitation is that it can be difficult to diagnose TB when the bacterial load is less than 10,000 per millilitre of the sputum sample. This can lead to erroneous negative results for some patients. In a paper published in the Indian Journal of Tuberculosis scientists working at the New Delhi Tuberculosis Centre discuss how an alternative and rapid method can be used as an alternative: MGIT (Mycobacteria growth indicator tube). With this method, organism growth is detected by a non-radioactive detection system using fluorochromes for detection and drug screening. See: “Can sputum microscopy be replaced?”
Applying artificial intelligence
In a paper published in the journal Infection, Disease & Health, researchers from St. Luke’s International University, Tokyo and City University of Hong Kong show how AI methods can be developed to enable effective analysis of massive infectious disease and surveillance data, including tuberculosis, to support risk and resource analysis for government agencies, healthcare service providers, and medical professionals in the future. See: “Artificial Intelligence for infectious disease Big Data Analytics.”
