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academies
September 20-21, 2017 | Philadelphia, USA
Global summit on
TUBERCULOSIS AND LUNG DISEASE
Int J Respir Med 2017 Volume 2 Issue 2
T
uberculosis (TB) is a global public health threat and a major leading cause of
death in the world. In 2015, 10.4 million people fell ill with TB and 1.8 million
died from the disease (including 0.4 million among people with HIV). Over 95% of
TB deaths occur in low- and middle-income countries, Swaziland inclusive. The TB
problem is compounded by the emergence of multidrug resistant TB in which there
is annual estimate of 480 000 people having multidrug-resistant TB (MDR-TB). TB in
Swaziland has reached an epidemic stage and the rate and pattern of transition to
multidrug resistant TB from individuals having TB is unknown, yet treatment and care
for clients with multidrug resistant TB poses serious burden on the economy of the
nation and leads to high mortality, hence the need for the nation to prepare adequate
human resources and finance to mitigate the impact of the disease. Predicting the
rate of transition to multidrug resistant among individuals with TB using computational
intelligence will assist government in preparing manpower and materials to deal with
the menace thus reducing the effect of the epidemic. Computational intelligence
uses computational methodologies and approaches to address complex real-world
problems such as prediction of transition from TB to MDR-TB. Results revealed that
given the current prevalence of TB in the country, three out of every ten individuals
with TB develop MDR-TB most of the transition occurs from second year of contracting
TB and among those who default treatment. In the light of the result, government
should scale up prevention strategies and procure diagnostic and treatment resources.
Training of human resources for the diagnosis, treatment and care of MDR-TB has been
recommended to the government.
e:
nducnytia@gmail.comPrediction of transition to Multidrug resistant Tuberculosis (MDR-TB) among individuals with
tuberculosis using computational intelligence.
Cynthia Chinagorom Nwokonna
Good Shepherd College of Nursing, Swaziland