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Background: Tuberculosis (TB) is a fatal disease globally, if not managed well, with a million or more people dying by the disease annually in low and middle-income countries (LMIC). Around two billion people are thought to be asymptomatically (latently) infected with Mycobacterium tuberculosis, putting them at risk of acquiring active tuberculosis. Tests that identify immunoreactivity to mycobacterial antigens rather than live bacteria, as well as mathematical modelling, are used to estimate the prevalence of latent tuberculosis infection. According to reports, tuberculosis (TB) was the cause of 1.3 million fatalities among HIV-negative people in 2016, surpassing the global number of HIV/acquired immune deficiency syndrome (AIDS) deaths. In addition, TB was a factor in 374,000 HIV-related deaths. Despite the effectiveness of chemotherapy over the last seven decades, tuberculosis remains the world's leading infectious killer. In 2016, 10.4 million new cases were reported, a number that has remained constant since the dawn of the twenty-first century, confounding public health specialists tasked with designing and implementing measures to lessen the global burden of tuberculosis disease. As a result, the current study aims to look into the epidemiology of tuberculosis in Lesotho in order to help policymakers make decisions on TB control in the country.
Methodology:. In the current investigation, a cross-sectional, retrospective descriptive study design was used, as well as a probability sampling strategy. The National TB-Database from the Ministry of Health in Lesotho was used as the source of data for this quantitative investigation, which was analyzed using STATA statistical software version 12 for Windows (STATA Corporation, College Station, Texas). A Chi-Squared test was used to compare categorical variables, while a t-test was used to examine continuous variables. A statistically significant P-value of 0.05 was used.
Results: A total of 18 836 TB patient records were recovered, with 45 percent of the TB patients being females. The average age of the TB patients was 35.9 years, with a standard deviation of 12.7%, and the ages ranged from one year to 84 years. There
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was a statistically significant difference between the age groups (p value 0.001), with 33.1 percent of TB patents being in the age group 25–34 years, followed by 29 percent, 15.4 percent, 11.2 percent, and 5.5 percent in the age groups 35–44 years, 45–55 years, 15–24 years, and 55–64 years 65 years.. There has been a fluctuating treatment outcome of TB from 63.5% for cured patients in 2012 to 57.2% in 2013 and this rose to 60.4% in 2014 then eventually reached 76.7% in 2019. The TB treatment success rate in Lesotho also showed a similar trend as the cure rate. The overall TB death rates in the current study was found to be increasing on an annual basis from 7.4% in 2012 to 9.2% in 2018 then dropped to 8.5% in 2019. The TB patients who have not been evaluated for treatment outcomes have been decreasing annually from 4.4% in 2012 to 0.8% in 2019. The proportion of TB patients with known HIV status increased from 22.3% in 2015 to 90.5% in 2019 and similarly to the proportion of TB patients with HIV status positive increased from 15.1% in 2015 to 60.4% in 2019. The proportion of TB patients with HIV status positive increased with increasing age group all age groups.
Conclusion: TB is still a concern in Lesotho, where treatment target goals have not yet been fulfilled, the findings of this study underline the importance of addressing the underlying socio-economic causes of TB. The most important goal in TB control is to detect 70% and cure at least 85% of sputum smear positive cases. If these goals are met, the prevalence, incidence, transmission, and medication resistance to tuberculosis (TB) could all decrease. Despite the National Tuberculosis Control Programme's attempts to enhance TB patients' access to treatment and adherence to therapy, the percentage of patients who have good treatment outcomes remains low. Despite having an 84 percent detection rate and using the DOTS technique, the available data did not identify the types of tuberculosis, therefore we were unable to forecast multidrug-resistant tuberculosis (MDR-TB). |
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