Non-Tuberculous Mycobacteria (NTM) transmission to humans occurs through inhalation of dust particles or vaporized water containing NTM leading to pulmonary manifestations. NTM infections are often misdiagnosed for tuberculosis (TB) due to their similar clinical and radiological manifestations. We, therefore, performed a species-level identification of NTM in symptomatic TB negative patients through sequencing of the hsp65 gene. We conducted a cross-sectional study at the National Tuberculosis Reference Laboratory in the period between January to November 2020. One hundred and sixty-six mycobacterial culture-positive samples that tested negative for TB using capilia underwent Polymerase Chain Reaction targeting the hsp65 gene. Isolates showing a band with gel electrophoresis at 441 bp position were sequenced using Sanger technology. Geneious software was used to analyze the obtained sequences, and the National Center for Biotechnology Information gene database identified NTM species for each isolate. A phylogenetic tree was constructed from the DNA sequences and evolutionary distances computed using the general time-reversible method. Pearson chi-square was used to determine the association between NTM infection and participants’ characteristicsOur study identified 43 different NTM species. The dominant NTM belonged to Mycobacterium avium complex 37 (31%). Slow-growing NTM were the majority at 86 (71%) while rapid-growing NTM were 36 (29%). A significant association (p<0.05) was observed for regions and age, while patient type had a week likelihood of NTM infection. Our study characterized the diversity of NTM in Kenya for the first time and showed a high diversity of NTM species.
Background: Non-Tuberculous Mycobacteria (NTM) transmission to humans occurs through inhalation of dust particles or vaporized water containing NTM leading to pulmonary manifestations. NTM infections are often misdiagnosed for tuberculosis (TB) due to their similar clinical and radiological manifestations. Aims and Objectives: We, therefore, performed a species-level identification of NTM in symptomatic TB negative patients through sequencing of the hsp65 gene. Materials and Methods: We conducted a cross-sectional study at the National Tuberculosis Reference Laboratory in the period between January to November 2020. One hundred and sixty-six mycobacterial culture-positive samples that tested negative for TB using capilia underwent Polymerase Chain Reaction targeting the hsp65 gene. Isolates showing a band with gel electrophoresis at 441 bp position were sequenced using Sanger technology. Geneious software was used to analyze the obtained sequences, and the National Center for Biotechnology Information gene database identified NTM species for each isolate. A phylogenetic tree was constructed from the DNA sequences and evolutionary distances computed using the general time-reversible method. Pearson chi-square was used to determine the association between NTM infection and participants' characteristics. Results: Our study identified 43 different NTM species. The dominant NTM belonged to Mycobacterium avium complex 37 (31%). Slow-growing NTM were the majority at 86 (71%) while rapid-growing NTM were 36 (29%). A significant association (P<0.05) was observed for regions and age, while patient type had a weak likelihood of NTM infection. Conclusion: Our study characterized the diversity of NTM in Kenya for the first time and showed that species belonging to M. Avium Complex are the most prevalent in the country.
Background: HIV drug resistance (HIVDR) threatens progress achieved in response to the HIV epidemic. Understanding the costs of implementing HIVDR testing programs for patient management and surveillance in resource-limited settings is critical in optimizing resource allocation. Here, we estimate the unit cost of HIVDR testing and identify major cost drivers while documenting challenges and lessons learnt in implementation of HIVDR testing at a tertiary level hospital in Kenya. Methods: We employed a mixed costing approach to estimate the costs associated with performing a HIVDR test from the provider’s perspective. Data collection involved a time and motion study of laboratory procedures and interviewing laboratory personnel and the management personnel. Cost analysis was based on estimated 1000 HIVDR tests per year. Data entry and analysis were done using Microsoft Excel and costs converted to US dollars (2019). Results: The estimated unit cost for a HIVDR test was $271.78 per test. The main cost drivers included capital ($102.42, 37.68%) and reagents (101.50, 37.35%). Other costs included: personnel ($46.81, 17.22%), utilities ($14.69, 5.41%), equipment maintenance costs ($2.37, 0.87%) and quality assurance program ($4, 1.47%). Costs in relation to specific laboratory processes were as follows: sample collection ($2.41, 0.89%), RNA extraction ($22.79, 8.38%), amplification ($56.14, 20.66%), gel electrophoresis ($10.34, 3.80%), sequencing ($160.94, 59.22%), and sequence analysis ($19.16, 7.05%). A user-initiated modification of halving reagent volumes for some laboratory processes (amplification and sequencing) reduced the unit cost for a HIVDR test to $233.81 (13.97%) reduction. Conclusions: Capital expenditure and reagents remain the most expensive components of HIVDR testing. This cost is bound to change as the sequencing platform is utilized towards maximum capacity or leveraged for use with other tests. Cost saving in offering HIVDR testing services is also possible through reagent volume reduction without compromising on the quality of test results.
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