Driving the Usage of Tuberculosis Diagnostic Data through Capacity Building in Low- and Middle-Income Countries

Driving the Usage of Tuberculosis Diagnostic Data through Capacity Building in Low- and Middle-Income Countries

By: Natasha Gous, Alaine U. Nyaruhirira, Bradford Cunningham, Chris Macek
Publication: African Journal of Laboratory Medicine Nov. 2020; Vol. 9 (2): a1092. DOI: https://doi.org/10.4102/ajlm.v9i2.1092.



Connectivity platforms collect a wealth of data from connected GeneXpert instruments, with the potential to provide valuable insights into the burden of disease and effectiveness of tuberculosis programmes. The challenge faced by many countries is a lack of training, analytical skills, and resources required to understand and translate this data into patient management and programme improvement.


We describe a novel training programme, the tuberculosis Data Fellowship, designed to build capacity in low- and middle-income countries for tuberculosis data analytics.


The programme consisted of classroom and remote training plus mentorship over a 12-month period. The focus was on skills development in Tableau software, followed by training in exploration, analysis, and interpretation of GeneXpert tuberculosis data across five key programme areas: patient services, programme monitoring, quality of testing, inventory management, and disease burden.


The programme was piloted in six countries (Bangladesh, Ethiopia, Ghana, Malawi, Mozambique) in July 2018 and Nigeria in September 2018; 20 participants completed the training. A number of key outputs have been achieved, such as improved instrument utilisation rates, decreased error rates, and improved instrument management.


The training programme empowers local tuberculosis programme staff to discover and fix critical inefficiencies, provides high-level technical and operational support to the tuberculosis programme, and provides a platform for continued sharing of insights and best practices between countries. It supports the notion that connectivity can increase efficiencies and clinical benefits with better data for decision making, if coupled with commensurate capacity building in data analysis and interpretation.