Lessons from the Field

Driving the usage of tuberculosis diagnostic data through capacity building in low- and middle-income countries

Natasha Gous, Alaine U. Nyaruhirira, Bradford Cunningham, Chris Macek
African Journal of Laboratory Medicine | Vol 9, No 2 | a1092 | DOI: https://doi.org/10.4102/ajlm.v9i2.1092 | © 2020 Natasha Gous, Alaine U. Nyaruhirira, Bradford Cunningham, Chris Macek | This work is licensed under CC Attribution 4.0
Submitted: 04 September 2019 | Published: 18 November 2020

About the author(s)

Natasha Gous, Global Health, SystemOne, LLC, Johannesburg, South Africa
Alaine U. Nyaruhirira, Management Sciences for Health, Pretoria, South Africa
Bradford Cunningham, Strategic Initiatives, SystemOne, LLC, Johannesburg, United States
Chris Macek, Business Development, SystemOne, LLC, Northampton, Massachusetts, United States

Abstract

Background: 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.

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

Methods: 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.

Results: 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.

Conclusion: 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.


Keywords

tuberculosis; GeneXpert; diagnostic data; monitoring and evaluation; data analysis; programmatic

Metrics

Total abstract views: 3336
Total article views: 3104

 

Crossref Citations

1. Use of automatic SQL generation interface to enhance transparency and validity of health-data analysis
Kavishwar B. Wagholikar, David Zelle, Layne Ainsworth, Kira Chaney, Alexander J. Blood, Angela Miller, Rupendra Chulyadyo, Michael Oates, William J. Gordon, Samuel J. Aronson, Benjamin M. Scirica, Shawn N. Murphy
Informatics in Medicine Unlocked  vol: 31  first page: 100996  year: 2022  
doi: 10.1016/j.imu.2022.100996