Revolutionizing TB Diagnosis: The Power of Diagnostic Network Optimization (DNO)

Tuberculosis (TB), a major global health threat, has remained at the forefront of public health challenges due to its high morbidity and mortality rates worldwide. Particularly prevalent in low- and middle-income countries, TB’s impact is exacerbated by social and economic factors that hinder effective disease management and control. Despite significant strides in medical science that have improved diagnostic and treatment options, TB continues to claim lives due to systemic challenges in healthcare systems. The advent of Diagnostic Network Optimization (DNO) offers a beacon of hope, promising to transform TB diagnosis through enhanced effectiveness and efficiency of diagnostic services. DNO is a geospatial analytics approach that supports visualization of current (“as-is”) diagnostic networks and creates various scenarios to achieve an optimized (“ideal”) network presenting trade-offs to choose from. By integrating data-driven strategies to optimize the deployment and utilization of diagnostic resources, DNO is poised to make a profound impact on TB control efforts globally.

The Urgent Need for Enhanced TB Diagnostics

Current diagnostic methods for TB are hindered by a combination of factors, including limited accessibility, high costs, and variable accuracy, which collectively contribute to substantial diagnostic gaps. Smear microscopy, the standard diagnostic method in many settings, often fails to detect less active forms of TB and is unable to determine drug resistance, which is crucial for guiding treatment. These limitations are particularly critical in the context of rising TB drug resistance worldwide, making the need for accurate and rapid diagnostics more pressing. Advanced molecular diagnostics, such as Xpert MTB/RIF, offer a solution with their higher sensitivity and rapid detection capabilities. However, their deployment is not widespread, often restricted by the high costs and infrastructural requirements that are not feasible in many regions hardest hit by TB.

The 2022 Global TB Report by the World Health Organization (WHO) underlines the severe implications of these diagnostic gaps. It reported that millions remain untreated and unreported within the health systems, with an estimated 4.1 million people with TB going undiagnosed or not reported in 2021, highlighting the urgent need for enhanced diagnostic strategies (World Health Organization).

The Power of Molecular Diagnostics

Molecular diagnostics revolutionize TB control by providing rapid, accurate detection of both TB and drug-resistant strains, essential for appropriate treatment. The WHO has emphasized the need for broader access to these technologies, setting targets for universal access to rapid molecular diagnostics by 2025. However, in 2021, only a third of all diagnosed TB cases utilized these advanced methods at the point of initial diagnosis, showcasing a significant gap in the deployment of these life-saving technologies (Pai and Swaminathan). DNO can play a critical role in cost effective planning for the scale-up of molecular diagnostics to meet current and future diagnostic needs aligned with the Sustainable Development Goals (SDG) of ending the TB epidemic by 2030.

Image Source: FIND

Diagnostic Network Optimization to the rescue

Diagnostic Network Optimization (DNO) is a transformative approach that uses data analytics to optimize the placement and utilization of the available diagnostic resources. By analyzing current diagnostic networks, identifying gaps, and strategically reconfiguring resources, DNO ensures that effective diagnostic tools reach the populations most in need. This not only improves the speed and accuracy of TB diagnoses but also extends the reach of advanced molecular diagnostics across diverse populations.

Image Source: FIND

Building blocks of DNO

The advancement of diagnostic network optimization heavily relies on the integration of sophisticated data analytics, which has transformative potential in reshaping diagnostic networks within low- and middle-income countries. Data analytics enables health systems to strategically design and implement diagnostic networks that are not only cost-efficient but also patient-centered (Nichols et al.). This methodological approach is crucial for understanding disease patterns, predicting diagnostic needs, and deploying resources in a manner that maximally enhances patient access to essential diagnostic services. DNO is implemented in 5 steps of defining a scope, data collection (or data system integration), baseline outputs, running scenarios for optimization and/or future state, and selecting an output for implementation. Data needs can vary based on the scope of the analysis from site-level GIS coordinates, equipment, and testing information.

Success Stories with DNO Implementation

Kenya

The application of DNO in Kenya led to a substantial reconfiguration of TB diagnostic services. Before DNO, there were a total of 162 GeneXpert GXIV instruments, performing around 275,000 Xpert MTB/RIF tests annually—significantly underutilizing their capacity of approximately 466,500 tests per year. With the strategic insights provided by DNO, projections indicated that testing volumes could increase to 682,000 by 2021 and potentially reach 1.4 million by 2023. To meet this demand, the DNO model recommended expanding the number of testing sites to 450-500 by 2023, many targeted at hard-to-reach counties to improve accessibility and reduce diagnosis turnaround time (Albert et al.).

Philippines

In the Philippines, DNO was pivotal in optimizing the placement of GeneXpert devices. By applying constraints such as a maximum allowable distance of 20 km from health facilities to testing sites, the model efficiently reduced the estimated need from approximately 1500 devices to 796 and 889 devices by 2019 and 2022, respectively. This optimization significantly impacted planning and resource allocation, demonstrating the potential of DNO in maximizing diagnostic network efficiency while enhancing patient access to TB diagnostic services (Albert et al.).

India

The application of DNO in India highlighted a critical shortfall in the existing network’s capacity to meet future demands for culture and DST testing. Projections made using the DNO model suggested that to meet the needs by 2025, substantial enhancements in diagnostic capacity would be required. This included the integration of additional diagnostic tools and expanding testing capabilities across various states to ensure that the rising demand could be met effectively, emphasizing the model’s role in facilitating strategic planning and investment in health infrastructure (Pai and Swaminathan).

Challenges and Future Directions

Despite its potential, the implementation of DNO faces significant challenges, including substantial initial costs and complexities in integrating it across varied healthcare environments. The data-driven nature of DNO also demands continuous, accurate data collection and analysis, which can be a significant hurdle in regions with limited technological infrastructure. Moreover, the sustainability of DNO projects hinges on consistent funding and political will, both of which can be unpredictable and vary greatly between regions.

Conclusion

The integration of DNO into global TB control efforts represents not just an innovation in combating this deadly disease, but a necessary evolution in public health strategy. As TB continues to pose a significant threat to global health, especially in under-resourced settings, the role of optimized diagnostic strategies like DNO becomes all the more crucial. DNO provides a sustainable and efficient approach to bridging the gap between advanced diagnostic technologies and the populations most in need. By enhancing the accessibility and effectiveness of TB diagnostics, DNO not only aims to reduce the burden of the disease but also supports the broader goal of global TB elimination. Moving forward, the lessons learned from DNO implementations could guide future public health responses not just for TB, but also for other infectious diseases, potentially transforming the landscape of global health diagnostics and intervention strategies as a whole.

About the Author:

Dr Archana Beri is a Physician trained in infectious diseases and laboratory medicine. She has 18 years experience in public health supporting Countries and National Programs in improving access to quality diagnostic services, applying tools and approaches for Diagnostic Network Optimization and accessibility, Laboratory quality (ISO 15189, TB-SLMTA), Lab-based surveillance, and Biosafety.

At FIND, she is the Principal Scientist-Access and the regional lead for Diagnostic Network Optimization with projects in India, Cambodia, Bangladesh, Mongolia, and PNG. Before this, she has worked with various national and international organizations including U.S. Centers for Disease Control and Preventions (CDC), the American Society of Microbiologists (ASM), the Indian Council of Medical Research (ICMR), and the Ministry of Health and Family Welfare (MoHFW), Government of India (GOI) for the TB, HIV, STI and Global Health Security (GHS) related programs. She is also a contributor to various national guidelines for HIV and STI programs in India.

References:

World Health Organization. “Global tuberculosis report 2022.” Available at: https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2022. Accessed January 27, 2023.
Pai, Madhukar, and Soumya Swaminathan. “India is well placed to scale innovations in tuberculosis diagnostics.” Indian Journal of Medical Research 157 (2023): 114-117.
Albert, Heidi, et al. “Optimizing diagnostic networks to increase patient access to TB diagnostic services: Development of the diagnostic network optimization (DNO) approach and learnings from its application in Kenya, India and the Philippines.” PLOS ONE (2023).
Nichols, Kameko, et al. “Bringing Data Analytics to the Design of Optimized Diagnostic Networks in Low- and Middle-Income Countries: Process, Terms and Definitions.” Diagnostics 11.22 (2021): 1-14. DOI: 10.3390/diagnostics11010022.

The Urgent Need for Enhanced TB Diagnostics

The Power of Molecular Diagnostics

Image Source: FIND

Diagnostic Network Optimization to the rescue

Image Source: FIND

Building blocks of DNO

Success Stories with DNO Implementation

Kenya

Philippines

India

Challenges and Future Directions

Conclusion

About the Author:

References:

Publication Date: 21st June 2024

Share

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The Urgent Need for Enhanced TB Diagnostics

The Power of Molecular Diagnostics

Diagnostic Network Optimization to the rescue

Building blocks of DNO

Success Stories with DNO Implementation

Kenya

Philippines

India

Challenges and Future Directions

Conclusion

About the Author:

References:

Publication Date: 21st June 2024

Share

Related posts

Contact Us

3rd Floor, Elphinstone Building, Veer Nariman Road, Horniman Circle, Fort, Mumbai 400001 contact@indiahealthfund.org

Contact Us

Pitch to us

Know More

2021 All rights reserved