Globally, diagnosis is the biggest gap in the cascade of care. According to the recent Lancet Commission on diagnostics, 35-62% of populations lack access to diagnostics for six common medical conditions in low- and middle-income countries, including in Africa, and this gap in access is particularly exacerbated at the primary healthcare level. Diagnostic network optimization (DNO), a geospatial data analytics approach that informs diagnostic strategies and operational planning, is an intervention that can allow countries to determine how to provide the greatest access to equitable and timely diagnostic services, while maximising overall efficiencies of laboratory systems. Activities in this course will help students better understand DNO.

LabCoP's DNO Sub-CoP

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Activity

LabCoP DNO Sub-CoP ECHO Sessions

View the latest ECHO sessions of LabCoP's DNO Sub-CoP

Session 01

Launching The Labcop Diagnostic Network Optimisation Sub-Community Of Practice

Session 02

Global Fund Priorities for DNO at Country Level: the What, How and When: Early insight from Gabon

November 2022 LabCoP ECHO Session

Local Manufacturing of Diagnostics in Africa - the case of MAScIR Diagnostic tests

August 2022 LabCoP ECHO Session

Measuring Progress of Integrated Sample Transport Systems for Clinical and Surveillance Purposes

June 2022 LabCoP ECHO Session

Rollout and Implementation of SMS Results Relay and Electronic Result Reporting: The Zimbabwe Experience

Dec 2021 Special ECHO Session

The Diagnostic Evidence Hub: Its Role in Accelerating Uptake of Diagnostic Innovations

Oct 2021 LabCoP Extended ECHO Session

German Epidemic Preparedness Team Laboratory Strengthening for External Quality Assessment. Experiences

July 2021 LabCoP ECHO Session

Unlocking the Potential of Tiered Laboratory Networks Through Laboratory Mapping

February 2021 LabCoP Extended ECHO Session

Roche & CDC Partnership: Developing Capacity and Improving Efficiencies in Laboratory Operations

January 2021 LabCoP ECHO Session

PEPFAR Country Operational Plan (COP) 2021: laboratory systems strengthening

September 2020 LabCoP ECHO Session

Using Strategic Communications as a Tool for Social Change and Advocacy

Special LabCoP ECHO Session

Establishing Diagnostic Connectivity for Laboratory Network Data Flow Across Conventional & POC Systems

April 2023 DNO SubCoP ECHO Session

Integrating TB and HIV Testing on GeneXpert to Improve Access and Same-Day Diagnosis: Zambia Experience

June 2023 LabCoP ECHO Session

Outcome Driven Lab Networks – Eswatini’s Experience Using Key Performance Indicators to Drive Laboratory

June 2023 LabCoP Extended ECHO Session

The World Health Assembly Diagnostic Resolution: What Is Next?

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What is LabCoP’s DNO Sub-CoP?

The Diagnostic Network Optimisation Sub-community of Practice (DNO Sub-CoP) is a dedicated segment of ASLM’s LabCoP. The DNO Sub-CoP is a collaboration of ASLM and FIND, funded by the Bill & Melinda Gates Foundation. The DNO Sub-CoP gathers country teams (made up of laboratorians, clinicians, and representatives from ministries of health who support DNO activities in their country) and stakeholders (implementing partners, regulatory and technical agencies) who share challenges, solutions and best practices for optimising their diagnostic network.

Why a DNO Sub-CoP?

Globally, diagnosis is the biggest gap in the cascade of care. In low- and middle-income countries (LMICs), including in Africa, 35–62% of populations are lacking access to essential diagnostics for six common medical conditions. This gap in access is exacerbated at primary healthcare level. The situation is mirrored for outbreak response, where the capacity to detect outbreaks in the African region, as assessed through the Joint External Evaluation (JEE) process, was only scored at 44%.

These data evidence how laboratory networks in Africa do not effectively deliver services for clinical (UHC) and public health (IHR) functions and how diagnostic services are generally not equitably available or accessible to the population. Furthermore, investments for optimising laboratory networks in the context of limited resources are often lacking or are not driven by context relevant and geo-localised (GIS) data on laboratory capacity.