Diagnostics services are an essential component of healthcare systems, advancing universal health coverage and ensuring global health security, but are often unavailable or under-resourced in low- and middle-income (LMIC) countries. Typically, diagnostics are delivered at various tiers of the laboratory network based on population needs, and resource and infrastructure constraints. A diagnostic network additionally incorporates screening and includes point-of-care testing that may occur outside of a laboratory in the community and clinic settings; it also emphasizes the importance of supportive network elements, including specimen referral systems, as being critical for the functioning of the diagnostic network. To date, design and planning of diagnostic networks in LMICs has largely been driven by infectious diseases such as TB and HIV, relying on manual methods and expert consensus, with a limited application of data analytics. Recently, there have been efforts to improve diagnostic network planning, including diagnostic network optimization (DNO). The DNO process involves the collection, mapping, and spatial analysis of baseline data; selection and development of scenarios to model and optimize; and lastly, implementing changes and measuring impact. This review outlines the goals of DNO and steps in the process, and provides clarity on commonly used terms.

Diagnostic network optimization (DNO), a geospatial optimization technique, can improve access to diagnostics and reduce costs through informing policy-makers’ decisions on diagnostic network changes. In Zambia, viral load (VL) testing and early infant diagnosis (EID) for HIV has been performed at centralized laboratories, whilst the TB-programme utilizes a decentralized network of GeneXpert platforms. Recently, the World Health Organization (WHO) has recommended point-of-care (POC) EID/VL to increase timely diagnosis. This analysis modelled the impact of integrating EID/VL testing for children and pregnant/breastfeeding-women (priority-HIV) with TB on GeneXpert in Zambia. Using OptiDx, we established the baseline diagnostic network using inputs for testing demand (October 2019-September 2020), referrals, testing sites, testing platforms, and costs for HIV/TB testing (transport, test, device) respectively in Zambia. Next, we integrated priority-HIV testing on GeneXpert platforms, historically only utilized by the TB-programme. 228,265 TB tests were conducted on GeneXpert devices and 167,458 (99%) of priority-HIV tests on centralized devices at baseline, of which 10% were tested onsite at the site of sample collection. With integration, the average distance travelled by priority-HIV tests decreased 10-fold (98km to 10km) and the proportion tested onsite increased (10% to 48%). 52% of EID tests are likely to be processed within the same-day from a baseline of zero. There were also benefits to the TB-programme: the average distance travelled/specimen decreased (11km to 7km), alongside potential savings in GeneXpert device-operating costs (30%) through cost-sharing with the HIV-programme. The total cost of the combined testing programmes reduced marginally by 1% through integration/optimization. DNO can be used to strategically leverage existing capacity to achieve the WHO’s recommendation regarding POC VL/EID testing. Through DNO of the Zambian network, we have shown that TB/HIV testing integration can improve the performance of the diagnostic network and increase the proportion of specimens tested closer to the patient whilst not increasing costs.

Efficient specimen transport systems are critical for early disease detection and reporting by laboratory networks. In Burkina Faso, centralized reference laboratories receive specimens from multiple surveillance sites for testing, but transport methods vary, resulting in potential delays and risk to specimen quality. The ministry of health and partners, under the Global Health Security Agenda implementation, piloted a specimen transport system for severe acute respiratory illness (SARI) surveillance in 4 Burkina Faso districts. A baseline assessment was conducted of the current specimen transport network structure and key stakeholders. Assessment results and guidelines for processing SARI specimens informed the pilot specimen transport system design and implementation. Monitoring and evaluation performance indicators included: proportion of packages delivered, timeliness, and quality of courier services (missed or damaged packages). Our baseline assessment found that laboratorians routinely carried specimens from the health center to reference laboratories, resulting in time away from laboratory duties and potential specimen delays or loss of quality. The pilot specimen transport system design engaged Sonapost, the national postal service, to transport specimens from SARI sites to the influenza national reference laboratory. From May 2017 to December 2018, the specimen transport system transported 557 packages containing 1,158 SARI specimens; 95% (529/557) were delivered within 24 hours of pickup and 77% (892/1,158) within 48 hours of collection. No packages were lost. This article highlights lessons learned that may be useful for other countries considering establishment of a specimen transport system to strengthen laboratory system infrastructure in global health security implementation.