Monitoring viral load for the last mile: what will it cost?

Abstract

Introduction Routine viral load testing is the WHO‐recommended method for monitoring HIV‐infected patients on ART, and many countries are rapidly scaling up testing capacity at centralized laboratories. Providing testing access to the most remote populations and facilities (the “last mile”) is especially challenging. Using a geospatial optimization model, we estimated the incremental costs of accessing the most remote 20% of patients in Zambia by expanding the transportation network required to bring blood samples from ART clinics to centralized laboratories and return results to clinics. Methods The model first optimized a sample transportation network (STN) that can transport 80% of anticipated sample volumes to centralized viral load testing laboratories on a daily or weekly basis, in line with Zambia's 2020 targets. Data incorporated into the model included the location and infrastructure of all health facilities providing ART, location of laboratories, measured distances and drive times between the two, expected future viral load demand by health facility, and local cost estimates. We then continued to expand the modelled STN in 5% increments until 100% of all samples could be collected. Results and Discussion The cost per viral load test when reaching 80% patient volumes using centralized viral load testing was a median of $18.99. With an expanded STN, the incremental cost per test rose to $20.29 for 80% to 85% and $20.52 for 85% to 90%. Above 90% coverage, the incremental cost per test increased substantially to $31.57 for 90% to 95% and $51.95 for 95% to 100%. The high numbers of kilometres driven per sample transported and large number of vehicles needed increase costs dramatically for reaching the clinics that serve the last 5% of patients. Conclusions Providing sample transport services to the most remote clinics in low‐ and middle‐income countries is likely to be cost‐prohibitive. Other strategies are needed to reduce the cost and increase the feasibility of making viral load monitoring available to the last 10% of patients. The cost of alternative methods, such as optimal point‐of‐care viral load equipment placement and usage, dried blood/plasma spot specimen utilization, or use of drones in geographically remote facilities, should be evaluated.

Point of Care Diagnostics in Resource-Limited Settings: A Review of the Present and Future of PoC in Its Most Needed Environment

“…While many simulations of cost effectiveness base their assumption on high-prevalence areas, others remark that especially for “the last mile“, in areas with lack of infrastructure, PoC might be one of the only viable alternatives for the hardest to reach 10% of patients, as transport networks get more and more difficult to establish in remote areas [ 75 ]. Despite the low volume of patients making cost-effectiveness more difficult, it is estimated that an optimal placement of PoC viral load tests on-site and in PoC hubs still can reduce the price of a test by 6–35% by avoiding high transport cost in remote areas [ 76 ].…”

Accelerating progress towards the elimination of mother‐to‐child transmission of HIV: a narrative review

“…Due to the delays in turnaround time, particularly in remote and rural areas, the integration of point‐of‐care (POC) viral load assays can increase coverage of virological monitoring. However, such POC instruments should be placed in a way that maximizes centralized laboratories (and their existing specimen transport networks) and minimizes overall costs [93,94].…”

Preparing national tiered laboratory systems and networks to advance diagnostics in Africa and meet the continent’s health agenda: Insights into priority areas for improvement

“…Initiatives aimed at collecting geographical information system data on laboratory capacity are gaining momentum to improve the performance of HIV and tuberculosis control programmes. 30 , 31 The ASLM and Africa CDC have implemented a continent-wide laboratory capacity mapping programme (LabMap 32 ) across diseases, based on open-source tools (Ona [Ona Systems Inc., Nairobi, Kenya], PlanWise [InSTEDD, Sunnyvale, California, United States] 33 ) and fostering country ownership. This system allows the easy collection, curation and analysis of geospatial data to make informed decisions on national laboratory networks and is interoperable with the District Health Information System (DHIS2, University of Oslo, Norway) 2 34 .…”