Optimizing viral load testing access for the last mile: Geospatial cost model for point of care instrument placement

Abstract

Introduction Viral load (VL) monitoring programs have been scaled up rapidly, but are now facing the challenge of providing access to the most remote facilities (the “last mile”). For the hardest-to-reach facilities in Zambia, we compared the cost of placing point of care (POC) viral load instruments at or near facilities to the cost of an expanded sample transportation network (STN) to deliver samples to centralized laboratories. Methods We extended a previously described geospatial model for Zambia that first optimized a STN for centralized laboratories for 90% of estimated viral load volumes. Amongst the remaining 10% of volumes, facilities were identified as candidates for POC placement, and then instrument placement was optimized such that access and instrument utilization is maximized. We evaluated the full cost per test under three scenarios: 1) POC placement at all facilities identified for POC; 2)an optimized combination of both on-site POC placement and placement at facilities acting as POC hubs; and 3) integration into the centralized STN to allow use of centralized laboratories. Results For the hardest-to-reach facilities, optimal POC placement covered a quarter of HIV-treating facilities. Scenario 2 resulted in a cost per test of $39.58, 6% less than the cost per test of scenario 1, $41.81. This is due to increased POC instrument utilization in scenario 2 where facilities can act as POC hubs. Scenario 3 was the most costly at $53.40 per test, due to high transport costs under the centralized model ($36 per test compared to $12 per test in scenario 2). Conclusions POC VL testing may reduce the costs of expanding access to the hardest-to-reach populations, despite the cost of equipment and low patient volumes. An optimal combination of both on-site placement and the use of POC hubs can reduce the cost per test by 6–35% by reducing transport costs and increasing instrument utilization.

Comparative cost analysis of point-of-care versus laboratory-based testing to initiate and monitor HIV treatment in South Africa

“…This was primarily accomplished by reduction in transportation costs and better instrument utilization. [33]…”

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 ]. Finally, it has to be noted that, although PoC tests could significantly improve the healthcare system in LICs, their impact will depend on the specific disease or condition they are employed for.…”

Geospatial Hotspots Need Point-of-Care Strategies to Stop Highly Infectious Outbreaks

“…Mapping by means of GIS analysis could provide real-time and geo-localized data transmission, improving antimalarial strategies in Uganda. Girdwood 11…”