Viral load scale-up optimization: Zambia (Completed)

Zambia has approximately 1500 clinics and hospitals that provide ART. Viral load testing is currently highly centralized at 19 laboratories. These 19 centralized laboratories provide VL testing capacity across the country, thus requiring a vast sample transport network across diverse geography for blood samples to move from the clinics to the laboratories. Currently, samples are transported largely on an ad hoc basis, with a formalized system currently under development. Only plasma specimens are currently approved for use, limiting the reach of the current system due to the logistical challenge of drawing, storing, and transporting blood transport samples to a VL laboratory within 24 hours of blood draw. An integrated geospatial, cost, and outcomes model was developed to optimize the use of different viral load technologies to improve viral load access and patient outcomes in Zambia.

The objectives of this project were to:

1. Improve viral load testing access and minimize the cost of a  national viral load sample network using an innovative geospatial optimization model
2. Calculate the cost of providing viral load access to the 10% most remote patients
3. Determine the use-case for dried specimens for viral load testing in Zambia
4. Optimize the placement of point of care equipment for viral load testing

Publications and other documents

Girdwood S, Nichols BE, Moyo C, Crompton T, Chimhamhiwa D, Rosen S. Optimizing viral load testing access for the last mile: Geospatial cost model for point of care instrument placement. PLoS ONE; 14(8):e0221586

Nichols BE, Girdwood S, Crompton T, Stewart-Isherwood L, Berrie L, Chimhamhiwa D, Moyo C, Kuehnle J, Stevens W, Rosen S. Monitoring viral load for the last mile: what will it cost? J Int AIDS Soc 2019, 22:e25337.

Nichols BE, Girdwood SJ, Shibemba A, Sikota S, Gill C, Mwananyanda L, Noble L, Stewart-Isherwood L, Scott L, Carmona S, Rosen S, Stevens W. Cost and impact of dried blood spot versus plasma separation card for scale-up of viral load testing in resource limited settings. Clin Inf Dis 2019; published ahead of print.

Nichols BE, Girdwood SJ, Crompton T, Stewart-Isherwood L, Berrie L, Chimhamhiwa D, Moyo C, Kuenhle J, Stevens W, Rosen S, for EQUIP Health. Impact of a borderless sample transport network for scaling up viral load monitoring: results of a geospatial optimization model for Zambia. J Int AIDS Soc 2018; 21:e25206.