Oral Presentation First Malaria World Congress 2018

Leveraging modeled data to estimate the market size for tafenoquine and glucose-6-phosphate dehydrogenase deficiency diagnostics to support decision-making on service delivery models  (#210)

Michael Kalnoky 1
  1. PATH, Seattle, WA, United States

Plasmodium vivax (P. vivax) is estimated to be responsible for more than 100 million clinical infections annually. The 8-aminoquinoline family of drugs, such as tafenoquine, can completely clear P.vivax parasites by killing malaria gametocytes and consequently blocking vector-borne transmission; however, they can also cause severe hemolysis in patients with reduced activity of the glucose-6-phosphate dehydrogenase (G6PD) enzyme. Therefore, the World Health Organization recommends identifying the G6PD status of patients infected with P. vivax malaria prior to drug administration. Point-of-care (POC) tests that can rapidly and affordably identify G6PD status are needed to support safer and wider use of the 8-aminoquinoline family of drugs.

PATH has been supporting the development and market entry of POC G6PD quantitative and qualitative tests. As part of these efforts, we have developed a computer application called GeoDx to estimate P. vivax burden per health facility i.e., number of patients who visit a facility who test positive for P. vivax, the total quantity of tafenoquine, G6PD tests per country, and the associated costs.

To estimate the P. vivax burden per health facility GeoDx leverages the following data modeled by the Malaria Atlas Project: P. vivax prevalence and population per geographical area, geospatial facility data, population and demographics data, geospatial G6PD prevalence maps and modeled data of facility catchment regions. Further, this estimate can be used to calculate the total quantity of tafenoquine and G6PD tests required per country and the associated costs over a ten-year period.

Not only is the development of the GeoDx application a useful platform for drug and diagnostic market sizing and demand forecasting, it represents a good example of how modeled data generated by the Malaria Atlas Project can be leveraged to provide answers currently unobtainable through empirical data sources.