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Background Rapid diagnostic tests (RDTs) for malaria are a vital part of global malaria control. Over the past decade, RDT prices have declined, and quality has improved. However, the relationship between price and product quality and their larger implications on the market have yet to be characterized. This analysis used purchase data from the Global Fund together with product quality data from the World Health Organization (WHO) and Foundation for Innovative New Diagnostics (FIND) Malaria RDT Product Testing Programme to understand three unanswered questions: (1) Has the market share by quality of RDTs in the Global Fund’s procurement orders changed over time? (2) What is the relationship between unit price and RDT quality? (3) Has the market for RDTs financed by the Global Fund become more concentrated over time? Methods Data from 10,075 procurement transactions in the Global Fund’s database, which includes year, product, volume, and price, was merged with product quality data from all eight rounds of the WHO-FIND programme, which evaluated 227 unique RDT products. To describe trends in market share by quality level of RDT, descriptive statistics were used to analyse trends in market share from 2009 to 2018. A generalized linear regression model was then applied to characterize the relationship between price and panel detection score (PDS), adjusting for order volume, year purchased, product type, and manufacturer. Third, a Herfindahl–Hirschman Index (HHI) score was calculated to characterize the degree of market concentration. Results Lower-quality RDTs have lost market share between 2009 and 2018, as have the highest-quality RDTs. No statistically significant relationship between price per test and PDS was found when adjusting for order volume, product type, and year of purchase. The HHI was 3,570, indicating a highly concentrated market. Conclusions Advancements in RDT affordability, quality, and access over the past decade risk stagnation if health of the RDT market as a whole is neglected. These results suggest that from 2009 to 2018, this market was highly concentrated and that quality was not a distinguishing feature between RDTs. This information adds to previous reports noting concerns about the long-term sustainability of this market. Further research is needed to understand the causes and implications of these trends.

In this paper, we present an efficiency improvement for the algorithm called AMOEBA, A Multidirectional Optimum Ecotope-Based Algorithm, devised by Aldstadt and Getis (Geogr Anal 38(4):327–343, 2006 ). AMOEBA embeds a local spatial autocorrelation statistic in an iterative procedure in order to identify spatial clusters (ecotopes) of related spatial units. We provide an analysis of the computational complexity of the original AMOEBA and develop an alternative formulation that reduces computational time without losing optimality. Empirical evidence is provided using georeferenced socio-demographic data in Accra, Ghana.