Molecular Surveillance of Antimalarial Partner Drug Resistance in Sub-Saharan Africa: A Spatial-Temporal Evidence Mapping Study

Abstract

Background: Molecular markers for antimalarial drug resistance can be used to rapidly monitor the emergence and spatial distribution of resistance to artemisinin-based combination therapies (ACTs). There has been little effort to analyze molecular surveillance efforts or to standardize data collection and management strategies. This study develops an evidence map to characterize the spatial-temporal distribution and sampling methodologies on partner drug resistance surveillance in sub-Saharan Africa. Methods: Through a systematic search, we identified studies that reported data on the following partner drug resistance-associated mutations: pfmdr1 N86Y, Y184F, D1246Y, or pfcrt K76T, with sample collection occurring in sub-Saharan Africa from 2004–2018, corresponding to the uptake of ACTs. We examined trends associated with surveillance and transmission of partner drug resistance. Findings: Our search yielded a total of 254 studies encompassing 501 year- and location-specific surveys from 35 malaria endemic countries, the most complete set of molecular partner drug surveillance data to date. We observed an average time lag of 3·2 years from sample acquisition to publication. Over half of the countries in the study region conducted only a single survey, on average, every 3–4 years. The amount of surveillance data per country was positively associated with reported donor funding. Drug resistance survey sites were 5.6 times as likely to be located in urban areas than malaria prevalence surveys. In countries where data was acquired in a more spatially representative pattern, heterogeneity in molecular marker prevalence was evident across time and space. Interpretation: In the majority of sub-Saharan countries, molecular data on antimalarial resistance remains temporally and geographically limited, likely failing to represent the true heterogeneous distribution of partner drug resistance. We discuss several inefficiencies in surveillance efforts that can be improved upon to develop more accurate data landscapes. Funding Statement: HYE was supported by the NIH Ruth L. Kirschstein National Research Service Award (F31AI150168). Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: Not required.