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. 2019 Oct 22;19(1):872.
doi: 10.1186/s12879-019-4523-0.

The changing landscape of Plasmodium falciparum drug resistance in the Democratic Republic of Congo

Molly Deutsch-Feldman  1 , Ozkan Aydemir  2 , Margaret Carrel  3 , Nicholas F Brazeau  4 , Samir Bhatt  5 , Jeffrey A Bailey  2 , Melchior Kashamuka  6 , Antoinette K Tshefu  6 , Steve M Taylor  7 , Jonathan J Juliano  4   8   9 , Steven R Meshnick  4 , Robert Verity  5
Affiliations

The changing landscape of Plasmodium falciparum drug resistance in the Democratic Republic of Congo

Molly Deutsch-Feldman et al. BMC Infect Dis. .

Abstract

Background: Drug resistant malaria is a growing concern in the Democratic Republic of the Congo (DRC), where previous studies indicate that parasites resistant to sulfadoxine/pyrimethamine or chloroquine are spatially clustered. This study explores longitudinal changes in spatial patterns to understand how resistant malaria may be spreading within the DRC, using samples from nation-wide population-representative surveys.

Methods: We selected 552 children with PCR-detectable Plasmodium falciparum infection and identified known variants in the pfdhps and pfcrt genes associated with resistance. We compared the proportion of mutant parasites in 2013 to those previously reported from adults in 2007, and identified risk factors for carrying a resistant allele using multivariate mixed-effects modeling. Finally, we fit a spatial-temporal model to the observed data, providing smooth allele frequency estimates over space and time.

Results: The proportion of co-occurring pfdhps K540E/A581G mutations increased by 16% between 2007 and 2013. The spatial-temporal model suggests that the spatial range of the pfdhps double mutants expanded over time, while the prevalence and range of pfcrt mutations remained steady.

Conclusions: This study uses population-representative samples to describe the changing landscape of SP resistance within the DRC, and the persistence of chloroquine resistance. Vigilant molecular surveillance is critical for controlling the spread of resistance.

Keywords: Drug resistance; Malaria; Spatial-temporal modeling.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
DHS cluster locations of the children included in the analysis. Clusters are from 2007 (a) and 2013 (b). The 26 DRC municipal province borders are outlined in black
Fig. 2
Fig. 2
Frequencies of pfdhps and pfcrt mutations in 2007 and 2013. Wild-type genotypes are highlighted in red. Chi-squared tests were performed to statistically compare proportions; asterisks indicate a statistically significant difference in proportion between years
Fig. 3
Fig. 3
Spatial prediction maps comparing prevalence and spatial distribution of pfdhps and pfcrt mutations. Predictions were generated for 2007 (left panels) and 2013 (right panels). Clusters with at least one mutation are marked with a white “x”, clusters with no mutations are marked in grey circles. Horizontal black lines represent a 10% increase in prevalence
See this image and copyright information in PMC

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