Modelling the impact of antimalarial quality on the transmission of sulfadoxine-pyrimethamine resistance in Plasmodium falciparum

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Modelling the impact of antimalarial quality on the transmission of sulfadoxine-pyrimethamine resistance in Plasmodium falciparum. Aleisha R.Brock a , Joshua V. Ross b , Scott Greenhalgh c , David P.Durham d , Alison Galvani d , Sunil Parikh e , Adrian Esterman f g . Infectious Disease Modelling 2 (2017) 161e187. https://doi.org/10.1016/j.idm.2017.04.001


Abstract

Background

The use of poor quality antimalarial medicines, including the use of non-recommended medicines for treatment such as sulfadoxine-pyrimethamine (SP) monotherapy, undermines malaria control and elimination efforts. Furthermore, the use of subtherapeutic doses of the active ingredient(s) can theoretically promote the emergence and transmission of drug resistant parasites.

Methods

We developed a deterministic compartmental model to quantify the impact of antimalarial medicine quality on the transmission of SP resistance, and validated it using sensitivity analysis and a comparison with data from Kenya collected in 2006. We modelled human and mosquito population dynamics, incorporating two Plasmodium falciparum subtypes (SP-sensitive and SP-resistant) and both poor quality and good quality (artemether-lumefantrine) antimalarial use.

Findings

The model predicted that an increase in human malaria cases, and among these, an increase in the proportion of SP-resistant infections, resulted from an increase in poor quality SP antimalarial use, whether it was full- or half-dose SP monotherapy.

Interpretation

Our findings suggest that an increase in poor quality antimalarial use predicts an increase in the transmission of resistance. This highlights the need for stricter control and regulation on the availability and use of poor quality antimalarial medicines, in order to offer safe and effective treatments, and work towards the eradication of malaria.

 

a School of Nursing & Midwifery, University of South Australia, Adelaide, SA, Australia b School of Mathematical Sciences, The University of Adelaide, Adelaide, SA, Australia
c Department of Mathematics and Statistics, Queen’s University, Kingston, ON, Canada
d Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, USA
e Yale School of Public Health, New Haven, CT, USA
f Sansom Institute for Research Health, University of South Australia, Adelaide, SA, Australia
g Australian Institute of Tropical Health and Medicine, James Cook University, Cairns, QLD, Australia

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