Surveillance of travellers: an additional tool for tracking antimalarial drug resistance in endemic countries

Abstract

Introduction: There are growing concerns about the emergence of resistance to artemisinin-based combination therapies (ACTs). Since the widespread adoption of ACTs, there has been a decrease in the systematic surveillance of antimalarial drug resistance in many malaria-endemic countries. The aim of this work was to test whether data on travellers returning from Africa with malaria could serve as an additional surveillance system of local information sources for the emergence of drug resistance in endemic-countries.

Methodology: Data were collected from travellers with symptomatic Plasmodium falciparum malaria returning from Senegal (n = 1,993), Mali (n = 2,372), Cote d'Ivoire (n = 4,778) or Cameroon (n = 3,272) and recorded in the French Malaria Reference Centre during the period 1996-2011. Temporal trends of the proportion of parasite isolates that carried the mutant genotype, pfcrt 76T, a marker of resistance to chloroquine (CQ) and pfdhfr 108N, a marker of resistance to pyrimethamine, were compared for travellers and within-country surveys that were identified through a literature review in PubMed. The in vitro response to CQ was also compared between these two groups for parasites from Senegal.

Results: The trends in the proportion of parasites that carried pfcrt 76T, and pfdhfr 108N, were compared for parasites from travellers and patients within-country using the slopes of the curves over time; no significant differences in the trends were found for any of the 4 countries. These results were supported by in vitro analysis of parasites from the field in Senegal and travellers returning to France, where the trends were also not significantly different.

Conclusion: The results have not shown different trends in resistance between parasites derived from travellers or from parasites within-country. This work highlights the value of an international database of drug responses in travellers as an additional tool to assess the emergence of drug resistance in endemic areas where information is limited.

Figure 1. Map of Africa illustrating the emergence of CQ resistance in East, Central and West Africa detected through travellers’ surveillance from the late 1970s to the early 1980s.

The dates of detection of index cases are displayed. The red arrows show the spread of antimalarial resistance from East Africa to West Africa.

Figure 2. Observed data, fitted model (by logistic regression) and 95% confidence interval (shaded area) for the prevalence of the pfcrt 76 mutant isolates from 2000 to 2011 for travellers (red) and field studies (blue) for A-Senegal, B-Mali, C-Cote d’Ivoire and D-Cameroon.

Each data point represents the prevalence of resistant isolates per year for travellers’ data and per study for field studies, where the size of the circle is proportional to the number of isolates in the sample.

Figure 3. Observed data, fitted model (by Generalized Linear Model) and 95% confidence interval (shaded area) for the in vitro CQ response (IC₅₀) isolates from 1996 to 2011 for travellers (red) and field studies (blue) from Senegal.

Each data point represents the ln (mean IC₅₀₎ per year for travellers’ data and per study for field studies, where the size of the circle is proportional to the number of isolates in the sample.

Figure 4. Observed data, fitted model (by logistic regression) and 95% confidence interval (shaded area) for the prevalence of the pfdhfr 108 mutant isolates from 1996 to 2011 for travellers (red) and field studies (blue) for A-Senegal, B-Mali, C-Cote d’Ivoire and D-Cameroon.

Each data point represents the prevalence of resistant isolates per year for travellers’ data and per study for field studies, where the size of the circle is proportional to the number of isolates in the sample.