Statistical model confirms trends in antimalarial drug use and policy change in Africa
Resistance to artemisinin-based treatments is confirmed in several locations in South-East Asia. Researchers are calling for increased attention to the threat of resistance spreading or emerging in Africa. In response, WWARN is developing a series of mathematical modelling techniques that analyse how national policy changes relate to trends in malaria drug use in 40 African countries. If artemisinin resistance does spread to Africa, these new methodologies will be a critical tool to model actual drug use and predict how that may impact drug efficacy.
The extensive use of drugs is a known driver of drug resistance. Over the last decade, resistance to first line antimalarials such as chloroquine (CQ) and sulphadoxine-pyrimethamine (SP) has evolved and African countries have changed national policies to artemisinin combination therapies (ACTs). Although increased access to ACTs is confirmed in most countries, use of non-recommended antimalarials like CQ and SP has continued for many years after these policy changes.
The delay between, implementation of policy changes and emergence of antimalarial resistance greatly affects malaria morbidity and mortality. Detailed analysis of trends suggests that policy change sometimes occurs many years after the emergence of resistance. As data on ACTs treatment in Africa become available, it will be possible to use these models to estimate actual ACT drug use and the role this plays in the spread of drug resistance. By developing these approaches now, we will be able to monitor key changes faster and more effectively.
How is this possible?
In collaboration with ACTwatch1, our project team developed a methodology to quantify the treatment changes in CQ and SP both from 1999-2011 and also after the change in policy in each country. In addition, the approach correlates measures of drug use and drug resistance. To measure drug use, the project team used data on antimalarial treatments taken by children under five years to treat fever2. CQ treatment levels were then modelled in relation to the national policy changes on a country-by-country basis.
A short video visualises changing treatment policy in Africa 1999-2011 (click full screen to view enlarged)
A new statistical model now exists to estimate both the drug use in each country and the trend in reduction of CQ use after policy changes from 1999-2011. SP treatment estimates were correlated with the prevalence of a molecular marker associated with SP resistance.
See also a short video demonstrating the changes in SP resistance, as measured by the dhps540E marker prevalence.
What did the analysis confirm?
Overall, the model shows the routine usage of CQ and SP in children under five years across Africa3 .
The results also demonstrate no obvious spatial pattern in the rate of reduction of CQ use after it was no longer the first line drug in a given country. This suggests that drug use depends less on official country policy than on factors that vary from country to country such as prescription habits, costs of treatments and the integrity of the supply chain. The model also suggests that reductions in the rate of SP treatment were related to the spread of resistance: the quicker SP use fell, the slower the increase in the dhps540E resistance marker. This was particularly evident for Kenya, Malawi and Tanzania.
Continuous monitoring will be essential to guide malaria control and resistance containment policies until eradication is achieved. Support for initiatives such as the Demographic Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS) and ACTwatch is critical to ensure that appropriate data are available to analyse and use to strengthen our collective understanding of the ongoing drivers of resistance.
For further details download the recently published paper Flegg J, Metcalf CJE, Gharbi M et al. Trends in antimalarial drug use in Africa. American Journal of Tropical Medicine and Hygiene 2013; ref no. hyperlinked.
This article was written by Dr Jennifer Flegg, Senior Postdoctoral Mathematical Modeller at WWARN.
1 ACTwatch is a research project of Population Services International in collaboration with the London School of Hygiene & Tropical Medicine.
2 Household surveys used for this study were conducted between 1999-2011 by the Demographic Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS) in Africa.
3 Graph legend (Figure 7): CQ (red), SP (blue) and ACT (green) usage data over the time period 1999-2011 and logistic regression model fits to the data. The upper subplot shows the number of countries per two-year interval that changed national antimalarial policy away from CQ (red) and SP (blue).