Malaria control in Africa: progress but still much to do

Abstract

During the past two decades, the international community has invested heavily in malaria control, especially in sub-Saharan Africa, with support increasing from around US$100 million in 2000 to nearly $2 billion in 2013.1 How eff ective has this investment been? Measurement of the eff ect of enhanced eff orts to control malaria has proved challenging. The main approaches used— measurement of changes in deaths from malaria or clinical episodes of the infection—each have major methodological challenges.2 Estimates of the number of deaths due to malaria rely to a large extent on verbal autopsy, an imprecise method. Many infections can cause illness resembling malaria; thus, estimates of numbers of cases of malaria can be unreliable unless supported by laboratory diagnoses. Strenuous eff orts are being made to improve these approaches; for example, ensuring that all reported clinical cases have been confi rmed by a diagnostic test. However, continuing imprecision is shown by the major diff erences in the number of deaths attributed to malaria in 2010 by WHO and the Institute of Health Metrics: 655 000 and 1 238 000, respectively.3,4 An alternative is measurement of changes in the prevalence of malaria infection detected by microscopy or by a rapid diagnostic test. This approach could be more precise because these methods are more specifi c than verbal autopsy or clinical diagnosis of malaria. This approach was adopted by Abdisalan Noor and colleagues5 in an important new study in The Lancet. The association between prevalence of malaria infection and incidence of clinical malaria or malaria mortality is not linear,6 so a reduction in parasite prevalence cannot be assumed to be associated with a reduction in the number of deaths from malaria or cases of severe malaria. However, measurement of changes in parasite prevalence can provide a valuable way of assessing changes in the incidence of malaria infection over time. Noor and colleagues collected data from 26 746 malaria parasite prevalence surveys co vering 3 575 418 person-observations done across Africa since 1980—a remarkable achievement. These data, together with spatially matched covariates, were used in a Bayesian hierarchical space–time model to derive estimates of the proportion of the population aged 2–10 years with diff erent levels of malaria parasitaemia (PfPR2–10) across Africa in 2000 and 2010.