Professor Bob Snow has developed a large programme of work on the phenotype of malaria disease, its relationship to parasite exposure and its wider public health burden.
Technical advisor to the Kenyan Government (and member of a number of international malaria advisory panels), Professor Snow provides the bridge between basic malaria epidemiology and malaria control policy in the region.
Quality data is vital to design better malaria control programmes. This project helps various African countries gather epidemiological evidence to better control malaria. Professor Bob Snow showed how sub-regional, evidence-based platforms can effectively change malaria treatment policies.
Ultimately, medical research must translate into improved treatments for patients. At the Nuffield Department of Medicine, our researchers collaborate to develop better health care, improved quality of life, and enhanced preventative measures for all patients. Our findings in the laboratory are translated into changes in clinical practice, from bench to bedside.
The future of malaria control in Africa has to be anchored in quality data, quality assured data that countries can have complete ownership of, and they begin to use to design the future of malaria control.
I think there is too much emphasis at the moment on models that are generated in the north. There is far too little use of data that exists in African countries, to support African governments in their decision-making. There needs to be a complete seed change in this use of evidence, and this is what this project is all about. Basically, it’s about increasing the capacity of countries to assemble the information that exists within their national borders and how they might use that to change the future.
The link programme as it is now is based at the London School of Hygiene and Tropical Medicine but it had an earlier history as it were, which began in Nairobi about two or three years ago. We started working with the Kenyan government to try and assemble all the malaria epidemiological evidence, so that they could better design the distribution of insecticide treated bed nets, distribution of ACTs (Artemisinin based Combination Therapies), IRS (Indoor Residual Spraying) and what they might do to improve progress towards reducing malaria morbidity and mortality in Kenya. That was really a science and government collaboration where we were assembling the data, spatially modelling the data, working closely with the National Malaria Control Programme. That relationship worked really well; it helped the Kenyan government secure money from the Global Fund, helped them design their National Malaria Strategy, and was actually a classic example of assembling epidemiological data to design control. I would say it was probably one of the first times it has been done for over 20 or 30 years in Africa. At the time that the Roll Back Malaria initiative started, things were so bad across Africa, everybody just did everything, irrespective of what the epidemiology was. You just threw everything and the kitchen sink at malaria, ATCs everywhere, bed nets everywhere, irrespective of whether or not people needed it, or it had been most effective.
After 10 years, we began to nuance that and fine-tune it using data. We have been working from the Kenyan Medical Research Institute in Nairobi; we have been working with other countries in Somalia, Djibouti and Sudan to do similar things. The Department for International Development from the UK thought that this was something that was needed beyond our sub-region, across Africa, and they asked us to form a project that would do an epidemiological assessment in 8 countries.
That was a difficult job because there was lots of data available but the National Malaria Control Programmes hadn’t pulled it all together in one centralised set of data bases. That was part of our job, to actually find all this data that was held by different research groups, different NGO groups, and pull it all together, put it on maps, model it and begin to develop an intelligence as it were, what we call a Malaria Intelligence System.
In the initial phase, we worked in the Democratic Republic of Congo, Tanzania, Uganda, Ethiopia, Malawi, Mali, Ghana and Nigeria. It took us a year to complete all those epidemiological profiles. We engaged with the National Malaria Control Programmes in each of those countries, and with the research partners that helped and support them provide the evidence in-country. I would say that one of the strengths of that, which wasn’t an anticipated strength, was bringing research and policy control communities together. In some instances, it was the first time that they had opened up a real dialogue as to how you might use epidemiological science to design control.
Now the ambition is to try and do that in 22 countries, try and support those countries in building up an evidence base, and get them to think - and I mean this in the nicest possible way - intelligently about designing control, so that they are rational with the use of their resources and likely to have the biggest benefit.