This article discusses the results of a Harvard School of Public Health study which was released this month regarding the use of mobile phones in tracking malaria in Kenya. In this study, researchers tracked the timing and location of calls and texts from 15 million mobile phones in Kenya between June 2008 – June 2009 and compared this data with data on malaria prevalence by region. By studying the movements of people relative to the movement of the disease, the researchers were able to identify primary sources of malaria, as well as those most at risk for infection. They concluded that the most significant amount of malaria transmission in Kenya is from travel from Lake Victoria to Nairobi, Kenya’s capital.
This study differs from some of the others we read about and discussed this week in that it does not use mobile technology to directly communicate with mobile phone users. It is interesting to see how the prevalence of mobile phone use, particularly in Africa, can be utilized for development in ways other than direct communication. Because this was a large scale study, rather than using mobile phones for direct communication within a smaller population, these results might be immediately applicable to policy decisions. In addition they can be applied to mHealth efforts more similar to the ones we read about this week, which directly engage and interact with their target populations to help more effectively educate about, prevent, and treat malaria. One of the researches associated with the study suggested warning texts to travelers to and from malaria hot spots.
While this study has the potential to provide a great deal of insight regarding patterns of malaria transmission, the data is biased due to the digital divide, and population differences between those who have mobile phones and those who do not. Younger, wealthier, male, and urban populations are more likely to have mobile phones and therefore be reflected in this data, so initiatives and policy based off these results may disproportionately benefit those groups. These results, and this method of data collection, may be less effective particularly in terms of helping rural, female, lower-income, and less geographically mobile populations. Of course, any knowledge which can promote health, even disproportionately, should be considered and used to implement public health initiatives – however hopefully there will be other measures taken to make up for disparities in data, as not to further disadvantage already disadvantaged groups within the population.