Using Information Design to Visualize the Global Burden of Disease By Region

Sunday, 15 February 2015
Exhibit Hall (San Jose Convention Center)
Lindsey A. Stegall, Clemson University, Clemson, SC
In the current research era, there is exponential growth and increased availability of data.  Although large data sets are revolutionizing science and technology, they present challenges in analysis and in the communication of findings within and beyond the scientific community.  Data on the global burden of diseases are important because they provide a framework for decisions about interventions and resource allocation.  In 2012, British data journalist, David McCanless, published a network-theory-based infographic illustrating the causes of death in the 20th century.  The piece was commissioned by the Wellcome Trust, a U.K. charity focused on human health, and provided a thought-provoking snapshot of the major causes of death from the years 1900-2000.  Since disease burdens may be transformed by advances in medicine, changes in lifestyle, income, and access to health care, it may be useful to visualize changes in diseases burdens between among regions. Disability-adjusted life year (DALYs) statistics may provide a better measure of disease burden than do mortality statistics, because the measure includes diseases that are more important because of the disabilities they cause rather than their lethality. We used DALY statistics from 2012, which were made available by the World Health Organization’s (WHO’s) Global Burden of Disease (GBD) project, to design an infographic.  Specifically, DALY statistics were represented as pie charts and overlayed  onto WHO-defined data-collection areas, which included Africa, the Americas, Southeast Asia, Europe, the Eastern Mediterranean, and the Western Pacific. The resulting image provided a means by which global burden of disease could be rapidly compared within and among regions.   Such information is not readily apparent from raw numbers.  The resulting infographic illustrates one of the ways to visualize large data sets, which, in turn, may reveal answers to scientific and societal problems.