Case Study: The Institute for Health Metrics & Evaluation
Global Burden of Disease
To understand the state of health around the world, we must identify, access, compile, correct, and analyze all these data comprehensively—a seemingly insurmountable challenge.
Health data are collected in many places, including censuses, surveys, vital registration systems, disease-specific registries, health system records, insurance claims, electronic medical records, satellite images, and scientific literature.
In the early 1990s, Professors Christopher Murray and Alan Lopez accepted that challenge and founded the Global Burden of Disease (GBD) project. It measures the impact of diseases, injuries, and risk factors that prevent us from living long lives in full health.
Three metrics are used to measure health loss:
Years of Life Lost (YLL) due to premature death
Years Lived with Disability (YLD), which include short and long-term illnesses
Disability-Adjusted Life Year (DALY), which essentially represents one year of healthy life lost
The GBD is updated annually and includes more than 300 diseases, injuries, and risk factors for more than 700 different countries, states, and localities. The latest updates can be found at healthdata.org. The findings are the result of a collaboration among more than 2,000 researchers in more than 125 countries.
The results dataset has more than 13 billion data points. There were a number of significant challenges in creating this dataset, from identifying and accessing data, to cleaning and preparing data for analysis, to the actual analysis.
GBD Results by Audience
To interest a broader representation from the general public in health data, IHME teamed up with Fast Company and the Gates Foundation in February 2017 to illustrate key findings related to child mortality. Five designers worked to humanize the data by visually telling the stories around the data to engage a larger audience.
IHME has created one tool, GBD Compare, that allows data actors to explore data in a wide variety of ways, supporting them in driving toward evidence-based conclusions to effect change. By using the cause patterns bar charts in GBD Compare, they can see a condensed view of broad cause groups and review patterns by age, geography, sex, and chronology. The arrow diagram view allows a quick comparison of disease and risk factor rankings between any year from 1990 to 2015. It helps users look at the big picture and identify key priorities, based on size or growth of burden. Users can then drill down by age and sex to see what populations are most affected.
They can download the data in a CSV file or share a permalink with the specific settings they are using via email or social media.
The Kenya Red Cross is a well-respected data promoter within Kenya. It partnered with IHME in 2016 to illustrate the latest health challenges there and recommend some policy solutions to them. The result was an illustrated report that became the touchstone for a series of events with the Kenya Ministry of Health.
Experts and analysts understand global health, specific diseases, injuries, and risk factors. They also understand the metrics used to measure them.
Their real interest lies in reviewing the data to find patterns and trends, and to answer questions. Then, they can use the data to plan interventions and programs. Just like researchers, they want lots of detail, but also more intuitive ways to interact with the data.
To satisfy this level of need, IHME built a results tool that makes it easy for users to find individual data points or small tables with the information they choose. Users can select diseases, risks, and injuries to explore. They can choose specific geographies, years, and the types of indicator they want in their final dataset.
For example, a data analyst might want to compare both mortality and disease burden by country from gun violence and from road traffic injuries between an emerging economy like Brazil and a more established economy like France. They may be interested in the overall numbers of both and also the rates, in order to accurately compare populations of disparate sizes. They may want to show these trends at three points in time, 1995, 2005, and 2015. After selecting all the relevant data categories, they could download a CSV file from the results tool and then perform any additional analysis necessary to explore their questions. Because the data come with ID numbers that can be used to link datasets. They could go back and download more data for Brazil and France and even add additional countries to make a larger dataset.
Researchers and other data experts want to review and use the results at the most comprehensive level. They are comfortable working with databases, and would rather manipulate the data themselves. In addition, they need to understand the methods and data used to generate the results. Fully detailed accounts of these methods are available across 8 papers and appendices (together these exceed 1,000 pages), accessible on the Lancet website.
To provide researchers with information about the more than 20,000 datasets used for GBD, the IHME team catalogued the data in the Global Health Data Exchange (GHDx) with a link to the data provider, as well as extensive metadata like geography, years, summary, keywords, publisher, and more.
Full results of from the GBD are available for download in CSV files in the GHDx. Given the size of the results database, the data have to be downloaded separately by disease, injury, risk factor, or country.
IHME also created specific visualizations aimed at providing researchers with windows into the estimation process, showing underlying input data points as well as the trend line created through the GBD estimation process. These tools include: