Today, the Institute for Health Metrics and Evaluation (IHME, my employer) is launching 8 new interactive data visualizations that bring to life the results of the 5-year Global Burden of Disease (GBD) study at the country level. The GBD study compiled all available data on health outcomes for 187 countries in the world for 1990 and 2010, and provides estimates for the burden caused by different diseases and risk factors that are comparable across countries and over time. Regional results were published in a dedicated triple-issue of the Lancet in December 2012 (see my related post here). Managing the Data Team at IHME, I have been lucky enough to support the project with finding and managing data over the past 4 years, as well as overseeing the creation of these visualizations.

The data visualizations play a key role in the GBD project for several reasons. It started with IHME’s need to review the results of GBD. Tables and static graphs just don’t provide the flexibility to properly assess results and identify patterns and trends.

GBD uses four key metrics: number of deaths, years of live lost (YLL), years of life lost to disability (YLD), and disability adjusted life-years (DALY). The results datasets are massive, broken down by several dimensions:

• 291 causes of disease and injuries at the most granular end of a 5-level cause hierarchy
• 66 risk factors
• 1100 cause-risk factor attributions (i.e. burden caused by a given risk factor via a particular disease or injury)
• 187 countries, 21 GBD regions, global
• 27 age groups: early neonatal, late neonatal, post neonatal, 1-4 years, 5-9, 10-14 and so on until 75-79, 80+, as well as under 5, 5-14, 15-49, 50-69, 70+, all ages, and age-standardized
• Male, female, both
• 3 years: 1990, 2005, 2010
• Estimates expressed as total number, rate, and %, as well as ranked by country
• 95% uncertainty intervals: lower bound, mean, and upper bound (not strictly a dimensions but adds to the size of the database)

In total, about 1 billion (!) results were calculated for the project, and then there are aggregations by cause, age, and geography. A nightmare to review, but a gold mine for visualizations. The results datasets are fully imputed for all dimensions, i.e. there are no gaps in the datasets. And consistent use of methods ensure comparability of results across all dimensions.

Initially, we tried off-the-shelf visualization tools, but they didn’t give us the flexibility to dive into all the dimensions and properly explore patterns and trends in the data. Then we discovered D3.js (Data-Driven Documents). D3 is a JavaScript library for manipulating documents based on data; it allows developers to build powerful visualizations very efficiently (but you be the judge about how powerful our resulting visualizations really are). And we did what visual.ly recommended in a blog post today: iterate early, iterate often.

We improved the tools as we reviewed our results, then started using the tools to show the results to collaborators and country experts to obtain feedback, review our estimates, and discuss what data were used for analysis (and what data may be available to further inform and improve the estimates). Realizing how powerful these tools are for different audiences to explore the results of GBD, we decided to make them publicly available. In December 2012, we launched 5 visualization tools with the regional results of GBD (available here) with the publication of the GBD papers in The Lancet.

Updates for these tools are now available with country-level results. In addition, we created three new tools that allow users to review and explore the data from completely new angles. Here is a quick overview of the country-level visualizations:

• GBD Compare is a powerful platform that visualizes the data in treemaps, maps, time plots, age plots and stacked bar charts. The most powerful feature is the 2-panel view that allows users to review any two of these charts simultaneously to compare and review trends across causes, risks, countries, ages etc. The panels are interactive, e.g. the map can be used to select countries in the other panel and quickly explore countries around the world. It’s a powerful tool, but requires a bit of commitment to make use of all the features. My video tutorial for GBD Compare can be found here.
• GBD Cause Patterns provides results for 21 cause groups in stacked column charts. It allows quick exploration of trends across geographies, ages, gender and time (see options at the bottom of the screen).
• GBD Arrow Diagram shows very concisely the rank of causes and risks for a given country or region in 1990 and 2010, along with the related growth trend. The connecting arrows quickly show how fast causes and risks have grown or decreased between 1990 and 2010. A version of the GBD Arrow Diagram is embedded below.
• GBD Heatmap ranks causes and risks by burden within a country, but then allows comparisons of those ranks across countries and/or regions (you can compare the ranks within a country with the ranks for a given region or the world).
• GBD Uncertainty Visualization allows users to compare uncertainty bounds across causes and risks for all dimensions. Countries or causes/risks where the data were more sparse or inconsistent will have wide uncertainty intervals.
• HALE/LE Visualizations shows the relationship between total life expectancy and healthy life expectancy, i.e. the number of years people can expect to spend in good health over their lifetime.
• Mortality Visualization provides an interesting addition to the results: users can look at all-cause mortality estimates and uncertainty bounds in the context of the underlying input data points. The hovers provide detailed metadata about the source of the data point.
• COD Visualization show the input data points for cause of death data by country, cause, and sex, also with detailed metadata.

All visualizations also feature “share” functionality that creates a unique URL for the chosen settings that can be shared via email, Twitter, Facebook or other social media. This should be useful to bring up the tools in online conversations about the health situation in different countries, disease patterns and international comparisons.

These tools will be used extensively in policy and country consultations, and many of these conversations will be conducted in locations that have less than reliable internet connections. To facilitate use, we created offline versions of these tools as well. The sheer size of the data provided a substantial challenge, but the tools are now performing well offline.

If you are interested in building additional visualizations with the GBD results, you should start with the regional results of GBD, all available for download on the GHDx here. The country-level results will be made available via the GHDx in September 2013.

I would love get your feedback on your experience with using the visualizations. Are they intuitive? Are there features that you like or don’t like? Are there things you would like to see or do with the data that aren’t possible yet? Leave suggestions in the comments, and I will make sure to include them in our discussions for future development

 

Example: GBD Arrow Diagram

On Thursday, 12/13/2012, The Lancet published seven papers with the results of the Global Burden of Diseases, Injuries and Risk Factors Study 2010. The epic, 5-year study involved hundreds of collaborators to compile and analyze all available data on health outcomes globally. My role in the project focused on finding and obtaining input data, managing data at IHME, and creating visualizations. This is the first in a series of blog posts in which I’ll discuss the sources of data used in the different components of the study, the availability of health outcomes data in general, and the metrics that were generated, and share some stories from the trenches. This post provides an introduction to the study. Follow me on Twitter or subscribe to my RSS feed to find out about future installments on mortality, causes of death, non-fatal health outcomes, and covariates.

The Global Burden of Diseases, Injuries and Risk factors Study 2010 (GBD 2010) is arguably the most comprehensive assessment on human health ever conducted. Richard Horton, Editor of The Lancet, and Peter Piot, Director of the London School of Hygiene and Tropical Medicine, compared the GBD 2010 to the Human Genome Project in terms of scope and importance. The results were published by The Lancet in seven papers that took up an entire triple issue of the journal. It's the first time in the 189-year history of the Lancet that an entire issue was dedicated to one study (the Lancet was founded in 1823). The results were officially presented at a launch event at the Royal Society in London last week (picture on the left).

GBD 2010 was coordinated by the Institute for Health Metrics and Evaluation (IHME) – my employer – in collaboration with 6 other organizations, the University of Queensland, Harvard School of Public Health, Johns Hopkins Bloomberg School of Public Health, the University of Tokyo, Imperial College London, and the World Health Organization (WHO). Professors Christopher Murray, director of IHME, and Alan Lopez, head of the school of population health at the University of Queensland, developed approach and methodology for global burden of disease analysis in the 1990s, and oversaw this iteration with a complete revision of all the steps of the analytic process (on the right, editor-in-chief of The Lancet Richard Horton is taking a picture of Chris and Alan at the GBD Study 2010 press conference).

GBD 2010 mapped all known diseases and injuries to 291 causes that were then analyzed for the burden they caused through fatal and non-fatal outcomes. This required compiling and analyzing all available published and unpublished data and evidence on health outcomes (notice that available is in italics? More on that later). Data sources include censuses, surveys, vital statistics, disease registries, hospital records, and many more. Especially for non-fatal outcomes and risk factors, systematic literature reviews were a key source of data. Hundreds of researchers provided data and expertise, and the seven published papers included 486 authors from over 50 countries. The analysis encompassed 18 different components that are highly interconnected (see the overview paper for details).

Compiling these data was a monumental task, but analyzing the overall global burden provided a key advantage compared to studies that focus on one or few diseases or injuries. There are 235 causes that can lead to death, and in GBD 2010, deaths from these causes always sum up to all-cause mortality in each age-sex-region group, i.e. every death is counted only once or - in scientific terms - all-cause mortality estimates constrain the cause-specific estimates. Studies that estimate mortality for only one or few causes don’t have this constrain and will often provide higher numbers of death.

A key challenge for all parts of this study was the availability of input data (this is the main reason for me to write this series). For many developed countries, data to estimate mortality and non-fatal health outcomes by cause and risk factors are readily available. However, in many of the 187 countries that are part of the 21 GBD regions, these data are not being collected, incomplete, of poor quality, insufficiently documented, only available on paper, or stuck on obsolete storage media. In addition, data are often simply not being shared for political or other reasons, even for this kind of research that provides a global public good. A fundamental take-away from this study is that we need to improve the collection and distribution of health-related data, in developing but also developed countries. For mortality data, this means improving civil registration and vital statistics systems to make sure that we track every death and its cause everywhere in the world. Survey, census and other health-related data are just as important for governments to share. With regard to understanding non-fatal health outcomes, access to health record, disease registry and other detailed health data is essential, of course with proper attention to privacy, confidentiality, and consent of the individuals. All of these can be overcome but that requires commitment and political will by the data owners.

GBD 2010 compiled all data on health outcomes available to us. For the first time ever, we now have estimates on mortality and non-fatal health outcomes by cause and risk factor that were developed with a consistent methodology for several points in time (1990, 2005, and 2010). To provide information about the availability and consistency of input data, 95% uncertainty intervals were calculated at each step, propagated throughout the analytic process, and are available for all results. GBD 2010 provides estimates even for causes, risk factors, regions, or age groups where data was limited or no data was available; these were imputed using different statistical methods and covariates like GDP, education, and many others to inform the estimates. We believe that estimates based on limited data are better for policy and decision making than no evidence at all. The result is a gigantic database with structured results by age and sex that are comparable across geographies and time, all publicly available. The data will allow global health practitioners, policy makers, donors, media, researchers and others to explore patterns and trends in health over time.

Here are your key resources to explore further:

• Publications in The Lancet (7 papers plus commentaries). All papers are available for free but require registration. You should especially read the very accessible comments by The Lancet Editor-in-Chief Richard Horton and the GBD Core Team, as well as the study overview
• IHME’s GBD overview page and News Release.
• Watch videos from the launch event at the Royal Society with lots of background on study and results (or simply review the presentations)
• IHME data visualizations. They allow you to explore the details and different dimensions of the very complex results datasets in a much more compelling way than studying tables. The Guardian data team was impressed enough to embed them on their data blog. Here are the direct links to the five visualizations:
  • GBD 2010 patterns by broad cause group
  • GBD 2010 leading causes and risks by region heat map
  • GBD 2010 change in leading causes and risks between 1990 and 2010
  • GBD 2010 Uncertainty intervals for causes and risks
  • GBD 2010 healthy years lost vs life expectancy
• GBD detailed results data for download on IHME’s Global Health Data Exchange
• Extensive press coverage by the Guardian, the New York Times, the Washington Post, the Economist, Forbes, Nature, National Geographic, and Spiegel Online (for my fellow German readers).
• Blog post by Bill Gates in the gatesnotes