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Synopsis

Previous analyses of cause-specific mortality in the United States have either focused on just one or a few causes of death or have analyzed national trends. We extend this work to describe cause-specific mortality in the US by county, age, sex, year, and a collectively exhaustive set of conditions. First, we describe trends in causes of death across these five dimensions. Next, we use novel Bayesian small area modeling techniques to jointly estimate and forecast cause-specific mortality. Finally, we present cutting-edge data visualizations to explore the results and convey them to a broader audience. 

Bio

Kyle Foreman is currently finishing his PhD in Biostatistics and Epidemiology at Imperial College London. He also works at Symcat.com, an online health app. He previously earned his MPH from the University of Washington while enrolled in the Post-Bachelor Fellowship program at the Institute for Health Metrics and Evaluation (IHME) and subsequently worked as IHME’s first Data Scientist. While there, he developed the CODEm model used to create Cause of Death estimates for the Global Burden of Diseases, Injuries, and Risk Factors Study 2010 (GBD 2010) and created several data visualization tools, such as GBD Compare and iCod.