What attracted you to the health metrics field?
I’ve always been drawn to quantitative fields – I majored in physics in college, and for a while I seriously considered pursuing a PhD in particle physics. But working toward addressing issues I care deeply about is extremely important to me, and health is often at the intersection of the social, economic, and environmental justice issues that are most pressing to communities around the globe. At the same time, I often found myself frustrated by the lack of quantitative rigor that went into important decisions made both by organizations I volunteered for and by policymakers on a larger scale. Because these decisions often have permanent and far-reaching impact, it is critical that they are informed by the best evidence available – yet in many cases, the relevant and available data are underutilized. I therefore see working in the health metrics field as an effective way to follow my passion for quantitative work while applying my skill set to the problems that matter to me.
What work are you doing at IHME?
I work on two projects at IHME: forecasting and cause of death modeling. The forecasting team takes the previous 30 years of available health data and projects the incidence and prevalence of diseases into the future. The team uses this model to simulate different scenarios – for example, what would be the impact on different populations’ health if ART (antiretroviral therapy, a treatment for HIV) prices plateaued instead of decreasing according to current trends? Currently I’m working on developing a testing framework for the core functions in the forecasting code. I feel very fortunate to be working with a very skilled, motivated, and patient group of people on forecasting, and I’m looking forward to continuing to learn from and contribute to this team!
I also work on modeling causes of death. Because many locations have sparse mortality data (or none at all), we use data on factors that we know affect the diseases we’re interested in (for example, tobacco consumption for estimating lung cancer), as well as data from neighboring locations and trends across time in order to estimate mortality for every cause, year, sex, age group, and location. To do this, we use a combination of models, called an ensemble model, in order to capture the different strengths of various types of models to make the best possible estimates. Currently I’m working on adding an additional family of models to the ensemble model, as well as preparing the code to be used on a new round of inputs with additional causes and locations. I’m very excited to be part of this process and am constantly learning as I encounter new facets of what’s possible in the near-term.
How do you think your experience at IHME will contribute to your future work?
Working at IHME, I’m always learning new things – not just about my work specifically, but also about the global health landscape, work that other people are doing, and what the future of health metrics looks like. I’m acquiring new skills and improving my existing skill set, but I’m also finding that the work is broadening my impression of what careers are possible in the health metrics field and which paths I might be interested in taking. Interacting with other PBFs, each with different future plans, and working with people in more senior positions at IHME, each with a different path to where they are now, opens up a wide variety of perspectives. These perspectives, perhaps even more than the skills I am developing, will be critical in informing my future work.
GBD Causes of Death Collaborators. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet. 14 Sept 2017: 390;1151–210.
GBD DALYs and HALE Collaborators. Global, regional, and national disability-adjusted life years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet. 14 Sept 2017: 390;1260-344.
GBD 2016 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet. 14 Sept 2017: 390; 1211–59.
GBD 2016 SDG Collaborators. Measuring progress and projecting attainment on the basis of past trends of the health-related Sustainable Development Goals in 188 countries: an analysis from the Global Burden of Disease Study 2016. The Lancet. 12 Sept 2017: 390; 1423–59.