What attracted you to the health metrics field?
During my undergraduate studies I decided I wanted to apply the programming and quantitative skills I was learning to my interests in science and health. One way I used those skills was by contributing to epidemiology and evolution research that helps to track viral disease outbreaks like flu, Zika, and Ebola in as close to real time as possible. Contributing to work that helped inform the world’s understanding of those outbreaks helped me confirm that I want to continue doing research that takes advantage of the vast amounts of data available today in order to improve the world’s health.
After learning of the work done at IHME, I immediately recognized the importance of providing accurate and timely information about the causes of health loss and making this information readily available to policymakers. In order to improve population health, we first have to know what the biggest health problems actually are and where in the world we need to address those problems to reduce health disparities. IHME provides the opportunity to use data to help improve health at the largest scale possible.
What work are you doing at IHME?
Often measurements of health are made at the national or state level, but variation between lower administrative divisions can be very large. The US Counties team makes county-level health estimates in order to inform health policy at a more local level. In order to make reasonable estimates from data with small sample sizes for each county, we use small area estimation models to borrow strength from covariates, and across space and time.
How do you think your experience at IHME will contribute to your future work?
Working at IHME will help me advance in many different ways. I’m excited to improve my understanding of global health, gain experience using quantitative and statistical tools to glean insights from data, and advance my research and analytical skills. IHME will also help me develop professionally and allow me to make valuable connections with faculty, staff, and fellow PBFs. I’m not exactly sure what I’ll do after my experience here, but I know the skills I’m gaining will allow me to continue making an impact in health metrics or similar fields in the future.
GBD 2017 Population and Fertility Collaborators. Population and fertility by age and sex for 195 countries and territories, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet. 8 Nov 2018;392:1995-2051. doi: http://dx.doi.org/10.1016/S0140-6736(18)32278-5.
GBD 2017 Mortality Collaborators. Global, regional, and national age-sex-specific mortality and life expectancy, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet. 8 Nov 2018;392:1684-735. doi: http://dx.doi.org/10.1016/S0140-6736(18)31891-9.
GBD 2017 Causes of Death Collaborators. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet. 8 Nov 2018;392:1736-88. doi: http://dx.doi.org/10.1016/S0140-6736(18)32203-7.
GBD 2017 SDG Collaborators. Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet. 8 Nov 2018; 392:2091–138. doi: http://dx.doi.org/10.1016/S0140-6736(18)32281-5.
Dwyer-Lindgren L, Stubbs RW, Bertozzi-Villa A, Morozoff C, Callender C, Finegold SB, Shirude S, Flaxman AD, Laurent A, Kern E, Duchin JS, Fleming D, Mokdad AH, Murray CJL. Variation in life expectancy and mortality by cause among neighborhoods in King County, WA, USA, 1990–2014: a census tract-level analysis for the Global Burden of Disease Study 2015. Lancet Public Health. 5 Sept 2017.