What attracted you to the health metrics field?
In college, I studied the relationship between geography and public health, seeking to situate health outcomes and environments in a place-specific context. I’m interested in the intersection of geographic data and health, and in using place as a tool for understanding population health at local, national, and international scales. At IHME, we’re using increasingly refined geographic data to inform our understanding of how geography impacts the burden of disease and explains differences in health outcomes.
What work are you doing at IHME?
As health data become available at increasingly granular geographic scales, we’re taking advantage of this data to produce health metrics at finer geographic scales. The geospatial team uses population surveys, rasters of covariate data, and statistical modeling to produce continuous surfaces of health outcomes such as under-5 mortality, child growth failure, and malaria at a 5 km x 5 km pixel level. Other examples of our work include predicting the zoonotic niche of Ebola Virus Disease through species distribution modeling to help inform the global health community’s understanding of risk and potential outbreaks.
How do you think your experience at IHME will contribute to your future work?
On the geospatial team, I’m learning about model-based geostatistics, the challenges of using big population-level and geographic data, and the mechanics of running complex models in cluster computing environments, as well as the extension of health metrics to an increasingly refined geographic context. I aim to draw on this statistics and modeling knowledge in the future, using this understanding of research to inform a career in public health.
Osgood-Zimmerman A, Millear AI, Stubbs RW, Shields C, Pickering BV, Earl L, Graetz N, Kinyoki DK, Ray SE, Bhatt S, Browne AJ, Burstein R, Cameron E, Casey DC, Deshpande A, Fullman N, Gething PW, Gibson HS, Henry NJ, Herrero M, Krause LK, Letourneau ID, Levine AJ, Liu PY, Longbottom J, Mayala BK, Mosser JF, Noor AM, Pigott DM, Piwoz EG, Rao P, Rawat R, Reiner RC, Smith DL, Weiss DJ, Wiens KE, Mokdad AH, Lim SS, Murray CJL, Kassebaum NJ, Hay SI. Mapping child growth failure in Africa between 2000 and 2015. Nature. 28 Feb 2018. doi:10.1038/nature25760
GBD 2016 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 2016 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 Risk Factors Collaborators. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet. 14 Sept 2017: 390;1345-1422.
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.
Pigott DM, Millear AI, Earl L, Morozoff C, Han BA, Shearer FM, Weiss DJ, Brady OJ, Kraemer MUG, Moyes CL, Bhatt S, Gething PW, Golding N, Hay SI. Updates to the zoonotic niche map of Ebola virus disease in Africa. eLife. 2016; 5:e16412. doi: 10.7554/eLife.16412.