Seeing is believing, or so is the case with Pop Health Analytics’ practicum student and visualization virtuoso Emma Buajitti. After completing her Honours Bachelor of Science from UofT, with majors in physiology and health studies, Emma joined the Dalla Lana School of Public Health, where she is currently a Master’s of Public Health student in epidemiology specializing in, among other things, the geographic visualization of health data. Put simply, she is a cartographer of public health.
Earlier this year, Emma created an atlas of LHIN-level geographic trends in adult mortality across Ontario, which revealed striking trends in the geographic, socioeconomic and sex-related differences in mortality trends over the past two decades.
This atlas aligns with Emma’s research interests, which involve understanding geographic health disparities in Ontario, and the way that demographics, socioeconomic status, and behaviours contribute to health outcomes (she is currently focused on premature mortality outcomes). In the future, Emma is interested in examining how Ontario’s health system plays a role in either mitigating or exacerbating these geographic disparities.
Besides the Atlas, Emma has in the past year contributed to the 2016 Annual Report of the Chief Medical Officer of Health of Ontario to the Legislative Assembly of Ontario, titled “Improving the Odds: Championing Health Equity in Ontario”, and also participated in a technical working group for Health Quality Ontario to review geographic stratifiers.
On top of all this, Emma has continued working on her Capstone project: a Bayesian spatial analysis of premature mortality in Ontario’s LHIN sub-regions, which also happens to be the first spatial analysis of mortality in Ontario, and among the principal research projects to utilize the new LHIN sub-regions. She is preparing the analysis for submission to a peer-reviewed journal later this spring.
The full impact of Emma’s work comes in the way she transforms daunting tables and charts, full of complex multi-variable relationships, into visually dynamic and powerful representations of the statistical findings they house. This translation of technical information into an accessible, easy to understand message is indispensable in the health analytics field.