Professor Monica Alexander was featured and interviewed in UofT News for her research on how marginalized populations are disproportionately vulnerable to conditions or events that can cause an early or unnatural death. By analyzing data on when, how and who dies, researchers like Professor Alexander can learn a lot about how to improve or even save people’s lives. Professor Alexander explains that passion for serving disadvantaged people led her to contribute to research projects that have informed the work of organizations such as UNICEF, the Bill and Melinda Gates Foundation, the World Health Organization, and the Human Mortality Database.
Professor Alexander is an Assistant Professor of Sociology and Statistical Sciences at the University of Toronto. Her research focuses on developing statistical methods to help measure and understand disparities in health outcomes. Specifically, her research interests include statistical demography, mortality and health inequalities, and small-area population issues.
An excerpt of her interview is included below (the full article can be found here).
What motivates you to do the kind of work you do?
I know this sounds pretty cliché, but I wanted to do something that could help people, especially marginalized people. There’s a lot of inequality in death because vulnerable populations tend to be just so much more affected.
I knew I was never going to become a doctor. I’m terrible with blood, but I realized that I can use my skills in analyzing health and mortality outcomes to impact social policy.
Your work focuses on developing statistical methods to overcome data gaps. Why is it that data on disadvantaged populations seems especially sparse?
Populations that have the highest death rates unfortunately also have the worst data. Child mortality, for example, is substantially higher in places like sub-Saharan Africa than it is in Canada, but because we don’t have a good registration system for deaths in sub-Saharan Africa, we don’t have a lot of information to work with. That makes it difficult to get a sense of how mortality is changing or what efforts to improve health are successful. That’s where my work comes in. I might have some data from surveys or small-scale surveillances and then I try to figure out how we can get a good picture of what is going on, despite the data not being perfect.
How do you do that?
By applying different statistical methods on sets of data and using the trends that I see to inform my estimates – in combination with a demographic approach.
A classical demographic approach is to use strong empirical regularities. We humans are actually quite predictable about when we’re going to die. When you look at mortality by age groups, there’s a very distinct shape that looks like a “J”. The rate of deaths is a bit higher in the first years of life, but then it dips down. With old age, it increases again. You can use prior knowledge like that to adjust and model biases in the data.