Naomi Hamm

Tell us a bit about your background and how you ended up studying in this field.

My background is in Kinesiology. My undergraduate degree focused on exercise physiology and my MSc research looked at using physical activity interventions for chronic disease management and prevention. During my MSc, I fell in love with statistics, which I know is a weird thing to say, but the heart wants what it wants. I always loved math, but I had only taken one statistics course and didn’t like it at first. It wasn’t until I was able to explore stats on my own that I came to appreciate it like my mathematics training.

Pharmacoepidemiology is not a path I would have expected to take. During my master’s I became really interested in looking at chronic diseases at the population level. When I came back to start my PhD, methods for chronic disease epidemiology was a perfect fit given my background and interest in methodology. From there Dr. Lisa Lix, my advisor, connected me with Dr. Dan Chateau and CNODES.

I started working with CNODES as a part of the Visual and Automated Data Analytics Training Program (VADA). As a part of this, I did a 4-month internship with Dr. Dan Chateau funded by CNODES. It was a nice way to branch out from chronic diseases while staying in a related field. Also, Dr. Chateau had a really cool project for me, which was a bonus!

Give us a short summary of your current research.

Broadly, my research looks at epidemiology methods. My thesis work is looking specifically at how we can incorporate longitudinal methods to improve chronic disease surveillance. My internship project with CNODES looked at assessing how well the high dimensional propensity score (HDPS) could account for unmeasured confounding due to the social determinants of health. To do this we looked at the impact of prescription opioid exposure in utero on infant and childhood outcomes. We are in the process of carrying out supplementary analyses before writing up the paper to be published.

Tell us about your experiences related with CNODES.

My experience with CNODES has been great! I gained a lot of skills working in SAS. It was also really good to get experience working with administrative health data. Much of my previous experience has been with clinical data, which is a bit of a different beast. I especially appreciated being able to work on the HDPS. Being more a statistics person than computer science, it was interesting to take a machine learning approach and ask “ok, great, but how are we using it and is this appropriate?” With all the buzz about machine learning and artificial intelligence recently, I think it important to still think about why we are doing what we’re doing and what the implications for research results are, while also recognizing the benefits automated approaches can bring.

I was also fortunate to work with the Analyst Training Program Development team. This opportunity, as well as attending the CNODES semi-annual meeting, allowed me to get a better grasp of how a research network like CNODES works. Sometimes, as a graduate student, you can get a little wrapped up in your own bubble, so it was really quite something to step out and realize just what it takes for a national research network to be able to do what they do. That was a really cool experience!

What excites you the most about the research you are doing or hope to do in the future?

The applied aspect is most exciting to me. It’s one of the reasons I moved into population health to begin with. I wanted to be able to contribute to the research that would be used to inform decision-makers and health policymakers. With my kinesiology background, the research is very much interventions-based and, depending on what intervention you’re doing, it may or may not translate into health policy. With CNODES there are connections in place where the results are passed on to policymakers. Immediately you have their attention, and the ability to see results from across Canada as opposed to just one site. You have more potential.

Are there aspects of the work that you find particularly challenging?

For me, with my own research, the challenging part is taking a research idea and being able to work it into a feasible research question with clear objectives and hypotheses. The more I do it and the more I realize how hard it is.

What are your career goals?

I would like to continue in academia and end up at a research-intensive university as a professor. Getting a job in academia is incredibly competitive so being connected to a national research network like CNODES is definitely helpful.

How has CNODES impacted your studies or career trajectory?

I am definitely interested in pharmacoepidemiology research in a way that I was not before! CNODES has opened a lot of doors for me to explore, especially as I start to carve out my own research area. One of the biggest things I learned through CNODES is that there are different career possibilities like being a data analyst at a university. When you’re a grad student you don’t think outside of academia for research opportunities and in CNODES you can see that there are so many other positions out there and so many opportunities. All of the positions, expertise, and collaboration are what make the network run.

Outside of work and studies, what are one or two things that you are really passionate about?

I’m really big into physical activity of any kind. My kinesiology background probably has a lot to do with that. Currently, I’ve been doing a lot of running and yoga, but I go through phases. I was a ballet dancer for most of my life and taught dance during my undergraduate, although now I do a lot more watching than dancing myself. Food is another passion of mine—both cooking and consuming.

Selected recent publications:

Lix L, Ayles J, Bartholomew S, Cooke C, Ellison J, Emond V, et al. The Canadian Chronic Disease Surveillance System: A model for collaborative surveillance. International Journal of Population Data Science. 2018;3(3). https://ijpds.org/article/view/433

Hamm NC, Pelletier L, Ellison J, Tennenhouse L, Reimer K, Paterson JM, et al. Original quantitative research Trends in chronic disease incidence rates from the Canadian Chronic Disease Surveillance System. Health Promotion and Chronic Disease Prevention in Canada : Research, Policy and Practice. 2019;39(6-7):216-24. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6699608/