Alumni stories: Solving society's problems, statistically speaking

The Master of Biostatistics has helped alumni Cattram Nguyen and Mathew Spittal carve out careers quantifying the impact of vaccines on disease, and health practitioner misconduct.

Associate Professor Matthew Spittal
Australian Research Council Future Fellow,
Melbourne School of Population and Global Health

Matthew Spittal
(Master of Biostatistics, 2012) has a longstanding interest in improving health and preventing injury through behaviour change. His research links behavioural research, biostatistics and epidemiology. Matthew's research program focuses on finding ways of improving how we regulate health practitioners with the goal of reducing patient harm.

What would you say you got the most out of during your time in the Master of Biostatistics at Melbourne? Learning how to think mathematically about a problem. By that I mean, listening to people about the problems they are trying to solve, and figuring out how to turn that into a testable hypothesis that can be evaluated with statistical methods.

Tell us what you love about what you are doing now, and why it’s important. I’m leading a research program that is trying to identify health practitioners who are at high risk of accruing multiple misconduct allegations. This is important because we want to reduce the many adverse events that happen to patients in the health care system. We use modern statistical methods to do this research. In fact, attempts to do this in the early 1990s largely failed, and partly this is because the statistical methodology hadn’t yet become mainstream.

What challenges in your field fire you up? When I meet with policy-makers and regulators, I find they always have important problems that they are trying to solve and they have collected really good data too. But what they need is someone to bring these two things together. I find that really exciting and the process of doing that can be really illuminating for everyone.

For instance, in one of the early studies we did in this area, we showed that 3 per cent of doctors accounted for nearly 50 per cent  of patient complaints, and we found that there were clear differences between doctors with no complaints and doctors with many complaints. This is really useful if we want to start thinking prospectively about remediation.

Cattram Nguyen
Biostatistician (Senior Research Fellow)
Clinical Epidemiology & Biostatistics Unit, New Vaccines 
Murdoch Children's Research Institute

During her Masters, Cattram spent several periods abroad, including internships at the World Health Organisation and the Research and Training Centre for Community Development in Vietnam. She returned to Australia in 2011 and commenced a PhD, which focused on multiple imputation, a statistical method for handling missing data.

Prior to joining MCRI, Cattram worked at the Centre for Biostatistics and Clinical Trials at the Peter MacCallum Cancer Centre, where she coordinated seven multi-centre cancer clinical trials at various stages of development and conduct.

What would you say you got the most out of during your time in the Master of Biostatistics at Melbourne?
The Masters program gave me a foundation in biostatistics that has been essential to my applied work in health research, as well as the research I do in statistical methodology. It also gave me practical and versatile skills that I can apply to many different areas, which is invaluable, given the increasing need for expertise in handling data

Tell us what you love about what you are doing now, and why it’s important.
I am working as a biostatistician in child health research. What I love about my role is that I get to work with passionate researchers, such as clinicians, epidemiologists, laboratory scientists, and other statisticians, on important questions that will make a difference to health policy and practice.

What makes you passionate about statistics and what are the challenges you are working on or intellectually curious about? Many of my statistical interests arise from the challenges that I encounter in my day-to-day work. For example, my colleagues and I are working on studies that investigate the impact of vaccines on disease as they are rolled out in countries in the Asia-Pacific. There are many statistical issues that we need to deal with – such as confounding, selection bias, missing data – which means my work is always challenging and interesting.