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Turning the mirror around: reflecting on reflections

For this reflection I have interviewed Allyson Essex, a friend of mine with a formidable intellect and capacity for rigorous debate. Allyson is a senior bureaucrat within the federal Department of Health and spends much of her time interrogating data in order to properly advise the relevant Minister. This isn’t just a job–more than once Allyson has spent a weekend creating models to find an answer to a demographic question I have idly asked. This sort of work isn’t just a job for Allyson. It’s a passion. So here’s the text of the interview, followed by my own reflection on our conversation.

C: Can you briefly describe your job?

A: I am the senior executive in charge of dynamic and microsimulation modelling, evaluation and the health economics practice of the Commonwealth Department of Health.

C: How did you come to be in that role?

A: I have worked in social policy as a senior executive developing policy and programmes to improve the lives of Australians for the last 12 years.Iwas the creator and developer of The Australian Priority Approach to Welfare. I was interested in whether you could apply the same kinds of dynamic models and analysis to the health system, and link it with the social security system to understand both the pathways of health and the social determinants of health, so I applied for a job at the Department of Health and was asked to take on the Chief Economist role.

C: How important is mathematics in your job? How do you use it?

A: Maths  is really important, we use statistics to understand the risk of someone having a particular injury r disease. We use mathematical algorithms to predict demand and the effects of that demand in couples systems. We use mathematical models to develop machine learning algorithms to find patterns in administrative data (for example will a particular kind of medicine hurt you or heal you, how likely are you to die with a particular treatment)  and I use mathematical techniques to forecast costs and expenditure. I use maths every day to give advice to the Minster for Health and The Australian Government.

C: Did you like maths in school? Why or why not? Did the maths you learned in school prepare you for the work you did now?

A: I liked doing maths but I didn’t like maths at school. I had 3 maths teachers in a row that told me that girls weren’t good at maths and that they thought it would be conceptually hard for me to study 3 unit or 4 unit maths. I always felt that the teacher thought I was stupid because I was a girl. I gave don maths fairly early on as a result.

C: What’s one thing you would have liked your teachers to have done differently with regards to your mathematical education?

A: Foster my curiosity and self directed learning. And not say that girls can’t do maths!!!

C: Do you think the average person understands maths well enough? 

A: I think people understand quite a lots but are scared of calling it maths. My most talented modellers did not do maths or science degrees at university because they were scared off by technically obtuse teaching. But they are brilliant mathematicians. Similarly, people didn’t realise how useful maths is for answering questions they have, and that they are using quite sophisticated maths to solve problems every day.

C: Do you wish yourself wishing that people had a better understanding of mathematics, especially in relation to statistics?

A: I wish people understood relative risk in statistics better. If you double your risk of cancer from 0.0002 to 0.0004 its still a very low risk but people receive it has high because it has doubled. People sent very good at understanding how big or small the chances of an event are, and can’t distinguish between dependent and independent events.

C: Do you wish interviewers would stop asking someone in your position silly questions about the ‘average person?’

A: No- but I wish people like me got totally to people studying maths so we could excite them with the possibilities that being competent with mathematical tools brings you.

Any other thoughts?

Good mathematicians are not always the best technical exponents but they are the most curious. We need to foster mathematical curiosity and useful tips (like working out when speed changes from the tangent of a curve)

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