On the third Thursday of every month, we speak to a recent graduate about their thesis and their studies. This month’s guest is Dr Caroline Vass who has a PhD from the University of Manchester. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.
Using discrete choice experiments to value benefits and risks in primary care
Katherine Payne, Stephen Campbell, Daniel Rigby
Are there particular challenges associated with asking people to trade-off risks in a discrete choice experiment?
The challenge of communicating risk in general, not just in DCEs, was one of the things which drew me to the PhD. I’d heard a TED talk discussing a study which asked people’s understanding of weather forecasts. Although most people think they understand a simple statement like “there’s a 30% chance of rain tomorrow”, few people correctly interpreted that as meaning it will rain 30% of the days like tomorrow. Most interpret it to mean there will be rain 30% of the time or in 30% of the area.
My first ever publication was reviewing the risk communication literature, which confirmed our suspicions; even highly educated samples don’t always interpret information as we expect. Therefore, testing if the communication of risk mattered when making trade-offs in a DCE seemed a pretty important topic and formed the overarching research question of my PhD.
Most of your study used data relating to breast cancer screening. What made this a good context in which to explore your research questions?
All women are invited to participate in breast screening (either from a GP referral or at 47-50 years old) in the UK. This makes every woman a potential consumer and a potential ‘patient’. I conducted a lot of qualitative research to ensure the survey text was easily interpretable, and having a disease which many people had heard of made this easier and allowed us to focus on the risk communication formats. My supervisor Prof. Katherine Payne had also been working on a large evaluation of stratified screening which made contacting experts, patients and charities easier.
There are also national screening participation figures so we were able to test if the DCE had any real-world predictive value. Luckily, our estimates weren’t too far off the published uptake rates for the UK!
How did you come to use eye-tracking as a research method, and were there any difficulties in employing a method not widely used in our field?
I have to credit my supervisor Prof. Dan Rigby with planting the seed and introducing me to the method. I did a bit of reading into what psychologists thought you could measure using eye-movements and thought it was worth further investigation. I literally found people publishing with the technology at our institution and knocked on doors until someone would let me use it! If the University of Manchester didn’t already have the equipment, it would have been much more challenging to collect these data.
I then discovered the joys of lab-based work which I think many health economists, fortunately, don’t encounter in their PhDs. The shared bench, people messing with your experiment set-up, restricted lab time which needs to be booked weeks in advance etc. I’m sure it will all be worth it… when the paper is finally published.
What are the key messages from your research in terms of how we ought to be designing DCEs in this context?
I had a bit of a null-result on the risk communication formats, where I found it didn’t affect preferences. I think looking back that might have been with the types of numbers I was presenting (5%, 10%, 20% are easier to understand) and maybe people have a lot of knowledge about the risks of breast screening. It certainly warrants further research to see if my finding holds in other settings. There is a lot of support for visual risk communication formats like icon arrays in other literatures and their addition didn’t seem to do any harm.
Some of the most interesting results came from the think-aloud interviews I conducted with female members of the public. Although I originally wanted to focus on their interpretation of the risk attributes, people started verbalising all sorts of interesting behaviour and strategies. Some of it aligned with economic concepts I hadn’t thought of such as feelings of regret associated with opting-out and discounting both the costs and health benefits of later screens in the programme. But there were also some glaring violations, like ignoring certain attributes, associating cost with quality, using other people’s budget constraints to make choices, and trying to game the survey with protest responses. So perhaps people designing DCEs for benefit-risk trade-offs specifically or in healthcare more generally should be aware that respondents can and do adopt simplifying heuristics. Is this evidence of the benefits of qualitative research in this context? I make that argument here.
Your thesis describes a wealth of research methods and findings, but is there anything that you wish you could have done that you weren’t able to do?
Achieved a larger sample size for my eye-tracking study!