Talking risk: how to communicate health outcomes to non-health economists

You’ve scrutinized the data, crunched the numbers and arrived at a solid risk assessment. But how do you communicate that information so that great-aunt Betty can decide whether to take your drug for her heart condition?

While other health economists understand you when you talk about the probabilities, costs and benefits of a particular outcome, it’s a different picture when it comes to a math-phobic general public.

So how do you get your message across to someone who may struggle to calculate the price of that shirt on sale for 30 per cent off? Keep the following tips in mind:

1. Express risks in absolute terms rather than relative ones. If patients are told a particular drug doubles their probability of developing liver cancer, for example, many will decide the potential risks outweigh the benefits. But phrase it in absolute terms –2 people in 10,000 who take the drug will develop liver cancer, compared to 1 in 10,000 who don’t take it – and their decision might well change.
2. When it comes to probabilities, frequencies are easier to grasp than percentages, so say “6 in 1,000 people will experience this side effect,” rather than “you have a 0.6% risk.” If you mention several different frequencies, keep the denominator consistent to avoid confusion.
3. Include comparisons to other treatments and to no treatments to help patients make a well-informed decision. If taking a particular birth control pill carries a risk of dying from a blood clot, for example, compare that to the risk of side effects from other birth control measures or the risk of dying during childbirth.
4. Different people absorb information more easily in different ways, so include diagrams to reach visual learners. A pictorial representation of the number of people affected – a Paling Palette© highlighting three human figures out of a thousand, for example – is an excellent way to convey percentages, while line graphs quickly convey trends and bar graphs make comparisons easier. (See issues 11, 12 and 13 of Heath Outcomes Communicator for tips on making the best use of graphics.)
5. Avoid vague language that even other health economists or doctors will have trouble interpreting. How frequently does a “rare” side effect occur? How probable is a “moderate” probability?
6. Similarly, avoid economic language that can convey a very different meaning to Joe Public. To you, “a significant risk” may mean a statistically significant risk, but a non-economist will interpret it to be a highly probable outcome or a risk that carries grave consequences. Similarly, a layperson might believe a “conservative estimate” actually underestimates the likelihood of a particular outcome, while a “positive trend” brings exclusively good news.

Ultimately, patients will evaluate risks and make health decisions based on a number of factors, including many that aren’t rational. That’s not going to change. But by presenting your information as clearly as possible, you can make sure they make a truly informed decision, not one based on misunderstanding or confusion.

Risk/benefit decisions – who should be involved, and are there valid measurement methods?

By Clare Gurton (cgurton@rxcomms.com)

Here we summarise a recent article from ISPOR Connections 2006; December 15: 3–5

Patients are generally not included in decision-making yet they are the group to whom the benefits and risk of treatment apply.

Treatment decision-making policy and treatment guidelines are led by objective evidence of benefit, that outweighs any risks, are collected from clinical studies under conditions of an experiment rather than clinical practice, and are designed to satisfy regulatory requirements prior to approval.

The question is: how to include end-users and to generate a valid way of assessing the preferences of a representative sample of the patient population and, further, to incorporate their preferences into risk/benefit decisions.

In a recent article, Hauber et al discuss two approaches for overcoming this problem. The incremental net health benefits (INHB) approach has been used in two recent studies. It quantifies the benefits and adverse events of a treatment and assigns weights to each outcome.

The difference between the sum of the weighted benefits and weighted risks represents the net health benefit and the INHB is calculated as the difference between the net health benefit of the treatment under assessment and a comparative treatment (most often standard care).

Another approach is to calculate the maximum acceptable risk (MAR). This estimates the maximum risk that patients are willing to tolerate in order to achieve the therapeutic benefits of a treatment that incorporates patient preferences over risk and benefit outcomes.

The MAR can then be compared with actual or expected risk to determine patient acceptability. MAR is calculated using choice experiment or conjoint-analysis methods and so includes patient’s subjective assessment of the risks versus benefits directly; MAR is the patients’ appraisal of the risk level at which the benefits are zero.

Both of these methods are relatively new and there is little data on their use in practice; however, they highlight the increasing importance of quantifiable patient preferences in risk/benefit analysis.