The power of the MicroMort


Audiences tend to giggle nervously when first presented with the term MicroMort because it combines a somewhat flippant name with a deadly association. But this intuitive reaction helps to focus attention on a very useful term that aims to get beyond the usual rather opaque risk statements in terms of person-years or 100 000 population. Its primary purpose is for comparisons, as the absolute size of small risks is tricky to grasp. You can tell people that a 1-in-a-million chance is about the same as the chance of getting 20 heads in a row when flipping a fair coin, but this still does not convey magnitude. To do this adequately we need to use the power of analogy, and not just a single one.

In this issue of BJOG, Walker et al. focus mainly on the analogy of someone of a different age but with the same daily risk of dying as a baby on the day of its birth. Figure 1 shows the daily risk of death, in MicroMorts for men and women in England and Wales in 2011,[1] from which we can immediately read off the author's observation that the daily risk on the first day of life is not reached until the early nineties. A horizontal line at 1 MicroMort/day represents the average daily risk from ‘non-natural causes’ (accidents and violence).

Figure 1.

MicroMorts per day for average person in England and Wales—dashed line shows rate for first day of life.

We note in passing that our 7-year-olds have about the lowest mortality of anyone, anywhere, ever, in the history of the human race. But (ignoring the lump of risk-taking youth between 15 and 25 years) from aged 7 the daily dose of MicroMorts, traditionally known as the ‘Force of Mortality’, increases linearly on a log-scale, and therefore exponentially at a rate of 9% per year, and so doubles every 8 years. Gompertz observed this linearity back in the 1820s.[2]

But this is only one analogy, and we can consider a whole lot more for balance. Table 1, based on values in our recent book The Norm Chronicles,[3] shows situations that expose people, on average, to 480 MicroMorts, which the authors consider an average risk on the first day of life.

Table 1. Some rough MicroMort equivalents to the first day of life
Cause of deathContextBirth equivalents
‘Non-natural causes’England & Wales 201016 months
All accidents—under 14England & Wales 20102.5 years
DrivingUK 2010160 000 miles
MotorcyclingUK 20103400 miles
CyclingUK 201013 440 miles
Travelling by trainUK 20103 600 000 miles
Travelling by light aircraftUSA 1992–20117200 miles
Travelling by passenger jetUSA 20102 900 000 miles
Anaesthesia for non-emergency operationUK48 operations
Serving in Afghanistan (peak risk period)

All UK forces

May–October 2009

12 days
Flying in Bomber Command in WW2Royal Air Force 1939–19458 minutes
Scuba divingUK 1998–200996 dives
Hang-glidingUK60 jumps
SkydivingUK48 jumps
Running marathonsUSA 1975–200470 runs
Base-jumpingKjerag Massif, Norway1 jump
Personal risk from asteroid 34 000 years

The Table deliberately shows a wide range of activities to try and avoid undue influence of ‘framing’. Single analogies, such as driving 160 000 miles, will tend to minimise the apparent risk if driving is perceived as being very safe, whereas others, such as serving 12 days in Afghanistan or hurling yourself off a rock in Norway, may tend to exaggerate the risk due to the extreme negative connotations. Therefore a mixed selection may be appropriate.

We note that we do not include chronic risks, such as cigarette smoking, obesity or pork pies. These are unlikely to kill you on the spot (unless you choke on the pork pie) and so their impact is better expressed as a change in lifelong-risks, such as reduction in life-expectancy, additional risk of early death, or increase in annual risk of death. For example, smoking 20 a day roughly doubles your daily MicroMorts, or equivalently is expected to take about 8 years off your life. Due to Gompertz's observation, we can also consider the smoking as essentially adding 8 years to your ‘effective age’, and this is independent of your current age.

The type of comparisons shown in Table 1 can be controversial. When the government drugs advisor Professor David Nutt rather mischievously compared the risks of ecstasy with the addiction to horse-riding, which he termed equasy,[4] and showed they were of roughly equivalent harm, this was greeted with political uproar and his services were eventual dispensed with. But why the fuss? Illegal drugs and horse-riding are both leisure activities, voluntarily undertaken by young people for fun. But one is wholesome and politically acceptable, and the other is not.

Why are we trying to communicate risk anyway? It needs to be considered whether we are trying to increase understanding, change behaviour, or encourage shared-care decisions based on an informed choice. My favourite objective was explored by psychologists who were trying to encourage ‘immunity to misleading anecdote’,[5] in which they showed in a randomised trial that transparent risk communication led to participants being less influenced by unrepresentative stories of the type frequently found in families, media and the internet–someone who smoked all their life and lived to 90, drank carrot juice and their cancer went away and so on. These stories may be true, and may reflect ‘possibilities’, but they do not reflect probabilities.

Of course all of these comparisons can only be rough assessments—they are only averages and do not reflect individual circumstances. But they provide ballpark figures to start important conversations, and if they use story-telling techniques of analogy and emotional response, without seeking to unduly manipulate behaviour, then these ideas should be welcomed.

Disclosure of interests

Nothing to disclose.