Nobody in Particular

Why do we believe something? How do we know that we are right? When I was a child, I was certain that the Fleetwood television set my parents had just purchased, was the best. So was the make of our car -and our vacuum cleaner too, come to think of it. But why? Was it simply because authority figures in my young life had told me, or was there an objective reality to their assertions? For that matter, how did they know, anyway? Other parents had different opinions, so who was right?

I was too young to question these things then, but gradually, I came to seek other sources of knowledge. And yet, even these sometimes differed. It’s difficult to know in what direction to face when confronted with disparate opinions. Different ‘truths’. Everybody can’t be right. Usually, in fact, the correct answer lies somewhere in the middle of it all, and it becomes a matter of knowing which truths to discard -choosing the ‘correct’ truth.

Despite the fact that most of us rely on some method like this, it sounds completely counterintuitive. How many truths can there be? Is each a truth, or merely an opinion? And what’s wrong with having a particular opinion? Again, how would we know? How could we know?

Nowadays, with social media algorithms selecting which particular news they report on the basis of our past choices, it’s difficult to know if we are in an echo chamber unless we purposely and critically examine whatever truths we hold dear -step back to burst the bubble. Canvas different people, and sample different opinions. But, even then, without resorting to mythology, or a presumed ‘revealed’ truth that substantiates a particular religious dogma, is there an objective truth that somehow transcends all the others? Conversely is all truth relative -situationally contextualized, temporally dependent, and ultimately socially endorsed?

Should we, in fact, rely on a random sample of opinions to arrive at an answer to some questions that are only a matter of values, but not about realistically verifiable facts -such as the height of a building, say, or maybe the type of bacterium that causes a particular disease? Would that bring us closer to the truth, or simply yet another truth?

Well, it turns out that the average of a large group of diverse and even contrary opinions has some statistical merit: http://www.bbc.com/future/story/20140708-when-crowd-wisdom-goes-wrong  ‘[T]here is some truth underpinning the idea that the masses can make more accurate collective judgements than expert individuals.’ The Wisdom of Crowds ‘is generally traced back to an observation by Charles Darwin’s cousin Francis Galton in 1907. Galton pointed out that the average of all the entries in a ‘guess the weight of the ox’ competition at a country fair was amazingly accurate – beating not only most of the individual guesses but also those of alleged cattle experts. This is the essence of the wisdom of crowds: their average judgement converges on the right solution.’

But the problem is in the sampling -the diversity of the members of that crowd. ‘If everyone let themselves be influenced by each other’s guesses, there’s more chance that the guesses will drift towards a misplaced bias.’ Of course ‘This finding challenges a common view in management and politics that it is best to seek consensus in group decision making. What you can end up with instead is herding towards a relatively arbitrary position. Just how arbitrary depends on what kind of pool of opinions you start off with. […] copycat behaviour has been widely regarded as one of the major contributing factors to the financial crisis, and indeed to all financial crises of the past. [And] this detrimental herding effect is likely to be even greater for deciding problems for which no objectively correct answer exists. […] All of these findings suggest that knowing who is in the crowd, and how diverse they are, is vital before you attribute to them any real wisdom.’

This might imply that ‘you should add random individuals whose decisions are unrelated to those of existing group members. That would be good, but it’s better still to add individuals who aren’t simply independent thinkers but whose views are ‘negatively correlated’ – as different as possible – from the existing members. In other words, diversity trumps independence. If you want accuracy, then, add those who might disagree strongly with your group.’

Do you see where I’m going with all this? We should try to be open enough to consider all sides of an argument before making a considered decision. Let’s face it, you have to know what it is that you’re up against before you can arrive at a compromise. And perhaps, the thing you thought you were opposing is not so different from your own view after all.

Even our values fluctuate. Unless we are willing to be historical revisionists, it’s obvious that people in the past often assigned values differently to how we do today -sexual orientation, for example, or racial characteristic and stereotyping. And who nowadays would dare argue that women are not the equal of men, and deserve the same rights?

There are some things about which we will continue to disagree, no doubt. And yet, even a willingness to listen to an opposing opinion instead of shutting it down without a fair acknowledgment of whatever merits it might have hidden within it, or commonalities it might share with ours, is a step in the right direction.

I’m not at all sure that it’s healthy to agree about everything, anyway, nor to assume we possess the truth. It’s our truth. I think that without some dissenting input, we’d be bored, condemned to float in the increasingly stagnant backwater we chose, while just beyond our banks, a creek runs merrily past, excited to discover another view that lies beyond and behind the next hill.

After all, remember what happened to Caesar after Shakespeare had him boast: “I am constant as the northern star, of whose true-fix’d and resting quality there is no fellow in the firmament.”

Just saying…

 

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Statistics and Gender

Statistics, the collation, analysis and ultimately, the interpretation of data, have never been easy – at least for me. They have never reached the level of intuitive and, indeed, have barely climbed past manipulative in my head. And I readily admit to occasional cognitive dissonance even when they are used to support what I already believe. Or, rather, want to believe… I wonder if the sources from which I have accessed the numbers might be those that already pander to my own biases. In the cloud of assertions that cover me, everything is obscure and up for grabs.

I suppose it’s like that for us all, though –we hear what seems important to us and sift clumsily through the rest, filing most of it somewhere else, if at all. Especially if what has been measured is not crystal clear –or at least what has been reported is not. A classic example was that of a survey of shared parental leave in the UK. It was initially reported that only 1% of men were opting for this –much less than the rest of Europe. In fact, however, the figure reported was 1% of all men, not 1% of men who had just had a baby.

We have to examine what we read before we arrive at our conclusions; most of us don’t. Most of us have neither the interest, nor the tools to know if what is presented to us is reasonable, or at least free of bias – especially our own confirmation biases. A lot slips through the net.

A good example of this are the statistics on women and girls: http://www.bbc.com/news/magazine-36314061

‘There is a black hole in our knowledge of women and girls around the world. They are often missing from official statistics, and areas of their lives are ignored completely.’ For example, a record of their participation in the labour force in various countries. The data are often biased towards employment in the formal sector, which in those countries, is where men work. ‘Buvinic [an expert from the Center for Global Development, a think tank] argues that many women get missed out because they consider themselves primarily as housewives, when in reality they work on farms, do part-time jobs and seasonal work or run their own businesses.’

‘There are other problems too, Buvinic says. Not all countries collect statistics on other aspects of women’s lives, such as domestic violence or maternal mortality rates, and when they do collect this data they often do it in different ways, making international comparisons difficult.’ And, ‘There are many statistics that are collected without being broken down by sex, which makes it hard to tell when women are not being treated equally.’ For example, “Until recently, very few banks disaggregated their customer data by sex, leading to difficulties in understanding reasons behind the persistent gender gap in access to and use of financial services,” says Megan O’Donnell, one of Buvinic’s colleagues at the Center for Global Development.’

That I find all of this surprising speaks to my naïveté, I suppose, and yet I have my doubts that many of us would take the time from our busy lives to consider what this neglect might mean. David McNair – Director of Transparency at the One Campaign, a group that fights poverty- even uses the weighted ‘sexist’ epithet and summarizes the problem succinctly: “The reason why it is sexist is that women and girls are disproportionately left out of data collection. They are uncounted, therefore they don’t matter.”

Roughly half the population on the planet doesn’t matter? And it’s the half that has gestated and succoured that other half -the only half that is counted? Even if I try my best not to be an historical revisionist, it does not make sense to me.  Perhaps McNair, again, had the best explanation: “If you have robust data then you can be held to account for your decisions. There are people who have a vested interest in not having that information in the public domain.”

But I suppose we have to look for any encouraging little cracks in the imposing male edifice: ‘Recently the UN’s International Labour Organisation (or ILO) held a conference, where labour statisticians agreed how to start collecting data on unpaid and domestic work, for example time spent cleaning your house. Ten countries have volunteered to take part in a pilot to use this new framework to measure unpaid work.’

Whoa, ten countries have decided to put their toes in the water…? Or rather, their statisticians in the water? How brave. Maybe Macbeth was on to something when he said that ‘tomorrow creeps in this petty pace from day to day.’ It’s the end of his soliloquy that has me worried though: ‘It is a tale told by an idiot, full of sound and fury, signifying nothing.’

Oh, I hope not…

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Risk Perception

Risk is something we all need to assess from time to time. The problem lies in how we do it. If there are factors we fail to take into account that affect our risk perception then our evaluation may as likely be wildly unrealistic, as appropriate. Emotion tends to skew things in one direction or other, as does as the degree of perceived asymmetry between the benefits and dangers –if we really desire or enjoy something, we might be less risk averse than if we were not keen on it in the first place. The status or acceptance acquired from smoking for a teenager, say, might counterbalance the long-term dangers; it might not be seen that way by an older, more confident adult. And immediately experienced risk –driving a car with faulty brakes- may be more influential than future risk such as lifestyle changes for cardiac disease prevention. And then, of course, there are cognitive biases –our own subjective mythologies, expectations and intuitions- not to mention cultural biases, all contributing to the overall assessment of the acceptability or not of the risk.

But I suspect that a major obstacle in risk perception lies in its probabilistic nature. People have enough difficulty in understanding the simple Bell Curve distribution of likelihood let alone the mathematical Baye’s theorem which ‘describes the probability of an event based on conditions that might be related to the event.’ Our estimates are more intuitively driven than statistically. Understandable, to be sure, but unreliable in most appraisals. Misleading. Dangerous, even.

It’s hard to grasp the concept that even doubling a risk when it is almost imperceptably low already, still leaves it almost imperceptable. So, at what stage does it become unacceptable, if it wasn’t really perceived to be that in the first place? Is any risk acceptable if we know about it, no matter how small and unlikely? What about the background risks that are inherent in most things –be they visible and published or not? Riding a bicycle, for example, or running on a treadmill, walking to work… At what point do we merely turn off and get on with life?

The problem is certainly manifest in Medicine. Most of us invest health –especially our own- with an appropriately significant amount of concern. But it is more of an emotional than intellectual process, and we tend to interpret the concept ‘improbable’ as the much more personal ‘but possible’. Shadows, despite their insubstantiality, hide ‘what-ifs’ –or worse still, unrealistic fears that favourable probability cannot disguise.

Martha was one of those. I would never have guessed it to look at her, though. She sat relaxed and confident in the waiting room, surveying her adopted realm like a tall queen. Crowned with long brown hair, the curls danced from her shoulders as she stooped to pick up a child’s toy, then returned it to him with a smile that would have melted an older stranger. Fifty-ish and surprisingly thin, she was dressed in loose, faded jeans, orange sneakers and a light blue designer tee shirt that said ‘Dare Me, eh?’ She was in control of all she surveyed: monarch of the room.

She stood when she saw me approaching and extended her hand before I was half way across the floor. “I’m so glad to meet you, doctor,” she said loud enough for all to hear, and squeezed my hand like she was doing an exercise in the gym. A full head taller than me, I had to look up to see her face. For a moment, I felt like that little boy whose toy she had rescued, and as I led her back down the corridor to my office I had the distinct impression that, despite her being behind me, nonetheless it was me being taken for a walk like a small dog on a leash.

She sat down on the chair opposite my desk and waited for me to settle into mine before starting the interview. That’s how it felt: she was interviewing me like a reporter hot on a story.

“I’m here,” she said without the usual preliminary pleasantries, “because of a disagreement with my family doctor…” She left her thought unfinished so she could study my face for its reaction, and when she saw nothing but curiosity written on it, continued. “She seemed to feel that my worries about hormone replacement therapies were unfounded.”

She immediately folded her arms across her chest to -as her tee shirt invited me to do- dare her to defend herself. I wondered if she’d chosen it specifically for the visit. I smiled to diffuse her arms, but her body had hardened into place; everything remained on guard, and her eyes perched on her face like a pair of eagles watching me from their aerie in a tree.

I thought I’d keep it simple. Basic. “She felt you needed hormones?” A regal, no-nonsense nod. “And why was that?”

“Hot flushes.”

I duly typed this on my laptop, although I sensed it might be only the tip of a rather unpredictable iceburg. So I waited.

I could sense she was testing me, and the eagles shifted impatiently on their branches. “I don’t need hormones, doctor. I was just going in for my pap smear and she asked me about hot flushes.” A smile passed across her face like a shadow crossing a stage. “I think she just wanted to compare notes with me…”

I tried to concentrate on her mouth, her eyebrows, hair –anything but those unnerving eyes that seemed constantly on the verge of attack. “So you’re not bothered by them?”

She shrugged, but if I hadn’t been staring at her, I might have missed it. I sat back in my chair, wondering where the thread-bare conversation was taking us.

She could see my confusion, although I had tried to hide it behind an Oslerian mask of Aequanamitas. I sometimes find it doesn’t quite cover everything, no matter how I wear it. “Look, she’s a nice woman and I think she was just trying to be kind.” She hooded the eagles and looked over my shoulder at something for a moment. “It was a girl thing, I suspect –you know, an attempt at empathy, wearing my shoes, or something.” One of my eyebrows started to move before I could rein it in and she noticed it and grinned sheepishly. “After I left her office, it dawned on me that she’s probably on hormones herself. An example of the play within the play of Hamlet: The lady doth protest too much, methinks.”

Martha was obviously not your average patient –she even put the ‘methinks’ at the end, where its supposed to be. I was impressed. “You think she was trying to convince herself that hormones were safe?” Might as well cut right to the chase.

She nodded. “I made the mistake of arguing with her and it rolled downhill from there. I shouldn’t have been so righteous, but from my reading, I felt she was mistaken.”

I could tell being a referee in a contest where one party has done extensive research on the subject, and the other was speaking to the contrary out of vested interests would not be easy. Martha had probably read more Shakespeare than me as well. I approached the issue carefully. “What did you find troubling about her opinion?”

She smiled; her trap was laid. “Well,” she started equally carefully, “for a start, the risk of phlebitis is increased by about three hundred per cent on hormone replacement therapy…”

I inclined my head slightly –it was meant to acknowledge the number, but not succumb to it. “Well, in fact the exact incidence of DVT” –I used the acronym for deep vein thrombophlebitis, to show her I had some trifling knowledge of the subject- “is unknown because of the inaccuracy of clinical diagnosis, but if you want to look at another way, its incidence is up to six hundred per cent higher in the first year of use…”

Her smile broadened –she’d been validated. She had been right to worry.

“But,” I added when her smile looked as if it was going to split her face in two, “Six hundred per cent of what?” I let it sink in; I didn’t expect an answer.

“Well, six hundred per cent higher than in non-users…” Her eyes were hunters again –hawks this time, I think. The tone of her voice said that it was obvious. “Six hundred per cent higher, doctor! Six hundred…”

I briefly flirted with some sort of aerial fight, our eyes meeting each other somewhere over the desk in a dominance combat . But I’m not like that. “So, six hundred per cent higher than in non-users in the first year? Otherwise, -what did you quote, three hundred per cent higher?- after the first year of use, I guess you mean?” She nodded impatiently, as if she was being patronized. “And the rate in non-users?”

This time I did expect and answer… Or did I? Three times anything seems like an awful lot more. And six times…? She shrugged as if it were not that important. The relative increase was what mattered. “Well, given that I said that the exact incidence was hard to determine, the figure in many studies has been estimated at around 80 cases…” -I stretched it out for effect- “ 80 cases per 100,000 people. Give or take.” I paused for a moment again. “So, even six times that is –what?- 480 people per one hundred thousand. That’s…uhmm… 0.48 cases per hundred people per year?” It sounded about right… But I have trouble with decimals sometimes.

“Whether I’ve got the numbers exactly right is not the issue, really. The point is more that six times very little, is still very little.”

I could see her mulling it over in her head; doubt lingered on her face, but at least she’d put the hawks away for the day. “An interesting way of looking at it, I have to say… but certainly not intuitive at all, is it?”

I allowed myself a smile that I hoped was non patronizing. “Probability –statistics- is not very intuitive.” Her face stayed neutral. “If I told you I had a way of increasing your chances of winning the lottery by 100% would you be interested?” She nodded, as I knew she would. “It’s simple, really…” She rolled her eyes –no eagles there anymore. “Just buy two tickets.”

She sat back, but I couldn’t tell from her expression whether or not I’d convinced her –or even held her interest. “So tell me, doctor, if I were your sister, would you suggest I go on hormone replacement?”

I sighed; she’d asked for honesty, not medical rhetoric. I locked eyes with her. “If you were my sister?” She nodded –earnestly, I think. “No.”

She seemed surprised after my attempt at explaining probability and risk. “Why not?”

“You’d argue with me every time we met…”

Breast and Ovarian Cancer Screening

I am sometimes troubled by the concept of risk. I mean how can we possibly decide whether or not a risk is acceptable? No matter the statistics, if the issue under consideration doesn’t happen, then the risk assumed was acceptable. So far, so good. But of course the converse is also true: no matter how low the risk, if it does occur, well…

Ours is a culture of prediction. Statistics. Guessing. I rationalize buying a lottery ticket by convincing myself that if I don’t buy it, I won’t win -no matter how low the odds, no matter how unreasonable it would be to assume that I would be the one in –what?- ten million who wins the jackpot. Or anything, for that matter…  And no matter that without a year of such profligate spending, I could treat myself to a sumptuous dinner at a good restaurant.

Of course, we all live in hope, and if the lottery ticket funds some worthwhile government project, then it is an almost enjoyable form of indirect taxation. Assimilable because it is freely chosen. Optional.

It is a different proposition entirely if the risk is one to which we do not wish to subscribe but have no choice: genetic defects in a developing pregnancy, cancers, diseases, to name but a few. It is likely to our advantage to interrogate these, if possible. Of course, the question then becomes who should undergo the screening. Only those at the highest risk –those with a family member with the condition, say- or everybody? Just in case.

Screening always seems to be bathed in a soft, warm glow. If you can test, then why not? Just pop in to your local lab and get that PSA; find out if your prostate is betraying you. Demand yearly mammograms as soon as you feel concerned. As soon as a friend or even a friend-once-removed has a cancer scare. And at any age, because you never know…

If only screening was that good; if only all negative tests were reliable –and, for that matter, didn’t have to be repeated at intervals to keep pace with the ravages of Time wreaking its not so subtle havoc on our aging bodies.

Screening for specific inherited genetic mutations for breast and ovarian cancers are the relatively new species of Wunderkind: BRCA1 and BRCA2. These are tumour suppressor genes broadly speaking; we all have them, and they are located on chromosomes 17 (BRCA1) and 13 (BRCA2). But if they contain defects -mutations- they may no longer function efficiently and so be unable to winnow out mistakes such as tumours from proliferating. The mutations are inherited in an autosomal dominant manner and women with these particular mutated genes have a lifetime breast cancer risk of 50-85%. .

So why not screen all women for these genes? Indeed, a recent study published in the Proceedings of the National Academy of Sciences (USA) suggested just that: http://www.pnas.org/content/111/39/14205.abstract

On first reading, it sounds like a reasonable approach. But I’m not so sure. First of all let’s put the whole issue into context. Less than 10% of breast cancers (and <15% of ovarian cancers) seem to be associated with BRCA1 or BRCA2 mutations. And, although even less common, there are hereditary breast cancers associated with other genes, so there might be a false sense of security from testing only the BRCAs.

And then there’s the uncomfortable fact that there have been over a thousand different mutations in BRCA1 and 2 discovered so far. You’d have to know which one to look for. Of course, some populations have more prevalent mutations –so called Founder effects– which might simplify the search. Two per cent of Ashkenazi Jews, for example, carry specific mutations of BRCA1 or BRCA2. And there are other populations carrying unusual founder mutations that might facilitate searches in them as well: people from the Netherlands, Quebec, Iceland, to name a few. Or in still other groups -some families, for example- if the particular mutation resulting in their tumours has been identified, then the process is obviously easier.

The most successful screening is in people with identifiable risks, however. With breast cancer, such things as family history -especially a young age of developing the breast or ovarian cancers (the younger, the more chance there is a risk that can be  inherited), or a family history of so-called triple negative breast cancers –progesterone, estrogen and HER2 receptor negative. Males with breast cancer (yes it happens) are another, albeit infrequent clue to increased risk.

But screening everybody? Let’s get back to risk assimilability. Just what risk is acceptable? Less than 50%? Less than 25%? No risk at all..? Sometimes the answer is easy: a 50-85% lifetime risk of breast cancer if specific BRCA1 or 2 mutations are present is likely not tolerable. But what about the odds if only 2% of the population had that risk, as is the case for BRCA1 and 2 mutations in the Ashkenazim? Or if the chances of those mutations are even lower: 1/800-1/1000 as it is in the general population?

And what if you are not a member of a high risk population, or if there are no cases of breast or ovarian cancer in the family? Should you still be screened? And if so, with what? Remember there are many different mutations possible on the BRCAs -not all of which may result in an increased cancer risk. And there are other genes than BRCA that may play a similar role sometimes. So if you are just concerned that you might be at some risk, or worse, merely curious… Well, its best to remember that we are all exposed to dangers each day that we don’t even think about -and there’s no avoiding them: everything from tripping and falling down the stairs, to slipping on some ice; from having a heart attack, to getting hit by a car crossing the street to shop. We have to put things in perspective: life is a risk, and we are fragile creatures. Remember Shakespeare’s Hotspur in Henry IV:

‘Tis dangerous to take a
cold, to sleep, to drink; but I tell you, my lord fool, out of
this nettle, danger, we pluck this flower, safety.

So, if there is reason to believe there is a risk on the horizon, then it’s best to mitigate it. But don’t go looking for it in places it doesn’t exist.

 

 

 

Understanding Risk

There are more things in heaven and earth, Horatio, than are dreamt of in your philosophy. This quote from Hamlet has always stuck in my memory; it reminds me to be humble, especially in the face of the unknown. Uncertainty has always been anathema to most of us. We need explanations and we crave stability; anything falling short of those expectations leaves us feeling anxious. Suspicious. It’s why we have experts, after all.

The recent verdict of an Italian court that a group of scientists failed to adequately warn the citizens of L’Aquila of a deadly earthquake in 2009 is perhaps a case in point. That guilt could be assigned because the risk, although quantifiable, could not be accurately assimilated, means to me that it was meant as an indictment of Science and its methods. A vilification, really. That statistics are misunderstood is probably the explanation.

I don’t pretend to understand them myself, so I can see why things seem so mysterious. Saying there’s a 99% chance that something won’t happen this year, or this time, or in June, for example, means to most of us that it won’t happen. We’re willing to give weather forecasters some leeway, perhaps, but not experts that are supposed to help us avoid tragedy. After all, it’s their field; they’re supposed to know something about it…

Medicine is not exempt from this expectation either. Patients ask me what possible complications might happen with or after a particular operation -a simple question. I should know the answer. But the answer really depends on how it’s understood, doesn’t it? For example, if I state that there is a 1% chance, say, of needing a blood transfusion after a Caesarian section, that might be heard as “Really unlikely! You won’t need one unless things go very wrong.” But it could equally be heard as “Caesarian sections should not be undertaken lightly and things can go wrong.” Both are correct, and yet we hear what we need to hear. What we want to hear.

How unlikely should a risk be before it is not mentioned? Or should every risk be mentioned? Is it really helpful to tell a woman in labour with a baby in distress that there is a risk she could die during a Caesarian section, but that the baby could die if the surgery is not performed? Or that there is a -what?- small chance that she could end up with permanent paralysis if she has an epidural inserted to ease the pain of her labour? I agree that discussion of risks is important, of course, but I’m just wondering at what level it might become counter productive. Think of a map of a shoreline of a country. On a small scale it serves the purpose of indicating where the country lies in relation to its neighbours. A larger scale identifies harbours or perhaps small outcrops of land. At some stage as we increase the scale, however, it becomes unusable: boulders at the foot of trees growing at the edge appear, small indentations worn away by waves emerge; what appeared to be a smooth shoreline now seems to be a random squiggle of smaller and smaller indentations. They’re all part of the shoreline, of course, but the inclusion of more and more details obscures the original intent of the map.

I sympathize with the Italian scientists. Detailed description of the risks of each mode of delivery of a baby, for example, inevitably leads to closed loops. If I describe the possibility of maternal perineal injury from a vaginal birth (incontinence, painful scarring, infection -the list is interminable, depending on how minor the trauma)- I am then forced to describe the possible complications of the alternative: Caesarian Section. And depending on the level of seriousness of complications that is demanded, I am forced to admit that they are both dangerous procedures with unpredictable consequences. Now what? Select from the possible risks and consequences using the very statistics that were probably the source of the confusion in the first place?

I am and have always been in favour of full disclosure of risks and consequences. The equation of hazards, as it were, needs to be solved. And yet what is it that the patient is really asking? What were the citizens of L’Aquila asking? Translation is required: explanation in context. In the case of the patient asking whether or not to have an elective Caesarian, the answer may well be an exploration of why they needed to ask in the first place. Are they afraid of labour? Of pain? Of severe and irreparable injury?Addressing those issues is likely to be a more fruitful first step on the journey, than taking the one of playing with figures.

And in terms of the earthquake tragedy? How to negotiate that equation? I don’t know; I suppose it all comes down to meaningful, understandable and contextual communication. Perspective -both that of the public and the science. Respect.