Bruce Hugman1
(1)
Uppsala Monitoring Centre, Uppsala, Sweden
Bruce Hugman
Email: brucehugman@hotmail.com
Introduction
The purpose of risk communication in clinical practice is to inform and protect; to support wise, balanced and rational decisions that match patients’ wishes and needs.1 Risk communication touches almost all aspects of our lives. The quality of the information on which risk communication is based is important, but much more influential for effectiveness is the quality of the communication through which the information is mediated and the way in which those who deliver it are perceived.
Such communication must be perfectly tailored to the reality of the audience and, in medicine, to the individual patient, delivered by a credible, trusted source. One critical variable in any audience profile is sex (commonly conflated with gender, the sociological concept). In this chapter we shall explore the radical impact for risk communication practice of having a female audience. We shall see how particular are the demands and challenges, how complex, how variable, and how very different from those of communications with men.
In this chapter we shall review the nature of the differences, the basics of risk communication theory and practice, and examine some of the powerful, influential variables that determine the nature of the risks women face and that must shape the risk communications they are offered.
Part 1: The Basics of Risk Communication
A Woman Is Not Just Like a Man
Given the profound biological, physiological, cerebral, philosophical and psychological differences between the sexes, it is a small miracle that so many of us manage to muddle along together as well as we do. On the other hand, many, in all parts of the world, do not muddle along at all, but live in a state of repressed or open conflict between the sexes. The much debated causes of these differences and conflicts are not issues we shall explore in this chapter; we must accept that they exist and that we must deal with them.
Somewhat over-simplified, the theoretical position, on which the contents of this and the next chapter are based is this: sex- and gender-stereotypes and gender-preferences have a profound influence on the way the world is organised, the opportunities people have, the risks they face, the choices they make and the roles they adopt. They facilitate, maybe determine, certain kinds of behaviour by men and women, at the same time as they inhibit or exclude other types of choices and behaviour. Men and women display an immense variety of character and behaviour, with enormous variation across and within cultures.
The influence of gender-stereotypes is strong everywhere, most obviously in the tendency of men to hold social and economic power and to manage and lead, and the tendency of women to run the domestic infrastructure, rear children and provide the support services that allow men and children to flourish. While some might challenge the truth of this view in relation to Western countries, where some slight shifts have taken place, I believe they are largely mistaken and that progress has been patchy and intermittent, at best. This view is shared by critical observers (Singh et al. 2001) as well as contemporary feminist thinkers such as Naomi Wolf who writes, for example: “‘Healthy’ and ‘diseased,’ as Susan Sontag points out…are often subjective judgments that society makes for its own purposes. Women have long been defined as sick as a means of subjecting them to social control” (Wolf 2002).
Men and women are different; they have different needs and preferences; their relationships and communications with their own and the opposite sex have quite different characteristics; their physiology and their relationship with their own bodies are dramatically divergent.
In a world largely structured and managed by men, women are, in some important respects, disadvantaged, even when they are not trying to compete with males or to imitate or follow male patterns of behaviour, where the problems are only too well known and documented (Economist 2014). Nowhere is this disadvantage clearer than in certain aspects of healthcare: nurses, with intrinsic compassionate commitment to patient welfare, are increasingly being de-skilled in systems hostile to communication and relationship, with managerial and accounting priorities taking precedence over professional discretion and comprehensive nursing care (Allnurses 2012); drug information and risk communication focus on the male preference for data and evidence and do not illuminate issues of concern to women such as the psychological, social, domestic, and spiritual aspects of safety and healthcare delivery, especially in pregnancy and post-natal care (Tabak and Ozmen 2008).
Women in many cultures prefer to be treated by a healthcare professional of their own sex, but this preference is rarely negotiated when it cannot be met, and its impact on welfare, trust and adherence rarely discussed. In many cultures, women value community, peer and family advice and support in health matters, but are often forced into isolating one-to-one relationships, frequently with male providers, when there is any affordable provision at all (Nicholls 1987; Chen 2010). Major aspects of women’s life experience, notably pregnancy, menopause, and body-image have been increasingly medicalised and pathologised in ways that men’s conditions have not; and menopause has remained, in some respects, a largely neglected area of research and development (Erhardt 2003).
Empathy, Sympathy, Altruism and Compassion
Empathy, that unique ability to grasp the reality of another person, to envisage accurately how they are thinking and feeling, to sense what it is like to be them, is at the very heart of all good relationships and communications. We can reach others only when they feel that we perceive and understand them in their own authentic terms. Without empathy, communications have only a superficial or transitory effect or miss the target completely.
The art of medicine embraces many qualities besides empathy: sympathy (the supportive expression of felt emotion about the lives of others); altruism (active concern for the welfare of others beyond self-interest); and compassion (a heartfelt concern for the comfort, welfare and inner peace of others). Some commentators, like Ralph Crawshaw, believe that these essential qualities are leaching from medical practice, that medical education ‘leaves too many young people bereft of compassionate imagination and altruistic ideals’ (Crawshaw 2002). There is probably no excess or deficit in essential compassion determined by sex, but its expression may be shaped by gender socialisation with women at greater liberty than men to be tender (Seppala 2013). Divergence in compassionate needs and delivery between the sexes may at times be problematic.
One Size Does Not Fit All
So much communication in healthcare, especially risk communication, has been a one size fits all transmission; so much healthcare delivery has been driven by the technical body as object framework (including biological essentialism; see, e.g. the debate at (javier44 2013)), mostly through male values and habits. Empathy for audiences, collective and individual, especially for women, and sensitive adaptation of messages and behaviour, have been patchy or absent.
Package inserts (PIs) and patient information leaflets (PILs), those core elements of official risk communication practice, fail by the most basic of audience segmentation criteria: they are often difficult and off-putting for even for highly educated, literate, motivated patients (even physicians); for the illiterate, the partially-sighted, speakers of minority languages, those for whom the printed word is not the primary mode of learning, they are entirely useless. There are no PIs or PILs that exclusively address women as consumers of medicines. One size hardly meets the needs of anyone (Nink 2006).
There has been some progress. Women (as professionals and patients) have, in some places, taken a leading role in challenging out-of-date and disadvantageous practice. In the West, we can see evidence of improvement in society at large (more female surgeons and engineers, for example, in some places, (Gomez 2012) and changing attitudes to fatherhood), but we are, in some respects and in many places, still in the dark ages. In China, it is current state policy, and the subject of a major, official, sexist campaign, to roll back such progress as women have made towards independence, to make them fear being single and ‘leftover’; to browbeat them into becoming wives and mothers (Lovell 2014; Fincher 2004). While the situation in most places is not as dark as this, women’s rights and preferences and needs are substantially neglected, as this chapter will show.
Risk
Some general discussion of the meaning of risk is necessary before we approach the substance of this and the next chapter on health risks and risk communication for women.
While ‘the chance or odds of something bad happening’ is the starting point, it is very far from being the whole story. Paul Slovic is a researcher who has illuminated many aspects of risk. Among his insights is the characterisation of three kinds of risk: risk as feelings, risk as analysis and risk as politics. He explains it thus (Slovic 2010; Slovic et al. 2003):
Risk in the modern world is confronted and dealt with in three fundamental ways. Risk as feelings refers to our fast, instinctive, and intuitive reactions to danger. Risk as analysis brings logic, reason, and scientific deliberation to bear on hazard management. When our ancient instincts and our modern scientific analyses clash, we become painfully aware of a third reality … risk as politics.
For the first, risk as feelings, which Slovic regards as the prime influence across all risk perception and assessment, there is strong experimental and real life evidence that even the most sophisticated, rational, statistical risk data is constructed and perceived with a greater or lesser degree of affect, that is covert, hidden or explicit feeling about the matter in hand.
Epstein, a polymath Professor of Family Medicine, observed:
There is no dearth of evidence in everyday life that people apprehend reality in two fundamentally different ways, one variously labeled intuitive, automatic, natural, non-verbal, narrative, and experiential, and the other analytical, deliberative, verbal, and rational. (Epstein and Peters 2009)
Slovic’s genius was to show the extent to which the latter (analysis) was often influenced, coloured or skewed by the former (automatic).
Nowhere are these issues more evident than in perceptions of risk and public reaction than in immunisation programmes, where risk analysis can drop out of the picture and be replaced entirely by risk as feelings and risk as politics. No amount of factual risk and safety data or information was initially going to change the minds of religious leaders in northern Nigeria about the acceptability of polio vaccination for their communities (Agbeyegbe 2007; Kaufmann and Feldbaum 2009); women in India were not assessing the risk of vaccination in relation to the health of their children or their communities when resisting interventions, but rather reacting to the fact that programme personnel were men from distant parts, strangers, and they wanted local, familiar women to vaccinate their children.
In the West, scares about MMR (Deer 2004) and pertussis vaccines (College of Physicians of Philadelphia 2014) have resisted almost all rational rebuttal, driven by powerful emotions and agendas with little sound science or fact (Gross 2012). Many individual and public health issues have been damaged or derailed by the failure of risk communication to take into account the potent non-scientific, social and psychological elements of human feeling, perception and reaction to risk, mothers’ fears for their children being a prime example (Hugman 2010).
Another important concept comes from Peter Sandman, a distinguished thinker in the field of crisis management (Sandman 1993). He represents risk thus:
Sandman points out that the degree of outrage is not necessarily proportionate to the scale of the hazard: people can get very upset about even low, remote risks while, on the other hand, paying little attention to major and immediate hazards (drink-driving, obesity and climate change being examples). The occurrence of a single, rare, serious or fatal ADR, for example, maybe amplified in the media, may cause far more outrage and upset than more pervasive disadvantage or failure in healthcare.
The risk of not providing patients with good safety information about their medications or medical procedures, is not just the possible occurrence of unexpected and inconvenient events, it is also the outrage, the emotional response, that individuals or populations will feel when things about which they were not fully informed go wrong. The risk is not just the patient’s possible experience of some temporary or chronic, even life-threatening disease or disability, but also the radical emotional and social implications – and a much higher risk of litigation (Renkema et al. 2014). Risk is not just a matter of scientific data and probabilities (Box 18.1).
Box 18.1: A Recent Non-medical Example: Outrage and Failure of Empathy
As I write this, I have just watched the Philippines Interior Minister, standing in the midst of the stinking débris of Tacloban City, respond to accusations that the Government’s relief effort, 5 days after super-typhoon Haiyan, was slow and inadequate. His answer? A recital of (utterly unconvincing) claims about the number of trucks delivering aid and collecting corpses, the distribution of body-bags, the volume of food and medical supplies on the road, all in a defensive tone of barely-restrained indignation (CNN 2013). Apart from failing to acknowledge and address the fact that no-one in the area had seen any trucks or food or body-collection, there was not a hint of his understanding the outrage, the despair and disillusionment, being felt by tens of thousands of people. The risk to the Government is not just of being seen to fail in providing rapid and effective aid, but of being judged as distant and uncaring, utterly out of touch with the feelings of the people and their need for reassurance, for understanding, for empathy with their dangerous and tragic plight. Regulatory authorities and pharmaceutical companies sometimes have a tendency to behave in comparably alienating ways too.
People in power, and it is often men, frequently behave like this at times of crisis and it does untold damage to the reputation of governments, regulatory authorities, hospital managements, public health programmes, companies, to anyone in the public eye.
Differential Perception of Risk
Covello’s work illuminates the extent to which our perception of risk is influenced by a multitude of issues, quite apart from the nature of the intrinsic hazard in question (Covello et al. 1989). Most people, for example, will take much greater risks in activities that are voluntary and self-determined, such as extreme sports, driving, and drinking, but will be less tolerant of much smaller risks when they are imposed on them, or are beyond their direct control, such as environmental hazards, food safety and medications. Unfamiliar threats, risks to children, hazards that might affect future generations, and other factors influence how we perceive risk, tolerate it and respond to harm in prospect or when it occurs. Patients will react to different risks differentially, not least based on the extent to which they trust the source of the risk information. The same information coming from different sources may be assessed in even conflicting ways, according to the recipient’s perception of the source (a media anecdote as opposed to a regulatory declaration, for example).
In order to make satisfactory decisions about most things in our lives, including medicines, we need to have information about benefit and risk presented in ways that make sense to us and that meet our needs and preferences. We also need information about alternative choices, for example, the risks of the disease or condition itself, the risks of no treatment, the benefits and risks of the treatment proposed by a health professional and the benefits and risks of other possible options. While the population probabilities may be well known, we still have to understand what the implications are for us individually (including genetic or other idiosyncrasies), and process them through our feelings, our preferences and our personal priorities. It is well known, for example, that patients with chronic or terminal diseases will accept greater risks for the potential benefit of short- or long-term relief; that mothers will be anxious about accepting any risk whatsoever to their fetus or children; that the more persuasive the benefits are the greater the risk that will be tolerated. Benefit-risk dilemmas of this kind are particularly associated with the use of anti-convulsants in pregnancy, contraceptives and HRT (see Chap. 19) and, for example, with prophylactic surgery in breast cancer.
As risk-perception is subject to great variability, so is benefit-perception. Effectiveness alone may not be adequate benefit in a patient’s eyes if, for example, the medication has side effects that interfere in some way with perceived quality of life. On the other hand, for some, the reduction of pain may be a desired benefit, irrespective, for example, of its impact on mobility or clarity of mind. Benefit must be defined by patients in their own terms, often well beyond anything in the imagination of bureaucrats or that might appear in the Summary of Product Characteristics (SPC), the patient information leaflet or other regulatory information.
Tailoring Risk Information
Risk information must be presented in ways that make sense to us, emotionally, conceptually and factually. It must match our abilities, literacy, knowledge (or absence) of science and statistics, and a host of other variables, many of them discussed later in this chapter.
Probability is a concept not well understood by most people: for example, many people won’t think that a natural disaster with a probability of occurrence of once in a 100 years could happen tomorrow. When an adverse reaction is described as ‘rare,’ many may not feel it is a threat to them personally, while ‘1 in 10,000’ (customarily regarded as ‘rare’ by the authorities) may seem risky to some people. If a tossed coin lands heads-up three times in a row, many will bet on heads for the next toss, believing or feeling that the odds have changed from the ultimately implacable 50/50 for any such series (the gambler’s illusion of a ‘winning streak’). Many people suffer from the self-deceiving optimistic bias that reassures them that that they are less at risk than others, that, ‘It won’t happen to me’ (a rationalisation employed by smokers, for example) (Klein).
So, if we say that an adverse reaction, or a negative outcome, or disease susceptibility has a probability of 1 in 10,000, or 0.0001 % or is ‘rare,’ we have first to ensure that our subject understands what we mean in absolute, balanced terms (‘This happens to one person in 10,000; 9,999 are not affected’). If the epidemiological population is large, then the numbers affected may also be large which may appear to make the risk larger. However small the risk, someone suffers the harm, so a small risk does not mean a ‘safe’ medicine or procedure which many people might instinctively seek. There is always a degree of uncertainty in risk statistics because unexpected events happen, and the past is not a certain guide to the future. This is very clear from post-marketing surveillance of drugs, when previously unknown adverse events, minor or serious, may take years to emerge. We can never assert more confidence than, ‘The best current evidence available.’
Individuals differ markedly in their perception of risk, their preferences for the form in which risk information is given, and the ways in which they process such information. Research shows us that the effectiveness of risk communication varies enormously and by no means in ways one might anticipate. All these issues emerge in Part 2 of this chapter in the discussion of healthcare issues which primarily affect women, whose preferences and needs are, not surprisingly, often very different from those of men and whose risks and risk communication needs are very different too.
Risk Information: The Basics
Here I will briefly chart a course from the mathematics and the technicalities to the endpoints of good communication and decision-making for physicians and patients. Among the primary sources for this material are Cochrane Collaboration open learning material and Gigerenzer, who both represent the toughest, clearest and most intelligent approach to risk communication (Collaboration; Gigerenzer et al. 2007; Gigerenzer 2002).
How Is Risk Expressed?
Cochrane summarises the meaning of the major terms in risk research and statistics for studies with dichotomous outcomes (when one group is compared with another):
· The risk describes the number of participants having the event in a group divided by the total number of participants
· The odds describe the number of participants having the event divided by the number of participants not having the event
· The risk ratio (relative risk) describes the risk of the event in the intervention group divided by the risk of the event in the control group
· The odds ratio describes the odds of the event in the intervention group divided by the odds of the event in the control group
· The risk difference describes the absolute change in risk that is attributable to the experimental intervention
· The number needed to treat (NNT) gives the number of people you would have to treat with the experimental intervention (compared with the control) to prevent one event.
Physicians need to understand these terms and be able to evaluate the strength of research conclusions on their understanding of the data and the calculations. They need to assess the significance of results in relation to sample size; control and placebo group matching; the relative usefulness of surrogate outcomes (hypertension, for example); actual endpoint outcomes such as morbidity, survival rates (and their interpretative hazards) and mortality; a difference in risk and odds ratios; confidence intervals, p-values, and much more. Patients do not need to understand the mathematics of all this, but they do need to understand some basic concepts and be informed about the extent to which figures are reliable.
The question for us is how to build a bridge from this bewildering and complex field to expressions of risk that are useful in clinical practice and for patient decision-making.
Gigerenzer advises us:
A major precondition for statistical literacy is transparent risk communication. We recommend using frequency statements instead of single-event probabilities, absolute risks instead of relative risks, mortality rates instead of survival rates, and natural frequencies instead of conditional probabilities.
Let’s look at some of these in more detail:
Frequency Statements Instead of Single-Event Probabilities
Probability is a guess or a hypothesis, or, at best, an estimate of the likelihood of an event occurring. The probability of a fair coin landing heads up when tossed is 1 in 2, or 1/2 (0.5) or 50 %. The probability of drawing any particular card from a full pack (without jokers) is 1 in 52, or 1/52 (0.19) or 1.9 %. In the technical jargon, an impossibility has a value of 0 (0 %), a certainty, of 1.0 (100 %). A probability is not a prediction for the next event: you may toss a coin a dozen times and get only one heads; you may pick a card a hundred times from a pack and never draw the target card. Calculations of the probabilities of tossing coins or picking cards are relatively simple because there is a finite and defined range of possibilities (2 and 52 respectively), but you would still need an enormous number of tosses or draws to have evidence to support the hypothesis because chance (unpredictability) also affects every event. The longer the series or the larger the data-set, the more accurately a probability can be calculated: that is one of the reasons sample size and time-scales in trials are so important.
If an American woman’s lifetime risk (probability) of breast cancer is in the region of 12.4 %, (a probability of 0.124) based on whole-population figures (Lifetime risk of breast cancer 2014; Institute 2012), then it is probably reasonably reliable. As a single event probability, however, what do those figures tell an individual woman about her risk? Not a lot! The frequency statement of a risk of about 1 in 8 is much clearer and more helpful.
A Cochrane review of the benefits of statins in low-risk patients was summarised in Medscape as follows (Hughes 2011):
…in the eight trials that reported on total mortality, none of the individual trials showed strong evidence of a reduction in total mortality, but when the data were pooled, a relative risk reduction of 17 % was observed with statin treatment. On combined fatal and nonfatal CHD events, nine trials reported on this end point, with four trials showing evidence of a reduction in this combined outcome, which was maintained in the pooled analysis, with a 28 % relative reduction. Seven trials reported on fatal and nonfatal stroke, and on pooled analysis, statin treatment was associated with a 22 % relative reduction.
That really doesn’t tell us very much of interest to patients. It’s all relative risk figures (see below for more on this) and needs frequency figures to make sense to most people.
The actual Cochrane review conclusions (Ebrahim et al. 2011) included this:
Of 1000 people treated with a statin for five years, 18 would avoid a major CVD event which compares well with other treatments used for preventing cardiovascular disease. Taking statins did not increase the risk of serious adverse effects such as cancer.
This tells us plainly how many people benefit (without serious harm), and it states that statins are comparable in benefit to other treatments. But it’s not the whole story.
Ebrahim commented (Ebrahim et al. 2011):
If you look at the hard end points of all deaths and coronary deaths, the effects are consistent with both benefit and with the play of chance. But importantly, the absolute benefits are really rather small--1000 people have to be treated for one year to prevent one death.
(If we present the opposite ratio: of 1,000 people treated over 1 year, 999 will not die (i.e. no effect) – then we are speaking true to the data, but are implying a different value judgement of the data in choosing that framing.)
Here, we also have the introduction of Number Needed to Treat (NNT) that is another useful and important clarification of odds, chances, probabilities (all three terms commonly found in everyday language).
Absolute Risks Instead of Relative Risks
In Chap. 19, we look in detail at the communications issues at the heart of the ‘pill scare’ in the UK and Western Europe in 1995. Of interest to us here is that the official announcement was that women taking third generation combined oral contraceptives (OCs) doubled their risk of venous thrombosis; that is to say, women taking third generation OCs were at twice the risk of those who were not taking them, a relative risk of 2. Alarming? Apparently. But the absolute figures tell a very different story: doubling meant an increase from 15 in 100,000 to 30 in 100,000 (the figures have since been refined, but the risks are comparable).
The Cochrane open learning material summarises the issues in a vivid, non-medical example:
Take the example of buying two lottery tickets instead of one. We could say you are doubling your chances of winning, or we could say your chances of winning have gone up by 1 in 400,000. Both versions give you incomplete information because neither tells us clearly what the chance of winning is in the first place. The statements are likely to be interpreted differently, because many people would think an increase of 1 in 400,000 sounds a lot less attractive than a doubling of the chance of winning.
This takes us to the thorny problem of framing, and the potentially manipulative use of data. The statins data, above, presented as 1 in 1,000 or 999 in 1,000 illustrates this: a proponent of statins may emphasise the one life saved, while a statins sceptic (or a financial manager) might emphasise the 999 people who take the pill with no benefit at all. The same alternative perspectives would apply equally if the 1 in 1,000 referred to the harm of a medicine, rather than a benefit: which way are we influencing or manipulating a patient with our choice of data presentation? (See Chap. 19, p. 585 for practical answers to this.)
Good, clear, simple figures can be hard to find. The FDA’s fact sheet on statins for consumers, for example, doesn’t give the relevant figures in any form (FDA 2013). And there are some fierce controversies raging among researchers too.
On the statins question, Baigent, responding to Ebrahim above, quoted in heartwire (Hughes 2011), says:
I object to the conclusions they have drawn from their review. They say there is not good evidence of benefit, but their own data show significant reductions in deaths and cardiac events.
What is meant by ‘significant’ is a big issue and depends on whether you are a statistician, a physician or a patient: what is statistically significant (‘the mathematical determination of an event, symptom, behavior, etc. not occurring merely by chance’ (DeMaria 2011)), may or may not be clinically significant or seem important to a patient.
Other matters of individual judgement include: what you regard as a proportionate treatment effort to gain any particular benefit (a thousand patient years to prevent one death, for example); and what you regard as an acceptable risk of harm (1 in 10,000, for example). Such judgements take on a very different nature when they are made by officials drawing up public health policy. Their judgements, of course, may be strongly influenced by cost-benefit analysis which may or may not be relevant to a patient or physician deciding on individual therapy or indeed to good health.
Anyone reading popular, easily accessible material on the internet (like www.curezone.com) will find their minds being frazzled by the arguments and paradoxes raging in professional circles in relation to lipids, saturated fat, statins and much more. (In this and the next chapter I try to offer some tentative guidance about how patients can negotiate this hazardous maze.)
Understanding Terms Used to Define Frequency
‘Common,’ ‘less common,’ ‘rare,’ and all the rest, mean very different things to different people. If we are using such terms, we have to be very certain how the terms are being used in our source information, clear what we mean when we use them, and very sensitive to what they mean to patients.
The Council for International Organisations of Medical Sciences (CIOMS 1999) has suggested definitions for the quantification of risk (e.g. for adverse events) as follows:
|
Very rare |
<0.01 % |
<1 in 10,000 |
|
Rare |
0.01–0.1 % |
1–10 in 10,000 |
|
Uncommon |
0.1–1 % |
1–10 in 1,000 |
|
Common |
1–10 % |
1–10 in 100 |
A patient may not feel that ‘10 in 1,000’ is ‘uncommon’ when she thinks of her city of half a million; a patient who is told that a side effect is ‘rare’ might ask, ‘So how many people in [say] Accra?’ Well, it’s about 2.5 million. ‘So, that’s…’ she says with a bit of quick mental arithmetic, ‘that’s 250 for the whole city at the low end, 2,500 at the high end. That’s not rare.’
This may seem absurd, but it’s not a foolish inference if it is not made absolutely clear that the figure applies only to the relatively few people like her taking the drug. And if we were looking at national figures for the number of prescriptions for (say) OCs, statins or HRT, the ‘rare’ numbers would look large too, whatever their statistical value.
The point is that these statistical thresholds are not based on popular consensus or usage, nor, certainly, on the unique mental set of the patient in front of us. If our perception of ‘uncommon’ does not match that of a patient, we risk offending her and losing credibility. The use of the word ‘uncommon’ from the doctor’s routine vocabulary prejudices the discussion and may appear manipulative. The figures should come first, followed by an exploration of the patient’s assessment of them, which may be very different from the doctor’s.
Another problem is that the denominators are often different, so comparing risks and benefits might require some mental agility if they fall into different categories: how can a patient easily compare a chance of benefit of 1 in 100 for one medicine, with a risk of harm of 1 in 1,000 for another (or with the figures reversed?) Some may feel that a bigger denominator means a bigger risk. In this (very simple) case, it would probably be helpful to equalise the denominators, expressing benefit as 10 in 1,000, harm as 1 in 1,000 (or vice versa). Reducing the higher denominator wouldn’t work, because the result would be one tenth of a person in 100 (nobody can make sense of ‘one tenth of a person’, or anything less than one whole person).
Reporting how often side effects may occur is often dealt with very unsatisfactorily: this from the Mayo Clinic (Clinic):
Very rarely, statins can cause life-threatening muscle damage called rhabdomyolysis.
The most common statin side effect is muscle pain
How rare is ‘very rare?’ How common is ‘common’ (1 in 2; 1 in 10; 1 in 100)? And if, as the Mayo guidance suggests, women are more at risk of statin side effects than men, we need to know how much more at risk and the actual risk figures on which that relative risk assessment is based. (Risks and benefits may be different for men and women (Sigurdsson 2013; Rosenberg 2012).)
WebMD, in its article headed, Statin Risks Outweighed by Statin Benefits, see also (Therapeutics Initiative (2014); Nissen 2012; Rutishauser 2011; Tonelli et al. 2011), mentions:
A more common risk seen with statins is muscle tenderness, which occurs in some 5 % of patients. This can be extremely severe.
We do at least have a comprehensible figure here (‘more common’ seems to mean 1 in 20), but it does appear disingenuous in relation to the risk of rhabdomyolosis (1 in 10,000 according to about.com (Harper and Jacobson 2007), to say nothing of other serious stuff like diabetes, liver myopathy and peripheral neuropathy which aren’t mentioned at all).
So what will you tell your healthy patients who have no previous history of CHD for whom you are proposing primary prevention?
You could say:
There was a 12 % proportional reduction in all-cause mortality per mmol/l in LDL cholesterol. This reflected a 19 % reduction in coronary mortality’ (Armitage 2007)
Hmm. Margaret McCartney points out (McCartney 2012) this is relative risk (and pretty much impenetrable). A bit more work is needed:
Adding up all the ‘major vascular events’ there is a difference between the control group and the statin group of 17.7 % versus 14.1 % – a difference in absolute risk of 3.7 %. The difference between deaths related to cardiovascular disease is smaller: statins can reduce this, the study found, from 4.4 % to 3.4 %. That’s a difference of 1 %… It’s not good being told that your risk of death can fall by 12 % if you take the statin (the relative risk) when the figure is really on 1 % (absolute risk).
Mortality Rates Instead of Survival Rates
Wegwarth et al. (2012) vividly expose this issue in their study of physicians’ responses to evidence about cancer screening.
Primary care physicians were more enthusiastic about the screening test supported by irrelevant evidence (5-year survival increased from 68 % to 99 %) than about the test supported by relevant evidence (cancer mortality reduced from 2 to 1.6 in 1000 persons). When presented with irrelevant evidence, 69 % of physicians recommended the test, compared with 23 % when presented with relevant evidence (P < 0.001). When asked general knowledge questions about screening statistics, many physicians did not distinguish between irrelevant and relevant screening evidence; 76 % versus 81 %, respectively, stated that each of these statistics proves that screening saves lives (P = 0.39). About one half (47 %) of the physicians incorrectly said that finding more cases of cancer in screened as opposed to unscreened populations “proves that screening saves lives.”
Their conclusion:
Most primary care physicians mistakenly interpreted improved survival and increased detection with screening as evidence that screening saves lives. Few correctly recognized that only reduced mortality in a randomized trial constitutes evidence of the benefit of screening.
Some patients may be persuaded by the benefits of longer survival, even if, as is sometimes the case, it is mere weeks, but survival statistics are not the whole picture on which patients can make rational choices. For a more detailed treatment of the issue, see Gigerenzer and Wegwarth (2013).
Lead-time bias is an important element in survival rates. Screening may lead to earlier diagnosis, for example, but does a screened patient live longer than one who was not screened and not diagnosed as a result? The survival rate for early-diagnosed screened patients may be 5 years, but do the non-screened patients, whose diagnosis was known about for a shorter time, die at the same time anyway? Survival rates, or symptom-free survival periods can also be very misleading in relation to short-term surrogate endpoints (like hypertension or hyperlipidemia) as opposed to all-cause mortality over a long period.
Gigerenzer points out (WHO 2014) “… 5-year survival rates and mortality rates are uncorrelated (r = 0.0) across the 20 most common solid tumours”.
And Gigerenzer also points out that there may be a further bias in cancer survival rates:
…overdiagnosis, the detection of non-progressive cancers – abnormalities that meet the pathological definition of cancer but will never progress to cause symptoms in the patient’s lifetime. Non-progressive cancers inflate survival rates.
This serious error might affect interpretation of data relating to any medicine or procedure.
Causality
There is much popular confusion about causality. Events that have a close temporal association are often mistaken as having a causal link. It’s too easy to leap to the conclusion that the vaccination or the drug must be the cause of an adverse event that follows shortly afterwards, but it may or may not be so. Coincidence is an experience to which we are all apt to attach too much significance, not least because we don’t pay attention to the thousands of other occasions when there’s no surprising association between similar kinds of events at all. At its simplest, it’s obvious that out of a million children, there’s a good chance that one or more of them will have a medical episode of some kind or other on the day of vaccination, whether they are vaccinated or not.
We may attach undue significance to chance events. Any random scattering of events is likely to show occasional clusters, which may or may not point to common causal factors (cancer and overhead power-lines, for example). Inference of causality may also be driven by prejudice: ‘I don’t trust doctors so they must be to blame for this;’ or: ‘We’ve spent a lot of money developing this drug and the results look good.’
Establishing causality in assessment of adverse drug reactions based on an individual patient in clinical practice or on a single case report in pharmacovigilance (i.e. proving the adverse event was undoubtedly caused by the medicine) is, in most situations, difficult, if not impossible. Risk factors, other variables and confounders often prohibit confident conclusions and the result will usually be a qualified probability. In assessing a patient case report, de-challenge and re-challenge information are the most reliable indicators of causality. However, in the real life clinical situation, a patient will have to believe that there is an element of doubt about the reaction that has to be tested before they may agree to a rechallenge (defined as readministration of the medicine at the same dose). The Bradford Hill criteria are among the most trusted algorithms for assessing causality (Lucas and McMichael 2005). Even these, however, may result in an outcome where causality is far from certain and far from the certainty that patients and the public hanker for. Explanation of that to patients, related to the tentative and evolving nature of science, is, in itself, a complex challenge. Guidance on causality assessment can be found in many places (e.g. MHRA 2013a).
Large ADR databases, such as the WHO’s VigiBase (Uppsala Monitoring Centre 2013), data-mining and rigorous signal detection may provide additional information about the likely strength of an association, but there is no real substitute for clinical observation and acumen and the collection of quality data. Clinical trials and pharmacovigilance can provide historic safety information of reasonable reliability and those sources are the basis of most of our risk information. Things do change over time, however, and old data are risky data.
Placebo (and nocebo) effect raises the question of causes being inaccurately attributed: did this patient get better because of the treatment or because they were cared for in ways that made them feel better? Did they fail to improve, or get worse, because of their suspicion, mistrust or lack of confidence in relation to the treatment or the provider?
So, patients may assert confidence about the causes of their troubles, while a doctor may be uncertain or even sceptical. Negotiating such differences requires some patience and skill in the performance of risk communication. This is particularly pertinent in highly-charged situations like vaccine and other medicines scares (see Chap. 19).
Trade-Offs (How We Assess Risks and Benefits)
As everyone perceives risk differently, so everyone calculates trade-offs differently. If I am in great pain, and if my personal tolerance of pain is low, I may want to trade-off the risk of harm of even potentially serious side effects from a medication that will give me the benefit of immediate relief, whatever my doctor feels about the decision. If, on the other hand, I am healthy and have risk factors but no disease symptoms (hypertension, hyperlipidemia, smoking or being overweight, for example), my trade-off is between more or less distant or hypothetical risks, current inconvenience or discomfort, and possible benefits at some time in the future.
The professional risk of preventive treatment of healthy people is, as Margaret McCartney characterises it, of turning a person into a dependent patient (a truly radical transformation) or, in the case of a woman and hormone replacement therapy (HRT) of exposing her to the risks of the medicine and its consequences for limited or uncertain benefit. The all-mortality risks of smoking are clearly much more vivid than those, for example, of borderline hypertension, but there are still difficult decisions and trade-offs to be made.
These issues must be part of the risk communication initiated by doctors, on the understanding that ordinary people (pre-patients) or patients will not necessarily think or act in the ways that professionals would. The trade-offs that patients make will not be decided by absolute risk or frequencies alone.
Regulatory authorities and patient information customarily use terms with specific meaning, like ‘serious,’ (leading to death, hospitalisation, and so on (MHRA 2013b)) to categorise adverse effects. For patients, on the other hand, the subjective meaning of some technically ‘non-serious’ events may be quite different. Someone whose tropical holiday is ruined by the (officially non-serious) effects of the antimalarial Mefloquine they were prescribed may well regard such effects as ‘serious’ and a poor trade-off, though it was never a threat to their essential health. The dancer who finds her muscles deteriorating as she takes her statins may be in a terrible bind between anxiety about the risk of vascular events and distress about the loss of physical capacity. What trade-offs should she be making and should her doctor be helping her to make in relation to her quality of life? (McCartney 2012, p. 45). (There is also the technical distinction between ‘serious’ and ‘severe’ (see MHRA reference above) that may not be clear to patients.)
‘How will this affect my quality of life?’ is an essential risk information question patients will want to have answered. Doctors may not always be the best people to answer that question; other patients, in forums and internet communities, may be better qualified. Many patients do trust their doctors (Gallup 2011) (though there are mixed opinions on this (Riner 2014)) but even the best professional cannot provide the richness of understanding of any disease or medication that experienced patients can on the best forums such as HealthTalkOnline and PatientsLikeMe (Healthtalkonline; patientslikeme)
This is an elementary account of some of the main aspects of risk communication theory and practice. There are immense printed and electronic resources for those who want to learn more. John Paling’s Helping Patients Understand Risk is among the most helpful and accessible and a good starting point (Paling 2006).
Part 2: Gender Specific Issues in Risk Communication for Women
Introduction
Around the world, the interplay of biology and culture, or nature and nurture, brings about differences in men’s and women’s health, which have been largely overlooked in clinical studies that [mainly] use only men as subjects. Although women live longer than men almost everywhere, they suffer from more illnesses and disabilities throughout their lives. Women’s health disadvantages often arise from gender inequalities, which are pervasive particularly among the poor in the developing world. (WHO 2000)
The purpose of this section is to examine a range of diseases, medicines, procedures, issues and questions that concern a wide spectrum of women uniquely or predominantly, especially the macro-context in which health is defined and treated; to develop some general and specific insights into the multiple risk factors and risk communication challenges for women. These, in turn, have radical implications for how female patients are perceived, how their diagnoses are made and how their risk decisions are negotiated. This material provides the broad context for a mature interpretation of everything in this book. Suggestions and methods for risk communication in practice, will be discussed in the following chapter.
Disease, diagnosis, prescribing, taking medicines and communication of all kinds take place in a socio-cultural-political context (see Chap. 16) and in relation to a host of individual variables. Among these multiple factors, many of them have particular impact on women as citizens and on women as patients. Healthcare as a whole must take account of these factors if women are to get equal and sufficient service. Health workers must take account of these factors when they manage relationships, prescribe medicines and communicate risk. While the material in this section applies to all aspects of healthcare, it is primarily focused on medicines and risk communication for women.
But for biological sex and its dependent variables such as longevity, the differences between the women of the world vary from minor to radical across many dimensions, just as they do between all men and between men and women. In taking ‘women’ as a category for discussion, there is always a risk of generalisation and oversimplification. In this part of the chapter we identify common themes and concerns where they are plausible (for example, women everywhere experience menopause and are at risk of physical and sexual violence). However, much of the research on which we are drawing and many of the observations we shall make are limited to specific populations with unique characteristics and risk factors (for example, Latinas in the US or elderly women in Japan). Some of these may raise issues that are useful for thinking and action in other contexts, but their utility can rarely be more than speculative without supporting research in different settings. We cannot expect to have specific, evidence-based material for every aspect of every patient’s character and conditions, but we can draw on the widest range of accumulated knowledge, in this case about women and healthcare across the world, that has any kind of bearing on the patient in front of us. Good practice requires understanding and wisdom far beyond clinical diagnostic skills, evidence-based therapy and grasp of basic demographic characteristics, as we shall see.
Women exist in every imaginable socio-economic and cultural milieu, from the most affluent, secure and independent to the most deprived, oppressive and dangerous; in circumstances where the very best of modern medical care is available, to those who have no access to resources beyond the wisdom of grandmothers and the local medicine man or fetish-priest. From this immense variety, we must try to find those areas where women have common interests and needs and those where conditions are unique.
The priorities and methods for risk communication differ immensely, therefore, across the world’s population of women and within individual countries and regions. In Chap. 19, we shall look at principles and methods in more detail, but these can never be more than suggestive signposts on a very complex journey with many possible routes for each individual. In some countries, for example, the greatest risks to women’s health are not adverse effects from medicines, but poverty or male indifference or hostility to women’s symptoms and needs (Anonymous 2014; Watch 2009), or women’s fears of stigma or rejection for being ill and their prioritising family and domestic responsibilities far above their own health.
Furthermore, access to good quality medical care is problematic for hundreds of millions of women even when they are motivated and free to seek it (see Chap. 17). In the West, and elsewhere, the male norm influences institutional and individual scientific and practice behaviour, biases diagnoses and treatments, and leads to neglect of research into the diseases and medicines that matter most to women. These are risks arising from fundamental socio-political and economic structures, values and practices that must be factored into the understanding of patients and their diseases and into their diagnoses, treatments and communications.
The Urgency and Complexity of Good Communication
Sound, trusting relationships with health professionals and tailored, appropriate information are essential for effective healthcare, for safety, for good outcomes and for patient satisfaction.
The evidence for the positive impact of good physician communication skills is now extensive. In their review of the literature, Wong and Lee conclude:
Good doctor patient communication is important and has multiple impacts on various aspects of health outcomes. The impacts included better health outcomes, higher compliance to therapeutic regimens in patients, higher patient and clinician satisfaction and a decrease in malpractice risk. (Wong and Lee 2006)
To address the challenges of risk communication in women’s health we have to take account of at least these issues:
· The immense variation between individuals within even apparently homogeneous groups
· The political, economic, religious and cultural context, particularly as it affects the dignity and autonomy of women
· The dominant norms, values and practices within healthcare
Scoping the Territory
In order to come to some useful conclusions about effective risk communication for women, we must review the relevant risk factors that affect women’s lives and the social and cultural contexts in which they occur. These will all affect the nature of the risks faced, how they are perceived and, if they can be managed at all, how that is to be done. This overview includes many of the topics that are dealt with individually and in greater detail elsewhere in this book.
This chapter makes no attempt at comprehensive coverage of all major or lesser causes of risk nor of morbidity and mortality, but rather draws some tentative conclusions from a representative, but highly selective list.
Based on Buvinic et al. (1999), Table 18.1 presents one approach, directing attention to some of the broad categories for consideration that mark the priority territory of concerns for women.
Table 18.1
An approach to differential grouping of women’s conditions and diseases
|
Reproductive concerns |
|
Cancer |
|
Diseases associated with women’s greater longevity |
|
Diseases arising from the interaction of sex and gender with higher prevalence in women |
|
Gender-based injuries |
|
Diseases common to both sexes where characteristics and needs differ for women |
|
Risks for women that may be inadequately diagnosed, understood, characterised and communicated |
Within all the groups in Table 18.1, the clinical picture of diseases and conditions as well as the socio-cultural-psychological dimensions will be either unique to women, or distinctly different from those presented by men. They will be different from those understood by men whose perceptions or practice are ill-informed, gender-biased in one way or another, or based on evidence from studies or trials that do not include women or women of the group being treated. Gender equity requires that conditions unique to women and drugs with unique effects on women should be researched with equal thoroughness to those conditions and drugs common to men and to men and women; that in research into common conditions and drugs, women should be equally represented with sex differences always disaggregated.
The safety profile of drugs tested or used in male populations may be quite different when prescribed for women. Symptom presentation and treatment (in myocardial infarction, for example) may be quite different in men and women (Bankhead 2013). Then there are the multiple, non-clinical variables that will differ across age, ethnicity, socio-economic and geopolitical groups, and, of course between the sexes.
The WHO’s radical Gender Policy (WHO 2000) proposed the framework shown in Table 18.2 for understanding gender profiles in health.
Table 18.2
WHO framework (WHO Gender Policy 2000)
|
Biological differences |
|
(a) Anatomical/physiological |
|
(b) Anatomical, physiological and genetic susceptibilities |
|
(c) Anatomical, physiological and genetic resistances/immunities |
|
Social differences |
|
(a) Roles and responsibilities |
|
(b) Access and control |
|
(c) Cultural influences and expectations |
|
(d) Subjective identity |
|
Health situations, conditions and/or problems |
|
(a) Sex specific |
|
(b) Higher prevalence in one or other sex |
|
(c) Different characteristics for men and women |
|
(d) Generate different response by individuals/family/institutions depending on whether the person is male or female |
Themes from this list will recur in the following discussion.
Concomitant Risk Factors
Apart from important biological, genetic, metabolic and other intrinsic risk factors, which do not fall within the core ambit of this chapter, there are many other influential issues for consideration that the WHO framework begins to elucidate. All of these are relevant to men and women but have quite distinct weight and meaning in application to each of the sexes. Table 18.3 shows a much modified version of Warren et al’s Predictors of Adherence to long-term statins (Warren James et al. 2013) which, though prepared for quite different purposes, provides a useful starting point and some synthesis of the issues raised so far. Each of the factors in the list may have a unique and specific effect on a patient’s health and welfare, presentation of symptoms, perception of risk and risk communication needs.
Table 18.3
Contextual risk factors in women’s health
|
Age |
|
Gender |
|
Ethnicity |
|
Religion |
|
Highest educational qualification |
|
Language spoken at home (i.e. the official national language or a minority language) |
|
Partnership status |
|
Sexual orientation |
|
Domestic and caretaking responsibilities |
|
Remoteness index (how far from city, town and health facilities) |
|
Insurance status |
|
Employment status |
|
Annual income |
|
Alcohol consumption |
|
Current smoking status |
|
Level of physical activity |
|
Self-rated health |
|
Functional limitation, disability |
|
Psychological distress |
|
Co-morbidity |
Based on Warren James et al. (2013)
Warren et al. alert us to some important influences on the risk factors associated with adherence to statins that may have significance well beyond the borders of their Australian study. For example, and a high alert for risk communication, the strongest predictor of non-adherence in this study was when the language spoken at home was not English.
We can extrapolate major questions relevant to all patients even from this study:
· Risks may not be well managed even among populations we may assume to be mature and well-organised (employed or with higher education, for example)
· Lifestyle choices (smoking and drinking, for example) may be indicative of predispositions or tendencies that manifest themselves in other aspects of choice and behaviour
· Psychological distress may strongly influence perception, choice and behaviour
· Self-image with regard to health status (excellent to poor) may influence responses to symptoms, to risk, to diagnosis and to medication regimens
· The primary language (as spoken at home) may have a critical impact on adherence, safe medication use, and on the care of self and others
The interim message is this: the variables affecting patients’ lives are multiple and interacting. If we now add sex and gender issues to our considerations, we must elaborate the risk factor list presented earlier as two distinct versions that would apply quite separately to women’s and men’s preferences and needs.
Gender Bias
Illustrative Evidence About Gender-Bias
As an illustration of the issues as they affect women, here’s an illuminating summary of the influence of gender-bias in the treatment of women for heart disease at the beginning of the century. It’s from the Beth Israel Deaconess Medical Centre (Ricciotti 2003).
Gender bias in medicine usually happens in one of two ways: (1) a doctor assumes that women’s and men’s health situations and risks are similar, when in fact they are not; or (2) a doctor assumes there are differences where there are actually similarities. Despite the medical profession’s best effort, gender bias may still play an insidious role in influencing physicians’ decision-making. The following studies exemplify cases of gender bias in the care of women with heart disease:
· In 1996, a national survey of physicians found that more than 65 % of respondents were unaware of gender differences in the symptoms, warning signs, and tests used to diagnose heart disease. Less than 40 % had received special training in the diagnosis of heart disease in female patients. Finally, a full 50 % of respondents did not know that heart disease is the number one health risk of women after menopause.
· A 1997 article published in the Archives of Internal Medicine found that of 677 heart attack survivors over the age of 65, women underwent fewer tests and were less likely to be prescribed aspirin in the prevention of another heart attack.
· A 1998 study published in the British Medical Journal found that of the almost 32,000 patients who received artificial pacemakers in 1992 and 1993, women were more likely to receive less sophisticated models. The authors believed that the patient’s gender influenced the physicians’ decision of which pacemaker to use.
· A 1999 study published in the New England Journal of Medicine found that a patient’s race and gender could significantly influence treatment recommendations. The study was conducted using actors as patients who reported the same symptoms and had the same lab test results. Yet black males and white females were 40 % less likely to be recommended for a potentially life-saving cardiac surgery than white males were. Black females were 60 % less likely to be recommended for the surgery.
· A 2000 article published in the New England Journal of Medicine reported that of 10,000 people who reported to a hospital emergency room, a small number had heart problems but were mistakenly sent home instead of being hospitalized. These people were more likely to be women under the age of 55, minorities, and people whose electrocardiogram (EKG) was normal.
· Another 2000 article published in the Journal of the American Medical Association suggested that hospitalized women with heart disease were less likely to have tests or procedures (e.g., catheter-based procedures) done while in the hospital.
· A 2000 study published in the Archives of Internal Medicine found that men were more likely to be prescribed cholesterol-reducing drugs than women were, despite a 1999 report published in the Journal of the American Medical Association stating that men and women benefit equally from the drugs.
It seems that things have not significantly changed in the decade since then. In a large study in Sweden, Sederholm Lawesson (2012) concluded:
In STEMI [ST elevated myocardial infarction], women had a higher risk of in-hospital mortality but the long-term risk of death was higher in men. More studies are needed in the primary percutaneous coronary intervention (pPCI) era that are designed to determine why women fare worse than men after STEMI during the first phase when they are in hospital.
In a project the previous year, this was their research objective:
In ST elevation myocardial infarction women received less evidence-based medicine and had worse outcome during the fibrinolytic era. With the shift to primary percutaneous coronary intervention (pPCI) as preferred reperfusion strategy, the authors aimed to investigate whether these gender differences had diminished.
They found:
In spite of an intense gender debate, focus on guideline adherence and the change in reperfusion strategy, the last decade gender differences in use of reperfusion therapy and evidence-based therapy at discharge did not decline during the study period, rather the opposite. Moreover, higher mortality in women persisted.
In a study published in GlobalHeart (Sharma and Gulati 2013), the authors found:
… that women who go to the hospital with chest pain or other urgent heart symptoms are less likely to receive blood thinners and less likely to undergo cardiac catheterization. Women with heart symptoms were also less likely to be given early aspirin, beta-blockers, or timely treatment to restore blood flow through blocked arteries… Women with CAD tend to develop the disease about 10 years later in life than men do, but the consequences are worse. Women under 50 who have a heart attack are twice as likely to die, and women over 65 are more likely than men to die in the first year after having a heart attack.
Although they raise some doubts that these differences can be attributed solely to treatment failures, they conclude:
… much work remains to be done to raise the visibility of heart disease in women, expand treatment, and prevent unnecessary deaths.
Risk and Domicile
The extent of gender-bias, and, therefore, the risks of unequal treatment, are likely to be determined, to a large extent, by the country of domicile. The World Economic Forum’s Global Gender Gap index (Forum 2013) gives us some indications of where women are likely to get a better deal and where they are disadvantaged across a wide range of variables. The index is based on an assessment of: Economic Participation and Opportunity; Educational Attainment; Health and Survival; Political Empowerment. The top 20 and bottom 10 countries in 2013 are shown in Table 18.4.
Table 18.4
The World Economic Forum’s global gender gap index (Forum 2013)
|
Countries are ranked according to how much of their gender gap they have closed, measured by a number of specific parameters for all countries |
|||
|
Rank |
Country |
Rank |
Country |
|
1 |
Iceland |
11 |
Belgium |
|
2 |
Finland |
12 |
Latvia |
|
3 |
Norway |
13 |
Netherlands |
|
4 |
Sweden |
14 |
Germany |
|
5 |
Philippines |
15 |
Cuba |
|
6 |
Ireland |
16 |
Lesotho |
|
7 |
New Zealand |
17 |
South Africa |
|
8 |
Denmark |
18 |
United Kingdom |
|
9 |
Switzerland |
19 |
Austria |
|
10 |
Nicaragua |
20 |
Canada |
|
The bottom 10: |
|||
|
Rank |
Country |
||
|
127 |
Saudi Arabia |
||
|
128 |
Mali |
||
|
129 |
Morocco |
||
|
130 |
Iran, Islamic Rep |
||
|
131 |
Côte d’Ivoire |
||
|
132 |
Mauritania |
||
|
133 |
Syria |
||
|
134 |
Chad |
||
|
135 |
Pakistan |
||
|
136 |
Yemen |
||
The Index Report’s conclusion gives a picture of exactly what the rankings represent:
The four highest ranked countries—Iceland, Finland, Norway and Sweden—have closed between 81 % and 87 % of their gender gaps, while the lowest ranked country—Yemen—has closed a little over half of its gender gap.
While readers may well have questions about some of the rankings and about the validity of the processes that gave rise to them, the Index does provide an important perspective on the position of women across the world. It alerts us to the relative advantages and disadvantages, and therefore to the wide spectrum of risks and opportunities for women depending on where they live. Health workers in Yemen have very different issues to manage and very different risk communication challenges from those in Iceland. A national perspective does not, of course, represent the multiple gaps and divisions within a single country which may, for some, be as wide as those between the first and last in the international list.
Professional Values, Attitudes and Behaviour
Beyond technical competence, the quality of care a woman receives, her health and her safety, will, to a large extent, depend on the values and behaviour of the institutions and individuals who care for her. This is a huge area of medical philosophy and sociology that we can only allude to here. In the material that follows, we shall see how the values and behaviour of medical professionals vary in decision-making style, in assumptions about women’s symptoms and in the establishment of trust.
Every institution and individual health worker must have awareness of hidden values (for example, on the spectrum of patriarchy to democracy, in relation to notions of professional expertise and patient naivety, in relation to differential perception of the sexes), and understand how they affect their every thought and decision. No-one can be aware of everything at any one time, but alertness to mystery, complexity and hidden operators and pitfalls is essential to intelligent practice (training has an important part to play in this). This is not to suggest that there is a single, virtuous position to achieve in all situations (because women’s preferences and needs differ so widely), but it is to assert that taken-for granted values and assumptions must be recognised and modified to the existential reality of each patient; if they are not, there has to be some understanding that the best quality of care cannot be delivered. Healthcare professionals themselves are one element in the matrix of risk that women face.
Women’s Preferences and Needs
Across all healthcare provision, the meeting or explicit negotiating of patients’ needs, expectations and preferences are likely to lead to greater satisfaction as well as better outcomes. They are likely to encourage patients to pay more attention to available information, including safety information, improve risk management, adherence and persistence. Dissatisfied, frustrated, unhappy or angry patients are very unlikely to enjoy optimal healthcare experiences and outcomes. The same negative prediction applies also to those patients who are neither explicitly satisfied nor dissatisfied with their healthcare but are, for one reason or another, disengaged or ill-informed about their disease and the benefits and harms of their medications.
The more we know about preferences and needs, the more we are likely to provide risk communications that have a positive effect, delivered in ways that sensitively take account of individual preferences and circumstances. It is impossible to predict what variables will be most influential in any given individual, but an understanding of the range and variability of individual preferences enables us to respond with greater sensitivity and accuracy. Generalisations do not answer the case, but a mental library of research and insight is an essential tool for good patient care.
First, a caveat about preference evidence provided by research. Montori et al remind us:
Access to patients’ preferences is complex. Individuals form their preferences when they have to make a decision, in a context replete with emotional and social influences. This context is often absent when volunteers, not facing a decision, report preferences. Hindsight bias, cognitive dissonance, and regret can reduce the validity of surveys of preferences in patients who are living with the consequences of a prior decision. (Montori et al. 2013)
The influence of local socio-ethnic-political-religious factors also affects preferences, so we must be cautious about generalizing from research in developed, predominantly Caucasian countries to the situation for women in developing countries living in utterly contrasting environments. (This caution also applies to the use of medicines tested in the West when they are provided for entirely different populations.)
We must be careful about believing we have unearthed the immutable truth when all we have done is to open the door to a complex and mutable landscape, especially when that landscape relates only to a specific culture or ethnic group in one country.
In an extensive review of the literature, published in 2004, Sampietro-Colom et al observed:
The literature on preferences in women’s health care is limited to a fairly homogeneous population (white women from the United States, United Kingdom, and Canada)… Women’s preferences are not necessarily uniform even when asked similar questions using similar tools. Little information on women’s preferences exists to inform policy-makers about women’s health care. (Sampietro-Colom et al. 2004)
Since then, there has been an accumulation of research across the world, but the primary populations studied remain predominantly the same. The following material illustrates some of the important insights that can be gained by asking women in many different places about their preferences and also casts light on a number of risk and risk communication issues across cultures, race, age and class.
Healthcare Provider Gender
Many women seem to have a preference for same-sex providers, female adolescents particularly, but that preference is also mediated by other factors, such as perception of the best available facility or doctor for the occasion (see Sacks, below). There is a stronger preference for female providers in breast screening, PAP smears, gynaecology, obstetrics and urology, but not exclusively so except in Muslim women (studied, for example, in Egypt (Zaghloul et al. 2005)). Female patients of the US Veterans’ Administration (VA) showed a preference for all-female facilities for comprehensive medical care, views shaped by their perception of gender discrimination elsewhere (Mattocks et al. 2011).
In a small study of middle-Class African American women, with the intersecting variables of sex, class and race, there was evidence of a preference for female providers irrespective of race, and though respondents did express an affinity with black providers in some circumstances, this did not constitute a race preference. The researchers conclude:
Although increasing racial diversity among the healthcare workforce is generally positive, the black middle-class women in this study suggest that alone will not ameliorate racial disparities in healthcare. The complexities of the healthcare encounter, including time pressure, clinical uncertainty and the patient’s desire for expertise regardless of race or gender, all impinge on respondents’ race preferences. Moreover, understanding the dual identities of minority women, i.e., black and female, highlight the importance of both race and gender preference for black women. Lastly, women noted that site-level factors may be conflated with the race of provider. That is, having a black provider does not necessarily lead to better care or protect women from discrimination or bias. As such, they do not necessarily prefer a black provider or rely solely on black providers to mitigate institutional-level bias. (Sacks 2013)
Here, amongst many interesting elements, we also see that the institutional context of healthcare, its values and behaviour, can have a marked effect on the confidence and trust of patients even when the provider-encounter seems satisfactory.
Henderson et al investigated the evidence from a 2002 evaluation of the National Centers of Excellence in Women’s Health (CoE) suggesting that women receive higher-quality primary health care, as indicated by receipt of recommended preventive care and patient satisfaction, when they receive their care in comprehensive women’s health centers (Henderson et al. 2004). Comparing CoE patients with others in the community, both receiving care primarily from female providers, they observed that there was a positive effect from institutional standards. Their conclusions are illuminating:
The findings confirm a positive CoE effect for many of the quality of care indicators that were observed in the original evaluation. Women seen in CoEs are more likely to receive physical breast examinations and mammograms (ages ≥50). In addition, positive CoE findings for counseling on domestic violence, sexually transmitted diseases, family or relationship concerns, and sexual function or concerns were upheld. The CoE model of care delivers advantages to women that are not explained by the greater number of female physicians in these settings.
Racz et al found exactly what the title of their paper states: Gender Preference for a Female Physician Diminishes as Women Have Increased Experience With Intimate Examinations (Racz et al. 2008). This presupposes, of course, a sensitively calibrated first-encounter with a doctor of either sex.
Schmittdiel et al found that women who made a conscious choice of a female physician were the least satisfied among four groups of men and women who either chose the gender of their physician or were assigned a physician (Schmittdiel et al. 2000). Men, on the other hand, who chose the same female physicians were the most satisfied, suggesting influential differences in expectations between the sexes. While the reasons for these differences are not self-evident, the authors suggest that:
One explanation for lower ratings may be that the female physicians in this study may not have achieved ‘gender-based’ care ideals such as better communication on social, lifestyle, prevention, and emotional concerns. Patients who had selected female physicians hoping for such qualities would therefore be disappointed.
What does all this mean for healthcare practice and for risk communication? Women’s preferences for provider-sex vary across medical condition, age, class, race and religion, from strong and exclusive to negotiable on the basis of other influential variables, such as perceived quality of care and institutional discrimination. Individuals’ preferences may change over time as they have reassuring experience after initial caution and we could assume that change in a reverse direction may also be likely. Optimal healthcare provision and risk communication can take place only when such opinions, expectations and preferences are investigated and taken fully into account in a consultation. If they are underestimated or ignored in patients for whom they are a genuine concern, they will become obstacles to communication and their effects may be damaging to both treatment and safety. When preferences cannot be met, the divergence needs to be factored into the way relationships and communication are established. (A brief sample of how this might be done appears in Box 18.2).
In many countries where the number of health care workers per head of population is very low (as noted in Chap. 17), this issue, and the illustrative conversation, could be seen as a luxury or a first world problem. Nevertheless, it needs to be recognised and managed, in some way, everywhere.
Box 18.2: Managing Provider Gender Preference Problems
Scenario:
Male doctor in clinic; female patient enters and show signs of surprise as she notices the doctor
Doc:
Hello. Please come in and sit down. My name’s Dr Sanjay. You’re Fatima Shah, is that right?
Patient:
Er, yes. [Sits down looking anxious]
Doc:
You seem worried. Is something troubling you?
Patient:
Well, er, I hoped, I, well, wanted to see a woman doctor.
Doc:
Oh I’m sorry. There are none of our lady doctors available just now. Is it a physical examination you’re worried about?
Patient:
Well, that yes. But I thought a woman would understand my problem better.
Doc:
OK. I won’t ask you to let me examine you physically, but could we just talk about the problem first? You can judge if you think I can help – even though I’m a man! – and if you’re not satisfied we’ll make another appointment with a lady doctor. Many of my patients are women, so you may find I can help you. Is that OK?
Patient:
OK
Commentary:
This doctor is empathetic and responsive and engages in very skilled management of the encounter. He notices the patient’s anxiety and quickly seeks its cause, inferring, correctly, the main issue (physical examination). He reassures her about that concern and asks permission to try the less risky path of verbal problem exploration. He leaves power of decision in the patient’s hands; makes a light-hearted remark that engages with the patient’s concern; reassures her by implying that other female patients find him a satisfactory provider, but that the patient may still decide to ask for a female doctor. It is only too obvious that failure to manage the patient’s preferences and expectations would be likely to lead to a tense, incomplete and unsatisfactory consultation. This very short encounter demonstrates several of the fundamental skills of the best communication.
Decision-Making Participation
Lopéz et al investigated ‘differences in treatment decision-making participation, satisfaction, and regret among Latinas and non-Latina whites with ductile carcinoma in situ (DCIS)’ (López et al. 2014). In their group of 745 Latinas and whites, they found that Spanish-speaking Latinas,
…had the highest mean preference for involvement in decision-making score and the lowest mean participatory decision-making score and were more likely to defer their final treatment decision to their physicians than English-speaking Latinas or whites… More participatory decision-making increased the odds of satisfaction and decreased the odds of treatment regret, independent of ethnicity-language.
Their conclusion also has a tragic thread to it:
Language barriers impede the establishment of decision-making partnerships between Latinas and their physicians, and result in less satisfaction with the decision-making process and more treatment regret.
A particularly intriguing piece of research was carried out in Japan by Watanabe et al. (2008). Their paper, Japanese cancer patient participation in and satisfaction with treatment-related decision-making : A qualitative studyprovides some vivid signposts for our understanding of the impact of patient preferences on satisfaction with physician behaviour and has profound implications for the handling of treatment and risk information. In a historically paternalistic culture, the Japanese Medical Association has, for over a decade, been strongly promoting patient involvement in decision-making and the ideal of informed consent, in contrast to the traditional Omakase (entrusting) value of submission to experts.
The intensive interviews with cancer survivors (a small group with a slight majority of women), revealed two clear issues:
· Patient preferences varied considerably from individual to individual (this finding is unsurprising and common to other studies in the West)
· Satisfaction was determined by the extent to which physicians actively sought and engaged with a patient’s specific preferences; failure to do so resulted in dissatisfied, resigned, angry, sometimes bitter patient reactions
The key findings in relation to the patient-doctor decision-making process in this Japanese study, with relevance and applicability across healthcare were five categories as shown in Box 18.3.
The authors point out:
While the informants under the first 3 categories were fairly satisfied with the decision-making process, those under the latter 2 were extremely dissatisfied. Informants’ views regarding their preferred role in the decision-making process varied substantially from complete physician control to complete patient control; the key factor for their satisfaction was the relation between their preferred involvement in decision-making and their actual level of involvement, irrespective of who the decision maker was [our emphasis]
The model missing from this research is that of true partnership and joint decision-making through genuine interactive discussion, the ideal that has been promoted in the West for some years now and is the underlying value of this chapter. That commitment does not imply, however, that it should be imposed on those who do not want it.
The range of preferences across all patients includes those who wish their doctors to make decisions for them either in the spirit of Omakase or at the end of a review of options. The conscientious pursuit of concordance and informed consent may, for some patients, be as oppressive as imposed decisions are for others. Unless we know what our patients prefer, we cannot make them comfortable nor rely on their paying attention to what we say. Their safety and health and their wellbeing are at stake in these matters. The risk information that patients want, its extent, detail, repetition and modes of access and delivery must be determined by them.
There is much useful wisdom in the conclusions of a small study by Wrede-Sach et al in Germany (Wrede-Sach et al. 2013). The authors sought to discover a typology for Decision-Making of Older Patients in Context of the Doctor-Patient Relationship. Good relationships with providers facilitated satisfying decision making, and participants (half of whom were female) provided these useful insights into their feelings:
Trust evolved as a response to a good doctor-patient relationship, and it helped patients to be more relaxed with decisions and led to better adherence. The patients often pointed out how doctors contribute to a good relationship: provide time, listen, pay attention and be open (patients 7 and 12), explain and give information (patients 14, 42, and 45), be truthful (patients 11, 14, and 32), be reassuring (patient 46), be a long-standing companion and share experiences (patients 13, 27, 28, and 45), know the patient through and through (patients 27, 28, 32, and 45), and be at eye level (Patient 33).
While the project found that older patients protected their autonomy to make decisions that affected their domestic and social life, they were inclined to rely quite heavily on their physicians for guidance and decision making in medical matters. Nevertheless, the range of preferences was broad, leading to the conclusions that:
Owing to the varied patient decision-making types, it is not easy for doctors to anticipate the desired level of patient involvement. However, the decision matter and the self-determination of patients provide good starting points in preparing the ground for shared decision making. A good relationship with the doctor facilitates satisfying decision-making experiences.
(I cannot resist comment on one intriguing detail from that summary of patient hopes: that the physician should be at eye level. This and a number of other very basic behaviours and provisions may have much more impact on patient attention and satisfaction than might at first seem likely, and with differential effects for women and men. Computer screens invisible to the patient; the doctor sitting behind a desk piled with books and files; the patient sitting on a low or uncomfortable chair; the decoration, smell and temperature of the room; the doctor’s mode of dealing with phone calls or interruptions; these and much more will have an impact, from minor to significant on a patient’s mood, morale, attentiveness and trust. Do we pay enough attention to such mundane matters?)
An interesting footnote to the issues of good information is the question of situations in which information-sharing may not be helpful. An Ohio State University study (Epstein and Peters 2009) reported as follows:
Being well-informed about their disease may lead to depression in women with heart failure who repress their anger and other emotions about their condition… The women’s coping styles affected their levels of depression or anxiety… some of the women felt worse emotionally when they had more information about heart disease. For women who tend to deny their emotions, less knowledge about their disease may be better.
In his piece, Identifying High Risk Abortion Patients, Reardon remarks:
Some pro-abortion researchers even argue that women should not be told of the psychological risks associated with abortion because such “demoralizing” information may make them even more prone to an adverse outcome. It is better, they would claim, to be ignorantly optimistic about the future than informed and worried. (Reardon 1993)
Reardon, like the author of this chapter, is sceptical about the ethics and humanity of such an approach in relation to abortion, or, indeed, any other procedure or condition.
Box 18.3: Japanese Cancer Patient Participation in and Satisfaction with Treatment-Related Decision-Making (Watanabe et al. 2008)
· Patient as the active decision maker, including rejecting physician advice
· Doctor selection (reviewing physician options and choosing a particular one to trust)
· Wilfully entrusting the physician
· Compelled decision-making
· Surrendering decision-making
Values, Beliefs and Personality
Montori et al. (2013) draw our attention to the major influence that patient preferences play in healthcare decisions and outcomes:
Patient preferences refer to patient perspectives, beliefs, expectations, and goals for health and life, and to the processes that individuals use in considering the potential benefits, harms, costs, and inconveniences of the management options in relation to one another. Patients may have preferences when it comes to defining the problem, identifying the range of management options, selecting the outcomes used to compare these options, and ranking these outcomes by importance.
The authors are particularly concerned with the writing of over-assertive, evidence-based clinical guidelines that do not allow room for negotiating the patient’s preferences. They remind clinicians that rigidly following guidelines may not be such good practice after all:
Clinicians should remember that taking care of patients is supposed to be difficult. Although guidelines may simplify this task, when patient preferences and context matter, guidelines must not replace clinicians’ compassionate and mindful engagement of the patient in making decisions together. This is the optimal practice of evidence-based medicine.
That is the challenge that has been implicit and explicit in much of this chapter and it takes us into the difficult territory of values, beliefs and personality.
This is a topic worthy of a book in its own right; here we can sketch only some of the issues most pertinent to risk communication. We need to consider, for example, a patient’s disposition with regards to the future, their degree of optimism or fatalism: do they believe that a partnership for change and improvement is desirable and possible or, in fatalistic mode, that what will be will be and not much can be done about it? Is their view of the trade-offs between benefit and harm short- or long-term? Do they value quality of life above continuity and length of life? Are they risk-averse or adventurous? How far do rational, scientific considerations moderate their emotions and fears or are they driven by superstition? Do they trust officials and professionals or rely on family, community and informal networks? Are they (as we have discussed above) self-determining and autonomous, seeking to make their own decisions or disempowered, or trusting and dependent looking to others to shoulder responsibility? What is their response to pain?
These multiple variables impinge on every aspect of healthcare and certainly on every incident of risk communication. Many of them have been implicit in the discussion of specific aspects in this chapter. A health professional must make an accurate assessment of the entirety of the person in front of them and shape their relationships and communications to the specific, unique individual. They must, especially, moderate their own natural tendencies if they are to any extent divergent from the wishes and expectations of patients.
Satisfaction with Service Provision
Elliott et al found that women were less satisfied with their hospital experience than men, especially with regard to communication about medicines, discharge information, and cleanliness (Elliott et al. 2012). The gender gap increased with age and for patients with worse self-reported health status. The authors concluded:
One of the more marked differences was the amount of information about medications or discharge plans that patients needed to feel sufficiently informed. Women generally wanted more information than they received, while men were satisfied with what they were told.
Stewart et al also found that women were far from satisfied with communications when recovering after an acute ischemic coronary event (ICE):
Patients after ICE, especially women, reported receiving much less information than they wanted from all health professionals. Most patients wanted a shared or autonomous treatment decision-making role with their doctor, but only a minority experienced this. (Stewart et al. 2004)
Clinicians must do better, they say,
…because meeting patients’ information needs and respecting their decisional preferences are shown to be associated with better self-efficacy, satisfaction, and health-promoting behavior.
In the NHS (England) National Cancer Patient Experience Survey for 2013, covering over 66,000 patients, more than half of whom were women, levels of satisfaction for men were overall higher than for women, though there were variations across some of the 70 questions, and sex was a less significant factor in differences than other demographic variables such as age (Health 2013). While the comprehensive review of patient opinion and experience, and comparisons across years, does suggest many areas for improvement, it does not explicitly address the question of women’s preferences, though the areas in which men expressed more satisfaction may be indicators of areas of concern to women who rated their experience of them less favourably. The main issues in this category were:
· Staff and staff working well together
· Privacy, being given respect and dignity, being told enough about their condition and treatment, and about being treated as a person rather than as a set of symptoms
· Discharge and post discharge arrangements
· Receipt of written information on types of cancer, and on free prescriptions
The questions are highly illuminating in terms of patient experience and also provide an agenda of good practice and topics for investigation for cancer (and most other) patients. For example:
Q.17 Were the possible side effects of treatment(s) explained in a way you could understand?
The results:
Of those patients saying they needed an explanation, 75 % said possible side effects of treatment were definitely explained to them in a way they could understand; a further 21 % said the explanation was understandable to some extent. 4 % said side effects were not explained to them.
Q.18 Before you started your treatment, were you given written information about the side effects of treatment(s)?
The results:
82 % of patients said that they had received written information about the side effects of treatment and that it was easy to understand; a further 5 % were given written information but it was difficult to understand. 13 % of patients said they were not given written information about side effects.
Q.19 Before you started your treatment, were you also told about any side effects of the treatment that could affect you in the future rather than straight away?
The results:
55 % of those patients who needed to be told said they were definitely told about longer term side effects; 26 % said they were to some extent. 19 % said future side effects were not explained to them. 6 % said they did not need an explanation
These, and most of the results across the whole survey are, on the whole, pretty positive, but they are in considerable contrast with many of the results from the 2012 National Inpatient Survey in which issues such as involvement in decisions about treatment and getting understandable answers from doctors most of the time, were rated very much lower (Commission 2012).
Specific Preferences in Pregnancy
Hill et al looked into the preferences of women and health professional in relation to non-invasive prenatal diagnosis and current invasive tests for Down syndrome (Hill et al. 2012). Distinct preferences were very clear:
Safe tests, conducted early in pregnancy, with high accuracy and information about Down syndrome and other conditions were preferred. The key attribute affecting women’s preferences for testing was no risk of miscarriage, whereas for health professionals it was accuracy.
Their conclusions have very clear implications for good risk communication through attention to women’s preferences as opposed to professionals’:
Women’s strong preference for tests with no risk of miscarriage demonstrates that consideration for safety of the fetus is paramount in decision making. Effective pretest counseling is therefore essential to ensure women understand the possible implications of results.
This example represents one of the many decision-making and benefit-risk dilemmas in pregnancy in which women’s preferences and priorities need careful discovery. (See also the section on anti-convulsants in Chap. 19.)
Preferences in Contraception
The effectiveness of medications, patient adherence and safety are influenced by many complex variables, as we have seen.
An ambitious, online study, by Hooper, across eight countries, with over 5,000 participants, discovered a wide range of variables in women’s decision-making about contraception (Hooper 2010). This is an extract from his results:
Many women did not plan on having children in the next 3 years (range 44 % in Russia to 77 % in the US and UK), but a quick return of fertility upon contraceptive discontinuation was desired by the majority of women in all countries (range 54 % in the US to 91 % in Russia). Rates of discontinuation or switching to a different hormonal contraceptive in the past year ranged from 30 % in Germany to 81 % in Brazil. Requests to switch because of side effects ranged from 24 % in Spain to 57 % in Brazil. Results from the Eight-Country Survey questionnaire indicated that 42 % of women would consider using one of the most effective contraceptive methods even if their menstrual cycle changed, 58 % would accept irregular bleeding initially if they had fewer periods over time, 53 % did not want/had concerns about foreign/additional estrogen in their body, 85 % would prefer a monthly option with a lower hormone dose over a daily pill, 80 % would consider switching contraceptives to minimize estrogen exposure and 74 % would prefer an estrogen-free/progestin (progesterone congener)-only pill to avoid potential side effects from foreign/extra estrogen.
This is just a glimpse of the complexity of women’s critical thinking and needs in relation to their contraceptive use. Hooper concludes:
[Our] findings demonstrate that a range of factors influence a woman’s choice of contraceptive. This highlights the importance of individualized counselling during contraceptive selection to ensure that the option recommended is tailored to the personal preferences of each woman to improve compliance, continuance and prevention of an unwanted pregnancy.
(There is further discussion of contraceptive risks and issues in Chap. 19.)
An issue of enormous importance in patient safety is adherence and satisfaction right across healthcare: we may have the right patient and the right drug and the right dose, but do we have the right formulation? That is to say a formulation that suits the individual needs and preferences of the patient in front of us. Of all the hormonal-based contraceptive methods and others does a woman have enough information about their effectiveness and risks and how far they suit her body, her lifestyle and her capacity for adherence?
Beyond contraception, we know, for example, that multiple tablets or tablet-splitting and other complications are an obstacle to adherence and safety (Mishra et al. 2011) but, given that there are often alternatives, have we chosen the options, formulations and packaging that really have the best chance of being used reliably, safely and effectively? (This issue is further discussed in the material on oral contraception in Chap. 19, p. 600.)
Patient preferences themselves are not always rational or sound: in Africa and Thailand, for example, there is a strong popular preference for injections over oral formulations for all types of medicine (Leo 2013). This is not easily amenable to modification, whatever the risks and benefits (and costs) of either course may be. The way in which such preferences are negotiated affects satisfaction, trust and adherence.
On the other hand, the mass roll-out of the injectible contraceptive Depo-Provera in developing countries brings its own ethical and professional concerns, noted as early as 1984 (Potts and Paxman 1984). Are women given adequate risk information about the drug, including the FDA black box warnings about menstrual and bone-density problems? Despite its cheapness and convenience, are the side effects and the continuing risk of STDs adequately explained and acceptable? Is informed consent sought and given?
Preferred Sources of Information
If risk communication is to be effective in protecting safety and facilitating patient decision-making, it must be available when and where it is needed, from a source that is trusted, and in a form that is acceptable.
Holton et al, in their systematic review, The childbearing concerns and related information needs and preferences of women of reproductive age with a chronic, noncommunicable health condition, reported (Holton et al. 2012):
There are serious evidence gaps about the childbearing concerns and related information needs and preferences of women with chronic, noncommunicable health conditions. Research is required to address these gaps and to inform the development of appropriate tools to assist women in this situation with their childbearing decisions.
Their conclusion applies across many of women’s major health concerns, but there are many studies that provide us with insights into specific contexts and needs. These studies also raise our gaze to much wider issues, particularly to the deep levels of empathy and ingenuity that are required to meet the communication needs of women across their immense variety. This sub-section offers a selection of those that will lead readers into contemplation of the broader challenges and, it is hoped, some pleasure at the revelation of many otherwise obscure issues.
Plutzer et al (Adelaide, Australia) investigated the Effect of Motherhood on Women’s Preferences for Sources of Health Information (Plutzer and Keirse 2012). They concluded:
Parents were listed most frequently as a preferred source of health information during pregnancy (67.8 %) followed by health care practitioners (48.8 %). By the time the child reached school age, 78 % listed health care practitioners as their preferred source compared with 15.5 % listing parents, 21.7 % friends and relatives, and 13 % the Internet. Data from the population-based comparison group of mothers with a first child of similar age mimicked those of mothers enrolled in the trial. Mothers put a lot more trust in information received from health care professionals than they did before their child was born.
In Preferences for Perinatal Health Communication of Women in Rural Tibet, Le et al found a very strong preference for close family, specifically, mother, as the most trusted source of information for perinatal women, with health workers and public health education initiatives very little favoured (Le et al. 2009). Their conclusions demonstrate how finely targeted and calibrated communications must be if they are to reach an audience at risk, and how unreliable the uncritical use of common, default, obvious methods may be:
Despite recent efforts in Tibet to use group teaching, television/radio programs, and health professionals visiting patients’ homes as health communication modalities, participants preferred to learn pregnancy-related health messages from their close family, especially their mothers. Future health communication interventions in rural Tibet and similar communities should consider targeting close family members as well as pregnant women to maximize acceptability of advice on healthy pregnancy and delivery.
Raine et al examined women’s experiences of communication in antenatal care (Raine et al. 2010). Their conclusions provide the elements of a basic communications course for health providers, with some very specific pointers about general and risk information provision:
From the users’ perspective, constructive communication on the part of health care providers was characterised by an empathic conversational style, openness to questions, allowing sufficient time to talk through any concerns, and pro-active contact by providers (e.g. text message appointment reminders). These features created reassurance, facilitated information exchange, improved appointment attendance and fostered tolerance in stressful situations. Salient features of poor communication were a lack of information provision, especially about the overall arrangement and the purpose of antenatal care, insufficient discussion about possible problems with the pregnancy and discourteous styles of interaction. Poor communication led some women to become assertive to address their needs; others became reluctant to actively engage with providers.
Vahabi focused on Breast cancer and screening information needs and preferred communication medium among Iranian immigrant women in Toronto (Vahabi 2011). Her work demonstrates the complexity of communications in such a minority group:
This group is influenced by historical, sociopolitical, and cultural experiences pre- and post-immigration… Mere translation of breast cancer and screening information from generic materials, without considering and respecting women’s unique historical, political, and cultural experiences, is insufficient.
Methods with the potential to reach such women require specific and sensitive tailoring:
For instance, video presentations conducted by a trusted Iranian healthcare professional focusing on socioculturally relevant breast cancer risk factors, symptoms, and screening methods, as well as a list of available breast health resources, could improve Iranian women’s knowledge and uptake of breast health practices.
On the question of health literacy, Ellis et al. looked into the information-seeking behaviour of patients with chronic arthritis and found, as might be expected, quite different levels of engagement depending on level of health literacy.
Participants with low health literacy were less likely to be engaged with health information-seeking behaviour. Participants with intermediate health literacy were more likely to source arthritis-focused health information from newspapers, television, and within their informal social network. Those with high health literacy sourced information from the internet and specialist health sources and were providers of information within their informal social network. (Ellis et al. 2012)
The further, and vital aspect of this issue is that level of health literacy will have an impact on every aspect of attention, comprehension and retention of information given in any form and, particularly, by health providers. With maybe 50 % of even educated and literate populations having some level of difficulty with health information (especially risk statistics), the onus is on providers of information to ensure the content of their messages is exactly matched with the abilities and preferences of their patients. No assumptions can be made, however articulate and literate the individual may seem at first encounter.
The material above also reminds us that the most effective channels of communication may be intermediaries – mothers in Tibet, literate arthritis sufferers in their own community, for example. Patient forums and communities on the internet are popular and have a wide reach. Exclusive reliance on public health initiatives or direct contact with health workers may leave untapped the power of more influential voices.
Young People and Risk
The US annual Youth Risk Behaviour Survey (2012) provides comprehensive information about the lives of young people across the country, including diverse matters such as wearing a helmet when cycling, texting and emailing while driving, alcohol and tobacco use, patterns of sexual behaviour and contraceptive use, incidents of physical violence, patterns of diet and exercise, suicidal thoughts and suicide attempts (Eaton et al. 2012).
A glimpse of this – sometimes surprising and alarming – material is given here because it reveals dimensions of young people’s lives that affect their health, welfare, safety and, of course, their relationships with healthcare professionals. Perception of risk in medicine and the kind of risk communication that will reach and influence them will be determined by their perception and management of risk in their own lives.
Although the 2011 data show some reduction in overall risky behaviour from previous surveys, some of the figures are quite stunning (7.8 % of the sample had attempted suicide, for example). In spite of some decrease, the report observes:
…many high school students continue to engage in behaviors that place them at risk for the leading causes of morbidity and mortality. Variations were observed in many health-risk behaviors by sex, race/ethnicity, and grade. The prevalence of some health-risk behaviors varied substantially among states and large urban school districts.
Roughly half the sample was female. The overall results and trends are not disaggregated by sex, but the tables record male and female response (as well as age and ethnicity), so issues where females seem relatively more at risk can be extracted. Among those and other issues are:
· Electronic bullying
· Forced sex
· Feeling sad or hopeless
· Having serious thoughts about suicide or making a suicide plan
· Smoking, though overall less than men and more women than men try to quit, still large numbers
· Less drinking than men, but one fifth record binge drinking
· Cocaine and other illegal substances, less than men, but still considerable numbers
· One third of age group (females, Grades 9–12) sexually active
· Fifteen percent of females used no contraception
· Eighteen percent drank alcohol before sex
· Women exercised much less than men
· Around one third used computers or watched TV for three hours a day
· Percentage of males and females overweight roughly the same, but females trying harder to lose weight, including diet pills and fasting, with 6 % taking extreme measures like vomiting and laxatives
· Thirteen percent were currently suffering from asthma
· One fifth used indoor tanning equipment/services
Here’s a couple of examples of analysed data that highlight the extent to which young females are exposed:
Made a suicide plan
During the 12 months before the survey, 12.8 % of students nationwide had made a plan about how they would attempt suicide (Table 23). Overall, the prevalence of having made a suicide plan was higher among female (15.0 %) than male (10.8 %) students; higher among white female (13.7 %), black female (13.9 %), and Hispanic female (17.6 %) than white male (10.6 %), black male (8.4 %), and Hispanic male (11.1 %) students, respectively; and higher among 9th-grade female (16.9 %), 10th-grade female (17.9 %), and 12th-grade female (12.0 %) than 9th-grade male (10.4 %), 10th-grade male (11.3 %), and 12th-grade male (9.5 %) students, respectively plan
Attempted suicide
Nationwide, 7.8 % of students had attempted suicide one or more times during the 12 months before the survey (Table 25). Overall, the prevalence of having attempted suicide was higher among female (9.8 %) than male (5.8 %) students; higher among white female (7.9 %) and Hispanic female (13.5 %) than white male (4.6 %) and Hispanic male (6.9 %) students, respectively
Did Not Use Any Method to Prevent Pregnancy
Among the 33.7 % of currently sexually active students nationwide, 12.9 % had not used any method to prevent pregnancy during last sexual intercourse (Table 71). Overall, the prevalence of not having used any method to prevent pregnancy was higher among female (15.1 %) than male (10.6 %) students; higher among white female (11.7 %), black female (17.5 %), and Hispanic female (22.6 %) than white male (8.3 %), black male (9.9 %), and Hispanic male (14.7 %) students, respectively
Do healthcare providers know what is going on in the lives of their young patients?
Hill et al. examined Gaps between Adolescent Risk Behaviors and Disclosure during Outpatient Visits (Hill et al. 2013) in a group of 221 young people aged 13–19 in a study on latent tuberculosis treatment; the majority (96 %) were identified as Hispanic, 45 % were foreign-born, and 46 % were male. The objective of the study was:
…to determine the gaps between disclosed high-risk behaviors in low-income, mainly Hispanic youth and the identification of these risks by health care providers.
The gap turned out to be enormous, indeed shockingly so:
A total of 399 risk behaviors were revealed to research staff by the participants; only 24 risk behaviors were revealed to [health] providers…The majority of risk behaviors based on the chart review were neither queried nor disclosed to the physicians. Physicians providing care to adolescents should consider strategies to improve disclosure as a necessary precursor to interventions.
The advice for practitioners is very clear:
In order for teens to volunteer information about their own behavior, questions need to be asked in a nonjudgmental, confidential, and teen-friendly way… this study suggests a deficiency in provider-patient rapport…[and] revealed multiple barriers to the identification of risk factors by physicians of high-risk teens. Physicians should be aware that their adolescent patients are often engaging in high-risk behaviors and that adolescents limit their disclosure of this information.
What, then, are the implications for risk communication? The first, somewhat self-evident conclusion, is that a physician cannot help a patient manage a specific risk if the general risks in her life are not disclosed and shared. Second, diagnosis and treatment, and associated communication, cannot be complete and effective if the physician doesn’t have a full picture of the patient’s state of health and wellbeing and of the risk factors in her life. Third, the prognosis for a productive, trusting partnership between patient and provider is rather poor when major aspects of the patient’s life are excluded from discussion, for one reason or another. Fourth, particularly with regard to risk communication about disease and medicine, it is unlikely that patients who have secrets, especially about risky feelings or behaviour, will put much faith in advice from providers who they know have little clue about their inner life or social reality.
(A vivid illustration of this are the health-related problems experienced by lesbians and gay men, for whom disclosure to a stranger physician, especially male, may be deeply problematic (Channel 2014) Yet sexual orientation may be at the root of psychological or physical problems, as well as having a significant impact on general health and conditions unrelated to sexual preference at all (Center 2010). The disadvantages and challenges for lesbians and bisexual women in healthcare deserve more extensive treatment than they are being given here; we note simply that it is another very important area for study and the remedying of prejudice, discrimination and disadvantage; it is a critical area of understanding for effective risk communication.)
Body Image
Women in every culture are under pressure to look good. Media images, advertising, models, film stars and male fantasies all form part of what might be regarded as the cultural conspiracy to promote ideal images and engender dissatisfaction. The icons are slim in the West, ample in Africa, pale in Asia. Cosmetics, clothes and shoes are amongst the tools of the trade. Dieting, forced feeding, cosmetic surgery, skin-whiteners, are amongst the risky remedial measures adopted. Eating disorders, depression, muscle and skeletal damage from high-heels, low self-esteem and premature death are amongst some of the effects.
These pressures in themselves put healthy women at risk, but they may also be important in women’s perception of risk and the decisions they make with regard to medicines (or surgery). Compensation for ‘loss of femininity’ in menopause, for example, may be a powerful driving force in all kinds of decisions; mastectomy or hair-loss may be profound threats to self-image and quality of life. Decisions about medicines are not just about therapeutic effectiveness.
In the United States, 20 million women and 10 million men suffer from a clinically significant eating disorder at some time in their life, including anorexia nervosa, bulimia nervosa, binge eating disorder, or an eating disorder not otherwise specified (EDNOS) (Wade, Keski-Rahkonen, & Hudson, 2011). (NEDA)
Vulnerability to concerns about body-image, to dieting and clinical eating disorders is not an exclusively Western phenomenon. Szabo and Allowood showed prevalence in a racially mixed, urban, sample of adolescent of females in South Africa to be close to that in the US and Western countries (Szabo and Allwood 2004). They mention studies in Nigeria and Egypt demonstrating that the problem is growing there too. A study of young, rural women in Tanzania showed the extent of negative impact of exposure to Western media on their self-image and eating habits, with a third of the sample being affected negatively in some way (IB Times 2011). Young women in Pakistan are also increasingly the victims of damaging body-image aspirations and eating disorders (Zain-Ul-Abideen et al. 2011).
In Africa, the traditional ideals are very different, as Yoknyam Dabale explains in her entertaining and informative blog (Dabale 2010):
…most African men love, I mean love, Fat babes (When African men use the word fat, they mean curvy and voluminous – big breasts and ass – like the shape of a soft drink bottle or an hour glass). Growing up in rural Middle Belt Nigeria, I frequently witnessed the execution of this unwritten constitution. Every man and woman was aware of its power. A woman was considered beautiful if she carried extra weight around the chest, and most importantly, her ass. The complexion should be very dark, the hair needed to be braided at least once a week, and let us not forget it is a must for her to know how to husband her husband’s home and those eight children she birthed from her ample hips.
This vivid ideal, driven by male preferences (and, to some extent, by female complicity and submission), has oppressive and risky aspects, not least the months a young bride-to-be might spend in a fattening farm. Pursuit of bulk cannot but be contributing to the rapid increase in the burden of non-communicable disease in Africa, projected by WHO to exceed the combined deaths of communicable and nutritional diseases, and maternal and perinatal deaths’ by 2030 (WHO 2011).
These preferences and practices bring an added dimension of complexity to risk communication and benefit-harm assessment for female patients. The discussion is no longer simply about the risks of disease and the benefits and risks of treatment, but also about the personal and social benefits and risks of a particular body-image. Weight loss or gain as goals for improved health or resisting disease may be in flat contradiction to a woman’s image of her own attractiveness. Here the negotiation may be with powerful social ideals and norms that may be hostile to good health and drive some women to behaviour that is self-harming. These are matters that have to be sensitively negotiated in every element of risk communication.
The risks of skin-whitening preparations are well known among professionals (Choices 2012), but millions of women, especially young women, all over the world, are buying and using them every day. In Thailand, the Philippines and Indonesia recent years have seen multiple brands and products banned because of their toxic ingredients, but dangerous products are still easily available OTC and on the streets in many countries. Conversely (and perversely), of course, in the West, along with the pale, slim, effete models of the catwalk, there are those many who seek the real sun of the beaches or the artificial beams of the tanning salon, pursuing that exotic bronzed look – and taking the well known risk of skin cancer (75,000 cases annually in the UK and 1,800 deaths) (BBC 2014a).
These issues all raise the question of risk communication in regulatory activity, at the level of public health education and in relationships with individual patients. Risk communication with individuals takes place within the context of social perceptions of risk; an individual may be deaf to sound advice when it goes against the tide of social norms; women are especially vulnerable. Such issues, and especially public scares about medicines, vaccines or cosmetics, attack women where they are most vulnerable: in the daily routines of femininity and in their roles as mothers. This is the uncertain and confusing context in which decisions about all kinds of risks must be made; where scientific evidence may take a back seat to plausible scare-mongering; where precautionary values and habits may win the day. This has enormous implications for risk communication in healthcare, especially for practitioners’ confidence and authority in providing good advice about such risks.
When the natural body is disfigured by disease, surgery or old age, then the risks and problems may multiply.
In summary: these matters have a profound impact on the lives of women and may give rise to intense emotions and anxieties. Risk communication about disease or medicine may have little or no effect if it is not rooted in a deeply empathetic understanding of the pressures and conflicts a woman may be suffering. Patients who are depressed or in mourning often cannot pay attention to communications that do not touch their immediate feelings; the same applies to women who are, for one reason or another, disturbed or depressed about their body image.
Women and Pain
Launching the Global Year Against Pain in Women (2007–2008) The International Association for the Study of Pain (IASP) made the case as follows:
Every day millions of women around the world suffer from chronic pain but many remain untreated. Several reasons may explain why barriers to treatment still exist. Psychosocial factors, such as gender roles, pain coping strategies and mood may influence how pain is perceived and communicated. In addition, there may be a lack of acceptance or understanding of the biological differences between men and women that may impact how pain is perceived. These psychosocial and biological factors, coupled with the economic and political barriers that still exist in many countries, have left millions of women living in pain without proper treatment. (Pain)
The issues raised in this proposition will now be very familiar to readers of this chapter. The claims about women’s suffering and about sex differences are well supported by research (see IASP website). The literature suggests that women report more pain than men, while Butte et al. found that women also report a greater intensity of pain across a large range of conditions:
It’s still not clear if women actually feel more pain than men do…But they’re certainly reporting more pain than men do. We don’t know why. But it’s not just a few diseases here and there, it’s a bunch of them—in fact, it may well turn out to be all of them. No matter what the disease, women appear to report more-intense levels of pain than men do. (Goldman 2012)
Old prejudices need to be cleared away:
While pain has long been considered a troublesome female complaint rather than a legitimate symptom that something physically is wrong…the problem is not in a woman’s head. (Kornblum 2014)
For women, pain, and its relief, may be important factors in their treatment and risk considerations and in ways that are quite different from the needs of men. (See Chap. 10 for a detailed discussion of chronic pelvic pain).
Physical Threats
In this sub-section we shall briefly look at four disparate, serious risks that women face: domestic violence, genital mutilation, ‘honour killings’ and so-called ‘dowry fires’ (estimated at around 100,000 per year in India, for example) (BBC 2014b). These are highly relevant to our concerns because they or their risk may, one way or another, deeply affect a woman’s perception of risk, and her health, safety and engagement with healthcare.
Women are overwhelmingly the victims of domestic violence in all its forms (though it is not an exclusively female problem as is sometimes assumed). According to the Domestic Violence Statistics website:
· Every 9 seconds in the US a woman is assaulted or beaten.
· Around the world, at least one in every three women has been beaten, coerced into sex or otherwise abused during her lifetime. Most often, the abuser is a member of her own family.
· Domestic violence is the leading cause of injury to women—more than car accidents, muggings, and rapes combined. (Domestic Violence Statistics 2014)
In the work by Hill et al we saw how limited was the extent of voluntary disclosure of risky behaviour by teenagers to health providers. With regard to the victimisation of women, the extent of disclosure also appears to fall dramatically short of actual incidents. A global study of gender-based violence (GBV) reports as follows:
Surveys from 24 countries, which revealed 93,656 women as survivors of GBV. They found that only 7 percent of women globally who are survivors of physical or sexual violence report GBV to formal sources, including legal, medical, or social support services. Additionally, disclosure of GBV to family, friends, or neighbors of the victims was low (37 percent). In 20 of the 24 countries studied, the majority of women told no one at all. (Medical 2013)
It suggests that the rates of underreporting are staggeringly high:
..estimates of gender-based violence (GBV) prevalence based on health systems data or on police reports may underestimate the actual total prevalence by 11- to 128-fold.
This is alarming enough, but it seems that even when presented with victims of domestic violence, health professionals may not always recognise it:
While recent research has found that 40 % of patients in North American orthopedic trauma clinics reported having experienced intimate partner violence at some point, a survey has found 74 % of orthopedic surgeons substantially underestimate its prevalence among their patients [estimated at a rate of 5 %]. Additionally, only 23 % had training to recognize such injuries.
The recent research referred to (Della Rocca 2013) was published by The Lancet in 2013 and concluded:
Orthopaedic surgeons should be confident in the assumption that one in six women have a history of physical abuse, and that one in 50 injured women will present to the clinic as a direct result of IPV [intimate personal violence]. Our findings warrant serious consideration for fracture clinics to improve identification of, respond to, and provide referral services for, victims of IPV.
A study in Australia found that:
There is an overwhelming link between gender violence and key indicators of women’s mental health, wellbeing and risk of suicide attempts… (Wales 2011)
If, as we now know, something between 15 % and 61 % of women in countries across the world are victims of violence of one sort or another, we are talking about truly enormous numbers. Of the patients who present themselves directly as a result of injury from violence, some of them will admit the cause, many will not; many of those seeking help for unrelated medical issues will have been abused at some time (median lifetime prevalence maybe around 30 %) and may or may not talk about it and any possible association with their presenting condition; many may present with apparently unrelated conditions that have, in truth, an immediate, direct association or causal link with violence.
Professional awareness of these risks is critical to good healthcare. Risk communication about directly related matters or matters ostensibly distinct cannot afford to be ignorant of such realities and the profound impact they will have on every aspect of women’s lives. Diagnosis, treatment, safety, adherence, may all fall short of optimal standards. Recognition in orthopedic and other clinics is an obvious example of good risk assessment, while, on the other hand, a looming threat to the safety of a fetus in pregnancy may easily be passed by (Hall et al. 2014).
Female genital mutilation, honour killings and dowry fires are radical risks for specific populations of women in developed and developing countries. Risk communication for women such as these must embrace the dramatic impact such experiences will have had on their perception of risk, their priorities in managing disease and medicines, and on their lives as a whole.
Summary
Risk communication is about the assessment and management of risk. For professionals, it is about understanding the risks that patients have faced or may face and making a full and empathetic judgement about what should be done, could be done and can be done in the light of the patient’s experience, wishes and priorities. The general life-time risks that women face as well as the medical risks are complex and multifarious. Professional communications need to be proportionately insightful, knowledgeable and authoritative in their process and content.
A summary of some of the key questions raised and discussed in this chapter is presented in Box 18.4:
Box 18.4: A Summary of the Basis of Sound Risk Communication: Some Key Questions from This Chapter
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
Reality check: with a few minutes for each patient, no doctor can possibly work through a checklist of questions like this alongside the demanding process of making a good diagnosis and negotiating the best treatment. Just as a good diagnostician will often be able to come to rapid conclusions on complex questions, based on accumulated reading, wisdom, and experience, so a good communicator has these questions (and others too) integrated into the functioning of her perception, her behaviour and the questions she asks. Through training, practice, repetition, she instinctively and automatically feels, perceives or actively seeks evidence about the critical issues in her patient’s life. This is not so much about length of time available, but about the intensity and quality of perception and interaction during whatever time is available.
Conclusions
This chapter has presented and discussed some of the basic concepts and tools of risk communication and suggested many of the best practice options.
It has also reviewed some of the multiple risks, issues and concerns that form the context in which healthcare is delivered to women and in which risk communication with women must take place. We have seen that reliable generalisations about women’s priorities and preferences cannot be made, but that we can show very clearly the areas in which their priorities and their preferences must be identified. For example, we must know about preferences with regard to provider sex and mode of decision making; we must know about perceptions of screening tests and the forms of medications; we must know who patients trust and how they want information delivered. We must know that some patients will withhold critical information about doubts, anxieties, assaults or risky behaviour. We must know about the social, cultural and idiosyncratic pressures and influences in each patient’s life. And so the list goes on. For effective risk communication in healthcare, no less than for all communication between patients and their providers, we must anticipate the infinite variety of the people we care for and ensure that we make genuine, accurate, empathetic contact with every single individual.
Take Home Messages
· Women are different from men on a range of important dimensions, both hidden and apparent
· Women are (often) disadvantaged in a number of significant ways in life and in healthcare
· Empathy is the primary quality in all effective communication, risk communication included
· Risk and benefit are perceived and experienced very differently by different people
· Risk communication should always be based on absolute figures and natural frequencies
· Women have very specific preferences with regard to the extent and quality of their involvement in risk decisions and the amount of risk information they want
· Risk must be communicated to people in clear, simple ways that they can genuinely understand and use in their decision-making
· Subjective understanding of risk and benefit, including causality, trade-offs and quality of life issues, are critical to making good decisions, especially about medicines
· There are many conditions that are unique to women and women experience all other conditions in ways that are unique to their sex
· Women’s experience of disease takes place within a complex social, cultural, political context and is influenced by the interaction of the multiple variables of their unique individuality with that context
· Some women will be preoccupied with the risks, pressures or dangers of their lives that will provide significant obstacles to risk communication about medicines
· Risk communication about medicines cannot reach or benefit women if it does not take account of the risks in their lives, their perception of risk and their individual assessment of the relative weight of benefits and harms
Acknowledgements for this chapter appear at the end of Chap. 19.
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Footnotes
1
This is closely related to but goes much further than the WHO definition of the rational use of medicines which requires that, ‘patients receive medications appropriate to their clinical needs, in doses that meet their own individual requirements, for an adequate period of time, and at the lowest cost to them and their community.’ (WHO 2012)