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Explaining risk. A guide for health professionals
Maturitas 64 (2009) 143–144
Contents lists available at ScienceDirect
Maturitas journal homepage: www.elsevier.com/locate/maturitas
Editorial
Explaining risk. A guide for health professionals
What is a ‘risk’, and when do we need to be concerned about it? From an epidemiological perspective a risk exists when the incidence of a disease is greater among exposed than among nonexposed persons (relative risk, >1.0), and we need to be concerned when the absolute risk (the incidence in the exposed minus the incidence in the non-exposed) is large. As a practical matter, the decision as to when a risk exists, and what its import may be, can sometimes be straightforward and sometimes be tricky. When a disease is common, and the relative risk is large and unbiased, the decision is easy. For example, no one disputes that smoking causes lung cancer: the risk is some 10-fold greater among smokers than among non-smokers, and some 30-fold greater among heavy smokers (relative risk, 10.0–30.0), and since the disease is common, the absolute risk is catastrophic. At the other extreme, however, when an association is small and tenuous, the perception of ‘risk’ can be in the eye of the beholder. To turn again to the example of lung cancer, in a recent study it has been claimed that hormone-replacement therapy (HRT) increases the risk of a fatal outcome (relative risk, 1.71 (p = 0.01)), and especially of fatal non-small-cell cancer (1.87 (p = 0.004)) [1]. Although at baseline that study had information on the number of cigarettes smoked and on duration, all that was done in the analysis was to classify women as never, past and current smokers; during 7.9 years of follow-up changes in smoking status, or in the amount smoked, were not allowed for. Had proper allowance been made for smoking, a relative risk estimate of 1.87 or less might readily have been nullified. HRT may or may not increase the risk of fatal lung cancer, but it cannot be claimed that an increased risk has been established. Yet this was not the perception: an accompanying editorial was given the headline, ‘Another nail in the coffin for hormone-replacement therapy?’ [2]. In short, if a study is reasonably well designed and executed, the difficulty is not in the identification and interpretation of large risks, but of small ones. To get around the difficulty, some epidemiologists now propose that statistics is the key to reaching a decision as to whether a small association is indeed a ‘risk’, and if so, how it should be interpreted. I believe this is an error. The outcomes at issue are clinical outcomes, and the evaluation of what any purported risk may mean, especially for individual patients, must be based predominantly on experienced clinical judgement. To be sure, that judgement must be informed by epidemiological, statistical, and other evidence (pathological, pharmacological, experimental, etc.), but reaching a judgement remains a clinical responsibility, not a statistical one.
0378-5122/$ – see front matter © 2009 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.maturitas.2009.09.022
Epidemiology is a simple discipline, but the jargon can be intimidating, which is a pity, since it can easily be decoded [3]: there is no reason why clinicians cannot evaluate the evidence for themselves. Some guidelines that may be of assistance in doing so follow: Firstly, good science is sceptical science, investigators should do everything they can to falsify their own hypothesis, and they should be the first to draw attention to the limitations of their own data [3]. Only then can a perception of increased risk be entertained—and as illustrated by the HRT/lung cancer example, that principle is not always adhered to. Secondly, no study is perfect, and for a small risk increment it is seldom possible to judge it as definitively causal, rather than biased or confounded. In the face of that uncertainty, evidence from sources such as pathology or pharmacology may be helpful, but even then, a causal inference, and hence any inference of risk, must usually remain tentative. Thirdly, the determination of causality is dependent on the relative risk, while its impact on health depends on the absolute risk. Assume, for example, that the annual incidence of a disease is one per million: a relative risk of, say, 1.5, would translate to an absolute risk of 0.5 per million (1 × 1.5–1.0). Clinically, such an association, even if causal, would hardly matter. Indeed, it would hardly matter even if the relative risk were 10.0. On the other hand, however, for an annual incidence of, say, 2% (e.g., myocardial infarction in elderly men), a relative risk of 10.0 would be truly catastrophic; even a relative risk of 1.5 would translate to an absolute risk of 1% (2 × 1.5–2), instead of 0.5 per million. But. . .regardless of how much it may matter, for a relative risk of 1.5, it is seldom possible to be sure that either the relative or absolute risk is ‘real’. In that circumstance we have no choice but to live with uncertainty. Finally, and most important of all, the epidemiological concept of ‘risk’ applies to populations, not to individuals. When it comes to the management of any individual patient, population risk is relevant as a background consideration, but it does not govern. To illustrate with another relatively common disease: among menopausal women the annual incidence of breast cancer is about 2 per 1000, and it has been claimed that HRT increases the risk by about 1.2-fold [4]. That claim is disputed, but if for the sake of the argument it were to be accepted, the absolute risk would be 0.4 per 1.000 (2 × 1.2–2). In deciding whether or not to prescribe HRT to a woman with menopausal symptoms, that risk would be only one of many relevant considerations, others being her age, age at menarche and menopause, obstetrical history, history of benign
144
Editorial / Maturitas 64 (2009) 143–144
breast disease, family history, obesity, and osteoporosis—and the list is incomplete. The belief that such complex considerations can be synthesised and quantified into a single risk estimate for any individual patent is illusory. Unfortunately that belief has nevertheless become widespread. Health professionals need to be aware of risk, but also of the limitations to its interpretability, and good patient care will continue to be based on clinical judgement. Competing interest The author presently consults, and in the past has consulted, with manufacturers of products discussed in this article. Funding None acknowledged.
References [1] Chlebowski RT, Schwartz AG, Anderson GL, et al. Oestrogen plus progestin and lung cancer in postmenopausal women (Women’s Health Initiative Trial): a post hoc analysis of a randomised controlled trial. Published online September 20, 2009; doi:10.1016/SO140-6736(09)61526-9 [www.theancet.com]. [2] Ganti AK. Another nail in the coffin for hormone-replacement therapy? Published online September 20, 2009; doi:10.1016/SO140-6736(09)61571-3 [www.lancet.com]. [3] Shapiro S. Causation, bias and confounding: a hitchhiker’s guide to the epidemiological galaxy. J Fam Plann Reprod Health Care 2008;34, 81–7;185–90;26–4. [4] Rossouw JE, Anderson GL, Prentice RL, et al. Risks and benefits of estrogen plus progestin on healthy postmenopausal women: principal results from the Women’s health Initiative randomised trial. JAMA 2002;290:1729–38.
Emeritus Director, Emeritus Professor, Visiting Professor Samuel Shapiro (MB, FCP(SA), FRCP(E)) a,b a Slone Epidemiology Center, Boston University, United States b University of Cape Town, South Africa E-mail address: [email protected]
Provenance 30 September 2009 Commissioned and not externally peer reviewed.
Contents lists available at ScienceDirect
Maturitas journal homepage: www.elsevier.com/locate/maturitas
Editorial
Explaining risk. A guide for health professionals
What is a ‘risk’, and when do we need to be concerned about it? From an epidemiological perspective a risk exists when the incidence of a disease is greater among exposed than among nonexposed persons (relative risk, >1.0), and we need to be concerned when the absolute risk (the incidence in the exposed minus the incidence in the non-exposed) is large. As a practical matter, the decision as to when a risk exists, and what its import may be, can sometimes be straightforward and sometimes be tricky. When a disease is common, and the relative risk is large and unbiased, the decision is easy. For example, no one disputes that smoking causes lung cancer: the risk is some 10-fold greater among smokers than among non-smokers, and some 30-fold greater among heavy smokers (relative risk, 10.0–30.0), and since the disease is common, the absolute risk is catastrophic. At the other extreme, however, when an association is small and tenuous, the perception of ‘risk’ can be in the eye of the beholder. To turn again to the example of lung cancer, in a recent study it has been claimed that hormone-replacement therapy (HRT) increases the risk of a fatal outcome (relative risk, 1.71 (p = 0.01)), and especially of fatal non-small-cell cancer (1.87 (p = 0.004)) [1]. Although at baseline that study had information on the number of cigarettes smoked and on duration, all that was done in the analysis was to classify women as never, past and current smokers; during 7.9 years of follow-up changes in smoking status, or in the amount smoked, were not allowed for. Had proper allowance been made for smoking, a relative risk estimate of 1.87 or less might readily have been nullified. HRT may or may not increase the risk of fatal lung cancer, but it cannot be claimed that an increased risk has been established. Yet this was not the perception: an accompanying editorial was given the headline, ‘Another nail in the coffin for hormone-replacement therapy?’ [2]. In short, if a study is reasonably well designed and executed, the difficulty is not in the identification and interpretation of large risks, but of small ones. To get around the difficulty, some epidemiologists now propose that statistics is the key to reaching a decision as to whether a small association is indeed a ‘risk’, and if so, how it should be interpreted. I believe this is an error. The outcomes at issue are clinical outcomes, and the evaluation of what any purported risk may mean, especially for individual patients, must be based predominantly on experienced clinical judgement. To be sure, that judgement must be informed by epidemiological, statistical, and other evidence (pathological, pharmacological, experimental, etc.), but reaching a judgement remains a clinical responsibility, not a statistical one.
0378-5122/$ – see front matter © 2009 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.maturitas.2009.09.022
Epidemiology is a simple discipline, but the jargon can be intimidating, which is a pity, since it can easily be decoded [3]: there is no reason why clinicians cannot evaluate the evidence for themselves. Some guidelines that may be of assistance in doing so follow: Firstly, good science is sceptical science, investigators should do everything they can to falsify their own hypothesis, and they should be the first to draw attention to the limitations of their own data [3]. Only then can a perception of increased risk be entertained—and as illustrated by the HRT/lung cancer example, that principle is not always adhered to. Secondly, no study is perfect, and for a small risk increment it is seldom possible to judge it as definitively causal, rather than biased or confounded. In the face of that uncertainty, evidence from sources such as pathology or pharmacology may be helpful, but even then, a causal inference, and hence any inference of risk, must usually remain tentative. Thirdly, the determination of causality is dependent on the relative risk, while its impact on health depends on the absolute risk. Assume, for example, that the annual incidence of a disease is one per million: a relative risk of, say, 1.5, would translate to an absolute risk of 0.5 per million (1 × 1.5–1.0). Clinically, such an association, even if causal, would hardly matter. Indeed, it would hardly matter even if the relative risk were 10.0. On the other hand, however, for an annual incidence of, say, 2% (e.g., myocardial infarction in elderly men), a relative risk of 10.0 would be truly catastrophic; even a relative risk of 1.5 would translate to an absolute risk of 1% (2 × 1.5–2), instead of 0.5 per million. But. . .regardless of how much it may matter, for a relative risk of 1.5, it is seldom possible to be sure that either the relative or absolute risk is ‘real’. In that circumstance we have no choice but to live with uncertainty. Finally, and most important of all, the epidemiological concept of ‘risk’ applies to populations, not to individuals. When it comes to the management of any individual patient, population risk is relevant as a background consideration, but it does not govern. To illustrate with another relatively common disease: among menopausal women the annual incidence of breast cancer is about 2 per 1000, and it has been claimed that HRT increases the risk by about 1.2-fold [4]. That claim is disputed, but if for the sake of the argument it were to be accepted, the absolute risk would be 0.4 per 1.000 (2 × 1.2–2). In deciding whether or not to prescribe HRT to a woman with menopausal symptoms, that risk would be only one of many relevant considerations, others being her age, age at menarche and menopause, obstetrical history, history of benign
144
Editorial / Maturitas 64 (2009) 143–144
breast disease, family history, obesity, and osteoporosis—and the list is incomplete. The belief that such complex considerations can be synthesised and quantified into a single risk estimate for any individual patent is illusory. Unfortunately that belief has nevertheless become widespread. Health professionals need to be aware of risk, but also of the limitations to its interpretability, and good patient care will continue to be based on clinical judgement. Competing interest The author presently consults, and in the past has consulted, with manufacturers of products discussed in this article. Funding None acknowledged.
References [1] Chlebowski RT, Schwartz AG, Anderson GL, et al. Oestrogen plus progestin and lung cancer in postmenopausal women (Women’s Health Initiative Trial): a post hoc analysis of a randomised controlled trial. Published online September 20, 2009; doi:10.1016/SO140-6736(09)61526-9 [www.theancet.com]. [2] Ganti AK. Another nail in the coffin for hormone-replacement therapy? Published online September 20, 2009; doi:10.1016/SO140-6736(09)61571-3 [www.lancet.com]. [3] Shapiro S. Causation, bias and confounding: a hitchhiker’s guide to the epidemiological galaxy. J Fam Plann Reprod Health Care 2008;34, 81–7;185–90;26–4. [4] Rossouw JE, Anderson GL, Prentice RL, et al. Risks and benefits of estrogen plus progestin on healthy postmenopausal women: principal results from the Women’s health Initiative randomised trial. JAMA 2002;290:1729–38.
Emeritus Director, Emeritus Professor, Visiting Professor Samuel Shapiro (MB, FCP(SA), FRCP(E)) a,b a Slone Epidemiology Center, Boston University, United States b University of Cape Town, South Africa E-mail address: [email protected]
Provenance 30 September 2009 Commissioned and not externally peer reviewed.