Guide Health, Disease, and Causal Explanations in Medicine

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Ta kontakt med Kundesenteret. Avbryt Send e-post. Les mer. Om boka Health, Disease, and Causal Explanations in Medicine On May , , some 50 scientists and scholars - physicians, philos- ophers and social scientists - convened at Hasselby Castle in Stockholm for the first Nordic Symposium on the Philosophy of Medicine. The topics for the symposium included 1 the concepts of health and disease, 2 classification in medicine, and 3 causality and causal explanations in medicine.

The majority of the participants were Scandinavian but the symposium was also able to welcome four distinguished guests from other parts of the world, Professors Stuart F. Spicker and H. Becoming a doctor is, in part, internalizing frameworks. Early in training you carry lists and papers that remind you how to evaluate acidosis, or the physiology of heart failure or suspected meningitis.

After time and repetition these lists are internalized and you can evaluate the problems without resorting to lists. As an intern one of the papers I carried around was The New England Journal of Medicine article on the physiology of the Swan-Ganz catheter, and I would refer to it with each patient who had a Swan. One day I did not need to refer to the paper. I had internalized the information, and tossed the paper into the trash.

Frameworks are not the be all and end all, but do serve as foundation upon which to build ideas. However, I never had a formal framework for thinking about what constitutes causality in medicine. It is worth reading in the original if no other reason as an appreciation of a time when the medical literature was not dry as dust and devoid of humor and style. Current medical journal writing is often an excellent replacement for Ambien, even when you are fascinated by the topic.

How strong is the association between the cause and the effect? Hill uses the example of chimney sweeps, who died of scrotal cancer at rates times the normal population. It killed Bert, or so I was lead to believe. He points out that a strong association like scrotal cancer and chimney sweeps is good evidence in favor of causality from an environmental exposure. Not a huge increase in mortality, but a strong association none the less.

If acupuncture or homeopathy were times superior to placebo, there would no discussion of its validity. Many medical therapies are not times as effective as placebo, but the strength of the association between cause and effect is well above background noise. Almost every study should support the association for there to be causation. Over time, as studies progressed, there was a consistent association between smoking and cancer. Patients admitted to hospital for operation for peptic ulcer are questioned about recent domestic anxieties or crises that may have precipitated the acute illness.

As controls, patients admitted for operation for a simple hernia are similarly quizzed. But, as Heady points out, the two groups may not be in pari materia. If your wife ran off with the lodger last week you still have to take your perforated ulcer to hospital without delay.

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No number of exact repetitions would remove or necessarily reveal that fallacy. Since diseases can have multiple etiologies and therapies can have multiple effects, this is a weaker criteria. However, given the knowledge of physiology and biochemistry since , we have more sophisticated techniques for measuring and determining specificity.

From the perspective of opportunistic infections, with no knowledge of viral pathophysiology, HIV is hardly a specific cause of disease. Fools all; infections are the one true cause of all disease. The order should be exposure, disease, treatment, resolution. Cause should proceed effect. Does a particular diet lead to disease or do the early stages of the disease lead to those particular dietetic habits? This temporal problem may not arise often, but it certainly needs to be remembered.

Also known as dose-response. A little exposure should result in a little effect, a large exposure should cause a large effect. Certainly well known to anyone who drinks alcohol; I suppose all homeopaths must be teetotallers. The comparison would be weakened, though not necessarily destroyed, if it depended upon, say, a much heavier death rate in light smokers and a lower rate in heavier smokers.

We should then need to envisage some much more complex relationship to satisfy the cause and effect hypothesis. The clear dose-response curve admits of a simple explanation and obviously puts the case in a clearer light. I suppose that a chiropractor could say your spine is partly unsubluxed as a result of half a spinal manipulation or an acupuncturist saying, your chi is partly unblocked as I used too few needles. I suppose.

Causal selection

I assume a reader will comment on the validity of this observation. The effect must have biologic plausibility. I would take it a slightly differently: not only should it be biologically plausible, but should not violate well known laws of the universe. What is biologically plausible depends upon the biological knowledge of the day. This explanatory function of a diagnosis is desirable for a number of purposes. First, it can support predictions about likely future outcomes.

Second, where treatment is available, an explanation can inform decisions regarding therapeutic interventions. Third, some theorists note that the explanation provided by a diagnosis is of value to the patient because it legitimises illness [ 7 , 10 ] and can offer a sense of relief [ 11 , 12 ]. The aim of this article is to provide an account that characterises how a diagnosis explains patient data.

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I argue that traditional covering law models of explanation fail to adequately capture this explanatory relation. Instead, I propose that where a diagnosis successfully explains patient data, it does so 1 by identifying the cause of the patient data and 2 in the presence of theoretical understanding of the mechanisms that link the identified cause to the patient data. My approach in this article is predominantly descriptive, but has normative implications. On the descriptive side, the model of explanation I provide is intended to capture, with fidelity, the nature of explanation in paradigm cases where diagnoses explain the patient data.

However, in the course of my discussion, I show that some of the traditional models of explanation fail to capture how diagnoses explain patient data on the grounds that they permit spurious or incorrect diagnoses, which has normative implications for how physicians should and should not reason. Although a substantial amount of literature has been dedicated to the logic of diagnostic reasoning in medicine [ 2 , 13 — 17 ] and the nature of explanation in the biomedical sciences [ 4 , 5 , 18 ], this particular topic of how diagnoses serve as explanations of patient data has been underexplored in the philosophy of medicine.

The literature on diagnostic reasoning has largely focused on analysing the inferential process leading from the patient data to the diagnostic hypothesis [ 2 , 13 — 17 ], but little has been written about the nature of the explanatory relation that goes in the opposite direction, from the diagnosis to the patient data. The same can also be said of the literature on artificial intelligence and expert diagnostic systems, which looks at the development of statistical algorithms to simulate and enhance clinical decision-making [ 3 , 16 , 19 ].


Again, the focus of this literature is the process of analysing patient data to arrive at a diagnosis. While the outcome of this process may be an explanation of the patient data, an account of precisely what makes it explanatory is still wanting. Hence, the account of explanation I provide can be seen as complementing, rather than challenging, the above mentioned work on diagnostic reasoning.

The philosophical literature on the nature of explanation in the biomedical sciences has largely focused on explanation in the context of medical research, rather than explanation in the context of clinical practice [ 4 , 5 , 18 ]. Nonetheless, there are notable exceptions of particular relevance to my discussion. One is Kenneth Schaffner, who argues that explanation in the biomedical sciences is different from that in the physical sciences because the former involves qualitative and analogical reasoning from loose theoretical generalisations rather than the subsumption under laws that is involved in the latter [ 18 , 20 ].

In his paper [ 20 ], Schaffner shows how such analogical reasoning is used to understand individual cases in medicine. However, his is a general account of how theoretical knowledge of medical science is applied to individual cases, not a specific analysis of how diagnoses in particular serve as explanations of patient data. As such, it is not made explicitly clear how this extension of theoretical knowledge exactly relates to the specific epistemic role of a diagnosis in the clinical encounter.

Another theorist relevant to my discussion is Margherita Benzi, who, in a recent paper [ 21 ], makes the important step of specifically and explicitly applying philosophical theory on causal explanation to the topic of diagnosis. Benzi proposes that diagnoses are not explanatory in virtue of general causal regularities between them and the patient data but rather because they pick out the actual causes of the patient data in each individual case. While I take this to be correct, I argue that it is incomplete as it stands and needs to be supplemented with consideration of how the intelligibility of the explanation also rests on knowledge of the mechanisms linking the identified cause and the patient data.

The rest of the article is structured as follows.

Health, Disease, and Causal Explanations in Medicine

In the second section that follows, I argue that some of the traditional models of explanation in the philosophy of science fail to adequately capture the explanatory relation between diagnosis and patient data. In the fifth section, I suggest that the former is the outcome of the diagnostic search, while the latter is provided by the theoretical framework in which the physician operates. Before I proceed, I offer two clarifications. The first is a note on terminology. Second, I do not claim that all diagnoses function as explanations of symptoms and signs.

In some cases, the diagnoses may be syndromic. That is to say, they do not refer to underlying diseases, but to the constellations of symptoms and signs themselves. Examples include some diagnoses in psychiatry and some of the so-called medically unexplained syndromes, whose explanatory statuses are hotly debated [ 7 , 8 , 12 ]. A covering law explanation has the form of an argument, whereby the explanandum is concluded from a set of premises, of which at least one must be a general law that is necessary for the argument.

The argument can be either deductive or inductive. At first glance, it might appear that this is getting things backwards, as physicians do not typically begin with a diagnosis and deduce the patient data, but begin with the patient data and then infer a diagnosis as an explanation. However, this problem is only apparent and disappears with a more accurate understanding of what the deductive-nomological formulation is intended to capture.

The deductive-nomological formulation is not intended to be a historical representation that captures the psychological process of hypothesis formation, but rather an atemporal representation that captures the logical relation between the hypothesis and the data. Under the model, then, the explanatory relation between the explanans and the explanandum does not depend on how we arrived at the former, but on whether the latter can be deductively entailed from the former.

Nonetheless, the deductive-nomological model has a serious limitation in the context of clinical practice. Many regularities in medicine are probabilistic rather than deterministic and so do not enable sound deductions of the patient data from the diagnoses [ 16 , p. In the above mentioned example, the correlation between heart failure and leg oedema is not absolute, and it is possible to have heart failure without leg oedema. Therefore, the deductive-nomological model is only applicable to a very limited number of cases of diagnostic explanation.

Hempel concedes that the deductive-nomological model cannot account for cases of explanation that do not involve deterministic laws and introduces the latter kind of covering law argument, known as inductive-statistical explanation, to make up for these cases. According to this, to explain a phenomenon is to inductively infer it from a statistical generalisation about previously observed cases.

The inductive-statistical model accommodates the fact that many relations between diagnosis and symptoms in medicine are probabilistic [ 4 , p. Therefore, a charitable rendering of a covering law account of diagnostic explanation needs to allow inductive-statistical as well as deductive-nomological explanations. I accept that some instances of diagnostic explanation may be formulated as covering law arguments of the inductive-statistical kind. There is a certain feature of diagnoses that permits such a formulation.

Covering law explanations appeal to laws or regularities, which in turn depend on the presupposition of repeatable types that instantiate these laws or regularities. In medicine, diagnoses are often treated as such repeatable types [ 16 , pp. They are generalised categories, whose tokens are taken to share certain properties. Individual cases of heart failure are tokens of this type that instantiate this feature. This characterisation of diagnoses as repeatable types enables them to support the kinds of regularity and inductive inference that feature in inductive-statistical explanations.

However, it has long been argued that the inductive-statistical model as it stands is too permissive to be a complete account of explanation. There are well-known counterexamples that fulfil the requirements of the inductive-statistical model yet are not genuinely explanatory.

Causal reasoning and the diagnostic process.

One kind of counterexample concerns explanatory irrelevancies. Peter Achinstein gives the hypothetical case of Jones, who dies within a day of eating a pound of arsenic [ 27 ]. If this is the case, then his eating a pound of arsenic is explanatorily irrelevant to his dying. To take another example, a significant proportion of patients diagnosed with left hemispheric stroke present with right-sided paralysis. Now, consider the case of a patient diagnosed with left hemispheric stroke but who already has right-sided paralysis for a different reason, such as cerebral palsy.

Another kind of counterexample concerns spurious correlations. Wesley Salmon gives the example of a correlation between a falling barometer reading and a storm [ 28 ]. Although there is a significant statistical regularity between these two event types, a falling barometer reading is not a legitimate explanation of a storm.

Rather, both have a common explanation, namely, the preceding drop in atmospheric pressure. Applying this to a medical example, there is a statistical regularity between calf pain and pulmonary embolism, such that the probability of a patient having calf pain is higher if he or she also has a pulmonary embolism than the probability of his or her having calf pain under any circumstance. Rather, both the calf pain and the pulmonary embolism, as well as the statistical relation between the two, can be explained by the diagnosis of deep vein thrombosis.

The above counterexamples show that genuine explanatory relations are underdetermined by covering law arguments. In the example of the patient with right-sided paralysis, there are two possible explanations for the patient data, each supported by a different inductive-statistical argument. These are left hemispheric stroke and cerebral palsy, respectively.

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  • Here, the correct explanation cannot be determined by the inductive-statistical model on its own. Rather, confronted with two inductive-statistical arguments supporting different diagnoses, the physician has to make a choice, or an inference to the best explanation, based on some other criterion. Hence, the covering law account at best describes only a part of the relation between the actual diagnosis and the clinical data. What seems to be suggested by the above counterexamples is that the necessary criterion for the relation between the diagnosis and the patient data to be genuinely explanatory is causation.

    In the case of the patient with cerebral palsy, the reason why left hemispheric stroke does not explain his or her right-sided paralysis is that the right-sided paralysis was caused by another condition, namely, cerebral palsy. Also, in the case of the patient with deep vein thrombosis and pulmonary embolism, the reason why the former but not the latter explains his or her chest pain is because it is the former that had caused it. However, inductive-statistical relations are not specifically causal, so on their own, they cannot distinguish between diagnoses that genuinely explain the patient data and those that are merely correlated with the patient data.

    The upshot, then, is that while the covering law account as described above may capture a part of the relation between a diagnosis and the patient data, it fails to pick out specifically what it is that makes this relation genuinely explanatory. The above considerations suggest that an adequate model of diagnostic explanation must take causation into account. Over the past half century, the causal model of explanation has attracted a large number of proponents in the field of philosophy of science [ 28 — 32 ]. The basic claim of the causal model is that to explain something is to provide information about its cause.

    This certainly has intuitive appeal with respect to diagnostic explanation as it is commonly suggested that the aim of the diagnostic process is to search for the cause of the clinical manifestation [ 6 , 9 , 15 ]. As noted in the previous section, physicians seeking explanations of patient data may be confronted with various factors that are correlated with the patient data, some of which may be causally irrelevant or spurious but nonetheless may satisfy the requirements for inductive-statistical explanations. Under the causal model of explanation, though, only those correlations which are genuinely causal would qualify as being explanatory.

    Physics and chemistry contain such examples. For example, one could explain why the ice cube in a glass of water melts by appealing more generally to the laws describing how high temperatures influence the hydrogen bonds between H 2 O molecules. As noted in the section above, some instances of diagnostic explanation can be formulated as covering law arguments, which suggests that they could be considered cases of covering law explanation that appeal to causal, rather than merely statistical, regularities. Benzi notes that in covering law explanations, the causal regularities hold between general types [ 21 , p.

    I have already shown in the previous section how a diagnosis, such as heart failure, is treated as a repeatable type. In diagnostic explanation, however, the explanandum is not a generality, but a particular fact.

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    That is to say, in the case where the diagnosis of heart failure successfully explains leg oedema, what is being explained is not why leg oedema occurs in general at the total population level but rather why this particular patient has leg oedema. To particularise the general regularity to the individual case, the covering law account treats the individual case as a token of the general type to which the regularity applies.

    Indeed, in many cases, the explanation of the individual case as if it is a token of a homogeneous type would turn out to yield the correct diagnosis. If a particular type of condition is statistically the commonest cause of a type of symptom in the total population, then it follows that most individual cases of this symptom would be caused by this condition.

    However, Benzi argues that this does not capture all cases of diagnostic explanation [ 21 , pp. Far from being tokens of a homogeneous type, the particular cases of a certain clinical presentation are affected by so many contingencies as to make each case unique. Given this uniqueness, the general causal regularity appealed to in a covering law argument may fail to pick out the actual causal relation in a given case. In other words, the likeliest cause of a clinical presentation in the relevant reference class may not be the actual cause of the clinical presentation in a particular patient.

    Also consider that this patient is known to already have a longstanding history of heart failure. Under the covering law account, the leg oedema could be explained with appeal to a causal regularity between kidney disease and leg oedema. However, in the primary care population, leg oedema is more likely to be caused by heart failure than by kidney disease [ 34 ]. Hence, the causal regularity between heart failure and leg oedema would also satisfy the requirements of a covering law explanation, despite this not being the actual cause of the leg oedema for this particular patient.

    The upshot is that appealing to general causal regularities cannot discern the actual explanation from the spurious one in the particular case, and so fails to capture what it is that makes the relation between a diagnosis and a set of patient data genuinely explanatory. That is to say, a diagnosis explains the patient data if it identifies the actual cause of that patient data. Hence, in the above mentioned example, heart failure may be a more common cause of leg oedema than kidney disease in the general population, but the correct explanation of leg oedema in the given patient is kidney disease, not heart failure, because kidney disease is the actual cause of the leg oedema in that particular case.

    The proponent of the covering law account might respond by suggesting that the relevant reference class to which the general causal regularity applies could be narrowed down by including the details of the contingencies emphasised by Gorovitz and MacIntyre [ 33 ] in the description of the reference class. However, there are two problems with this suggestion. First, as argued by Nancy Cartwright [ 35 ] and restated by Stefan Dragulinescu [ 36 ], a complete description that achieves absolute concordance between the reference class and the correct diagnosis may not be possible.

    Although we can include certain known risk factors in the description of a reference class, there are also many other contingencies for which we cannot account due to our ignorance of them [ 33 , p. To paraphrase Cartwright, there may be no available complete description, but simply individual variation [ 35 ]. So, an adequate causal account of diagnostic explanation cannot be based on general causal regularities, but it needs to appeal to the notion of actual causation in each individual case.

    The explanans , or the diagnosis, explains by identifying the actual cause of the clinical presentation in the particular patient. What has been presented here is a descriptive account of what constitutes the explanatory relation between a diagnosis and the patient data, but it does have normative implications for how physicians should reason. It supports the idea, suggested by Dominick Rizzi [ 15 , p. The importance of this is that one of the key functions of a diagnosis is to help determine the correct intervention for the given patient.