The Price of Health (Philosophy and Medicine)

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Why a Partnership?

Medicine, morals and money have, for centuries, lived in uneasy cohabitation. Dwelling in the social institution of care of the sick, each needs the other, yet each is embarrassed to admit the other's presence. Morality, in particular, suffers embarrassment, for it is often required to explain how money and medicine are not inimical.

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Centuries later, a medieval medical moralist, Henri de Mandeville, said: "The chief object of the patient This incompatibility, while general, is not universal. Throughout history, medical practitioners have resolved the problem - either in conscience or to their satisfaction. Some physicians have been so reluctant to make a profit from the ills of those whom they treated that they preferred to live in poverty.

Samuel Johnson described his friend, Dr. Robert Levet, a Practiser of Physic: No summons mock'd by chill delay, No petty gain disdain'd by pride; The modest wants of ev'ry day The toil of ev'ry day supplied [3]. Convert currency. Add to Basket. Book Description Kluwer, Condition: New. Seller Inventory More information about this seller Contact this seller. Book Description Springer, Book Description Springer, Netherlands, Language: English. Brand new Book.

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Throughout the history of Western medicine, morality's explanations have been con sistently ambiguous. Centuries later, a medieval medical moralist, Henri de Mandeville, said: "The chief object of the patient.

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Established seller since Seller Inventory IQ Delivered from our UK warehouse in 4 to 14 business days. John Worrall argues that, at the end of the day, RCTs are a powerful means to control selection bias, but no more than that Worrall , a,b. As he uses the term, selection bias occurs when treatment and control group are unbalanced with respect to some prognostic factors because a medical researcher has selected which patients will receive which treatment.

Selection bias in this sense obviously cannot occur in an RCT because in an RCT the allocation is made by a random process. But it is also clear that randomization is at best sufficient but not necessary to achieve the result.

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A large number of alternative designs may be used to the same effect: allocation can be made by a strict, albeit non-random protocol; allocation is made by non-experts who are unrelated to the treatment development and therefore have no expectations concerning outcomes; treatment and control groups are deliberately matched again by persons who have nothing at stake or according to some protocol ; and so on.

A controversial issue is the role of mechanistic knowledge, that is, knowledge about the biological and physiological mechanisms responsible for medical outcomes and thus treatment efficacy should play in EBM. Nevertheless, a number of philosophers of medicine have pointed out that mechanistic knowledge is in fact important in EBM or that it should receive more attention.

Federica Russo and Jon Williamson have, for instance, argued that causal claims need both statistical evidence as well as evidence about the mechanisms that connect an intervention with the outcome variable in order to be established Russo and Williamson Others disagree Reiss or qualify the claim Gillies ; Howick a; Illari Further, it has been pointed out that mechanistic knowledge plays an important role in the design and preparation of an RCT, as well as in the interpretation and application of RCT results La Caze ; Solomon Especially when it comes to extrapolating research results from a test to another population, mechanistic knowledge is supposed to be vital Steel ; see also next section.

On the other hand, knowledge about mechanisms is often highly problematic and should not be relied on too heavily in applications Andersen New therapies are often trialed using animal models before they are tested on humans in a randomized trial. Animal models also play important roles in establishing whether or not a substance is toxic for humans. The International Agency for Research on Cancer IARC , for example, classifies substances with respect to the quality of the evidence for their carcinogenicity into five groups.

Evidence from animal models is referred to in the characterization of each group IARC This raises questions about how such extrapolations from animal models to humans work, and how reliable they are. Animal models are widely used in biomedical research because experimental interventions on animals are easier to conduct and cheaper than experiments on humans. Both kinds of experiments involve ethical dilemmas, but animal experimentation is usually regarded as less problematic from an ethical point of view than experimentation with humans.

At any rate, the number of animals killed, maimed, or made sick in biomedical research is much higher than the number of humans adversely affected in this research. The problem is essentially this. What is true of a model can be presumed to be true of the target only to the extent that the model is similar to the target in relevant respects.

The reason we experiment on models in the first place is, however, that the model differs in important respects from the target if animals were just like humans, we would not find experiments on the former to be ethically less problematic than experiments on the latter. Extrapolation—the inference from model to target—is therefore only worthwhile to the extent that there are significant limitations in our ability to study the target directly.

If so, there can be no good grounds to decide whether a model is a good one for the target. To do so, we would have to investigate whether the target is relevantly similar to the model; but if we could do so, there would be no reason to study the model in the first place. This inferential problem has led some commentators to maintain highly skeptical views concerning our ability to use animals as models for humans in biomedical research. Hugh LaFollette and Niall Shanks argue that animal models cannot be reliably used for extrapolation at all, but at best only heuristically, as sources of hypotheses that have to be tested on humans LaFollette and Shanks The former can be used to make reliable predictions about target populations of interest; the latter only heuristically.

The main premise in their argument that animal models in biomedical research are at best HAMs but not CAMs is that for a model to be a CAM there cannot be causally relevant disanalogies between model and target—a condition which is rarely if ever met by animal models again, this is why we study animals in the laboratory in the first place.

Daniel Steel ch. Whether a claim about a model can be extrapolated depends, he argues, also on the strength of the claim to be exported. It is one thing, say, to reason from. He assumes that causes C such as medical interventions or the ingestion of toxic substances bring about their effects E such as the appearance of symptoms or improvements or deteriorations of symptoms through a series of steps or stages. To trace a causal process means to investigate through what set of stages C brings about E.

Process tracing is comparative when the set of stages through which C brings about E in one species or population is compared to the set through which it does so if it does so indeed in another.

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  8. Comparative process tracing would be futile if, in order to know that C causes E in the target species or population, we would have to compare all the stages of the process between model and target. This is because in order to do so, we would have to know all stages of the process through which C causes E , but if we did, we would already know that C causes E.

    Thus, if we compare an intermediate stage of the process which obtains in the model with that stage in the target and find them to be relevantly similar, then the only differences that may still obtain will be downstream from this stage. How useful comparative process tracing is as a method for extrapolation for the biomedical sciences depends on how reliable the assumption that only downstream differences matter to extrapolation is, the reliability with which stages where there might be differences between model and target can be identified and the reliability of our mechanistic knowledge more generally.

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    If, say, our reasons for supposing that C causes E through a series of stages X , Y , Z in the model, or that X and Z are the stages where model and target are likely to differ, are not very strong, then the method does not get off the ground. This is an issue that depends on the quality of the existing knowledge about a given case and cannot be addressed for the biomedical sciences as a whole.

    There are certainly some examples of well-established causal claims where it is known only that C causes E but the details of the causal process are entirely beyond our current grasp Reiss forthcoming-a. An alternative to comparative process tracing that has been proposed is extrapolation by knowledge of causal capacities. If C has a causal capacity to bring about E , then C causes E in a somewhat stable or invariant manner.

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    Specifically, C will then continue to contribute to the production of E even when disturbing factors are present Cartwright And therefore, if C causes E in a model species or population and C has the causal capacity to bring about E , then there is some reason to believe that C causes E also in the target species or population for a defense, see Cartwright The usefulness of the method of extrapolation by causal capacities depends, among other things, on the extent to which biomedical factors can be characterized as having capacities.

    Many biomedical causes do indeed have some degree of stability. These figures suggest a reading along the lines of,. Reiss b: But there is a high degree of interaction with other factors as well. Whether or not a substance is toxic for an organism depends on minute details of its metabolic system, and unless the conditions are just right, the organism may not be affected by the substance at all.

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    To what extent this method will be successful therefore similarly case-dependent as comparative process tracing. As we can see, there is no general answer to the question whether or not animal studies are valuable from a purely epistemic as opposed to ethical, economic, or combined view. Other authors have developed a practice-based taxonomy of animal modes to allow more accurate assessment of the epistemic merits and shortcomings, and predictive capacities of specific modeling practices Degeling and Johnson There is much evidence that species differ enormously with respect to their susceptibility to have toxic reactions to substances.

    Thus, while it is very likely that for any one toxin, there is some species that is predictive of the human response, it is often hard to tell which one is most appropriate for any particular toxin. A species that predicts the human response well for one substance may be a bad model for another. However, some authors suggest that extrapolations from animal models have been made successfully in at least some cases Steel discusses the extrapolation of claims concerning the carcinogenicity of aflatoxin from Fisher rats to humans; see Reiss a for a critical appraisal and Steel for a response.

    Frequently, in the biomedical sciences, reliable animal or other non-human models are not available and RCTs on humans are infeasible for ethical or practical reasons. In these and other cases, biomedical hypotheses can be established using observational methods. As we have seen in Section 5 , evidence-based medicine regards observational methods as generally less reliable than RCTs and other experimental methods. This is because observational studies are subject to a host of confounders and biases that can be controlled when the hypothesis is tested by a—well-designed and well-conducted—RCT.

    But it is not the case that observational methods cannot deliver reliable results. In fact, it is well possible that the medical knowledge that has been established observationally by far exceeds the knowledge that comes from RCTs.

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