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M Parascandola

Publications and source records attributed to M Parascandola.

10 recordsLinked to original sources
PubMed2001

Causation in epidemiology.

Causation is an essential concept in epidemiology, yet there is no single, clearly articulated definition for the discipline. From a systematic review of the literature, five categories can be delineated: production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic. Strengths and weaknesses of these categories are examined in terms of proposed characteristics of a useful scientific definition of causation: it must be specific enough to distinguish causation from mere correlation, but not so narrow as to eliminate apparent causal phenomena from consideration. Two categories-production and counterfactual-are present in any definition of causation but are not themselves sufficient as definitions. The necessary and sufficient cause definition assumes that all causes are deterministic. The sufficient-component cause definition attempts to explain probabilistic phenomena via unknown component causes. Thus, on both of these views, heavy smoking can be cited as a cause of lung cancer only when the existence of unknown deterministic variables is assumed. The probabilistic definition, however, avoids these assumptions and appears to best fit the characteristics of a useful definition of causation. It is also concluded that the probabilistic definition is consistent with scientific and public health goals of epidemiology. In debates in the literature over these goals, proponents of epidemiology as pure science tend to favour a narrower deterministic notion of causation models while proponents of epidemiology as public health tend to favour a probabilistic view. The authors argue that a single definition of causation for the discipline should be and is consistent with both of these aims. It is concluded that a counterfactually-based probabilistic definition is more amenable to the quantitative tools of epidemiology, is consistent with both deterministic and probabilistic phenomena, and serves equally well for the acquisition and the application of scientific knowledge.

Causality↗
PubMed2001

Cigarettes and the US Public Health Service in the 1950s.

The conclusion of the United States Surgeon General's Advisory Committee on Smoking and Health in 1964 that excessive cigarette smoking causes lung cancer is cited as the major turning point for public health action against cigarettes. But the surgeon general and US Public Health Service (PHS) scientists had concluded as early as 1957 that smoking was a cause of lung cancer, indeed, "the principal etiologic factor in the increased incidence of lung cancer." Throughout the 1950s, however, the PHS rejected further tobacco-related public health actions, such as placing warning labels on cigarettes or creating educational programs for schools. Instead, the agency continued to gather information and provided occasional assessments of the evidence as it came available. It was not until pressure mounted from outside the PHS in the early 1960s that more substantive action was taken. Earlier action was not taken because of the way in which PHS scientists (particularly those within the National Institutes of Health) and administrators viewed their roles in relation to science and public health.

Epidemiologic Studies↗
PubMed2000

Ethics and breast cancer risk assessment.

Clinical risk assessment holds great promise for identifying individuals who might benefit from preventive interventions. In the case of breast cancer, statistical models, notably the Gail model, have been developed to assess an individual woman's future risk of developing disease. However, the estimates derived from these models are subject to substantial uncertainty and there is controversy over how to translate risk information into prevention and control measures.In light of these uncertainties, ethical concerns have been raised about appropriate use of these models. The potential benefits of individualized risk assessment must be weighed against current limitations and potential harms. The fact that breast cancer is a significant source of anxiety for many women suggests that the potential harms from misinformation are substantial.This paper concludes that the profound uncertainties surrounding breast cancer risk assessment warrant caution in the use of such models. Breast cancer risk assessment tools occupy a grey area between public health education and individualized clinical attention. When public health officials promise individualized risk information, there is potential for women to place too much importance and trust in these risk estimates. Moreover, use of these models in counseling women about participation in clinical trials should be responsive to the complexity of informed consent.

Journal Article↗
PubMedDate not supplied

Epidemiology: second-rate science?

In recent years epidemiology has come under increasing criticism in regulatory and public arenas for being "unscientific." The tobacco industry has taken advantage of this, insisting for decades that evidence linking cigarettes and lung cancer falls short of proof. Moreover, many epidemiologists remain unduly skeptical and self-conscious about the status of their own causal claims. This situation persists in part because of a widespread belief that only the laboratory can provide evidence sufficient for scientific proof. Adherents of this view erroneously believe that there is no element of uncertainty or inductive inference in the "direct observation" of the laboratory researcher and that epidemiology provides mere "circumstantial" evidence. The historical roots of this attitude can be traced to philosopher John Stuart Mill and physiologist Claude Bernard and their influence on modern experimental thinking. The author uses the debate over cigarettes and lung cancer to examine ideas of proof in medical science and public health, concluding that inductive inference from a limited sample to a larger population is an element in all empirical science.

England↗