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Personality and Stress as Causal Factors in Cancer and Coronary Heart Disease

  • Hans J. Eysenck
Part of the Contributions to Psychology and Medicine book series (CONTRIBUTIONS)

Abstract

In this chapter, Eysenck relates that since the times of Hippocrates and Galen there has been the tradition in medicine that certain personality characteristics are related to specific diseases. For example, the lack of emotional expression and feelings of impotence in the presence of unavoidable stress are characteristics attributed to victims of cancer. Cancer and heart disease are the focus here, as the evidence of consistent but contrasting personality characteristics being related to each is described. Past attempts at research in this area are criticized for their lack of theory, as Eysenck emphasizes the importance of putting forward hypotheses that can be tested, rather than pointing to correlations that cannot be interpreted causally. The bulk of the paper is given over to a description of two large-scale prospective studies, in two different parts of Europe: Crevenka, Yugoslavia and Heidelberg, West Germany. The striking thing about the two studies is the remarkable similarity of the data with relating personality characteristics and cancer/cardiovascular disease. Subjects were divided into four broad categories, the principal ones being Type I (cancer-prone—hopelessness, helplessness, depressive feelings, and repression in the face of stress) and Type II (coronary-prone—responding to stress with emotional expression, anger, irritation, and having unstable emotional relations). The hypothesized relationship was demonstrated. In both samples, Type I subjects were more likely than others to die of cancer, while Type II subjects were more like to suffer mortality due to infarct or stroke.

Keywords

Coronary Heart Disease Behavior Therapy Emotional Expression Personality Type Adverse Life Event 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer-Verlag New York Inc. 1988

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  • Hans J. Eysenck

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