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American Journal of Epidemiology Advance Access originally published online on November 17, 2005
American Journal of Epidemiology 2006 163(1):76-83; doi:10.1093/aje/kwj011
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American Journal of Epidemiology Copyright © 2005 by the Johns Hopkins Bloomberg School of Public Health All rights reserved; printed in U.S.A.

Original Contribution

Estimating the Proportion of Disease due to Classes of Sufficient Causes

Kurt Hoffmann, Christin Heidemann, Cornelia Weikert, Matthias B. Schulze and Heiner Boeing

From the Department of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbrücke, Germany

Correspondence to Dr. Kurt Hoffmann, Department of Epidemiology, German Institute of Human Nutrition, Arthur-Scheunert-Allee 114–116, 14558 Nuthetal, Germany (e-mail: khoff{at}mail.dife.de).

Disease can be caused by different mechanisms. A possible causal model proposed by Rothman is a complete causal mechanism or a so-called "sufficient cause" consisting of a set of component causes that can be illustrated in a pie chart. However, this model does not allow finding out what sufficient causes produce the majority of cases. The authors' objective was to extend Rothman's work by quantifying the proportion of disease that can be attributed to a class of sufficient causes. The underlying idea was to consider all combinations of a given set of known risk factors and to assign each combination to a class of sufficient causes. This assignment makes it possible to evaluate a class of sufficient causes by the population attributable fraction of the corresponding combination of risk factors. The approach presented was applied to sufficient causes of myocardial infarction by use of data on participants recruited between 1994 and 1998 into the European Prospective Investigation into Cancer and Nutrition-Potsdam Study. As a result, 51.8% of cases were attributed to only four different classes of sufficient causes. In conclusion, the statistical method described in the paper may be beneficial for quantifying the importance of different sufficient causes and for improving the efficiency of public health programs.

models, statistical; myocardial infarction; risk factors; statistics

Abbreviations: EPIC, European Prospective Investigation into Cancer and Nutrition; PAF, population attributable fraction; PDC, proportion of disease due to a class of sufficient causes


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