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K J Rothman. Oxford University Press, 2002. (Pp 223; price not stated). ISBN: 0-19-513553-9 (hardback); 0-19-513554-7 (paperback).
The aim of this book is clearly stated by K J Rothman in the preface: “… to present a simple overview of the concepts that are the underpinnings of epidemiology, so that a coherent picture of epidemiology thinking emerges for the student. The emphasis is not on statistics, formulas, or computation, but on epidemiologic principles and concepts”. In fact, this is the essence of the book: conceptual, simple, and introductory to the epidemiological logic. It has been conceived as an introductory text to a general course in epidemiology.
The book is structured into 11 chapters. Chapter 1 is an introduction to epidemiological thinking, based on the concept of confounding that “illustrates that epidemiology is more than just common sense”. Chapters 2 to 4 deal with the topics of causation, measuring disease ocurrence and causal effects, and types of epidemiological studies. Chapters 5 and 6 deal with measurement error (biases and random error). Chapters 7 to 10 are devoted to the methods for analysing epidemiological effects, including an introduction to some more advanced issues, as controlling of confounding by stratifying data, measuring interactions, and using regression models in epidemiological analysis. Finally, chapter 11 deals with clinical epidemiology, including some concepts related to diagnosis and clinical trials.
The book has successfully met its pedagogical goal. Main epidemiological concepts and principles are presented in a simple language, as if they were being explained in a classroom, illustrated with clear and attractive examples, and all chapters contain a set of questions for further study. Moreover, a web site that supports reader participation and provides answers to these questions is available (http://www.oup-usa.org/epi/rothman).
Though the aim of the author was to write a basic text, he also introduces the principles for more advanced issues, establishing the basics for understanding interaction and multivariable regression models.
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