Aschengrau and Seage begin by introducing measures of disease frequency, sources of public health data, and the basics of descriptive epidemiology. Four chapters then cover study design, looking in turn at experimental studies, cohort studies, and case-control studies: the key features of each approach are explained, along with their limitations and strengths and their different variants. There are chapters on bias, confounding, and random error. There's a checklist for critically reviewing studies, with some example applications. And there are chapters on effect measure modification, epidemiological approaches to causation, and the uses (and abuses) of screening.
The text is not repetitive, but it does dot its i's and cross its t's, with plentiful examples that make it impossible for readers not to grasp the key ideas. These are also listed as "learning goals" at the front of each chapter and tested by simple exercises at the end of each chapter, but these "textbook" elements don't dominate. There's some treatment of history and philosophical issues, but the focus is on how epidemiological studies are planned, carried out and interpreted.
The approach is resolutely non-mathematical, with no statistics until page 300, with the chapter on random error. Surprisingly, this is not frustrating, but it is limiting. At one point the authors write: "Although they are powerful, multivariate analyses are conducted in a 'black-box,' and so the investigator loses sight of the data" — but their approach to this seems likely to be self-fulfilling, producing investigators who are forced to use key mathematical tools as black boxes because they don't understand them.
Essentials of Epidemiology in Public Health is aimed at general health sciences students and clinical practitioners, rather than at students of statistics or epidemiology. With that proviso, it is highly recommended: it is lucid and clearly explained, and covers a good range of material.
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