In the past thirty years epidemiology has matured from a fledgling scientific field into a vibrant discipline
that brings together the biological and social sciences, and in doing so draws upon disciplines ranging from statistics
and survey sampling to the philosophy of science. These areas of knowledge have converged into a modern theory
of epidemiology that has been slow to penetrate into textbooks, particularly at the introductory level. Epidemiology:
An Introduction closes the gap. It begins with a brief, lucid discussion of causal thinking and causal inference
and then takes the reader through the elements of epidemiology, focusing on the measures of disease occurrence
and causal effects. With these building blocks in place, the reader learns how to design, analyze and interpret
problems that epidemiologists face, including confounding, the role of chance, and the exploration of interactions.
All these topics are layered on the foundation of basic principles presented in simple language, with numerous
examples and questions for further thought.
Table of Contents
1. Introduction to Epidemiologic Thinking
2. What is Causation?
3. Measuring Disease Occurrence and Causal Effects
4. Types of Epidemiologic Study
5. Biases of Study Design
6. Random Error and the Role of Statistics
7. Analyzing Simple Epidemiologic Data
8. Controlling Confounding by Stratifying Data
9. Measuring Interactions
10. Using Regression Models in Epidemiologic Analysis
11. Epidemiology in Clinical Settings
