## Epidemiology: An IntroductionIn 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. |

### From inside the book

Results 1-5 of 47

Page 2

Source: U.S. Census Bureau, International Data Base. a given year, despite the

lower death rates within age categories in Sweden

situation illustrates what epidemiologists call confounding. In this example, age ...

Source: U.S. Census Bureau, International Data Base. a given year, despite the

lower death rates within age categories in Sweden

**compared**with Panama. Thissituation illustrates what epidemiologists call confounding. In this example, age ...

Page 6

These examples reflect the fallacy of

or disease strikes rather than

same age. We will explore the proper way to make epidemiologic comparisons in

...

These examples reflect the fallacy of

**comparing**the average age at which deathor disease strikes rather than

**comparing**the risk of death between groups of thesame age. We will explore the proper way to make epidemiologic comparisons in

...

Page 7

Why or why not? 7. What is the underlying problem in

age at death or the average age at which a person gets a specific disease

between two populations? How should you avert this problem? References 1.

MacMahon ...

Why or why not? 7. What is the underlying problem in

**comparing**the averageage at death or the average age at which a person gets a specific disease

between two populations? How should you avert this problem? References 1.

MacMahon ...

Page 17

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### Contents

1 | |

8 | |

3 Measuring Disease Occurrence and Causal Effects | 24 |

4 Types of Epidemiologic Study | 57 |

5 Biases in Study Design | 94 |

6 Random Error and the Role of Statistics | 113 |

7 Analyzing Simple Epidemiologic Data | 130 |

8 Controlling Confounding by Stratifying Data | 144 |

9 Measuring Interactions | 168 |

10 Using Regression Models in Epidemiologic Analysis | 181 |

11 Epidemiology in Clinical Settings | 198 |

Appendix | 218 |

Index | 221 |

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### Common terms and phrases

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