Epidemiology: An IntroductionAcross the last forty years, epidemiology has developed into a vibrant scientific discipline that brings together the social and biological sciences, incorporating everything from statistics to the philosophy of science in its aim to study and track the distribution and determinants of health events. A now-classic text, the second edition of this essential introduction to epidemiology presents the core concepts in a unified approach that aims to cut through the fog and elucidate the fundamental concepts. Rather than focusing on formulas or dogma, the book presents basic epidemiologic principles and concepts in a coherent and straightforward exposition. By emphasizing a unifying set of ideas, students will develop a strong foundation for understanding the principles of epidemiologic research. |
From inside the book
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... chapter, new to this second edition, adds a historical perspective for the reader who is new to epidemiology, in the form of capsule profiles of pioneers in epidemiology and public health. The chapter illustrates the deep historical ...
... chapter, new to this second edition, adds a historical perspective for the reader who is new to epidemiology, in the form of capsule profiles of pioneers in epidemiology and public health. The chapter illustrates the deep historical ...
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... Chapter 7, and random error in Chapter 8. Chapter 9 introduces the basic analytic methods for estimating epidemiologic effects; these methods are extended in Chapter 10 to stratified data. Chapters 11 and 12 address the more advanced ...
... Chapter 7, and random error in Chapter 8. Chapter 9 introduces the basic analytic methods for estimating epidemiologic effects; these methods are extended in Chapter 10 to stratified data. Chapters 11 and 12 address the more advanced ...
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... Chapter 7. Here I aim only to demonstrate the kind of problem that epidemiology deals with routinely. I will also point out some basic fallacies, the kind that can be found in the newspapers on a regular basis and that occur commonly ...
... Chapter 7. Here I aim only to demonstrate the kind of problem that epidemiology deals with routinely. I will also point out some basic fallacies, the kind that can be found in the newspapers on a regular basis and that occur commonly ...
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... Chapter 10, I will return to these data and show how to calculate the effect of smoking on the risk of death after removal of the age confounding. Table 1–2 RISK OF DEATH IN A 20YEAR PERIOD AMONG WOMEN IN WHICKHAM, ENGLAND, ACCORDING TO ...
... Chapter 10, I will return to these data and show how to calculate the effect of smoking on the risk of death after removal of the age confounding. Table 1–2 RISK OF DEATH IN A 20YEAR PERIOD AMONG WOMEN IN WHICKHAM, ENGLAND, ACCORDING TO ...
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... chapters, I will explore the proper way to make epidemiologic comparisons. The point of these examples is to illustrate that what may appear to be a commonsense approach to a simple problem can be overtly wrong until we educate our ...
... chapters, I will explore the proper way to make epidemiologic comparisons. The point of these examples is to illustrate that what may appear to be a commonsense approach to a simple problem can be overtly wrong until we educate our ...
Contents
Measuring Disease Occurrence and Causal Effects | |
Types of Epidemiologic Studies | |
Infectious Disease Epidemiology | |
Dealing with Biases | |
Random Error and the Role of Statistics | |
Controlling Confounding by Stratifying Data | |
Measuring Interactions | |
Using Regression Models in Epidemiologic Analysis | |
13 | |
Epidemiology in Clinical Settings | |
Appendix | |
Index | |
Analyzing Simple Epidemiologic Data | |
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Common terms and phrases
age categories age distribution asbestos attributable fraction biologic interaction birth order breast cancer calculated casecontrol data casecontrol study causal mechanisms Chapter cholera cigarette smoking clinical cohort study compared component causes confidence interval confounding factor control confounding control series curve data in Table denominator described effect epidemic epidemiologic epidemiologic study evaluation example experiment exposed and unexposed Figure flutamide incidence proportion incidence rate ratio infection influenza investigator lung cancer matching measure misclassification mortality rate myocardial infarction nonsmokers null hypothesis obtain occur odds ratio outbreak outcome patients person persontime personyears placebo pooled estimate population at risk predicted prevalence propensity score public health Pvalue function random assignment randomized trial rate difference rate ratio regression model relation result risk data risk difference risk factors risk of death risk ratio sampling selection bias significance testing source population specific standard statistical significance strata stratified analysis subjects Suppose tolbutamide treatment unexposed group vaccine variable women