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. |
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... person who was just born. Why? Because they have a 40year head start; if they had died before age 40, they could not have been part of a group in which everyone is 40 years old. To determine whether conducting an orchestra is beneficial ...
... person who was just born. Why? Because they have a 40year head start; if they had died before age 40, they could not have been part of a group in which everyone is 40 years old. To determine whether conducting an orchestra is beneficial ...
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... person can understand epidemiology, but without considering the principles outlined in this book, even a sensible person using what appears to be common sense is apt to go astray. By mastering a few fundamental epidemiologic principles ...
... person can understand epidemiology, but without considering the principles outlined in this book, even a sensible person using what appears to be common sense is apt to go astray. By mastering a few fundamental epidemiologic principles ...
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... person gets a specific disease, between two populations? How should you avert this problem? REFERENCES 1. 2. MacMahon B, Pugh TF. Epidemiology: Principles and Methods. Boston: Little, Brown; 1970:137–198,175–184. Gaylord Anderson, as ...
... person gets a specific disease, between two populations? How should you avert this problem? REFERENCES 1. 2. MacMahon B, Pugh TF. Epidemiology: Principles and Methods. Boston: Little, Brown; 1970:137–198,175–184. Gaylord Anderson, as ...
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... person to person, and he suggested a theory that disease was spread through selfreplicating particles. He postulated that these particles, which he called seminaria, or seeds, were too small to see and were specific for each disease ...
... person to person, and he suggested a theory that disease was spread through selfreplicating particles. He postulated that these particles, which he called seminaria, or seeds, were too small to see and were specific for each disease ...
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... person was wearing, the lack of a handrail along the path, a sudden gust of wind, and the weight of the person. The complete causal mechanism involves a multitude of factors. Some factors, such as the earlier injury that resulted in the ...
... person was wearing, the lack of a handrail along the path, a sudden gust of wind, and the weight of the person. The complete causal mechanism involves a multitude of factors. Some factors, such as the earlier injury that resulted in the ...
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