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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... risks for each age group are calculated by dividing the number who died in each smoking ... risk of death. Few died among those in the youngest age categories ... population as a whole? The reason is evident in Table 1–2: A much greater ...
... risks for each age group are calculated by dividing the number who died in each smoking ... risk of death. Few died among those in the youngest age categories ... population as a whole? The reason is evident in Table 1–2: A much greater ...
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... risk of death among orchestra conductors with the risk of death among other ... population are studied. Here is another example that makes this point ... risk of death. When one looks at the average age at death, one looks only at those ...
... risk of death among orchestra conductors with the risk of death among other ... population are studied. Here is another example that makes this point ... risk of death. When one looks at the average age at death, one looks only at those ...
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... risk of cancer in the general population. Looking at the age at which cancer develops among those who get cancer cannot address the question of risk for cancer. These examples reflect the fallacy of comparing the average age at which ...
... risk of cancer in the general population. Looking at the age at which cancer develops among those who get cancer cannot address the question of risk for cancer. These examples reflect the fallacy of comparing the average age at which ...
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... risk because smokers have about 10 times the risk of lung cancer as ... population. We can then define a strong cause to be a component cause that ... population level, it is considered a strong cause of lung cancer because it causes such ...
... risk because smokers have about 10 times the risk of lung cancer as ... population. We can then define a strong cause to be a component cause that ... population level, it is considered a strong cause of lung cancer because it causes such ...
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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