Epidemiology: An Introduction
Across 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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1 Introduction to Epidemiologic Thinking
2 Pioneers in Epidemiology and Public Health
3 What Is Causation?
4 Measuring Disease Occurrence and Causal Effects
5 Types of Epidemiologic Studies
6 Infectious Disease Epidemiology
7 Dealing with Biases
8 Random Error and the Role of Statistics
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age categories approach asbestos bare-metal stents biologic interaction birth order breast cancer calculated case-control study causal mechanism Chapter cholera clinical cohort study compared component causes confidence interval confounding factor control confounding control series curve data in Table denominator described drug-eluting stent epidemic epidemiologic epidemiologic study evaluation example experiment exposed and unexposed Figure flutamide follow-up incidence proportion incidence rate ratio induction infection influenza investigator matching measure misclassification mortality rate nonsmokers null hypothesis obtain occur odds ratio outbreak outcome P-value P-value function patients period person person-time person-years placebo population at risk predicted prevalence prevent confounding propensity score random assignment randomized trial rate difference rate ratio regression model relation reproductive number result risk data risk difference risk factor risk of death risk ratio Rothman KJ sampling selection bias significance testing source population specific standard statistical significance strata stratified analysis subjects Suppose tion tolbutamide treatment unexposed group vaccine variable women