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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Page 2
... data are reproduced in Table 1–1. Only 24% of the women who were smokers at the time of the initial survey died during the 20-year follow-up period. In contrast, 31% of those who were Table 1–1 Risk of Death in a 20-Year Period Among.
... data are reproduced in Table 1–1. Only 24% of the women who were smokers at the time of the initial survey died during the 20-year follow-up period. In contrast, 31% of those who were Table 1–1 Risk of Death in a 20-Year Period Among.
Page 3
... data within age categories, as shown in Table 1–2. (The risks for each age group are calculated by dividing the number who died in each smoking group by the total number of those dead or alive.) Table 1–1 combines all of the age ...
... data within age categories, as shown in Table 1–2. (The risks for each age group are calculated by dividing the number who died in each smoking group by the total number of those dead or alive.) Table 1–1 combines all of the age ...
Page 4
... data of Table 1–1. In 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. Confounding is a problem that pervades many epidemiologic studies ...
... data of Table 1–1. In 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. Confounding is a problem that pervades many epidemiologic studies ...
Page 6
... data in Table 1–2 to plot the 20-year risk of death against age. Put age on the horizontal axis and the 20-year risk of death on the vertical axis. Describe the shape of the curve. What biological forces account for the shape? 5. A ...
... data in Table 1–2 to plot the 20-year risk of death against age. Put age on the horizontal axis and the 20-year risk of death on the vertical axis. Describe the shape of the curve. What biological forces account for the shape? 5. A ...
Page 12
... data resource, the weekly Bills of Mortality, which summarized data ... table. It included the first reports of time trends for various diseases ... data collection for the Bills of Mortality5: These Bills were Printed and published, not ...
... data resource, the weekly Bills of Mortality, which summarized data ... table. It included the first reports of time trends for various diseases ... data collection for the Bills of Mortality5: These Bills were Printed and published, not ...
Contents
1 | |
8 | |
3 What Is Causation? | 23 |
4 Measuring Disease Occurrence and Causal Effects | 38 |
5 Types of Epidemiologic Studies | 69 |
6 Infectious Disease Epidemiology | 110 |
7 Dealing with Biases | 124 |
8 Random Error and the Role of Statistics | 148 |
9 Analyzing Simple Epidemiologic Data | 164 |
10 Controlling Confounding by Stratifying Data | 176 |
11 Measuring Interactions | 198 |
12 Using Regression Models in Epidemiologic Analysis | 211 |
13 Epidemiology in Clinical Settings | 235 |
Appendix | 254 |
Index | 257 |
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Common terms and phrases
age categories approach asbestos bare-metal stents biologic interaction birth order breast cancer calculated case-cohort study 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 sampling selection bias significance testing source population specific standard statistical significance strata stratified analysis subjects Suppose tion tolbutamide treatment unexposed group vaccine variable women