This chapter covered two new measures of effect—absolute and relative effects—that may be used as aids in the interpretation of epidemiologic studies. In addition, the chapter presented guidelines tha

This chapter covered two new measures of effect—absolute and relative effects—that may be used as aids in the interpretation of epidemiologic studies. In addition, the chapter presented guidelines that should be taken into account when one is interpreting an epidemiologic finding. Absolute effects, the first variety of which is called risk differences, are determined by finding the difference in measures of disease frequency between exposed and nonexposed individuals. A second type of absolute effect, called population risk difference, is found by computing the difference in measures of disease frequency between the exposed segment of the population and the total population. Relative effects are characterized by the inclusion of an absolute effect in the numerator and a reference group in the denominator. One type of relative effect, the etiologic fraction, attempts to quantify the amount of a disease that is attributable to a given exposure. The second type of relative effect, the population etiologic fraction, provides an estimate of the possible impact on the population rates of disease that can be anticipated by removal of the offending exposure. With respect to interpretation of epidemiologic findings, one should be cognizant of the influence of sample size on the statistical significance of the results. Large sample sizes may lead to clinically unimportant, yet statistically significant, results; small sample sizes may yield statistically nonsignificant results that are clinically important. Therefore, we presented a series of five questions that should be asked when one attempts to interpret an epidemiologic observation. The chapter closed with a discourse on causal models, which may be particularly instructive when trying to interpret epidemiologic data.

Study Questions and Exercises

1) Calculate the etiologic fraction when the RR for disease associated with a given exposure is 1.2, 1.8, 3, and 15.)

2) The impact of an exposure on a population does not depend on:

a) the strength of the association between exposure and disease.

b) the prevalence of the exposure.

c) the case fatality rate.

d) the overall incidence rate of disease in the population.

The next seven questions (3–9) are based on the following data: The death rate per 100,000 for lung cancer is 7 among nonsmokers and 71 among smokers. The death rate per 100,000 for coronary thrombosis is 422 among nonsmokers and 599 among smokers. The prevalence of smoking in the population is 55%. (If necessary, refer to the chapter on cohort studies for formulas for RR.)

3) What is the RR of dying of lung cancer for smokers versus nonsmokers?

4) What is the RR of dying of coronary thrombosis for smokers versus nonsmokers?

5) What is the etiologic fraction of disease due to smoking among individuals with lung cancer?

6) What is the etiologic fraction of disease due to smoking among individuals with coronary thrombosis?

7) What is the population etiologic fraction of lung cancer due to smoking?

8) What is the population etiologic fraction of coronary thrombosis due to smoking?

9) On the basis of the RR and etiologic fractions associated with smoking from lung cancer and coronary thrombosis, which one a) of the following statements is most likely to be correct?

b) Smoking seems much more likely to be causally related to coronary thrombosis than to lung cancer.

c) Smoking seems much more likely to be causally related to lung cancer than to coronary thrombosis.

e) Smoking seems to be equally causally related to both lung cancer and coronary thrombosis.

d) Smoking does not seem to be causally related to either lung cancer or coronary thrombosis.

e) No comparative statement is possible between smoking and lung cancer or coronary thrombosis.

Please take your time and answer all of them correctly