The following activities are designed to be conducted in order—answers from the first section feed into items in the second, and so on. Sections 1 and 2 ask you to conduct calculations similar to those discussed in class, in narrated slides, and in your text. Section 3 asks you to think about ways to interpret the results, including the possibility that they are misleading, and what you might do with the information.
Section 1. Prevalence
Prevalence is simply the proportion of those in a given population which have the condition of interest at the time of assessment. For instance, if 23 of 350 H100 class members have colds at our first class meeting, the prevalence (point prevalence, to be technical) is 23/350, or 6.6/100 or 6.6%. In this section, you will calculate the prevalence of strength training for college men and women, using the data table provided to you below.
NCHA participants were asked the number of days in an average week in which they did strength training exercise. The national recommendation is to engage in such activity at least twice per week. The table below reports the number of college women and college men who engaged in muscle strengthening activity at least two days per week (i.e. two or more days/week), and the number of men and women responding to the item. Using the numbers in the table below, calculate the prevalence of muscle strengthening activity for men, women, and overall, and enter your results in the table below. Prevalence can be reported as either a proportion or a percentage. In either case, you need not report more than three digits (e.g. 0.235 or 23.5%).
Total 44,719 118,198
In your own words, and in no more than a couple of sentences or bullets, provide a summary of the prevalence pattern you see here; in other words, what is the overall prevalence and does it seem to differ for women and men? Don’t worry for now about the interpretation or implications of the patterns you see (i.e. whether there is some causal association between gender and muscle strengthening exercise).
Section 2. Relative risk
Using the methods illustrated in the narrated slides, in class, and in your text, calculate the relative risk below using the prevalence estimates you calculated above (show the fractions you create as well as the relative risk). The prevalence estimates from the table above are your best estimates of absolute risk for each gender category; in this instance, it is the absolute risk of engaging in strengthening exercises at least twice per week. (“Risk” is used here in the epidemiologic sense rather than common usage—“risk” does not necessarily refer only to unpleasant outcomes, but for any outcome; in this case we are dealing with the “risk” of a health-promoting activity.)
Using the prevalence of ≥ 2/week strengthening exercise in women as the “non-exposed” group (i.e. the denominator), calculate the relative risk of ≥ 2/week strengthening exercise for men. So, for the table below, you will calculate one relative risk. In the center column, provide the fraction you use to calculate relative risk (i.e. the two numbers you use to calculate RR). In the right column, provide the relative risk that comes from that fraction.
Measure Fraction Relative risk
RR for men
Using your own words, how do you quantitatively interpret the results? That is, how would you translate the relative risk for someone who didn’t know the term “relative risk”? You must interpret the actual relative risk you found (i.e. as a number), not just comment about how to interpret relative risks generally.
Section 3. Interpretation and action
As our session on epidemiology emphasized, getting a result is only part of conducting an epidemiologic study. The results must be interpreted correctly to be of use. One possible interpretation of results is that they are causal—that is, that the predictor (or exposure) causes the outcome (or event). For instance, you may look at the results on gender and weight bearing exercise and determine that the association is somehow causal (i.e. that gender—in the broadest definition, including social definitions—is really associated with strength training via some causal mechanism). You then have the task of explaining how that could be so—what cause or causes lead to the connection? There are other reasons that two variables (e.g. gender and reported strength training) might be associated but without one causing the other. A third variable might be leading to engagement in weight bearing exercise, but be associated with gender, for instance. A variable statistically associated with gender may be leading to strength training frequency, and not gender itself (“confounding”, as we have discussed). Or, it could be that the students in the survey were either sampled in a way that led to misleading results, or there were problems with the way the survey collected data that led to misleading results (i.e. bias).
Your task in this section is to look at the results, and try to decide whether you think they are causal, that is, do you think that gender (or a component of gender) actually influences whether a student engages in strength training? If you think it is causal, explain how—if not, give a plausible alternative to causation.
How could gender be causally associated with strength training? Explain how, in a way consistent with the results you found. How might the results be the result of confounding or bias (i.e. the result of a third variable or a problem with the way the survey was conducted)? Provide one example; again, be sure your answer is consistent with the data and results you found earlier. If you argue that the sample is biased, you may need to refer to the initial table describing the survey sample.
Epidemiology is an applied science. Ultimately, the goal of the field is to find results that allow public health organizations (and others) to take action to improve health. Based on everything you have calculated and reported for this exercise, in the box below briefly (2 bullets or sentences) say 1) whether you would recommend action based on these results, and 2) what that action would be?
Would you take public health action based on these results? If so, what? If not, what would
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