An intrepid epidemiology student was examining statistics from the National Highway Traffic Safety Administration

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Epidemiology

Chapter 9 Exercises

Part I


1. An intrepid epidemiology student was examining statistics from the National Highway Traffic Safety Administration when she made a startling discovery: smoking among drivers appeared to be associated with fatal car accidents. She gathered 2009 data from Florida and arranged it in the 2x2 table below:
Fatal Accident
Smoking
1289
No Smoking 1269
Total
2558

Nonfatal Accident
164723
688421
853144

Total
166012
689690
855702

Calculate and interpret the risk ratio and risk difference based on the above table.

2. With such compelling results, the student immediately began thinking of reasons for the observed association. Could it be that people who smoked were more distracted? That they tended to drive with only one hand on the wheel and were at greater risk of losing control? That they had reduced visibility because of the smoky car interior? Her mentor had another idea, however, and suggested that she consider whether a third variable, such as alcohol consumption, might be driving the results. The student first considered whether the drivers testing positive for blood alcohol at the scene of the accident was associated with smoking in her data:
Smoking
Alcohol
124337
No Alcohol 41675
Total
166012

No Smoking
335475
354215
689690

Total
459812
395890
855702

Calculate and interpret the risk ratio and risk difference based on the above table.

3. She then considered whether testing positive for alcohol was associated with being in a fatal car crash in her data:

Fatal Accident
Alcohol
1639
No Alcohol 919
Total
2558

Nonfatal Accident
458173
394971
853144

Total
459812
395890
855702

Calculate and interpret the risk ratio and risk difference based on the above table.

4. Do you think that the association between smoking and fatal car accidents originally observed by the student may have been confounded by alcohol? Explain.

Part II
You undertake an investigation to assess the effects of hormone replacement therapy (HRT) on coronary heart disease (CHD). You conduct a cohort study whereby you follow women with no history of CHD for
ten years. Assume complete follow-up on all women.


We will assume that the data table below is the TRUTH (i.e., not what you measured in your study, but what you would have measured if you had conducted the study perfectly and there were no noncomparability). In the absence of any misclassification of exposure status, this is what you would have observed.
CHD+
CHDTotal
HRT+
200
5110
5310
HRT170
5259
5429
Total
370
10369
10739


1. Calculate and interpret the risk ratio and risk difference based on these data.

Misclassification Scenario 1:
Women were asked to recall their HRT exposure experience; recall is almost never perfect. Twenty percent of women who actually had taken HRT said that they hadnt, whereas ten percent of women
who actually had not taken HRT said that they had. This occurred regardless of disease status.

2. Use the table below to calculate the 2x2 table with misclassification.
CHD+

CHD-

Total

HRT+
HRTFinal table

Total

with misclassification:
CHD+

CHD-

Total

HRT+
HRTTotal
3. What are the risk ratio and risk difference in the misclassified table? Calculate and interpret.

4. Compare the risk ratio in question 3 to the true risk ratio in question 1. How did misclassification of the exposure independent of disease status (non-differential misclassification of exposure) affect the
estimates in the study?

Misclassification Scenario 2:
Twenty percent of the nonusers with CHD were categorized as users. Everyone else was classified accurately according to the TRUTH table above.

5. Is this an example of misclassification of exposure or disease?

6. Fill in the table below with the misclassified 2x2 table.
CHD+
HRT+
HRTTotal
Table with misclassification:

CHD-

Total

CHD+

CHD-

Total

HRT+
HRTTotal

7. What are the risk ratio and risk difference in the misclassified table? Calculate and interpret.

8. Compare the risk ratio in question 7 to the true risk ratio in question 1. How did this misclassification affect the estimates in the study?

Part III
1. In a prospective study of depression and dementia, you recruit participants who are between ages 65 and 75 and not suffering dementia. At intake, you give them a screening test for depression. Five years later, you give them the Mini Mental State Examination, which assesses cognitive function. There is no loss to follow-up. Your observed risk ratio is 2.40: elderly people who are depressed have 2.40 times the risk of dementia compared with elderly people who are not depressed over 5 years.

a. If, instead of perfect follow-up, you ended up with 20% fewer participants after 5 years. It turns out that depressed people were more likely to drop out than non-depressed. What would the effect be on your risk ratio?

b. Alternatively, what would be the effect on the risk ratio if the 20% of participants lost to followup were all people who were depressed and demented?

Part IV
What are some strategies that may be deployed in the design and data collection phases of epidemiologic studies to minimize misclassification?

An intrepid epidemiology student was examining statistics from the National Highway Traffic Safety Administration
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