A study of the relationship between exercise and heart attacks that is conducted among women who do not smoke. Explain whether gender is a confounder.

Answer: Yes, gender would be a confounder being that men and women have different  lifestyle and behavior characteristics that cause a distinguish between the two groups. Women have a tendency to engage in unhealthy eating habits, less exercise, and alcohol consumption can be factors that  could lead toward heart attacks.

A case-control study of the relationship between liver cirrhosis and alcohol use. In this study, smoking is associated with drinking alcohol and is a risk factor for liver cirrhosis among both non-alcoholics and alcoholics. Explain whether smoking is a confounder.

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Answer:

Assessing Random Error, Confounding, and Effect Modification

Although researchers do their best to reduce error within every study, there will always be error. It is important to identify and report any possible error within the research study in order to accurately interpret the research study’s findings. In epidemiologic research, the focus is on assessing confounding and effect modification along with normal statistical measures (p-values, confidence intervals, etc.).

For this Application Assignment, you will calculate and interpret the effects of confounding, random error, and effect modification within epidemiologic research. Read each of the following questions and answer them appropriately:

• Consider each of the following scenarios and explain whether the variable in question is a confounder:

1.    A study of the relationship between exercise and heart attacks that is conducted among women who do not smoke. Explain whether gender is a confounder.

2.    A case-control study of the relationship between liver cirrhosis and alcohol use. In this study, smoking is associated with drinking alcohol and is a risk factor for liver cirrhosis among both non-alcoholics and alcoholics. Explain whether smoking is a confounder.

• Interpret the results of the following studies

1.    An odds ratio of 1.2 (95% confidence interval: 0.8-1.5) is found for the association of low socioeconomic status and occurrence of obesity.

2.    A relative risk of 3.0 is reported for the association between consumption of red meat and the occurrence of colon cancer. The p-value of the association is 0.15.

3.    An odds ratio of 7 (95% confidence interval: 3.0 – 11.4) is found for the association of smoking and lung cancer.

• The relationship between cigarette smoking and lung cancer was conducted in a case-control study with 700 cases and 425 controls. Using the results below, calculate the crude odds ratio and explain what the ratio means:
  • Heavy Smoking—Cases: 450; Controls: 200
  • Not Heavy Smoking—Cases: 250; Controls: 225
  • A case-control study looked at the association of alcohol use with the occurrence of coronary heart disease (CHD). There were 300 participants in the study (150 cases and 150 controls). Of the cases, 90 participants drank alcohol; of the controls, 60 participants drank alcohol.

    Design the appropriate 2×2 table, calculate and interpret the appropriate measure of association.

    You suspect that the association between alcohol use and CHD might be confounded by smoking. You collect the following data:

• Calculate the appropriate measure of association between alcohol use and CHD in both smokers and non-smokers. Discuss whether smoking was a confounder of the association. What is the relationship of alcohol use to CHD after controlling for confounding?

• A study was conducted in young adults to look at the association between taking a driver’s education class and the risk of being in an automobile accident. 450 participants were included in the study, 150 cases who had been in an accident and 300 controls who had not. Of the 150 cases, 70 reported having taken a driver’s education class. Of the 300 controls, 170 reported having taken a driver’s education class.

Calculate and interpret the appropriate measure of association between driver’s education and accidents.

The question arose as to whether gender might be an effect modifier of this association. When gender was assessed, the data looked like the following:

Perform the appropriate calculations to test for effect modification. Interpret your results.

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